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Wang N, Qu S, Kong W, Hua Q, Hong Z, Liu Z, Shi Y. Establishment and validation of novel predictive models to predict bone metastasis in newly diagnosed prostate adenocarcinoma based on single-photon emission computed tomography radiomics. Ann Nucl Med 2024:10.1007/s12149-024-01942-4. [PMID: 38822897 DOI: 10.1007/s12149-024-01942-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 05/12/2024] [Indexed: 06/03/2024]
Abstract
PURPOSE To establish and validate novel predictive models for predicting bone metastasis (BM) in newly diagnosed prostate adenocarcinoma (PCa) via single-photon emission computed tomography radiomics. METHOD In a retrospective review of the clinical single-photon emission computed tomography (SPECT) database, 176 patients (training set: n = 140; validation set: n = 36) who underwent SPECT/CT imaging and were histologically confirmed to have newly diagnosed PCa from June 2016 to June 2022 were enrolled. Radiomic features were extracted from the region of interest (ROI) in a targeted lesion in each patient. Clinical features, including age, total prostate-specific antigen (t-PSA), and Gleason grades, were included. Statistical tests were then employed to eliminate irrelevant and redundant features. Finally, four types of optimized models were constructed for the prediction. Furthermore, fivefold cross-validation was applied to obtain sensitivity, specificity, accuracy, and area under the curve (AUC) for performance evaluation. The clinical usefulness of the multivariate models was estimated through decision curve analysis (DCA). RESULTS A radiomics signature consisting of 27 selected features which were obtained by radiomics' LASSO treatment was significantly correlated with bone status (P < 0.01 for both training and validation sets). Collectively, the models showed good predictive efficiency. The AUC values ranged from 0.87 to 0.98 in four models. The AUC values of the human experts were 0.655 and 0.872 in the training and validation groups, respectively. Most radiomic models showed better diagnostic accuracy than human experts in the training and validation groups. DCA also demonstrated the superiority of the radiomics models compared to human experts. CONCLUSION Radiomics models are superior to humans in differentiating between benign bone and prostate cancer bone metastases; it can be used to facilitate personalized prediction of BM in newly diagnosed PCa patients.
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Affiliation(s)
- Ning Wang
- Shanghai Key Laboratory of Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, China
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Shihui Qu
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Weiwei Kong
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Qian Hua
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Zhihui Hong
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Zengli Liu
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China
| | - Yizhen Shi
- Department of Nuclear Medicine, the Second Affiliated Hospital of Soochow University, 215004, Jiangsu, Suzhou, China.
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2
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Zhang RR, You HR, Geng YY, Li XG, Sun Y, Hou J, Ji LC, Shi JL, Zhang LB, Yang BQ. Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study. BMC Med Imaging 2024; 24:117. [PMID: 38773416 PMCID: PMC11110286 DOI: 10.1186/s12880-024-01295-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/07/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years. METHODS This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness. RESULTS The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit. CONCLUSION The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.
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Affiliation(s)
- Rong-Rong Zhang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China
- Jinzhou Medical University, Jinzhou, China
| | - Hong-Rui You
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China
- Jinzhou Medical University, Jinzhou, China
| | - Ya-Yuan Geng
- Shukun Technology Co., Ltd, West Beichen Road, Beijing, China
| | - Xiao-Gang Li
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Yu Sun
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Jie Hou
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Lian-Chang Ji
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | | | - Li-Bo Zhang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China
| | - Ben-Qiang Yang
- Department of Radiology, General Hospital of Northern Theater Command, 83 Wenhua Road, Shenyang, Liaoning Province, 110016, P.R. China.
- Key Laboratory of Cardiovascular Imaging and Research of Liaoning Province, Shenyang, China.
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3
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Pak S, Park SG, Park J, Cho ST, Lee YG, Ahn H. Applications of artificial intelligence in urologic oncology. Investig Clin Urol 2024; 65:202-216. [PMID: 38714511 PMCID: PMC11076794 DOI: 10.4111/icu.20230435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/24/2024] [Accepted: 03/11/2024] [Indexed: 05/10/2024] Open
Abstract
PURPOSE With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. MATERIALS AND METHODS We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery." RESULTS A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. CONCLUSIONS AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.
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Affiliation(s)
- Sahyun Pak
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sung Gon Park
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | | | - Sung Tae Cho
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Young Goo Lee
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Hanjong Ahn
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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Benzekry S, Mastri M, Nicolò C, Ebos JML. Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment. PLoS Comput Biol 2024; 20:e1012088. [PMID: 38701089 PMCID: PMC11095706 DOI: 10.1371/journal.pcbi.1012088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 05/15/2024] [Accepted: 04/18/2024] [Indexed: 05/05/2024] Open
Abstract
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically.
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Affiliation(s)
- Sebastien Benzekry
- Computational Pharmacology and Clinical Oncology (COMPO), Inria Sophia Antipolis–Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, Marseille, France
| | - Michalis Mastri
- Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America
| | - Chiara Nicolò
- InSilicoTrials Technologies S.P.A, Riva Grumula, Trieste, Italy
| | - John M. L. Ebos
- Department of Cancer Genetics and Genomics, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America
- Department of Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, New York, United States of America
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5
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Crombé A, Fadli D, Clinca R, Reverchon G, Cevolani L, Girolami M, Hauger O, Matcuk GR, Spinnato P. Imaging of Spondylodiscitis: A Comprehensive Updated Review-Multimodality Imaging Findings, Differential Diagnosis, and Specific Microorganisms Detection. Microorganisms 2024; 12:893. [PMID: 38792723 PMCID: PMC11123694 DOI: 10.3390/microorganisms12050893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 04/11/2024] [Accepted: 04/25/2024] [Indexed: 05/26/2024] Open
Abstract
Spondylodiscitis is defined by infectious conditions involving the vertebral column. The incidence of the disease has constantly increased over the last decades. Imaging plays a key role in each phase of the disease. Indeed, radiological tools are fundamental in (i) the initial diagnostic recognition of spondylodiscitis, (ii) the differentiation against inflammatory, degenerative, or calcific etiologies, (iii) the disease staging, as well as (iv) to provide clues to orient towards the microorganisms involved. This latter aim can be achieved with a mini-invasive procedure (e.g., CT-guided biopsy) or can be non-invasively supposed by the analysis of the CT, positron emission tomography (PET) CT, or MRI features displayed. Hence, this comprehensive review aims to summarize all the multimodality imaging features of spondylodiscitis. This, with the goal of serving as a reference for Physicians (infectious disease specialists, spine surgeons, radiologists) involved in the care of these patients. Nonetheless, this review article may offer starting points for future research articles.
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Affiliation(s)
- Amandine Crombé
- Department of Musculoskeletal Imaging, Pellegrin University Hospital, Bordeaux University, Place Amélie Raba-Léon, F-33000 Bordeaux, France
| | - David Fadli
- Department of Musculoskeletal Imaging, Pellegrin University Hospital, Bordeaux University, Place Amélie Raba-Léon, F-33000 Bordeaux, France
| | - Roberta Clinca
- Department of Radiology, IRCCS Policlinico di Sant’Orsola, 40138 Bologna, Italy
| | - Giorgio Reverchon
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
| | - Luca Cevolani
- Orthopedic Oncology Unit, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
| | - Marco Girolami
- Department of Spine Surgery Unit, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
| | - Olivier Hauger
- Department of Musculoskeletal Imaging, Pellegrin University Hospital, Bordeaux University, Place Amélie Raba-Léon, F-33000 Bordeaux, France
| | - George R. Matcuk
- Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
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6
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Tompkins A, Gray ZN, Dadey RE, Zenkin S, Batavani N, Newman S, Amouzegar A, Ak M, Ak N, Pak TY, Peddagangireddy V, Mamindla P, Behr S, Goodman A, Ploucha DL, Kirkwood JM, Zarour HM, Najjar YG, Davar D, Colen R, Luke JJ, Bao R. Radiomic analysis of patient and inter-organ heterogeneity in response to immunotherapies and BRAF targeted therapy in metastatic melanoma. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.26.24306411. [PMID: 38712112 PMCID: PMC11071587 DOI: 10.1101/2024.04.26.24306411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Background Variability in treatment response may be attributable to organ-level heterogeneity in tumor lesions. Radiomic analysis of medical images can elucidate non-invasive biomarkers of clinical outcome. Organ-specific radiomic comparison across immunotherapies and targeted therapies has not been previously reported. Methods We queried UPMC Hillman Cancer Center registry for patients with metastatic melanoma (MEL) treated with immune checkpoint inhibitors (ICI) (anti-PD1/CTLA4 [ipilimumab+nivolumab; I+N] or anti-PD1 monotherapy) or BRAF targeted therapy. Best overall response was measured using RECIST v1.1. Lesions were segmented into discrete volume-of-interest with 400 radiomics features extracted. Overall and organ-specific machine-learning models were constructed to predict disease control (DC) versus progressive disease (PD) using XGBoost. Results 291 MEL patients were identified, including 242 ICI (91 I+N, 151 PD1) and 49 BRAF. 667 metastases were analyzed, including 541 ICI (236 I+N, 305 PD1) and 126 BRAF. Across cohorts, baseline demographics included 39-47% female, 24-29% M1C, 24-46% M1D, and 61-80% with elevated LDH. Among patients experiencing DC, the organs with the greatest reduction were liver (-88%±12%, I+N; mean±S.E.M.) and lung (-72%±8%, I+N). For patients with multiple same-organ target lesions, the highest inter-lesion heterogeneity was observed in brain among patients who received ICI while no intra-organ heterogeneity was observed in BRAF. 267 patients were kept for radiomic modeling, including 221 ICI (86 I+N, 135 PD1) and 46 BRAF. Models consisting of optimized radiomic signatures classified DC/PD across I+N (AUC=0.85) and PD1 (0.71) and within individual organ sites (AUC=0.72∼0.94). Integration of clinical variables improved the models' performance. Comparison of models between treatments and across organ sites suggested mostly non-overlapping DC or PD features. Skewness, kurtosis, and informational measure of correlation (IMC) were among the radiomic features shared between overall response models. Kurtosis and IMC were also utilized by multiple organ-site models. Conclusions Differential organ-specific response was observed across BRAF and ICI with within organ heterogeneity observed for ICI but not for BRAF. Radiomic features of organ-specific response demonstrated little overlap. Integrating clinical factors with radiomics improves the prediction of disease course outcome and prediction of tumor heterogeneity.
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7
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Louis T, Lucia F, Cousin F, Mievis C, Jansen N, Duysinx B, Le Pennec R, Visvikis D, Nebbache M, Rehn M, Hamya M, Geier M, Salaun PY, Schick U, Hatt M, Coucke P, Lovinfosse P, Hustinx R. Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence. Sci Rep 2024; 14:9028. [PMID: 38641673 PMCID: PMC11031577 DOI: 10.1038/s41598-024-58551-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 04/01/2024] [Indexed: 04/21/2024] Open
Abstract
The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.
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Affiliation(s)
- Thomas Louis
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
| | - François Lucia
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium.
- Radiation Oncology Department, University Hospital of Brest, Brest, France.
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France.
| | - François Cousin
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Carole Mievis
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Nicolas Jansen
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Bernard Duysinx
- Division of Pulmonology, University Hospital of Liège, Liège, Belgium
| | - Romain Le Pennec
- Nuclear Medicine Department, University Hospital of Brest, Brest, France
- GETBO INSERM UMR 1304, University of Brest, UBO, Brest, France
| | | | - Malik Nebbache
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Martin Rehn
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Mohamed Hamya
- Radiation Oncology Department, University Hospital of Brest, Brest, France
| | - Margaux Geier
- Medical Oncology Department, University Hospital of Brest, Brest, France
| | - Pierre-Yves Salaun
- Nuclear Medicine Department, University Hospital of Brest, Brest, France
- GETBO INSERM UMR 1304, University of Brest, UBO, Brest, France
| | - Ulrike Schick
- Radiation Oncology Department, University Hospital of Brest, Brest, France
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Mathieu Hatt
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - Philippe Coucke
- Department of Radiotherapy Oncology, University Hospital of Liège, Liège, Belgium
| | - Pierre Lovinfosse
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
| | - Roland Hustinx
- Division of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, Liège, Belgium
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Li K, Zhu Q, Yang J, Zheng Y, Du S, Song M, Peng Q, Yang R, Liu Y, Qi L. Imaging and Liquid Biopsy for Distinguishing True Progression From Pseudoprogression in Gliomas, Current Advances and Challenges. Acad Radiol 2024:S1076-6332(24)00162-4. [PMID: 38614827 DOI: 10.1016/j.acra.2024.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/14/2024] [Accepted: 03/18/2024] [Indexed: 04/15/2024]
Abstract
RATIONALE AND OBJECTIVES Gliomas are aggressive brain tumors with a poor prognosis. Assessing treatment response is challenging because magnetic resonance imaging (MRI) may not distinguish true progression (TP) from pseudoprogression (PsP). This review aims to discuss imaging techniques and liquid biopsies used to distinguish TP from PsP. MATERIALS AND METHODS This review synthesizes existing literature to examine advances in imaging techniques, such as magnetic resonance diffusion imaging (MRDI), perfusion-weighted imaging (PWI) MRI, and liquid biopsies, for identifying TP or PsP through tumor markers and tissue characteristics. RESULTS Advanced imaging techniques, including MRDI and PWI MRI, have proven effective in delineating tumor tissue properties, offering valuable insights into glioma behavior. Similarly, liquid biopsy has emerged as a potent tool for identifying tumor-derived markers in biofluids, offering a non-invasive glimpse into tumor evolution. Despite their promise, these methodologies grapple with significant challenges. Their sensitivity remains inconsistent, complicating the accurate differentiation between TP and PSP. Furthermore, the absence of standardized protocols across platforms impedes the reliability of comparisons, while inherent biological variability adds complexity to data interpretation. CONCLUSION Their potential applications have been highlighted, but gaps remain before routine clinical use. Further research is needed to develop and validate these promising methods for distinguishing TP from PsP in gliomas.
