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Carriero A, Groenhoff L, Vologina E, Basile P, Albera M. Deep Learning in Breast Cancer Imaging: State of the Art and Recent Advancements in Early 2024. Diagnostics (Basel) 2024; 14:848. [PMID: 38667493 PMCID: PMC11048882 DOI: 10.3390/diagnostics14080848] [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: 02/29/2024] [Revised: 04/07/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The rapid advancement of artificial intelligence (AI) has significantly impacted various aspects of healthcare, particularly in the medical imaging field. This review focuses on recent developments in the application of deep learning (DL) techniques to breast cancer imaging. DL models, a subset of AI algorithms inspired by human brain architecture, have demonstrated remarkable success in analyzing complex medical images, enhancing diagnostic precision, and streamlining workflows. DL models have been applied to breast cancer diagnosis via mammography, ultrasonography, and magnetic resonance imaging. Furthermore, DL-based radiomic approaches may play a role in breast cancer risk assessment, prognosis prediction, and therapeutic response monitoring. Nevertheless, several challenges have limited the widespread adoption of AI techniques in clinical practice, emphasizing the importance of rigorous validation, interpretability, and technical considerations when implementing DL solutions. By examining fundamental concepts in DL techniques applied to medical imaging and synthesizing the latest advancements and trends, this narrative review aims to provide valuable and up-to-date insights for radiologists seeking to harness the power of AI in breast cancer care.
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Affiliation(s)
| | - Léon Groenhoff
- Radiology Department, Maggiore della Carità Hospital, 28100 Novara, Italy; (A.C.); (E.V.); (P.B.); (M.A.)
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Lokaj B, Pugliese MT, Kinkel K, Lovis C, Schmid J. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. Eur Radiol 2024; 34:2096-2109. [PMID: 37658895 PMCID: PMC10873444 DOI: 10.1007/s00330-023-10181-6] [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: 03/02/2023] [Revised: 06/07/2023] [Accepted: 07/10/2023] [Indexed: 09/05/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) has demonstrated promise in enhancing breast cancer diagnosis, the implementation of AI algorithms in clinical practice encounters various barriers. This scoping review aims to identify these barriers and facilitators to highlight key considerations for developing and implementing AI solutions in breast cancer imaging. METHOD A literature search was conducted from 2012 to 2022 in six databases (PubMed, Web of Science, CINHAL, Embase, IEEE, and ArXiv). The articles were included if some barriers and/or facilitators in the conception or implementation of AI in breast clinical imaging were described. We excluded research only focusing on performance, or with data not acquired in a clinical radiology setup and not involving real patients. RESULTS A total of 107 articles were included. We identified six major barriers related to data (B1), black box and trust (B2), algorithms and conception (B3), evaluation and validation (B4), legal, ethical, and economic issues (B5), and education (B6), and five major facilitators covering data (F1), clinical impact (F2), algorithms and conception (F3), evaluation and validation (F4), and education (F5). CONCLUSION This scoping review highlighted the need to carefully design, deploy, and evaluate AI solutions in clinical practice, involving all stakeholders to yield improvement in healthcare. CLINICAL RELEVANCE STATEMENT The identification of barriers and facilitators with suggested solutions can guide and inform future research, and stakeholders to improve the design and implementation of AI for breast cancer detection in clinical practice. KEY POINTS • Six major identified barriers were related to data; black-box and trust; algorithms and conception; evaluation and validation; legal, ethical, and economic issues; and education. • Five major identified facilitators were related to data, clinical impact, algorithms and conception, evaluation and validation, and education. • Coordinated implication of all stakeholders is required to improve breast cancer diagnosis with AI.
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Affiliation(s)
- Belinda Lokaj
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.
| | - Marie-Thérèse Pugliese
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
| | - Karen Kinkel
- Réseau Hospitalier Neuchâtelois, Neuchâtel, Switzerland
| | - Christian Lovis
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland
| | - Jérôme Schmid
- Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Delémont, Switzerland
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Fiste O, Gkiozos I, Charpidou A, Syrigos NK. Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC. Cancers (Basel) 2024; 16:831. [PMID: 38398222 PMCID: PMC10887017 DOI: 10.3390/cancers16040831] [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: 01/31/2024] [Revised: 02/12/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.
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Affiliation(s)
- Oraianthi Fiste
- Oncology Unit, Third Department of Internal Medicine and Laboratory, Medical School, National and Kapodistrian University of Athens, 11527 Athens, Greece; (I.G.); (A.C.); (N.K.S.)
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Kanan M, Alharbi H, Alotaibi N, Almasuood L, Aljoaid S, Alharbi T, Albraik L, Alothman W, Aljohani H, Alzahrani A, Alqahtani S, Kalantan R, Althomali R, Alameen M, Mufti A. AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis. Cancers (Basel) 2024; 16:674. [PMID: 38339425 PMCID: PMC10854661 DOI: 10.3390/cancers16030674] [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: 11/19/2023] [Revised: 01/20/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
(1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI's performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI's role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI's potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI's accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI's potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.
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Affiliation(s)
- Mohammed Kanan
- Department of Clinical Pharmacy, King Fahad Medical City, Riyadh 12211, Saudi Arabia
| | - Hajar Alharbi
- Department of Medicine, Gdansk Medical University, 80210 Gdansk, Poland
| | - Nawaf Alotaibi
- Department of Clinical Pharmacy, Northern Border University, Rafha 73213, Saudi Arabia
| | - Lubna Almasuood
- Department of Pharmacy, Qassim University, Buraydah 52571, Saudi Arabia
| | - Shahad Aljoaid
- Department of Medicine, University of Tabuk, Tabuk 47911, Saudi Arabia
| | - Tuqa Alharbi
- Department of Medicine, Qassim University, Buraydah 52571, Saudi Arabia
| | - Leen Albraik
- Department of Medicine, Al-Faisal University, Riyadh 12385, Saudi Arabia;
| | - Wojod Alothman
- Department of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31411, Saudi Arabia
| | - Hadeel Aljohani
- Department of Medicine and Surgery, King Abdulaziz University, Jeddah 22230, Saudi Arabia; (H.A.); (R.K.)
| | - Aghnar Alzahrani
- Department of Medicine, Al-Baha University, Al Bahah 65964, Saudi Arabia
| | - Sadeem Alqahtani
- Department of Pharmacy, King Khalid University, Abha 62217, Saudi Arabia
| | - Razan Kalantan
- Department of Medicine and Surgery, King Abdulaziz University, Jeddah 22230, Saudi Arabia; (H.A.); (R.K.)
| | - Raghad Althomali
- Department of Medicine, Taif University, Taif 26311, Saudi Arabia
| | - Maram Alameen
- Department of Medicine, Taif University, Taif 26311, Saudi Arabia
| | - Ahdab Mufti
- Department of Medicine, Ibn Sina National College, Jeddah 22230, Saudi Arabia
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Shen S, Li C, Fan Y, Lu S, Yan Z, Liu H, Zhou H, Zhang Z. Development and validation of a multi-modality fusion deep learning model for differentiating glioblastoma from solitary brain metastases. ZHONG NAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF CENTRAL SOUTH UNIVERSITY. MEDICAL SCIENCES 2024; 49:58-67. [PMID: 38615167 PMCID: PMC11017031 DOI: 10.11817/j.issn.1672-7347.2024.230248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Indexed: 04/15/2024]
Abstract
OBJECTIVES Glioblastoma (GBM) and brain metastases (BMs) are the two most common malignant brain tumors in adults. Magnetic resonance imaging (MRI) is a commonly used method for screening and evaluating the prognosis of brain tumors, but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited. In recent years, deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system. This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases (SBMs), and to further explore the impact of multimodality data fusion on classification tasks. METHODS Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed. First, structural T1-weight, T1C-weight, and T2-weight were selected as 3 inputs to the entire model, regions of interest (ROIs) were manually delineated on the registered three modal MR images, and multimodality radiomics features were obtained, dimensions were reduced using a random forest (RF)-based feature selection method, and the importance of each feature was further analyzed. Secondly, we used the method of contrast disentangled to find the shared features and complementary features between different modal features. Finally, the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. RESULTS The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs. Furthermore, compared with single-modal data, the multimodal fusion models using machine learning algorithms such as support vector machine (SVM), Logistic regression, RF, adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) achieved significant improvements, with area under the curve (AUC) values of 0.974, 0.978, 0.943, 0.938, and 0.947, respectively; our comparative disentangled multi-modal MR fusion method performs well, and the results of AUC, accuracy (ACC), sensitivity (SEN) and specificity(SPE) in the test set were 0.985, 0.984, 0.900, and 0.990, respectively. Compared with other multi-modal fusion methods, AUC, ACC, and SEN in this study all achieved the best performance. In the ablation experiment to verify the effects of each module component in this study, AUC, ACC, and SEN increased by 1.6%, 10.9% and 15.0%, respectively after 3 loss functions were used simultaneously. CONCLUSIONS A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.
