1
|
Isaak AJ, Clements GR, Buenaventura RGM, Merlino G, Yu Y. Development of Personalized Strategies for Precisely Battling Malignant Melanoma. Int J Mol Sci 2024; 25:5023. [PMID: 38732242 PMCID: PMC11084485 DOI: 10.3390/ijms25095023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2024] [Revised: 04/27/2024] [Accepted: 04/30/2024] [Indexed: 05/13/2024] Open
Abstract
Melanoma is the most severe and fatal form of skin cancer, resulting from multiple gene mutations with high intra-tumor and inter-tumor molecular heterogeneity. Treatment options for patients whose disease has progressed beyond the ability for surgical resection rely on currently accepted standard therapies, notably immune checkpoint inhibitors and targeted therapies. Acquired resistance to these therapies and treatment-associated toxicity necessitate exploring novel strategies, especially those that can be personalized for specific patients and/or populations. Here, we review the current landscape and progress of standard therapies and explore what personalized oncology techniques may entail in the scope of melanoma. Our purpose is to provide an up-to-date summary of the tools at our disposal that work to circumvent the common barriers faced when battling melanoma.
Collapse
Affiliation(s)
| | | | | | | | - Yanlin Yu
- Laboratory of Cancer Biology and Genetics, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| |
Collapse
|
2
|
Nakajima EC, Simpson A, Bogaerts J, de Vries EGE, Do R, Garalda E, Goldmacher G, Kinahan PE, Lambin P, LeStage B, Li Q, Lin F, Litière S, Perez-Lopez R, Petrick N, Schwartz L, Seymour L, Shankar L, Laurie SA. Tumor Size Is Not Everything: Advancing Radiomics as a Precision Medicine Biomarker in Oncology Drug Development and Clinical Care. A Report of a Multidisciplinary Workshop Coordinated by the RECIST Working Group. JCO Precis Oncol 2024; 8:e2300687. [PMID: 38635935 DOI: 10.1200/po.23.00687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/08/2024] [Accepted: 03/05/2024] [Indexed: 04/20/2024] Open
Abstract
Radiomics, the science of extracting quantifiable data from routine medical images, is a powerful tool that has many potential applications in oncology. The Response Evaluation Criteria in Solid Tumors Working Group (RWG) held a workshop in May 2022, which brought together various stakeholders to discuss the potential role of radiomics in oncology drug development and clinical trials, particularly with respect to response assessment. This article summarizes the results of that workshop, reviewing radiomics for the practicing oncologist and highlighting the work that needs to be done to move forward the incorporation of radiomics into clinical trials.
Collapse
Affiliation(s)
| | | | | | | | - Richard Do
- Memorial Sloan-Kettering Cancer Center, NY, NY
| | - Elena Garalda
- Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | | | | | | | | | | | - Frank Lin
- University of Sydney, Sydney, Australia
| | | | | | | | | | - Lesley Seymour
- Canadian Cancer Trials Group, Queen's University, Kingston, ON, Canada
| | - Lalitha Shankar
- National Cancer Institute, National Institutes of Health, Bethesda, MD
| | - Scott A Laurie
- The Ottawa Hospital Cancer Centre, University of Ottawa, Ottawa, ON, Canada
| |
Collapse
|
3
|
Zheng S, He A, Chen C, Gu J, Wei C, Chen Z, Liu J. Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index. Front Immunol 2024; 15:1343425. [PMID: 38571962 PMCID: PMC10987686 DOI: 10.3389/fimmu.2024.1343425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 02/29/2024] [Indexed: 04/05/2024] Open
Abstract
Introduction Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction. Methods In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment. Results Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature. Discussion Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.
Collapse
Affiliation(s)
- Shaoluan Zheng
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Anqi He
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Chenxi Chen
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China
| | - Jianying Gu
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- Artificial Intelligence Center for Plastic Surgery and Cutaneous Soft Tissue Cancers, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Chuanyuan Wei
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Zhiwei Chen
- Big Data and Artificial Intelligence Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jiaqi Liu
- Department of Plastic and Reconstructive Surgery, Zhongshan Hospital, Fudan University, Shanghai, China
- Artificial Intelligence Center for Plastic Surgery and Cutaneous Soft Tissue Cancers, Zhongshan Hospital, Fudan University, Shanghai, China
| |
Collapse
|
4
|
He M, Chen ZF, Liu S, Chen Y, Zhang H, Zhang L, Zhao J, Yang J, Zhang XT, Shen L, Gao JB, Dong B, Tang L. Deep learning model based on multi-lesion and time series CT images for predicting the benefits from anti-HER2 targeted therapy in stage IV gastric cancer. Insights Imaging 2024; 15:59. [PMID: 38411839 PMCID: PMC10899559 DOI: 10.1186/s13244-024-01639-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 01/31/2024] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVE To develop and validate a deep learning model based on multi-lesion and time series CT images in predicting overall survival (OS) in patients with stage IV gastric cancer (GC) receiving anti-HER2 targeted therapy. METHODS A total of 207 patients were enrolled in this multicenter study, with 137 patients for retrospective training and internal validation, 33 patients for prospective validation, and 37 patients for external validation. All patients received anti-HER2 targeted therapy and underwent pre- and post-treatment CT scans (baseline and at least one follow-up). The proposed deep learning model evaluated the multiple lesions in time series CT images to predict risk probabilities. We further evaluated and validated the risk score of the nomogram combining a two-follow-up lesion-based deep learning model (LDLM-2F), tumor markers, and clinical information for predicting the benefits from treatment (Nomo-LDLM-2F). RESULTS In the internal validation and prospective cohorts, the one-year AUCs for Nomo-LDLM-2F using the time series medical images and tumor markers were 0.894 (0.728-1.000) and 0.809 (0.561-1.000), respectively. In the external validation cohort, the one-year AUC of Nomo-LDLM-2F without tumor markers was 0.771 (0.510-1.000). Patients with a low Nomo-LDLM-2F score derived survival benefits from anti-HER2 targeted therapy significantly compared to those with a high Nomo-LDLM-2F score (all p < 0.05). CONCLUSION The Nomo-LDLM-2F score derived from multi-lesion and time series CT images holds promise for the effective readout of OS probability in patients with HER2-positive stage IV GC receiving anti-HER2 therapy. CRITICAL RELEVANCE STATEMENT The deep learning model using baseline and early follow-up CT images aims to predict OS in patients with stage IV gastric cancer receiving anti-HER2 targeted therapy. This model highlights the spatiotemporal heterogeneity of stage IV GC, assisting clinicians in the early evaluation of the efficacy of anti-HER2 therapy. KEY POINTS • Multi-lesion and time series model revealed the spatiotemporal heterogeneity in anti-HER2 therapy. • The Nomo-LDLM-2F score was a valuable prognostic marker for anti-HER2 therapy. • CT-based deep learning model incorporating time-series tumor markers improved performance.
Collapse
Affiliation(s)
- Meng He
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Zi-Fan Chen
- Center for Data Science, Peking University, Beijing, China
| | - Song Liu
- Department of Radiology, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, No. 321, Zhongshan Road, Nanjing CityJiangsu Province, 210008, China
| | - Yang Chen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Li Zhang
- Center for Data Science, Peking University, Beijing, China
| | - Jie Zhao
- National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, China
- Peking University Changsha Institute for Computing and Digital Economy, Changsha, China
| | - Jie Yang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China
| | - Xiao-Tian Zhang
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China
| | - Lin Shen
- Department of Gastrointestinal Oncology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, China.
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Bin Dong
- Peking University Changsha Institute for Computing and Digital Economy, Changsha, China.
- Beijing International Center for Mathematical Research, Peking University, Beijing, China.
- Center for Machine Learning Research, Peking University, Beijing, China.
| | - Lei Tang
- Department of Radiology, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, China.
| |
Collapse
|
5
|
Ligero M, Gielen B, Navarro V, Cresta Morgado P, Prior O, Dienstmann R, Nuciforo P, Trebeschi S, Beets-Tan R, Sala E, Garralda E, Perez-Lopez R. A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking? NPJ Precis Oncol 2024; 8:42. [PMID: 38383736 PMCID: PMC10881558 DOI: 10.1038/s41698-024-00534-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 01/26/2024] [Indexed: 02/23/2024] Open
Abstract
The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.
Collapse
Affiliation(s)
- Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Bente Gielen
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Victor Navarro
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Pablo Cresta Morgado
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
- Prostate Cancer Translational Research Group, Institute of Oncology (VHIO), Vall d'Hebron University Hospital, Barcelona, Spain
| | - Olivia Prior
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Rodrigo Dienstmann
- Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain
| | - Paolo Nuciforo
- Molecular Oncology Group, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Stefano Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Regina Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Evis Sala
- Dipartimento Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
- Dipartimento di Scienze Radiologiche ed Ematologiche, Universita Cattolica del Sacro Cuore, Rome, Italy
| | - Elena Garralda
- Department of Medical Oncology, Vall d'Hebron University Hospital and Institute of Oncology (VHIO), Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain.
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
Zakariya F, Salem FK, Alamrain AA, Sanker V, Abdelazeem ZG, Hosameldin M, Tan JK, Howard R, Huang H, Awuah WA. Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence. Eur J Med Res 2024; 29:25. [PMID: 38183141 PMCID: PMC10768232 DOI: 10.1186/s40001-023-01625-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 12/25/2023] [Indexed: 01/07/2024] Open
Abstract
Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.
