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Safarian A, Mirshahvalad SA, Farbod A, Nasrollahi H, Pirich C, Beheshti M. Artificial intelligence for tumor [ 18F]FDG-PET imaging: Advancement and future trends-part I. Semin Nucl Med 2025; 55:328-344. [PMID: 40158896 DOI: 10.1053/j.semnuclmed.2025.03.003] [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: 02/28/2025] [Revised: 03/19/2025] [Accepted: 03/19/2025] [Indexed: 04/02/2025]
Abstract
The advent of sophisticated image analysis techniques has facilitated the extraction of increasingly complex data, such as radiomic features, from various imaging modalities, including [18F]FDG PET/CT, a well-established cornerstone of oncological imaging. Furthermore, the use of artificial intelligence (AI) algorithms has shown considerable promise in enhancing the interpretation of these quantitative parameters. Additionally, AI-driven models enable the integration of parameters from multiple imaging modalities along with clinical data, facilitating the development of comprehensive models with significant clinical impact. However, challenges remain regarding standardization and validation of the AI-powered models, as well as their implementation in real-world clinical practice. The variability in imaging acquisition protocols, segmentation methods, and feature extraction approaches across different institutions necessitates robust harmonization efforts to ensure reproducibility and clinical utility. Moreover, the successful translation of AI models into clinical practice requires prospective validation in large cohorts, as well as seamless integration into existing workflows to assess their ability to enhance clinicians' performance. This review aims to provide an overview of the literature and highlight three key applications: diagnostic impact, prediction of treatment response, and long-term patient prognostication. In the first part, we will focus on head and neck, lung, breast, gastroesophageal, colorectal, and gynecological malignancies.
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Affiliation(s)
- Alireza Safarian
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Rajaie Cardiovascular Medical and Research Center, Rajaie Cardiovascular Institute, Iran University of Medical Sciences, Tehran, Iran
| | - Seyed Ali Mirshahvalad
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Joint Department of Medical Imaging, University Medical Imaging Toronto, University of Toronto, Toronto, Ontario, Canada
| | - Abolfazl Farbod
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria; Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Hadi Nasrollahi
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Christian Pirich
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Mohsen Beheshti
- Division of Molecular Imaging & Theranostics, Department of Nuclear Medicine, University Hospital, Paracelsus Medical University, Salzburg, Austria.
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Lee JW, Won YK, Ahn H, Lee JE, Han SW, Kim SY, Jo IY, Lee SM. Peritumoral Adipose Tissue Features Derived from [ 18F]fluoro-2-deoxy-2-d-glucose Positron Emission Tomography/Computed Tomography as Predictors for Response to Neoadjuvant Chemotherapy in Breast Cancer Patients. J Pers Med 2024; 14:952. [PMID: 39338206 PMCID: PMC11432773 DOI: 10.3390/jpm14090952] [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: 08/13/2024] [Revised: 09/02/2024] [Accepted: 09/06/2024] [Indexed: 09/30/2024] Open
Abstract
This study investigated whether the textural features of peritumoral adipose tissue (AT) on [18F]fluoro-2-deoxy-2-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) can predict the pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. We retrospectively enrolled 147 female breast cancer patients who underwent staging FDG PET/CT and completed NAC and underwent curative surgery. We extracted 10 first-order features, 6 gray-level co-occurrence matrix (GLCM) features, and 3 neighborhood gray-level difference matrix (NGLDM) features of peritumoral AT and evaluated the predictive value of those imaging features for pathological complete response (pCR) and PFS. The results of our study demonstrated that GLCM homogeneity showed the highest predictability for pCR among the peritumoral AT imaging features in the receiver operating characteristic curve analysis. In multivariate logistic regression analysis, the mean standardized uptake value (SUV), 50th percentile SUV, 75th percentile SUV, SUV histogram entropy, GLCM entropy, and GLCM homogeneity of the peritumoral AT were independent predictors for pCR. In multivariate survival analysis, SUV histogram entropy and GLCM correlation of peritumoral AT were independent predictors of PFS. Textural features of peritumoral AT on FDG PET/CT could be potential imaging biomarkers for predicting the response to NAC and disease progression in breast cancer patients.
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Affiliation(s)
- Jeong Won Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea
| | - Yong Kyun Won
- Department of Radiation Oncology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea
| | - Hyein Ahn
- Department of Pathology, CHA Gangnam Medical Center, CHA University School of Medicine, 569 Nonhyon-ro, Gangnam-gu, Seoul 06135, Republic of Korea
| | - Jong Eun Lee
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea
| | - Sun Wook Han
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea
| | - Sung Yong Kim
- Department of Surgery, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea
| | - In Young Jo
- Department of Radiation Oncology, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea
| | - Sang Mi Lee
- Department of Nuclear Medicine, Soonchunhyang University Cheonan Hospital, 31 Suncheonhyang 6-gil, Dongnam-gu, Cheonan 31151, Republic of Korea
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Li Y, Long W, Zhou H, Tan T, Xie H. Revolutionizing breast cancer Ki-67 diagnosis: ultrasound radiomics and fully connected neural networks (FCNN) combination method. Breast Cancer Res Treat 2024; 207:453-468. [PMID: 38853220 DOI: 10.1007/s10549-024-07375-x] [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: 12/28/2023] [Accepted: 05/14/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE This study aims to assess the diagnostic value of ultrasound habitat sub-region radiomics feature parameters using a fully connected neural networks (FCNN) combination method L2,1-norm in relation to breast cancer Ki-67 status. METHODS Ultrasound images from 528 cases of female breast cancer at the Affiliated Hospital of Xiangnan University and 232 cases of female breast cancer at the Affiliated Rehabilitation Hospital of Xiangnan University were selected for this study. We utilized deep learning methods to automatically outline the gross tumor volume and perform habitat clustering. Subsequently, habitat sub-regions were extracted to identify radiomics features and underwent feature engineering using the L1,2-norm. A prediction model for the Ki-67 status of breast cancer patients was then developed using a FCNN. The model's performance was evaluated using accuracy, area under the curve (AUC), specificity (Spe), positive predictive value (PPV), negative predictive value (NPV), Recall, and F1. In addition, calibration curves and clinical decision curves were plotted for the test set to visually assess the predictive accuracy and clinical benefit of the models. RESULT Based on the feature engineering using the L1,2-norm, a total of 9 core features were identified. The predictive model, constructed by the FCNN model based on these 9 features, achieved the following scores: ACC 0.856, AUC 0.915, Spe 0.843, PPV 0.920, NPV 0.747, Recall 0.974, and F1 0.890. Furthermore, calibration curves and clinical decision curves of the validation set demonstrated a high level of confidence in the model's performance and its clinical benefit. CONCLUSION Habitat clustering of ultrasound images of breast cancer is effectively supported by the combined implementation of the L1,2-norm and FCNN algorithms, allowing for the accurate classification of the Ki-67 status in breast cancer patients.
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Affiliation(s)
- Yanfeng Li
- Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China
| | - Wengxing Long
- Department of Interventional Vascular Surgery, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China
| | - Hongda Zhou
- Department of Oncology, Affiliated Hospital of Xiangnan University, Chenzhou, 423000, Hunan, People's Republic of China
| | - Tao Tan
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China
| | - Hui Xie
- Department of Oncology, Affiliated Hospital of Xiangnan University, Chenzhou, 423000, Hunan, People's Republic of China.
- Faulty of Applied Sciences, Macao Polytechnic University, Macao, 999078, People's Republic of China.
- Department of Radiation Oncology, Affiliated Hospital (Clinical College) of Xiangnan University, Chenzhou, 423000, People's Republic of China.