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Affiliation(s)
- Shanshan Shen
- Department of Oncology, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000.
- Information Engineering School of Nanchang University, Nanchang 330031.
| | - Chunquan Li
- Information Engineering School of Nanchang University, Nanchang 330031
| | - Yaohua Fan
- Department of Oncology, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000
| | - Shanfu Lu
- Perception Vision Medical Technologies Co., Ltd., Guangzhou 510530
| | - Ziye Yan
- Perception Vision Medical Technologies Co., Ltd., Guangzhou 510530
| | - Hu Liu
- Department of Radiology, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000
| | - Haihang Zhou
- Department of Neurosurgery, Second Hospital of Jiaxing, Jiaxing Zhejiang 314000
| | - Zijian Zhang
- Department of Oncology, Xiangya Hospital, Central South University, Changsha 410008, China.
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Wang H, Zhang Y. Exploring non-invasive precision treatment in non-small cell lung cancer patients through deep learning radiomics across imaging features and molecular phenotypes. Biomark Res 2024; 12:12. [PMID: 38273398 PMCID: PMC10809593 DOI: 10.1186/s40364-024-00561-5] [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] [Accepted: 01/10/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUND Accurate prediction of tumor molecular alterations is vital for optimizing cancer treatment. Traditional tissue-based approaches encounter limitations due to invasiveness, heterogeneity, and molecular dynamic changes. We aim to develop and validate a deep learning radiomics framework to obtain imaging features that reflect various molecular changes, aiding first-line treatment decisions for cancer patients. METHODS We conducted a retrospective study involving 508 NSCLC patients from three institutions, incorporating CT images and clinicopathologic data. Two radiomic scores and a deep network feature were constructed on three data sources in the 3D tumor region. Using these features, we developed and validated the 'Deep-RadScore,' a deep learning radiomics model to predict prognostic factors, gene mutations, and immune molecule expression levels. FINDINGS The Deep-RadScore exhibits strong discrimination for tumor molecular features. In the independent test cohort, it achieved impressive AUCs: 0.889 for lymphovascular invasion, 0.903 for pleural invasion, 0.894 for T staging; 0.884 for EGFR and ALK, 0.896 for KRAS and PIK3CA, 0.889 for TP53, 0.895 for ROS1; and 0.893 for PD-1/PD-L1. Fusing features yielded optimal predictive power, surpassing any single imaging feature. Correlation and interpretability analyses confirmed the effectiveness of customized deep network features in capturing additional imaging phenotypes beyond known radiomic features. INTERPRETATION This proof-of-concept framework demonstrates that new biomarkers across imaging features and molecular phenotypes can be provided by fusing radiomic features and deep network features from multiple data sources. This holds the potential to offer valuable insights for radiological phenotyping in characterizing diverse tumor molecular alterations, thereby advancing the pursuit of non-invasive personalized treatment for NSCLC patients.
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Affiliation(s)
- Xingping Zhang
- School of Medical Information Engineering, Gannan Medical University, 341000, Ganzhou, China
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, 341000, Ganzhou, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, 110189, Shenyang, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, 510006, Guangzhou, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia
| | - Yanchun Zhang
- School of Computer Science and Technology, Zhejiang Normal University, 321000, Jinhua, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, 3011, Melbourne, Australia.
- Department of New Networks, Peng Cheng Laboratory, 518000, Shenzhen, China.
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Kasai A, Miyoshi J, Sato Y, Okamoto K, Miyamoto H, Kawanaka T, Tonoiso C, Harada M, Goto M, Yoshida T, Haga A, Takayama T. A novel CT-based radiomics model for predicting response and prognosis of chemoradiotherapy in esophageal squamous cell carcinoma. Sci Rep 2024; 14:2039. [PMID: 38263395 PMCID: PMC10806175 DOI: 10.1038/s41598-024-52418-4] [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: 04/16/2023] [Accepted: 01/18/2024] [Indexed: 01/25/2024] Open
Abstract
No clinically relevant biomarker has been identified for predicting the response of esophageal squamous cell carcinoma (ESCC) to chemoradiotherapy (CRT). Herein, we established a CT-based radiomics model with artificial intelligence (AI) to predict the response and prognosis of CRT in ESCC. A total of 44 ESCC patients (stage I-IV) were enrolled in this study; training (n = 27) and validation (n = 17) cohorts. First, we extracted a total of 476 radiomics features from three-dimensional CT images of cancer lesions in training cohort, selected 110 features associated with the CRT response by ROC analysis (AUC ≥ 0.7) and identified 12 independent features, excluding correlated features by Pearson's correlation analysis (r ≥ 0.7). Based on the 12 features, we constructed 5 prediction models of different machine learning algorithms (Random Forest (RF), Ridge Regression, Naive Bayes, Support Vector Machine, and Artificial Neural Network models). Among those, the RF model showed the highest AUC in the training cohort (0.99 [95%CI 0.86-1.00]) as well as in the validation cohort (0.92 [95%CI 0.71-0.99]) to predict the CRT response. Additionally, Kaplan-Meyer analysis of the validation cohort and all the patient data showed significantly longer progression-free and overall survival in the high-prediction score group compared with the low-prediction score group in the RF model. Univariate and multivariate analyses revealed that the radiomics prediction score and lymph node metastasis were independent prognostic biomarkers for CRT of ESCC. In conclusion, we have developed a CT-based radiomics model using AI, which may have the potential to predict the CRT response as well as the prognosis for ESCC patients with non-invasiveness and cost-effectiveness.
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Affiliation(s)
- Akinari Kasai
- Department of Gastroenterology and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Jinsei Miyoshi
- Department of Gastroenterology and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
- Department of Gastroenterology, Kawashima Hospital, 6-1 Kitasakoichiban-cho, Tokushima, 770-0011, Japan
| | - Yasushi Sato
- Department of Gastroenterology and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Koichi Okamoto
- Department of Gastroenterology and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hiroshi Miyamoto
- Department of Gastroenterology and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Takashi Kawanaka
- Department of Radiology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Chisato Tonoiso
- Department of Radiology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Masafumi Harada
- Department of Radiology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Masakazu Goto
- Department of Thoracic, Endocrine Surgery and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Takahiro Yoshida
- Department of Thoracic, Endocrine Surgery and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
- Yoshida Clinic, 1-18 shinuchimachi, Tokushima, 770-0845, Japan
| | - Akihiro Haga
- Department of Medical Image Informatics, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Tetsuji Takayama
- Department of Gastroenterology and Oncology, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
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Fang F, Sun Y, Huang H, Huang Y, Luo X, Yao W, Wei L, Xie G, Wu Y, Lu Z, Zhao J, Li C. Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: a two-center study. J Cancer Res Clin Oncol 2024; 150:18. [PMID: 38240867 PMCID: PMC10798931 DOI: 10.1007/s00432-023-05549-6] [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: 10/16/2023] [Accepted: 11/22/2023] [Indexed: 01/22/2024]
Abstract
OBJECTIVE To develop an ultrasound-driven clinical deep learning radiomics (CDLR) model for stratifying the risk of testicular masses, aiming to guide individualized treatment and minimize unnecessary procedures. METHODS We retrospectively analyzed 275 patients with confirmed testicular lesions (January 2018 to April 2023) from two hospitals, split into training (158 cases), validation (68 cases), and external test cohorts (49 cases). Radiomics and deep learning (DL) features were extracted from preoperative ultrasound images. Following feature selection, we utilized logistic regression (LR) to establish a deep learning radiomics (DLR) model and subsequently derived its signature. Clinical data underwent univariate and multivariate LR analyses, forming the "clinic signature." By integrating the DLR and clinic signatures using multivariable LR, we formulated the CDLR nomogram for testicular mass risk stratification. The model's efficacy was gauged using the area under the receiver operating characteristic curve (AUC), while its clinical utility was appraised with decision curve analysis(DCA). Additionally, we compared these models with two radiologists' assessments (5-8 years of practice). RESULTS The CDLR nomogram showcased exceptional precision in distinguishing testicular tumors from non-tumorous lesions, registering AUCs of 0.909 (internal validation) and 0.835 (external validation). It also excelled in discerning malignant from benign testicular masses, posting AUCs of 0.851 (internal validation) and 0.834 (external validation). Notably, CDLR surpassed the clinical model, standalone DLR, and the evaluations of the two radiologists. CONCLUSION The CDLR nomogram offers a reliable tool for differentiating risks associated with testicular masses. It augments radiological diagnoses, facilitates personalized treatment approaches, and curtails unwarranted medical procedures.
