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Min J, Dong F, Chen Y, Li W, Wu Y, Tan Y, Yang F, Wu P, Chai Y. The NSCLC immunotherapy response predicted by tumor-infiltrating T cells via a non-invasive radiomic approach. Front Immunol 2024; 15:1379812. [PMID: 39315096 PMCID: PMC11416977 DOI: 10.3389/fimmu.2024.1379812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 08/12/2024] [Indexed: 09/25/2024] Open
Abstract
Introductions Identifying patients with non-small cell lung cancer (NSCLC) who are optimal candidates for immunotherapy is a cornerstone in clinical decision-making. The tumor immune microenvironment (TIME) is intricately linked with both the prognosis of the malignancy and the efficacy of immunotherapeutic interventions. CD8+ T cells, and more specifically, tissue-resident memory CD8+ T cells [CD8+ tissue-resident memory T (TRM) cells] are postulated to be pivotal in orchestrating the immune system's assault on tumor cells. Nevertheless, the accurate quantification of immune cell infiltration-and by extension, the prediction of immunotherapeutic efficacy-remains a significant scientific frontier. Methods In this study, we introduce a cutting-edge non-invasive radiomic model, grounded in TIME markers (CD3+ T, CD8+ T, and CD8+ TRM cells), to infer the levels of immune cell infiltration in NSCLC patients receiving immune checkpoint inhibitors and ultimately predict their response to immunotherapy. Data from patients who had surgical resections (cohort 1) were employed to construct a radiomic model capable of predicting the TIME. This model was then applied to forecast the TIME for patients under immunotherapy (cohort 2). Conclusively, the study delved into the association between the predicted TIME from the radiomic model and the immunotherapeutic outcomes of the patients. Result For the immune cell infiltration radiomic prediction models in cohort 1, the AUC values achieved 0.765, 0.763, and 0.675 in the test set of CD3+ T, CD8+ T, and CD8+ TRM, respectively. While the AUC values for the TIME-immunotherapy predictive value were 0.651, 0.763, and 0.829 in the CD3-immunotherapy response model, CD8-immunotherapy response model, and CD8+ TRM-immunotherapy response model in cohort 2, respectively. The CD8+ TRM-immunotherapy model exhibited the highest predictive value and was significantly better than the CD3-immunotherapy model in predicting the immunotherapy response. The progression-free survival (PFS) analysis based on the predicted levels of CD3+ T, CD8+ T, and CD8+ TRM immune cell infiltration showed that the CD8+ T cell infiltration level was an independent factor (P=0.014, HR=0.218) with an AUC value of 0.938. Discussion Our empirical evidence reveals that patients with substantial CD8+ T cell infiltration experience a markedly improved PFS compared with those with minimal infiltration, asserting the status of the CD8+ T cell as an independent prognosticator of PFS in the context of immunotherapy. Although CD8+ TRM cells demonstrated the greatest predictive accuracy for immunotherapy response, their predictive strength for PFS was marginally surpassed by that of CD8+ T cells. These insights advocate for the application of the proposed non-invasive radiomic model, which utilizes TIME analysis, as a reliable predictor for immunotherapy outcomes and PFS in NSCLC patients.
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Affiliation(s)
- Jie Min
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Fei Dong
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yongyuan Chen
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Wenshan Li
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yimin Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yanbin Tan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Fan Yang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Pin Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Ying Chai
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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Liu J, Sui C, Bian H, Li Y, Wang Z, Fu J, Qi L, Chen K, Xu W, Li X. Radiomics based on 18F-FDG PET/CT for prediction of pathological complete response to neoadjuvant therapy in non-small cell lung cancer. Front Oncol 2024; 14:1425837. [PMID: 39132503 PMCID: PMC11310012 DOI: 10.3389/fonc.2024.1425837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 07/09/2024] [Indexed: 08/13/2024] Open
Abstract
Purpose This study aimed to establish and evaluate the value of integrated models involving 18F-FDG PET/CT-based radiomics and clinicopathological information in the prediction of pathological complete response (pCR) to neoadjuvant therapy (NAT) for non-small cell lung cancer (NSCLC). Methods A total of 106 eligible NSCLC patients were included in the study. After volume of interest (VOI) segmentation, 2,016 PET-based and 2,016 CT-based radiomic features were extracted. To select an optimal machine learning model, a total of 25 models were constructed based on five sets of machine learning classifiers combined with five sets of predictive feature resources, including PET-based alone radiomics, CT-based alone radiomics, PET/CT-based radiomics, clinicopathological features, and PET/CT-based radiomics integrated with clinicopathological features. Area under the curves (AUCs) of receiver operator characteristic (ROC) curves were used as the main outcome to assess the model performance. Results The hybrid PET/CT-derived radiomic model outperformed PET-alone and CT-alone radiomic models in the prediction of pCR to NAT. Moreover, addition of clinicopathological information further enhanced the predictive performance of PET/CT-derived radiomic model. Ultimately, the support vector machine (SVM)-based PET/CT radiomics combined clinicopathological information presented an optimal predictive efficacy with an AUC of 0.925 (95% CI 0.869-0.981) in the training cohort and an AUC of 0.863 (95% CI 0.740-0.985) in the test cohort. The developed nomogram involving radiomics and pathological type was suggested as a convenient tool to enable clinical application. Conclusions The 18F-FDG PET/CT-based SVM radiomics integrated with clinicopathological information was an optimal model to non-invasively predict pCR to NAC for NSCLC.