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Kuntner C, Alcaide C, Anestis D, Bankstahl JP, Boutin H, Brasse D, Elvas F, Forster D, Rouchota MG, Tavares A, Teuter M, Wanek T, Zachhuber L, Mannheim JG. Optimizing SUV Analysis: A Multicenter Study on Preclinical FDG-PET/CT Highlights the Impact of Standardization. Mol Imaging Biol 2024; 26:668-679. [PMID: 38907124 PMCID: PMC11281957 DOI: 10.1007/s11307-024-01927-9] [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: 02/11/2024] [Revised: 05/29/2024] [Accepted: 06/04/2024] [Indexed: 06/23/2024]
Abstract
PURPOSE Preclinical imaging, with translational potential, lacks a standardized method for defining volumes of interest (VOIs), impacting data reproducibility. The aim of this study was to determine the interobserver variability of VOI sizes and standard uptake values (SUVmean and SUVmax) of different organs using the same [18F]FDG-PET and PET/CT datasets analyzed by multiple observers. In addition, the effect of a standardized analysis approach was evaluated. PROCEDURES In total, 12 observers (4 beginners and 8 experts) analyzed identical preclinical [18F]FDG-PET-only and PET/CT datasets according to their local default image analysis protocols for multiple organs. Furthermore, a standardized protocol was defined, including detailed information on the respective VOI size and position for multiple organs, and all observers reanalyzed the PET/CT datasets following this protocol. RESULTS Without standardization, significant differences in the SUVmean and SUVmax were found among the observers. Coregistering CT images with PET images improved the comparability to a limited extent. The introduction of a standardized protocol that details the VOI size and position for multiple organs reduced interobserver variability and enhanced comparability. CONCLUSIONS The protocol offered clear guidelines and was particularly beneficial for beginners, resulting in improved comparability of SUVmean and SUVmax values for various organs. The study suggested that incorporating an additional VOI template could further enhance the comparability of the findings in preclinical imaging analyses.
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Affiliation(s)
- Claudia Kuntner
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Vienna, Austria.
- Medical Imaging Cluster (MIC), Medical University of Vienna, Vienna, Austria.
| | | | | | | | - Herve Boutin
- Division of Neuroscience & Experimental Psychology, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
- INSERM, UMR 1253, iBrainUniversité de Tours, Tours, France
| | - David Brasse
- Institut Pluridisciplinaire Hubert Curien, UMR7178, Université de Strasbourg, CNRS, Strasbourg, France
| | - Filipe Elvas
- Molecular Imaging Center Antwerp, University of Antwerpen, Antwerp, Belgium
| | - Duncan Forster
- Division of Informatics, Imaging and Data Sciences, Manchester Molecular Imaging Centre, The University of Manchester, Manchester, UK
| | | | | | | | - Thomas Wanek
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Vienna, Austria
| | - Lena Zachhuber
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Vienna, Austria
| | - Julia G Mannheim
- Department of Preclinical Imaging and Radiopharmacy Werner Siemens Imaging Center, Eberhard-Karls University Tuebingen, Tuebingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", Tuebingen, Germany
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Lim CH, Um SW, Kim HK, Choi YS, Pyo HR, Ahn MJ, Choi JY. 18F-Fluorodeoxyglucose Positron Emission Tomography-Based Risk Score Model for Prediction of Five-Year Survival Outcome after Curative Resection of Non-Small-Cell Lung Cancer. Cancers (Basel) 2024; 16:2525. [PMID: 39061165 PMCID: PMC11274931 DOI: 10.3390/cancers16142525] [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: 05/30/2024] [Revised: 07/09/2024] [Accepted: 07/10/2024] [Indexed: 07/28/2024] Open
Abstract
The aim of our retrospective study is to develop and assess an imaging-based model utilizing 18F-FDG PET parameters for predicting the five-year survival in non-small-cell lung cancer (NSCLC) patients after curative surgery. A total of 361 NSCLC patients who underwent curative surgery were assigned to the training set (n = 253) and the test set (n = 108). The LASSO regression model was used to construct a PET-based risk score for predicting five-year survival. A hybrid model that combined the PET-based risk score and clinical variables was developed using multivariate logistic regression analysis. The predictive performance was determined by the area under the curve (AUC). The individual features with the best predictive performances were co-occurrence_contrast (AUC = 0.675) and SUL peak (AUC = 0.671). The PET-based risk score was identified as an independent predictor after adjusting for clinical variables (OR 5.231, 95% CI 1.987-6.932; p = 0.009). The hybrid model, which integrated clinical variables, significantly outperformed the PET-based risk score alone in predictive accuracy (AUC = 0.771 vs. 0.696, p = 0.022), a finding that was consistent in the test set. The PET-based risk score, especially when integrated with clinical variables, demonstrates good predictive ability for five-year survival in NSCLC patients following curative surgery.
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Affiliation(s)
- Chae Hong Lim
- Department of Nuclear Medicine, Soonchunhyang University College of Medicine, Seoul 04401, Republic of Korea
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Hong Kwan Kim
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Yong Soo Choi
- Department of Thoracic and Cardiovascular Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Hong Ryul Pyo
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
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Amrane K, Meur CL, Thuillier P, Berthou C, Uguen A, Deandreis D, Bourhis D, Bourbonne V, Abgral R. Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes. Cancer Imaging 2024; 24:87. [PMID: 38970050 PMCID: PMC11225300 DOI: 10.1186/s40644-024-00732-5] [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: 11/02/2023] [Accepted: 06/24/2024] [Indexed: 07/07/2024] Open
Abstract
Over the past decade, several strategies have revolutionized the clinical management of patients with cutaneous melanoma (CM), including immunotherapy and targeted tyrosine kinase inhibitor (TKI)-based therapies. Indeed, immune checkpoint inhibitors (ICIs), alone or in combination, represent the standard of care for patients with advanced disease without an actionable mutation. Notably BRAF combined with MEK inhibitors represent the therapeutic standard for disease disclosing BRAF mutation. At the same time, FDG PET/CT has become part of the routine staging and evaluation of patients with cutaneous melanoma. There is growing interest in using FDG PET/CT measurements to predict response to ICI therapy and/or target therapy. While semiquantitative values such as standardized uptake value (SUV) are limited for predicting outcome, new measures including tumor metabolic volume, total lesion glycolysis and radiomics seem promising as potential imaging biomarkers for nuclear medicine. The aim of this review, prepared by an interdisciplinary group of experts, is to take stock of the current literature on radiomics approaches that could improve outcomes in CM.
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Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, 29600, France.
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France.
| | - Coline Le Meur
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
| | - Christian Berthou
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Hematology, University Hospital of Brest, Brest, France
| | - Arnaud Uguen
- Lymphocytes B et Autoimmunité, Inserm, UMR1227, Univ Brest, Inserm, LabEx IGO, Brest, France
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Désirée Deandreis
- Department of Nuclear Medicine, Gustave Roussy Institute, University of Paris Saclay, Paris, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
| | - Vincent Bourbonne
- Department of Radiotherapy, University Hospital of Brest, Brest, France
- Inserm, UMR1101, LaTIM, University of Western Brittany, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, University of Western Brittany, Brest, IFR 148, France
- Department of Nuclear Medicine, University Hospital of Brest, Brest, France
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Kairemo K, Gouda M, Chuang HH, Macapinlac HA, Subbiah V. Deciphering Tumor Response: The Role of Fluoro-18-d-Glucose Uptake in Evaluating Targeted Therapies with Tyrosine Kinase Inhibitors. J Clin Med 2024; 13:3269. [PMID: 38892979 PMCID: PMC11173296 DOI: 10.3390/jcm13113269] [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: 05/06/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 06/21/2024] Open
Abstract
Background/Objectives: The inhibitory effects of tyrosine kinase inhibitors (TKIs) on glucose uptake through their binding to human glucose transporter-1 (GLUT-1) have been well documented. Thus, our research aimed to explore the potential impact of various TKIs of GLUT-1 on the standard [18F]FDG-PET monitoring of tumor response in patients. Methods: To achieve this, we conducted an analysis on three patients who were undergoing treatment with different TKIs and harbored actionable alterations. Alongside the assessment of FDG data (including SUVmax, total lesion glycolysis (TLG), and metabolic tumor volume (MTV)), we also examined the changes in tumor sizes through follow-up [18F]FDG-PET/CT imaging. Notably, our patients harbored alterations in BRAFV600, RET, and c-KIT and exhibited positive responses to the targeted treatment. Results: Our analysis revealed that FDG data derived from SUVmax, TLG, and MTV offered quantifiable outcomes that were consistent with the measurements of tumor size. Conclusions: These findings lend support to the notion that the inhibition of GLUT-1, as a consequence of treatment efficacy, could be indirectly gauged through [18F] FDG-PET/CT imaging in cancer patients undergoing TKI therapy.