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Affiliation(s)
- Fuxiang Fang
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China
| | - Yan Sun
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China
| | - Hualin Huang
- Department of Emergency, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Yueting Huang
- Department of Epidemiology and Health Statistics, School of Public Health of Guangxi Medical University, Nanning, 530021, China
| | - Xing Luo
- Department of Urology, Baise People's Hospital, Baise, 533099, China
| | - Wei Yao
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China
| | - Liyan Wei
- Department of Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China
| | - Guiwu Xie
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China
| | - Yongxian Wu
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China
| | - Zheng Lu
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China
| | - Jiawen Zhao
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China.
| | - Chengyang Li
- Department of Urology, The First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning, 530021, China.
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Yang M, Meng S, Wu F, Shi F, Xia Y, Feng J, Zhang J, Li C. Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging. Front Med (Lausanne) 2024; 11:1305565. [PMID: 38283620 PMCID: PMC10811129 DOI: 10.3389/fmed.2024.1305565] [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: 10/02/2023] [Accepted: 01/02/2024] [Indexed: 01/30/2024] Open
Abstract
Purpose Early and rapid diagnosis of mild cognitive impairment (MCI) has important clinical value in improving the prognosis of Alzheimer's disease (AD). The hippocampus and parahippocampal gyrus play crucial roles in the occurrence of cognitive function decline. In this study, deep learning and radiomics techniques were used to automatically detect MCI from healthy controls (HCs). Method This study included 115 MCI patients and 133 normal individuals with 3D-T1 weighted MR structural images from the ADNI database. The identification and segmentation of the hippocampus and parahippocampal gyrus were automatically performed with a VB-net, and radiomics features were extracted. Relief, Minimum Redundancy Maximum Correlation, Recursive Feature Elimination and the minimum absolute shrinkage and selection operator (LASSO) were used to reduce the dimensionality and select the optimal features. Five independent machine learning classifiers including Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Bagging Decision Tree (BDT), and Gaussian Process (GP) were trained on the training set, and validated on the testing set to detect the MCI. The Delong test was used to assess the performance of different models. Result Our VB-net could automatically identify and segment the bilateral hippocampus and parahippocampal gyrus. After four steps of feature dimensionality reduction, the GP models based on combined features (11 features from the hippocampus, and 4 features from the parahippocampal gyrus) showed the best performance for the MCI and normal control subject discrimination. The AUC of the training set and test set were 0.954 (95% CI: 0.929-0.979) and 0.866 (95% CI: 0.757-0.976), respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The GP classifier based on 15 radiomics features of bilateral hippocampal and parahippocampal gyrus could detect MCI from normal controls with high accuracy based on conventional MR images. Our fully automatic model could rapidly process the MRI data and give results in 1 minute, which provided important clinical value in assisted diagnosis.
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Affiliation(s)
- Mingguang Yang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Shan Meng
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Jinan, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Junbang Feng
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Jinrui Zhang
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, Chongqing, China
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10
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Zhang M, Wang Y, Lv M, Sang L, Wang X, Yu Z, Yang Z, Wang Z, Sang L. Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis. Technol Cancer Res Treat 2024; 23:15330338241235769. [PMID: 38465611 DOI: 10.1177/15330338241235769] [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: 03/12/2024] Open
Abstract
Objectives: The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Methods: Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. Results: The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Conclusion: Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. Advances in knowledge: This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research.
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Affiliation(s)
- Minghui Zhang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Yan Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Mutian Lv
- Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Li Sang
- Department of Acupuncture and Massage, Shouguang Hospital of Traditional Chinese Medicine, Weifang, P. R. China
| | - Xuemei Wang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zijun Yu
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Ziyi Yang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Zhongqing Wang
- Department of Information Center, The First Hospital of China Medical University, Shenyang, P. R. China
| | - Liang Sang
- Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China
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11
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Zhang X, Zhong Y, Jin C, Hu D, Tian M, Zhang H. Medical image Generative Pre-Trained Transformer (MI-GPT): future direction for precision medicine. Eur J Nucl Med Mol Imaging 2024; 51:332-335. [PMID: 37803245 DOI: 10.1007/s00259-023-06450-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Affiliation(s)
- Xiaohui Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Daoyan Hu
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Mei Tian
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China.
- Human Phenome Institute, Fudan University, 825 Zhangheng Road, Shanghai, 201203, China.
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
- Institute of Nuclear Medicine and Molecular Imaging of Zhejiang University, Hangzhou, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China.
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.
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Zhang J, Zhang Y, Jin Y, Xu J, Xu X. MDU-Net: multi-scale densely connected U-Net for biomedical image segmentation. Health Inf Sci Syst 2023; 11:13. [PMID: 36925619 PMCID: PMC10011258 DOI: 10.1007/s13755-022-00204-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 11/02/2022] [Indexed: 03/18/2023] Open
Abstract
Biomedical image segmentation plays a central role in quantitative analysis, clinical diagnosis, and medical intervention. In the light of the fully convolutional networks (FCN) and U-Net, deep convolutional networks (DNNs) have made significant contributions to biomedical image segmentation applications. In this paper, we propose three different multi-scale dense connections (MDC) for the encoder, the decoder of U-shaped architectures, and across them. Based on three dense connections, we propose a multi-scale densely connected U-Net (MDU-Net) for biomedical image segmentation. MDU-Net directly fuses the neighboring feature maps with different scales from both higher layers and lower layers to strengthen feature propagation in the current layer. Multi-scale dense connections, which contain shorter connections between layers close to the input and output, also make a much deeper U-Net possible. Besides, we introduce quantization to alleviate the potential overfitting in dense connections, and further improve the segmentation performance. We evaluate our proposed model on the MICCAI 2015 Gland Segmentation (GlaS) dataset. The three MDC improve U-Net performance by up to 1.8% on test A and 3.5% on test B in the MICCAI Gland dataset. Meanwhile, the MDU-Net with quantization obviously improves the segmentation performance of original U-Net.
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Affiliation(s)
- Jiawei Zhang
- The Department of New Networks, Peng Cheng Laboratory, Shenzhen, Guangdong China
- Department of Cardiovascular Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences),Southern Medical University, Guangzhou, Guangdong China
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, Guangdong China
- Institute for Sustainable Industries & Livable Cities, Victoria University, Melbourne, VIC Australia
| | - Yanchun Zhang
- The Department of New Networks, Peng Cheng Laboratory, Shenzhen, Guangdong China
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, Guangdong China
- Institute for Sustainable Industries & Livable Cities, Victoria University, Melbourne, VIC Australia
| | - Yuzhen Jin
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
| | - Jilan Xu
- Shanghai Key Laboratory of Data Science, School of Computer Science, Fudan University, Shanghai, China
| | - Xiaowei Xu
- Department of Cardiovascular Surgery, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences),Southern Medical University, Guangzhou, Guangdong China
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Wang H, Chen X, He L. A narrative review of radiomics and deep learning advances in neuroblastoma: updates and challenges. Pediatr Radiol 2023; 53:2742-2755. [PMID: 37945937 DOI: 10.1007/s00247-023-05792-6] [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: 08/24/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023]
Abstract
Neuroblastoma is an extremely heterogeneous tumor that commonly occurs in children. The diagnosis and treatment of this tumor pose considerable challenges due to its varied clinical presentations and intricate genetic aberrations. Presently, various imaging modalities, including computed tomography, magnetic resonance imaging, and positron emission tomography, are utilized to assess neuroblastoma. Nevertheless, these conventional imaging modalities have limitations in providing quantitative information for accurate diagnosis and prognosis. Radiomics, an emerging technique, can extract intricate medical imaging information that is imperceptible to the human eye and transform it into quantitative data. In conjunction with deep learning algorithms, radiomics holds great promise in complementing existing imaging modalities. The aim of this review is to showcase the potential of radiomics and deep learning advancements to enhance the diagnostic capabilities of current imaging modalities for neuroblastoma.
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Affiliation(s)
- Haoru Wang
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- Department of Radiology, Children's Hospital of Chongqing Medical University, 136 Zhongshan Road 2, Yuzhong District, Chongqing, 400014, China.
- National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
- Chongqing Key Laboratory of Pediatrics, Chongqing, China.
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Thunold HH, Riegler MA, Yazidi A, Hammer HL. A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering. Diagnostics (Basel) 2023; 13:3413. [PMID: 37998548 PMCID: PMC10670034 DOI: 10.3390/diagnostics13223413] [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: 09/18/2023] [Revised: 11/03/2023] [Accepted: 11/06/2023] [Indexed: 11/25/2023] Open
Abstract
An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one.