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Affiliation(s)
- Kaishu Li
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China; Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China.; Department of Neurosurgery, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China
| | - Qihui Zhu
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Junyi Yang
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Yin Zheng
- Department of Neurosurgery, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Siyuan Du
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Meihui Song
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Qian Peng
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China
| | - Runwei Yang
- Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China
| | - Yawei Liu
- Department of Neurosurgery & Medical Research Center, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), 1# Jiazi Road, Foshan, Guangdong 528300, China
| | - Ling Qi
- Institute of Digestive Disease of Guangzhou Medical University, Affiliated Qingyuan Hospital,Guangzhou Medical University,Qingyuan People's Hospital, Qingyuan 511518, China.
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9
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Emori M, Tsuchie H, Takashima H, Teramoto A, Murahashi Y, Imura Y, Outani H, Nakai S, Takenaka S, Hirota R, Nakahashi N, Shimizu J, Murase K, Takasawa A, Nagasawa H, Sugita S, Takada K, Hasegawa T, Okada S, Miyakoshi N, Yamashita T. Coefficient of variation of T2-weighted MRI may predict the prognosis of malignant peripheral nerve sheath tumor. Skeletal Radiol 2024; 53:657-664. [PMID: 37755491 DOI: 10.1007/s00256-023-04457-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023]
Abstract
BACKGROUND We investigated whether non-enhancement MRI features, including measurement of the heterogeneity of the tumor with MR T2 imaging by calculating coefficient of variation (CV) values, were associated with the prognosis of non-metastatic malignant peripheral nerve sheath tumors (MPNST). METHODS This retrospective study included 42 patients with MPNST who had undergone surgical resection (mean age, 50 years ± 21; 20 male participants). Non-enhancement MR images were evaluated for signal intensity heterogeneity on T1- and T2-weighted imaging, tumor margin definition on T1- and T2-weighted imaging, peritumoral edema on T2-weight imaging, and CV. We measured the signal intensities of MR T2-weighted images and calculated the corresponding CV values. CV is defined as the ratio of the standard deviation to the mean. The associations between factors and overall survival (OS) were investigated via the Kaplan-Meier method with log-rank tests and the Cox proportional hazards model. RESULTS The mean CV value of MR T2 images was 0.2299 ± 0.1339 (standard deviation) (range, 0.0381-0.8053). Applying receiver operating characteristics analysis, the optimal cut-off level for CV value was 0.137. This cut-off CV value was used for its stratification into high and low CV values. At multivariate survival analysis, a high CV value (hazard ratio = 3.63; 95% confidence interval = 1.16-16.0; p = 0.047) was identified as an independent predictor of OS. CONCLUSION The CV value of the signal intensity of heterogenous MPNSTs MR T2-weighted images is an independent predictor of patients' OS.
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Affiliation(s)
- Makoto Emori
- Department of Orthopedic Surgery, Sapporo Medical University School of Medicine, West 16, South 1, Chuo-Ku, Sapporo, 060-8543, Japan.
| | - Hiroyuki Tsuchie
- Department of Orthopedic Surgery, Akita University School of Medicine, Akita, Akita, 010-8543, Japan
| | - Hiroyuki Takashima
- Faculty of Health Sciences, Hokkaido University, Sapporo, Hokkaido, 060-0812, Japan
| | - Atsushi Teramoto
- Department of Orthopedic Surgery, Sapporo Medical University School of Medicine, West 16, South 1, Chuo-Ku, Sapporo, 060-8543, Japan
| | - Yasutaka Murahashi
- Department of Orthopedic Surgery, Sapporo Medical University School of Medicine, West 16, South 1, Chuo-Ku, Sapporo, 060-8543, Japan
| | - Yoshinori Imura
- Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
| | - Hidetatsu Outani
- Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
| | - Sho Nakai
- Musculoskeletal Oncology Service, Osaka International Cancer Institute, Osaka, Osaka, 541-8567, Japan
| | - Satoshi Takenaka
- Musculoskeletal Oncology Service, Osaka International Cancer Institute, Osaka, Osaka, 541-8567, Japan
| | - Ryosuke Hirota
- Department of Orthopedic Surgery, Sapporo Medical University School of Medicine, West 16, South 1, Chuo-Ku, Sapporo, 060-8543, Japan
| | - Naoya Nakahashi
- Department of Orthopedic Surgery, Sapporo Medical University School of Medicine, West 16, South 1, Chuo-Ku, Sapporo, 060-8543, Japan
| | - Junya Shimizu
- Department of Orthopedic Surgery, Sapporo Medical University School of Medicine, West 16, South 1, Chuo-Ku, Sapporo, 060-8543, Japan
| | - Kazuyuki Murase
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, 060-8543, Japan
| | - Akira Takasawa
- Departments of Pathology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, 060-8543, Japan
| | - Hiroyuki Nagasawa
- Department of Orthopedic Surgery, Akita University School of Medicine, Akita, Akita, 010-8543, Japan
| | - Shintaro Sugita
- Department of Surgical Pathology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, 060-8543, Japan
| | - Kohichi Takada
- Department of Medical Oncology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, 060-8543, Japan
| | - Tadashi Hasegawa
- Department of Surgical Pathology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, 060-8543, Japan
| | - Seiji Okada
- Department of Orthopedic Surgery, Osaka University Graduate School of Medicine, Suita, Osaka, 565-0871, Japan
| | - Naohisa Miyakoshi
- Department of Orthopedic Surgery, Akita University School of Medicine, Akita, Akita, 010-8543, Japan
| | - Toshihiko Yamashita
- Department of Orthopedic Surgery, Sapporo Medical University School of Medicine, West 16, South 1, Chuo-Ku, Sapporo, 060-8543, Japan
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Ravanelli M, Rondi P, Di Meo N, Farina D. The added value of radiomics in determining patient responsiveness to laryngeal preservation strategies. Curr Opin Otolaryngol Head Neck Surg 2024; 32:134-137. [PMID: 38259164 DOI: 10.1097/moo.0000000000000963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
PURPOSE OF REVIEW Laryngeal cancer (LC) is a highly aggressive malignancy of the head and neck and represents about 1-2% of cancer worldwide.Treatment strategies for LC aim both to complete cancer removal and to preserve laryngeal function or maximize larynx retention.Predicting with high precision response to induction chemotherapy (IC) is one of the main fields of research when considering LC, since this could guide treatment strategies in locally advanced LC. RECENT FINDINGS Radiomics is a noninvasive method to extract quantitative data from the whole tumor using medical imaging. This signature could represent the underlying tumor heterogeneity and phenotype.During the last five years, some studies have highlighted the potential of radiomics in the pretreatment assessment of LC, in the prediction of response to IC, and in the early assessment of response to radiation therapy. Although these represent promising results, larger multicentric studies are demanded to validate the value of radiomics in this field. SUMMARY The role of radiomics in laryngeal preservation strategies is still to be defined. There are some early promising studies, but the lack of validation and larger multicentric studies limit the value of the papers published in the literature and its application in clinical practice.
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Affiliation(s)
- Marco Ravanelli
- Department of Radiology, University of Brescia, ASST Spedali Civili Brescia, Italy
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Duan W, Wang Z, Ma Z, Zheng H, Li Y, Pei D, Wang M, Qiu Y, Duan M, Yan D, Ji Y, Cheng J, Liu X, Zhang Z, Yan J. Radiomic profiling for insular diffuse glioma stratification with distinct biologic pathway activities. Cancer Sci 2024; 115:1261-1272. [PMID: 38279197 PMCID: PMC11007007 DOI: 10.1111/cas.16089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 01/28/2024] Open
Abstract
Current literature emphasizes surgical complexities and customized resection for managing insular gliomas; however, radiogenomic investigations into prognostic radiomic traits remain limited. We aimed to develop and validate a radiomic model using multiparametric magnetic resonance imaging (MRI) for prognostic prediction and to reveal the underlying biological mechanisms. Radiomic features from preoperative MRI were utilized to develop and validate a radiomic risk signature (RRS) for insular gliomas, validated through paired MRI and RNA-seq data (N = 39), to identify core pathways underlying the RRS and individual prognostic radiomic features. An 18-feature-based RRS was established for overall survival (OS) prediction. Gene set enrichment analysis (GSEA) and weighted gene coexpression network analysis (WGCNA) were used to identify intersectional pathways. In total, 364 patients with insular gliomas (training set, N = 295; validation set, N = 69) were enrolled. RRS was significantly associated with insular glioma OS (log-rank p = 0.00058; HR = 3.595, 95% CI:1.636-7.898) in the validation set. The radiomic-pathological-clinical model (R-P-CM) displayed enhanced reliability and accuracy in prognostic prediction. The radiogenomic analysis revealed 322 intersectional pathways through GSEA and WGCNA fusion; 13 prognostic radiomic features were significantly correlated with these intersectional pathways. The RRS demonstrated independent predictive value for insular glioma prognosis compared with established clinical and pathological profiles. The biological basis for prognostic radiomic indicators includes immune, proliferative, migratory, metabolic, and cellular biological function-related pathways.
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Affiliation(s)
- Wenchao Duan
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zilong Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zeyu Ma
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Hongwei Zheng
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yinhua Li
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Dongling Pei
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Minkai Wang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuning Qiu
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Mengjiao Duan
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Dongming Yan
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Yuchen Ji
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Jingliang Cheng
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Xianzhi Liu
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Zhenyu Zhang
- Department of NeurosurgeryThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
| | - Jing Yan
- Department of MRIThe First Affiliated Hospital of Zhengzhou UniversityZhengzhouHenanChina
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Huang H, Chen H, Zheng D, Chen C, Wang Y, Xu L, Wang Y, He X, Yang Y, Li W. Habitat-based radiomics analysis for evaluating immediate response in colorectal cancer lung metastases treated by radiofrequency ablation. Cancer Imaging 2024; 24:44. [PMID: 38532520 DOI: 10.1186/s40644-024-00692-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/20/2024] [Indexed: 03/28/2024] Open
Abstract
PURPOSE To create radiomics signatures based on habitat to assess the instant response in lung metastases of colorectal cancer (CRC) after radiofrequency ablation (RFA). METHODS Between August 2016 and June 2019, we retrospectively included 515 lung metastases in 233 CRC patients who received RFA (412 in the training group and 103 in the test group). Multivariable analysis was performed to identify independent risk factors for developing the clinical model. Tumor and ablation regions of interest (ROI) were split into three spatial habitats through K-means clustering and dilated with 5 mm and 10 mm thicknesses. Radiomics signatures of intratumor, peritumor, and habitat were developed using the features extracted from intraoperative CT data. The performance of these signatures was primarily evaluated using the area under the receiver operating characteristics curve (AUC) via the DeLong test, calibration curves through the Hosmer-Lemeshow test, and decision curve analysis. RESULTS A total of 412 out of 515 metastases (80%) achieved complete response. Four clinical variables (cancer antigen 19-9, simultaneous systemic treatment, site of lung metastases, and electrode type) were utilized to construct the clinical model. The Habitat signature was combined with the Peri-5 signature, which achieved a higher AUC than the Peri-10 signature in the test set (0.825 vs. 0.816). The Habitat+Peri-5 signature notably surpassed the clinical and intratumor radiomics signatures (AUC: 0.870 in the test set; both, p < 0.05), displaying improved calibration and clinical practicality. CONCLUSIONS The habitat-based radiomics signature can offer precise predictions and valuable assistance to physicians in developing personalized treatment strategies.
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Affiliation(s)
- Haozhe Huang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Hong Chen
- Department of Medical Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 South Wanping Road, Xuhui District, Shanghai, 200030, China
| | - Dezhong Zheng
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, 500 Yutian Road, Hongkou District, Shanghai, 200083, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, 100049, China
| | - Chao Chen
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Ying Wang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Lichao Xu
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Yaohui Wang
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Xinhong He
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China
| | - Yuanyuan Yang
- Laboratory for Medical Imaging Informatics, Shanghai Institute of Technical Physics, Chinese Academy of Science, 500 Yutian Road, Hongkou District, Shanghai, 200083, China.
- University of Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan District, Beijing, 100049, China.
| | - Wentao Li
- Department of Interventional Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Xuhui District, Shanghai, 200032, China.
- Department of Oncology, Shanghai Medical College, Fudan University, Xuhui District, 130 Dongan Road, Shanghai, 200032, China.
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Wang SX, Yang Y, Xie H, Yang X, Liu ZQ, Li HJ, Huang WJ, Luo WJ, Lei YM, Sun Y, Ma J, Chen YF, Liu LZ, Mao YP. Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. Eur Radiol 2024:10.1007/s00330-024-10678-8. [PMID: 38514481 DOI: 10.1007/s00330-024-10678-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/13/2024] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
OBJECTIVES This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC). MATERIALS AND METHODS A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods. RESULTS The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients. CONCLUSION The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT. CLINICAL RELEVANCE STATEMENT The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment. KEY POINTS • The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.
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Affiliation(s)
- Shun-Xin Wang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Yi Yang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Hui Xie
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Xin Yang
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Zhi-Qiao Liu
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Hao-Jiang Li
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Wen-Jie Huang
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Wei-Jie Luo
- Department of Medical Oncology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China
| | - Yi-Ming Lei
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Ying Sun
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Jun Ma
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China
| | - Yan-Feng Chen
- Department of Head and Neck Surgery, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China.
| | - Li-Zhi Liu
- Department of Radiology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China.
| | - Yan-Ping Mao
- Department of Radiation Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Guangzhou, 510060, China.