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Affiliation(s)
- Kalevi Kairemo
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Mohamed Gouda
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hubert H. Chuang
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Homer A. Macapinlac
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Vivek Subbiah
- Department of Investigational Cancer Therapeutics, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
- Sarah Cannon Research Institute, Nashville, TN 37203, USA
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Yazdani E, Geramifar P, Karamzade-Ziarati N, Sadeghi M, Amini P, Rahmim A. Radiomics and Artificial Intelligence in Radiotheranostics: A Review of Applications for Radioligands Targeting Somatostatin Receptors and Prostate-Specific Membrane Antigens. Diagnostics (Basel) 2024; 14:181. [PMID: 38248059 PMCID: PMC10814892 DOI: 10.3390/diagnostics14020181] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/11/2024] [Accepted: 01/12/2024] [Indexed: 01/23/2024] Open
Abstract
Radiotheranostics refers to the pairing of radioactive imaging biomarkers with radioactive therapeutic compounds that deliver ionizing radiation. Given the introduction of very promising radiopharmaceuticals, the radiotheranostics approach is creating a novel paradigm in personalized, targeted radionuclide therapies (TRTs), also known as radiopharmaceuticals (RPTs). Radiotherapeutic pairs targeting somatostatin receptors (SSTR) and prostate-specific membrane antigens (PSMA) are increasingly being used to diagnose and treat patients with metastatic neuroendocrine tumors (NETs) and prostate cancer. In parallel, radiomics and artificial intelligence (AI), as important areas in quantitative image analysis, are paving the way for significantly enhanced workflows in diagnostic and theranostic fields, from data and image processing to clinical decision support, improving patient selection, personalized treatment strategies, response prediction, and prognostication. Furthermore, AI has the potential for tremendous effectiveness in patient dosimetry which copes with complex and time-consuming tasks in the RPT workflow. The present work provides a comprehensive overview of radiomics and AI application in radiotheranostics, focusing on pairs of SSTR- or PSMA-targeting radioligands, describing the fundamental concepts and specific imaging/treatment features. Our review includes ligands radiolabeled by 68Ga, 18F, 177Lu, 64Cu, 90Y, and 225Ac. Specifically, contributions via radiomics and AI towards improved image acquisition, reconstruction, treatment response, segmentation, restaging, lesion classification, dose prediction, and estimation as well as ongoing developments and future directions are discussed.
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Affiliation(s)
- Elmira Yazdani
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran
| | - Najme Karamzade-Ziarati
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran 14117-13135, Iran
| | - Mahdi Sadeghi
- Medical Physics Department, School of Medicine, Iran University of Medical Sciences, Tehran 14496-14535, Iran
- Finetech in Medicine Research Center, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran 14496-14535, Iran
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC V5Z 1L3, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC V5Z 1L3, Canada
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Kong X, Mao Y, Xi F, Li Y, Luo Y, Ma J. Development of a nomogram based on radiomics and semantic features for predicting chromosome 7 gain/chromosome 10 loss in IDH wild-type histologically low-grade gliomas. Front Oncol 2023; 13:1196614. [PMID: 37781185 PMCID: PMC10541227 DOI: 10.3389/fonc.2023.1196614] [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: 03/30/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023] Open
Abstract
Purpose To predict chromosome 7 gain and chromosome 10 loss (+7/-10) in IDH wild-type (IDH-wt) histologically low-grade gliomas (LGG) by machine learning models based on MRI radiomics and semantic features. Methods A total of 122 patients diagnosed as IDH-wt histologically LGG were retrospectively included in this study. The patients were randomly divided into a training group and a test group in a ratio of 7:3. The radiomics features were extracted from axial T1WI, T2WI, FLAIR and CET1 sequences, respectively. The distance correlation (DC) and least absolute shrinkage and selection operator (LASSO) were used to select the radiomics signatures. Three machine learning algorithms including neural network (NN), support vector machine (SVM), and linear discriminant analysis (LDA) were used to construct radiomics models. In addition, a nomogram was developed by combining the optimal radiomics signature with clinical risk factors, and the potential clinical utility of the nomogram was evaluated using decision curve analysis. Results The LDA+DC model was identified as the optimal classifier among the six radiomics models. Necrosis was determined as a risk factor for +7/-10 in IDH-wt histologically LGG. The nomogram achieved the best performance, with an AUC of 0.854 and an accuracy of 0.778 in the independent test group. The decision curve of the nomogram confirmed its clinical usefulness in a wide range of thresholds. Conclusion The nomogram combining radiomics and semantic features can predict the +7/-10 status effectively, which may contribute to the risk stratification and individualized treatment planning of patients with IDH-wt histologically LGG.
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Affiliation(s)
- Xin Kong
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yu Mao
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fengjun Xi
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yan Li
- Department of Radiology, Beijing Fengtai Hospital, Beijing, China
| | - Yuqi Luo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jun Ma
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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Deantonio L, Vigna L, Paolini M, Matheoud R, Sacchetti GM, Masini L, Loi G, Brambilla M, Krengli M. Application of a smart 18F-FDG-PET adaptive threshold segmentation algorithm for the biological target volume delineation in head and neck cancer. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF... 2023; 67:238-244. [PMID: 35238518 DOI: 10.23736/s1824-4785.22.03405-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND The aim of the present study is to evaluate the reliability of a 18F-fluorodeoxyglucose (18F-FDG) PET adaptive threshold segmentation (ATS) algorithm, previously validated in a preclinical setting on several scanners, for the biological target volume (BTV) delineation of head and neck radiotherapy planning. METHODS [18F]FDG PET ATS algorithm was studied in treatment plans of head and neck squamous cell carcinoma on a dedicated workstation (iTaRT, Tecnologie Avanzate, Turin, Italy). BTVs segmented by the present ATS algorithm (BTVATS) were compared with those manually segmented for the original radiotherapy treatment planning (BTVVIS). We performed a qualitative and quantitative volumetric analysis with a comparison tool within the ImSimQA TM software package (Oncology Systems Limited, Shrewsbury, UK). We reported figures of merit (FOMs) to convey complementary information: Dice Similarity Coefficient, Sensitivity Index, and Inclusiveness Index. RESULTS The study was conducted on 32 treatment plans. Median BTVATS was 11 cm3 while median BTVVIS was 14 cm3. The median Dice Similarity Coefficient, Sensitivity Index, Inclusiveness Index were 0.72, 63%, 88%, respectively. Interestingly, the median volume and the median distance of the voxels that are over contoured by ATS were respectively 1 cm3 and 1 mm. CONCLUSIONS ATS algorithm could be a smart and an independent operator tool when implemented for 18F-FDG-PET-based tumor volume delineation. Furthermore, it might be relevant in case of BTV-based dose painting.