Collapse
Affiliation(s)
- Farida Zakariya
- Faculty of Pharmaceutical Sciences, Ahmadu Bello University, Zaria, Nigeria
- Division of Experimental Medicine, Faculty of Medicine and Health Sciences, McGill University, Montreal, Canada
| | - Fatma K Salem
- Faculty of Veterinary Medicine, South Valley University, Qena, 83523, Egypt
| | | | - Vivek Sanker
- Research Assistant, Dept. Of Neurosurgery, Trivandrum Medical College, Trivandrum, India
| | - Zainab G Abdelazeem
- Division of Molecular Biology, Department of Zoology, Faculty of Science, Alexandria University, Alexandria, Egypt
| | | | | | - Rachel Howard
- School of Clinical Medicine, University of Cambridge, Cambridge, England
| | - Helen Huang
- Faculty of Medicine and Health Science, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Wireko Andrew Awuah
- Medical Institute, Sumy State University, Zamonstanksya 7, Sumy, 40007, Ukraine.
| |
Collapse
|
8
|
Prelaj A, Miskovic V, Zanitti M, Trovo F, Genova C, Viscardi G, Rebuzzi SE, Mazzeo L, Provenzano L, Kosta S, Favali M, Spagnoletti A, Castelo-Branco L, Dolezal J, Pearson AT, Lo Russo G, Proto C, Ganzinelli M, Giani C, Ambrosini E, Turajlic S, Au L, Koopman M, Delaloge S, Kather JN, de Braud F, Garassino MC, Pentheroudakis G, Spencer C, Pedrocchi ALG. Artificial intelligence for predictive biomarker discovery in immuno-oncology: a systematic review. Ann Oncol 2024; 35:29-65. [PMID: 37879443 DOI: 10.1016/j.annonc.2023.10.125] [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: 07/05/2023] [Revised: 08/31/2023] [Accepted: 10/08/2023] [Indexed: 10/27/2023] Open
Abstract
BACKGROUND The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. MATERIALS AND METHODS We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. RESULTS A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. CONCLUSION AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice.
Collapse
Affiliation(s)
- A Prelaj
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland.
| | - V Miskovic
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - M Zanitti
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - F Trovo
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - C Genova
- UO Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genoa; Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa
| | - G Viscardi
- Precision Medicine Department, Università degli Studi della Campania Luigi Vanvitelli, Naples
| | - S E Rebuzzi
- Department of Internal Medicine and Medical Specialties (Di.M.I.), University of Genoa, Genoa; Medical Oncology Unit, Ospedale San Paolo, Savona, Italy
| | - L Mazzeo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan; Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - L Provenzano
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - S Kosta
- Department of Electronic Systems, Aalborg University Copenhagen, Denmark
| | - M Favali
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - A Spagnoletti
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - L Castelo-Branco
- ESMO European Society for Medical Oncology, Lugano, Switzerland; NOVA National School of Public Health, Lisboa, Portugal
| | - J Dolezal
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - A T Pearson
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | - G Lo Russo
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Proto
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M Ganzinelli
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - C Giani
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - E Ambrosini
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| | - S Turajlic
- Cancer Dynamics Laboratory, The Francis Crick Institute, London
| | - L Au
- Renal and Skin Unit, The Royal Marsden NHS Foundation Trust, London, UK; Department of Medical Oncology, Peter MacCallum Cancer Centre, Melbourne; Sir Peter MacCallum Department of Medical Oncology, The University of Melbourne, Melbourne, Australia
| | - M Koopman
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation, Utrecht, The Netherlands; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - S Delaloge
- Department of Cancer Medicine, Gustave Roussy, Villejuif, France; ESMO Real World Data and Digital Health Working Group, ESMO, Lugano, Switzerland
| | - J N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - F de Braud
- Medical Oncology Department, Fondazione IRCCS Istituto Nazionale Tumori, Milan
| | - M C Garassino
- Section of Hematology/Oncology, Department of Medicine, University of Chicago, Chicago, USA
| | | | - C Spencer
- Cancer Dynamics Laboratory, The Francis Crick Institute, London.
| | - A L G Pedrocchi
- Nearlab, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milano, Italy
| |
Collapse
|
9
|
Fang Y, Chen X, Cao C. Cancer immunotherapy efficacy and machine learning. Expert Rev Anticancer Ther 2024; 24:21-28. [PMID: 38288663 DOI: 10.1080/14737140.2024.2311684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 01/25/2024] [Indexed: 02/03/2024]
Abstract
INTRODUCTION Immunotherapy is one of the major breakthroughs in the treatment of cancer, and it has become a powerful clinical strategy, however, not all patients respond to immune checkpoint blockade and other immunotherapy strategies. Applying machine learning (ML) techniques to predict the efficacy of cancer immunotherapy is useful for clinical decision-making. AREAS COVERED Applying ML including deep learning (DL) in radiomics, pathomics, tumor microenvironment (TME) and immune-related genes analysis to predict immunotherapy efficacy. The studies in this review were searched from PubMed and ClinicalTrials.gov (January 2023). EXPERT OPINION An increasing number of studies indicate that ML has been applied to various aspects of oncology research, with the potential to provide more effective individualized immunotherapy strategies and enhance treatment decisions. With advances in ML technology, more efficient methods of predicting the efficacy of immunotherapy may become available in the future.
Collapse
Affiliation(s)
- Yuting Fang
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China
| | - Xiaozhong Chen
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| | - Caineng Cao
- Department of Radiation Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences; Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
| |
Collapse
|
10
|
Poletto S, Paruzzo L, Nepote A, Caravelli D, Sangiolo D, Carnevale-Schianca F. Predictive Factors in Metastatic Melanoma Treated with Immune Checkpoint Inhibitors: From Clinical Practice to Future Perspective. Cancers (Basel) 2023; 16:101. [PMID: 38201531 PMCID: PMC10778365 DOI: 10.3390/cancers16010101] [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: 10/10/2023] [Revised: 12/11/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
The introduction of immunotherapy revolutionized the treatment landscape in metastatic melanoma. Despite the impressive results associated with immune checkpoint inhibitors (ICIs), only a portion of patients obtain a response to this treatment. In this scenario, the research of predictive factors is fundamental to identify patients who may have a response and to exclude patients with a low possibility to respond. These factors can be host-associated, immune system activation-related, and tumor-related. Patient-related factors can vary from data obtained by medical history (performance status, age, sex, body mass index, concomitant medications, and comorbidities) to analysis of the gut microbiome from fecal samples. Tumor-related factors can reflect tumor burden (metastatic sites, lactate dehydrogenase, C-reactive protein, and circulating tumor DNA) or can derive from the analysis of tumor samples (driver mutations, tumor-infiltrating lymphocytes, and myeloid cells). Biomarkers evaluating the immune system activation, such as IFN-gamma gene expression profile and analysis of circulating immune cell subsets, have emerged in recent years as significantly correlated with response to ICIs. In this manuscript, we critically reviewed the most updated literature data on the landscape of predictive factors in metastatic melanoma treated with ICIs. We focus on the principal limits and potentiality of different methods, shedding light on the more promising biomarkers.
Collapse
Affiliation(s)
- Stefano Poletto
- Department of Oncology, University of Turin, AOU S. Luigi Gonzaga, 10043 Orbassano, Italy;
| | - Luca Paruzzo
- Department of Oncology, University of Turin, 10124 Turin, Italy; (L.P.); (D.S.)
- Division of Hematology and Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alessandro Nepote
- Department of Oncology, University of Turin, AOU S. Luigi Gonzaga, 10043 Orbassano, Italy;
| | - Daniela Caravelli
- Medical Oncology Division, Candiolo Cancer Institute, FPO-IRCCs, 10060 Candiolo, Italy; (D.C.); (F.C.-S.)
| | - Dario Sangiolo
- Department of Oncology, University of Turin, 10124 Turin, Italy; (L.P.); (D.S.)