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Gu P, Mendonca O, Carter D, Dube S, Wang P, Huang X, Li D, Moore JH, McGovern DPB. AI-luminating Artificial Intelligence in Inflammatory Bowel Diseases: A Narrative Review on the Role of AI in Endoscopy, Histology, and Imaging for IBD. Inflamm Bowel Dis 2024:izae030. [PMID: 38452040 DOI: 10.1093/ibd/izae030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Indexed: 03/09/2024]
Abstract
Endoscopy, histology, and cross-sectional imaging serve as fundamental pillars in the detection, monitoring, and prognostication of inflammatory bowel disease (IBD). However, interpretation of these studies often relies on subjective human judgment, which can lead to delays, intra- and interobserver variability, and potential diagnostic discrepancies. With the rising incidence of IBD globally coupled with the exponential digitization of these data, there is a growing demand for innovative approaches to streamline diagnosis and elevate clinical decision-making. In this context, artificial intelligence (AI) technologies emerge as a timely solution to address the evolving challenges in IBD. Early studies using deep learning and radiomics approaches for endoscopy, histology, and imaging in IBD have demonstrated promising results for using AI to detect, diagnose, characterize, phenotype, and prognosticate IBD. Nonetheless, the available literature has inherent limitations and knowledge gaps that need to be addressed before AI can transition into a mainstream clinical tool for IBD. To better understand the potential value of integrating AI in IBD, we review the available literature to summarize our current understanding and identify gaps in knowledge to inform future investigations.
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Affiliation(s)
- Phillip Gu
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | | | - Dan Carter
- Department of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel
| | - Shishir Dube
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Paul Wang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Xiuzhen Huang
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Dermot P B McGovern
- F. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Demetriou D, Lockhat Z, Brzozowski L, Saini KS, Dlamini Z, Hull R. The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics. Cancers (Basel) 2024; 16:1076. [PMID: 38473432 DOI: 10.3390/cancers16051076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.
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Affiliation(s)
- Demetra Demetriou
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Zarina Lockhat
- Department of Radiology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Luke Brzozowski
- Translational Research and Core Facilities, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Kamal S Saini
- Fortrea Inc., 8 Moore Drive, Durham, NC 27709, USA
- Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Zodwa Dlamini
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
| | - Rodney Hull
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield, Pretoria 0028, South Africa
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16
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Yang F, Chen R, Yang Y, Yang Z, Su Y, Ji M, Pang Z, Wang D. Computed tomography-based radiomics model to predict adverse clinical outcomes in acute pulmonary embolism. J Thromb Thrombolysis 2024; 57:428-436. [PMID: 38280936 DOI: 10.1007/s11239-023-02929-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/18/2023] [Indexed: 01/29/2024]
Abstract
This preliminary study investigated the feasibility of a combined model constructed using radiomic features based on computed tomography (CT) and clinical features to predict adverse clinical outcomes in acute pulmonary embolism (APE). Currently, there is no widely recognized predictive model. Patients with confirmed APE who underwent CT pulmonary angiography were retrospectively categorized into good and poor prognosis groups. Seventy-four patients were randomized into a training (n = 51) or validation (n = 23) cohort. Feature extraction was performed using 3D-Slicer software. The least absolute shrinkage and selection operator regression was used to identify the optimal radiomics features and calculate the radiomics scores; subsequently, the radiomics model was developed. A combined predictive model was constructed based on radiomics scores and selected clinical features. The predictive efficacy of the three models (radiomics, clinical and combined) was assessed by plotting receiver operating characteristic curves. Furthermore, the calibration curves were graphed and the decision curve analysis was performed. Four radiomic features were screened to calculate the radiomic score. Right ventricular to left ventricular ratio (RV/LV) ≥ 1.0 and radiomics score were independent risk factors for adverse clinical outcomes. In the training and validation cohorts, the areas under the curve (AUCs) for the RV/LV ≥ 1.0 (clinical) and radiomics score prediction models were 0.778 and 0.833 and 0.907 and 0.817, respectively. The AUCs for the combined model of RV/LV ≥ 1.0 and radiomics score were 0.925 and 0.917, respectively. The combined and radiomics models had high clinical assessment efficacy for predicting adverse clinical outcomes in APE, demonstrating the clinical utility of both models. Calibration curves exhibited a strong level of consistency between the predictive and observed probabilities of poor and good prognoses in the combined model. The combined model of RV/LV ≥ 1.0 and radiomics score based on CT could accurately and non-invasively predict adverse clinical outcomes in patients with APE.
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Affiliation(s)
- Fei Yang
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Rong Chen
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Yue Yang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Zhixiang Yang
- Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Yaying Su
- Department of Nuclear Medicine, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Mengmeng Ji
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Zhiying Pang
- Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, China
| | - Dawei Wang
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Hebei North University, 12 Changqing Road, Zhangjiakou, 075000, Hebei, China.
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Koska IO, Ozcan HN, Tan AA, Beydogan B, Ozer G, Oguz B, Haliloglu M. Radiomics in differential diagnosis of Wilms tumor and neuroblastoma with adrenal location in children. Eur Radiol 2024:10.1007/s00330-024-10589-8. [PMID: 38311701 DOI: 10.1007/s00330-024-10589-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 02/06/2024]
Abstract
OBJECTIVES Machine learning methods can be applied successfully to various medical imaging tasks. Our aim with this study was to build a robust classifier using radiomics and clinical data for preoperative diagnosis of Wilms tumor (WT) or neuroblastoma (NB) in pediatric abdominal CT. MATERIAL AND METHODS This is a single-center retrospective study approved by the Institutional Ethical Board. CT scans of consecutive patients diagnosed with WT or NB admitted to our hospital from January 2005 to December 2021 were evaluated. Three distinct datasets based on clinical centers and CT machines were curated. Robust, non-redundant, high variance, and relevant radiomics features were selected using data science methods. Clinically relevant variables were integrated into the final model. Dice score for similarity of tumor ROI, Cohen's kappa for interobserver agreement among observers, and AUC for model selection were used. RESULTS A total of 147 patients, including 90 WT (mean age 34.78 SD: 22.06 months; 43 male) and 57 NB (mean age 23.77 SD:22.56 months; 31 male), were analyzed. After binarization at 24 months cut-off, there was no statistically significant difference between the two groups for age (p = .07) and gender (p = .54). CT clinic radiomics combined model achieved an F1 score of 0.94, 0.93 accuracy, and an AUC 0.96. CONCLUSION In conclusion, the CT-based clinic-radiologic-radiomics combined model could noninvasively predict WT or NB preoperatively. Notably, that model correctly predicted two patients, which none of the radiologists could correctly predict. This model may serve as a noninvasive preoperative predictor of NB/WT differentiation in CT, which should be further validated in large prospective models. CLINICAL RELEVANCE STATEMENT CT-based clinic-radiologic-radiomics combined model could noninvasively predict Wilms tumor or neuroblastoma preoperatively. KEY POINTS • CT radiomics features can predict Wilms tumor or neuroblastoma from abdominal CT preoperatively. • Integrating clinic variables may further improve the performance of the model. • The performance of the combined model is equal to or greater than human readers, depending on the lesion size.
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Affiliation(s)
- Ilker Ozgur Koska
- Department of Radiology, Behcet Uz Children's Hospital, Konak İzmir, Turkey.
| | - H Nursun Ozcan
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Aziz Anil Tan
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
- Department of Radiology, Sincan Training and Research Hospital, Ankara, Turkey
| | - Beyza Beydogan
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Gozde Ozer
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Berna Oguz
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
| | - Mithat Haliloglu
- Department of Radiology, Hacettepe University School of Medicine, Ankara, Turkey
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Zhao T, Yi J, Luo D, Liu J, Fan X, Wu Q, Wang W. Prognostic factors for invasive mucinous adenocarcinoma of the lung: systematic review and meta-analysis. World J Surg Oncol 2024; 22:41. [PMID: 38303008 PMCID: PMC10835932 DOI: 10.1186/s12957-024-03326-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 01/23/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Invasive mucinous adenocarcinoma of the lung (IMA) is a unique and rare subtype of lung adenocarcinoma with poorly defined prognostic factors and highly controversial studies. Hence, this study aimed to comprehensively identify and summarize the prognostic factors associated with IMA. METHODS A comprehensive search of relevant literature was conducted in the PubMed, Embase, Cochrane, and Web of Science databases from their inception until June 2023. The pooled hazard ratio (HR) and corresponding 95% confidence intervals (CI) of overall survival (OS) and/or disease-free survival (DFS) were obtained to evaluate potential prognostic factors. RESULTS A total of 1062 patients from 11 studies were included. In univariate analysis, we found that gender, age, TNM stage, smoking history, lymph node metastasis, pleural metastasis, spread through air spaces (STAS), tumor size, pathological grade, computed tomography (CT) findings of consolidative-type morphology, pneumonia type, and well-defined heterogeneous ground-glass opacity (GGO) were risk factors for IMA, and spiculated margin sign was a protective factor. In multivariate analysis, smoking history, lymph node metastasis, pathological grade, STAS, tumor size, and pneumonia type sign were found to be risk factors. There was not enough evidence that epidermal growth factor receptor (EGFR) mutations, anaplastic lymphoma kinase (ALK) mutations, CT signs of lobulated margin, and air bronchogram were related to the prognosis for IMA. CONCLUSION In this study, we comprehensively analyzed prognostic factors for invasive mucinous adenocarcinoma of the lung in univariate and multivariate analyses of OS and/or DFS. Finally, 12 risk factors and 1 protective factor were identified. These findings may help guide the clinical management of patients with invasive mucinous adenocarcinoma of the lung.
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Affiliation(s)
- Ting Zhao
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
- Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
| | - Jianhua Yi
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
- Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
| | - Dan Luo
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
- Faculty of Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine and University Hospital, Macau University of Science and Technology, Taipa, 999078, Macao, China
- Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
| | - Junjun Liu
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
- Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China
| | - Xianming Fan
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China.
- Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China.
| | - Qibiao Wu
- Faculty of Chinese Medicine, State Key Laboratory of Quality Research in Chinese Medicine and University Hospital, Macau University of Science and Technology, Taipa, 999078, Macao, China.
- Zhuhai MUST Science and Technology Research Institute, 51900, Zhuhai, Guangdong, China.
| | - Wenjun Wang
- Department of Respiratory and Critical Care Medicine, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China.
- Inflammation & Allergic Diseases Research Unit, The Affiliated Hospital, Southwest Medical University, 646099, Luzhou, Sichuan, China.
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Yang S, Zhang W, Liu C, Li C, Hua K. Predictive value and potential association of PET/CT radiomics on lymph node metastasis of cervical cancer. Ann Med Surg (Lond) 2024; 86:805-810. [PMID: 38333288 PMCID: PMC10849352 DOI: 10.1097/ms9.0000000000001412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 10/09/2023] [Indexed: 02/10/2024] Open
Abstract
Objective Due to the information-rich nature of positron emission tomography/computed tomography (PET/CT) images, the authors hope to explore radiomics features that could distinguish metastatic lymph nodes (LNs) from hypermetabolic benign LNs, in addition to conventional indicators. Methods PET/CT images of 106 patients with early-stage cervical cancer from 2019 to 2021 were retrospectively analyzed. The tumor lesions and LN regions of PET/CT images were outlined with SeeIt, and then radiomics features were extracted. The least absolute shrinkage and selection operator (LASSO) was used to select features. The final selected radiomics features of LNs were used as predictors to construct a machine learning model to predict LN metastasis. Results The authors determined two morphological coefficient characteristics of cervical lesions (shape - major axis length and shape - mesh volume), one first order characteristics of LNs (first order - 10 percentile) and two gray-level co-occurrence matrix (GLCM) characteristics of LNs (GLCM - id and GLCM - inverse variance) were closely related to LN metastasis. Finally, a neural network was constructed based on the radiomic features of the LNs. The area under the curve of receiver operating characteristic (AUC-ROC) of the model was 0.983 in the training set and 0.860 in the test set. Conclusion The authors constructed and demonstrated a neural network based on radiomics features of PET/CT to evaluate the risk of single LN metastasis in early-stage cervical cancer.
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Affiliation(s)
- Shimin Yang
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
| | - Wenrui Zhang
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People’s Republic of China
| | - Chunli Liu
- Shanghai Universal Medical Imaging Diagnostic Center, Shanghai, People’s Republic of China
| | - Chunbo Li
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital of Fudan University
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20
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Jin J, Jiang Y, Zhao YL, Huang PT. Radiomics-based Machine Learning to Predict the Recurrence of Hepatocellular Carcinoma: A Systematic Review and Meta-analysis. Acad Radiol 2024; 31:467-479. [PMID: 37867018 DOI: 10.1016/j.acra.2023.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 08/31/2023] [Accepted: 09/04/2023] [Indexed: 10/24/2023]
Abstract
RATIONALE AND OBJECTIVES Recurrence of hepatocellular carcinoma (HCC) is a major concern in its management. Accurately predicting the risk of recurrence is crucial for determining appropriate treatment strategies and improving patient outcomes. A certain amount of radiomics models for HCC recurrence prediction have been proposed. This study aimed to assess the role of radiomics models in the prediction of HCC recurrence and to evaluate their methodological quality. MATERIALS AND METHODS Databases Cochrane Library, Web of Science, PubMed, and Embase were searched until July 11, 2023 for studies eligible for the meta-analysis. Their methodological quality was evaluated using the Radiomics Quality Score (RQS). The predictive ability of the radiomics model, clinical model, and the combined model integrating the clinical characteristics with radiomics signatures was measured using the concordance index (C-index), sensitivity, and specificity. Radiomics models in included studies were compared based on different imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound/sonography (US), contrast-enhanced ultrasound (CEUS). RESULTS A total of 49 studies were included. On the validation cohort, radiomics model performed better (CT: C-index = 0.747, 95% CI: 0.70-0.79; MRI: C-index = 0.788, 95% CI: 0.75-0.83; CEUS: C-index = 0.763, 95% CI: 0.60-0.93) compared to the clinical model (C-index = 0.671, 95% CI: 0.65-0.70), except for ultrasound-based models (C-index = 0.560, 95% CI: 0.53-0.59). The combined model outperformed other models (CT: C-index = 0.790, 95% CI: 0.76-0.82; MRI: C-index = 0.826, 95% CI: 0.79-0.86; US: C-index = 0.760, 95% CI: 0.65-0.87), except for CEUS-based combined models (C-index = 0.707, 95% CI: 0.44-0.97). CONCLUSION Radiomics holds the potential to predict HCC recurrence and demonstrates enhanced predictive value across various imaging modalities when integrated with clinical features. Nevertheless, further studies are needed to optimize the radiomics approach and validate the results in larger, multi-center cohorts.
