Cited by 1,236Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and BiomarkersMedical Imaging Techniques and Applications

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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

Sun, Roger, Limkin, Elaine Johanna, Vakalopoulou, Maria, Dercle, Laurent, Champiat, Stéphane, Han, Shan Rong, Verlingue, Loïc, Brandao, David, Lancia, Andrea, Ammari, Samy, Hollebecque, Antoine, Scoazec, Jean-Yves, Marabelle, Aurélien, Massard, Christophe, Soria, Jean-Charles, Robert, Charlotte, Paragios, Nikos, Deutsch, Eric, Ferté, Charles·2018·The Lancet Oncology

DOI 10.1016/s1470-2045(18)30413-3

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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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A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs)
Obuchowski, N., Huang, E. P., Desouza, N., Raunig, D., Delfino, J., Buckler, A., Hatt, C., Wang, X., Moskowitz, C., Guimaraes, A. R., et al. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.08.031 · Cited by 12
Consecutive Serial Non-Contrast CT Scan-Based Deep Learning Model Facilitates the Prediction of Tumor Invasiveness of Ground-Glass Nodules
Xu, Y., Li, Y., Yin, H., Tang, W., & Fan, G.-H. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.725599 · Cited by 12
“Unfortunately, there are currently no relevant authoritative studies or guidance on the specific size of the perinodular region (38).”
Background
Identification of Stage IIIC/IV EGFR-Mutated Non-Small Cell Lung Cancer Populations Sensitive to Targeted Therapy Based on a PET/CT Radiomics Risk Model
Shao, D., Du, D., Liu, H., Lv, J., Cheng, Y., Zhang, H., Lv, W., Wang, S., & Lu, L. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2021.721318 · Cited by 13
Use of Radiomics to Predict Response to Immunotherapy of Malignant Tumors of the Digestive System
Ji, Z., Cui, Y., Peng, Z., Gong, J., Zhu, H.-T., Zhang, X.-T., Li, J., Lu, M., Lu, Z.-H., Shen, L., et al. (2020) · Medical Science Monitor · doi:10.12659/msm.924671 · Cited by 12
“For example, radiomics was able to predict response to immunotherapy by assessing tumor-infiltrating CD8 cells [19].”
Background
Immune Oncology Biomarkers in Lung Cancer: an Overview
Travert, C., Barlési, F., Greillier, L., & Tomasini, P. (2020) · Current Oncology Reports · doi:10.1007/s11912-020-00970-3 · Cited by 13
A PET-CT radiomics model for immunotherapy response and prognosis prediction in patients with metastatic colorectal cancer
Chen, W.-B., Zhu, P., Chen, Y.-D., & Sun, G. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1568755 · Cited by 11
MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images
Liu, L.-L., Jing, B.-Z., Liu, X., Li, R.-G., Wan, Z.-Y., Zhang, J.-Y., Ouyang, X.-M., Kong, Q., Kang, X., Wang, D.-D., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102099 · Cited by 11
Prediction of soft tissue sarcoma grading using intratumoral habitats and a peritumoral radiomics nomogram: a multi-center preliminary study
Wang, B., Guo, H., Zhang, M., Huang, Y., Duan, L., Huang, C., Xu, J., & Wang, H. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1433196 · Cited by 8
Predicting response to immunotherapy in oral squamous cell carcinoma via a CT-based radiomics model
Ma, Q., Ren, J.-L., Wang, R., Yuan, Y., & Tao, X.-F. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01444-9 · Cited by 11
“Previous studies have explored the prediction of ICIs response in a variety of different tumor therapies by radiomics signature [21, 22].”
Background
Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling
Yao, Y., Chen, Y. F., & Zhang, Q. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae547 · Cited by 9
Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer
Zhang, X., Wu, S., Zu, X., Li, X.-J., Zhang, Q., Ren, Y., Qian, X., Tong, S., & Li, H. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1438923 · Cited by 11
Radiomics nomogram based on CT radiomics features and clinical factors for prediction of Ki-67 expression and prognosis in clear cell renal cell carcinoma: a two-center study
Li, B., Zhu, J., Wang, Y.-M., Xu, Y.-C., Gao, Z.-S., Shi, H., Nie, P., Zhang, J., Zhuang, Y., Wang, Z., et al. (2024) · Cancer Imaging · doi:10.1186/s40644-024-00744-1 · Cited by 11
“Radiomics has been proven to facilitate the precise assessment of tumour heterogeneity, which has been shown to have an important prognostic relationship in various malignancies [37], including lung cancer [38], breast cancer [39], esophageal cancer [40], hepatocellular carcinoma [41], and RCC [42].”