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Affiliation(s)
- Håvard Horgen Thunold
- Department of Compute Science, Faculty of Technology, Art and Design, Oslo Metropolitan University, 0176 Oslo, Norway; (H.H.T.); (M.A.R.); (A.Y.)
| | - Michael A. Riegler
- Department of Compute Science, Faculty of Technology, Art and Design, Oslo Metropolitan University, 0176 Oslo, Norway; (H.H.T.); (M.A.R.); (A.Y.)
- Department of Holistic Systems, SimulaMet, 0176 Oslo, Norway
| | - Anis Yazidi
- Department of Compute Science, Faculty of Technology, Art and Design, Oslo Metropolitan University, 0176 Oslo, Norway; (H.H.T.); (M.A.R.); (A.Y.)
| | - Hugo L. Hammer
- Department of Compute Science, Faculty of Technology, Art and Design, Oslo Metropolitan University, 0176 Oslo, Norway; (H.H.T.); (M.A.R.); (A.Y.)
- Department of Holistic Systems, SimulaMet, 0176 Oslo, Norway
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Jiang C, Qian C, Jiang Z, Teng Y, Lai R, Sun Y, Ni X, Ding C, Xu Y, Tian R. Robust deep learning-based PET prognostic imaging biomarker for DLBCL patients: a multicenter study. Eur J Nucl Med Mol Imaging 2023; 50:3949-3960. [PMID: 37606859 DOI: 10.1007/s00259-023-06405-y] [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: 06/19/2023] [Accepted: 08/16/2023] [Indexed: 08/23/2023]
Abstract
OBJECTIVE To develop and independently externally validate robust prognostic imaging biomarkers distilled from PET images using deep learning techniques for precise survival prediction in patients with diffuse large B cell lymphoma (DLBCL). METHODS A total of 684 DLBCL patients from three independent medical centers were included in this retrospective study. Deep learning scores (DLS) were generated from PET images using deep convolutional neural network architecture known as VGG19 and DenseNet121. These DLSs were utilized to predict progression-free survival (PFS) and overall survival (OS). Furthermore, multiparametric models were designed based on results from the Cox proportional hazards model and assessed through calibration curves, concordance index (C-index), and decision curve analysis (DCA) in the training and validation cohorts. RESULTS The DLSPFS and DLSOS exhibited significant associations with PFS and OS, respectively (P<0.05) in the training and validation cohorts. The multiparametric models that incorporated DLSs demonstrated superior efficacy in predicting PFS (C-index: 0.866) and OS (C-index: 0.835) compared to competing models in training cohorts. In external validation cohorts, the C-indices for PFS and OS were 0.760 and. 0.770 and 0.748 and 0.766, respectively, indicating the reliable validity of the multiparametric models. The calibration curves displayed good consistency, and the decision curve analysis (DCA) confirmed that the multiparametric models offered more net clinical benefits. CONCLUSIONS The DLSs were identified as robust prognostic imaging biomarkers for survival in DLBCL patients. Moreover, the multiparametric models developed in this study exhibited promising potential in accurately stratifying patients based on their survival risk.
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Affiliation(s)
- Chong Jiang
- Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China
| | - Chunjun Qian
- School of Electrical and Information Engineering, Changzhou Institute of Technology, Changzhou, 213032, Jiangsu, China
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213003, China
- Center of Medical Physics, Nanjing Medical University, Changzhou, 213003, China
| | - Zekun Jiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yue Teng
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Ruihe Lai
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Yiwen Sun
- Department of Nuclear Medicine, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, China
| | - Xinye Ni
- The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, 213003, China
- Center of Medical Physics, Nanjing Medical University, Changzhou, 213003, China
| | - Chongyang Ding
- Department of Nuclear Medicine, the First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Jiangsu Province, No. 321, Zhongshan Road, Nanjing, 210008, China.
| | - Yuchao Xu
- School of Nuclear Science and Technology, University of South China, Hengyang City, China
| | - Rong Tian
- Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37, Guoxue Alley, Chengdu, 610041, Sichuan, China.
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Yang J, Cao Y, Zhou F, Li C, Lv J, Li P. Combined deep-learning MRI-based radiomic models for preoperative risk classification of endometrial endometrioid adenocarcinoma. Front Oncol 2023; 13:1231497. [PMID: 37909025 PMCID: PMC10613647 DOI: 10.3389/fonc.2023.1231497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 10/02/2023] [Indexed: 11/02/2023] Open
Abstract
Background Differences exist between high- and low-risk endometrial cancer (EC) in terms of whether lymph node dissection is performed. Factors such as tumor grade, myometrial invasion (MDI), and lymphovascular space invasion (LVSI) in the European Society for Medical Oncology (ESMO), European SocieTy for Radiotherapy & Oncology (ESTRO) and European Society of Gynaecological Oncology (ESGO) guidelines risk classification can often only be accurately assessed postoperatively. The aim of our study was to estimate the risk classification of patients with endometrial endometrioid adenocarcinoma before surgery and offer individualized treatment plans based on their risk classification. Methods Clinical information and last preoperative pelvic magnetic resonance imaging (MRI) of patients with postoperative pathologically determined endometrial endometrioid adenocarcinoma were collected retrospectively. The region of interest (ROI) was subsequently plotted in T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), and diffusion-weighted imaging (DWI) MRI scans, and the traditional radiomics features and deep-learning image features were extracted. A final radiomics nomogram model integrating traditional radiomics features, deep learning image features, and clinical information was constructed to distinguish between low- and high-risk patients (based on the 2020 ESMO-ESGO-ESTRO guidelines). The efficacy of the model was evaluated in the training and validation sets of the model. Results We finally included 168 patients from January 1, 2020 to July 29, 2021, of which 95 patients in 2021 were classified as the training set and 73 patients in 2020 were classified as the validation set. In the training set, the area under the curve (AUC) of the radiomics nomogram was 0.923 (95%CI: 0.865-0.980) and in the validation set, the AUC of the radiomics nomogram was 0.842 (95%CI: 0.762-0.923). The nomogram had better predictions than both the traditional radiomics model and the deep-learning radiomics model. Conclusion MRI-based radiomics models can be useful for preoperative risk classification of patients with endometrial endometrioid adenocarcinoma.
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Affiliation(s)
| | | | | | | | | | - Pu Li
- Clinical School of Obstetrics and Gynecology Center, Tianjin Medical University, Tianjin, China
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Zhang X, Zhang G, Qiu X, Yin J, Tan W, Yin X, Yang H, Liao L, Wang H, Zhang Y. Radiomics under 2D regions, 3D regions, and peritumoral regions reveal tumor heterogeneity in non-small cell lung cancer: a multicenter study. LA RADIOLOGIA MEDICA 2023; 128:1079-1092. [PMID: 37486526 DOI: 10.1007/s11547-023-01676-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 06/29/2023] [Indexed: 07/25/2023]
Abstract
PURPOSE Lung cancer has significant genetic and phenotypic heterogeneity, leading to poor prognosis. Radiomic features have emerged as promising predictors of the tumor phenotype. However, the role of underlying information surrounding the cancer remains unclear. MATERIALS AND METHODS We conducted a retrospective study of 508 patients with NSCLC from three institutions. Radiomics models were built using features from six tumor regions and seven classifiers to predict three prognostically significant tumor phenotypes. The models were evaluated and interpreted by the mean area under the receiver operating characteristic curve (AUC) under nested cross-validation and Shapley values. The best-performing predictive models corresponding to six tumor regions and three tumor phenotypes were identified for further comparative analysis. In addition, we designed five experiments with different voxel spacing to assess the sensitivity of the experimental results to the spatial resolution of the voxels. RESULTS Our results demonstrated that models based on 2D, 3D, and peritumoral region features yielded mean AUCs and 95% confidence intervals of 0.759 and [0.747-0.771] for lymphovascular invasion, 0.889 and [0.882-0.896] for pleural invasion, and 0.839 and [0.829-0.849] for T-staging in the testing cohort, which was significantly higher than all other models. Similar results were obtained for the model combining the three regional features at five voxel spacings. CONCLUSION Our study revealed the predictive role of the developed methods with multi-regional features for the preoperative assessment of prognostic factors in NSCLC. The analysis of different voxel spacing and model interpretability strengthens the experimental findings and contributes to understanding the biological significance of the radiological phenotype.
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Affiliation(s)
- Xingping Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000, China
| | - Guijuan Zhang
- Department of Respiratory and Critical Care, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, China
| | - Xingting Qiu
- Department of Radiology, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, China
| | - Jiao Yin
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia
| | - Wenjun Tan
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189, China
| | - Xiaoxia Yin
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China
| | - Hong Yang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China
| | - Liefa Liao
- School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China
| | - Hua Wang
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia.
| | - Yanchun Zhang
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
- Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, 3011, Australia.
- Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000, China.
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Huynh BN, Groendahl AR, Tomic O, Liland KH, Knudtsen IS, Hoebers F, van Elmpt W, Malinen E, Dale E, Futsaether CM. Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics. Front Med (Lausanne) 2023; 10:1217037. [PMID: 37711738 PMCID: PMC10498924 DOI: 10.3389/fmed.2023.1217037] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/07/2023] [Indexed: 09/16/2023] Open
Abstract
Background Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI. Purpose The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images. Materials and methods FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately. Results CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours. Conclusion High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.
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Affiliation(s)
- Bao Ngoc Huynh
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | | | - Oliver Tomic
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Kristian Hovde Liland
- Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
| | - Ingerid Skjei Knudtsen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
| | - Frank Hoebers
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Wouter van Elmpt
- Department of Radiation Oncology (MAASTRO), Maastricht University Medical Center, Maastricht, Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, Netherlands
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Oslo, Norway
- Department of Physics, University of Oslo, Oslo, Norway
| | - Einar Dale
- Department of Oncology, Oslo University Hospital, Oslo, Norway
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Lum RTW, Wang X, Zhang M, Zhang X, Ho JYK, Chow SCY, Fujikawa T, Wong RHL. Emerging role of radiogenomics in genetically triggered thoracic aortic aneurysm and dissection: a narrative review. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:356. [PMID: 37675315 PMCID: PMC10477616 DOI: 10.21037/atm-22-6149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 03/17/2023] [Indexed: 09/08/2023]
Abstract
Background and Objective Thoracic aortic aneurysm and dissection (TAAD) and its complications are life-threatening conditions. Hypertension and atherosclerosis had all along been recognized as the predominant risk factors for the development of TAAD. However, it was increasingly reported that genetic factors, such as single nucleotide polymorphisms (SNPs), are playing an important role in the disease development. The development of next-generation sequencing (NGS) and the rapid growth in radiomics provide a promising new platform to evaluate genetically triggered thoracic aortic aneurysm and dissection (GTAAD) from a new angle. This review is to present an overview of currently available knowledge regarding the use of radiomics and radiogenomics in GTAAD. Methods We performed literature searches in PubMed, EMBASE and Cochrane database from 2012 to 2022 regarding the use of radiomics and radiogenomics in GTAAD. Key Content and Findings There were only 13 studies on radiomics and 4 studies on radiogenomics integration retrieved from the search and it signifies there is still a significant knowledge gap in this field of translational medicine. An overview of the current knowledge of GTAAD, the workflow and role of radiomics, the radiogenomics integration for GTAAD including its potential role in the development of polygenic scores, as well as the implications, challenges, and limitations of radiogenomics research were discussed. Conclusions In the contemporary era, radiogenomics has been emerging as a state-of-the art approach to establish statistical correlation with radiomics features with genomic information in diagnosis, risk modeling and prediction and treatment decision in TAAD.
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Affiliation(s)
- Ray Tak Wai Lum
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Xin Wang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Miaoru Zhang
- Department of Surgery, The Chinese University of Hong Kong, Hong Kong, China
| | - Xianrui Zhang
- Department of Biomedical Science, The City University of Hong Kong, Hong Kong, China
| | - Jacky Yan Kit Ho
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Simon Chi Ying Chow
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Takuya Fujikawa
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
| | - Randolph Hung Leung Wong
- Division of Cardiothoracic Surgery, Department of Surgery, Prince of Wales Hospital, The Chinese University of Hong Kong, Hong Kong, China
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20
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Alamgeer M, Alruwais N, Alshahrani HM, Mohamed A, Assiri M. Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification. Cancers (Basel) 2023; 15:3982. [PMID: 37568800 PMCID: PMC10417684 DOI: 10.3390/cancers15153982] [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: 05/25/2023] [Revised: 07/27/2023] [Accepted: 07/31/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is the main cause of cancer deaths all over the world. An important reason for these deaths was late analysis and worse prediction. With the accelerated improvement of deep learning (DL) approaches, DL can be effectively and widely executed for several real-world applications in healthcare systems, like medical image interpretation and disease analysis. Medical imaging devices can be vital in primary-stage lung tumor analysis and the observation of lung tumors from the treatment. Many medical imaging modalities like computed tomography (CT), chest X-ray (CXR), molecular imaging, magnetic resonance imaging (MRI), and positron emission tomography (PET) systems are widely analyzed for lung cancer detection. This article presents a new dung beetle optimization modified deep feature fusion model for lung cancer detection and classification (DBOMDFF-LCC) technique. The presented DBOMDFF-LCC technique mainly depends upon the feature fusion and hyperparameter tuning process. To accomplish this, the DBOMDFF-LCC technique uses a feature fusion process comprising three DL models, namely residual network (ResNet), densely connected network (DenseNet), and Inception-ResNet-v2. Furthermore, the DBO approach was employed for the optimum hyperparameter selection of three DL approaches. For lung cancer detection purposes, the DBOMDFF-LCC system utilizes a long short-term memory (LSTM) approach. The simulation result analysis of the DBOMDFF-LCC technique of the medical dataset is investigated using different evaluation metrics. The extensive comparative results highlighted the betterment of the DBOMDFF-LCC technique of lung cancer classification.
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Affiliation(s)
- Mohammad Alamgeer
- Department of Information Systems, College of Science & Art at Mahayil, King Khalid University, Abha 61421, Saudi Arabia
| | - Nuha Alruwais
- Department of Computer Science and Engineering, College of Applied Studies and Community Services, King Saud University, P.O. Box 22459, Riyadh 11495, Saudi Arabia;
| | - Haya Mesfer Alshahrani
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia;
| | - Abdullah Mohamed
- Research Centre, Future University in Egypt, New Cairo 11845, Egypt;
| | - Mohammed Assiri
- Department of Computer Science, College of Sciences and Humanities-Aflaj, Prince Sattam bin Abdulaziz University, Aflaj 16273, Saudi Arabia;
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21
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Tonneau M, Phan K, Manem VSK, Low-Kam C, Dutil F, Kazandjian S, Vanderweyen D, Panasci J, Malo J, Coulombe F, Gagné A, Elkrief A, Belkaïd W, Di Jorio L, Orain M, Bouchard N, Muanza T, Rybicki FJ, Kafi K, Huntsman D, Joubert P, Chandelier F, Routy B. Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study. Front Oncol 2023; 13:1196414. [PMID: 37546399 PMCID: PMC10400292 DOI: 10.3389/fonc.2023.1196414] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/28/2023] [Indexed: 08/08/2023] Open
Abstract
Background Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers. Methods Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6). Results The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59. Conclusion We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.