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Affiliation(s)
- Jin Jin
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.)
| | - Ying Jiang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.)
| | - Yu-Lan Zhao
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.)
| | - Pin-Tong Huang
- Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (J.J., Y.J., Y.-L.Z., P.-L.H.); Research Center of Ultrasound in Medicine and Biomedical Engineering, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, P.R. China (P.-L.H.); Research Center for Life Science and Human Health, Binjiang Institute of Zhejiang University, Hangzhou, P.R. China (P.-L.H.).
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21
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Liu H, Cui Y, Chang C, Zhou Z, Zhang Y, Ma C, Yin Y, Wang R. Development and validation of a 18F-FDG PET/CT radiomics nomogram for predicting progression free survival in locally advanced cervical cancer: a retrospective multicenter study. BMC Cancer 2024; 24:150. [PMID: 38291351 PMCID: PMC10826285 DOI: 10.1186/s12885-024-11917-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 01/24/2024] [Indexed: 02/01/2024] Open
Abstract
BACKGROUND The existing staging system cannot meet the needs of accurate survival prediction. Accurate survival prediction for locally advanced cervical cancer (LACC) patients who have undergone concurrent radiochemotherapy (CCRT) can improve their treatment management. Thus, this present study aimed to develop and validate radiomics models based on pretreatment 18Fluorine-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)-computed tomography (CT) images to accurately predict the prognosis in patients. METHODS The data from 190 consecutive patients with LACC who underwent pretreatment 18F-FDG PET-CT and CCRT at two cancer hospitals were retrospectively analyzed; 176 patients from the same hospital were randomly divided into training (n = 117) and internal validation (n = 50) cohorts. Clinical features were selected from the training cohort using univariate and multivariate Cox proportional hazards models; radiomic features were extracted from PET and CT images and filtered using least absolute shrinkage and selection operator and Cox proportional hazard regression. Three prediction models and a nomogram were then constructed using the previously selected clinical, CT and PET radiomics features. The external validation cohort that was used to validate the models included 23 patients with LACC from another cancer hospital. The predictive performance of the constructed models was evaluated using receiver operator characteristic curves, Kaplan Meier curves, and a nomogram. RESULTS In total, one clinical, one PET radiomics, and three CT radiomics features were significantly associated with progression-free survival in the training cohort. Across all three cohorts, the combined model displayed better efficacy and clinical utility than any of these parameters alone in predicting 3-year progression-free survival (area under curve: 0.661, 0.718, and 0.775; C-index: 0.698, 0.724, and 0.705, respectively) and 5-year progression-free survival (area under curve: 0.661, 0.711, and 0.767; C-index, 0.698, 0.722, and 0.676, respectively). On subsequent construction of a nomogram, the calibration curve demonstrated good agreement between actually observed and nomogram-predicted values. CONCLUSIONS In this study, a clinico-radiomics prediction model was developed and successfully validated using an independent external validation cohort. The nomogram incorporating radiomics and clinical features could be a useful clinical tool for the early and accurate assessment of long-term prognosis in patients with LACC patients who undergo concurrent chemoradiotherapy.
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Affiliation(s)
- Huiling Liu
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
| | - Yongbin Cui
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Cheng Chang
- Department of Nuclear Medicine, Third Affiliated Hospital of Xinjiang Medical University, State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia, Urumqi, China
| | - Zichun Zhou
- School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China
| | - Yalin Zhang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China
- Xinjiang Key Laboratory of Oncology, Urumqi, China
- Key Laboratory of Cancer Immunotherapy and Radiotherapy, Chinese Academy of Medical Sciences, Urumqi, China
| | - Changsheng Ma
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China.
| | - Ruozheng Wang
- Department of Radiation Oncology, The Third Affillated Teaching Hospital of Xinjiang Medical University, Affilated Cancer Hospital, Urumuqi, China.
- Xinjiang Key Laboratory of Oncology, Urumqi, China.
- Key Laboratory of Cancer Immunotherapy and Radiotherapy, Chinese Academy of Medical Sciences, Urumqi, China.
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Brancato V, Cerrone M, Garbino N, Salvatore M, Cavaliere C. Current status of magnetic resonance imaging radiomics in hepatocellular carcinoma: A quantitative review with Radiomics Quality Score. World J Gastroenterol 2024; 30:381-417. [PMID: 38313230 PMCID: PMC10835534 DOI: 10.3748/wjg.v30.i4.381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/05/2023] [Accepted: 01/10/2024] [Indexed: 01/26/2024] Open
Abstract
BACKGROUND Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) for different tasks related to the management of patients with hepatocellular carcinoma (HCC). However, its implementation in clinical practice is still far, with many issues related to the methodological quality of radiomic studies. AIM To systematically review the current status of MRI radiomic studies concerning HCC using the Radiomics Quality Score (RQS). METHODS A systematic literature search of PubMed, Google Scholar, and Web of Science databases was performed to identify original articles focusing on the use of MRI radiomics for HCC management published between 2017 and 2023. The methodological quality of radiomic studies was assessed using the RQS tool. Spearman's correlation (ρ) analysis was performed to explore if RQS was correlated with journal metrics and characteristics of the studies. The level of statistical signi-ficance was set at P < 0.05. RESULTS One hundred and twenty-seven articles were included, of which 43 focused on HCC prognosis, 39 on prediction of pathological findings, 16 on prediction of the expression of molecular markers outcomes, 18 had a diagnostic purpose, and 11 had multiple purposes. The mean RQS was 8 ± 6.22, and the corresponding percentage was 24.15% ± 15.25% (ranging from 0.0% to 58.33%). RQS was positively correlated with journal impact factor (IF; ρ = 0.36, P = 2.98 × 10-5), 5-years IF (ρ = 0.33, P = 1.56 × 10-4), number of patients included in the study (ρ = 0.51, P < 9.37 × 10-10) and number of radiomics features extracted in the study (ρ = 0.59, P < 4.59 × 10-13), and time of publication (ρ = -0.23, P < 0.0072). CONCLUSION Although MRI radiomics in HCC represents a promising tool to develop adequate personalized treatment as a noninvasive approach in HCC patients, our study revealed that studies in this field still lack the quality required to allow its introduction into clinical practice.
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Affiliation(s)
- Valentina Brancato
- Department of Information Technology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Cerrone
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Nunzia Garbino
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Marco Salvatore
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
| | - Carlo Cavaliere
- Department of Radiology, IRCCS SYNLAB SDN, Naples 80143, Italy
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Yang Z, Dong H, Fu C, Zhang Z, Hong Y, Shan K, Ma C, Chen X, Xu J, Pang Z, Hou M, Zhang X, Zhu W, Liu L, Li W, Sun J, Zhao F. A nomogram based on CT intratumoral and peritumoral radiomics features preoperatively predicts poorly differentiated invasive pulmonary adenocarcinoma manifesting as subsolid or solid lesions: a double-center study. Front Oncol 2024; 14:1289555. [PMID: 38313797 PMCID: PMC10834705 DOI: 10.3389/fonc.2024.1289555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 01/02/2024] [Indexed: 02/06/2024] Open
Abstract
Background The novel International Association for the Study of Lung Cancer (IASLC) grading system suggests that poorly differentiated invasive pulmonary adenocarcinoma (IPA) has a worse prognosis. Therefore, prediction of poorly differentiated IPA before treatment can provide an essential reference for therapeutic modality and personalized follow-up strategy. This study intended to train a nomogram based on CT intratumoral and peritumoral radiomics features combined with clinical semantic features, which predicted poorly differentiated IPA and was tested in independent data cohorts regarding models' generalization ability. Methods We retrospectively recruited 480 patients with IPA appearing as subsolid or solid lesions, confirmed by surgical pathology from two medical centers and collected their CT images and clinical information. Patients from the first center (n =363) were randomly assigned to the development cohort (n = 254) and internal testing cohort (n = 109) in a 7:3 ratio; patients (n = 117) from the second center served as the external testing cohort. Feature selection was performed by univariate analysis, multivariate analysis, Spearman correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the model performance. Results The AUCs of the combined model based on intratumoral and peritumoral radiomics signatures in internal testing cohort and external testing cohort were 0.906 and 0.886, respectively. The AUCs of the nomogram that integrated clinical semantic features and combined radiomics signatures in internal testing cohort and external testing cohort were 0.921 and 0.887, respectively. The Delong test showed that the AUCs of the nomogram were significantly higher than that of the clinical semantic model in both the internal testing cohort(0.921 vs 0.789, p< 0.05) and external testing cohort(0.887 vs 0.829, p< 0.05). Conclusion The nomogram based on CT intratumoral and peritumoral radiomics signatures with clinical semantic features has the potential to predict poorly differentiated IPA manifesting as subsolid or solid lesions preoperatively.
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Affiliation(s)
- Zebin Yang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Hao Dong
- Department of Radiology, Affiliated Xiaoshan Hospital of Wenzhou Medical University, Hangzhou, China
| | - Chunlong Fu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zening Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yao Hong
- Department of Radiology, Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Yiwu, China
| | - Kangfei Shan
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Chijun Ma
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Xiaolu Chen
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jieping Xu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhenzhu Pang
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Min Hou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiaowei Zhang
- Department of Pathology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Weihua Zhu
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Linjiang Liu
- Medical Imaging Department, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Weihua Li
- Medical Imaging Department, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China
| | - Jihong Sun
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Fourth Affiliated Hospital, College of Medicine, Zhejiang University, Yiwu, China
- Cancer Center, Zhejiang University, Hangzhou, China
| | - Fenhua Zhao
- Department of Radiology, Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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Fusco R, Granata V, Simonetti I, Setola SV, Iasevoli MAD, Tovecci F, Lamanna CMP, Izzo F, Pecori B, Petrillo A. An Informative Review of Radiomics Studies on Cancer Imaging: The Main Findings, Challenges and Limitations of the Methodologies. Curr Oncol 2024; 31:403-424. [PMID: 38248112 PMCID: PMC10814313 DOI: 10.3390/curroncol31010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
The aim of this informative review was to investigate the application of radiomics in cancer imaging and to summarize the results of recent studies to support oncological imaging with particular attention to breast cancer, rectal cancer and primitive and secondary liver cancer. This review also aims to provide the main findings, challenges and limitations of the current methodologies. Clinical studies published in the last four years (2019-2022) were included in this review. Among the 19 studies analyzed, none assessed the differences between scanners and vendor-dependent characteristics, collected images of individuals at additional points in time, performed calibration statistics, represented a prospective study performed and registered in a study database, conducted a cost-effectiveness analysis, reported on the cost-effectiveness of the clinical application, or performed multivariable analysis with also non-radiomics features. Seven studies reached a high radiomic quality score (RQS), and seventeen earned additional points by using validation steps considering two datasets from two distinct institutes and open science and data domains (radiomics features calculated on a set of representative ROIs are open source). The potential of radiomics is increasingly establishing itself, even if there are still several aspects to be evaluated before the passage of radiomics into routine clinical practice. There are several challenges, including the need for standardization across all stages of the workflow and the potential for cross-site validation using real-world heterogeneous datasets. Moreover, multiple centers and prospective radiomics studies with more samples that add inter-scanner differences and vendor-dependent characteristics will be needed in the future, as well as the collecting of images of individuals at additional time points, the reporting of calibration statistics and the performing of prospective studies registered in a study database.
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Affiliation(s)
- Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Naples, Italy;
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Igino Simonetti
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Maria Assunta Daniela Iasevoli
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Filippo Tovecci
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Ciro Michele Paolo Lamanna
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
| | - Francesco Izzo
- Division of Epatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Biagio Pecori
- Division of Radiation Protection and Innovative Technology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale—IRCCS di Napoli, 80131 Naples, Italy (S.V.S.); (M.A.D.I.); (F.T.); (C.M.P.L.); (A.P.)
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Zhou L, Sun J, Long H, Zhou W, Xia R, Luo Y, Fang J, Wang Y, Chen X. Imaging phenotyping using 18F-FDG PET/CT radiomics to predict micropapillary and solid pattern in lung adenocarcinoma. Insights Imaging 2024; 15:5. [PMID: 38185779 PMCID: PMC10772036 DOI: 10.1186/s13244-023-01573-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 11/22/2023] [Indexed: 01/09/2024] Open
Abstract
OBJECTIVES To develop and validate a machine learning model using 18F-FDG PET/CT radiomics signature and clinical features to predict the presence of micropapillary and solid (MP/S) components in lung adenocarcinoma. METHODS Eight hundred and forty-six patients who underwent preoperative PET/CT with pathologically confirmed adenocarcinoma were enrolled. After segmentation, 1688 radiomics features were extracted from PET/CT and selected to construct predictive models. Then, we developed a nomogram based on PET/CT radiomics integrated with clinical features. Receiver operating curves, calibration curves, and decision curve analysis (DCA) were performed for diagnostics assessment and test of the developed models for distinguishing patients with MP/S components from the patients without. RESULTS PET/CT radiomics-clinical combined model could well distinguish patients with MP/S components from those without MP/S components (AUC = 0.87), which performed better than PET (AUC = 0.829, p < 0.05) or CT (AUC = 0.827, p < 0.05) radiomics models in the training cohort. In test cohorts, radiomics-clinical combined model outperformed the PET radiomics model in test cohort 1 (AUC = 0.859 vs 0.799, p < 0.05) and the CT radiomics model in test cohort 2 (AUC = 0.880 vs 0.829, p < 0.05). Calibration curve indicated good coherence between all model prediction and the actual observation in training and test cohorts. DCA revealed PET/CT radiomics-clinical model exerted the highest clinical benefit. CONCLUSION 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of MP/S components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies. The PET/CT radiomics-clinical combined model performed best. CRITICAL RELEVANCE STATEMENT: 18F-FDG PET/CT radiomics signatures could achieve promising prediction efficiency to identify the presence of micropapillary and solid components in adenocarcinoma patients to help the clinician decide on personalized treatment and surveillance strategies.