| | | |
Collapse
|
11
|
Dercle L, Yang M, Gönen M, Flynn J, Moskowitz CS, Connors DE, Yang H, Lu L, Reidy-Lagunes D, Fojo T, Karovic S, Zhao B, Schwartz LH, Henick BS. Ethnic diversity in treatment response for colorectal cancer: proof of concept for radiomics-driven enrichment trials. Eur Radiol 2023; 33:9254-9261. [PMID: 37368111 DOI: 10.1007/s00330-023-09862-z] [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: 10/28/2022] [Revised: 05/11/2023] [Accepted: 05/15/2023] [Indexed: 06/28/2023]
Abstract
BACKGROUND Several barriers hamper recruitment of diverse patient populations in multicenter clinical trials which determine efficacy of new systemic cancer therapies. PURPOSE We assessed if quantitative analysis of computed tomography (CT) scans of metastatic colorectal cancer (mCRC) patients using imaging features that predict overall survival (OS) can unravel the association between ethnicity and efficacy. METHODS We retrospectively analyzed CT images from 1584 mCRC patients in two phase III trials evaluating FOLFOX ± panitumumab (n = 331, 350) and FOLFIRI ± aflibercept (n = 437, 466) collected from August 2006 to March 2013. Primary and secondary endpoints compared RECIST1.1 response at month-2 and delta tumor volume at month-2, respectively. An ancillary study compared imaging phenotype using a peer-reviewed radiomics-signature combining 3 imaging features to predict OS landmarked from month-2. Analysis was stratified by ethnicity. RESULTS In total, 1584 patients were included (mean age, 60.25 ± 10.57 years; 969 men). Ethnicity was as follows: African (n = 50, 3.2%), Asian (n = 66, 4.2%), Caucasian (n = 1413, 89.2%), Latino (n = 27, 1.7%), Other (n = 28, 1.8%). Overall baseline tumor volume demonstrated Africans and Caucasians had more advanced disease (p < 0.001). Ethnicity was associated with treatment response. Response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. Overall delta tumor volume at month-2 demonstrated that Latino patients more likely experienced response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023). CONCLUSION This study highlights how clinical trials that inadequately represent minority groups may impact associated translational work. In appropriately powered studies, radiomics features may allow us to unravel associations between ethnicity and treatment efficacy, better elucidate mechanisms of resistance, and promote diversity in trials through predictive enrichment. CLINICAL RELEVANCE STATEMENT Radiomics could promote clinical trial diversity through predictive enrichment, hence benefit to historically underrepresented racial/ethnic groups that may respond variably to treatment due to socioeconomic factors and built environment, collectively referred to as social determinants of health. KEY POINTS •Findings indicate ethnicity was associated with treatment response across all 3 endpoints. First, response per RECIST1.1 at month-2 was distinct between ethnicities (p = 0.048) with higher response rate (55.6%) in Latinos. •Second, the overall delta tumor volume at month-2 demonstrated that Latino patients were more likely to experience response to treatment (p = 0.021). Radiomics phenotype was also distinct in terms of tumor radiomics heterogeneity (p = 0.023).
Collapse
Affiliation(s)
- Laurent Dercle
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA.
| | - Melissa Yang
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Jessica Flynn
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Chaya S Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Dana E Connors
- Foundation for the National Institutes of Health (FNIH), 11400 Rockville Pike, Suite 600, North Bethesda, MD, 20852, USA
| | - Hao Yang
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Lin Lu
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Diane Reidy-Lagunes
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA
| | - Tito Fojo
- Columbia University Herbert Irving Comprehensive Cancer Center, 161 Fort Washington Ave, New York, NY, 10032, USA
| | - Sanja Karovic
- Inova Center for Personalized Health and Schar Cancer Institute, 8100 Innovation Park Dr, Fairfax, VA, 22031, USA
- University of Virginia Cancer Center, 1240 Lee St, Charlottesville, VA, 22903, USA
| | - Binsheng Zhao
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center and New York Presbyterian Hospital, 710 West 168th St, New York, NY, 10032, USA
| | - Brian S Henick
- Columbia University Herbert Irving Comprehensive Cancer Center, 161 Fort Washington Ave, New York, NY, 10032, USA
| |
Collapse
|
12
|
Roisman LC, Kian W, Anoze A, Fuchs V, Spector M, Steiner R, Kassel L, Rechnitzer G, Fried I, Peled N, Bogot NR. Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer. NPJ Precis Oncol 2023; 7:125. [PMID: 37990050 PMCID: PMC10663598 DOI: 10.1038/s41698-023-00473-x] [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: 07/17/2023] [Accepted: 10/24/2023] [Indexed: 11/23/2023] Open
Abstract
Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.
Collapse
Affiliation(s)
- Laila C Roisman
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
- Ben-Gurion University of the Negev, Be'er Sheva, Israel.
| | - Waleed Kian
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
- The Institute of Oncology, Assuta Ashdod, Ashdod, Israel
| | - Alaa Anoze
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Vered Fuchs
- Ben-Gurion University of the Negev, Be'er Sheva, Israel
| | - Maria Spector
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Roee Steiner
- The Institute for Nuclear Medicine, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Levi Kassel
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Gilad Rechnitzer
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Iris Fried
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel
| | - Nir Peled
- The Hebrew University, Helmsley Cancer Center, Shaare Zedek Medical Center, Jerusalem, Israel.
| | - Naama R Bogot
- The Department of Radiology, Shaare Zedek Medical Center, Jerusalem, Israel
| |
Collapse
|
13
|
Higgins H, Nakhla A, Lotfalla A, Khalil D, Doshi P, Thakkar V, Shirini D, Bebawy M, Ammari S, Lopci E, Schwartz LH, Postow M, Dercle L. Recent Advances in the Field of Artificial Intelligence for Precision Medicine in Patients with a Diagnosis of Metastatic Cutaneous Melanoma. Diagnostics (Basel) 2023; 13:3483. [PMID: 37998619 PMCID: PMC10670510 DOI: 10.3390/diagnostics13223483] [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: 09/20/2023] [Revised: 10/27/2023] [Accepted: 10/31/2023] [Indexed: 11/25/2023] Open
Abstract
Standard-of-care medical imaging techniques such as CT, MRI, and PET play a critical role in managing patients diagnosed with metastatic cutaneous melanoma. Advancements in artificial intelligence (AI) techniques, such as radiomics, machine learning, and deep learning, could revolutionize the use of medical imaging by enhancing individualized image-guided precision medicine approaches. In the present article, we will decipher how AI/radiomics could mine information from medical images, such as tumor volume, heterogeneity, and shape, to provide insights into cancer biology that can be leveraged by clinicians to improve patient care both in the clinic and in clinical trials. More specifically, we will detail the potential role of AI in enhancing detection/diagnosis, staging, treatment planning, treatment delivery, response assessment, treatment toxicity assessment, and monitoring of patients diagnosed with metastatic cutaneous melanoma. Finally, we will explore how these proof-of-concept results can be translated from bench to bedside by describing how the implementation of AI techniques can be standardized for routine adoption in clinical settings worldwide to predict outcomes with great accuracy, reproducibility, and generalizability in patients diagnosed with metastatic cutaneous melanoma.
Collapse
Affiliation(s)
- Hayley Higgins
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Abanoub Nakhla
- Department of Clinical Medicine, American University of the Caribbean School of Medicine, 33027 Cupecoy, Sint Maarten, The Netherlands;
| | - Andrew Lotfalla
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - David Khalil
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Parth Doshi
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Vandan Thakkar
- Department of Clinical Medicine, Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA; (D.K.); (P.D.); (V.T.)
| | - Dorsa Shirini
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
| | - Maria Bebawy
- Department of Clinical Medicine, Touro College of Osteopathic Medicine, Middletown, NY 10940, USA; (A.L.); (M.B.)
| | - Samy Ammari
- Département d’Imagerie Médicale Biomaps, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France;
- ELSAN Département de Radiologie, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, 20089 Rozzano, Italy;
| | - Lawrence H. Schwartz
- Department of Radiology, New York-Presbyterian, Columbia University Irving Medical Center, New York, NY 10032, USA;
| | - Michael Postow
- Melanoma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Weill Cornell Medical College, New York, NY 10065, USA
| | - Laurent Dercle
- Department of Radiology, Shahid Beheshti University of Medical Sciences, Tehran 1981619573, Iran;
| |
Collapse
|
14
|
Shirini D, Schwartz LH, Dercle L. Artificial intelligence for aging research in cancer drug development. Aging (Albany NY) 2023; 15:12699-12701. [PMID: 37980599 DOI: 10.18632/aging.204914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Indexed: 11/21/2023]
Affiliation(s)
- Dorsa Shirini
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
- Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Lawrence H Schwartz
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY10032, USA
| | - Laurent Dercle
- Department of Radiology, Columbia University Medical Center, New York, NY 10032, USA
| |
Collapse
|
15
|
Peisen F, Gerken A, Hering A, Dahm I, Nikolaou K, Gatidis S, Eigentler TK, Amaral T, Moltz JH, Othman AE. Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study. Diagnostics (Basel) 2023; 13:3210. [PMID: 37892030 PMCID: PMC10605712 DOI: 10.3390/diagnostics13203210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/06/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy. METHODS A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented (n = 4727). RESULTS Compared with the baseline model, the extended radiomics model did not add significantly more information to the best-response prediction (AUC [95% CI] 0.548 (0.188, 0.808) vs. 0.487 (0.139, 0.743)), the prediction of PFS after six months (AUC [95% CI] 0.699 (0.436, 0.958) vs. 0.604 (0.373, 0.867)), or the overall survival prediction after six and twelve months (AUC [95% CI] 0.685 (0.188, 0.967) vs. 0.766 (0.433, 1.000) and AUC [95% CI] 0.554 (0.163, 0.781) vs. 0.616 (0.271, 1.000), respectively). CONCLUSIONS The results showed no additional value of baseline whole-body CT radiomics for best-response prediction, progression-free survival prediction for six months, or six-month and twelve-month overall survival prediction for stage IV melanoma patients receiving first-line targeted therapy. These results need to be validated in a larger cohort.
Collapse
Affiliation(s)
- Felix Peisen
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
| | - Annika Gerken
- Fraunhofer MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
| | - Alessa Hering
- Fraunhofer MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
- Diagnostic Image Analysis Group, Radboud University Medical Center (Radboudumc), Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Isabel Dahm
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
- Image-Guided and Functionally Instructed Tumor Therapies (iFIT), The Cluster of Excellence (EXC 2180), 72076 Tuebingen, Germany
| | - Sergios Gatidis
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
- Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tuebingen, Germany
| | - Thomas K. Eigentler
- Center of Dermato-Oncology, Department of Dermatology, Tuebingen University Hospital, Eberhard Karls University, Liebermeisterstraße 25, 72076 Tuebingen, Germany; (T.K.E.); (T.A.)
