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Liu Z, Yao Y, Zhao M, Zhao Q, Xue J, Huang Y, Qin S. Radiomics Models Derived From Arterial-Phase-Enhanced CT Reliably Predict Both PD-L1 Expression and Immunotherapy Prognosis in Non-small Cell Lung Cancer: A Retrospective, Multicenter Cohort Study. Acad Radiol 2024:S1076-6332(24)00463-X. [PMID: 39084935 DOI: 10.1016/j.acra.2024.07.028] [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: 06/03/2024] [Revised: 07/15/2024] [Accepted: 07/16/2024] [Indexed: 08/02/2024]
Abstract
RATIONALE AND OBJECTIVES Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC) and programmed cell death-ligand 1 (PD-L1) is a companion biomarker. This study aims to use baseline arterial-phase enhanced CT (APECT) to construct efficient radiomic models for predicting PD-L1 expression and immunotherapy prognosis in NSCLC. MATERIALS AND METHODS We extracted radiomics features from the baseline APECT images of 204 patients enrolled in a published multicenter clinical trial that commenced on August 23, 2018, and concluded on November 15, 2019 (ClinicalTrials.gov: NCT03607539). Of these patients, 146 patients from selected centers were assigned to the training cohort. The least absolute shrinkage and selection operator (LASSO) method was used to reduce dimensionality of radiomics features and calculate tumor scores. Models were created using naive bayes, decision trees, XGBoost, and random forest algorithms according to tumor scores. These models were then validated in an independent validation cohort comprising 58 patients from the remaining centers. RESULTS The random forest algorithm outperformed the other methods. In the three-classification scenario, the random forest model achieving the area under the curve (AUC) values of 0.98 and 0.94 in the training and validation cohorts, respectively. In the two-classification scenario, the random forest model achieved AUCs of 0.99 (95%CI: 0.97-1.0, P < 0.0001) and 0.93 (95%CI: 0.83-0.98, P < 0.0001) in the training and validation cohorts, respectively. Furthermore, patients classified as PD-L1 high-expression by this model can predict treatment response (AUC=0.859, 95%CI: 0.7-0.96, P < 0.001) and improved survival (HR=0.2, 95%CI: 0.08-0.53, P = 0.001) only in validation sintilimab arm. CONCLUSION Radiomics models based on APECT represent a potential non-invasive approach to robustly predict PD-L1 expression and ICI treatment outcomes in patients with NSCLC, which could significantly improve precision cancer immunotherapy.
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Affiliation(s)
- Zhenhua Liu
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China; Department of Radiotherapy, Yancheng First Hospital Affiliated Hospital of Nanjing University Medical School, The First people's Hospital of Yancheng, 66 Renmin Road, Yancheng 224005, China; National Clinical Research Center for Hematologic Diseases, Cyrus Tang Medical Institute, Collaborative Innovation Center of Hematology, State Key Laboratory of Radiation Medicine and Prevention, Soochow University, Suzhou 215123, China
| | - Yimin Yao
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China
| | - Miaomiao Zhao
- Department of Ultrasound, Yancheng First Hospital Affiliated Hospital of Nanjing University Medical School, The First people's Hospital of Yancheng, 66 Renmin Road, Yancheng 224005, China
| | - Qi Zhao
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China
| | - Jiao Xue
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China
| | - Yuhui Huang
- National Clinical Research Center for Hematologic Diseases, Cyrus Tang Medical Institute, Collaborative Innovation Center of Hematology, State Key Laboratory of Radiation Medicine and Prevention, Soochow University, Suzhou 215123, China
| | - Songbing Qin
- Department of Radiation Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou 215000, China.
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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.
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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.