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Affiliation(s)
- Linyi Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - He Long
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Yi Luo
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fang
- Department of Ultrasound, Daping Hospital, Army Medical University, Chongqing, China.
| | - Yi Wang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
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Martin A, Marcelin C, Petitpierre F, Jambon E, Maaloum R, Grenier N, Le Bras Y, Crombé A. Clinical, Technical, and MRI Features Associated with Patients' Outcome at 3 Months and 2 Years following Prostate Artery Embolization: Is There an Added Value of Radiomics? J Pers Med 2024; 14:67. [PMID: 38248768 PMCID: PMC10817287 DOI: 10.3390/jpm14010067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/26/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024] Open
Abstract
Our aim was to investigate which features were associated with clinical successes at short- and mid-terms following prostate artery embolization (PAE) for symptomatic benign prostate hypertrophy (BPH). All adults treated by PAE for BPH at our referral center between January 2017 and March 2021, with pre-treatment MRI, technical success, and follow-up at 3 months and 2 years were included in this single-center retrospective study. Radiologists reviewed the prostatic protrusion index (PPI), adenomatous dominant BPH (adBPH), and Wasserman classification on pre-treatment MRI. Radiomics analysis was achieved on the transitional zone on pre-treatment T2-weighted imaging (WI) and ADC, and comprised reproducibility assessment, unsupervised classifications, and supervised radiomics scores obtained with cross-validated Elasticnet regressions. Eighty-eight patients were included (median age: 65 years), with 81.8% clinical successes at 3 months and 60.2% at 2 years. No feature was associated with success at 3 months, except the radiomics score trained on T2-WI and ADC (AUROC = 0.694). Regarding success at 2 years, no radiomics approaches provided significant performances; however, Wasserman type-1 and change in international prostate symptom score (IPSS) at 3 months ≤ -35% were associated with success in multivariable analysis (OR = 5.82, p = 0.0296, and OR = 9.04, p = 0.0002). Thus, while radiomics provided limited interest, Wasserman classification and early IPSS changes appeared predictive of mid-term outcomes.
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Affiliation(s)
- Antoine Martin
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
| | - Clément Marcelin
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
- BRIC Bordeaux Institute of Oncology, INSERM U1312, 2 Rue Dr Hoffmann Martinot, F-33000 Bordeaux, France
| | - François Petitpierre
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
- Department of Radiology, Clinique Mutualiste de Pessac, 46 Avenue du Dr Albert Schweitzer, F-33600 Pessac, France
| | - Eva Jambon
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
| | - Rim Maaloum
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
| | - Nicolas Grenier
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
| | - Yann Le Bras
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
| | - Amandine Crombé
- Department of Diagnostic and Interventional Radiology, Pellegrin University Hospital, Place Amélie-Raba-Léon, F-33076 Bordeaux, France (F.P.); (R.M.); (N.G.)
- BRIC Bordeaux Institute of Oncology, INSERM U1312, 2 Rue Dr Hoffmann Martinot, F-33000 Bordeaux, France
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Feliciani G, Serra F, Menghi E, Ferroni F, Sarnelli A, Feo C, Zatelli MC, Ambrosio MR, Giganti M, Carnevale A. Radiomics in the characterization of lipid-poor adrenal adenomas at unenhanced CT: time to look beyond usual density metrics. Eur Radiol 2024; 34:422-432. [PMID: 37566266 DOI: 10.1007/s00330-023-10090-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 06/29/2023] [Accepted: 07/04/2023] [Indexed: 08/12/2023]
Abstract
OBJECTIVES In this study, we developed a radiomic signature for the classification of benign lipid-poor adenomas, which may potentially help clinicians limit the number of unnecessary investigations in clinical practice. Indeterminate adrenal lesions of benign and malignant nature may exhibit different values of key radiomics features. METHODS Patients who had available histopathology reports and a non-contrast-enhanced CT scan were included in the study. Radiomics feature extraction was done after the adrenal lesions were contoured. The primary feature selection and prediction performance scores were calculated using the least absolute shrinkage and selection operator (LASSO). To eliminate redundancy, the best-performing features were further examined using the Pearson correlation coefficient, and new predictive models were created. RESULTS This investigation covered 50 lesions in 48 patients. After LASSO-based radiomics feature selection, the test dataset's 30 iterations of logistic regression models produced an average performance of 0.72. The model with the best performance, made up of 13 radiomics features, had an AUC of 0.99 in the training phase and 1.00 in the test phase. The number of features was lowered to 5 after performing Pearson's correlation to prevent overfitting. The final radiomic signature trained a number of machine learning classifiers, with an average AUC of 0.93. CONCLUSIONS Including more radiomics features in the identification of adenomas may improve the accuracy of NECT and reduce the need for additional imaging procedures and clinical workup, according to this and other recent radiomics studies that have clear points of contact with current clinical practice. CLINICAL RELEVANCE STATEMENT The study developed a radiomic signature using unenhanced CT scans for classifying lipid-poor adenomas, potentially reducing unnecessary investigations that scored a final accuracy of 93%. KEY POINTS • Radiomics has potential for differentiating lipid-poor adenomas and avoiding unnecessary further investigations. • Quadratic mean, strength, maximum 3D diameter, volume density, and area density are promising predictors for adenomas. • Radiomics models reach high performance with average AUC of 0.95 in the training phase and 0.72 in the test phase.
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Affiliation(s)
- Giacomo Feliciani
- Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Francesco Serra
- Department of Translational Medicine - Section of Radiology, University of Ferrara, Ferrara, Italy
| | - Enrico Menghi
- Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy.
| | - Fabio Ferroni
- Radiology Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Anna Sarnelli
- Medical Physics Unit, IRCCS Istituto Romagnolo per lo Studio dei Tumori (IRST) "Dino Amadori", Meldola, Italy
| | - Carlo Feo
- Department of Medical Sciences, University of Ferrara, Ferrara, Italy
| | - Maria Chiara Zatelli
- Department of Medical Sciences - Section of Endocrinology and Internal Medicine, University of Ferrara, Ferrara, Italy
| | - Maria Rosaria Ambrosio
- Department of Medical Sciences - Section of Endocrinology and Internal Medicine, University of Ferrara, Ferrara, Italy
| | - Melchiore Giganti
- Department of Translational Medicine - Section of Radiology, University of Ferrara, Ferrara, Italy
| | - Aldo Carnevale
- Department of Translational Medicine - Section of Radiology, University of Ferrara, Ferrara, Italy
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Hong MP, Zhang R, Fan SJ, Liang YT, Cai HJ, Xu MS, Zhou B, Li LS. Interpretable CT radiomics model for invasiveness prediction in patients with ground-glass nodules. Clin Radiol 2024; 79:e8-e16. [PMID: 37833141 DOI: 10.1016/j.crad.2023.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 09/20/2023] [Accepted: 09/21/2023] [Indexed: 10/15/2023]
Abstract
AIM To evaluate the performance of an interpretable computed tomography (CT) radiomic model in predicting the invasiveness of ground-glass nodules (GGNs). MATERIALS AND METHODS The study was conducted retrospectively from 1 August 2017 to 1 August 2022, at three different centres. Two hundred and thirty patients with GGNs were enrolled at centre I as a training cohort. Centres II (n=157) and III (n=156) formed two external validation cohorts. Radiomics features extracted based on CT were reduced by a coarse-fine feature screening strategy. A radiomic model was developed through the use of the LASSO (least absolute shrinkage and selection operator) and XGBoost algorithms. Then, a radiological model was established through multivariate logistic regression analysis. Finally, the interpretability of the model was explored using SHapley Additive exPlanations (SHAP). RESULTS The radiomic XGBoost model outperformed the radiomic logistic model and radiological model in assessing the invasiveness of GGNs. The area under the curve (AUC) values for the radiomic XGBoost model were 0.885 (95% confidence interval [CI] 0.836-0.923), 0.853 (95% CI 0.790-0.906), and 0.838 (95% CI 0.773-0.902) in the training and the two external validation cohorts, respectively. The SHAP method allowed for both a quantitative and visual representation of how decisions were made using a given model for each individual patient. This can provide a deeper understanding of the decision-making mechanisms within the model and the factors that contribute to its prediction effectiveness. CONCLUSIONS The present interpretable CT radiomics model has the potential to preoperatively evaluate the invasiveness of GGNs. Furthermore, it can provide personalised, image-based clinical-decision support.
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Affiliation(s)
- M P Hong
- Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
| | - R Zhang
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde), Foshan, China
| | - S J Fan
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Y T Liang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - H J Cai
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - M S Xu
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.
| | - B Zhou
- Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.
| | - L S Li
- Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.
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Ge L, Wu J, Jin Y, Xu D, Wang Z. Noninvasive Assessment of Tumor Histological Grade in Invasive Breast Carcinoma Based on Ultrasound Radiomics and Clinical Characteristics: A Multicenter Study. Technol Cancer Res Treat 2024; 23:15330338241257424. [PMID: 38780506 PMCID: PMC11119369 DOI: 10.1177/15330338241257424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 04/16/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
Rationale and Objectives: We aimed to develop and validate prediction models for histological grade of invasive breast carcinoma (BC) based on ultrasound radiomics features and clinical characteristics. Materials and Methods: A number of 383 patients with invasive BC were retrospectively enrolled and divided into a training set (207 patients), internal validation set (90 patients), and external validation set (86 patients). Ultrasound radiomics features were extracted from all the eligible patients. The Boruta method was used to identify the most useful features. Seven classifiers were adopted to developed prediction models. The output of the classifier with best performance was labeled as the radiomics score (Rad-score) and the classifier was selected as the Rad-score model. A combined model combining clinical factors and Rad-score was developed. The performance of the models was evaluated using receiver operating characteristic curve. Results: Seven radiomics features were selected from 788 candidate features. The logistic regression model performing best among the 7 classifiers in the internal and external validation sets was considered as Rad-score model, with areas under the receiver operating characteristic curve (AUC) values of 0.731 and 0.738. The tumor size was screened out as the risk factor and the combined model was developed, with AUC values of 0.721 and 0.737 in the internal and external validation sets. Furthermore, the 10-fold cross-validation demonstrated that the 2 models above were reliable and stable. Conclusion: The Rad-score model and combined model were able to predict histological grade of invasive BC, which may enable tailored therapeutic strategies for patients with BC in routine clinical use.
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Affiliation(s)
- Lifang Ge
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Jiangfeng Wu
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Yun Jin
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
| | - Dong Xu
- Department of Ultrasonography, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Zhengping Wang
- Department of Ultrasonography, Dongyang People's Hospital, Dongyang, Zhejiang, China
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Sheng L, Yang C, Chen Y, Song B. Machine Learning Combined with Radiomics Facilitating the Personal Treatment of Malignant Liver Tumors. Biomedicines 2023; 12:58. [PMID: 38255165 PMCID: PMC10813632 DOI: 10.3390/biomedicines12010058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/13/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
In the realm of managing malignant liver tumors, the convergence of radiomics and machine learning has redefined the landscape of medical practice. The field of radiomics employs advanced algorithms to extract thousands of quantitative features (including intensity, texture, and structure) from medical images. Machine learning, including its subset deep learning, aids in the comprehensive analysis and integration of these features from diverse image sources. This potent synergy enables the prediction of responses of malignant liver tumors to various treatments and outcomes. In this comprehensive review, we examine the evolution of the field of radiomics and its procedural framework. Furthermore, the applications of radiomics combined with machine learning in the context of personalized treatment for malignant liver tumors are outlined in aspects of surgical therapy and non-surgical treatments such as ablation, transarterial chemoembolization, radiotherapy, and systemic therapies. Finally, we discuss the current challenges in the amalgamation of radiomics and machine learning in the study of malignant liver tumors and explore future opportunities.
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Affiliation(s)
- Liuji Sheng
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Chongtu Yang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yidi Chen
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, China; (L.S.); (C.Y.)
- Functional and Molecular Imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China
- Department of Radiology, Sanya People’s Hospital, Sanya 572000, China
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Zheng X, Huang Y, Lin Y, Zhu T, Zou J, Wang S, Wang K. 18F-FDG PET/CT-based deep learning radiomics predicts 5-years disease-free survival after failure to achieve pathologic complete response to neoadjuvant chemotherapy in breast cancer. EJNMMI Res 2023; 13:105. [PMID: 38052965 DOI: 10.1186/s13550-023-01053-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 11/19/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND This study aimed to assess whether a combined model incorporating radiomic and depth features extracted from PET/CT can predict disease-free survival (DFS) in patients who failed to achieve pathologic complete response (pCR) after neoadjuvant chemotherapy. RESULTS This study retrospectively included one hundred and five non-pCR patients. After a median follow-up of 71 months, 15 and 7 patients experienced recurrence and death, respectively. The primary tumor volume underwent feature extraction, yielding a total of 3644 radiomic features and 4096 depth features. The modeling procedure employed Cox regression for feature selection and utilized Cox proportional-hazards models to make predictions on DFS. Time-dependent receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were utilized to evaluate and compare the predictive performance of different models. 2 clinical features (RCB, cT), 4 radiomic features, and 7 depth features were significant predictors of DFS and were included to develop models. The integrated model incorporating RCB, cT, and radiomic and depth features extracted from PET/CT images exhibited the highest accuracy for predicting 5-year DFS in the training (AUC 0.943) and the validation cohort (AUC 0.938). CONCLUSION The integrated model combining radiomic and depth features extracted from PET/CT images can accurately predict 5-year DFS in non-pCR patients. It can help identify patients with a high risk of recurrence and strengthen adjuvant therapy to improve survival.