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Affiliation(s)
- Marion Tonneau
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
- Université de Médecine, Lille, France
| | - Kim Phan
- Imagia Canexia Health, Montreal, QC, Canada
| | - Venkata S. K. Manem
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
- Department of Mathematics and Computer Science, University of Quebec at Trois-Rivières, Trois-Rivières, QC, Canada
| | | | | | - Suzanne Kazandjian
- Department of Medical Oncology, Jewish General Hospital, Montreal, QC, Canada
| | - Davy Vanderweyen
- Department of Radiology, Centre Hospitalier de Sherbrooke (CHUS), Sherbrooke, QC, Canada
| | - Justin Panasci
- Department of Medical Oncology, Jewish General Hospital, Montreal, QC, Canada
| | - Julie Malo
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
| | - François Coulombe
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
| | - Andréanne Gagné
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
| | - Arielle Elkrief
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
- Hemato-Oncology Division, Centre Hospitalier de l’université de Montreal, Montreal, QC, Canada
| | - Wiam Belkaïd
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
| | | | - Michele Orain
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
| | - Nicole Bouchard
- Department of Oncology, Centre Hospitalier de Sherbrooke (CHUS), Sherbrooke, QC, Canada
| | - Thierry Muanza
- Department of Medical Oncology, Jewish General Hospital, Montreal, QC, Canada
- Department of Radiation Oncology, Lady Davis Institute of the Jewish General Hospital, Montreal, QC, Canada
| | | | - Kam Kafi
- Imagia Canexia Health, Montreal, QC, Canada
| | | | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Quebec, Université Laval, Québec City, QC, Canada
- Department of Pathology, Institut Universitaire de Cardiologie et de Pneumologie de Québec, Québec, QC, Canada
| | | | - Bertrand Routy
- Department of Cancer Research, Centre de Recherche du Centre Hospitalier Universitaire de Montréal (CRCHUM), Montreal, QC, Canada
- Hemato-Oncology Division, Centre Hospitalier de l’université de Montreal, Montreal, QC, Canada
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22
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Wang Y, Chen Z, Liu C, Chu R, Li X, Li M, Yu D, Qiao X, Kong B, Song K. Radiomics-based fertility-sparing treatment in endometrial carcinoma: a review. Insights Imaging 2023; 14:127. [PMID: 37466860 DOI: 10.1186/s13244-023-01473-y] [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: 01/10/2023] [Accepted: 06/25/2023] [Indexed: 07/20/2023] Open
Abstract
In recent years, with the increasing incidence of endometrial carcinoma in women of child-bearing age, to decision of whether to preserve patients' fertility during treatment has become increasingly complex, presenting a formidable challenge for both physicians and patients. Non-fertility-sparing treatment can remove lesions more thoroughly than fertility-sparing treatment. However, patients will permanently lose their fertility. In contrast, fertility-sparing treatment can treat tumors without impairing fertility, but the risk of disease progression is high as compared with non-fertility-sparing treatment. Therefore, it is extremely important to accurately identify patients who are suitable for fertility-sparing treatments. The evaluation of prognostic factors, including myometrial invasion, the presence of lymph node metastases, and histopathological type, is vital for determining whether a patient can receive fertility-sparing treatment. As a non-invasive and quantitative approach, radiomics has the potential to assist radiologists and other clinicians in determining more precise judgments with regard to the above factors by extracting imaging features and establishing predictive models. In this review, we summarized currently available fertility-sparing strategies and reviewed the performance of radiomics in predicting risk factors associated with fertility-sparing treatment. This review aims to assist clinicians in identifying patients suitable for fertility-sparing treatment more accurately and comprehensively and informs more appropriate and rigorous treatment decisions for endometrial cancer patients of child-bearing age.Critical relevance statement: Radiomics is a promising tool that may assist clinicians identify risk factors about fertility-sparing more accurately and comprehensively.
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Affiliation(s)
- Yuanjian Wang
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Zhongshao Chen
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Chang Liu
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Ran Chu
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xiao Li
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China.
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China.
| | - Mingbao Li
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China.
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China.
| | - Dexin Yu
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Xu Qiao
- School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Beihua Kong
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China
| | - Kun Song
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China.
- Gynecology Oncology Key Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, China.
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23
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Elahmadawy MA, Ashraf A, Moustafa H, Kotb M, Abd El-Gaid S. Prognostic value of initial [ 18 F]FDG PET/computed tomography volumetric and texture analysis-based parameters in patients with head and neck squamous cell carcinoma. Nucl Med Commun 2023; 44:653-662. [PMID: 37038954 DOI: 10.1097/mnm.0000000000001695] [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: 04/12/2023]
Abstract
AIM OF WORK To determine the predictive value of initial [ 18 F]FDG PET/computed tomography (CT) volumetric and radiomics-derived analyses in patients with head and neck squamous cell carcinoma (HNSCC). METHODS Forty-six adult patients had pathologically proven HNSCC and underwent pretherapy [ 18 F]FDG PET/CT were enrolled. Semi-quantitative PET-derived volumetric [(maximum standardized uptake value (SUVmax) and mean SUV (SUVmean), total lesion glycolysis (TLG) and metabolic tumor volume (MTV)] and radiomics analyses using LIFEx 6.73.3 software were performed. RESULTS In the current study group, the receiver operating characteristic curve marked a cutoff point of 21.105 for primary MTV with area under the curve (AUC) of 0.727, sensitivity of 62.5%, and specificity of 86.8% ( P value 0.041) to distinguish responders from non-responders, while no statistically significant primary SUVmean or max or primary TLG cut off points could be determined. It also marked the cutoff point for survival prediction of 10.845 for primary MTV with AUC 0.728, sensitivity of 80%, and specificity of 77.8% ( P value 0.026). A test of the synergistic performance of PET-derived volumetric and textural features significant parameters was conducted in an attempt to develop the most accurate and stable prediction model. Therefore, multivariate logistic regression analysis was performed to detect independent predictors of mortality. With a high specificity of 97.1% and an overall accuracy of 89.1%, the combination of primary tumor MTV and the textural feature gray-level co-occurrence matrix correlation provided the most accurate prediction of mortality ( P value < 0.001). CONCLUSION Textural feature indices are a noninvasive method for capturing intra-tumoral heterogeneity. In our study, a PET-derived prediction model was successfully generated with high specificity and accuracy.
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Affiliation(s)
| | - Aya Ashraf
- Nuclear Medicine Unit, National Cancer Institute
| | - Hosna Moustafa
- Nuclear Medicine Unit, Kasr Al-Ainy (NEMROCK Center), Cairo University, Cairo, Egypt
| | - Magdy Kotb
- Nuclear Medicine Unit, National Cancer Institute
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Liu H, Hou CJ, Tang JL, Sun LT, Lu KF, Liu Y, Du P. Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images. Sci Rep 2023; 13:10500. [PMID: 37380667 DOI: 10.1038/s41598-023-37319-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 06/20/2023] [Indexed: 06/30/2023] Open
Abstract
This study aimed to evaluate the performance of traditional-deep learning combination model based on Doppler ultrasound for diagnosing malignant complex cystic and solid breast nodules. A conventional statistical prediction model based on the ultrasound features and basic clinical information was established. A deep learning prediction model was used to train the training group images and derive the deep learning prediction model. The two models were validated, and their accuracy rates were compared using the data and images of the test group, respectively. A logistic regression method was used to combine the two models to derive a combination diagnostic model and validate it in the test group. The diagnostic performance of each model was represented by the receiver operating characteristic curve and the area under the curve. In the test cohort, the diagnostic efficacy of the deep learning model was better than traditional statistical model, and the combined diagnostic model was better and outperformed the other two models (combination model vs traditional statistical model: AUC: 0.95 > 0.70, P = 0.001; combination model vs deep learning model: AUC: 0.95 > 0.87, P = 0.04). A combination model based on deep learning and ultrasound features has good diagnostic value.
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Affiliation(s)
- Han Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Chun-Jie Hou
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Jing-Lan Tang
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China.
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China.
| | - Li-Tao Sun
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ke-Feng Lu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Ying Liu
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
| | - Pei Du
- Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou, 310011, Zhejiang, China
- Key Laboratory for Diagnosis and Treatment of Upper Limb Edema and Stasis of Breast Cancer, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, 310011, Zhejiang, China
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25
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Wang H, Zha H, Du Y, Li C, Zhang J, Ye X. An integrated radiomics nomogram based on conventional ultrasound improves discriminability between fibroadenoma and pure mucinous carcinoma in breast. Front Oncol 2023; 13:1170729. [PMID: 37427125 PMCID: PMC10324567 DOI: 10.3389/fonc.2023.1170729] [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: 02/21/2023] [Accepted: 04/14/2023] [Indexed: 07/11/2023] Open
Abstract
Objective To evaluate the ability of integrated radiomics nomogram based on ultrasound images to distinguish between breast fibroadenoma (FA) and pure mucinous carcinoma (P-MC). Methods One hundred seventy patients with FA or P-MC (120 in the training set and 50 in the test set) with definite pathological confirmation were retrospectively enrolled. Four hundred sixty-four radiomics features were extracted from conventional ultrasound (CUS) images, and radiomics score (Radscore) was constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different models were developed by a support vector machine (SVM), and the diagnostic performance of the different models was assessed and validated. A comparison of the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) was performed to evaluate the incremental value of the different models. Results Finally, 11 radiomics features were selected, and then Radscore was developed based on them, which was higher in P-MC in both cohorts. In the test group, the clinic + CUS + radiomics (Clin + CUS + Radscore) model achieved a significantly higher area under the curve (AUC) value (AUC = 0.86, 95% CI, 0.733-0.942) when compared with the clinic + radiomics (Clin + Radscore) (AUC = 0.76, 95% CI, 0.618-0.869, P > 0.05), clinic + CUS (Clin + CUS) (AUC = 0.76, 95% CI, 0.618-0.869, P< 0.05), Clin (AUC = 0.74, 95% CI, 0.600-0.854, P< 0.05), and Radscore (AUC = 0.64, 95% CI, 0.492-0.771, P< 0.05) models, respectively. The calibration curve and DCA also suggested excellent clinical value of the combined nomogram. Conclusion The combined Clin + CUS + Radscore model may help improve the differentiation of FA from P-MC.
