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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Association between intra- and peritumoral heterogeneity on B-mode ultrasound and overall survival in patients with liver metastases treated with immunotherapy
Lawrance, L., Leguerney, I., Benane-Benatsou, B., Dupont, J., Ammari, S., Bône, A., Balleyguier, C., Choucair, A., Massard, C., Lassau, N., et al. (2026) · European Radiology Experimental · doi:10.1186/s41747-026-00771-6
“Innovative quantitative imaging biomarkers offer a noninvasive alternative to capture global and spatially resolved tumor heterogeneity [13], potentially overcoming biases of purely dimensional assessments, which can be confounded by immune-related phenomena such as pseudoprogression or transient…”
Self-citation
Prediction of microvascular invasion in hepatocellular carcinoma using contrast-enhanced ultrasound and deep learning
Pang, C., Ru, J., Liu, Y., Ding, W.-Z., Chai, S., Yao, J.-D., Liu, S.-H., Feng, H., Liu, J., Chen, M., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-74985-y
“Sun et al. proposed a contrast-enhanced CT based radiomics approach to assess tumor-in ﬁ ltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy 31 .”
Research on Predicting the Efficacy of Immunotherapy for Non-small Cell Lung Cancer Based on CT Radiomics and Peripheral Blood Inflammatory Markers
Wen, X., Li, Y., Hu, Y.-D., Chen, A.-Q., Wang, K., Zou, W., Xie, B., & Ma, Y.-C. (2026) · Technology in Cancer Research and Treatment · doi:10.1177/15330338261465744
Treatment-specific CT Radiomics Models To Predict Response To Neoadjuvant Therapy And Explore Individualized Treatment Selection In Gastric Cancer
Liu, J., Gao, X., Ma, T., Li, X., Ye, L., Wang, L., Ding, X., Xiao, J., & Ye, Z. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.06.038
Development and validation of a multiparametric MRI habitat radiomics nomogram for predicting early complete response to induction chemoimmunotherapy in locally advanced nasopharyngeal carcinoma.
Zhu, B., Chen, K.-H., Wen, Z., Zhu, L.-R., Li, L., & Zhu, X.-D. (2026) · European Journal of Radiology · doi:10.1016/j.ejrad.2026.113112
A clinical-radiomic model based on best subset regression for prognostic prediction in natural killer/T-cell lymphoma
Tian, F.-F., Chen, L.-L., Chen, X.-L., & Pang, H.-Y. (2026) · Cancer Imaging · doi:10.1186/s40644-026-01074-0
“This critical distinction suggests that pre-treatment imaging may more accurately reflect the fundamental, innate biological properties of the tumor which ultimately dictate long-term survival outcomes [17].”
Dynamic multimodal radiomic model for survival prediction in cervical cancer: a multi-cohort study
Yang, G., Wang, W.-P., Zhang, C.-L., Liu, Y.-L., Yang, S.-L., Dai, J.-J., Xiang, Z.-Y., Zhang, F.-Q., Qiu, J., Yang, B., et al. (2026) · npj Precision Oncology · doi:10.1038/s41698-026-01545-4
“Through systematic analysis of these features, radiomics may provide indirect information related to tumor heterogeneity, aggressiveness, and treatment-associated imaging changes 12 – 14 , thereby supporting personalized radiotherapy decision-making 9,15 .”
Deep learning of CT imaging predicts PD-L1 expression and immunotherapy response in metastatic NSCLC: A multi-center study
Muneer, A., Showkatian, E., Saad, M. B., Hong, L., Li, S.-D., Salehjahromi, M., Aminu, M., Sujit, S., Xu, H., Waqas, M., et al. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218687
An interpretable radiomics–machine learning model for early risk stratification of invasive fungal infections in community-acquired pneumonia: a dual-center study
He, W.-Z., Xiong, Y., Huang, X., Luo, H.-R., Zhou, S.-Q., Tu, Y., Deng, T., & Gu, M. (2026) · Scientific Reports · doi:10.1038/s41598-026-56091-7
Artificial Intelligence and the Evolving Paradigm of Lung Cancer Management
Martins, R. S., Hanna, Y., & Axtell, A. L. (2026) · Cancers · doi:10.3390/cancers18122012
“…growth factor receptor (EGFR) [24] status, lymph node [25] and distant organ [26] metastasis, treatment response and adverse reactions to immunotherapy [27], post-treatment response to immunotherapy [28,29], overall survival after treatment [30,31], and local or distant recurrence [32,33].”