Development and validation of an MRI-Based nomogram to predict the effectiveness of immunotherapy for brain metastasis in patients with non-small cell lung cancer
Xu, J.-H., Wang, P.-L., Li, Y.-K., Shi, X., Yin, T.-W., Yu, J.-M., & Teng, F. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1373330 · Cited by 11
Magnetic resonance imaging-based radiomics was used to evaluate the level of prognosis-related immune cell infiltration in breast cancer tumor microenvironment
Qian, H., Ren, X., Xu, M.-S., Fang, Z., Zhang, R.-X., Bu, Y.-Y., & Zhou, C.-Y. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01212-9 · Cited by 11
“Sun et al. constructed a CT-based radiolabeling, which included eight variables to assess CD8 + T cell infiltration determined by RNA-seq data [29].”
Historical perspective and future directions: computational science in immuno-oncology
Ricker, C. A., Meli, K., & Allen, E. M. V. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-008306 · Cited by 14
“211 While other studies have developed radiomics-based machine learning models to predict response to ICB, a number of these studies were found to have a limited sample size and do not correct for overfitting.”
Background
Predictive Factors in Metastatic Melanoma Treated with Immune Checkpoint Inhibitors: From Clinical Practice to Future Perspective
Poletto, S., Paruzzo, L., Nepote, A., Caravelli, D., Sangiolo, D., & Carnevale‐Schianca, F. (2023) · Cancers · doi:10.3390/cancers16010101 · Cited by 13
“Future studies should define if a locoregional approach direct to a lesion at a higher risk of progression could improve a patient’s prognosis [99,100].”
Background
Immune Checkpoint Inhibitors in “Special” NSCLC Populations: A Viable Approach?
Bronte, G., Cosi, D. M., Magri, C., Frassoldati, A., Crinò, L., & Calabrò, L. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241612622 · Cited by 10
An AI Approach to Generating MIDD Assets Across the Drug Development Continuum
Barrett, J. S., Goyal, R. K., Gobburu, J. V. S., Baran, S. W., & Varshney, J. (2023) · The AAPS Journal · doi:10.1208/s12248-023-00838-x · Cited by 13
“Computed tomography (CT) image data and RNA-sequencing genomic data were retrospectively collected from the said trial for the purpose of this analysis [22].”
BackgroundMethod
Application of Radiomics in Prognosing Lung Cancer Treated with Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitors: A Systematic Review and Meta-Analysis
Wang, T.-W., Hsu, M.-S., Lin, Y.-H., Chiu, H.-Y., Chao, H.-S., Liao, C.-Y., Lu, C.-F., Wu, Y.-T., Huang, J.-W., & Chen, Y.-M. (2023) · Cancers · doi:10.3390/cancers15143542 · Cited by 11
Nonoperative Management of dMMR/MSI-H Colorectal Cancer following Neoadjuvant Immunotherapy: A Narrative Review
Xiao, B.-Y., Yu, J.-H., & Ding, P.-R. (2023) · Clinics in Colon and Rectal Surgery · doi:10.1055/s-0043-1767703 · Cited by 11
Development of a Molecular-Subtype-Associated Immune Prognostic Signature That Can Be Recognized by MRI Radiomics Features in Bladder Cancer
Liu, S., Chen, H.-T., Zheng, Z.-T., He, Y., & Yao, X. (2023) · Bioengineering · doi:10.3390/bioengineering10030318 · Cited by 11
The other immuno-PET: Metabolic tracers in evaluation of immune responses to immune checkpoint inhibitor therapy for solid tumors
Levi, J., & Hong, S. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1113924 · Cited by 9
PET/CT radiomics for prediction of hyperprogression in metastatic melanoma patients treated with immune checkpoint inhibitors
Gabryś, H., Basler, L., Burgermeister, S., Hogan, S., Ahmadsei, M., Pavic, M., Bogowicz, M., Vuong, D., Tanadini-Lang, S., Förster, R., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.977822 · Cited by 11
A Support Vector Machine Based on Liquid Immune Profiling Predicts Major Pathological Response to Chemotherapy Plus Anti-PD-1/PD-L1 as a Neoadjuvant Treatment for Patients With Resectable Non-Small Cell Lung Cancer
Peng, J., Zou, D., Han, L., Yin, Z., & Hu, X. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.778276 · Cited by 12
CT-Based Peritumoral and Intratumoral Radiomics as Pretreatment Predictors of Atypical Responses to Immune Checkpoint Inhibitor Across Tumor Types: A Preliminary Multicenter Study
He, S., Feng, Y., Lin, Q., Wang, L.-H. H., Wei, L.-J., Tong, J., Zhang, Y.-W., Liu, Y., Ye, Z.-X., Guo, Y., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.729371 · Cited by 11
“, who used radiomics from the peri-tumoral region to detect CD8 cells andpredict immunotherapy response inmultipledatasets (28).”