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Peng J, Zou D, Zhang X, Ma H, Han L, Yao B. A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma. J Transl Med 2024; 22:87. [PMID: 38254087 PMCID: PMC10802066 DOI: 10.1186/s12967-024-04904-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 01/14/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Identifying precise biomarkers of immunotherapy response for non-small cell lung carcinoma (NSCLC) before treatment is challenging. This study aimed to construct and investigate the potential performance of a sub-regional radiomics model (SRRM) as a novel tumor biomarker in predicting the response of patients with NSCLC treated with immune checkpoint inhibitors, and test whether its predictive performance is superior to that of conventional radiomics, tumor mutational burden (TMB) score and programmed death ligand-1 (PD-L1) expression. METHODS We categorized 264 patients from retrospective databases of two centers into training (n = 159) and validation (n = 105) cohorts. Radiomic features were extracted from three sub-regions of the tumor region of interest using the K-means method. We extracted 1,896 features from each sub-region, resulting in 5688 features per sample. The least absolute shrinkage and selection operator regression method was used to select sub-regional radiomic features. The SRRM was constructed and validated using the support vector machine algorithm. We used next-generation sequencing to classify patients from the two cohorts into high TMB (≥ 10 muts/Mb) and low TMB (< 10 muts/Mb) groups; immunohistochemistry was performed to assess PD-L1 expression in formalin-fixed, paraffin-embedded tumor sections, with high expression defined as ≥ 50% of tumor cells being positive. Associations between the SRRM and progression-free survival (PFS) and variant genes were assessed. RESULTS Eleven sub-regional radiomic features were employed to develop the SRRM. The areas under the receiver operating characteristic curve (AUCs) of the proposed SRRM were 0.90 (95% confidence interval [CI] 0.84-0.96) and 0.86 (95% CI 0.76-0.95) in the training and validation cohorts, respectively. The SRRM (low vs. high; cutoff value = 0.936) was significantly associated with PFS in the training (hazard ratio [HR] = 0.35 [0.24-0.50], P < 0.001) and validation (HR = 0.42 [0.26-0.67], P = 0.001) cohorts. A significant correlation between the SRRM and three variant genes (H3C4, PAX5, and EGFR) was observed. In the validation cohort, the SRRM demonstrated a higher AUC (0.86, P < 0.001) than that for PD-L1 expression (0.66, P = 0.034) and TMB score (0.54, P = 0.552). CONCLUSIONS The SRRM had better predictive performance and was superior to conventional radiomics, PD-L1 expression, and TMB score. The SRRM effectively stratified the progression-free survival (PFS) risk among patients with NSCLC receiving immunotherapy.
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Affiliation(s)
- Jie Peng
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China.
| | - Dan Zou
- Department of Oncology, The Second Affiliated Hospital, Guizhou Medical University, Kaili, China
| | - Xudong Zhang
- Department of Radiation Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Honglian Ma
- Department of Radiation Oncology, Cancer Hospital of the University of Chinese Academy of Sciences, Hangzhou, China
| | - Lijie Han
- Department of Hematology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Biao Yao
- Department of Oncology, Tongren People's Hospital, Tongren, China
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Cellina M, De Padova G, Caldarelli N, Libri D, Cè M, Martinenghi C, Alì M, Papa S, Carrafiello G. Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy. Crit Rev Oncog 2024; 29:1-13. [PMID: 38505877 DOI: 10.1615/critrevoncog.2023050439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Lung cancer remains a global health challenge, leading to substantial morbidity and mortality. While prevention and early detection strategies have improved, the need for precise diagnosis, prognosis, and treatment remains crucial. In this comprehensive review article, we explore the role of artificial intelligence (AI) in reshaping the management of lung cancer. AI may have different potential applications in lung cancer characterization and outcome prediction. Manual segmentation is a time-consuming task, with high inter-observer variability, that can be replaced by AI-based approaches, including deep learning models such as U-Net, BCDU-Net, and others, to quantify lung nodules and cancers objectively and to extract radiomics features for the characterization of the tissue. AI models have also demonstrated their ability to predict treatment responses, such as immunotherapy and targeted therapy, by integrating radiomic features with clinical data. Additionally, AI-based prognostic models have been developed to identify patients at higher risk and personalize treatment strategies. In conclusion, this review article provides a comprehensive overview of the current state of AI applications in lung cancer management, spanning from segmentation and virtual biopsy to outcome prediction. The evolving role of AI in improving the precision and effectiveness of lung cancer diagnosis and treatment underscores its potential to significantly impact clinical practice and patient outcomes.