Deep learning and radiomics models in patients with advanced non-small cell lung cancer treated with immunotherapy combined with stereotactic radiotherapy
Kothari, G., Hardcastle, N., Perera, R., Jackson, P., Lee, S., Silver, J. D., Gaudreault, M., Li, J., Brown, C., Mitchell, P. L., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-53520-5
“Radiomic models incorporating both intra-tumoural and peri-tumoural features have shown to correlate with CD8 cell tumour infiltration, suggesting that the peri-tumoural microenvironment may provide important information about immune activity 12 .”
Influential
Early prediction of immune checkpoint inhibitor-related pneumonitis in advanced non-small cell lung cancer based on primary tumor delta-radiomics features
Xie, D., Xu, L.-G., Xu, J.-X., Chen, H.-F., Yu, J.-N., He, C., Qiu, Y.-G., Fu, L.-F., Han, Q., Kong, L., et al. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1795169
The digital revolution in medical imaging: The Role of artificial intelligence (AI) in the future of radiology: A subject review
Saad, F. F., & Bashara, D. M. (2026) · JOURNAL OF MEDICAL AND HEALTH SCIENCE · doi:10.21070/anamnetic.v4i1.1655
“These representations may be indications of a complex process referred to as "Intratumoral Heterogeneity, known as one of the most important resistance concepts that has been ignored in single-site needle biopsies [45, 46].”
Quality over quantity: biopsy-anchored CT radiogenomics models outperform all-lesion training in a multi-tumour cohort despite a smaller sample size
Sánchez, D. I. R., Middelkoop, J., Vanneste, T., Maxouri, O., Ursprung, S., Rostami, S., Bogveradze, N., Chupetlovska, K., Castagnoli, F., Landolfi, F., et al. (2026) · European Radiology · doi:10.1007/s00330-026-12601-9
A Statistical Multi-Objective Framework for Assessing Sensitivity of Radiomic AI Models to Acquisition Parameters
Gil, D., Sanchez, I., & Sanchez, C. (2026) · doi:10.48550/arxiv.2605.14667
“Mixed-effects modeling has previously been used Sun, Limkin, Vakalopoulou et al. (2018) to include acquisition parameters as factors in regression models for the development of a radiomic signature predicting response to immunotherapy treatments.”
Explainable 1-mm Peritumoral CT Radiomics for EGFR Mutation Prediction in Non-small Cell Lung Cancer: A Vietnamese Real-World Cohort Study
Le, M. N., Nguyen, T. M., Ngô, L. L., Le, T. D., Nguyen, D. A., Nguyen, N., Dinh, H., Le, N., Bùi, T. T. N., & Bui, T. O. (2026) · Cancer Control: Journal of the Moffitt Cancer Cente · doi:10.1177/10732748261455532
“18,19 Earlier studies often viewed the tumor as a single, uniform structure, without accounting for local heterogeneity that could reflect EGFR related molecular differences.”
Three‐Discipline Collaborative Radiation Therapy (3DCRT) debate: Medical physics should focus less on geometric precision and more on biological outcomes
de-Kermenguy, F., Ellsworth, S. G., Facciabene, A., Durante, M., Pacelli, R., St-Aubin, J., & Wang, D.-X. (2026) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.70636
Advancing the understanding of tumor microenvironment through medical imaging based on bibliometric analysis
Huang, K., Han, Z.-Q., Xia, Y.-F., Lou, X., Wu, X., Fu, R.-Z., Wu, L.-Y., & Gao, C. (2026) · Translational Cancer Research · doi:10.21037/tcr-2026-1-0308
“Notably, a 2018 article by Sun et al. on radiomics analysis of the TME was the most frequently cited in the co-citation analysis, significantly influencing radiomics research within the TME field (27).”
