A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study
Abstract
Background Because responses of patients with cancer to immunotherapy can vary in success, innovative predictors of response to treatment are urgently needed to improve treatment outcomes. We aimed to develop and independently validate a radiomics-based biomarker of tumour-infiltrating CD8 cells in patients included in phase 1 trials of anti-programmed cell death protein (PD)-1 or anti-programmed cell death ligand 1 (PD-L1) monotherapy. We also aimed to evaluate the association between the biomarker, and tumour immune phenotype and clinical outcomes of these patients. Methods In this retrospective multicohort study, we used four independent cohorts of patients with advanced solid tumours to develop and validate a radiomic signature predictive of immunotherapy response by combining contrast-enhanced CT images and RNA-seq genomic data from tumour biopsies to assess CD8 cell tumour infiltration. To develop the radiomic signature of CD8 cells, we used the CT images and RNA sequencing data of 135 patients with advanced solid malignant tumours who had been enrolled into the MOSCATO trial between May 1, 2012, and March 31, 2016, in France (training set). The genomic data, which are based on the CD8B gene, were used to estimate the abundance of CD8 cells in the samples and data were then aligned with the images to generate the radiomic signatures. The concordance of the radiomic signature (primary endpoint) was validated in a Cancer Genome Atlas [TGCA] database dataset including 119 patients who had available baseline preoperative imaging data and corresponding transcriptomic data on June 30, 2017. From 84 input variables used for the machine-learning method (78 radiomic features, five location variables, and one technical variable), a radiomics-based predictor of the CD8 cell expression signature was built by use of machine learning (elastic-net regularised regression method). Two other independent cohorts of patients with advanced solid tumours were used to evaluate this predictor. The immune phenotype internal cohort (n=100), were randomly selected from the Gustave Roussy Cancer Campus database of patient medical records based on previously described, extreme tumour-immune phenotypes: immune-inflamed (with dense CD8 cell infiltration) or immune-desert (with low CD8 cell infiltration), irrespective of treatment delivered; these data were used to analyse the correlation of the immune phenotype with this biomarker. Finally, the immunotherapy-treated dataset (n=137) of patients recruited from Dec 1, 2011, to Jan 31, 2014, at the Gustave Roussy Cancer Campus, who had been treated with anti-PD-1 and anti-PD-L1 monotherapy in phase 1 trials, was used to assess the predictive value of this biomarker in terms of clinical outcome. Findings We developed a radiomic signature for CD8 cells that included eight variables, which was validated with the gene expression signature of CD8 cells in the TCGA dataset (area under the curve [AUC]=0·67; 95% CI 0·57-0·77; p=0·0019). In the cohort with assumed immune phenotypes, the signature was also able to discriminate inflamed tumours from immune-desert tumours (0·76; 0·66-0·86; p<0·0001). In patients treated with anti-PD-1 and PD-L1, a high baseline radiomic score (relative to the median) was associated with a higher proportion of patients who achieved an objective response at 3 months (vs those with progressive disease or stable disease; p=0·049) and a higher proportion of patients who had an objective response (vs those with progressive disease or stable disease; p=0·025) or stable disease (vs those with progressive disease; p=0·013) at 6 months. A high baseline radiomic score was also associated with improved overall survival in univariate (median overall survival 24·3 months in the high radiomic score group, 95% CI 18·63-42·1; vs 11·5 months in the low radiomic score group, 7·98-15·6; hazard ratio 0·58, 95% CI 0·39-0·87; p=0·0081) and multivariate analyses (0·52, 0·35-0·79; p=0·0022). Interpretation The radiomic signature of CD8 cells was validated in three independent cohorts. This imaging predictor provided a promising way to predict the immune phenotype of tumours and to infer clinical outcomes for patients with cancer who had been treated with anti-PD-1 and PD-L1. Our imaging biomarker could be useful in estimating CD8 cell count and predicting clinical outcomes of patients treated with immunotherapy, when validated by further prospective randomised trials. Funding Fondation pour la Recherche Médicale, and SIRIC-SOCRATE 2.0, French Society of Radiation Oncology.
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Previous studies have primarily focused on tumor-infiltrating lymphocytes, which only provide a simplified and partial view of the TME.
“First, previous studies have primarily focused on tumor-infiltrating lymphocytes,12,26 which only provide a simplified and partial view of the TME.”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
Prior work shows that either knowledge-based radiomics or data-driven deep learning can extract useful information for assessing TME.