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Affiliation(s)
- Hui Wang
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hailing Zha
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Du
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Cuiying Li
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xinhua Ye
- Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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26
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Mei X, Liu Z, Singh A, Lange M, Boddu P, Gong JQX, Lee J, DeMarco C, Cao C, Platt S, Sivakumar G, Gross B, Huang M, Masseaux J, Dua S, Bernheim A, Chung M, Deyer T, Jacobi A, Padilla M, Fayad ZA, Yang Y. Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data. Nat Commun 2023; 14:2272. [PMID: 37080956 PMCID: PMC10119160 DOI: 10.1038/s41467-023-37720-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 03/29/2023] [Indexed: 04/22/2023] Open
Abstract
For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient's 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.
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Affiliation(s)
- Xueyan Mei
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Zelong Liu
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ayushi Singh
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Marcia Lange
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Priyanka Boddu
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jingqi Q X Gong
- Department of Pharmaceutical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Justine Lee
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Cody DeMarco
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Chendi Cao
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samantha Platt
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Benjamin Gross
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mingqian Huang
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joy Masseaux
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sakshi Dua
- Department of Medicine, Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Adam Bernheim
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Chung
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Timothy Deyer
- Department of Radiology, Cornell Medicine, New York, NY, USA
- Department of Radiology, East River Medical Imaging, New York, NY, USA
| | - Adam Jacobi
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria Padilla
- Department of Medicine, Pulmonary, Critical Care and Sleep Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Zahi A Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Yang Yang
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
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Li J, Yin W, Wang Y. CT classification model of pancreatic serous cystic neoplasm and mucinous cystic neoplasm based on deep transfer learning. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:167-180. [PMID: 36404568 DOI: 10.3233/xst-221281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND Pancreatic cancer is a highly lethal disease. The preoperative distinction between pancreatic serous cystic neoplasm (SCN) and mucinous cystic neoplasm (MCN) remains a clinical challenge. OBJECTIVE The goal of this study is to provide clinicians with supportive advice and avoid overtreatment by constructing a convolutional neural network (CNN) classifier to automatically identify pancreatic cancer using computed tomography (CT) images. METHODS We construct a CNN model using a dataset of 6,173 CT images obtained from 107 pathologically confirmed pancreatic cancer patients at Shanghai Changhai Hospital from January 2017 to February 2022. We divide CT slices into three categories namely, SCN, MCN, and no tumor, to train the DenseNet201-based CNN model with multi-head spatial attention mechanism (MSAM-DenseNet201). The attention module enhances the network's attention to local features and effectively improves the network performance. The trained model is applied to process all CT image slices and finally realize the two categories classification of MCN and SCN patients through a joint voting strategy. RESULTS Using a 10-fold cross validation method, this new MSAM-DenseNet201 model achieves a classification accuracy of 92.52%, a precision of 92.16%, a sensitivity of 92.16%, and a specificity of 92.86%, respectively. CONCLUSIONS This study demonstrates the feasibility of using a deep learning network or classification model to help diagnose MCN and SCN cases. This, the new method has great potential for developing new computer-aided diagnosis systems and applying in future clinical practice.
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Affiliation(s)
- Jin Li
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Wei Yin
- Department of Radiology, Changhai Hospital, The Naval Military Medical University, Shanghai, China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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Tabari A, Chan SM, Omar OMF, Iqbal SI, Gee MS, Daye D. Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers. Cancers (Basel) 2022; 15:cancers15010063. [PMID: 36612061 PMCID: PMC9817513 DOI: 10.3390/cancers15010063] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 12/24/2022] Open
Abstract
Gastrointestinal (GI) cancers, consisting of a wide spectrum of pathologies, have become a prominent health issue globally. Despite medical imaging playing a crucial role in the clinical workflow of cancers, standard evaluation of different imaging modalities may provide limited information. Accurate tumor detection, characterization, and monitoring remain a challenge. Progress in quantitative imaging analysis techniques resulted in "radiomics", a promising methodical tool that helps to personalize diagnosis and treatment optimization. Radiomics, a sub-field of computer vision analysis, is a bourgeoning area of interest, especially in this era of precision medicine. In the field of oncology, radiomics has been described as a tool to aid in the diagnosis, classification, and categorization of malignancies and to predict outcomes using various endpoints. In addition, machine learning is a technique for analyzing and predicting by learning from sample data, finding patterns in it, and applying it to new data. Machine learning has been increasingly applied in this field, where it is being studied in image diagnosis. This review assesses the current landscape of radiomics and methodological processes in GI cancers (including gastric, colorectal, liver, pancreatic, neuroendocrine, GI stromal, and rectal cancers). We explain in a stepwise fashion the process from data acquisition and curation to segmentation and feature extraction. Furthermore, the applications of radiomics for diagnosis, staging, assessment of tumor prognosis and treatment response according to different GI cancer types are explored. Finally, we discussed the existing challenges and limitations of radiomics in abdominal cancers and investigate future opportunities.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
- Correspondence:
| | - Shin Mei Chan
- Yale University School of Medicine, 330 Cedar Street, New Haven, CT 06510, USA
| | - Omar Mustafa Fathy Omar
- Center for Vascular Biology, University of Connecticut Health Center, Farmington, CT 06030, USA
| | - Shams I. Iqbal
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Michael S. Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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29
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Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28:6363-6379. [PMID: 36533112 PMCID: PMC9753055 DOI: 10.3748/wjg.v28.i45.6363] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/25/2022] [Accepted: 11/17/2022] [Indexed: 12/02/2022] Open
Abstract
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Hao-Ming Yan
- School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Zhong-Ren Wang
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Hon Ho Yu
- Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
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30
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Liu X, Elbanan MG, Luna A, Haider MA, Smith AD, Sabottke CF, Spieler BM, Turkbey B, Fuentes D, Moawad A, Kamel S, Horvat N, Elsayes KM. Radiomics in Abdominopelvic Solid-Organ Oncologic Imaging: Current Status. AJR Am J Roentgenol 2022; 219:985-995. [PMID: 35766531 PMCID: PMC10616929 DOI: 10.2214/ajr.22.27695] [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] [Indexed: 12/14/2022]
Abstract
Radiomics is the process of extraction of high-throughput quantitative imaging features from medical images. These features represent noninvasive quantitative biomarkers that go beyond the traditional imaging features visible to the human eye. This article first reviews the steps of the radiomics pipeline, including image acquisition, ROI selection and image segmentation, image preprocessing, feature extraction, feature selection, and model development and application. Current evidence for the application of radiomics in abdominopelvic solid-organ cancers is then reviewed. Applications including diagnosis, subtype determination, treatment response assessment, and outcome prediction are explored within the context of hepatobiliary and pancreatic cancer, renal cell carcinoma, prostate cancer, gynecologic cancer, and adrenal masses. This literature review focuses on the strongest available evidence, including systematic reviews, meta-analyses, and large multicenter studies. Limitations of the available literature are highlighted, including marked heterogeneity in radiomics methodology, frequent use of small sample sizes with high risk of overfitting, and lack of prospective design, external validation, and standardized radiomics workflow. Thus, although studies have laid a foundation that supports continued investigation into radiomics models, stronger evidence is needed before clinical adoption.
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Affiliation(s)
- Xiaoyang Liu
- Joint Department of Medical Imaging, Division of Abdominal Imaging, University Health Network, University of Toronto, ON, Canada
| | - Mohamed G Elbanan
- Department of Radiology, Yale New Haven Health, Bridgeport Hospital, Bridgeport, CT
| | | | - Masoom A Haider
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Joint Department of Medical Imaging, University Health Network, Sinai Health System and University of Toronto, Toronto, ON, Canada
| | - Andrew D Smith
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL
| | - Carl F Sabottke
- Department of Medical Imaging, University of Arizona College of Medicine, Tucson, AZ
| | - Bradley M Spieler
- Department of Radiology, University Medical Center, Louisiana State University Health Sciences Center, New Orleans, LA
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, NIH, Bethesda, MD
| | - David Fuentes
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Ahmed Moawad
- Department of Diagnostic and Interventional Radiology, Mercy Catholic Medical Center, Darby, PA
| | - Serageldin Kamel
- Department of Lymphoma, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Natally Horvat
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Khaled M Elsayes
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, 1400 Pressler St, Houston, TX 77030
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31
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Wei W, Jia G, Wu Z, Wang T, Wang H, Wei K, Cheng C, Liu Z, Zuo C. A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based on 18F-FDG PET/CT images. Jpn J Radiol 2022; 41:417-427. [PMID: 36409398 PMCID: PMC9676903 DOI: 10.1007/s11604-022-01363-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/09/2022] [Indexed: 11/22/2022]
Abstract
PURPOSE To explore a multidomain fusion model of radiomics and deep learning features based on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images to distinguish pancreatic ductal adenocarcinoma (PDAC) and autoimmune pancreatitis (AIP), which could effectively improve the accuracy of diseases diagnosis. MATERIALS AND METHODS This retrospective study included 48 patients with AIP (mean age, 65 ± 12.0 years; range, 37-90 years) and 64 patients with PDAC patients (mean age, 66 ± 11.3 years; range, 32-88 years). Three different methods were discussed to identify PDAC and AIP based on 18F-FDG PET/CT images, including the radiomics model (RAD_model), the deep learning model (DL_model), and the multidomain fusion model (MF_model). We also compared the classification results of PET/CT, PET, and CT images in these three models. In addition, we explored the attributes of deep learning abstract features by analyzing the correlation between radiomics and deep learning features. Five-fold cross-validation was used to calculate receiver operating characteristic (ROC), area under the roc curve (AUC), accuracy (Acc), sensitivity (Sen), and specificity (Spe) to quantitatively evaluate the performance of different classification models. RESULTS The experimental results showed that the multidomain fusion model had the best comprehensive performance compared with radiomics and deep learning models, and the AUC, accuracy, sensitivity, specificity were 96.4% (95% CI 95.4-97.3%), 90.1% (95% CI 88.7-91.5%), 87.5% (95% CI 84.3-90.6%), and 93.0% (95% CI 90.3-95.6%), respectively. And our study proved that the multimodal features of PET/CT were superior to using either PET or CT features alone. First-order features of radiomics provided valuable complementary information for the deep learning model. CONCLUSION The preliminary results of this paper demonstrated that our proposed multidomain fusion model fully exploits the value of radiomics and deep learning features based on 18F-FDG PET/CT images, which provided competitive accuracy for the discrimination of PDAC and AIP.