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Affiliation(s)
- Jianjing Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Chunxiao Sui
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Haiman Bian
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Yue Li
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Lung Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ziyang Wang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Jie Fu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Cancer Hospital Airport Hospital, Tianjin, China
| | - Lisha Qi
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
- Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
| | - Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China
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3
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Huang D, Lin C, Jiang Y, Xin E, Xu F, Gan Y, Xu R, Wang F, Zhang H, Lou K, Shi L, Hu H. Radiomics model based on intratumoral and peritumoral features for predicting major pathological response in non-small cell lung cancer receiving neoadjuvant immunochemotherapy. Front Oncol 2024; 14:1348678. [PMID: 38585004 PMCID: PMC10996281 DOI: 10.3389/fonc.2024.1348678] [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: 12/03/2023] [Accepted: 03/06/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To establish a radiomics model based on intratumoral and peritumoral features extracted from pre-treatment CT to predict the major pathological response (MPR) in patients with non-small cell lung cancer (NSCLC) receiving neoadjuvant immunochemotherapy. Methods A total of 148 NSCLC patients who underwent neoadjuvant immunochemotherapy from two centers (SRRSH and ZCH) were retrospectively included. The SRRSH dataset (n=105) was used as the training and internal validation cohort. Radiomics features of intratumoral (T) and peritumoral regions (P1 = 0-5mm, P2 = 5-10mm, and P3 = 10-15mm) were extracted from pre-treatment CT. Intra- and inter- class correlation coefficients and least absolute shrinkage and selection operator were used to feature selection. Four single ROI models mentioned above and a combined radiomics (CR: T+P1+P2+P3) model were established by using machine learning algorithms. Clinical factors were selected to construct the combined radiomics-clinical (CRC) model, which was validated in the external center ZCH (n=43). The performance of the models was assessed by DeLong test, calibration curve and decision curve analysis. Results Histopathological type was the only independent clinical risk factor. The model CR with eight selected radiomics features demonstrated a good predictive performance in the internal validation (AUC=0.810) and significantly improved than the model T (AUC=0.810 vs 0.619, p<0.05). The model CRC yielded the best predictive capability (AUC=0.814) and obtained satisfactory performance in the independent external test set (AUC=0.768, 95% CI: 0.62-0.91). Conclusion We established a CRC model that incorporates intratumoral and peritumoral features and histopathological type, providing an effective approach for selecting NSCLC patients suitable for neoadjuvant immunochemotherapy.
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Affiliation(s)
- Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chen Lin
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Yangyang Jiang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Enhui Xin
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yi Gan
- Department of Pathology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Rui Xu
- DUT-RU International School of Information Science and Engineering, Dalian University of Technology, Dalian, Liaoning, China
- DUT-RU Co-Research Center of Advanced Information Computing Technology (ICT) for Active Life, Dalian University of Technology, Dalian, Liaoning, China
| | - Fang Wang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haiping Zhang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Lei Shi
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- Medical Imaging International Scientific and Technological Cooperation Base of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 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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Wu S, Zhan W, Liu L, Xie D, Yao L, Yao H, Liao G, Huang L, Zhou Y, You P, Huang Z, Li Q, Xu B, Wang S, Wang G, Zhang DK, Qiao G, Chan LWC, Lanuti M, Zhou H. Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB-IV NSCLC (LCDigital-IO Study): a multicenter retrospective study. J Immunother Cancer 2023; 11:e007369. [PMID: 37865396 PMCID: PMC10603353 DOI: 10.1136/jitc-2023-007369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/18/2023] [Indexed: 10/23/2023] Open
Abstract
BACKGROUND The predictive efficacy of current biomarker of immune checkpoint inhibitors (ICIs) is not sufficient. This study investigated the causality between radiomic biomarkers and immunotherapy response status in patients with stage IB-IV non-small cell lung cancer (NSCLC), including its biological context for ICIs treatment response prediction. METHODS CT images from 319 patients with pretreatment NSCLC receiving immunotherapy between January 2015 and November 2021 were retrospectively collected and composed a discovery (n=214), independent validation (n=54), and external test cohort (n=51). A set of 851 features was extracted from tumorous and peritumoral volumes of interest (VOIs). The reference standard is the durable clinical benefit (DCB, sustained disease control for more than 6 months assessed via radiological evaluation). The predictive value of combined radiomic signature (CRS) for pathological response was subsequently assessed in another cohort of 98 patients with resectable NSCLC receiving ICIs preoperatively. The association between radiomic features and tumor immune landscape on the online data set (n=60) was also examined. A model combining clinical predictor and radiomic signatures was constructed to improve performance further. RESULTS CRS discriminated DCB and non-DCB patients well in the training and validation cohorts with an area under the curve (AUC) of 0.82, 95% CI: 0.75 to 0.88, and 0.75, 95% CI: 0.64 to 0.87, respectively. In this study, the predictive value of CRS was better than programmed cell death ligand-1 (PD-L1) expression (AUC of PD-L1 subset: 0.59, 95% CI: 0.50 to 0.69) or clinical model (AUC: 0.66, 95% CI: 0.51 to 0.81). After combining the clinical signature with CRS, the predictive performance improved further with an AUC of 0.837, 0.790 and 0.781 in training, validation and D2 cohorts, respectively. When predicting pathological response, CRS divided patients into a major pathological response (MPR) and non-MPR group (AUC: 0.76, 95% CI: 0.67 to 0.81). Moreover, CRS showed a promising stratification ability on overall survival (HR: 0.49, 95% CI: 0.27 to 0.89; p=0.020) and progression-free survival (HR: 0.43, 95% CI: 0.26 to 0.74; p=0.002). CONCLUSION By analyzing both tumorous and peritumoral regions of CT images in a radiomic strategy, we developed a non-invasive biomarker for distinguishing responders of ICIs therapy and stratifying their survival outcome efficiently, which may support the clinical decisions on the use of ICIs in advanced as well as patients with resectable NSCLC.