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Affiliation(s)
- Xingxing Zheng
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yuhong Huang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yingyi Lin
- Shantou University Medical College, Shantou, China
| | - Teng Zhu
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Jiachen Zou
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Medical University, Zhanjiang, China
| | - Shuxia Wang
- Department of Nuclear Medicine and PET Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
| | - Kun Wang
- Department of Breast Cancer, Cancer Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
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Zhou T, Tu W, Dong P, Duan S, Zhou X, Ma Y, Wang Y, Liu T, Zhang H, Feng Y, Huang W, Ge Y, Liu S, Li Z, Fan L. CT-Based Radiomic Nomogram for the Prediction of Chronic Obstructive Pulmonary Disease in Patients with Lung cancer. Acad Radiol 2023; 30:2894-2903. [PMID: 37062629 DOI: 10.1016/j.acra.2023.03.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 04/18/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and validate a model for predicting chronic obstructive pulmonary disease (COPD) in patients with lung cancer based on computed tomography (CT) radiomic signatures and clinical and imaging features. MATERIALS AND METHODS We retrospectively enrolled 443 patients with lung cancer who underwent pulmonary function test as the primary cohort. They were randomly assigned to the training (n = 311) or validation (n = 132) set in a 7:3 ratio. Additionally, an independent external cohort of 54 patients was evaluated. The radiomic lung nodule signature was constructed using the least absolute shrinkage and selection operator algorithm, while key variables were selected using logistic regression to develop the clinical and combined models presented as a nomogram. RESULTS COPD was significantly related to the radiomics signature in both cohorts. Moreover, the signature served as an independent predictor of COPD in the multivariate regression analysis. For the training, internal, and external cohorts, the area under the receiver operating characteristic curve (ROC, AUC) values of our radiomics signature for COPD prediction were 0.85, 0.85, and 0.76, respectively. Additionally, the AUC values of the radiomic nomogram for COPD prediction were 0.927, 0.879, and 0.762 for the three cohorts, respectively, which outperformed the other two models. CONCLUSION The present study presents a nomogram that incorporates radiomics signatures and clinical and radiological features, which could be used to predict the risk of COPD in patients with lung cancer with one-stop chest CT scanning.
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Affiliation(s)
- TaoHu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China; School of Medical Imaging, Weifang Medical University, Weifang, SD, China
| | - WenTing Tu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Peng Dong
- School of Medical Imaging, Weifang Medical University, Weifang, SD, China
| | - ShaoFeng Duan
- GE Healthcare, Precision Health Institution, Shanghai, China
| | - XiuXiu Zhou
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - YanQing Ma
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital of Hangzhou Medical College, Hangzhou, ZJ, China
| | - Yun Wang
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Tian Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - HanXiao Zhang
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, JS, China
| | - Yan Feng
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - WenJun Huang
- School of Medical Imaging, Weifang Medical University, Weifang, SD, China
| | - YanMing Ge
- Medical Imaging Center, Affiliated Hospital of Weifang Medical University, Weifang, SD, China
| | - Shiyuan Liu
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China
| | - Zhaobin Li
- Department of Radiation Oncology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Li Fan
- Department of Radiology, Second Affiliated Hospital of Naval Medical University, No. 415 Fengyang Road, Shanghai 200003, China.
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Chen Y, Kan K, Liu S, Lin H, Lue K. Impact of respiratory motion on 18 F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner. J Appl Clin Med Phys 2023; 24:e14200. [PMID: 37937706 PMCID: PMC10691638 DOI: 10.1002/acm2.14200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/13/2023] [Accepted: 10/30/2023] [Indexed: 11/09/2023] Open
Abstract
PURPOSE 18 F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on 18 F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical 18 F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner. MATERIALS AND METHODS A total of 101 patients who underwent oncological 18 F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. 18 F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability. RESULTS In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most 18 F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features. CONCLUSION Respiratory motion has a significant impact on 18 F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.
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Affiliation(s)
- Yu‐Hung Chen
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- School of MedicineCollege of MedicineTzu Chi UniversityHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Kuo‐Yi Kan
- Department of Nuclear MedicineFu Jen Catholic University HospitalNew Taipei CityTaiwan
| | - Shu‐Hsin Liu
- Department of Nuclear MedicineHualien Tzu Chi HospitalBuddhist Tzu Chi Medical FoundationHualienTaiwan
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
| | - Hsin‐Hon Lin
- Department of Medical Imaging and Radiological SciencesCollege of MedicineChang Gung UniversityTaoyuanTaiwan
- Department of Nuclear MedicineChang Gung Memorial HospitalLinkouTaiwan
| | - Kun‐Han Lue
- Department of Medical Imaging and Radiological SciencesTzu Chi University of Science and TechnologyHualienTaiwan
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Liu J, Sun L, Zhao X, Lu X. Development and validation of a combined nomogram for predicting perineural invasion status in rectal cancer via computed tomography-based radiomics. J Cancer Res Ther 2023; 19:1552-1559. [PMID: 38156921 DOI: 10.4103/jcrt.jcrt_2633_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 07/05/2023] [Indexed: 01/03/2024]
Abstract
AIM This study aimed to create and validate a clinic-radiomics nomogram based on computed tomography (CT) imaging for predicting preoperative perineural invasion (PNI) of rectal cancer (RC). MATERIAL AND METHODS This study enrolled 303 patients with RC who were divided into training (n = 242) and test datasets (n = 61) in an 8:2 ratio with all their clinical outcomes. A total of 3,296 radiomic features were extracted from CT images. Five machine learning (ML) models (logistic regression (LR)/K-nearest neighbor (KNN)/multilayer perceptron (MLP)/support vector machine (SVM)/light gradient boosting machine (LightGBM)) were developed using radiomic features derived from the arterial and venous phase images, and the model with the best diagnostic performance was selected. By combining the radiomics and clinical signatures, a fused nomogram model was constructed. RESULTS After using the Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) to remove redundant features, the MLP model proved to be the most efficient among the five ML models. The fusion nomogram based on MLP prediction probability further improves the ability to predict the PNI status. The area under the curve (AUC) of the training and test sets was 0.883 and 0.889, respectively, which were higher than those of the clinical (training set, AUC = 0.710; test set, AUC = 0.762) and radiomic models (training set, AUC = 0.840; test set, AUC = 0.834). CONCLUSIONS The clinical-radiomics combined nomogram model based on enhanced CT images efficiently predicted the PNI status of patients with RC.
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Affiliation(s)
- Jiaxuan Liu
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
| | - Lingling Sun
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
| | - Xiang Zhao
- Institute of Innovative Science and Technology, Shenyang University, Liaoning, China
| | - Xi Lu
- Department of Radiology, The Fourth Affiliated Hospital of China Medical University, Liaoning, China
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Crombé A, Spinnato P, Italiano A, Brisse HJ, Feydy A, Fadli D, Kind M. Radiomics and artificial intelligence for soft-tissue sarcomas: Current status and perspectives. Diagn Interv Imaging 2023; 104:567-583. [PMID: 37802753 DOI: 10.1016/j.diii.2023.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 09/18/2023] [Accepted: 09/19/2023] [Indexed: 10/08/2023]
Abstract
This article proposes a summary of the current status of the research regarding the use of radiomics and artificial intelligence to improve the radiological assessment of patients with soft tissue sarcomas (STS), a heterogeneous group of rare and ubiquitous mesenchymal malignancies. After a first part explaining the principle of radiomics approaches, from raw image post-processing to extraction of radiomics features mined with unsupervised and supervised machine-learning algorithms, and the current research involving deep learning algorithms in STS, especially convolutional neural networks, this review details their main research developments since the formalisation of 'radiomics' in oncologic imaging in 2010. This review focuses on CT and MRI and does not involve ultrasonography. Radiomics and deep radiomics have been successfully applied to develop predictive models to discriminate between benign soft-tissue tumors and STS, to predict the histologic grade (i.e., the most important prognostic marker of STS), the response to neoadjuvant chemotherapy and/or radiotherapy, and the patients' survivals and probability for presenting distant metastases. The main findings, limitations and expectations are discussed for each of these outcomes. Overall, after a first decade of publications emphasizing the potential of radiomics through retrospective proof-of-concept studies, almost all positive but with heterogeneous and often non-replicable methods, radiomics is now at a turning point in order to provide robust demonstrations of its clinical impact through open-science, independent databases, and application of good and standardized practices in radiomics such as those provided by the Image Biomarker Standardization Initiative, without forgetting innovative research paths involving other '-omics' data to better understand the relationships between imaging of STS, gene-expression profiles and tumor microenvironment.
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Affiliation(s)
- Amandine Crombé
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France; Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France; 'Sarcotarget' team, BRIC INSERM U1312 and Bordeaux University, 33000 Bordeaux France.
| | - Paolo Spinnato
- Diagnostic and Interventional Radiology, IRCCS Istituto Ortopedico Rizzoli, Bologna 40136, Italy
| | | | | | - Antoine Feydy
- Department of Radiology, Hopital Cochin-AP-HP, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006 Paris, France
| | - David Fadli
- Department of Radiology, Pellegrin University Hospital, 33000 Bordeaux, France
| | - Michèle Kind
- Department of Oncologic Imaging, Bergonié Institute, 33076 Bordeaux, France
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Wang P, Xie S, Wu Q, Weng L, Hao Z, Yuan P, Zhang C, Gao W, Wang S, Zhang H, Song Y, He J, Gao Y. Model incorporating multiple diffusion MRI features: development and validation of a radiomics-based model to predict adult-type diffuse gliomas grade. Eur Radiol 2023; 33:8809-8820. [PMID: 37439936 PMCID: PMC10667393 DOI: 10.1007/s00330-023-09861-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 05/06/2023] [Accepted: 05/14/2023] [Indexed: 07/14/2023]
Abstract
OBJECTIVES To develop and validate a radiomics-based model (ADGGIP) for predicting adult-type diffuse gliomas (ADG) grade by combining multiple diffusion modalities and clinical and imaging morphologic features. METHODS In this prospective study, we recruited 103 participants diagnosed with ADG and collected their preoperative conventional MRI and multiple diffusion imaging (diffusion tensor imaging, diffusion kurtosis imaging, neurite orientation dispersion and density imaging, and mean apparent propagator diffusion-MRI) data in our hospital, as well as clinical information. Radiomic features of the diffusion images and clinical information and morphological data from the radiological reports were extracted, and multiple pipelines were used to construct the optimal model. Model validation was performed through a time-independent validation cohort. ROC curves were used to evaluate model performance. The clinical benefit was determined by decision curve analysis. RESULTS From June 2018 to May 2021, 72 participants were recruited for the training cohort. Between June 2021 and February 2022, 31 participants were enrolled in the prospective validation cohort. In the training cohort (AUC 0.958), internal validation cohort (0.942), and prospective validation cohort (0.880), ADGGIP had good accuracy in predicting ADG grade. ADGGIP was also significantly better than the single-modality prediction model (AUC 0.860) and clinical imaging morphology model (0.841) (all p < .01) in the prospective validation cohort. When the threshold probability was greater than 5%, ADGGIP provided the greatest net benefit. CONCLUSION ADGGIP, which is based on advanced diffusion modalities, can predict the grade of ADG with high accuracy and robustness and can help improve clinical decision-making. CLINICAL RELEVANCE STATEMENT Integrated multi-modal predictive modeling is beneficial for early detection and treatment planning of adult-type diffuse gliomas, as well as for investigating the genuine clinical significance of biomarkers. KEY POINTS • Integrated model exhibits the highest performance and stability. • When the threshold is greater than 5%, the integrated model has the greatest net benefit. • The advanced diffusion models do not demonstrate better performance than the simple technology.
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Affiliation(s)
- Peng Wang
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
- Inner Mongolia Medical University, Hohhot, 010110, China
| | - Shenghui Xie
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Qiong Wu
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Lixin Weng
- Inner Mongolia Medical University, Hohhot, 010110, China
- Department of Pathology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Zhiyue Hao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Pengxuan Yuan
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Chi Zhang
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Weilin Gao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China
| | - Shaoyu Wang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Huapeng Zhang
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Yang Song
- MR Scientific Marketing, Siemens Healthineers, Shanghai, 201318, China
| | - Jinlong He
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China.
| | - Yang Gao
- Department of Radiology, Affiliated Hospital of Inner Mongolia Medical University, Hohhot, 010059, China.
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Lan L, Feng K, Wu Y, Zhang W, Wei L, Che H, Xue L, Gao Y, Tao J, Qian S, Cao W, Zhang J, Wang C, Tian M. Phenomic Imaging. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:597-612. [PMID: 38223684 PMCID: PMC10781914 DOI: 10.1007/s43657-023-00128-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 08/13/2023] [Accepted: 08/17/2023] [Indexed: 01/16/2024]
Abstract
Human phenomics is defined as the comprehensive collection of observable phenotypes and characteristics influenced by a complex interplay among factors at multiple scales. These factors include genes, epigenetics at the microscopic level, organs, microbiome at the mesoscopic level, and diet and environmental exposures at the macroscopic level. "Phenomic imaging" utilizes various imaging techniques to visualize and measure anatomical structures, biological functions, metabolic processes, and biochemical activities across different scales, both in vivo and ex vivo. Unlike conventional medical imaging focused on disease diagnosis, phenomic imaging captures both normal and abnormal traits, facilitating detailed correlations between macro- and micro-phenotypes. This approach plays a crucial role in deciphering phenomes. This review provides an overview of different phenomic imaging modalities and their applications in human phenomics. Additionally, it explores the associations between phenomic imaging and other omics disciplines, including genomics, transcriptomics, proteomics, immunomics, and metabolomics. By integrating phenomic imaging with other omics data, such as genomics and metabolomics, a comprehensive understanding of biological systems can be achieved. This integration paves the way for the development of new therapeutic approaches and diagnostic tools.