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Affiliation(s)
- Letizia Deantonio
- Department of Radiotherapy, Maggiore della Carità University Hospital, Novara, Italy -
| | - Luca Vigna
- Department of Medical Physics, Maggiore della Carità University Hospital, Novara, Italy
| | - Marina Paolini
- Department of Radiotherapy, Maggiore della Carità University Hospital, Novara, Italy
| | - Roberta Matheoud
- Department of Medical Physics, Maggiore della Carità University Hospital, Novara, Italy
| | - Gian M Sacchetti
- Department of Nuclear Medicine, Maggiore della Carità University Hospital, Novara, Italy
| | - Laura Masini
- Department of Radiotherapy, Maggiore della Carità University Hospital, Novara, Italy
| | - Gianfranco Loi
- Department of Medical Physics, Maggiore della Carità University Hospital, Novara, Italy
| | - Marco Brambilla
- Department of Medical Physics, Maggiore della Carità University Hospital, Novara, Italy
| | - Marco Krengli
- Department of Radiotherapy, Maggiore della Carità University Hospital, Novara, Italy
- Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy
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Amrane K, Thuillier P, Bourhis D, Le Meur C, Quere C, Leclere JC, Ferec M, Jestin-Le Tallec V, Doucet L, Alemany P, Salaun PY, Metges JP, Schick U, Abgral R. Prognostic value of pre-therapeutic FDG-PET radiomic analysis in gastro-esophageal junction cancer. Sci Rep 2023; 13:5789. [PMID: 37031233 PMCID: PMC10082755 DOI: 10.1038/s41598-023-31587-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/14/2023] [Indexed: 04/10/2023] Open
Abstract
The main aim of this study was to evaluate the prognostic value of radiomic approach in pre-therapeutic 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET/CT) in a large cohort of patients with gastro-esophageal junction cancer (GEJC). This was a retrospective monocenter study including 97 consecutive patients with GEJC who underwent a pre-therapeutic FDG-PET and were followed up for 3 years. Standard first-order radiomic PET indices including SUVmax, SUVmean, SUVpeak, MTV and TLG and 32 textural features (TFs) were calculated using LIFEx software on PET imaging. Prognostic significance of these parameters was assessed in univariate and multivariate analysis. Relapse-free survival (RFS) and overall survival (OS) were respectively chosen as primary and secondary endpoints. An internal validation cohort was used by randomly drawing one-third of included patients. The main characteristics of this cohort were: median age of 65 years [41-88], sex ratio H/F = 83/14, 81.5% of patients with a histopathology of adenocarcinoma and 43.3% with a stage IV disease. The median follow-up was 28.5 months [4.2-108.5]. Seventy-seven (79.4%) patients had locoregional or distant progression or recurrence and 71 (73.2%) died. In univariate analysis, SUVmean, Histogram-Entropy and 2 TFs (GLCM-Homogeneity and GLCM-Energy) were significantly correlated with RFS and OS, as well as 2 others TFs (GLRLM-LRE and GLRLM-GLNU) with OS only. In multivariate analysis, Histogram-Entropy remained an independent prognostic factor of both RFS and OS whereas SUVmean was an independent prognostic factor of OS only. These results were partially confirmed in our internal validation cohort of 33 patients. Our results suggest that radiomic approach reveals independent prognostic factors for survival in patients with GEJC.
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Affiliation(s)
- Karim Amrane
- Department of Oncology, Regional Hospital of Morlaix, Morlaix, France.
| | - Philippe Thuillier
- Department of Endocrinology, University Hospital of Brest, Brest, France
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
| | - David Bourhis
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | - Coline Le Meur
- Department of Oncology, University Hospital of Brest, Brest, France
| | - Chloe Quere
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Marc Ferec
- Department of Gastroenterology, Regional Hospital of Morlaix, Morlaix, France
| | | | - Laurent Doucet
- Department of Pathology, University Hospital of Brest, Brest, France
| | - Pierre Alemany
- Department of Pathology, Ouestpathology Brest, Brest, France
| | - Pierre-Yves Salaun
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France
| | | | - Ulrike Schick
- Department of Radiotherapy, University Hospital of Brest, Brest, France
| | - Ronan Abgral
- UMR Inserm 1304 GETBO, IFR 148, University of Western Brittany, Brest, France.
- Department of Nuclear Medicine, University Hospital of Brest, 2 Avenue Foch, 29609, Brest Cedex, France.
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Current Role of Delta Radiomics in Head and Neck Oncology. Int J Mol Sci 2023; 24:ijms24032214. [PMID: 36768535 PMCID: PMC9916410 DOI: 10.3390/ijms24032214] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 01/19/2023] [Indexed: 01/26/2023] Open
Abstract
The latest developments in the management of head and neck cancer show an increasing trend in the implementation of novel approaches using artificial intelligence for better patient stratification and treatment-related risk evaluation. Radiomics, or the extraction of data from various imaging modalities, is a tool often used to evaluate specific features related to the tumour or normal tissue that are not identifiable by the naked eye and which can add value to existing clinical data. Furthermore, the assessment of feature variations from one time point to another based on subsequent images, known as delta radiomics, was shown to have even higher value for treatment-outcome prediction or patient stratification into risk categories. The information gathered from delta radiomics can, further, be used for decision making regarding treatment adaptation or other interventions found to be beneficial to the patient. The aim of this work is to collate the existing studies on delta radiomics in head and neck cancer and evaluate its role in tumour response and normal-tissue toxicity predictions alike. Moreover, this work also highlights the role of holomics, which brings under the same umbrella clinical and radiomic features, for a more complex patient characterization and treatment optimisation.
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13
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Allard B, Dissaux B, Bourhis D, Dissaux G, Schick U, Salaün PY, Abgral R, Querellou S. Hotspot on 18F-FET PET/CT to Predict Aggressive Tumor Areas for Radiotherapy Dose Escalation Guiding in High-Grade Glioma. Cancers (Basel) 2022; 15:cancers15010098. [PMID: 36612093 PMCID: PMC9817533 DOI: 10.3390/cancers15010098] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 12/28/2022] Open
Abstract
The standard therapy strategy for high-grade glioma (HGG) is based on the maximal surgery followed by radio-chemotherapy (RT-CT) with insufficient control of the disease. Recurrences are mainly localized in the radiation field, suggesting an interest in radiotherapy dose escalation to better control the disease locally. We aimed to identify a similarity between the areas of high uptake on O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) positron emission tomography/computed tomography (PET) before RT-CT, the residual tumor on post-therapy NADIR magnetic resonance imaging (MRI) and the area of recurrence on MRI. This is an ancillary study from the IMAGG prospective trial assessing the interest of FET PET imaging in RT target volume definition of HGG. We included patients with diagnoses of HGG obtained by biopsy or tumor resection. These patients underwent FET PET and brain MRIs, both after diagnosis and before RT-CT. The follow-up consisted of sequential brain MRIs performed every 3 months until recurrence. Tumor delineation on the initial MRI 1 (GTV 1), post-RT-CT NADIR MRI 2 (GTV 2), and progression MRI 3 (GTV 3) were performed semi-automatically and manually adjusted by a neuroradiologist specialist in neuro-oncology. GTV 2 and GTV 3 were then co-registered on FET PET data. Tumor volumes on FET PET (MTV) were delineated using a tumor to background ratio (TBR) ≥ 1.6 and different % SUVmax PET thresholds. Spatial similarity between different volumes was performed using the dice (DICE), Jaccard (JSC), and overlap fraction (OV) indices and compared together in the biopsy or partial surgery group (G1) and the total or subtotal surgery group (G2). Another overlap index (OV') was calculated to determine the threshold with the highest probability of being included in the residual volume after RT-CT on MRI 2 and in MRI 3 (called "hotspot"). A total of 23 patients were included, of whom 22% (n = 5) did not have a NADIR MRI 2 due to a disease progression diagnosed on the first post-RT-CT MRI evaluation. Among the 18 patients who underwent a NADIR MRI 2, the average residual tumor was approximately 71.6% of the GTV 1. A total of 22% of patients (5/23) showed an increase in GTV 2 without diagnosis of true progression by the multidisciplinary team (MDT). Spatial similarity between MTV and GTV 2 and between MTV and GTV 3 were higher using a TBR ≥ 1.6 threshold. These indices were significantly better in the G1 group than the G2 group. In the FET hotspot analysis, the best similarity (good agreement) with GTV 2 was found in the G1 group using a 90% SUVmax delineation method and showed a trend of statistical difference with those (poor agreement) in the G2 group (OV' = 0.67 vs. 0.38, respectively, p = 0.068); whereas the best similarity (good agreement) with GTV 3 was found in the G1 group using a 80% SUVmax delineation method and was significantly higher than those (poor agreement) in the G2 group (OV'= 0.72 vs. 0.35, respectively, p = 0.014). These results showed modest spatial similarity indices between MTV, GTV 2, and GTV 3 of HGG. Nevertheless, the results were significantly improved in patients who underwent only biopsy or partial surgery. TBR ≥ 1.6 and 80-90% SUVmax FET delineation methods showing a good agreement in the hotspot concept for targeting standard dose and radiation boost. These findings need to be tested in a larger randomized prospective study.