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Affiliation(s)
- Shaowei Wu
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Weijie Zhan
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Lan Liu
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, People's Republic of China
| | - Daipeng Xie
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Lintong Yao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
- Shantou University Medical College, Shantou, China
| | - Henian Yao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
- Guangdong Medical University, Zhanjiang, China
| | - Guoqing Liao
- Department of Thoracic Surgery, Cancer Hospital of Shantou University Medical College, Shantou, China
| | - Luyu Huang
- Department of Surgery, Competence Center of Thoracic Surgery, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Yubo Zhou
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Peimeng You
- Department of Radiology, Jiangxi Cancer Hospital, Nanchang, People's Republic of China
| | - Zekai Huang
- Guangdong Medical University, Zhanjiang, China
| | - Qiaxuan Li
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
- Shantou University Medical College, Shantou, China
| | - Bin Xu
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Siyun Wang
- Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Guangyi Wang
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Dong-Kun Zhang
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Guibin Qiao
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Lawrence Wing-Chi Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Michael Lanuti
- Department of Thoracic Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Haiyu Zhou
- Department of Thoracic Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
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Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
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7
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Frankowska K, Zarobkiewicz M, Dąbrowska I, Bojarska-Junak A. Tumor infiltrating lymphocytes and radiological picture of the tumor. Med Oncol 2023; 40:176. [PMID: 37178270 PMCID: PMC10182948 DOI: 10.1007/s12032-023-02036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023]
Abstract
Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.
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Affiliation(s)
- Karolina Frankowska
- Department of Clinical Immunology, Medical University of Lublin, Lublin, Poland
| | - Michał Zarobkiewicz
- Department of Clinical Immunology, Medical University of Lublin, Lublin, Poland.
| | - Izabela Dąbrowska
- Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, Lublin, Poland
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Milanese G, Mazzaschi G, Ledda RE, Balbi M, Lamorte S, Caminiti C, Colombi D, Tiseo M, Silva M, Sverzellati N. The radiological appearances of lung cancer treated with immunotherapy. Br J Radiol 2023; 96:20210270. [PMID: 36367539 PMCID: PMC10078868 DOI: 10.1259/bjr.20210270] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 09/29/2022] [Accepted: 10/06/2022] [Indexed: 11/13/2022] Open
Abstract
Therapy and prognosis of several solid and hematologic malignancies, including non-small cell lung cancer (NSCLC), have been favourably impacted by the introduction of immune checkpoint inhibitors (ICIs). Their mechanism of action relies on the principle that some cancers can evade immune surveillance by expressing surface inhibitor molecules, known as "immune checkpoints". ICIs aim to conceal tumoural checkpoints on the cell surface and reinvigorate the ability of the host immune system to recognize tumour cells, triggering an antitumoural immune response.In this review, we will focus on the imaging patterns of different responses occurring in patients treated by ICIs. We will also discuss imaging findings of immune-related adverse events (irAEs), along with current and future perspectives of metabolic imaging. Finally, we will explore the role of radiomics in the setting of ICI-treated patients.
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Affiliation(s)
- Gianluca Milanese
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Giulia Mazzaschi
- Department of Medicine and Surgery, Unit of Medical Oncology, University of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Maurizio Balbi
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Sveva Lamorte
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Caterina Caminiti
- Unit of Research and Innovation, University Hospital of Parma, Parma, Italy
| | - Davide Colombi
- Department of Radiological Functions, Radiology Unit, Guglielmo da Saliceto Hospital, Piacenza, Italy
| | - Marcello Tiseo
- Department of Medicine and Surgery, Unit of Medical Oncology, University of Parma, Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery, Unit of Radiological Sciences, University of Parma, Parma, Italy
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Xue C, Zhou Q, Xi H, Zhou J. Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment. Diagn Interv Imaging 2023; 104:113-122. [PMID: 36283933 DOI: 10.1016/j.diii.2022.10.008] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 12/24/2022]
Abstract
With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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10
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Chen L, Chen L, Ni H, Shen L, Wei J, Xia Y, Yang J, Yang M, Zhao Z. Prediction of CD3 T cells and CD8 T cells expression levels in non-small cell lung cancer based on radiomic features of CT images. Front Oncol 2023; 13:1104316. [PMID: 36860311 PMCID: PMC9968855 DOI: 10.3389/fonc.2023.1104316] [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: 11/21/2022] [Accepted: 01/27/2023] [Indexed: 02/16/2023] Open
Abstract
Background In this work, radiomics characteristics based on CT scans were used to build a model for preoperative evaluation of CD3 and CD8 T cells expression levels in patients with non-small cell lung cancer (NSCLC). Methods Two radiomics models for evaluating tumor-infiltrating CD3 and CD8 T cells were created and validated using computed tomography (CT) images and pathology information from NSCLC patients. From January 2020 to December 2021, 105 NSCLC patients with surgical and histological confirmation underwent this retrospective analysis. Immunohistochemistry (IHC) was used to determine CD3 and CD8 T cells expression, and all patients were classified into groups with high and low CD3 T cells expression and high and low CD8 T cells expression. The CT area of interest had 1316 radiomic characteristics that were retrieved. The minimal absolute shrinkage and selection operator (Lasso) technique was used to choose components from the IHC data, and two radiomics models based on CD3 and CD8 T cells abundance were created. Receiver operating characteristic (ROC), calibration curve, and decision curve analyses were used to examine the models' ability to discriminate and their clinical relevance (DCA). Results A CD3 T cells radiomics model with 10 radiological characteristics and a CD8 T cells radiomics model with 6 radiological features that we created both demonstrated strong discrimination in the training and validation cohorts. The CD3 radiomics model has an area under the curve (AUC) of 0.943 (95% CI 0.886-1), sensitivities, specificities, and accuracy of 96%, 89%, and 93%, respectively, in the validation cohort. The AUC of the CD8 radiomics model was 0.837 (95% CI 0.745-0.930) in the validation cohort, with sensitivity, specificity, and accuracy values of 70%, 93%, and 80%, respectively. Patients with high levels of CD3 and CD8 expression had better radiographic results than patients with low levels of expression in both cohorts (p<0.05). Both radiomic models were therapeutically useful, as demonstrated by DCA. Conclusions When making judgments on therapeutic immunotherapy, CT-based radiomic models can be utilized as a non-invasive way to evaluate the expression of tumor-infiltrating CD3 and CD8 T cells in NSCLC patients.