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Affiliation(s)
- Wenting Wei
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China ,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Guorong Jia
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
| | - Zhongyi Wu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Tao Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
| | - Heng Wang
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China ,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Kezhen Wei
- School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun, 130022 China ,Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Chao Cheng
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
| | - Zhaobang Liu
- Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163 China
| | - Changjing Zuo
- Department of Nuclear Medicine, The First Affiliated Hospital of Naval Medical University (Changhai Hospital), 168 Changhai Road, Shanghai, 200433 China
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Popova E, Tkachev S, Reshetov I, Timashev P, Ulasov I. Imaging Hallmarks of Sarcoma Progression Via X-ray Computed Tomography: Beholding the Flower of Evil. Cancers (Basel) 2022; 14:cancers14205112. [PMID: 36291896 PMCID: PMC9600487 DOI: 10.3390/cancers14205112] [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: 09/27/2022] [Revised: 10/13/2022] [Accepted: 10/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Sarcomas represent the largest group of rare solid tumors that arise from mesenchymal stem cells and are a leading cause of cancer death in individuals younger than 20 years of age. There is an immediate need for the development of an algorithm for the early accurate diagnosis of sarcomas due to the high rate of diagnostic inaccuracy, which reaches up to 30%. X-ray computed tomography is a non-invasive imaging technique used to obtain detailed internal images of the human or animal body in clinical practice and preclinical studies. We summarized the main imaging features of soft tissue and bone sarcomas, and noted the development of new molecular markers to reach tumor type-specific imaging. Also, we demonstrated the possibility of the use X-ray computed microtomography for non-destructive 3D visualization of sarcoma progression in preclinical studies. Finding correlations between X-ray computed tomography modalities and the results of the histopathological specimen examination may significantly increase the accuracy of diagnostics, which leads to the initiation of appropriate management in a timely manner and, consequently, to improved outcomes. Abstract Sarcomas are a leading cause of cancer death in individuals younger than 20 years of age and represent the largest group of rare solid tumors. To date, more than 100 morphological subtypes of sarcomas have been described, among which epidemiology, clinical features, management, and prognosis differ significantly. Delays and errors in the diagnosis of sarcomas limit the number of effective therapeutic modalities and catastrophically worsen the prognosis. Therefore, the development of an algorithm for the early accurate diagnosis of sarcomas seems to be as important as the development of novel therapeutic advances. This literature review aims to summarize the results of recent investigations regarding the imaging of sarcoma progression based on the use of X-ray computed tomography (CT) in preclinical studies and in current clinical practice through the lens of cancer hallmarks. We attempted to summarize the main CT imaging features of soft-tissue and bone sarcomas. We noted the development of new molecular markers with high specificity to antibodies and chemokines, which are expressed in particular sarcoma subtypes to reach tumor type-specific imaging. We demonstrate the possibility of the use of X-ray computed microtomography (micro-CT) for non-destructive 3D visualization of solid tumors by increasing the visibility of soft tissues with X-ray scattering agents. Based on the results of recent studies, we hypothesize that micro-CT enables the visualization of neovascularization and stroma formation in sarcomas at high-resolution in vivo and ex vivo, including the novel techniques of whole-block and whole-tissue imaging. Finding correlations between CT, PET/CT, and micro-CT imaging features, the results of the histopathological specimen examination and clinical outcomes may significantly increase the accuracy of soft-tissue and bone tumor diagnostics, which leads to the initiation of appropriate histotype-specific management in a timely manner and, consequently, to improved outcomes.
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Affiliation(s)
- Elena Popova
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Sergey Tkachev
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Igor Reshetov
- University Clinical Hospital No. 1, I. M. Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
| | - Peter Timashev
- World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
| | - Ilya Ulasov
- Group of Experimental Biotherapy and Diagnostic, Institute for Regenerative Medicine, World-Class Research Centre “Digital Biodesign and Personalized Healthcare”, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119991 Moscow, Russia
- Correspondence: ; Tel.: +7-901-797-5406
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Lim EJ, Castellani D, So WZ, Fong KY, Li JQ, Tiong HY, Gadzhiev N, Heng CT, Teoh JYC, Naik N, Ghani K, Sarica K, De La Rosette J, Somani B, Gauhar V. Radiomics in Urolithiasis: Systematic Review of Current Applications, Limitations, and Future Directions. J Clin Med 2022; 11:jcm11175151. [PMID: 36079078 PMCID: PMC9457189 DOI: 10.3390/jcm11175151] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 08/29/2022] [Accepted: 08/29/2022] [Indexed: 11/25/2022] Open
Abstract
Radiomics is increasingly applied to the diagnosis, management, and outcome prediction of various urological conditions. Urolithiasis is a common benign condition with a high incidence and recurrence rate. The purpose of this scoping review is to evaluate the current evidence of the application of radiomics in urolithiasis, especially its utility in diagnostics and therapeutics. An electronic literature search on radiomics in the setting of urolithiasis was conducted on PubMed, EMBASE, and Scopus from inception to 21 March 2022. A total of 7 studies were included. Radiomics has been successfully applied in the field of urolithiasis to differentiate phleboliths from calculi and classify stone types and composition pre-operatively. More importantly, it has also been utilized to predict outcomes and complications after endourological procedures. Although radiomics in urolithiasis is still in its infancy, it has the potential for large-scale implementation. Its greatest potential lies in the correlation with conventional established diagnostic and therapeutic factors.
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Affiliation(s)
- Ee Jean Lim
- Department of Urology, Singapore General Hospital, Singapore 169608, Singapore
- Correspondence: ; Tel.: +65-6321-4693
| | - Daniele Castellani
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica Delle Marche, 60126 Ancona, Italy
| | - Wei Zheng So
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Khi Yung Fong
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
| | - Jing Qiu Li
- Department of Urology, Singapore General Hospital, Singapore 169608, Singapore
| | - Ho Yee Tiong
- Department of Urology, National University Hospital, Singapore 119074, Singapore
| | - Nariman Gadzhiev
- Department of Urology, Saint-Petersburg State University Hospital, 199034 St. Petersburg, Russia
| | - Chin Tiong Heng
- Department of Urology, Ng Teng Fong Hospital, Singapore 609606, Singapore
| | - Jeremy Yuen-Chun Teoh
- S.H. Ho Urology Centre, Department of Surgery, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Khurshid Ghani
- Department of Urology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kemal Sarica
- Department of Urology, Biruni University, 34010 Istanbul, Turkey
| | - Jean De La Rosette
- Istanbul Medipol University, TEM Avrupa Otoyolu Goztepe Cikisi No: 1, 34010 Istanbul, Turkey
| | - Bhaskar Somani
- Department of Urology, University Hospital Southampton NHS Foundation Trust, Southampton SO16 6YD, UK
| | - Vineet Gauhar
- Department of Urology, Ng Teng Fong Hospital, Singapore 609606, Singapore
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Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers (Basel) 2022; 14:cancers14153656. [PMID: 35954318 PMCID: PMC9367613 DOI: 10.3390/cancers14153656] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 02/04/2023] Open
Abstract
The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.
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