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Affiliation(s)
- Lizhen Lan
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Kai Feng
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Yudan Wu
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Wenbo Zhang
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Ling Wei
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Huiting Che
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Le Xue
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang China
| | - Yidan Gao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Ji Tao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Shufang Qian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009 Zhejiang China
| | - Wenzhao Cao
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Fudan University, Shanghai, 200040 China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
| | - Mei Tian
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Pudong New District, Shanghai, 201203 China
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Lin CH, Yan JL, Yap WK, Kang CJ, Chang YC, Tsai TY, Chang KP, Liao CT, Hsu CL, Chou WC, Wang HM, Huang PW, Fan KH, Huang BS, Tung-Chieh Chang J, Tu SJ, Lin CY. Prognostic value of interim CT-based peritumoral and intratumoral radiomics in laryngeal and hypopharyngeal cancer patients undergoing definitive radiotherapy. Radiother Oncol 2023; 189:109938. [PMID: 37806562 DOI: 10.1016/j.radonc.2023.109938] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/20/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND AND PURPOSE We aimed to investigate the prognostic value of peritumoral and intratumoral computed tomography (CT)-based radiomics during the course of radiotherapy (RT) in patients with laryngeal and hypopharyngeal cancer (LHC). MATERIALS AND METHODS A total of 92 eligible patients were 1:1 randomly assigned into training and validation cohorts. Pre-RT and mid-RT radiomic features were extracted from pre-treatment and interim CT. LASSO-Cox regression was used for feature selection and model construction. Time-dependent area under the receiver operating curve (AUC) analysis was applied to evaluate the models' prognostic performances. Risk stratification ability on overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method and Cox regression. The associations between radiomics and clinical parameters as well as circulating lymphocyte counts were also evaluated. RESULTS The mid-RT peritumoral (AUC: 0.77) and intratumoral (AUC: 0.79) radiomic models yielded better performance for predicting OS than the pre-RT intratumoral model (AUC: 0.62) in validation cohort. This was confirmed by Kaplan-Meier analysis, in which risk stratification depended on the mid-RT peritumoral (p = 0.009) and intratumoral (p = 0.003) radiomics could be improved for OS, in comparison to the pre-RT intratumoral radiomics (p = 0.199). Multivariate analysis identified mid-RT peritumoral and intratumoral radiomic models as independent prognostic factors for both OS and PFS. Mid-RT peritumoral and intratumoral radiomics were correlated with treatment-related lymphopenia. CONCLUSION Mid-RT peritumoral and intratumoral radiomic models are promising image biomarkers that could have clinical utility for predicting OS and PFS in patients with LHC treated with RT.
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Affiliation(s)
- Chia-Hsin Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Jiun-Lin Yan
- Department of Neurosurgery, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan; School of Traditional Chinese Medicine, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
| | - Wing-Keen Yap
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
| | - Chung-Jan Kang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Yun-Chen Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Tsung-You Tsai
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kai-Ping Chang
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Chun-Ta Liao
- Department of Otorhinolaryngology, Head and Neck Surgery, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Cheng-Lung Hsu
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Wen-Chi Chou
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Hung-Ming Wang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Pei-Wei Huang
- Department of Hematology-Oncology, Chang Gung Memorial Hospital, Medical College of Chang Gung University, Taoyuan, Taiwan.
| | - Kang-Hsing Fan
- Department of Radiation Oncology, New Taipei Municipal Tucheng Hospital, New Taipei City, Taiwan.
| | - Bing-Shen Huang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Graduate Institute of Clinical Medical Science, Chang Gung University, Taoyuan, Taiwan.
| | - Joseph Tung-Chieh Chang
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan; Department of Radiation Oncology, Xiamen Chang Gung Memorial Hospital, Xiamen, Fujian, China.
| | - Shu-Ju Tu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Medical Imaging and Intervention, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan.
| | - Chien-Yu Lin
- Proton and Radiation Therapy Center, Chang Gung Memorial Hospital-Linkou Medical Center, Department of Radiation Oncology, Chang Gung University, Taoyuan, Taiwan.
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Huang W, Xiong W, Tang L, Chen C, Yuan Q, Zhang C, Zhou K, Sun Z, Zhang T, Han Z, Feng H, Liang X, Zhong Y, Deng H, Yu L, Xu Y, Wang W, Shen L, Li G, Jiang Y. Non-invasive CT imaging biomarker to predict immunotherapy response in gastric cancer: a multicenter study. J Immunother Cancer 2023; 11:e007807. [PMID: 38179695 PMCID: PMC10668251 DOI: 10.1136/jitc-2023-007807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/24/2023] [Indexed: 01/06/2024] Open
Abstract
BACKGROUND Despite remarkable benefits have been provided by immune checkpoint inhibitors in gastric cancer (GC), predictions of treatment response and prognosis remain unsatisfactory, making identifying biomarkers desirable. The aim of this study was to develop and validate a CT imaging biomarker to predict the immunotherapy response in patients with GC and investigate the associated immune infiltration patterns. METHODS This retrospective study included 294 GC patients who received anti-PD-1/PD-L1 immunotherapy from three independent medical centers between January 2017 and April 2022. A radiomics score (RS) was developed from the intratumoral and peritumoral features on pretreatment CT images to predict immunotherapy-related progression-free survival (irPFS). The performance of the RS was evaluated by the area under the time-dependent receiver operating characteristic curve (AUC). Multivariable Cox regression analysis was performed to construct predictive nomogram of irPFS. The C-index was used to determine the performance of the nomogram. Bulk RNA sequencing of tumors from 42 patients in The Cancer Genome Atlas was used to investigate the RS-associated immune infiltration patterns. RESULTS Overall, 89 of 294 patients (median age, 57 years (IQR 48-66 years); 171 males) had an objective response to immunotherapy. The RS included 13 CT features that yielded AUCs of 12-month irPFS of 0.787, 0.810 and 0.785 in the training, internal validation, and external validation 1 cohorts, respectively, and an AUC of 24-month irPFS of 0.805 in the external validation 2 cohort. Patients with low RS had longer irPFS in each cohort (p<0.05). Multivariable Cox regression analyses showed RS is an independent prognostic factor of irPFS. The nomogram that integrated the RS and clinical characteristics showed improved performance in predicting irPFS, with C-index of 0.687-0.778 in the training and validation cohorts. The CT imaging biomarker was associated with M1 macrophage infiltration. CONCLUSION The findings of this prognostic study suggest that the non-invasive CT imaging biomarker can effectively predict immunotherapy outcomes in patients with GC and is associated with innate immune signaling, which can serve as a potential tool for individual treatment decisions.
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Affiliation(s)
- Weicai Huang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Cheng Zhang
- Department of Gastrointestinal Oncology, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Kangneng Zhou
- University of Science and Technology, Beijing, China
| | - Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Zhen Han
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Hao Feng
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Xiaokun Liang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, China
- Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong, China
| | - Yonghong Zhong
- Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Haijun Deng
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Lequan Yu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China
| | - Yikai Xu
- Department of Medical Imaging Center, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Wei Wang
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Southern Medical University Nanfang Hospital, Guangzhou, Guangdong, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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Akin O, Lema-Dopico A, Paudyal R, Konar AS, Chenevert TL, Malyarenko D, Hadjiiski L, Al-Ahmadie H, Goh AC, Bochner B, Rosenberg J, Schwartz LH, Shukla-Dave A. Multiparametric MRI in Era of Artificial Intelligence for Bladder Cancer Therapies. Cancers (Basel) 2023; 15:5468. [PMID: 38001728 PMCID: PMC10670574 DOI: 10.3390/cancers15225468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging-Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.
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Affiliation(s)
- Oguz Akin
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alfonso Lema-Dopico
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | - Ramesh Paudyal
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | | | | | - Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Lubomir Hadjiiski
- Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Hikmat Al-Ahmadie
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Alvin C. Goh
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Bernard Bochner
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jonathan Rosenberg
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Lawrence H. Schwartz
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medical Physics, Memorial Sloan Kettering Cancer, New York, NY 10065, USA
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Yang Q, Huang H, Zhang G, Weng N, Ou Z, Sun M, Luo H, Zhou X, Gao Y, Wu X. Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study. Thorac Cancer 2023; 14:3266-3274. [PMID: 37743537 PMCID: PMC10665784 DOI: 10.1111/1759-7714.15117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND In view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti-programmed death-1 (anti-PD-1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast-enhanced CT (CECT) radiomics features. METHODS Radiomic analysis of images was performed retrospectively for image samples before and after anti-PD-1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples were included in this study. Quantitative radiomic features were extracted from the images, and the least absolute shrinkage and selection operator method was applied to select radiomic features. After obtaining selected features, three classification models were used to establish a radiomics model to predict the ESCC status and efficacy of therapy. A cross-validation strategy utilizing three folds was employed to train and test the model. Performance evaluation of the model was done using the area under the curve (AUC) of receiver operating characteristic, sensitivity, specificity, and precision metric. RESULTS Wavelet and area of gray level change (log-sigma) were the most significant radiomic features for predicting therapy efficacy. Fifteen radiomic features from the whole tumor and peritumoral regions were selected and comprised of the fusion radiomics score. A radiomics classification was developed with AUC of 0.82 and 0.884 in the before and after-therapy cohorts, respectively. CONCLUSIONS The combined model incorporating radiomic features and clinical CECT predictors helps to predict the response to anti-PD-1therapy in patients with ESCC.
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Affiliation(s)
- Qinzhu Yang
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
| | - Haofan Huang
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
- Department of Biomedical EngineeringHong Kong Polytechnic UniversityHong Kong SARChina
| | - Guizhi Zhang
- Department of RadiologyThe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Nuoqing Weng
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
| | - Zhenkai Ou
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
| | - Meili Sun
- Department of RadiologySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouChina
| | - Huixing Luo
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
| | - Xuhui Zhou
- Department of RadiologyThe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Yi Gao
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
- Shenzhen Key Laboratory of Precision Medicine for Hematological MalignanciesShenzhenChina
- Marshall Laboratory of Biomedical EngineeringShenzhenChina
| | - Xiaobin Wu
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
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Zhang Y, Zheng J, Huang Z, Teng Y, Chen C, Xu J. Predicting visual recovery in pituitary adenoma patients post-endoscopic endonasal transsphenoidal surgery: Harnessing delta-radiomics of the optic chiasm from MRI. Eur Radiol 2023; 33:7482-7493. [PMID: 37488296 PMCID: PMC10598191 DOI: 10.1007/s00330-023-09963-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 05/18/2023] [Accepted: 05/23/2023] [Indexed: 07/26/2023]
Abstract
OBJECTIVES To investigate whether morphological changes after surgery and delta-radiomics of the optic chiasm obtained from routine MRI could help predict postoperative visual recovery of pituitary adenoma patients. METHODS A total of 130 pituitary adenoma patients were retrospectively enrolled and divided into the recovery group (n = 87) and non-recovery group (n = 43) according to visual outcome 1 year after endoscopic endonasal transsphenoidal surgery. Morphological parameters of the optic chiasm were measured preoperatively and postoperatively, including chiasmal thickness, deformed angle, and suprasellar extension. Delta-radiomics of the optic chiasm were calculated based on features extracted from preoperative and postoperative coronal T2-weighted images, followed by machine learning modeling using least absolute shrinkage and selection operator wrapped with support vector machine through fivefold cross-validation in the development set. The delta-radiomic model was independently evaluated in the test set, and compared with the combined model that incorporated delta-radiomics, significant clinical and morphological parameters. RESULTS Postoperative morphological changes of the optic chiasm could not significantly be used as predictors for the visual outcome. In contrast, the delta-radiomics model represented good performances in predicting visual recovery, with an AUC of 0.821 in the development set and 0.811 in the independent test set. Moreover, the combined model that incorporated age and delta-radiomics features of the optic chiasm achieved the highest AUC of 0.841 and 0.840 in the development set and independent test set, respectively. CONCLUSIONS Our proposed machine learning models based on delta-radiomics of the optic chiasm can be used to predict postoperative visual recovery of pituitary adenoma patients. CLINICAL RELEVANCE STATEMENT Our delta-radiomics-based models from MRI enable accurate visual recovery predictions in pituitary adenoma patients who underwent endoscopic endonasal transsphenoidal surgery, facilitating better clinical decision-making and ultimately improving patient outcomes. KEY POINTS • Prediction of the postoperative visual outcome for pituitary adenoma patients is important but challenging. • Delta-radiomics of the optic chiasm after surgical decompression represented better prognostic performances compared with its morphological changes. • The proposed machine learning models can serve as novel approaches to predict visual recovery for pituitary adenoma patients in clinical practice.
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Affiliation(s)
- Yang Zhang
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Junkai Zheng
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Zhouyang Huang
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Yuen Teng
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China
| | - Chaoyue Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
| | - Jianguo Xu
- Department of Neurosurgery, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
- Department of Radiology, West China Hospital, Sichuan University, No. 37, GuoXue Alley, Chengdu, 610041, China.
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Bizzarri N, Russo L, Dolciami M, Zormpas-Petridis K, Boldrini L, Querleu D, Ferrandina G, Pedone Anchora L, Gui B, Sala E, Scambia G. Radiomics systematic review in cervical cancer: gynecological oncologists' perspective. Int J Gynecol Cancer 2023; 33:1522-1541. [PMID: 37714669 DOI: 10.1136/ijgc-2023-004589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2023] Open
Abstract
OBJECTIVE Radiomics is the process of extracting quantitative features from radiological images, and represents a relatively new field in gynecological cancers. Cervical cancer has been the most studied gynecological tumor for what concerns radiomics analysis. The aim of this study was to report on the clinical applications of radiomics combined and/or compared with clinical-pathological variables in patients with cervical cancer. METHODS A systematic review of the literature from inception to February 2023 was performed, including studies on cervical cancer analysing a predictive/prognostic radiomics model, which was combined and/or compared with a radiological or a clinical-pathological model. RESULTS A total of 57 of 334 (17.1%) screened studies met inclusion criteria. The majority of studies used magnetic resonance imaging (MRI), but positron emission tomography (PET)/computed tomography (CT) scan, CT scan, and ultrasound scan also underwent radiomics analysis. In apparent early-stage disease, the majority of studies (16/27, 59.3%) analysed the role of radiomics signature in predicting lymph node metastasis; six (22.2%) investigated the prediction of radiomics to detect lymphovascular space involvement, one (3.7%) investigated depth of stromal infiltration, and one investigated (3.7%) parametrial infiltration. Survival prediction was evaluated both in early-stage and locally advanced settings. No study focused on the application of radiomics in metastatic or recurrent disease. CONCLUSION Radiomics signatures were predictive of pathological and oncological outcomes, particularly if combined with clinical variables. These may be integrated in a model using different clinical-pathological and translational characteristics, with the aim to tailor and personalize the treatment of each patient with cervical cancer.