“This idea is motivated by prior work showing that either approach can extract useful information for assessing TME.12,13,33,35”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
The feasibility of using imaging analysis to reveal relations between radiological phenotypes and pathophysiology has been demonstrated in previous studies.
“The feasibility of this idea has been demonstrated in previous studies.12,23,24,25”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
Sun et al. developed a radiomic signature of CD8 T cells, which was correlated with clinical response and outcomes of patients treated with anti-PD1 immunotherapy.
“Sun et al. developed a radiomic signature of CD8 T cells, which was correlated with clinical response and outcomes of patients treated with anti-PD1 immunotherapy.12”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
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“…to analyse radiographic characteristics, have been recently proposed as noninvasive biomarkers for response to immunotherapy and may be useful to improve patient stratification and for predicting clinical outcomes of patients treated with immunotherapy (Sun et al., 2018; Trebeschi et al., 2019).”
BackgroundSelf-citationDercle, L., Lü, L., Schwartz, L. H., Qian, M., Tejpar, S., Eggleton, P., Zhao, B., & Piessevaux, H. H. (2020) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djaa017 · Cited by 146 · see citing works
“The innovation of our work compared with the existing literature was that the signature was dynamic, generalized to four datasets acquired in multicenter studies, used a limited subset of features to reduce overfitting, and considered image quality according to new quality control tools (17,19,20).”
Self-citationAI-driven quantification, staging and outcome prediction of COVID-19 pneumonia
Chassagnon, G., Vakalopoulou, M., Battistella, E., Christodoulidis, S., Hoang-Thi, T.-N., Dangeard, S., Deutsch, É., André, F., Guillo, E., Halm, N., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101860 · Cited by 173 · see citing works
“A variety of works report that radiomics, quantitative features extracted from the images, provide valuable information about the severity and response to treatment for different diseases including cancer Sun et al., 2018 .”
InfluentialSelf-citationCombination of Immunotherapy With Targeted Therapy: Theory and Practice in Metastatic Melanoma
Yu, C., Liu, X., Yang, J., Zhang, M., Jin, H., Ma, X., & Shi, H. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.00990 · Cited by 126 · see citing works
“It can also predict clinical outcomes for patients (176).”
BackgroundIntroduction to radiomics for a clinical audience
McCague, C., Ramlee, S., Reinius, M., SELBY, I. A., Hulse, D., Piyatissa, P., Bura, V., Crispin‐Ortuzar, M., Sala, E., & Woitek, R. (2023) · Clinical Radiology · doi:10.1016/j.crad.2022.08.149 · Cited by 137 · see citing works
Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data
Holzinger, A., Haibe‐Kains, B., & Jurišica, I. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04382-9 · Cited by 142 · see citing works
Seban, R., Nemer, J. S., Marabelle, A., Yeh, R., Deutsch, É., Ammari, S., Moya‐Plana, A., Mokrane, F., Gartrell, R. D., Finkel, G. G., et al. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04411-7 · Cited by 133 · see citing works
Self-citationSaad, M. B., Hong, L., Aminu, M., Vokes, N. I., Chen, P., Salehjahromi, M., Qin, K., Sujit, S. J., Lu, X., Young, E., et al. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00082-1 · Cited by 121 · see citing works
“Previous radiomic analyses to predict benefit of immune checkpoint inhibition consisted of smaller pilot studies that focused on establishing imaging surrogates for specific molecular biomarkers, including CD8 + T-cell infiltrate, 19 EGFR mutation, 17,23 or PD-L1 expression.”
Jiang, Y., Zhou, K., Sun, Z., Wang, H., Xie, J., Zhang, T., Sang, S., Islam, M. T., Wang, J.-Y., Chen, C., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101146 · Cited by 120 · see citing works
“developed a radiomic signature of CD8 T cells, which was correlated with clinical response and outcomes of patients treated with anti-PD1 immunotherapy.(12) In our recent work, we developed a deep learning-based imaging signature of tumor stroma, which predicted prognosis and the benefit of adjuvant chemotherapy in GC.”
BackgroundKang, W., Qiu, X., Luo, Y., Luo, J., Liu, Y., Xi, J., Li, X., & Yang, Z. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04437-4 · Cited by 125 · see citing works
“…of advanced solid tumors, and the radiomic signature of CD8 cells was validated in three additional independent cohorts, providing precise predictions for distinguishing the immunophenotypes of tumors and clinical outcomes of cancer patients undergoing anti-PD-1 and PD-L1 immunotherapy [43].”