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Affiliation(s)
- Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, Milano, Piazza Principessa Clotilde 3, 20121, Milan, Italy
| | - Giuseppe De Padova
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Nazarena Caldarelli
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Dario Libri
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Carlo Martinenghi
- Radiology Department, Ospedale San Raffaele, Via Olgettina, 60 - 20132 Milan, Italy
| | - Marco Alì
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Sergio Papa
- Radiology Unit, CDI, Centro Diagnostico Italiano, Via Simone Saint Bon, 20, 20147 Milan, Italy
| | - Gianpaolo Carrafiello
- Postgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono, 7, 20122 Milan, Italy; Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, Università di Milano, 20122 Milan, Italy
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Hu Y, Jiang T, Wang H, Song J, Yang Z, Wang Y, Su J, Jin M, Chang S, Deng K, Jiang W. Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC. Phys Med 2024; 117:103200. [PMID: 38160516 DOI: 10.1016/j.ejmp.2023.103200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 08/18/2023] [Accepted: 12/21/2023] [Indexed: 01/03/2024] Open
Abstract
PURPOSE To develop and externally validate subregional radiomics for predicting therapeutic response to anti-PD1 therapy in non-small-cell lung cancer (NSCLC). METHODS Sixty-six patients from center 1 served as training and internal validation cohorts. Thirty patients from center 2 and thirty patients from center 3 served as external validation 1 and external validation 2 cohorts, respectively. The lesions identified on CT scans were subdivided into two phenotypically consistent subregions by automatic clustering on the patient-level and population-level (denoted as marginal S1 and inner S2). Handcrafted and deep learning-based features were extracted separately from the entire tumor region and subregions, then selected using the intraclass correlation coefficient and least absolute shrinkage and selection operator regression (LASSO). Radiomics signatures (RSs) were built integrating the selected features and correlation coefficients using a logistic regression method. Area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the RSs. RESULTS RSs derived from S1 outperformed those from S2 and the whole tumor region for both handcrafted and deep learning features. The Fusion-RS incorporating the two feature types achieved the best prediction performance in training (AUC = 0.947, 95 % Confidence Interval [CI] 0.905-0.989, SPE = 0.895, SEN = 0.878), internal validation (AUC = 0.875, 95 % CI: 0.782-0.969, SPE = 0.724, SEN = 0.952), external validation 1 (AUC = 0.836, 95 % CI: 0.694-0.977, SPE = 1.000, SEN = 0.533) and external validation 2 (AUC = 0.783, 95 % CI: 0.613-0.953, SPE = 0.765, SEN = 0.692) cohorts. CONCLUSIONS Subregional radiomics analysis can be useful for predicting therapeutic response to anti-PD1 therapy. The developed Fusion-RS may be considered as a potential non-invasive tool for individual treatment managements.
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Affiliation(s)
- Yue Hu
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Tao Jiang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Huan Wang
- Radiation Oncology Department Of Thoracic Cancer, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China
| | - Jiangdian Song
- School of Medical Informatics, China Medical University, Liaoning 110122, PR China
| | - Zhiguang Yang
- Department of Radiology, Shengjing Hospital, Shenyang 110004, PR China
| | - Yan Wang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Juan Su
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Meiqi Jin
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China
| | - Shijie Chang
- School of Intelligent Medicine, China Medical University, Liaoning, 110122, PR China.
| | - Kexue Deng
- Department of Radiology, The First Affiliated Hospital of University of Science and Technology of China, Anhui 230036, PR China.
| | - Wenyan Jiang
- Department of Scientific Research and Academic, Liaoning Cancer Hospital and Institute, Liaoning 110042, PR China.