- Department of Dermatology, Venereology and Allergology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humbolt-Universität zu Berlin, Luisenstraße 2, 10117 Berlin, Germany
| | - Teresa Amaral
- Center of Dermato-Oncology, Department of Dermatology, Tuebingen University Hospital, Eberhard Karls University, Liebermeisterstraße 25, 72076 Tuebingen, Germany; (T.K.E.); (T.A.)
| | - Jan H. Moltz
- Fraunhofer MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
| | - Ahmed E. Othman
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
- Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Langenbeckstraße 1, 55131 Mainz, Germany
| |
Collapse
|
16
|
Salawu A, Wang BX, Han M, Geady C, Heirali A, Berman HK, Pfister TD, Hernando-Calvo A, Al-Ezzi EM, Stayner LA, Gupta AA, Ayodele O, Lam B, Hansen AR, Spreafico A, Bedard PL, Butler MO, Avery L, Coburn B, Haibe-Kains B, Siu LL, Abdul Razak AR. Safety, Immunologic, and Clinical Activity of Durvalumab in Combination with Olaparib or Cediranib in Advanced Leiomyosarcoma: Results of the DAPPER Clinical Trial. Clin Cancer Res 2023; 29:4128-4138. [PMID: 37566240 DOI: 10.1158/1078-0432.ccr-23-1137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/21/2023] [Accepted: 08/08/2023] [Indexed: 08/12/2023]
Abstract
PURPOSE Non-inflamed (cold) tumors such as leiomyosarcoma do not benefit from immune checkpoint blockade (ICB) monotherapy. Combining ICB with angiogenesis or PARP inhibitors may increase tumor immunogenicity by altering the immune cell composition of the tumor microenvironment (TME). The DAPPER phase II study evaluated the safety, immunologic, and clinical activity of ICB-based combinations in pretreated patients with leiomyosarcoma. PATIENTS AND METHODS Patients were randomized to receive durvalumab 1,500 mg IV every 4 weeks with either olaparib 300 mg twice a day orally (Arm A) or cediranib 20 mg every day orally 5 days/week (Arm B) until unacceptable toxicity or disease progression. Paired tumor biopsies, serial radiologic assessments and stool collections were performed. Primary endpoints were safety and immune cell changes in the TME. Objective responses and survival were correlated with transcriptomic, radiomic, and microbiome parameters. RESULTS Among 30 heavily pretreated patients (15 on each arm), grade ≥ 3 toxicity occurred in 3 (20%) and 2 (13%) on Arms A and B, respectively. On Arm A, 1 patient achieved partial response (PR) with increase in CD8 T cells and macrophages in the TME during treatment, while 4 had stable disease (SD) ≥ 6 months. No patients on Arm B achieved PR or SD ≥ 6 months. Transcriptome analysis showed that baseline M1-macrophage and B-cell activity were associated with overall survival. CONCLUSIONS Durvalumab plus olaparib increased immune cell infiltration of TME with clinical benefit in some patients with leiomyosarcoma. Baseline M1-macrophage and B-cell activity may identify patients with leiomyosarcoma with favorable outcomes on immunotherapy and should be further evaluated.
Collapse
Affiliation(s)
- Abdulazeez Salawu
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Ben X Wang
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Ming Han
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Caryn Geady
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Alya Heirali
- Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada
| | - Hal K Berman
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Thomas D Pfister
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Alberto Hernando-Calvo
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Esmail Mutahar Al-Ezzi
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Lee-Anne Stayner
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Abha A Gupta
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Olubukola Ayodele
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Bernard Lam
- Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Aaron R Hansen
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Anna Spreafico
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Philippe L Bedard
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Marcus O Butler
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Lisa Avery
- Department of Statistics, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | | | - Benjamin Haibe-Kains
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
| | - Lillian L Siu
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Albiruni R Abdul Razak
- Princess Margaret Cancer Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
17
|
Cui S, Traverso A, Niraula D, Zou J, Luo Y, Owen D, El Naqa I, Wei L. Interpretable artificial intelligence in radiology and radiation oncology. Br J Radiol 2023; 96:20230142. [PMID: 37493248 PMCID: PMC10546466 DOI: 10.1259/bjr.20230142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 05/24/2023] [Accepted: 05/26/2023] [Indexed: 07/27/2023] Open
Abstract
Artificial intelligence has been introduced to clinical practice, especially radiology and radiation oncology, from image segmentation, diagnosis, treatment planning and prognosis. It is not only crucial to have an accurate artificial intelligence model, but also to understand the internal logic and gain the trust of the experts. This review is intended to provide some insights into core concepts of the interpretability, the state-of-the-art methods for understanding the machine learning models, the evaluation of these methods, identifying some challenges and limits of them, and gives some examples of medical applications.
Collapse
Affiliation(s)
- Sunan Cui
- Department of Radiation Oncology, University of Washington, Seattle, WA, United States
| | - Alberto Traverso
- Department of Radiotherapy, Maastro Clinic, Maastricht, Netherlands
| | - Dipesh Niraula
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Jiaren Zou
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| | - Yi Luo
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Dawn Owen
- Department of Radiation Oncology, Mayo Clinic, Rochester, MN, United States
| | - Issam El Naqa
- Department of Machine Learning, Moffitt Cancer Center, FL, United States
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, United States
| |
Collapse
|
18
|
McGale J, Hama J, Yeh R, Vercellino L, Sun R, Lopci E, Ammari S, Dercle L. Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy. Diagnostics (Basel) 2023; 13:3065. [PMID: 37835808 PMCID: PMC10573034 DOI: 10.3390/diagnostics13193065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Revised: 09/01/2023] [Accepted: 09/06/2023] [Indexed: 10/15/2023] Open
Abstract
Immunotherapy has greatly improved the outcomes of patients with metastatic melanoma. However, it has also led to new patterns of response and progression, creating an unmet need for better biomarkers to identify patients likely to achieve a lasting clinical benefit or experience immune-related adverse events. In this study, we performed a focused literature survey covering the application of artificial intelligence (AI; in the form of radiomics, machine learning, and deep learning) to patients diagnosed with melanoma and treated with immunotherapy, reviewing 12 studies relevant to the topic published up to early 2022. The most commonly investigated imaging modality was CT imaging in isolation (n = 9, 75.0%), while patient cohorts were most frequently recruited retrospectively and from single institutions (n = 7, 58.3%). Most studies concerned the development of AI tools to assist in prognostication (n = 5, 41.7%) or the prediction of treatment response (n = 6, 50.0%). Validation methods were disparate, with two studies (16.7%) performing no validation and equal numbers using cross-validation (n = 3, 25%), a validation set (n = 3, 25%), or a test set (n = 3, 25%). Only one study used both validation and test sets (n = 1, 8.3%). Overall, promising results have been observed for the application of AI to immunotherapy-treated melanoma. Further improvement and eventual integration into clinical practice may be achieved through the implementation of rigorous validation using heterogeneous, prospective patient cohorts.
Collapse
Affiliation(s)
- Jeremy McGale
- Department of Radiology, New York-Presbyterian Hospital, New York, NY 10032, USA
| | - Jakob Hama
- Queens Hospital Center, Icahn School of Medicine at Mt. Sinai, Queens, NY 10029, USA
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Laetitia Vercellino
- Nuclear Medicine Department, INSERM UMR S942, Hôpital Saint-Louis, Assistance-Publique, Hôpitaux de Paris, Université Paris Cité, 75010 Paris, France
| | - Roger Sun
- Department of Radiation Oncology, Gustave Roussy, 94800 Villejuif, France
| | - Egesta Lopci
- Nuclear Medicine Unit, IRCCS—Humanitas Research Hospital, 20089 Rozzano, MI, Italy
| | - Samy Ammari
- Department of Medical Imaging, BIOMAPS, UMR1281 INSERM, CEA, CNRS, Gustave Roussy, Université Paris-Saclay, 94800 Villejuif, France
- ELSAN Department of Radiology, Institut de Cancérologie Paris Nord, 95200 Sarcelles, France
| | - Laurent Dercle
- Department of Radiology, New York-Presbyterian Hospital, New York, NY 10032, USA
| |
Collapse
|
19
|
Wang X, Jiang Y, Chen H, Zhang T, Han Z, Chen C, Yuan Q, Xiong W, Wang W, Li G, Heng PA, Li R. Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning. Radiother Oncol 2023; 186:109793. [PMID: 37414254 DOI: 10.1016/j.radonc.2023.109793] [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: 01/20/2023] [Revised: 06/22/2023] [Accepted: 06/28/2023] [Indexed: 07/08/2023]
Abstract
BACKGROUND AND PURPOSE Immunotherapy is a standard treatment for many tumor types. However, only a small proportion of patients derive clinical benefit and reliable predictive biomarkers of immunotherapy response are lacking. Although deep learning has made substantial progress in improving cancer detection and diagnosis, there is limited success on the prediction of treatment response. Here, we aim to predict immunotherapy response of gastric cancer patients using routinely available clinical and image data. MATERIALS AND METHODS We present a multi-modal deep learning radiomics approach to predict immunotherapy response using both clinical data and computed tomography images. The model was trained using 168 advanced gastric cancer patients treated with immunotherapy. To overcome limitations of small training data, we leverage an additional dataset of 2,029 patients who did not receive immunotherapy in a semi-supervised framework to learn intrinsic imaging phenotypes of the disease. We evaluated model performance in two independent cohorts of 81 patients treated with immunotherapy. RESULTS The deep learning model achieved area under receiver operating characteristics curve (AUC) of 0.791 (95% CI 0.633-0.950) and 0.812 (95% CI 0.669-0.956) for predicting immunotherapy response in the internal and external validation cohorts. When combined with PD-L1 expression, the integrative model further improved the AUC by 4-7% in absolute terms. CONCLUSION The deep learning model achieved promising performance for predicting immunotherapy response from routine clinical and image data. The proposed multi-modal approach is general and can incorporate other relevant information to further improve prediction of immunotherapy response.