BackgroundReinventing 2D Convolutions for 3D Images
Yang, J., Huang, X., He, Y., Xu, J., Yang, C., Xu, G., & Ni, B.-B. (2019) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2021.3049452 · Cited by 118 · see citing works
“For certain rare diseases or novel applications ( e.g. , predicting response for novel treatment [43]), the data scale is naturally very small.”
BackgroundThe emerging roles of artificial intelligence in cancer drug development and precision therapy
Liang, G., Fan, W., Luo, H., & Zhu, X. M. (2020) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.110255 · Cited by 149 · see citing works
Jiang, Y., Yuan, Q., Lv, W., Xi, S., Huang, W., Sun, Z., Chen, H., Zhao, L., Liu, W., Hu, Y., et al. (2018) · Theranostics · doi:10.7150/thno.28018 · Cited by 122 · see citing works
“In contrast, Radiomics refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features, which may reflect changes of human tissues at the cellular and genetic levels and provide different information from TNM staging [11, 12, 38-41].”
BackgroundOvercoming barriers to data sharing with medical image generation: a comprehensive evaluation
Schütte, A. D., Hetzel, J., Gatidis, S., Hepp, T., Dietz, B., Bauer, S., & Schwab, P. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00507-3 · Cited by 93 · see citing works
“(4) Brain scan with subdural haemorrhage.”
Artificial intelligence in lung cancer: current applications and perspectives
Chassagnon, G., de Margerie‐Mellon, C., Vakalopoulou, M., Marini, R., Hoang-Thi, T.-N., Revel, M., & Soyer, P. A. (2022) · Japanese Journal of Radiology · doi:10.1007/s11604-022-01359-x · Cited by 125 · see citing works
“used a radiomic model to predict tumor infiltration by CD8 cells in patients treated by anti PD-1 or anti PD-L1 immunotherapy [64].”
MethodSelf-citationStage III Non-Small-Cell Lung Cancer: An Overview of Treatment Options
Petrella, F., Rizzo, S., Attili, I., Passaro, A., Zilli, T., Martucci, F., Bonomo, L. D., Grande, F. D., Casiraghi, M., de Marinis, F., et al. (2023) · Current Oncology · doi:10.3390/curroncol30030239 · Cited by 112 · see citing works
Radiomics in liver diseases: Current progress and future opportunities
Wei, J., Jiang, H., Gu, D., Niu, M., Fu, F., Han, Y., Song, B., & Tian, J. (2020) · Liver International · doi:10.1111/liv.14555 · Cited by 120 · see citing works
de Miguel‐Pérez, D., Russo, A., Arrieta, O. G., Ak, M., Barrón, F., Gunasekaran, M., Mamindla, P., Lara‐Mejía, L., Peterson, C. B., Er, M. E., et al. (2022) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-022-02379-1 · Cited by 117 · see citing works
“On the other hand, radiomics is a rapidly growing field in imaging, which can convert a patient’s imaging scans into mineable quantitative data to better understand the tumor heterogeneity and microenvironment [18].”
BackgroundLigero, M., Garcia-Ruiz, A., Viaplana, C., Villacampa, G., Raciti, M. V., Landa, J., Matos, I., Martin-Liberal, J., Ochoa-de-Olza, M., Hierro, C., et al. (2021) · Radiology · doi:10.1148/radiol.2021200928 · Cited by 107 · see citing works
Biology-guided deep learning predicts prognosis and cancer immunotherapy response
Jiang, Y., Zhang, Z., Wang, W., Huang, W., Chen, C., Xi, S., Ahmad, M. U., Ren, Y., Sang, S., Xie, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40890-x · Cited by 115 · see citing works
“Several groups including ours have developed radiomic signatures of immune biomarkers that were subsequently correlated treatment response and outcomes 29 – 33 .”
Wang, Y., Lang, J., Zuo, J. Z., Dong, Y., Hu, Z., Xu, X., Zhang, Y., Wang, Q., Yang, L., Wong, S. T. C., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08887-0 · Cited by 108 · see citing works
“The findings of the present study confirmed a previous study that heterogeneity and high gray levels might be associated with intertwined processes, such as chaotic vascularization [49].”
ResultApplication of Radiomics and Artificial Intelligence for Lung Cancer Precision MedicinePreprint
Tunali, I., Gillies, R. J., & Schabath, M. B. (2021) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a039537 · Cited by 126 · see citing works
“(Sun et al. 2018) developed models that assess CD8 cell tumor infiltration and utilized this model to predict immunotherapy response of NSCLC patients.”