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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.
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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
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Yang Q, Huang H, Zhang G, Weng N, Ou Z, Sun M, Luo H, Zhou X, Gao Y, Wu X. Contrast-enhanced CT-based radiomic analysis for determining the response to anti-programmed death-1 therapy in esophageal squamous cell carcinoma patients: A pilot study. Thorac Cancer 2023; 14:3266-3274. [PMID: 37743537 PMCID: PMC10665784 DOI: 10.1111/1759-7714.15117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 09/06/2023] [Accepted: 09/07/2023] [Indexed: 09/26/2023] Open
Abstract
BACKGROUND In view of the fact that radiomics features have been reported as predictors of immunotherapy to various cancers, this study aimed to develop a prediction model to determine the response to anti-programmed death-1 (anti-PD-1) therapy in esophageal squamous cell carcinoma (ESCC) patients from contrast-enhanced CT (CECT) radiomics features. METHODS Radiomic analysis of images was performed retrospectively for image samples before and after anti-PD-1 treatment, and efficacy analysis was performed for the results of two different time node evaluations. A total of 68 image samples were included in this study. Quantitative radiomic features were extracted from the images, and the least absolute shrinkage and selection operator method was applied to select radiomic features. After obtaining selected features, three classification models were used to establish a radiomics model to predict the ESCC status and efficacy of therapy. A cross-validation strategy utilizing three folds was employed to train and test the model. Performance evaluation of the model was done using the area under the curve (AUC) of receiver operating characteristic, sensitivity, specificity, and precision metric. RESULTS Wavelet and area of gray level change (log-sigma) were the most significant radiomic features for predicting therapy efficacy. Fifteen radiomic features from the whole tumor and peritumoral regions were selected and comprised of the fusion radiomics score. A radiomics classification was developed with AUC of 0.82 and 0.884 in the before and after-therapy cohorts, respectively. CONCLUSIONS The combined model incorporating radiomic features and clinical CECT predictors helps to predict the response to anti-PD-1therapy in patients with ESCC.
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Affiliation(s)
- Qinzhu Yang
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
| | - Haofan Huang
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
- Department of Biomedical EngineeringHong Kong Polytechnic UniversityHong Kong SARChina
| | - Guizhi Zhang
- Department of RadiologyThe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Nuoqing Weng
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
| | - Zhenkai Ou
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
| | - Meili Sun
- Department of RadiologySun Yat‐sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineGuangzhouChina
| | - Huixing Luo
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
| | - Xuhui Zhou
- Department of RadiologyThe Eighth Affiliated Hospital of Sun Yat‐sen UniversityShenzhenChina
| | - Yi Gao
- School of Biomedical EngineeringShenzhen University Medical School, Shenzhen UniversityShenzhenChina
- Shenzhen Key Laboratory of Precision Medicine for Hematological MalignanciesShenzhenChina
- Marshall Laboratory of Biomedical EngineeringShenzhenChina
| | - Xiaobin Wu
- Department of Gastrointestinal Surgery, The Eighth Affiliated HospitalSun Yat‐sen UniversityShenzhenChina
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Chen M, Lu H, Copley SJ, Han Y, Logan A, Viola P, Cortellini A, Pinato DJ, Power D, Aboagye EO. A Novel Radiogenomics Biomarker for Predicting Treatment Response and Pneumotoxicity From Programmed Cell Death Protein or Ligand-1 Inhibition Immunotherapy in NSCLC. J Thorac Oncol 2023; 18:718-730. [PMID: 36773776 DOI: 10.1016/j.jtho.2023.01.089] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 11/23/2022] [Accepted: 01/17/2023] [Indexed: 02/11/2023]