Collapse
Affiliation(s)
- Xi Wang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford 94305, CA, USA; Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China; Zhejiang Lab, Hangzhou, China
| | - Yuming Jiang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford 94305, CA, USA; Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Zhen Han
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wei Wang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford 94305, CA, USA.
| |
Collapse
|
20
|
Lin P, Lin YQ, Gao RZ, Wan WJ, He Y, Yang H. Integrative radiomics and transcriptomics analyses reveal subtype characterization of non-small cell lung cancer. Eur Radiol 2023; 33:6414-6425. [PMID: 36826501 DOI: 10.1007/s00330-023-09503-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 12/15/2022] [Accepted: 01/30/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVES To assess whether integrative radiomics and transcriptomics analyses could provide novel insights for radiomic features' molecular annotation and effective risk stratification in non-small cell lung cancer (NSCLC). METHODS A total of 627 NSCLC patients from three datasets were included. Radiomics features were extracted from segmented 3-dimensional tumour volumes and were z-score normalized for further analysis. In transcriptomics level, 186 pathways and 28 types of immune cells were assessed by using the Gene Set Variation Analysis (GSVA) algorithm. NSCLC patients were categorized into subgroups based on their radiomic features and pathways enrichment scores using consensus clustering. Subgroup-specific radiomics features were used to validate clustering performance and prognostic value. Kaplan-Meier survival analysis with the log-rank test and univariable and multivariable Cox analyses were conducted to explore survival differences among the subgroups. RESULTS Three radiotranscriptomics subtypes (RTSs) were identified based on the radiomics and pathways enrichment profiles. The three RTSs were characterized as having specific molecular hallmarks: RTS1 (proliferation subtype), RTS2 (metabolism subtype), and RTS3 (immune activation subtype). RTS3 showed increased infiltration of most immune cells. The RTS stratification strategy was validated in a validation cohort and showed significant prognostic value. Survival analysis demonstrated that the RTS strategy could stratify NSCLC patients according to prognosis (p = 0.009), and the RTS strategy remained an independent prognostic indicator after adjusting for other clinical parameters. CONCLUSIONS This radiotranscriptomics study provides a stratification strategy for NSCLC that could provide information for radiomics feature molecular annotation and prognostic prediction. KEY POINTS • Radiotranscriptomics subtypes (RTSs) could be used to stratify molecularly heterogeneous patients. • RTSs showed relationships between molecular phenotypes and radiomics features. • The RTS algorithm could be used to identify patients with poor prognosis.
Collapse
Affiliation(s)
- Peng Lin
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Yi-Qun Lin
- Department of Radiology, The 909th Hospital. School of Medicine, Xiamen University, Fujian, Zhangzhou, People's Republic of China
| | - Rui-Zhi Gao
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Wei-Jun Wan
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Yun He
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
| | - Hong Yang
- Department of Medical Ultrasound, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
| |
Collapse
|
21
|
Yolchuyeva S, Giacomazzi E, Tonneau M, Lamaze F, Orain M, Coulombe F, Malo J, Belkaid W, Routy B, Joubert P, Manem VSK. Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study. Sci Rep 2023; 13:11065. [PMID: 37422576 PMCID: PMC10329671 DOI: 10.1038/s41598-023-38076-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/03/2023] [Indexed: 07/10/2023] Open
Abstract
With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.
Collapse
Affiliation(s)
- Sevinj Yolchuyeva
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois Rivières, Canada
| | - Elena Giacomazzi
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois Rivières, Canada
| | - Marion Tonneau
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
- Université de médecine de Lille, Lille, France
| | - Fabien Lamaze
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
| | - Michele Orain
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
| | - François Coulombe
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
| | - Julie Malo
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
| | - Wiam Belkaid
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
| | - Bertrand Routy
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
| | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec, Canada
| | - Venkata S K Manem
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois Rivières, Canada.
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada.
| |
Collapse
|
22
|
Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
Collapse
Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| |
Collapse
|
23
|
Song R, Liu F, Ping Y, Zhang Y, Wang L. Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction. Biomark Res 2023; 11:57. [PMID: 37268978 DOI: 10.1186/s40364-023-00498-1] [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: 02/01/2023] [Accepted: 05/07/2023] [Indexed: 06/04/2023] Open
Abstract
Immune checkpoint inhibitors (ICIs) have dramatically enhanced the treatment outcomes for diverse malignancies. Yet, only 15-60% of patients respond significantly. Therefore, accurate responder identification and timely ICI administration are critical issues in tumor ICI therapy. Recent rapid developments at the intersection of oncology, immunology, biology, and computer science have provided an abundance of predictive biomarkers for ICI efficacy. These biomarkers can be invasive or non-invasive, depending on the specific sample collection method. Compared with invasive markers, a host of non-invasive markers have been confirmed to have superior availability and accuracy in ICI efficacy prediction. Considering the outstanding advantages of dynamic monitoring of the immunotherapy response and the potential for widespread clinical application, we review the recent research in this field with the aim of contributing to the identification of patients who may derive the greatest benefit from ICI therapy.
Collapse
Affiliation(s)
- Ruixia Song
- Biotherapy Center and Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory for Tumor Immunology and Biotherapy, Zhengzhou University, Zhengzhou, Henan, China
| | - Fengsen Liu
- Biotherapy Center and Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory for Tumor Immunology and Biotherapy, Zhengzhou University, Zhengzhou, Henan, China
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China
| | - Yu Ping
- Biotherapy Center and Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yi Zhang
- Biotherapy Center and Cancer Center, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
- Henan Key Laboratory for Tumor Immunology and Biotherapy, Zhengzhou University, Zhengzhou, Henan, China.
- School of Life Sciences, Zhengzhou University, Zhengzhou, Henan, China.
- State Key Laboratory of Esophageal Cancer Prevention & Treatment, Zhengzhou, Henan, China.
| | - Liping Wang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
| |
Collapse
|
24
|
Garralda E, Laurie SA, Seymour L, de Vries EGE. Towards evidence-based response criteria for cancer immunotherapy. Nat Commun 2023; 14:3001. [PMID: 37225715 DOI: 10.1038/s41467-023-38837-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/17/2023] [Indexed: 05/26/2023] Open
Affiliation(s)
- Elena Garralda
- Research Unit, Vall d'Hebron Institute of Oncology, Barcelona, Spain
| | - Scott A Laurie
- Division of Medical Oncology, The Ottawa Hospital Cancer Centre, Ottawa, Canada
| | - Lesley Seymour
- Canadian Cancer Trials Group, Queens University, Cancer Centre of South Eastern Ontario, Kingston, ON, Canada
| | - Elisabeth G E de Vries
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| |
Collapse
|
25
|
Prendergast CM, Capaccione KM, Lopci E, Das JP, Shoushtari AN, Yeh R, Amin D, Dercle L, De Jong D. More than Just Skin-Deep: A Review of Imaging's Role in Guiding CAR T-Cell Therapy for Advanced Melanoma. Diagnostics (Basel) 2023; 13:992. [PMID: 36900136 PMCID: PMC10000712 DOI: 10.3390/diagnostics13050992] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 02/21/2023] [Accepted: 02/24/2023] [Indexed: 03/08/2023] Open
Abstract
Advanced melanoma is one of the deadliest cancers, owing to its invasiveness and its propensity to develop resistance to therapy. Surgery remains the first-line treatment for early-stage tumors but is often not an option for advanced-stage melanoma. Chemotherapy carries a poor prognosis, and despite advances in targeted therapy, the cancer can develop resistance. CAR T-cell therapy has demonstrated great success against hematological cancers, and clinical trials are deploying it against advanced melanoma. Though melanoma remains a challenging disease to treat, radiology will play an increasing role in monitoring both the CAR T-cells and response to therapy. We review the current imaging techniques for advanced melanoma, as well as novel PET tracers and radiomics, in order to guide CAR T-cell therapy and manage potential adverse events.