Igase, M., Nemoto, Y., Itamoto, K., Tani, K., Nakaichi, M., Sakurai, M., Sakai, Y., Noguchi, S., Kato, M., Tsukui, T., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-75533-4 · Cited by 102 · see citing works
Iwatate, Y., Hoshino, I., Yokota, H., Ishige, F., Itami, M., Mori, Y., Chiba, S., Arimitsu, H., Yanagibashi, H., Nagase, H., et al. (2020) · British Journal of Cancer · doi:10.1038/s41416-020-0997-1 · Cited by 101 · see citing works
Radiogenomics: a key component of precision cancer medicine
Liu, Z.-Q., Duan, T., Zhang, Y.-Y., Weng, S.-Y., Xu, H.-X., Ren, Y.-Q., Zhang, Z.-Y., & Han, X.-W. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02317-8 · Cited by 98 · see citing works
Vaidya, P., Bera, K., Patil, P., Gupta, A., Jain, P., Alilou, M., Khorrami, M., Velcheti, V., & Madabhushi, A. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-001343 · Cited by 98 · see citing works
Tong, H.-P., Sun, J.-J., Fang, J.-Q., Zhang, M., Liu, H., Xia, R.-X., Zhou, W.-C., Liu, K.-J., & Chen, X. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.859323 · Cited by 97 · see citing works
“Recently, based on CT images, some machine learning models were reported to discriminate tumors with ‘hot’ versus ‘cold’ TIME, and were also used for prediction of immunotherapy efficacy in cancers (18, 42).”
MethodResultLuo, F., Lu, F.-T., Cao, J.-X., Ma, W.-J., Xia, Z.-F., Zhan, J.-H., Zeng, K.-M., Huang, Y., Zhao, H.-Y., & Zhang, L. (2022) · Cancer Letters · doi:10.1016/j.canlet.2022.01.027 · Cited by 95 · see citing works
Radiological tumour classification across imaging modality and histology
Wu, J., Li, C., Gensheimer, M. F., Padda, S. K., Kato, F., Shirato, H., Wei, Y., Schönlieb, C., Price, S. J., Jaffray, D. A., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00377-0 · Cited by 98 · see citing works
Fournier, L. S., Costaridou, L. I., Bidaut, L. M., Michoux, N., Lecouvet, F. E., de Geus‐Oei, L., Boellaard, R., Oprea‐Lager, D. E., Obuchowski, N. A., Caroli, A., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07598-8 · Cited by 110 · see citing works
Li, Y., Zhang, Y., Fang, Q., Zhang, X., Hou, P., Wu, H.-B., & Wang, X.-L. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05119-9 · Cited by 95 · see citing works
Sun, R., Sundahl, N., Hecht, M., Putz, F., Lancia, A., Rouyar, A., Milic, M., Carré, A., Battistella, E., Alvarez Andres, E., et al. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-001429 · Cited by 94 · see citing works
Self-citationAdvances in artificial intelligence to predict cancer immunotherapy efficacy
Xie, J., Luo, X., Deng, X., Tang, Y., Tian, W., Cheng, H., Zhang, J., Zou, Y., Guo, Z., & Xie, X. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1076883 · Cited by 108 · see citing works
“(29) selected 135 patients with tumors at different sites derived from phase I PD-1/ PD-L1 monotherapy clinical trials.”
BackgroundDercle, L., Zhao, B.-S., Gönen, M., Moskowitz, C., Firas, A., Beylergil, V., Connors, D. E., Yang, H., Lu, L., Fojo, T., et al. (2022) · JAMA Oncology · doi:10.1001/jamaoncol.2021.6818 · Cited by 91 · see citing works
Self-citationApplication of radiomics and machine learning in head and neck cancers
Peng, Z., Wang, Y.-M., Wang, Y.-X., Jiang, S., Fan, R.-H., Zhang, H., & Jiang, W.-H. (2021) · International Journal on Biological Sciences · doi:10.7150/ijbs.55716 · Cited by 91 · see citing works
“It based on a hypothesis is that mineable data can be extracted from medical images and provides additional information about tumors’ phenotype, genes, and proteins for use in patients [15, 23-24].”