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Affiliation(s)
- Lujiao Chen
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
| | - Lulin Chen
- Department of Ultrasound, Affiliated hospital of Shaoxing University, Shaoxing, Zhejiang, China
| | - Hongxia Ni
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
| | - Liyijing Shen
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
| | - Jianguo Wei
- Department of Pathology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
| | - Yang Xia
- Department of Radiology, Shaoxing Maternal and Child Health Hospital, Shaoxing, Zhejiang, China
| | - Jianfeng Yang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China
| | - Minxia Yang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China,*Correspondence: Minxia Yang, ; Zhenhua Zhao,
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, China,*Correspondence: Minxia Yang, ; Zhenhua Zhao,
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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: 1] [Impact Index Per Article: 1.0] [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.
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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,
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Investigation of radiomics based intra-patient inter-tumor heterogeneity and the impact of tumor subsampling strategies. Sci Rep 2022; 12:17244. [PMID: 36241749 PMCID: PMC9568579 DOI: 10.1038/s41598-022-20931-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 09/21/2022] [Indexed: 01/06/2023] Open
Abstract
While radiomics analysis has been applied for localized cancer disease, its application to the metastatic setting involves a non-exhaustive lesion subsampling strategy which may sidestep the intrapatient tumoral heterogeneity, hindering the reproducibility and the therapeutic response performance. Our aim was to evaluate if radiomics features can capture intertumoral intrapatient heterogeneity, and the impact of tumor subsampling on the computed heterogeneity. To this end, We delineated and extracted radiomics features of all visible tumors from single acquisition pre-treatment computed tomography of patients with metastatic lung cancer (cohort L) and confirmed our results on a larger cohort of patients with metastatic melanoma (cohort M). To quantify the captured heterogeneity, the absolute coefficient of variation (CV) of each radiomics index was calculated at the patient-level and a sensitivity analysis was performed using only a subset of all extracted features robust to the segmentation step. The extent of information loss by six commonly used tumor sampling strategies was then assessed. A total of 602 lesions were segmented from 43 patients (median age 57, 4.9% female). All robust radiomics indexes exhibited at least 20% of variation with significant heterogeneity both in heavily and oligo metastasized patients, and also at the organ level. None of the segmentation subsampling strategies were able to recover the true tumoral heterogeneity obtained by exhaustive tumor sampling. Image-based inter-tumor intra-patient heterogeneity can be successfully grasped by radiomics analyses. Failing to take into account this kind of heterogeneity will lead to inconsistent predictive algorithms. Guidelines to standardize the tumor sampling step and/or AI-driven tools to alleviate the segmentation effort are required.
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Yin X, Liao H, Yun H, Lin N, Li S, Xiang Y, Ma X. Artificial intelligence-based prediction of clinical outcome in immunotherapy and targeted therapy of lung cancer. Semin Cancer Biol 2022; 86:146-159. [PMID: 35963564 DOI: 10.1016/j.semcancer.2022.08.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/06/2022] [Accepted: 08/08/2022] [Indexed: 11/26/2022]
Abstract
Lung cancer accounts for the main proportion of malignancy-related deaths and most patients are diagnosed at an advanced stage. Immunotherapy and targeted therapy have great advances in application in clinics to treat lung cancer patients, yet the efficacy is unstable. The response rate of these therapies varies among patients. Some biomarkers have been proposed to predict the outcomes of immunotherapy and targeted therapy, including programmed cell death-ligand 1 (PD-L1) expression and oncogene mutations. Nevertheless, the detection tests are invasive, time-consuming, and have high demands on tumor tissue. The predictive performance of conventional biomarkers is also unsatisfactory. Therefore, novel biomarkers are needed to effectively predict the outcomes of immunotherapy and targeted therapy. The application of artificial intelligence (AI) can be a possible solution, as it has several advantages. AI can help identify features that are unable to be used by humans and perform repetitive tasks. By combining AI methods with radiomics, pathology, genomics, transcriptomics, proteomics, and clinical data, the integrated model has shown predictive value in immunotherapy and targeted therapy, which significantly improves the precision treatment of lung cancer patients. Herein, we reviewed the application of AI in predicting the outcomes of immunotherapy and targeted therapy in lung cancer patients, and discussed the challenges and future directions in this field.