Method
Distribution Atlas of COVID-19 Pneumonia on Computed Tomography: A Deep Learning Based Description
Huang, S., Wang, Y.-C., Zhou, Z., Yu, Q., Yu, Y.-Z., Yang, Y., & Ju, S. (2021) · Phenomics · doi:10.1007/s43657-021-00011-4 · Cited by 11
MR-Guided Radiotherapy: The Perfect Partner for Immunotherapy?
Hörner‐Rieber, J., Klüter, S., Debus, J., Adema, G. J., Ansems, M., & Verheij, M. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.615697 · Cited by 11
“Recently, a CT-based radiomic signature was developed and validated to assess tumorinfiltrating immune cells and response to immunotherapy in patients with advanced solid tumors (90).”
Background
Biomarkers in immunotherapy: literature review and future directions
Pharaon, R. R., Koczywas, M. A., Salgia, S. K., Mohanty, A. R., & Massarelli, E. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd.2020.04.15 · Cited by 11
“studied a radiomic signature for CD8 cells in a retrospective cohort of patients using imaging data applied to different patient cohorts in the TCGA (35).”
Method
Association Between the Size and 3D CT-Based Radiomic Features of Breast Cancer Hepatic Metastasis
Velichko, Y. S., Mozafarykhamseh, A., Trabzonlu, T. A., Zhang, Z.-L., Rademaker, A., & Yaghmai, V. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.03.004 · Cited by 11
Hypoxia-related radiomics predict immunotherapy response: A multi-cohort study of NSCLC
Tunali, I., Tan, Y., Gray, J., Katsoulakis, E., Eschrich, S., Saller, J. J., Boyle, T., Qi, J., Guvenis, A., Gillies, R., et al. (2020) · bioRxiv · doi:10.1101/2020.04.02.020859 · Cited by 11
Master protocols in immuno-oncology: do novel drugs deserve novel designs?
Mazzarella, L., Morganti, S., Marra, A., Trapani, D., Tini, G., Pelicci, P. G., & Curigliano, G. (2020) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2019-000475 · Cited by 12
Reinventing 2D Convolutions for 3D Medical Images
Yang, J., Huang, X., Ni, B.-B., Xu, J., Yang, C., & Xu, G. (2019) · arXiv.org · doi:10.48550/arxiv.1911.10477 · Cited by 11
“1 [55] Roger Sun, Elaine Johanna Limkin, Maria Vakalopoulou, Laurent Dercle, Stéphane Champiat, Shan Rong Han, Loı̈c Verlingue, David Brandao, Andrea Lancia, Samy Ammari, et al.”
Background
Circulating Tumor DNA in Melanoma: Advances in Detection, Clinical Applications, and Integration with Emerging Technologies
Charbel, N., Rizkallah, J., Bal, M., Masri, A. E., Armache, E., Ghezzawi, M., Awada, A., Kreidieh, L., Mehdi, J., & Kreidieh, F. (2026) · International Journal of Molecular Sciences · doi:10.3390/ijms27031569 · Cited by 11
“Finally, radiomics features associated with CD8 + T-cell infiltration suggest that integrating ctDNA with imaging could function not only as a prognostic biomarker but also as a surrogate for tumor–immune interactions, guiding rational treatment sequencing or combinatorial approaches [71,73].”