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Affiliation(s)
- Bastien Allard
- Nuclear Medicine Department, University Hospital, 29200 Brest, France
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
| | - Brieg Dissaux
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
- GETBO UMR U_1304, Inserm, University of Western Brittany (UBO), 29200 Brest, France
- Radiology Department, University Hospital, 29200 Brest, France
| | - David Bourhis
- Nuclear Medicine Department, University Hospital, 29200 Brest, France
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
- GETBO UMR U_1304, Inserm, University of Western Brittany (UBO), 29200 Brest, France
| | - Gurvan Dissaux
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
- Radiation Oncology Department, University Hospital, 29200 Brest, France
- LaTIM, INSERM 1101, 29200 Brest, France
| | - Ulrike Schick
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
- Radiation Oncology Department, University Hospital, 29200 Brest, France
- LaTIM, INSERM 1101, 29200 Brest, France
| | - Pierre-Yves Salaün
- Nuclear Medicine Department, University Hospital, 29200 Brest, France
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
- GETBO UMR U_1304, Inserm, University of Western Brittany (UBO), 29200 Brest, France
| | - Ronan Abgral
- Nuclear Medicine Department, University Hospital, 29200 Brest, France
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
- GETBO UMR U_1304, Inserm, University of Western Brittany (UBO), 29200 Brest, France
| | - Solène Querellou
- Nuclear Medicine Department, University Hospital, 29200 Brest, France
- UFR Médecine, University of Western Brittany (UBO), 29200 Brest, France
- GETBO UMR U_1304, Inserm, University of Western Brittany (UBO), 29200 Brest, France
- Correspondence:
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Mehta P, Sinha S, Kashid S, Chakraborty D, Mhatre R, Murthy V. Exploring Texture Analysis to Optimize Bladder Preservation in Muscle Invasive Bladder Cancer. Clin Genitourin Cancer 2022; 21:e138-e144. [PMID: 36628695 DOI: 10.1016/j.clgc.2022.11.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 11/08/2022] [Accepted: 11/14/2022] [Indexed: 11/21/2022]
Abstract
PURPOSE To explore if texture analysis of Muscle Invasive Bladder Cancer (MIBC) can aid in better patient selection for bladder preservation. METHODS Pretreatment noncontrast CT images of 41 patients of MIBC treated with bladder preservation were included. The visible tumor was contoured on all slices by a single observer. The primary endpoint was to identify texture parameters associated with disease recurrence posttreatment. The secondary endpoints included intra and interobserver variability, single and multislice analysis, and differentiating the texture features of normal bladder and tumor. For interobserver variability of bladder tumor texture features, 3 observers contoured the visible tumor on all slices independently. Observer 1 contoured again at an interval of 1 month for intraobserver variability. RESULTS The median follow-up was 30 months with 12 patients having a recurrence. In the primary endpoint analysis, the mean of the pixels at Spatial Scaling Filter (SSF) 2 for the no recurrence group and recurrence group was 6.44 v 13.73 respectively (P = .031) and the same at SSF-3 was 11.95 and 22.32 respectively (P = .034). The texture features that could significantly differentiate tumor and normal bladder were mean, standard deviation and kurtosis of the pixels at SSF-2 and entropy and kurtosis of the pixels at SSF-3. Overall, there was an excellent intra and interobserver concordance in texture features. Only multislice analysis and not single-slice could differentiate recurrence and no recurrence posttreatment. CONCLUSIONS Texture analysis can be explored as a modality for patient selection for bladder preservation along with the established clinical parameters to improve outcomes.
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Affiliation(s)
- Prachi Mehta
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Shwetabh Sinha
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Sheetal Kashid
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Debanjan Chakraborty
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Ritesh Mhatre
- Department of Medical Physics, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Vedang Murthy
- Department of Radiation Oncology, Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India.
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Creff G, Jegoux F, Palard X, Depeursinge A, Abgral R, Marianowski R, Leclere JC, Eugene T, Malard O, Crevoisier RD, Devillers A, Castelli J. 18F-FDG PET/CT-Based Prognostic Survival Model After Surgery for Head and Neck Cancer. J Nucl Med 2022; 63:1378-1385. [PMID: 34887336 PMCID: PMC9454462 DOI: 10.2967/jnumed.121.262891] [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: 07/11/2021] [Revised: 11/16/2021] [Indexed: 12/24/2022] Open
Abstract
The aims of this multicenter study were to identify clinical and preoperative PET/CT parameters predicting overall survival (OS) and distant metastasis-free survival (DMFS) in a cohort of head and neck squamous cell carcinoma patients treated with surgery, to generate a prognostic model of OS and DMFS, and to validate this prognostic model with an independent cohort. Methods: A total of 382 consecutive patients with head and neck squamous cell carcinoma, divided into training (n = 318) and validation (n = 64) cohorts, were retrospectively included. The following PET/CT parameters were analyzed: clinical parameters, SUVmax, SUVmean, metabolic tumor volume (MTV), total lesion glycolysis, and distance parameters for the primary tumor and lymph nodes defined by 2 segmentation methods (relative SUVmax threshold and absolute SUV threshold). Cox analyses were performed for OS and DMFS in the training cohort. The concordance index (c-index) was used to identify highly prognostic parameters. These prognostic parameters were externally tested in the validation cohort. Results: In multivariable analysis, the significant parameters for OS were T stage and nodal MTV, with a c-index of 0.64 (P < 0.001). For DMFS, the significant parameters were T stage, nodal MTV, and maximal tumor-node distance, with a c-index of 0.76 (P < 0.001). These combinations of parameters were externally validated, with c-indices of 0.63 (P < 0.001) and 0.71 (P < 0.001) for OS and DMFS, respectively. Conclusion: The nodal MTV associated with the maximal tumor-node distance was significantly correlated with the risk of DMFS. Moreover, this parameter, in addition to clinical parameters, was associated with a higher risk of death. These prognostic factors may be used to tailor individualized treatment.