BackgroundApplying artificial intelligence for cancer immunotherapy
Xu, Z., Wang, X., Zeng, S., Ren, X., Yan, Y., & Gong, Z. (2021) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2021.02.007 · Cited by 131 · see citing works
“Through combining RNA-seq genomic data from tumor biopsies and contrast-enhanced CT images, this radiomics approach provided a promising method to evaluate the tumor-infiltrating CD8 signature and infer clinical outcomes for cancer patients 21 .”
BackgroundMethodPseudoprogression and Hyperprogression as New Forms of Response to Immunotherapy
Frélaut, M., du Rusquec, P., Moura, A. D., Tourneau, C. L., & Borcoman, É. (2020) · BioDrugs · doi:10.1007/s40259-020-00425-y · Cited by 99 · see citing works
“developed a radiomicsbased score at initiation of ICI in a retrospective multi-tumor cohort, which was predictive of tumor-infiltrating CD8 cells and was associated with treatment response and OS [74].”
BackgroundBasler, L., Gabryś, H. S., Hogan, S. A., Pavic, M., Bogowicz, M., Vuong, D., Tanadini‐Lang, S., Forster, R. E., Kudura, K., Huellner, M. W., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-0020 · Cited by 117 · see citing works
Wang, Z., Liu, Y., & Niu, X. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.04.009 · Cited by 89 · see citing works
Huang, W., Jiang, Y., Xiong, W., Sun, Z., Chen, C., Yuan, Q., Zhou, K., Han, Z., Feng, H., Chen, H., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32816-w · Cited by 101 · see citing works
Vicini, S., Bortolotto, C., Rengo, M., Ballerini, D., Bellini, D., Carbone, I., Preda, L., Laghi, A., Coppola, F., & Faggioni, L. (2022) · La radiologia medica · doi:10.1007/s11547-022-01512-6 · Cited by 91 · see citing works
“To predict the clinical outcomes of patients receiving anti-programmed cell death ligand 1 (PD-L1) or antiprogrammed cell death protein 1 (PD-1) immunotherapy, Sun and colleagues [25] proposed a radiomic signature for the detection of immune phenotypes (classified by the presence and location of CD8 cells) as a biomar…”
BackgroundArtificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology
Benzekry, S. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1951 · Cited by 89 · see citing works
Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy
Dercle, L., McGale, J., Sun, S. H., Marabelle, A., Yeh, R., Deutsch, É., Mokrane, F., Farwell, M. D., Ammari, S., Schöder, H. M., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-005292 · Cited by 99 · see citing works
Self-citationWu, J., Mayer, A. T., & Li, R. (2020) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2020.12.005 · Cited by 89 · see citing works
- Authors
- Sun, R., Limkin, E. J., Vakalopoulou, M., Dercle, L., Champiat, S., Han, S. R., Verlingue, L., Brandao, D., Lancia, A., Ammari, S., Hollebecque, A., Scoazec, J.-Y., Marabelle, A., Massard, C., Soria, J.-C., Robert, C., Paragios, N., Deutsch, E., & Ferté, C.
- Affiliations
- Inserm (FR)
- Université Paris-Saclay (FR)
- Institut Gustave Roussy (FR)
- L'Hôpital Nord Franche-Comté (FR)
- Université Paris-Sud (FR)
- ORCID
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 19
- Issue
- 9
- Pages
- 1180-1191
- ISSN
- 1470-2045 (ISSN-L)
- OpenAlex
- W2885326465
- Cited by
- 1,236
- References
- 48
- Citations by year
- 2026: 109 · 2025: 118 · 2024: 186 · 2023: 180 · 2022: 165 · 2021: 221 · 2020: 155 · 2019: 89 · 2018: 9 · 2014: 1
- FWCI
- 81.87
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Fondation pour la Recherche Médicale
- American Society for Radiation Oncology
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics, anti-PD-1 therapy, PD-L1 blockade, immunotherapy response prediction, imaging biomarkers, advanced solid tumors, contrast-enhanced CT, radiomics signature, elastic net
- MeSH terms
- Antineoplastic Agents, Immunological, Adult, Aged, Female, Humans, Male, Middle Aged, Neoplasms, Phenotype, Predictive Value of Tests, Retrospective Studies, RNA, Neoplasm, Time Factors, Tomography, X-Ray Computed, Biomarkers, Tumor, Reproducibility of Results, Lymphocytes, Tumor-Infiltrating, Treatment Outcome, Sequence Analysis, RNA, CD8-Positive T-Lymphocytes, Gene Expression Profiling, Molecular Imaging, Transcriptome, B7-H1 Antigen, Programmed Cell Death 1 Receptor
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