Pretreatment CT Texture Analysis for Predicting Survival Outcomes in Advanced Nonsmall Cell Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta‐Analysis
Zhang, Y.-R., Lu, Y.-H., Lin, C.-M., & Ku, J.-W. (2025) · Thoracic Cancer · doi:10.1111/1759-7714.70144 · Cited by 10
“Four studies clustered in the upper portion of the funnel, indicating higher precision [40, 46, 49, 50].”
Influential
Prediction of immunotherapy response in nasopharyngeal carcinoma: a comparative study using MRI-based radiomics signature and programmed cell death ligand 1 expression score
Mai, H., Li, L., Xin, X., Jiang, Z., Tang, Y., Huang, J., Lei, Y., Chen, L.-Z., Dong, T., & Zhong, X. (2025) · European Radiology · doi:10.1007/s00330-025-11350-5 · Cited by 10
“Previous studies have demonstrated that radiomics features derived from CT/MR images are closely associated with TIME status and can also be used to predict immunotherapy response and prognosis in multiple solid tumors [18 – 21].”
Role of Radiomics-based Multiomics Panel in the Microenvironment and Prognosis of Hepatocellular Carcinoma.
Wu, Z., Ouyang, S.-Y., Gao, J., Hu, J., Guo, Q., Liu, D., & Ren, K. (2025) · Academic Radiology · doi:10.1016/j.acra.2024.12.039 · Cited by 10
Pushing the boundaries of radiotherapy-immunotherapy combinations: highlights from the 7 th immunorad conference
Laurent, P.-A., André, F., Bobard, A., Deandreis, D., Demaria, S., Depil, S., Eichmüller, S. B., Fernández-Palomo, C., Foijer, F., Galluzzi, L., et al. (2024) · OncoImmunology · doi:10.1080/2162402x.2024.2432726 · Cited by 10
“76,77 Similarly, the role of noninvasive approaches based on standard imaging for the preservation of lymphocytes and the optimization of immunotherapy in patients was highlighted in the presentation of Prof. Philippe Lambin (University of Maastricht, the Netherlands).”
Self-citation
Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma
Zhuang, Z., Lin, J.-X., Wan, Z., Weng, J.-R., Yuan, Z., Xie, Y.-M., Liu, Z., Xie, P., Mao, S., Wang, Z., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03573-y · Cited by 10
“The RSEA analysis indicated that TMB was associated with the imaging changes in peritumoral edema which was in concordance with previous studies that the peripheral tumor area was important in determining TMB and immunotherapy response [58, 59].”
Result
CT-based deep learning radiomics biomarker for programmed cell death ligand 1 expression in non-small cell lung cancer
Xu, T., Liu, X., Chen, Y., Wang, S., Jiang, C., & Gong, J. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01380-8 · Cited by 13
Deep Learning Based on Computed Tomography Predicts Response to Chemoimmunotherapy in Lung Squamous Cell Carcinoma
Peng, J., Xie, B., Ma, H., Wang, R., Hu, X., & Huang, Z. (2024) · Aging and Disease · doi:10.14336/ad.2024.0169 · Cited by 9
“The T cell CD8 plays an important role in the killing of tumors, and the abundance has been correlated with the efficacy of immunotherapy [42].”
A multicenter study: predicting KRAS mutation and prognosis in colorectal cancer through a CT-based radiomics nomogram
Li, M.-M., Yuan, Y.-W., Zhou, H., Feng, F., & Xu, G.-D. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04218-7 · Cited by 10
Ct-based subregional radiomics using hand-crafted and deep learning features for prediction of therapeutic response to anti-PD1 therapy in NSCLC
Hu, Y., Jiang, T., Wang, H., Song, J., Yang, Z., Wang, Y., Su, J., Jin, M., Chang, S., Deng, K., et al. (2023) · Physica Medica · doi:10.1016/j.ejmp.2023.103200 · Cited by 11
Immunoradiotherapy for NSCLC: mechanisms, clinical outcomes, and future directions
Weishan, H., Dong-lin, Z., Guangmei, D., Liu, W., Wu, F., & Chen, J. (2023) · Clinical & Translational Oncology · doi:10.1007/s12094-023-03337-9 · Cited by 10
“Radiomics is a powerful, non-invasive, economical, and reliable methodology for evaluating patient response to precision medicine [89].”