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Affiliation(s)
- Xiaomeng Yin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hu Liao
- Department of Thoracic Surgery, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Hong Yun
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Nan Lin
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Shen Li
- West China School of Medicine, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Yu Xiang
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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Radiomic Signatures Associated with CD8+ Tumour-Infiltrating Lymphocytes: A Systematic Review and Quality Assessment Study. Cancers (Basel) 2022; 14:cancers14153656. [PMID: 35954318 PMCID: PMC9367613 DOI: 10.3390/cancers14153656] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 07/23/2022] [Accepted: 07/25/2022] [Indexed: 02/04/2023] Open
Abstract
The tumour immune microenvironment influences the efficacy of immune checkpoint inhibitors. Within this microenvironment are CD8-expressing tumour-infiltrating lymphocytes (CD8+ TILs), which are an important mediator and marker of anti-tumour response. In practice, the assessment of CD8+ TILs via tissue sampling involves logistical challenges. Radiomics, the high-throughput extraction of features from medical images, may offer a novel and non-invasive alternative. We performed a systematic review of the available literature reporting radiomic signatures associated with CD8+ TILs. We also aimed to evaluate the methodological quality of the identified studies using the Radiomics Quality Score (RQS) tool, and the risk of bias and applicability with the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. Articles were searched from inception until 31 December 2021, in three electronic databases, and screened against eligibility criteria. Twenty-seven articles were included. A wide variety of cancers have been studied. The reported radiomic signatures were heterogeneous, with very limited reproducibility between studies of the same cancer group. The overall quality of studies was found to be less than desirable (mean RQS = 33.3%), indicating a need for technical maturation. Some potential avenues for further investigation are also discussed.
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Machine Learning for Computed Tomography Radiomics: Prediction of Tumor-Infiltrating Lymphocytes in Patients With Pancreatic Ductal Adenocarcinoma. Pancreas 2022; 51:549-558. [PMID: 35877153 DOI: 10.1097/mpa.0000000000002069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
OBJECTIVES The aims of the study were to develop and validate a machine learning classifier for preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS In this retrospective study of 183 PDAC patients who underwent multidetector computed tomography and surgical resection, CD4 + , CD8 + , and CD20 + expression was evaluated using immunohistochemistry, and TIL scores were calculated using the Cox regression model. The patients were divided into TIL-low and TIL-high groups. An extreme gradient boosting (XGBoost) classifier was developed using a training set consisting of 136 consecutive patients, and the model was validated in 47 consecutive patients. The discriminative ability, calibration, and clinical utility of the XGBoost classifier were evaluated. RESULTS The prediction model showed good discrimination in the training (area under the curve, 0.93; 95% confidence interval, 0.89-0.97) and validation (area under the curve, 0.79; 95% confidence interval, 0.65-0.92) sets with good calibration. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 0.93, 0.85, 0.90, 0.89, and 0.91, respectively, while those for the validation set were 0.63, 0.91, 0.77, 0.88, and 0.70, respectively. CONCLUSIONS The XGBoost-based model could predict PDAC TILs and may facilitate clinical decision making for immune therapy.
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Tong H, Sun J, Fang J, Zhang M, Liu H, Xia R, Zhou W, Liu K, Chen X. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study. Front Immunol 2022; 13:859323. [PMID: 35572597 PMCID: PMC9105942 DOI: 10.3389/fimmu.2022.859323] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/30/2022] [Indexed: 12/05/2022] Open
Abstract
Background The tumor immune microenvironment (TIME) phenotypes have been reported to mainly impact the efficacy of immunotherapy. Given the increasing use of immunotherapy in cancers, knowing an individual's TIME phenotypes could be helpful in screening patients who are more likely to respond to immunotherapy. Our study intended to establish, validate, and apply a machine learning model to predict TIME profiles in non-small cell lung cancer (NSCLC) by using 18F-FDG PET/CT radiomics and clinical characteristics. Methods The RNA-seq data of 1145 NSCLC patients from The Cancer Genome Atlas (TCGA) cohort were analyzed. Then, 221 NSCLC patients from Daping Hospital (DPH) cohort received18F-FDG PET/CT scans before treatment and CD8 expression of the tumor samples were tested. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images and develop a radiomics signature. The models were established by radiomics, clinical features, and radiomics-clinical combination, respectively, the performance of which was calculated by receiver operating curves (ROCs) and compared by DeLong test. Moreover, based on radiomics score (Rad-score) and clinical features, a nomogram was established. Finally, we applied the combined model to evaluate TIME phenotypes of NSCLC patients in The Cancer Imaging Archive (TCIA) cohort (n = 39). Results TCGA data showed CD8 expression could represent the TIME profiles in NSCLC. In DPH cohort, PET/CT radiomics model outperformed CT model (AUC: 0.907 vs. 0.861, P = 0.0314) to predict CD8 expression. Further, PET/CT radiomics-clinical combined model (AUC = 0.932) outperformed PET/CT radiomics model (AUC = 0.907, P = 0.0326) or clinical model (AUC = 0.868, P = 0.0036) to predict CD8 expression. In the TCIA cohort, the predicted CD8-high group had significantly higher immune scores and more activated immune pathways than the predicted CD8-low group (P = 0.0421). Conclusion Our study indicates that 18F-FDG PET/CT radiomics-clinical combined model could be a clinically practical method to non-invasively detect the tumor immune status in NSCLCs.