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Affiliation(s)
- Gwenaelle Creff
- Department of Otolaryngology-Head and Neck Surgery (HNS), University Hospital, Rennes, France;
| | - Franck Jegoux
- Department of Otolaryngology–Head and Neck Surgery (HNS), University Hospital, Rennes, France
| | - Xavier Palard
- Department of Nuclear Medicine, Cancer Institute, Rennes, France
| | | | - Ronan Abgral
- Department of Nuclear Medicine, University Hospital, Brest, France
| | - Remi Marianowski
- Department of Otolaryngology–HNS, University Hospital, Brest, France
| | | | - Thomas Eugene
- Department of Nuclear Medicine, University Hospital, Nantes, France
| | - Olivier Malard
- Department of Otolaryngology–HNS, University Hospital, Nantes, France
| | - Renaud De Crevoisier
- Department of Radiation Oncology, Cancer Institute, Rennes, France; and,LTSI (Image and Signal Processing Laboratory), INSERM, U1099, Rennes, France
| | - Anne Devillers
- Department of Nuclear Medicine, Cancer Institute, Rennes, France
| | - Joel Castelli
- Department of Radiation Oncology, Cancer Institute, Rennes, France; and,LTSI (Image and Signal Processing Laboratory), INSERM, U1099, Rennes, France
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Comte V, Schmutz H, Chardin D, Orlhac F, Darcourt J, Humbert O. Development and validation of a radiomic model for the diagnosis of dopaminergic denervation on [18F]FDOPA PET/CT. Eur J Nucl Med Mol Imaging 2022; 49:3787-3796. [PMID: 35567626 PMCID: PMC9399031 DOI: 10.1007/s00259-022-05816-7] [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: 01/28/2022] [Accepted: 04/23/2022] [Indexed: 11/30/2022]
Abstract
Purpose FDOPA PET shows good performance for the diagnosis of striatal dopaminergic denervation, making it a valuable tool for the differential diagnosis of Parkinsonism. Textural features are image biomarkers that could potentially improve the early diagnosis and monitoring of neurodegenerative parkinsonian syndromes. We explored the performances of textural features for binary classification of FDOPA scans. Methods We used two FDOPA PET datasets: 443 scans for feature selection, and 100 scans from a different PET/CT system for model testing. Scans were labelled according to expert interpretation (dopaminergic denervation versus no dopaminergic denervation). We built LASSO logistic regression models using 43 biomarkers including 32 textural features. Clinical data were also collected using a shortened UPDRS scale. Results The model built from the clinical data alone had a mean area under the receiver operating characteristics (AUROC) of 63.91. Conventional imaging features reached a maximum score of 93.47 but the addition of textural features significantly improved the AUROC to 95.73 (p < 0.001), and 96.10 (p < 0.001) when limiting the model to the top three features: GLCM_Correlation, Skewness and Compacity. Testing the model on the external dataset yielded an AUROC of 96.00, with 95% sensitivity and 97% specificity. GLCM_Correlation was one of the most independent features on correlation analysis, and systematically had the heaviest weight in the classification model. Conclusion A simple model with three radiomic features can identify pathologic FDOPA PET scans with excellent sensitivity and specificity. Textural features show promise for the diagnosis of parkinsonian syndromes. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05816-7.
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Affiliation(s)
- Victor Comte
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.
| | - Hugo Schmutz
- Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - David Chardin
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Fanny Orlhac
- Laboratoire d'Imagerie Translationnelle en Oncologie (LITO) U1288, Institut Curie, Inserm, Université Paris-Saclay, Orsay, France
| | - Jacques Darcourt
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
| | - Olivier Humbert
- Department of Nuclear Medicine, Centre Antoine Lacassagne, Université Côte d'Azur, Nice, France.,Laboratoire TIRO UMR E4320, Université Côte d'Azur, Nice, France
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Decentralized Distributed Multi-institutional PET Image Segmentation Using a Federated Deep Learning Framework. Clin Nucl Med 2022; 47:606-617. [PMID: 35442222 DOI: 10.1097/rlu.0000000000004194] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE The generalizability and trustworthiness of deep learning (DL)-based algorithms depend on the size and heterogeneity of training datasets. However, because of patient privacy concerns and ethical and legal issues, sharing medical images between different centers is restricted. Our objective is to build a federated DL-based framework for PET image segmentation utilizing a multicentric dataset and to compare its performance with the centralized DL approach. METHODS PET images from 405 head and neck cancer patients from 9 different centers formed the basis of this study. All tumors were segmented manually. PET images converted to SUV maps were resampled to isotropic voxels (3 × 3 × 3 mm3) and then normalized. PET image subvolumes (12 × 12 × 12 cm3) consisting of whole tumors and background were analyzed. Data from each center were divided into train/validation (80% of patients) and test sets (20% of patients). The modified R2U-Net was used as core DL model. A parallel federated DL model was developed and compared with the centralized approach where the data sets are pooled to one server. Segmentation metrics, including Dice similarity and Jaccard coefficients, percent relative errors (RE%) of SUVpeak, SUVmean, SUVmedian, SUVmax, metabolic tumor volume, and total lesion glycolysis were computed and compared with manual delineations. RESULTS The performance of the centralized versus federated DL methods was nearly identical for segmentation metrics: Dice (0.84 ± 0.06 vs 0.84 ± 0.05) and Jaccard (0.73 ± 0.08 vs 0.73 ± 0.07). For quantitative PET parameters, we obtained comparable RE% for SUVmean (6.43% ± 4.72% vs 6.61% ± 5.42%), metabolic tumor volume (12.2% ± 16.2% vs 12.1% ± 15.89%), and total lesion glycolysis (6.93% ± 9.6% vs 7.07% ± 9.85%) and negligible RE% for SUVmax and SUVpeak. No significant differences in performance (P > 0.05) between the 2 frameworks (centralized vs federated) were observed. CONCLUSION The developed federated DL model achieved comparable quantitative performance with respect to the centralized DL model. Federated DL models could provide robust and generalizable segmentation, while addressing patient privacy and legal and ethical issues in clinical data sharing.
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Maajem M, Leclère JC, Bourhis D, Tissot V, Icard N, Arnaud L, Le Pennec R, Dissaux G, Gujral DM, Salaün PY, Schick U, Abgral R. Comparison of Volumetric Quantitative PET Parameters Before and After a CT-Based Elastic Deformation on Dual-Time 18FDG-PET/CT Images: A Feasibility Study in a Perspective of Radiotherapy Planning in Head and Neck Cancer. Front Med (Lausanne) 2022; 9:831457. [PMID: 35223928 PMCID: PMC8873113 DOI: 10.3389/fmed.2022.831457] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 01/17/2022] [Indexed: 11/13/2022] Open
Abstract
Background The use of 18FDG-PET/CT for delineating a gross tumor volume (GTV, also called MTV metabolic tumor volume) in radiotherapy (RT) planning of head neck squamous cell carcinomas (HNSCC) is not included in current recommendations, although its interest for the radiotherapist is of evidence. Because pre-RT PET scans are rarely done simultaneously with dosimetry CT, the validation of a robust image registration tool and of a reproducible MTV delineation method is still required. Objective Our objective was to study a CT-based elastic registration method on dual-time pre-RT 18FDG-PET/CT images to assess the feasibility of PET-based RT planning in patients with HNSCC. Methods Dual-time 18FDG-PET/CT [whole-body examination (wbPET) + 1 dedicated step (headPET)] were selected to simulate a 2-times scenario of pre-RT PET images deformation on dosimetry CT. ER-headPET and RR-headPET images were, respectively, reconstructed after CT-to-CT rigid (RR) and elastic (ER) registrations of the headPET on the wbPET. The MTVs delineation was performed using two methods (40%SUVmax, PET-Edge). The percentage variations of several PET parameters (SUVmax, SUVmean, SUVpeak, MTV, TLG) were calculated between wbPET, ER-headPET, and RR-headPET. Correlation between MTV values was calculated (Deming linear regression). MTVs intersections were assessed by two indices (OF, DICE) and compared together (Wilcoxon test). Additional per-volume analysis was evaluated (Mann-Whitney test). Inter- and intra-observer reproducibilities were evaluated (ICC = intra-class coefficient). Results 36 patients (30M/6F; median age = 65 y) were retrospectively included. The changes in SUVmax, SUVmean and SUVpeak values between ER-headPET and RR-headPET images were <5%. The variations in MTV values between ER-headPET and wbPET images were −6 and −3% with 40%SUVmax and PET Edge, respectively. Their correlations were excellent whatever the delineation method (R2 > 0.99). The ER-headPET MTVs had significant higher mean OF and DICE with the wbPET MTVs, for both delineation methods (p ≤ 0.002); and also when lesions had a volume > 5cc (excellent OF = 0.80 with 40%SUVmax). The inter- and intra-observer reproducibilities for MTV delineation were excellent (ICC ≥ 0.8, close to 1 with PET-Edge). Conclusion Our study demonstrated no significant changes in MTV after an elastic deformation of pre-RT 18FDG-PET/CT images acquired in dual-time mode. This opens possibilities for HNSCC radiotherapy planning improvement by transferring GTV-PET on dosimetry CT.