Abstract
INTRODUCTION Patient selection for checkpoint inhibitor immunotherapy is currently guided by programmed death-ligand 1 (PD-L1) expression obtained from immunohistochemical staining of tumor tissue samples. This approach is susceptible to limitations resulting from the dynamic and heterogeneous nature of cancer cells and the invasiveness of the tissue sampling procedure. To address these challenges, we developed a novel computed tomography (CT) radiomic-based signature for predicting disease response in patients with NSCLC undergoing programmed cell death protein 1 (PD-1) or PD-L1 checkpoint inhibitor immunotherapy. METHODS This retrospective study comprises a total of 194 patients with suitable CT scans out of 340. Using the radiomic features computed from segmented tumors on a discovery set of 85 contrast-enhanced chest CTs of patients diagnosed with having NSCLC and their CD274 count, RNA expression of the protein-encoding gene for PD-L1, as the response vector, we developed a composite radiomic signature, lung cancer immunotherapy-radiomics prediction vector (LCI-RPV). This was validated in two independent testing cohorts of 66 and 43 patients with NSCLC treated with PD-1 or PD-L1 inhibition immunotherapy, respectively. RESULTS LCI-RPV predicted PD-L1 positivity in both NSCLC testing cohorts (area under the curve [AUC] = 0.70, 95% confidence interval [CI]: 0.57-0.84 and AUC = 0.70, 95% CI: 0.46-0.94). In one cohort, it also demonstrated good prediction of cases with high PD-L1 expression exceeding key treatment thresholds (>50%: AUC = 0.72, 95% CI: 0.59-0.85 and >90%: AUC = 0.66, 95% CI: 0.45-0.88), the tumor's objective response to treatment at 3 months (AUC = 0.68, 95% CI: 0.52-0.85), and pneumonitis occurrence (AUC = 0.64, 95% CI: 0.48-0.80). LCI-RPV achieved statistically significant stratification of the patients into a high- and low-risk survival group (hazard ratio = 2.26, 95% CI: 1.21-4.24, p = 0.011 and hazard ratio = 2.45, 95% CI: 1.07-5.65, p = 0.035). CONCLUSIONS A CT radiomics-based signature developed from response vector CD274 can aid in evaluating patients' suitability for PD-1 or PD-L1 checkpoint inhibitor immunotherapy in NSCLC.
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Affiliation(s)
- Mitchell Chen
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Haonan Lu
- Department of Surgery and Cancer, Imperial College, London, United Kingdom
| | - Susan J Copley
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Yidong Han
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Andrew Logan
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Patrizia Viola
- North West London Pathology, Charing Cross Hospital, London, United Kingdom
| | - Alessio Cortellini
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - David J Pinato
- Department of Surgery and Cancer, Imperial College, London, United Kingdom; Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom; Division of Oncology, Department of Translational Medicine, University of Piemonte Orientale, Novara, Italy
| | - Danielle Power
- Imperial College Healthcare NHS Trust, Hammersmith Hospital, London, United Kingdom
| | - Eric O Aboagye
- Department of Surgery and Cancer, Imperial College, London, United Kingdom.
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Cousin F, Louis T, Dheur S, Aboubakar F, Ghaye B, Occhipinti M, Vos W, Bottari F, Paulus A, Sibille A, Vaillant F, Duysinx B, Guiot J, Hustinx R. Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors. Cancers (Basel) 2023; 15:cancers15071968. [PMID: 37046629 PMCID: PMC10093736 DOI: 10.3390/cancers15071968] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/20/2023] [Accepted: 03/23/2023] [Indexed: 03/29/2023] Open
Abstract
The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65−0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56−0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.