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Affiliation(s)
- Haipeng Tong
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fang
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
| | - Mi Zhang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Huan Liu
- Advanced Application Team, GE Healthcare, Shanghai, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Kaijun Liu
- Department of Gastroenterology, Daping Hospital, Army Medical University, Chongqing, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China
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Bian Y, Liu C, Li Q, Meng Y, Liu F, Zhang H, Fang X, Li J, Yu J, Feng X, Ma C, Zhao Z, Wang L, Xu J, Shao C, Lu J. Preoperative Radiomics Approach to Evaluating Tumor-Infiltrating CD8 + T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging. J Magn Reson Imaging 2022; 55:803-814. [PMID: 34355834 DOI: 10.1002/jmri.27871] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/20/2021] [Accepted: 07/20/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND CD8+ T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8+ T-cell expression can better identify the population benefitting from immunotherapy. PURPOSE To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8+ T-cell expression in patients with PDAC. STUDY TYPE Retrospective cohort study. POPULATION Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts. FIELD STRENGTH/SEQUENCE/3 T: Breath-hold single-shot fast-spin echo T2-weighted sequence and noncontrast T1-weighted fat-suppressed sequences. ASSESSMENT CD8+ T-cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1- and T2-weighted images and reduced using the Wilcoxon rank-sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility. STATISTICAL TESTS Kaplan-Meier estimates, Student's t-test, the Kruskal-Wallis H test, and the chi-square test, receiver operating characteristic curve, and decision curve analysis. RESULTS A log-rank test showed that the survival duration in the CD8-high group (25.51 months) was significantly longer than that in the CD8-low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77-0.92) and 0.69 (95% CI, 0.53-0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77-0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61-0.87). DATA CONCLUSIONS This study developed a noncontrast MRI-based radiomics model that can preoperatively determine CD8+ T-cell expression in patients with PDAC and potentially immunotherapy planning. EVIDENCE LEVEL 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Cong Liu
- Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Zengrui Zhao
- Institute for AI in Medicine, School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jun Xu
- Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
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Min J, Dong F, Wu P, Xu X, Wu Y, Tan Y, Yang F, Chai Y. A Radiomic Approach to Access Tumor Immune Status by CD8 +TRMs on Surgically Resected Non-Small-Cell Lung Cancer. Onco Targets Ther 2021; 14:4921-4931. [PMID: 34611410 PMCID: PMC8486276 DOI: 10.2147/ott.s316994] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2021] [Accepted: 09/09/2021] [Indexed: 12/14/2022] Open
Abstract
Purpose Immunotherapy has made breakthroughs in the treatment of non-small-cell lung cancer (NSCLC); however, only a subset of patients achieved long-term survival, so it is of great importance to find a biomarker of lung cancer thus guide immunotherapy. Studies have shown that the infiltration level of tissue resident memory CD8+ T cells (CD8+ TRMs) is positively correlated with lung cancer prognosis and can be an ideal biomarker for assessing the tumor local immune status. We screened the radiomic features associated with CD8+ TRMs as targets in NSCLC surgical specimens by radiomic approaches, and established a radiomic predictive model to assess the local immune status, which may provide a scientific reference for lung cancer treatment strategies. Patients and Methods We retrospectively analyzed the NSCLC surgical specimens immune cell database and extracted CD8+ TRMs cell data, preoperative CT scan data were achieved. A total of 97 patients containing complete preoperative data were included, radiomic features were extracted from the preoperative CT image data. All the patients were divided into two groups, namely high-CD8+ TRMs infiltrated group and low-CD8+ TRMs infiltrated group, based on the proportion of CD8+ TRMs cells subset in the immune cell population. The most valuable radiomic features and semantic features were extracted and selected, and a neural network model was established to predict the level of CD8+ TRMs cell infiltration level to assess the tumor local immune status. Results The NSCLC tumor immune status predictive model was built to discriminate high- from low-CD8+ TRMs with an area under the curve (AUC) of 0.788 (95% CI) in the training set and 0.753 (95% CI) in the validation set. Conclusion The radiomic models using CT image data showed a good predictive performance for accessing NSCLC immune status thus has great potential for personalized therapeutic decision making.
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Affiliation(s)
- Jie Min
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
| | - Fei Dong
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
| | - Pin Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
| | - Xiaopei Xu
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
| | - Yimin Wu
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
| | - Yanbin Tan
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
| | - Fan Yang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
| | - Ying Chai
- Department of Thoracic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310009, Zhejiang Province, People's Republic of China
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Bian Y, Liu YF, Jiang H, Meng Y, Liu F, Cao K, Zhang H, Fang X, Li J, Yu J, Feng X, Li Q, Wang L, Lu J, Shao C. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2021; 46:4800-4816. [PMID: 34189612 DOI: 10.1007/s00261-021-03159-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS In this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use. RESULTS The XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79-0.93) and validation sets (AUC 0.79; 95% CI 0.64-0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier. CONCLUSION The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Yan Fang Liu
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Kai Cao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China.
- Department of Radiology, Changhai Hospital, 168 Changhai Road, Shanghai, 200433, China.
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Rundo L, Ledda RE, di Noia C, Sala E, Mauri G, Milanese G, Sverzellati N, Apolone G, Gilardi MC, Messa MC, Castiglioni I, Pastorino U. A Low-Dose CT-Based Radiomic Model to Improve Characterization and Screening Recall Intervals of Indeterminate Prevalent Pulmonary Nodules. Diagnostics (Basel) 2021; 11:1610. [PMID: 34573951 PMCID: PMC8471292 DOI: 10.3390/diagnostics11091610] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/25/2021] [Accepted: 08/30/2021] [Indexed: 12/25/2022] Open
Abstract
Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.
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Affiliation(s)
- Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Roberta Eufrasia Ledda
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Christian di Noia
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK;
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK
| | - Giancarlo Mauri
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, 20126 Milan, Italy;
| | - Gianluca Milanese
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Nicola Sverzellati
- Unit of Radiological Sciences, Department of Medicine and Surgery (DiMeC), University of Parma, 43126 Parma, Italy; (R.E.L.); (G.M.); (N.S.)
| | - Giovanni Apolone
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
| | - Maria Carla Gilardi
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
| | - Maria Cristina Messa
- School of Medicine and Surgery, University of Milano-Bicocca, 20126 Milan, Italy; (M.C.G.); (M.C.M.)