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Affiliation(s)
- Meriem Maajem
- Department of Nuclear Medicine, Brest University Hospital, Brest, France
| | | | - David Bourhis
- Department of Nuclear Medicine, Brest University Hospital, Brest, France
- European University of Brittany, UMR 1304 GETBO, IFR 148, Brest, France
| | - Valentin Tissot
- Department of Radiology, Brest University Hospital, Brest, France
| | - Nicolas Icard
- Department of Nuclear Medicine, Saint-Brieuc Regional Hospital, Saint-Brieuc, France
| | - Laëtitia Arnaud
- Department of Nuclear Medicine, Saint-Brieuc Regional Hospital, Saint-Brieuc, France
| | - Romain Le Pennec
- Department of Nuclear Medicine, Brest University Hospital, Brest, France
- European University of Brittany, UMR 1304 GETBO, IFR 148, Brest, France
| | - Gurvan Dissaux
- Department of Radiotherapy, Brest University Hospital, Brest, France
| | - Dorothy M Gujral
- Clinical Oncology Department, Imperial College Healthcare National Health Service (NHS) Trust, Charing Cross Hospital, London, United Kingdom
- Department of Cancer and Surgery, Imperial College London, London, United Kingdom
| | - Pierre-Yves Salaün
- Department of Nuclear Medicine, Brest University Hospital, Brest, France
- European University of Brittany, UMR 1304 GETBO, IFR 148, Brest, France
| | - Ulrike Schick
- Department of Radiotherapy, Brest University Hospital, Brest, France
| | - Ronan Abgral
- Department of Nuclear Medicine, Brest University Hospital, Brest, France
- European University of Brittany, UMR 1304 GETBO, IFR 148, Brest, France
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19
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Clinical Significance of Peritumoral Adipose Tissue PET/CT Imaging Features for Predicting Axillary Lymph Node Metastasis in Patients with Breast Cancer. J Pers Med 2021; 11:jpm11101029. [PMID: 34683170 PMCID: PMC8540268 DOI: 10.3390/jpm11101029] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 10/07/2021] [Accepted: 10/13/2021] [Indexed: 12/12/2022] Open
Abstract
We investigated whether textural parameters of peritumoral breast adipose tissue (AT) based on F-18 fluorodeoxyglucose (FDG) PET/CT could predict axillary lymph node metastasis in patients with breast cancer. A total of 326 breast cancer patients with preoperative FDG PET/CT were retrospectively enrolled. PET/CT images were visually assessed and the maximum FDG uptake of axillary lymph nodes (LN SUVmax) was measured. From peritumoral breast AT, 38 textural features of PET imaging were extracted. The diagnostic ability of PET based on visual analysis, LN SUVmax, and textural features of peritumoral breast AT for predicting axillary lymph node metastasis were assessed using the area under the receiver operating characteristic curve (AUC) values. Among the 38 peritumoral breast AT textural features, grey-level co-occurrence matrix (GLCM) entropy showed the highest AUC value (0.830) for predicting axillary lymph node metastasis. The value of GLCM entropy was higher than that of visual analysis (0.739; p < 0.05) and the AUC value was comparable to that of LN SUVmax (0.793; p > 0.05). In the subgroup analysis of patients with negative findings on visual analysis, GLCM entropy still showed a high diagnostic ability (AUC: 0.759) in predicting lymph node metastasis. The findings suggest a potential diagnostic role of PET/CT imaging features of peritumoral breast AT in predicting axillary lymph node metastasis in patients with breast cancer.
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20
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Orlhac F, Nioche C, Klyuzhin I, Rahmim A, Buvat I. Radiomics in PET Imaging:: A Practical Guide for Newcomers. PET Clin 2021; 16:597-612. [PMID: 34537132 DOI: 10.1016/j.cpet.2021.06.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Tips are provided for critical reading of the content of articles. The advantages and limitations of handcrafted radiomics compared with deep-learning approaches for the characterization of PET images are also discussed.
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Affiliation(s)
- Fanny Orlhac
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France.
| | - Christophe Nioche
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
| | - Ivan Klyuzhin
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada; Department of Radiology, University of British Columbia, 675 West 10th Avenue, Vancouver, BC V5Z 1L3, Canada
| | - Irène Buvat
- Institut Curie Centre de Recherche, Centre Universitaire, Bat 101B, Rue Henri Becquerel, CS 90030, 91401 Orsay Cedex, France
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21
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Yousefirizi F, Jha AK, Brosch-Lenz J, Saboury B, Rahmim A. Toward High-Throughput Artificial Intelligence-Based Segmentation in Oncological PET Imaging. PET Clin 2021; 16:577-596. [PMID: 34537131 DOI: 10.1016/j.cpet.2021.06.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Artificial intelligence (AI) techniques for image-based segmentation have garnered much attention in recent years. Convolutional neural networks have shown impressive results and potential toward fully automated segmentation in medical imaging, and particularly PET imaging. To cope with the limited access to annotated data needed in supervised AI methods, given tedious and prone-to-error manual delineations, semi-supervised and unsupervised AI techniques have also been explored for segmentation of tumors or normal organs in single- and bimodality scans. This work reviews existing AI techniques for segmentation tasks and the evaluation criteria for translational AI-based segmentation efforts toward routine adoption in clinical workflows.
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Affiliation(s)
- Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada.
| | - Abhinav K Jha
- Department of Biomedical Engineering, Washington University in St. Louis, St Louis, MO 63130, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Julia Brosch-Lenz
- Department of Integrative Oncology, BC Cancer Research Institute, 675 West 10th Avenue, Vancouver, British Columbia V5Z 1L3, Canada
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland Baltimore County, Baltimore, MD, USA; Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA
| | - Arman Rahmim
- Department of Radiology, University of British Columbia, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada; Department of Physics, University of British Columbia, Senior Scientist & Provincial Medical Imaging Physicist, BC Cancer, BC Cancer Research Institute, 675 West 10th Avenue, Office 6-112, Vancouver, British Columbia V5Z 1L3, Canada
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22
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Spohn SK, Bettermann AS, Bamberg F, Benndorf M, Mix M, Nicolay NH, Fechter T, Hölscher T, Grosu R, Chiti A, Grosu AL, Zamboglou C. Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics 2021; 11:8027-8042. [PMID: 34335978 PMCID: PMC8315055 DOI: 10.7150/thno.61207] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/17/2021] [Indexed: 12/14/2022] Open
Abstract
Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics - a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and improve PCa detection, tissue characterization and clinical outcome prediction. This article gives an overview on the current aspects of methodology and systematically reviews available literature on radiomics in PCa patients, showing its potential for personalized therapy approaches. The qualitative synthesis includes all imaging modalities and focuses on validated studies, putting forward future directions.
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Affiliation(s)
- Simon K.B. Spohn
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
| | - Alisa S. Bettermann
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Fabian Bamberg
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Matthias Benndorf
- Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Michael Mix
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Nils H. Nicolay
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Tobias Fechter
- Department of Radiation Oncology - Division of Medical Physics, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
| | - Tobias Hölscher
- Radiotherapy and Radiation Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden
- OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany
| | - Radu Grosu
- Institute of Computer Engineering, Vienne University of Technology, Vienna, Austria
| | - Arturo Chiti
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele - Milan, Italy
- IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano - Milan, Italy
| | - Anca L. Grosu
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
| | - Constantinos Zamboglou
- Department of Radiation Oncology, Medical Center - University of Freiburg, Faculty of Medicine. University of Freiburg, Germany
- German Cancer Consortium (DKTK). Partner Site Freiburg, Germany
- Berta-Ottenstein-Programme, Faculty of Medicine, University of Freiburg, Germany
- German Oncology Center, European University of Cyprus, Limassol, Cyprus
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23
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Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms. Biomedicines 2021; 9:biomedicines9030281. [PMID: 33801987 PMCID: PMC8001140 DOI: 10.3390/biomedicines9030281] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 03/01/2021] [Accepted: 03/04/2021] [Indexed: 12/20/2022] Open
Abstract
Aim: To evaluate if conventional Positron emission tomography (PET) parameters and radiomic features (RFs) extracted by 18F-FDG-PET/CT can differentiate among different histological subtypes of lung neuroendocrine neoplasms (Lu-NENs). Methods: Forty-four naïve-treatment patients on whom 18F-FDG-PET/CT was performed for histologically confirmed Lu-NEN (n = 46) were retrospectively included. Manual segmentation was performed by two operators allowing for extraction of four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG)) and 41 RFs. Lu-NENs were classified into two groups: lung neuroendocrine tumors (Lu-NETs) vs. lung neuroendocrine carcinomas (Lu-NECs). Lu-NETs were classified according to histological subtypes (typical (TC)/atypical carcinoid (AC)), Ki67-level, and TNM staging. The least absolute shrink age and selection operator (LASSO) method was used to select the most predictive RFs for classification and Pearson correlation analysis was performed between conventional PET parameters and selected RFs. Results: PET parameters, in particular, SUVmax (area under the curve (AUC) = 0.91; cut-off = 5.16) were higher in Lu-NECs vs. Lu-NETs (p < 0.001). Among RFs, HISTO_Entropy_log10 was the most predictive (AUC = 0.90), but correlated with SUVmax/SUVmean (r = 0.95/r = 0.94, respectively). No statistical differences were found between conventional PET parameters and RFs (p > 0.05) and TC vs. AC classification. Conventional PET parameters were correlated with N+ status in Lu-NETs. Conclusion: In our study, conventional PET parameters were able to distinguish Lu-NECs from Lu-NETs, but not TC from AC. RFs did not provide additional information.