Method
Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram
Ni, H., Zhou, G.-H., Chen, X., Ren, J., Yang, M.-Q., Zhang, Y.-H., Zhang, Q.-Y., Zhang, L., Mao, C., & Li, X. (2023) · Bioengineering · doi:10.3390/bioengineering10070828 · Cited by 10
Risk Assessment and Cholangiocarcinoma: Diagnostic Management and Artificial Intelligence
Granata, V., Fusco, R., Muzio, F. D., Cutolo, C., Grassi, F., Brunese, M. C., Simonetti, I., Catalano, O., Gabelloni, M., Pradella, S., et al. (2023) · Biology · doi:10.3390/biology12020213 · Cited by 11
Short-term peri- and intra-tumoral CT radiomics to predict immunotherapy response in advanced non-small cell lung cancer
Wang, T., Chen, L., Bao, X., Han, Z., Wang, Z.-Z., Nie, S.-D., Gu, Y.-J., & Gong, J. (2023) · Translational Lung Cancer Research · doi:10.21037/tlcr-24-973 · Cited by 10
“CT radiomics features, including tumor-infiltrating CD8 + T cells, somatic mutations, histopathologic grading, and metabolism, have been previously linked to clinical outcomes and tumor biology (27-29).”
A radiomics approach for predicting acute hematologic toxicity in patients with cervical or endometrial cancer undergoing external-beam radiotherapy
Le, Z., Wu, D., Chen, X., Wang, L., Xu, Y., Zhao, G., Zhang, C., Chen, Y., Hu, Y., Yao, S., et al. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109489 · Cited by 10
CT-based radiomics signature to predict CD8+ tumor infiltrating lymphocytes in non-small-cell lung cancer
Chen, Y., Xu, T., Jiang, C., You, S., Cheng, Z., & Gong, J. (2022) · Acta Radiologica · doi:10.1177/02841851221126596 · Cited by 11
“Two recent studies showed that the radiomics score derived from CT images of multiple tumor types could be used to predict abundance of CD8+ TILs and was associated with clinical outcomes of patients treated with immunotherapy (15,17).”
Background
Immune Profiling Reveals Molecular Classification and Characteristic in Urothelial Bladder Cancer
Yang, L., Li, A., Liu, F.-S., Zhao, Q.-T., Ji, S.-F., Zhu, W., Yu, W.-N., Zhang, R., Liu, Y.-Q., Li, W., et al. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.596484 · Cited by 10
“Moreover, Sun et al. (2018) developed a radiomic signature for CD8 cells, and a high baseline radiomic score was correlated with improved OS.”
Background
Radiomics in immuno-oncology
Bodalal, Z., Wamelink, I. J. H. G., Trebeschi, S., & Beets‐Tan, R. G. H. (2021) · Immuno-Oncology Technology · doi:10.1016/j.iotech.2021.100028 · Cited by 11
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Yang, J., Chen, J., Kuang, K., Lin, T., He, J., & Ni, B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59713-9_21 · Cited by 12
A novel panel based on immune infiltration and tumor mutational burden for prognostic prediction in hepatocellular carcinoma
Xie, C., Wu, H., Pan, T., Zheng, X., Yang, X., Zhang, G., Lian, Y., Lin, J., & Peng, L. (2021) · Aging · doi:10.18632/aging.202670 · Cited by 15
“PD-1/PD-L1 expression level(31), tumor mutation burden(13, 32), high microsatellite instability (MSI-H)(33), tumor infiltrating T cell content(34), and neutrophil-lymphocyte ratio (NLR)(35) are currently more widely accepted indicators of response efficacy.”
Background
Editorial: Deep Learning as a staging tool in gastric cancer.