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
- Fondazione Tecnomed, University of Milano-Bicocca, 20900 Monza, Italy
| | - Isabella Castiglioni
- Department of Physics “Giuseppe Occhialini”, University of Milano-Bicocca, 20126 Milan, Italy;
- Institute of Biomedical Imaging and Physiology, Italian National Research Council (IBFM-CNR), Segrate, 20090 Milan, Italy
| | - Ugo Pastorino
- Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, 20133 Milan, Italy; (G.A.); (U.P.)
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Li J, Shi Z, Liu F, Fang X, Cao K, Meng Y, Zhang H, Yu J, Feng X, Li Q, Liu Y, Wang L, Jiang H, Lu J, Shao C, Bian Y. XGBoost Classifier Based on Computed Tomography Radiomics for Prediction of Tumor-Infiltrating CD8 + T-Cells in Patients With Pancreatic Ductal Adenocarcinoma. Front Oncol 2021; 11:671333. [PMID: 34094971 PMCID: PMC8170309 DOI: 10.3389/fonc.2021.671333] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 04/26/2021] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVES This study constructed and validated a machine learning model to predict CD8+ tumor-infiltrating lymphocyte expression levels in patients with pancreatic ductal adenocarcinoma (PDAC) using computed tomography (CT) radiomic features. MATERIALS AND METHODS In this retrospective study, 184 PDAC patients were randomly assigned to a training dataset (n =137) and validation dataset (n =47). All patients were divided into CD8+ T-high and -low groups using X-tile plots. A total of 1409 radiomics features were extracted from the segmentation of regions of interest, based on preoperative CT images of each patient. The LASSO algorithm was applied to reduce the dimensionality of the data and select features. The extreme gradient boosting classifier (XGBoost) was developed using a training set consisting of 137 consecutive patients admitted between January 2017 and December 2017. The model was validated in 47 consecutive patients admitted between January 2018 and April 2018. The performance of the XGBoost classifier was determined by its discriminative ability, calibration, and clinical usefulness. RESULTS The cut-off value of the CD8+ T-cell level was 18.69%, as determined by the X-tile program. A Kaplan-Meier analysis indicated a correlation between higher CD8+ T-cell levels and better overall survival (p = 0.001). The XGBoost classifier showed good discrimination in the training set (area under curve [AUC], 0.75; 95% confidence interval [CI]: 0.67-0.83) and validation set (AUC, 0.67; 95% CI: 0.51-0.83). Moreover, it showed a good calibration. The sensitivity, specificity, accuracy, positive and negative predictive values were 80.65%, 60.00%, 0.69, 0.63, and 0.79, respectively, for the training set, and 80.95%, 57.69%, 0.68, 0.61, and 0.79, respectively, for the validation set. CONCLUSIONS We developed a CT-based XGBoost classifier to extrapolate the infiltration levels of CD8+ T-cells in patients with PDAC. This method could be useful in identifying potential patients who can benefit from immunotherapies.
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Affiliation(s)
- Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Zhang Shi
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Kai Cao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yanfang Liu
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
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Wang X, Wu K, Li X, Jin J, Yu Y, Sun H. Additional Value of PET/CT-Based Radiomics to Metabolic Parameters in Diagnosing Lynch Syndrome and Predicting PD1 Expression in Endometrial Carcinoma. Front Oncol 2021; 11:595430. [PMID: 34055595 PMCID: PMC8152935 DOI: 10.3389/fonc.2021.595430] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 04/12/2021] [Indexed: 01/13/2023] Open
Abstract
Purpose We aim to compare the radiomic features and parameters on 2-deoxy-2-[fluorine-18] fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. We also hope to explore the biologic significance of selected radiomic features. Materials and Methods We conducted a retrospective cohort study, first using the 18F-FDG PET/CT images and clinical data from 100 patients with endometrial cancer to construct a training group (70 patients) and a test group (30 patients). The metabolic parameters and radiomic features of each tumor were compared between patients with and without Lynch syndrome. An independent cohort of 23 patients with solid tumors was used to evaluate the value of selected radiomic features in predicting the expression of the programmed cell death 1 (PD1), using 18F-FDG PET/CT images and RNA-seq genomic data. Results There was no statistically significant difference in the standardized uptake values on PET between patients with endometrial cancer with Lynch syndrome and those with endometrial cancer without Lynch syndrome. However, there were significant differences between the 2 groups in metabolic tumor volume and total lesion glycolysis (p < 0.005). There was a difference in the radiomic feature of gray level co-occurrence matrix entropy (GLCMEntropy; p < 0.001) between the groups: the area under the curve was 0.94 in the training group (sensitivity, 82.86%; specificity, 97.14%) and 0.893 in the test group (sensitivity, 80%; specificity, 93.33%). In the independent cohort of 23 patients, differences in GLCMEntropy were related to the expression of PD1 (rs =0.577; p < 0.001). Conclusions In patients with endometrial cancer, higher metabolic tumor volumes, total lesion glycolysis values, and GLCMEntropy values on 18F-FDG PET/CT could suggest a higher risk for Lynch syndrome. The radiomic feature of GLCMEntropy for tumors is a potential predictor of PD1 expression.