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24
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A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021; 11:diagnostics11020380. [PMID: 33672285 PMCID: PMC7926413 DOI: 10.3390/diagnostics11020380] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/12/2022] Open
Abstract
Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.
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25
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Litvin A, Burkin D, Kropinov A, Paramzin F. Radiomics and Digital Image Texture Analysis in Oncology (Review). Sovrem Tekhnologii Med 2021; 13:97-104. [PMID: 34513082 PMCID: PMC8353717 DOI: 10.17691/stm2021.13.2.11] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Indexed: 12/12/2022] Open
Abstract
One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of "virtual biopsy" is of particular relevance in oncology. The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered. Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.
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Affiliation(s)
- A.A. Litvin
- Professor, Department of Surgical Disciplines, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia; Deputy Head Physician for Medical Aspects, Regional Clinical Hospital of the Kaliningrad Region, 74 Klinicheskaya St., Kaliningrad, 236016, Russia
| | - D.A. Burkin
- PhD Student in Information Science and Computer Engineering, Immanuel Kant Baltic Federal University, 14 A. Nevskogo St., Kaliningrad, 236016, Russia
| | - A.A. Kropinov
- Therapeutist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
| | - F.N. Paramzin
- Oncologist, Central City Clinical Hospital, 3 Letnyaya St., Kaliningrad, 236005, Russia
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26
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Bielak L, Wiedenmann N, Berlin A, Nicolay NH, Gunashekar DD, Hägele L, Lottner T, Grosu AL, Bock M. Convolutional neural networks for head and neck tumor segmentation on 7-channel multiparametric MRI: a leave-one-out analysis. Radiat Oncol 2020; 15:181. [PMID: 32727525 PMCID: PMC7392704 DOI: 10.1186/s13014-020-01618-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 07/13/2020] [Indexed: 12/22/2022] Open
Abstract
Background Automatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer. Methods Head&neck cancer patients underwent multi-parametric MRI including T2w, pre- and post-contrast T1w, T2*, perfusion (ktrans, ve) and diffusion (ADC) measurements at 3 time points before and during radiochemotherapy. The 7 different MRI contrasts (input channels) and manually defined gross tumor volumes (primary tumor and lymph node metastases) were used to train CNNs for lesion segmentation. A reference CNN with all input channels was compared to individually trained CNNs where one of the input channels was left out to identify which MRI contrast contributes the most to the tumor segmentation task. A statistical analysis was employed to account for random fluctuations in the segmentation performance. Results The CNN segmentation performance scored up to a Dice similarity coefficient (DSC) of 0.65. The network trained without T2* data generally yielded the worst results, with ΔDSCGTV-T = 5.7% for primary tumor and ΔDSCGTV-Ln = 5.8% for lymph node metastases compared to the network containing all input channels. Overall, the ADC input channel showed the least impact on segmentation performance, with ΔDSCGTV-T = 2.4% for primary tumor and ΔDSCGTV-Ln = 2.2% respectively. Conclusions We developed a method to reduce overall scan times in MRI protocols by prioritizing those sequences that add most unique information for the task of automatic tumor segmentation. The optimized CNNs could be used to aid in the definition of the GTVs in radiotherapy planning, and the faster imaging protocols will reduce patient scan times which can increase patient compliance. Trial registration The trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under register number DRKS00003830 on August 20th, 2015.
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Affiliation(s)
- Lars Bielak
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany. .,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.
| | - Nicole Wiedenmann
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiation Oncology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - Nils Henrik Nicolay
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiation Oncology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Deepa Darshini Gunashekar
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Leonard Hägele
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Thomas Lottner
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Anca-Ligia Grosu
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany.,Department of Radiation Oncology, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michael Bock
- Department of Radiology, Medical Physics, Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
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27
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Beaumont J, Acosta O, Devillers A, Palard-Novello X, Chajon E, de Crevoisier R, Castelli J. Voxel-based identification of local recurrence sub-regions from pre-treatment PET/CT for locally advanced head and neck cancers. EJNMMI Res 2019; 9:90. [PMID: 31535233 PMCID: PMC6751236 DOI: 10.1186/s13550-019-0556-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2019] [Accepted: 08/22/2019] [Indexed: 12/13/2022] Open
Abstract
Background Overall, 40% of patients with a locally advanced head and neck cancer (LAHNC) treated by chemoradiotherapy (CRT) present local recurrence within 2 years after the treatment. The aims of this study were to characterize voxel-wise the sub-regions where tumor recurrence appear and to predict their location from pre-treatment 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) images. Materials and methods Twenty-six patients with local failure after treatment were included in this study. Local recurrence volume was identified by co-registering pre-treatment and recurrent PET/CT images using a customized rigid registration algorithm. A large set of voxel-wise features were extracted from pre-treatment PET to train a random forest model allowing to predict local recurrence at the voxel level. Results Out of 26 expert-assessed registrations, 15 provided enough accuracy to identify recurrence volumes and were included for further analysis. Recurrence volume represented on average 23% of the initial tumor volume. The MTV with a threshold of 50% of SUVmax plus a 3D margin of 10 mm covered on average 89.8% of the recurrence and 96.9% of the initial tumor. SUV and MTV alone were not sufficient to identify the area of recurrence. Using a random forest model, 15 parameters, combining radiomics and spatial location, were identified, allowing to predict the recurrence sub-regions with a median area under the receiver operating curve of 0.71 (range 0.14–0.91). Conclusion As opposed to regional comparisons which do not bring enough evidence for accurate prediction of recurrence volume, a voxel-wise analysis of FDG-uptake features suggested a potential to predict recurrence with enough accuracy to consider tailoring CRT by dose escalation within likely radioresistant regions.
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Affiliation(s)
- J Beaumont
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, 35000, Rennes, France
| | - O Acosta
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, 35000, Rennes, France
| | - A Devillers
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, 35000, Rennes, France.,Department of Radiotherapy, Centre Eugene Marquis, avenue de la Bataille Flandre Dunkerque, 35000, Rennes, France
| | - X Palard-Novello
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, 35000, Rennes, France.,Department of Radiotherapy, Centre Eugene Marquis, avenue de la Bataille Flandre Dunkerque, 35000, Rennes, France
| | - E Chajon
- Department of Radiotherapy, Centre Eugene Marquis, avenue de la Bataille Flandre Dunkerque, 35000, Rennes, France
| | - R de Crevoisier
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, 35000, Rennes, France.,Department of Radiotherapy, Centre Eugene Marquis, avenue de la Bataille Flandre Dunkerque, 35000, Rennes, France
| | - J Castelli
- Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, 35000, Rennes, France. .,Department of Radiotherapy, Centre Eugene Marquis, avenue de la Bataille Flandre Dunkerque, 35000, Rennes, France.
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