Paepe, K. D., & Cunningham, D. (2020) · Annals of Oncology · doi:10.1016/j.annonc.2020.04.477 · Cited by 10
Non-small-cell lung cancer: what are the benefits and challenges of treating it with immune checkpoint inhibitors?
Rassy, E., Mezquita, L., Remón, J., & Besse, B. (2019) · Immunotherapy · doi:10.2217/imt-2019-0071 · Cited by 11
Radiomics to predict response to immunotherapy, bridging the gap from proof of concept to clinical applicability?
Deutsch, E., & Paragios, N. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz150 · Cited by 10
Self-citation
Deep-Learning Serial CT Prediction of Survival in Immunotherapy-Treated Non–Small Cell Lung Cancer
Sako, C., Kurland, B. F., Schmidt, T. G., He, J., de Groot, M., Patel, A. A., Owen, D. H., Amini, A., Curti, B. D., Kelly, R. J., et al. (2026) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.55759 · Cited by 13
Combination of radiotherapy and ICIs in advanced hepatocellular carcinoma: A systematic review of current evidence and future prospects (Review)
Ma, C., Yu, X., Zhang, X., Su, L., Jiang, O., & Cui, R. (2025) · Oncology Letters · doi:10.3892/ol.2025.15088 · Cited by 9
“These imaging techniques, when integrated into RT planning, enable more accurate tumor targeting, improved dose distribution and decreased toxicity to surrounding healthy tissue (46,47).”
Exploring radiomic features of lateral cerebral ventricles in postmortem CT for postmortem interval estimation
De-Giorgio, F., Guerreri, M., Gatta, R., Bergamin, E., Vita, V. D., Mancino, M., Boldrini, L., Sala, E., & Pascali, V. L. (2024) · Zeitschrift für Rechtsmedizin · doi:10.1007/s00414-024-03396-9 · Cited by 9
“Initially designed to improve diagnosis, prognosis, and treatment planning in the field of oncology [34–43], radiomics has quickly proven to be applicable in broader contexts [44–48].”
Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study.
Kang, W., Tang, P., Luo, Y.-G., Lian, Q., Zhou, X., Ren, J., Cong, T., Miao, L., Li, H., Huang, X.-Y., et al. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.10.038 · Cited by 9
Explainable 18F-FDG PET/CT radiomics model for predicting EGFR mutation status in lung adenocarcinoma: a two-center study
Zuo, Y., Liu, Q.-F., Li, N., Li, P., Fang, Y.-C., Bian, L.-J., Zhang, J.-P., & Song, S.-L. (2024) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-024-05998-7 · Cited by 9
“…between macroscopic image features and microscopic tumor-driven genes and signaling pathways, which have provided intelligent decision-making guidance for clinical doctors and patients and shown great potential in the field of medi - cal imaging (Sun et al. 2018; Mu et al. 2020 ; Wang et al. 2022).”
Background
Comparative assessment of the capability of machine learning-based radiomic models for predicting omental metastasis in locally advanced gastric cancer
Wu, A., Luo, L., Zeng, Q., Wu, C., Shu, X., Huang, P., Wang, Z., Hu, T., Feng, Z., Tu, Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-66979-x · Cited by 7
“Additionally, at the molecular level, radiomics has been employed to investigate immune cell infiltration and assess the sensitivity of immune therapy 20 .”
Background
Enhancing Immunotherapy Response Prediction in Metastatic Lung Adenocarcinoma: Leveraging Shallow and Deep Learning with CT-Based Radiomics across Single and Multiple Tumor Sites
Masson-Grehaigne, C., Lafon, M., Palussière, J., Leroy, L., Bonhomme, B., Jambon, E., Italiano, A., Cousin, S., & Crombé, A. (2024) · Cancers · doi:10.3390/cancers16132491 · Cited by 8
“Regarding NSCLC response to CPI, several radiomics studies have demonstrated significant associations between single-site radiomics and response [11–15].”
Background
Predicting response to immunotherapy in non-small cell lung cancer- from bench to bedside
Montoya, C., Spieler, B. O., Welford, S. M., Kwon, D., Pra, A. D., Junior, G. L. L., & Mihaylov, I. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1225720 · Cited by 11
“Radiomic analysis was performed with respect to the largest tumor on CT in accordance with prior radiomics studies in patients undergoing immunotherapy (39).”