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Affiliation(s)
- Xinghao Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Ke Wu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Xiaoran Li
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Junjie Jin
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Yang Yu
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
| | - Hongzan Sun
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.,Liaoning Provincial Key Laboratory of Medical Imaging Department of Radiology, Shenyang, China
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Trentini F, Mazzaschi G, Milanese G, Pavone C, Madeddu D, Gnetti L, Frati C, Lorusso B, Lagrasta CAM, Minari R, Ampollini L, Ledda RE, Silva M, Sverzellati N, Quaini F, Roti G, Tiseo M. Validation of a radiomic approach to decipher NSCLC immune microenvironment in surgically resected patients. TUMORI JOURNAL 2021; 108:86-92. [PMID: 33730957 DOI: 10.1177/03008916211000808] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
BACKGROUND Radiomics has emerged as a noninvasive tool endowed with the potential to intercept tumor characteristics thereby predicting clinical outcome. In a recent study on resected non-small cell lung cancer (NSCLC), we identified highly prognostic computed tomography (CT)-derived radiomic features (RFs), which in turn were able to discriminate hot from cold tumor immune microenvironment (TIME). We aimed at validating a radiomic model capable of dissecting specific TIME profiles bearing prognostic power in resected NSCLC. METHODS The validation cohort included 31 radically resected NSCLCs clinicopathologically matched with the training set (n = 69). TIME was classified in hot and cold according to a multiparametric immunohistochemical analysis involving PD-L1 score and incidence of immune effector phenotypes among tumor infiltrating lymphocytes (TILs). High-throughput radiomic features (n = 841) extracted from CT images were correlated to TIME parameters to ultimately define prognostic classes. RESULTS We confirmed PD-1 to CD8 ratio as best predictor of clinical outcome among TIME characteristics. Significantly prolonged overall survival (OS) was observed in patients carrying hot (median OS not reached) vs cold (median OS 22 months; hazard ratio 0.28, 95% confidence interval 0.09-0.82; p = 0.015) immune background, thus validating the prognostic impact of these two TIME categories in resected NSCLC. Importantly, in the validation setting, three out of eight previously identified RFs sharply distinguishing hot from cold TIME were endorsed. Among signature-related RFs, Wavelet-HHH_gldm_HighGrayLevelEmphasis highly performed as descriptor of hot immune contexture (area under the receiver operating characteristic curve 0.94, 95% confidence interval 0.81-1.00; p = 0.01). CONCLUSION Radiomics may decipher specific TIME profiles providing a noninvasive prognostic approach in resected NSCLC and an exploitable predictive strategy in advanced cases.
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Affiliation(s)
- Francesca Trentini
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Giulia Mazzaschi
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Claudio Pavone
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Denise Madeddu
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Letizia Gnetti
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Caterina Frati
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Bruno Lorusso
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Costanza Anna Maria Lagrasta
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Parma, Italy
| | - Roberta Minari
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
| | - Luca Ampollini
- Department of Medicine and Surgery, University of Parma, Thoracic Surgery, University Hospital of Parma, Parma, Italy
| | - Roberta Eufrasia Ledda
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Mario Silva
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Nicola Sverzellati
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Parma, Italy
| | - Federico Quaini
- Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Parma, Italy
| | - Giovanni Roti
- Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Parma, Italy
| | - Marcello Tiseo
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Parma, Italy
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24
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Ma Q, Chen Y, Xiao F, Hao Y, Song Z, Zhang J, Okuda K, Um SW, Silva M, Shimada Y, Si C, Liang C. A signature of estimate-stromal-immune score-based genes associated with the prognosis of lung adenocarcinoma. Transl Lung Cancer Res 2021; 10:1484-1500. [PMID: 33889524 PMCID: PMC8044489 DOI: 10.21037/tlcr-21-223] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background Immune and stromal component evaluation is necessary to establish accurate prognostic markers for the prediction of clinical outcomes in lung adenocarcinoma (LUAD). We aimed to develop a gene signature based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE)-stromal-immune score in LUAD. Methods The transcriptomic profiles of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA), and the immune and stromal scores were derived using the ESTIMATE algorithm. The prognostic signature genes were selected from the differentially expressed genes (DEGs) using the robust partial likelihood-based cox proportional hazards regression method. The negative log-likelihood and the Akaike Information Criterion (AIC) were used to identify the optimal gene signature. The validation was carried out in 2 independent datasets from the Gene Expression Omnibus (GSE68571 and GSE72094). Results Patients with high ESTIMATE, stromal, and immune scores had better overall survivals (P=0.0035, P=0.066, and P=0.0077). The expression of thirty-seven genes was related to ESTIMATE-stromal-immune score. A risk stratification model was developed based on a gene signature containing CD74, JCHAIN, and PTGDS. The ESTIMATE-stromal-immune risk score was revealed to be a prognostic factor (P=0.009) after multivariate analysis. Four groups were classified based on this risk stratification model, yielding increasing survival outcomes (log-rank test, P=0.0051). This risk stratification model and other clinicopathological factors were combined to generate a nomogram. The calibration curves showed perfect agreement between the nomogram-predicted outcomes and those actually observed. Similar observations were made in 2 independent cohorts. Conclusions The gene signature based on the ESTIMATE-stromal-immune score could predict the prognosis of patients with LUAD.
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Affiliation(s)
- Qianli Ma
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Yang Chen
- Department of Biochemistry and Molecular Biology, The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Fei Xiao
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Yang Hao
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Zhiyi Song
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Jin Zhang
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
| | - Katsuhiro Okuda
- Department of Oncology, Immunology and Surgery, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
| | - Sang-Won Um
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Mario Silva
- Section of Scienze Radiologiche, Department of Medicine and Surgery (DiMeC), University of Parma, Parma, Italy
| | - Yoshihisa Shimada
- Department of Thoracic Surgery, Tokyo Medical University, Tokyo, Japan
| | - Chaozeng Si
- Department of Information Management, China-Japan Friendship Hospital, Beijing, China
| | - Chaoyang Liang
- Department of Thoracic Surgery, China-Japan Friendship Hospital, Beijing, China
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