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Affiliation(s)
- François Cousin
- Department of Nuclear Medicine and Oncological Imaging, University Hospital (CHU) of Liège, 4000 Liège, Belgium
- Correspondence: ; Tel.: +32-475972109
| | - Thomas Louis
- Radiomics (Oncoradiomics SA), 4000 Liège, Belgium
| | - Sophie Dheur
- Department of Radiology, University Hospital (CHU) of Liège, 4000 Liège, Belgium
| | - Frank Aboubakar
- Department of Pulmonology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, 1200 Bruxelles, Belgium
- Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Bruxelles, Belgium
| | - Benoit Ghaye
- Institut de Recherche Expérimentale et Clinique (IREC), Université Catholique de Louvain, 1200 Bruxelles, Belgium
- Department of Radiology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, 1200 Bruxelles, Belgium
| | | | - Wim Vos
- Radiomics (Oncoradiomics SA), 4000 Liège, Belgium
| | | | - Astrid Paulus
- Department of Respiratory Medicine, University Hospital (CHU) of Liège, 4000 Liège, Belgium
| | - Anne Sibille
- Department of Respiratory Medicine, University Hospital (CHU) of Liège, 4000 Liège, Belgium
| | - Frédérique Vaillant
- Department of Respiratory Medicine, University Hospital (CHU) of Liège, 4000 Liège, Belgium
| | - Bernard Duysinx
- Department of Respiratory Medicine, University Hospital (CHU) of Liège, 4000 Liège, Belgium
| | - Julien Guiot
- Department of Respiratory Medicine, University Hospital (CHU) of Liège, 4000 Liège, Belgium
| | - Roland Hustinx
- Department of Nuclear Medicine and Oncological Imaging, University Hospital (CHU) of Liège, 4000 Liège, Belgium
- GIGA-CRC In Vivo Imaging, University of Liège, 4000 Liège, Belgium
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10
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McCague C, Ramlee S, Reinius M, Selby I, Hulse D, Piyatissa P, Bura V, Crispin-Ortuzar M, Sala E, Woitek R. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78:83-98. [PMID: 36639175 DOI: 10.1016/j.crad.2022.08.149] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/31/2022] [Indexed: 01/12/2023]
Abstract
Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.
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Affiliation(s)
- C McCague
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
| | - S Ramlee
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - M Reinius
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - I Selby
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - D Hulse
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - P Piyatissa
- Department of Radiology, University of Cambridge, Cambridge, UK
| | - V Bura
- Department of Radiology, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Department of Radiology and Medical Imaging, County Clinical Emergency Hospital, Cluj-Napoca, Romania
| | - M Crispin-Ortuzar
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK; Department of Oncology, University of Cambridge, Cambridge, UK
| | - E Sala
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - R Woitek
- Department of Radiology, University of Cambridge, Cambridge, UK; Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK; Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK; Research Centre for Medical Image Analysis and Artificial Intelligence (MIAAI), Department of Medicine, Faculty of Medicine and Dentistry, Danube Private University, Krems, Austria
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11
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CT-Based Radiomics Can Predict the Efficacy of Anlotinib in Advanced Non-Small-Cell Lung Cancer. JOURNAL OF ONCOLOGY 2022; 2022:4182540. [PMID: 36600966 PMCID: PMC9807313 DOI: 10.1155/2022/4182540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 11/25/2022] [Accepted: 11/29/2022] [Indexed: 12/27/2022]
Abstract
Anlotinib is a small-molecule RTK inhibitor that has achieved certain results in further-line treatment, but many patients do not respond to this drug and lack effective methods for identification. Although radiomics has been widely used in lung cancer, very few studies have been conducted in the field of antiangiogenic drugs. This study aims to develop a new model to predict the efficacy of patients receiving anlotinib by combining pretreatment computed tomography (CT) radiomic characters with clinical characters, in order to assist precision medicine of pulmonary cancer. 