Collapse
Affiliation(s)
- Conor M. Prendergast
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Kathleen M. Capaccione
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Egesta Lopci
- Department of Nuclear Medicine, IRCSS Humanitas Research Hospital, 20089 Milan, Italy
| | - Jeeban P. Das
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | | | - Randy Yeh
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Daniel Amin
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Laurent Dercle
- Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Dorine De Jong
- Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| |
Collapse
|
26
|
Shankar LK, Schöder H, Sharon E, Wolchok J, Knopp MV, Wahl RL, Ellingson BM, Hall NC, Yaffe MJ, Towbin AJ, Farwell MD, Pryma D, Poussaint TY, Wright CL, Schwartz L, Harisinghani M, Mahmood U, Wu AM, Leung D, de Vries EGE, Tang Y, Beach G, Reeves SA. Harnessing imaging tools to guide immunotherapy trials: summary from the National Cancer Institute Cancer Imaging Steering Committee workshop. Lancet Oncol 2023; 24:e133-e143. [PMID: 36858729 PMCID: PMC10119769 DOI: 10.1016/s1470-2045(22)00742-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/18/2022] [Accepted: 11/30/2022] [Indexed: 03/02/2023]
Abstract
As the immuno-oncology field continues the rapid growth witnessed over the past decade, optimising patient outcomes requires an evolution in the current response-assessment guidelines for phase 2 and 3 immunotherapy clinical trials and clinical care. Additionally, investigational tools-including image analysis of standard-of-care scans (such as CT, magnetic resonance, and PET) with analytics, such as radiomics, functional magnetic resonance agents, and novel molecular-imaging PET agents-offer promising advancements for assessment of immunotherapy. To document current challenges and opportunities and identify next steps in immunotherapy diagnostic imaging, the National Cancer Institute Clinical Imaging Steering Committee convened a meeting with diverse representation among imaging experts and oncologists to generate a comprehensive review of the state of the field.
Collapse
Affiliation(s)
- Lalitha K Shankar
- Clinical Trials Branch, National Cancer Institute, Rockville, MD, USA.
| | - Heiko Schöder
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Elad Sharon
- Investigational Drug Branch, National Cancer Institute, Rockville, MD, USA
| | - Jedd Wolchok
- Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Michael V Knopp
- Department of Radiology, Ohio State University, Columbus, OH, USA
| | - Richard L Wahl
- Department of Radiology, Washington University, St Louis, MO, USA
| | - Benjamin M Ellingson
- Department of Radiological Sciences, University of California Los Angeles, Los Angeles, CA, USA
| | - Nathan C Hall
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Martin J Yaffe
- Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Alexander J Towbin
- Department of Radiology and Medical Imaging, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Michael D Farwell
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel Pryma
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | | | | | - Umar Mahmood
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Anna M Wu
- Department of Immunology & Theranostics, City of Hope Comprehensive Cancer Center, Duarte, CA, USA
| | | | - Elisabeth G E de Vries
- Department of Medical Oncology, University Medical Centre Groningen, University of Groningen, Groningen, Netherlands
| | | | | | - Steven A Reeves
- Coordinating Center for Clinical Trials, National Cancer Institute, Rockville, MD, USA
| |
Collapse
|
27
|
ter Maat L, van Duin IA, Elias SG, Leiner T, Verhoeff JJ, Arntz ER, Troenokarso MF, Blokx WA, Isgum I, de Wit GA, van den Berkmortel FW, Boers-Sonderen MJ, Boomsma MF, van den Eertwegh FJ, de Groot JWB, Piersma D, Vreugdenhil A, Westgeest HM, Kapiteijn E, van Diest PJ, Pluim J, de Jong PA, Suijkerbuijk KP, Veta M. CT radiomics compared to a clinical model for predicting checkpoint inhibitor treatment outcomes in patients with advanced melanoma. Eur J Cancer 2023; 185:167-177. [PMID: 36996627 DOI: 10.1016/j.ejca.2023.02.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/10/2023] [Accepted: 02/17/2023] [Indexed: 03/18/2023]
Abstract
INTRODUCTION Predicting checkpoint inhibitors treatment outcomes in melanoma is a relevant task, due to the unpredictable and potentially fatal toxicity and high costs for society. However, accurate biomarkers for treatment outcomes are lacking. Radiomics are a technique to quantitatively capture tumour characteristics on readily available computed tomography (CT) imaging. The purpose of this study was to investigate the added value of radiomics for predicting clinical benefit from checkpoint inhibitors in melanoma in a large, multicenter cohort. METHODS Patients who received first-line anti-PD1±anti-CTLA4 treatment for advanced cutaneous melanoma were retrospectively identified from nine participating hospitals. For every patient, up to five representative lesions were segmented on baseline CT, and radiomics features were extracted. A machine learning pipeline was trained on the radiomics features to predict clinical benefit, defined as stable disease for more than 6 months or response per RECIST 1.1 criteria. This approach was evaluated using a leave-one-centre-out cross validation and compared to a model based on previously discovered clinical predictors. Lastly, a combination model was built on the radiomics and clinical model. RESULTS A total of 620 patients were included, of which 59.2% experienced clinical benefit. The radiomics model achieved an area under the receiver operator characteristic curve (AUROC) of 0.607 [95% CI, 0.562-0.652], lower than that of the clinical model (AUROC=0.646 [95% CI, 0.600-0.692]). The combination model yielded no improvement over the clinical model in terms of discrimination (AUROC=0.636 [95% CI, 0.592-0.680]) or calibration. The output of the radiomics model was significantly correlated with three out of five input variables of the clinical model (p < 0.001). DISCUSSION The radiomics model achieved a moderate predictive value of clinical benefit, which was statistically significant. However, a radiomics approach was unable to add value to a simpler clinical model, most likely due to the overlap in predictive information learned by both models. Future research should focus on the application of deep learning, spectral CT-derived radiomics, and a multimodal approach for accurately predicting benefit to checkpoint inhibitor treatment in advanced melanoma.
Collapse
|
28
|
Ghaffari Laleh N, Ligero M, Perez-Lopez R, Kather JN. Facts and Hopes on the Use of Artificial Intelligence for Predictive Immunotherapy Biomarkers in Cancer. Clin Cancer Res 2023; 29:316-323. [PMID: 36083132 DOI: 10.1158/1078-0432.ccr-22-0390] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/26/2022] [Accepted: 08/29/2022] [Indexed: 01/19/2023]
Abstract
Immunotherapy by immune checkpoint inhibitors has become a standard treatment strategy for many types of solid tumors. However, the majority of patients with cancer will not respond, and predicting response to this therapy is still a challenge. Artificial intelligence (AI) methods can extract meaningful information from complex data, such as image data. In clinical routine, radiology or histopathology images are ubiquitously available. AI has been used to predict the response to immunotherapy from radiology or histopathology images, either directly or indirectly via surrogate markers. While none of these methods are currently used in clinical routine, academic and commercial developments are pointing toward potential clinical adoption in the near future. Here, we summarize the state of the art in AI-based image biomarkers for immunotherapy response based on radiology and histopathology images. We point out limitations, caveats, and pitfalls, including biases, generalizability, and explainability, which are relevant for researchers and health care providers alike, and outline key clinical use cases of this new class of predictive biomarkers.
Collapse
Affiliation(s)
| | - Marta Ligero
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.,Department of Radiology, Vall d'Hebron University Hospital, Barcelona, Spain
| | - Jakob Nikolas Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.,Division of Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.,Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.,Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| |
Collapse
|
29
|
Dercle L, Fronheiser M, Rizvi NA, Hellmann MD, Maier S, Hayes W, Yang H, Guo P, Fojo T, Schwartz LH, Zhao B, Leung DK. Baseline Radiomic Signature to Estimate Overall Survival in Patients With NSCLC. J Thorac Oncol 2023; 18:587-598. [PMID: 36646209 DOI: 10.1016/j.jtho.2022.12.019] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 11/22/2022] [Accepted: 12/20/2022] [Indexed: 01/15/2023]
Abstract
INTRODUCTION We aimed to define a baseline radiomic signature associated with overall survival (OS) using baseline computed tomography (CT) images obtained from patients with NSCLC treated with nivolumab or chemotherapy. METHODS The radiomic signature was developed in patients with NSCLC treated with nivolumab in CheckMate-017, -026, and -063. Nivolumab-treated patients were pooled and randomized to training, calibration, or validation sets using a 2:1:1 ratio. From baseline CT images, volume of tumor lesions was semiautomatically segmented, and 38 radiomic variables depicting tumor phenotype were extracted. Association between the radiomic signature and OS was assessed in the nivolumab-treated (validation set) and chemotherapy-treated (test set) patients in these studies. RESULTS A baseline radiomic signature was identified using CT images obtained from 758 patients. The radiomic signature used a combination of imaging variables (spatial correlation, tumor volume in the liver, and tumor volume in the mediastinal lymph nodes) to output a continuous value, ranging from 0 to 1 (from most to least favorable estimated OS). Given a threshold of 0.55, the sensitivity and specificity of the radiomic signature for predicting 3-month OS were 86% and 77.8%, respectively. The signature was identified in the training set of patients treated with nivolumab and was significantly associated (p < 0.0001) with OS in patients treated with nivolumab or chemotherapy. CONCLUSIONS The radiomic signature provides an early readout of the anticipated OS in patients with NSCLC treated with nivolumab or chemotherapy. This could provide important prognostic information and may support risk stratification in clinical trials.