Method
Development and Validation of a Machine Learning-Based Model Using CT Radiomics for Predicting Immune Checkpoint Inhibitor-related Pneumonitis in Patients With NSCLC Receiving Anti-PD1 Immunotherapy: A Multicenter Retrospective CaseControl Study.
Zhang, G.-Y., Du, X.-Z., Xu, R., Chen, T., Wu, Y., Wu, X.-J., & Liu, S. (2023) · Academic Radiology · doi:10.1016/j.acra.2023.10.039 · Cited by 9
Imaging-Based Biomarkers Predict Programmed Death-Ligand 1 and Survival Outcomes in Advanced NSCLC Treated With Nivolumab and Pembrolizumab: A Multi-Institutional Study
Yolchuyeva, S., Giacomazzi, E., Tonneau, M., Lamaze, F. C., Orain, M., Coulombe, F., Malo, J., Belkaïd, W., Routy, B., Joubert, P., et al. (2023) · JTO Clinical and Research Reports · doi:10.1016/j.jtocrr.2023.100602 · Cited by 10
“8,9 Radiomics-based techniques have a number of bene ﬁ ts, including the fact that they are noninvasive and the features can be derived from routine medical images.”
Background
Challenges of artificial intelligence in precision oncology: public-private partnerships including national health agencies as an asset to make it happen.
Luu, V. P., Fiorini, M., Combes, S., Quemeneur, E., Bonneville, M., & Bousquet, P. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.09.3106 · Cited by 9
An Imaging Feature Predicts Efficacy of Atezolizumab Plus Bevacizumab in Unresectable Hepatocellular Carcinoma
Kuwano, A., Yada, M., Miyazaki, Y., TANAKA, K., KOGA, Y., Ohishi, Y., Masumoto, A., & Motomura, K. (2023) · Cancer Diagnosis & Prognosis · doi:10.21873/cdp.10241 · Cited by 9
Novel clinical radiomic nomogram method for differentiating malignant from non-malignant pleural effusions
Han, R., Huang, L., Zhou, S.-J., Shen, J.-R., Li, P.-L., Li, M., Wu, X.-W., & Wang, R. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e18056 · Cited by 9
Predicting Drug Treatment Outcomes in Children with Tuberous Sclerosis Complex–Related Epilepsy: A Clinical Radiomics Study
Hu, Z., Jiang, D., Zhao, X., Yang, J., Liang, D., Wang, H., Zhao, C., & Liao, J. (2023) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7911 · Cited by 15
Multiparameter prediction model of immune checkpoint inhibitors combined with chemotherapy for non-small cell lung cancer based on support vector machine learning
Zhou, Z., Guo, W., Liu, D., Micha, J. R. N., Song, Y., & Han, S. (2023) · Scientific Reports · doi:10.1038/s41598-023-31189-4 · Cited by 9
“Imaging reflects features of tumors, including PD-L1 expression status and CD8 infiltration 11,19 .”
BackgroundMethodSelf-citation
Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians
Zhou, H., Luo, Q., Wu, W., Li, N., Chunli, Y., & Zou, L. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1088874 · Cited by 11
“Therefore, the credibility of the radiomics signature would be affected (12).”
BackgroundInfluential

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: Sun, Roger
Limkin, Elaine Johanna
Vakalopoulou, Maria
Dercle, Laurent
Champiat, Stéphane
Verlingue, Loïc
Lancia, Andrea
Ammari, Samy
Hollebecque, Antoine
Scoazec, Jean-Yves
Marabelle, Aurélien
Massard, Christophe
Soria, Jean-Charles
Robert, Charlotte
Ferté, Charles
Venue: The Lancet Oncology
Publisher: Elsevier BV
Published: 2018-09
Volume: 19
Issue: 9
Pages: 1180-1191
ISSN: 1470-2045 (ISSN-L)
DOI: 10.1016/s1470-2045(18)30413-3
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
FWCI: 81.87
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: The Lancet Oncology (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
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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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

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