254 patients from seven institutions were involved in the study. Lesions were selected according to the RECIST 1.1 criteria, and the corresponding radiomic features were obtained. We constructed prediction models based on clinical, NCE-CT, and CE-CT radiomic features, respectively, and evaluated the prediction performance of the models for training sets, internal validation sets, and external validation sets. In the RAD score only model, the area under curve(AUC) of the NCE-CT cohort was 0.740 (95% CI: 0.622, 0.857) for the training set, 0.711 (95% CI: 0.480, 0.942) for the internal validation set, and 0.633(95% CI: 0.479, 0.787) for the external validation set, while that of the CE-CT cohort was 0.815 (95% CI: 0.705, 0.926) for the training set, 0.771 (95% CI: 0.539, 1.000) for the internal validation set, and 0.701 (95% CI: 0.489, 0.913) for the external validation set. In the RAD score-combined model, the AUC of the NCE-CT cohort was 0.796 (95% CI: 0.691, 0.901) for the training set, 0.579 (95% CI: 0.309, 0.848) for the internal validation set, and 0.590 (95% CI: 0.427, 0.753) for the external validation set, while that of the CE-CT cohort was 0.902 (95% CI: 0.828, 0.977) for the training set, 0.865 (95% CI: 0.696, 1.000) for the internal validation set, and 0.837 (95% CI: 0.682, 0.992) for the external validation set. In conclusion, radiomics has accurate predictions for the efficacy of anlotinib. CE-CT-based radiomic models have the best predictive potential in predicting the efficacy of anlotinib, and model predictions become better when they are combined with clinical characteristics.
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12
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Yu Y, Bai Y, Zheng P, Wang N, Deng X, Ma H, Yu R, Ma C, Liu P, Xie Y, Wang C, Chen H. Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers. BMC Cancer 2022; 22:1241. [PMID: 36451109 PMCID: PMC9710011 DOI: 10.1186/s12885-022-10344-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 11/21/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) represent an approved treatment for various cancers; however, only a small proportion of the population is responsive to such treatment. We aimed to develop and validate a plain CT-based tool for predicting the response to ICI treatment among cancer patients. METHODS Data for patients with solid cancers treated with ICIs at two centers from October 2019 to October 2021 were randomly divided into training and validation sets. Radiomic features were extracted from pretreatment CT images of the tumor of interest. After feature selection, a radiomics signature was constructed based on the least absolute shrinkage and selection operator regression model, and the signature and clinical factors were incorporated into a radiomics nomogram. Model performance was evaluated using the training and validation sets. The Kaplan-Meier method was used to visualize associations with survival. RESULTS Data for 122 and 30 patients were included in the training and validation sets, respectively. Both the radiomics signature (radscore) and nomogram exhibited good discrimination of response status, with areas under the curve (AUC) of 0.790 and 0.814 for the training set and 0.831 and 0.847 for the validation set, respectively. The calibration evaluation indicated goodness-of-fit for both models, while the decision curves indicated that clinical application was favorable. Both models were associated with the overall survival of patients in the validation set. CONCLUSIONS We developed a radiomics model for early prediction of the response to ICI treatment. This model may aid in identifying the patients most likely to benefit from immunotherapy.
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Affiliation(s)
- Yang Yu
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Yuping Bai
- grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China ,grid.411294.b0000 0004 1798 9345Department of MR, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Peng Zheng
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Na Wang
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Xiaobo Deng
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Huanhuan Ma
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Rong Yu
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Chenhui Ma
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China ,grid.32566.340000 0000 8571 0482The Second Clinical Medical College, Lanzhou University, Lanzhou, 730000 Gansu China
| | - Peng Liu
- grid.461867.a0000 0004 1765 2646Department of Radiology, Gansu Provincial Cancer Hospital, Lanzhou, 730050 Gansu China
| | - Yijing Xie
- grid.411294.b0000 0004 1798 9345Department of Radiology, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Chen Wang
- grid.411294.b0000 0004 1798 9345Department of General Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
| | - Hao Chen
- grid.411294.b0000 0004 1798 9345The Department of Tumor Surgery, Lanzhou University Second Hospital, Lanzhou, 730030 Gansu China
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