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Affiliation(s)
- Nicolò Bizzarri
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Russo
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Miriam Dolciami
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Konstantinos Zormpas-Petridis
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Luca Boldrini
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Denis Querleu
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Gabriella Ferrandina
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Luigi Pedone Anchora
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Benedetta Gui
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
| | - Evis Sala
- Department of Bioimaging, Radiation Oncology and Hematology, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giovanni Scambia
- UOC Ginecologia Oncologica, Dipartimento per la salute della Donna e del Bambino e della Salute Pubblica, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
- Università Cattolica del Sacro Cuore, Rome, Italy
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Chen X, Feng B, Xu K, Chen Y, Duan X, Jin Z, Li K, Li R, Long W, Liu X. Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes. Eur Radiol 2023; 33:6804-6816. [PMID: 37148352 DOI: 10.1007/s00330-023-09690-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/20/2023] [Accepted: 02/27/2023] [Indexed: 05/08/2023]
Abstract
OBJECTIVES Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs). METHODS Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage. The predictive capability of a DLRN incorporating clinical characteristics, subjective CT findings and DLS was evaluated by the area under the curve (AUC) of a receiver operating characteristic curve. RESULTS To construct a DLS, 25 deep learning features with non-zero coefficients were selected from 116 low-risk TETs (subtypes A, AB, and B1) and 141 high-risk TETs (subtypes B2, B3, and C). The combination of subjective CT features such as infiltration and DLS demonstrated the best performance in differentiating TETs risk status. The AUCs in the training, internal validation, external validation 1 and 2 cohorts were 0.959 (95% confidence interval [CI]: 0.924-0.993), 0.868 (95% CI: 0.765-0.970), 0.846 (95% CI: 0.750-0.942), and 0.846 (95% CI: 0.735-0.957), respectively. The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful model. CONCLUSIONS The DLRN comprised of CECT-derived DLS and subjective CT findings showed a high performance in predicting risk status of patients with TETs. CLINICAL RELEVANCE STATEMENT Accurate risk status assessment of thymic epithelial tumors (TETs) may aid in determining whether preoperative neoadjuvant treatment is necessary. A deep learning radiomics nomogram incorporating enhancement CT-based deep learning features, clinical characteristics, and subjective CT findings has the potential to predict the histologic subtypes of TETs, which can facilitate decision-making and personalized therapy in clinical practice. KEY POINTS • A non-invasive diagnostic method that can predict the pathological risk status may be useful for pretreatment stratification and prognostic evaluation in TET patients. • DLRN demonstrated superior performance in differentiating the risk status of TETs when compared to the deep learning signature, radiomics signature, or clinical model. • The DeLong test and decision in curve analysis revealed that the DLRN was the most predictive and clinically useful in differentiating the risk status of TETs.
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Affiliation(s)
- Xiangmeng Chen
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Bao Feng
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Kuncai Xu
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Yehang Chen
- Laboratory of Artificial Intelligence of Biomedicine, Guilin University of Aerospace Technology, Guilin, Guangxi Province, 541004, People's Republic of China
| | - Xiaobei Duan
- Department of Nuclear Medicine, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Zhifa Jin
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Kunwei Li
- Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-Sen University, Zhuhai, Guangdong Province, 519000, People's Republic of China
| | - Ronggang Li
- Department of Pathology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China
| | - Wansheng Long
- Department of Radiology, Jiangmen Central Hospital, Jiangmen, Guangdong Province, 529030, People's Republic of China.
| | - Xueguo Liu
- Department of Radiology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, Guangdong Province, 518107, People's Republic of China.
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Rong XC, Kang YH, Shi GF, Ren JL, Liu YH, Li ZG, Yang G. The use of mammography-based radiomics nomograms for the preoperative prediction of the histological grade of invasive ductal carcinoma. J Cancer Res Clin Oncol 2023; 149:11635-11645. [PMID: 37405478 DOI: 10.1007/s00432-023-05001-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 06/15/2023] [Indexed: 07/06/2023]
Abstract
BACKGROUND Accurate prediction of the grade of invasive ductal carcinoma (IDC) before treatment is vital for individualized therapy and improving patient outcomes. This study aimed to develop and validate a mammography-based radiomics nomogram that would incorporate the radiomics signature and clinical risk factors in the preoperative prediction of the histological grade of IDC. METHODS The data of 534 patients from our hospital with pathologically confirmed IDC (374 in the training cohort and 160 in the validation cohort) were retrospectively analyzed. A total of 792 radiomics features were extracted from the patients' craniocaudal and mediolateral oblique view images. A radiomics signature was generated using the least absolute shrinkage and selection operator method. Multivariate logistic regression was adopted to establish a radiomics nomogram, the utility of which was evaluated using a receiver-operating characteristic curve, calibration curve, and decision curve analysis (DCA). RESULTS The radiomics signature was found to have a significant correlation with histological grade (P < 0.01), but the efficacy of the model is limited. The radiomics nomogram, which incorporated the radiomics signature and spicule sign into mammography, showed good consistency and discrimination in both the training cohort [area under the curve (AUC) = 0.75] and the validation cohort (AUC = 0.75). The calibration curves and DCA demonstrated the clinical usefulness of the proposed radiomics nomogram model. CONCLUSIONS A radiomics nomogram based on the radiomics signature and spicule sign can be used to predict the histological grade of IDC and assist in clinical decision-making for patients with IDC.
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Affiliation(s)
- Xiao-Cui Rong
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
| | - Yi-He Kang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
| | - Gao-Feng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China.
| | - Jia-Liang Ren
- GE Healthcare China, Daxing District, Tongji South Road No.1, Beijing, 100176, China
| | - Yu-Hao Liu
- GE Healthcare China, Daxing District, Tongji South Road No.1, Beijing, 100176, China
| | - Zhi-Gang Li
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
| | - Guang Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, China
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Chu X, Niu L, Yang X, He S, Li A, Chen L, Liang Z, Jing D, Zhou R. Radiomics and deep learning models to differentiate lung adenosquamous carcinoma: A multicenter trial. iScience 2023; 26:107634. [PMID: 37664612 PMCID: PMC10474462 DOI: 10.1016/j.isci.2023.107634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/07/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
Adenosquamous carcinoma (ASC) is frequently misdiagnosed or overlooked in clinical practice due to its dual histological components and potential transformation from either adenocarcinoma (ADC) or squamous cell carcinoma (SCC). Our study aimed to differentiate ASC from ADC and SCC by incorporating features of enhanced CTs and clinical characteristics to build radiomics and deep learning models. The classification models were trained in Xiangya Hospital and validated in two other independent hospitals. The areas under the receiver operating characteristic curves (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were used to estimate the performance. The optimal three-class classification model achieved a maximum AUC of 0.89 and accuracy of 0.81 in external validation sets, AUC of 0.99 and accuracy of 0.99 in the internal test set. These findings highlight the efficacy of our models in differentiating ASC, providing a non-invasive, timely, and accurate diagnostic approach before and during the treatment.
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Affiliation(s)
- Xianjing Chu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Lishui Niu
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Xianghui Yang
- Department of Oncology, Changsha Central Hospital, Changsha 410004, China
| | - Shiqi He
- Department of Computer Science, University of British Columbia, 2329 West Mall, Vancouver, British Columbia, Canada
| | - Aixin Li
- Department of Radiotherapy, The First Affiliated Hospital, Hengyang Medical School, University of South, Hengyang 421001, China
| | - Liu Chen
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Zhan Liang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Di Jing
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
| | - Rongrong Zhou
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China
- Xiangya Lung Cancer Center, Xiangya Hospital, Central South University, Changsha 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
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Hou J, Wen X, Qu G, Chen W, Xu X, Wu G, Ji R, Wei G, Liang T, Huang W, Xiong L. A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy. Front Endocrinol (Lausanne) 2023; 14:1184608. [PMID: 37780621 PMCID: PMC10541026 DOI: 10.3389/fendo.2023.1184608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 08/30/2023] [Indexed: 10/03/2023] Open
Abstract
Background A model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool. Objective In this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians. Methods According to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model's predictive efficacy and clinical application using data from two different centers. Results The study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients' pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model's clinical utility and illustrated specificities of 0.935 and 0.806, respectively. Conclusion We found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.
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Affiliation(s)
- Jian Hou
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Xiangyang Wen
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Genyi Qu
- Department of Urology, Zhuzhou Central Hospital, Zhuzhou, China
| | - Wenwen Chen
- Department of Radiology, Zixing First People’s Hospital, Chenzhou, China
| | - Xiang Xu
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Guoqing Wu
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Ruidong Ji
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Genggeng Wei
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Tuo Liang
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Wenyan Huang
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
| | - Lin Xiong
- Division of Urology, Department of Surgery, The University of Hongkong-Shenzhen Hosipital, ShenZhen, China
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Meng F, Wu Q, Zhang W, Hou S. Shear-Wave Elastography-Based Radiomics Nomogram for the Prediction of Cardiovascular Disease in Patients with Diabetic Kidney Disease. Diabetes Metab Syndr Obes 2023; 16:2705-2716. [PMID: 37701720 PMCID: PMC10494864 DOI: 10.2147/dmso.s422364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/29/2023] [Indexed: 09/14/2023] Open
Abstract
Background Diabetic kidney disease (DKD) patients have a high risk of suffering from cardiovascular disease (CVD), placing a heavy cost on the public health system. In this study, we intended to develop and validate a shear-wave elastography (SWE)-based radiomics nomogram for predicting the development of CVD in DKD patients. This approach allows extensive use of the valuable information contained in ultrasound images, thus helping clinicians to identify CVD in DKD patients. Methods Totally 337 and 145 patients constituted the training and validation cohorts, respectively. The radiomics features of the segmented kidney in ultrasound images were extracted and selected to generate the rad-score of each patient. These rad-score, as well as the predictors of risk of CVD occurrence from the clinical characteristics, were included in the multivariate analysis to develop a nomogram. It was further assessed in the training and validation cohorts. Results Patients with CVD accounted for 30.9% (104/337) in the training cohort and 31.0% (45/145) in the validation cohort. The rad-score was calculated for each patient using 6 features extracted from the ultrasound images. The radiomics nomogram was built with the rad-score, age, systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C). It was superior to the clinical nomogram developed without the rad-score and demonstrated promising discrimination, calibration, and clinical utility in both training and validation cohorts. Conclusion We developed and validated an SWE-based radiomics nomogram to predict CVD risk in patients with DKD. The model was demonstrated to have a promising prediction performance, showing its potential to identify CVD in DKD patients and assist decision-making for appropriate early intervention.
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Affiliation(s)
- Fei Meng
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
| | - Qin Wu
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
| | - Wei Zhang
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
| | - Shirong Hou
- Department of Ultrasound, Xuan Cheng City Central Hospital, Xuancheng, Anhui, People’s Republic of China
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De Muzio F, Pellegrino F, Fusco R, Tafuto S, Scaglione M, Ottaiano A, Petrillo A, Izzo F, Granata V. Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics (Basel) 2023; 13:2877. [PMID: 37761243 PMCID: PMC10529975 DOI: 10.3390/diagnostics13182877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.
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Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Salvatore Tafuto
- Unit of Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Mariano Scaglione
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Alessandro Ottaiano
- Unit for Innovative Therapies of Abdominal Metastastes, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
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Cen C, Wang C, Wang S, Wen K, Liu L, Li X, Wu L, Huang M, Ma L, Liu H, Wu H, Han P. Clinical-radiomics nomogram using contrast-enhanced CT to predict histological grade and survival in pancreatic ductal adenocarcinoma. Front Oncol 2023; 13:1218128. [PMID: 37731637 PMCID: PMC10507255 DOI: 10.3389/fonc.2023.1218128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 08/15/2023] [Indexed: 09/22/2023] Open
Abstract
Objectives Tumor grading is important for prognosis of pancreatic ductal adenocarcinoma (PDAC). In this study, we developed preoperative clinical-radiomics nomograms using features from contrast-enhanced CT (CECT), to discriminate high-grade and low-grade PDAC and predict overall survival (OS). Methods In this single-center, retrospective study conducted from February 2014 to April 2021, consecutive PDAC patients who underwent CECT and had pathologically identified grading were randomized to training (n=200) and test (n=84) cohorts for development of model to predict histological grade based on radiomics scores from CECT (HGrad). Another 42 patients were used as external validation cohort of HGrad. A nomogram (HGnom) was constructed using radiomics score, CA12-5 and smoking to predict histological grade. A second nomogram (Pnom) was constructed using radiomics score, CA12-5, TNM, adjuvant treatment, resection margin and microvascular invasion to predict OS in radical resection patients (217 of 284). Results Among 326 patients, 122 were high-grade (120 poorly differentiated and 2 undifferentiated). The HGrad yielded AUCs of 0.75 (95% CI: 0.64, 0.85) and 0.76 (95% CI: 0.60, 0.91) in test and validation cohorts. The HGnom achieved AUCs of 0.77 (95% CI: 0.66, 0.87), and the predicted grades calibrated well with actual grades (P=.13). OS was different between the grades predicted by radiomics scores (P=.01). The integrated AUC of the Pnom for predicting OS was 0.80 (95% CI: 0.75, 0.88). Conclusion Compared with the HGrad using features from CECT, the HGnom demonstrated higher performance for predicting histological grade. The Pnom helped identify patients with high survival outcome in pancreatic ductal adenocarcinoma.
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Affiliation(s)
- Chunyuan Cen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Chunyou Wang
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Siqi Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Kan Wen
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Liying Liu
- Department of Radiology, the First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xin Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Linxia Wu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Mengting Huang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Ling Ma
- He Kang Corporate Management (SH) Co. Ltd, Shanghai, China
| | - Huan Liu
- Advanced Application Team, GE Healthcare, Shanghai, China
| | - Heshui Wu
- Department of Pancreatic Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Ping Han
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
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