Collapse
Affiliation(s)
- Laurent Dercle
- Department of Radiology, Columbia University Medical Center, New York, New York.
| | | | - Naiyer A Rizvi
- Department of Radiology, Columbia University Medical Center, New York, New York
| | - Matthew D Hellmann
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | | | | | - Hao Yang
- Department of Radiology, Columbia University Medical Center, New York, New York
| | - Pingzhen Guo
- Department of Radiology, Columbia University Medical Center, New York, New York
| | - Tito Fojo
- Department of Radiology, Columbia University Medical Center, New York, New York
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Medical Center, New York, New York
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, New York, New York
| | | |
Collapse
|
30
|
Suresh S, Rabbie R, Garg M, Lumaquin D, Huang TH, Montal E, Ma Y, Cruz NM, Tang X, Nsengimana J, Newton-Bishop J, Hunter MV, Zhu Y, Chen K, de Stanchina E, Adams DJ, White RM. Identifying the Transcriptional Drivers of Metastasis Embedded within Localized Melanoma. Cancer Discov 2023; 13:194-215. [PMID: 36259947 PMCID: PMC9827116 DOI: 10.1158/2159-8290.cd-22-0427] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 08/25/2022] [Accepted: 10/14/2022] [Indexed: 01/16/2023]
Abstract
In melanoma, predicting which tumors will ultimately metastasize guides treatment decisions. Transcriptional signatures of primary tumors have been utilized to predict metastasis, but which among these are driver or passenger events remains unclear. We used data from the adjuvant AVAST-M trial to identify a predictive gene signature in localized tumors that ultimately metastasized. Using a zebrafish model of primary melanoma, we interrogated the top genes from the AVAST-M signature in vivo. This identified GRAMD1B, a cholesterol transfer protein, as a bona fide metastasis suppressor, with a majority of knockout animals rapidly developing metastasis. Mechanistically, excess free cholesterol or its metabolite 27-hydroxycholesterol promotes invasiveness via activation of an AP-1 program, which is associated with increased metastasis in humans. Our data demonstrate that the transcriptional seeds of metastasis are embedded within localized tumors, suggesting that early targeting of these programs can be used to prevent metastatic relapse. SIGNIFICANCE We analyzed human melanoma transcriptomics data to identify a gene signature predictive of metastasis. To rapidly test clinical signatures, we built a genetic metastasis platform in adult zebrafish and identified GRAMD1B as a suppressor of melanoma metastasis. GRAMD1B-associated cholesterol overload activates an AP-1 program to promote melanoma invasion. This article is highlighted in the In This Issue feature, p. 1.
Collapse
Affiliation(s)
- Shruthy Suresh
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Roy Rabbie
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Manik Garg
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, United Kingdom
| | - Dianne Lumaquin
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, New York
| | - Ting-Hsiang Huang
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Emily Montal
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yilun Ma
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, New York
| | - Nelly M Cruz
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Xinran Tang
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
- Biochemistry and Structural Biology, Cellular and Developmental Biology and Molecular Biology Ph.D. Program, Weill Cornell Graduate School of Medical Sciences, New York, New York
| | - Jérémie Nsengimana
- Biostatistics Research Group, Population Health Sciences Institute, Faculty of Medical Sciences Newcastle University, Newcastle upon Tyne, United Kingdom
| | | | - Miranda V. Hunter
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Yuxin Zhu
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Kevin Chen
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Elisa de Stanchina
- Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, New York
| | - David J. Adams
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Richard M. White
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, New York
| |
Collapse
|
31
|
Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
Collapse
Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
| |
Collapse
|
32
|
Guerrisi A, Falcone I, Valenti F, Rao M, Gallo E, Ungania S, Maccallini MT, Fanciulli M, Frascione P, Morrone A, Caterino M. Artificial Intelligence and Advanced Melanoma: Treatment Management Implications. Cells 2022; 11:cells11243965. [PMID: 36552729 PMCID: PMC9777238 DOI: 10.3390/cells11243965] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.
Collapse
Affiliation(s)
- Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Italia Falcone
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
- Correspondence:
| | - Fabio Valenti
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Marco Rao
- Enea-FSN-TECFIS-APAM, C.R. Frascati, via Enrico Fermi, 45, 00146 Rome, Italy
| | - Enzo Gallo
- Pathology Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena Institute, 00144 Rome, Italy
| | - Maria Teresa Maccallini
- Departement of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy
| | - Maurizio Fanciulli
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Pasquale Frascione
- Oncologic and Preventative Dermatology, IFO-San Gallicano Dermatological Institute-IRCCS, 00144 Rome, Italy
| | - Aldo Morrone
- Scientific Direction, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Mauro Caterino
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| |
Collapse
|
33
|
Artificial intelligence for prediction of response to cancer immunotherapy. Semin Cancer Biol 2022; 87:137-147. [PMID: 36372326 DOI: 10.1016/j.semcancer.2022.11.008] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/02/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
Abstract
Artificial intelligence (AI) indicates the application of machines to imitate intelligent behaviors for solving complex tasks with minimal human intervention, including machine learning and deep learning. The use of AI in medicine improves health-care systems in multiple areas such as diagnostic confirmation, risk stratification, analysis, prognosis prediction, treatment surveillance, and virtual health support, which has considerable potential to revolutionize and reshape medicine. In terms of immunotherapy, AI has been applied to unlock underlying immune signatures to associate with responses to immunotherapy indirectly as well as predict responses to immunotherapy responses directly. The AI-based analysis of high-throughput sequences and medical images can provide useful information for management of cancer immunotherapy considering the excellent abilities in selecting appropriate subjects, improving therapeutic regimens, and predicting individualized prognosis. In present review, we aim to evaluate a broad framework about AI-based computational approaches for prediction of response to cancer immunotherapy on both indirect and direct manners. Furthermore, we summarize our perspectives about challenges and opportunities of further AI applications on cancer immunotherapy relating to clinical practicability.
Collapse
|
34
|
Litière S, Bogaerts J. Imaging endpoints for clinical trial use: a RECIST perspective. J Immunother Cancer 2022; 10:jitc-2022-005092. [PMID: 36424032 PMCID: PMC9693866 DOI: 10.1136/jitc-2022-005092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/11/2022] [Indexed: 11/27/2022] Open
Abstract
Twenty years after its initial introduction, Response Evaluation Criteria in Solid Tumors (RECIST) remains today a unique standardized tool allowing uniform objective evaluation of response in solid tumors in clinical trials across different treatment indications. Several attempts have been made to update or replace RECIST, but none have realized the general traction or uptake seen with RECIST. This communication provides an overview of some challenges faced by RECIST in the rapidly changing oncology landscape, including the incorporation of PET with 18F-fluorodeoxyglucose tracer as a tool for response assessment and the validation of criteria for use in trials involving immunotherapeutics. The latter has mainly been slow due to lack of data sharing. Work is ongoing to try to address this.We also aim to share our view as statistician representatives on the RECIST Working Group on what would be needed to validate new imaging endpoints for clinical trial use, with a specific focus on RECIST. Whether this could lead to an update of RECIST or replace RECIST altogether, depends on the changes being proposed. The ultimate goal remains to have a well defined, repeatable, confirmable and objective standard as provided by RECIST today.
Collapse
Affiliation(s)
- Saskia Litière
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| | - Jan Bogaerts
- European Organisation for Research and Treatment of Cancer, Brussels, Belgium
| |
Collapse
|
35
|
Vera J, Lai X, Baur A, Erdmann M, Gupta S, Guttà C, Heinzerling L, Heppt MV, Kazmierczak PM, Kunz M, Lischer C, Pützer BM, Rehm M, Ostalecki C, Retzlaff J, Witt S, Wolkenhauer O, Berking C. Melanoma 2.0. Skin cancer as a paradigm for emerging diagnostic technologies, computational modelling and artificial intelligence. Brief Bioinform 2022; 23:6761961. [PMID: 36252807 DOI: 10.1093/bib/bbac433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 07/28/2022] [Accepted: 09/08/2022] [Indexed: 12/19/2022] Open
Abstract
We live in an unprecedented time in oncology. We have accumulated samples and cases in cohorts larger and more complex than ever before. New technologies are available for quantifying solid or liquid samples at the molecular level. At the same time, we are now equipped with the computational power necessary to handle this enormous amount of quantitative data. Computational models are widely used helping us to substantiate and interpret data. Under the label of systems and precision medicine, we are putting all these developments together to improve and personalize the therapy of cancer. In this review, we use melanoma as a paradigm to present the successful application of these technologies but also to discuss possible future developments in patient care linked to them. Melanoma is a paradigmatic case for disruptive improvements in therapies, with a considerable number of metastatic melanoma patients benefiting from novel therapies. Nevertheless, a large proportion of patients does not respond to therapy or suffers from adverse events. Melanoma is an ideal case study to deploy advanced technologies not only due to the medical need but also to some intrinsic features of melanoma as a disease and the skin as an organ. From the perspective of data acquisition, the skin is the ideal organ due to its accessibility and suitability for many kinds of advanced imaging techniques. We put special emphasis on the necessity of computational strategies to integrate multiple sources of quantitative data describing the tumour at different scales and levels.