Non-invasive prediction of prognostic immune subtypes in lung adenocarcinoma using PET/CT-based radiomics
Zhang, S., Wang, Y., Dai, D., & Xu, W.-G. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1739125
CT-based immune radiomic signature for prognosis and prediction of immunotherapy and anticancer drug response in NSCLC
Ma, J., Guo, K.-X., Gao, Y.-P., Li, T., Li, F., Pang, C., Geng, Y., & Guo, J.-W. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1767389
“…low PD-L1 expression and high CD3+ T-cell in ﬁ ltration, consistent with an immune-activated state (33); similarly, radiomic signatures have been reported to discriminate immune-in ﬂ amed from immune-desert tumours and to correlate with CD8-related immune phenotypes and immunotherapy outcomes (34).”
A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images
Ye, Y.-Z., Chou, P.-Y., Ou, D., Tai, W.-H., Yu, C.-J., Lin, S.-Y., Chen, Y.-F., Wang, C.-Y., Liu, C.-H., Lee, C.-E., et al. (2026) · Scientific Reports · doi:10.1038/s41598-026-49064-3
“For instance, Sun et al. 22 was the first to use radiomics analysis to explore the relationship between immune phenotypes and imaging features across various cancers.”
Clinical value of habitat radiomics within the 2-cm edema zone surrounding the postoperative residual cavity in predicting glioma recurrence
Cheng, M.-Y., Bai, D., Liu, W.-J., Fan, J., Liang, Y.-Z., Li, W.-F., & Wang, Z.-Q. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1786939
Application of artificial intelligence in lung cancer diagnosis, therapy, and prognosis
Wang, Y., Tian, R., Liu, J., Duan, S., Zhang, Y., & Guo, S. (2026) · BMC Medical Genomics · doi:10.1186/s12920-026-02360-3
“Sun et al. [127] developed a radiomic feature of CD8 T cells that can predict the efficacy of anti-PD-1 or anti-PD-L1 immunotherapy.”
PET–CT radiomics and artificial intelligence for predicting lymph node metastasis in cervical cancer: a systematic review and meta-analysis
Liu, X., & Lei, J. (2026) · Clinical & Translational Oncology · doi:10.1007/s12094-026-04346-0
“This capability is uniquely valuable for detecting micro-metastases lacking morphological signs [33], thereby facilitating safer preoperative stratification.”
Imaging Immune Heterogeneity in Nasopharyngeal Carcinoma: A Multidimensional Radiomics Perspective.
Liang, J.-X., Wang, Z., & Zhang, Y.-Q. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.03.035
A Noninvasive Predictive Model of Portal Hypertension in Patients with HCC Based on Clinical Features and Intra- and Peritumoral Pre-Fusion Radiomic Features
Gao, Q., Zhu, C., Chen, M.-F., Mo, S.-T., He, Y.-F., Huang, K., Liao, Y., Liang, T.-Y., Han, C.-Y., & Peng, T. (2026) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s596500
“17,41,42 All extracted features underwent Z-score normalization to ensure data consistency.”
Prediction of depression development in Parkinson’s disease patients: a radiomics-based machine learning study
Yuan, Y., Zhang, Z., Shu, Z., Wei, Y., Song, Q., & Xiang, Y. (2026) · BMC Neurology · doi:10.1186/s12883-026-04779-8
“This process involved standardizing image pixel values to a resolution of 1 × 1 × 1 and adjusting the grayscale to encompass 32 levels [19].”