Collapse
Affiliation(s)
- Julio Vera
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Xin Lai
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Andreas Baur
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Michael Erdmann
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Cristiano Guttà
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Lucie Heinzerling
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany.,Department of Dermatology, LMU University Hospital, Munich, Germany
| | - Markus V Heppt
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Manfred Kunz
- Department of Dermatology, Venereology and Allergology, University of Leipzig, 04103 Leipzig, Germany
| | - Christopher Lischer
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Brigitte M Pützer
- Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, 18057 Rostock, Germany
| | - Markus Rehm
- Institute of Cell Biology and Immunology, University of Stuttgart, 70569 Stuttgart, Germany.,Stuttgart Research Center Systems Biology, University of Stuttgart, 70569 Stuttgart, Germany
| | - Christian Ostalecki
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | - Jimmy Retzlaff
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock 18051, Germany
| | - Carola Berking
- Department of Dermatology, FAU Erlangen-Nürnberg, Universitätsklinikum Erlangen, Comprehensive Cancer Center Erlangen and Deutsches Zentrum Immuntherapie (DZI), 91054 Erlangen, Germany
| |
Collapse
|
36
|
Beyond the AJR: Radiomics Meets Machine Learning to Improve Outcome Prediction. AJR Am J Roentgenol 2022; 219:844. [PMID: 35576518 DOI: 10.2214/ajr.22.27740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
|
37
|
Radiomic and Volumetric Measurements as Clinical Trial Endpoints—A Comprehensive Review. Cancers (Basel) 2022; 14:cancers14205076. [PMID: 36291865 PMCID: PMC9599928 DOI: 10.3390/cancers14205076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 10/12/2022] [Accepted: 10/14/2022] [Indexed: 11/23/2022] Open
Abstract
Simple Summary The extraction of quantitative data from standard-of-care imaging modalities offers opportunities to improve the relevance and salience of imaging biomarkers used in drug development. This review aims to identify the challenges and opportunities for discovering new imaging-based biomarkers based on radiomic and volumetric assessment in the single-site solid tumor sites: breast cancer, rectal cancer, lung cancer and glioblastoma. Developing approaches to harmonize three essential areas: segmentation, validation and data sharing may expedite regulatory approval and adoption of novel cancer imaging biomarkers. Abstract Clinical trials for oncology drug development have long relied on surrogate outcome biomarkers that assess changes in tumor burden to accelerate drug registration (i.e., Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST v1.1) criteria). Drug-induced reduction in tumor size represents an imperfect surrogate marker for drug activity and yet a radiologically determined objective response rate is a widely used endpoint for Phase 2 trials. With the addition of therapies targeting complex biological systems such as immune system and DNA damage repair pathways, incorporation of integrative response and outcome biomarkers may add more predictive value. We performed a review of the relevant literature in four representative tumor types (breast cancer, rectal cancer, lung cancer and glioblastoma) to assess the preparedness of volumetric and radiomics metrics as clinical trial endpoints. We identified three key areas—segmentation, validation and data sharing strategies—where concerted efforts are required to enable progress of volumetric- and radiomics-based clinical trial endpoints for wider clinical implementation.
Collapse
|
38
|
Dercle L, McGale J, Sun S, Marabelle A, Yeh R, Deutsch E, Mokrane FZ, Farwell M, Ammari S, Schoder H, Zhao B, Schwartz LH. Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy. J Immunother Cancer 2022; 10:jitc-2022-005292. [PMID: 36180071 PMCID: PMC9528623 DOI: 10.1136/jitc-2022-005292] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/01/2022] [Indexed: 11/04/2022] Open
Abstract
Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology’s role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57–180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10–16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.
Collapse
Affiliation(s)
- Laurent Dercle
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Jeremy McGale
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Shawn Sun
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Aurelien Marabelle
- Therapeutic Innovation and Early Trials, Gustave Roussy, Villejuif, Île-de-France, France
| | - Randy Yeh
- Molecular Imaging and Therapy Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Eric Deutsch
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France
| | | | - Michael Farwell
- Division of Nuclear Medicine and Molecular Imaging, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Samy Ammari
- Radiation Oncology, Gustave Roussy, Villejuif, Île-de-France, France.,Radiology, Institut de Cancérologie Paris Nord, Sarcelles, France
| | - Heiko Schoder
- Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Binsheng Zhao
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA
| |
Collapse
|
39
|
Sun R, Henry T, Laville A, Carré A, Hamaoui A, Bockel S, Chaffai I, Levy A, Chargari C, Robert C, Deutsch E. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022; 10:jitc-2022-004848. [PMID: 35793875 PMCID: PMC9260846 DOI: 10.1136/jitc-2022-004848] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/17/2022] Open
Abstract
Strong rationale and a growing number of preclinical and clinical studies support combining radiotherapy and immunotherapy to improve patient outcomes. However, several critical questions remain, such as the identification of patients who will benefit from immunotherapy and the identification of the best modalities of treatment to optimize patient response. Imaging biomarkers and radiomics have recently emerged as promising tools for the non-invasive assessment of the whole disease of the patient, allowing comprehensive analysis of the tumor microenvironment, the spatial heterogeneity of the disease and its temporal changes. This review presents the potential applications of medical imaging and the challenges to address, in order to help clinicians choose the optimal modalities of both radiotherapy and immunotherapy, to predict patient’s outcomes and to assess response to these promising combinations.
Collapse
Affiliation(s)
- Roger Sun
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Théophraste Henry
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Nuclear Medicine, Gustave Roussy, Villejuif, France
| | - Adrien Laville
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Alexandre Carré
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Anthony Hamaoui
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Sophie Bockel
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Ines Chaffai
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Antonin Levy
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Cyrus Chargari
- Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,Department of Radiation Oncology, Brachytherapy Unit, Gustave Roussy, Villejuif, France
| | - Charlotte Robert
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France.,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France
| | - Eric Deutsch
- Department of Radiation Oncology, Gustave Roussy, Villejuif, France .,Radiothérapie Moléculaire et Innovation Thérapeutique, Université Paris-Saclay, Institut Gustave Roussy, Inserm, Villejuif, France.,INSERM U1030, Gustave Roussy, Villejuif, France
| |
Collapse
|
40
|
Affiliation(s)
| | - Alexander T Pearson
- Biological Sciences Division, University of Chicago, Chicago, IL, USA.
- University of Chicago Comprehensive Cancer Center, University of Chicago, Chicago, IL, USA.
| |
Collapse
|
41
|
Kothari G. Role of radiomics in predicting immunotherapy response. J Med Imaging Radiat Oncol 2022; 66:575-591. [PMID: 35581928 PMCID: PMC9323544 DOI: 10.1111/1754-9485.13426] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 05/02/2022] [Indexed: 12/13/2022]
Abstract
Immunotherapies have revolutionised cancer management. Despite their success, durable responses are limited to a subset of patients. Prediction of immunotherapy response in patients has proven to be difficult due to a lack of robust biomarkers. Routinely collected imaging may offer an additional information source to personalise patient treatment, with advantages over tissue-based biomarkers. Quantitative image analysis or radiomics, which involves the high-throughput extraction of imaging features, has the potential to non-invasively predict cancer histology, outcomes and prognosis. This review evaluates the value of radiomics in patients undergoing immunotherapy, with a summary provided of the performance of radiomics models in predicting immunotherapy response and toxicity, as well as immune correlates. Much of the literature focussed on clinical endpoints and correlates to tissue biomarkers, particularly in lung cancer, while few studies investigated association with immune-related adverse events. Strengths of the studies included more frequent use of clinical trial datasets, homogenous patient cohorts and high-quality diagnostic scans. Limitations of the studies include heterogeneity in study methodology, lack of well-defined homogenous imaging datasets, limited open publishing of imaging datasets, coding and parameters used for radiomics signature development and limited use of external validation datasets. Future research should address the above limitations, as well as further explore the relationship between radiomics and immune-related adverse effects and less well-studied biological correlates such tumour mutational burden, and incorporate known clinical prognostic scores into radiomics models.
Collapse
Affiliation(s)
- Gargi Kothari
- Department of Radiation OncologyPeter MacCallum Cancer CentreMelbourneVictoriaAustralia
- Sir Peter MacCallum Department of Oncology, University of MelbournePeter MacCallum Cancer CentreMelbourneVictoriaAustralia
| |
Collapse
|
42
|
Predicting Objective Response Rate (ORR) in Immune Checkpoint Inhibitor (ICI) Therapies with Machine Learning (ML) by Combining Clinical and Patient-Reported Data. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12031563] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
ICIs are a standard of care in several malignancies; however, according to overall response rate (ORR), only a subset of eligible patients benefits from ICIs. Thus, an ability to predict ORR could enable more rational use. In this study a ML-based ORR prediction model was built, with patient-reported symptom data and other clinical data as inputs, using the extreme gradient boosting technique (XGBoost). Prediction performance for unseen samples was evaluated using leave-one-out cross-validation (LOOCV), and the performance was evaluated with accuracy, AUC (area under curve), F1 score, and MCC (Matthew’s correlation coefficient). The ORR prediction model had a promising LOOCV performance with all four metrics: accuracy (75%), AUC (0.71), F1 score (0.58), and MCC (0.4). A rather good sensitivity (0.58) and high specificity (0.82) of the model were seen in the confusion matrix for all 63 LOOCV ORR predictions. The two most important symptoms for predicting the ORR were itching and fatigue. The results show that it is possible to predict ORR for patients with multiple advanced cancers undergoing ICI therapies with a ML model combining clinical, routine laboratory, and patient-reported data even with a limited size cohort.
Collapse
|