Cross-dataset adaptation of voxel-level deep radiomics for predicting survival in inoperable locally advanced NSCLC treated with immunotherapy
Wang, J., Jiang, Z.-Y., Ji, W.-H., Cheng, H., Zhang, Z., Dekker, A., Wee, L., Yan, M., & Lai, X.-J. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1787518
Predicting hormone receptor status in tumors: An innovative approach using breast ultrasound-radiomics combined model
Yin, X., Wang, Z., Zhao, Q.-X., Zhang, X.-C., Sang, T., & Shao, C.-P. (2026) · Medicine · doi:10.1097/md.0000000000047576
“[14–16] US-based radiomics (US-radiomics) is becoming increasingly popular, although radiomics studies in breast cancer have primarily used MRI or PET/CT. [17,18] US-radiomics US-radiomics can measure tumor heterogeneity, internal echoes, and acoustic texture, which may indirectly reflect…”
Radiomics in sporadic microsatellite instable, mismatch repair deficient and Lynch syndrome-associated pancreatic ductal adenocarcinoma: a pilot study
Eikenboom, E. L., Magnin, J., Alessandris, R., Horvat, N. D. S. M. R., Gonen, M., Jarnagin, W. R., Drebin, J. A., D'Angelica, M. I., Kingham, T. P., Balachandran, V. P., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2025.1584167
Melanoma Beyond the Microscope in the Era of AI and Integrated Diagnostics
Aksoy, S., Demircioğlu, P., & Böğrekçi, İ. (2026) · Dermato · doi:10.3390/dermato6010006
“Predictive models based on AI are possibly one approach that could efficiently address these challenges by providing an integrated analysis of genomics, epigenomics, and immune parameters for more accurate predictions [78,79].”
Artificial Intelligence in Healthcare: Advances in Medical Image Processing For Diagnosis, Treatment, and Monitoring
Balaji, D. T., & Veeranan, V. A. G. (2026) · International Journal of Integrative Studies (IJIS) · doi:10.63856/wd8eqk05
“In precision oncology, AI-extracted radiomic features are used to match patients with therapies most likely to be effective based on the tumor’s phenotypic signature [19].”
Targeted nanodelivery of CRISPR/Cas9 for hepatocellular carcinoma gene therapy in murine models
Yue, F., Jiang, X., Yang, Y., Jiang, Y., Ma, L., Wang, F., & Luo, J. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.173751
Biomarkers of Immunotherapy Response in Surgical Oncology
Bae, J., & Cohen, S. (2026) · Surgical Oncology Clinics of North America · doi:10.1016/j.soc.2025.12.011
Multi-Institutional CT Scan-Based Radiomics for Predicting Tumor PD-L1 Expression in Patients with Advanced and Limited Non-Small Cell Lung Cancer
Saber, R., Tonneau, M., Salko, O., Liberman, M., Malo, J., Elkrief, A., Turcotte, S., Bouchard, N., Joubert, P., Kadoury, S., et al. (2026) · Cancers · doi:10.3390/cancers18040552
“Several studies have leveraged radiomics features and machine learning models to predict tumor infiltrating lymphocytes, namely cytotoxic T cells [19] and helper T cells [20] and the intratumoral expression of immune biomarkers [21] including PD-L1 expression [22,23].”
CT-Based Radiomics Predicts the Functional State of Tumor-Infiltrating CD8+ T Cells and Prognosis in NSCLC
Sang, B., Zang, X., Yu, J., Yang, L., Yang, G., Xiaodan, G., Zheng, M., Qi, H., Qiu, Q., Cao, F., et al. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.01.057
Discovery of biomarkers in the era of radioimmunotherapy against lung cancer: A bench‐to‐bedside promise
Wang, A.-N., Wang, S.-Y., Zhang, S.-J., Liu, F.-L., Zhou, T.-L., Li, Z.-Q., Wang, Y.-X., Xu, J.-X., Cai, J., Wang, X., et al. (2026) · International Journal of Cancer · doi:10.1002/ijc.70352
Prediction of tumor-infiltrating lymphocytes through habitat radiomics and exploration of response mechanisms in neoadjuvant immunochemotherapy-treated lung cancer
Geng, Z., Hu, Y., Zhou, S., Wu, G., Gong, Z., Feng, R., Huang, Z., Mei, P., Li, K., Ye, G., et al. (2026) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-025-04291-x · Cited by 1
Radiogenomics predicts immune microenvironment heterogeneity and response to combination immunotherapy in hepatocellular carcinoma
Xu, Z., Liu, Y.-W., Ji, Y., Cao, S.-Y., Wang, T., Wu, H.-Y., Tang, W., Wu, X.-F., Xia, Y.-X., Xu, Q., et al. (2026) · Journal of Translational Medicine · doi:10.1186/s12967-025-07627-4
“The “glszm_GrayLevelNonUniformity” measure the variability of gray-level intensity values in the image, with a higher value indicating more heterogeneity in intensity values and intertwined processes within the tumor, such as chaotic vascularization and necrosis [32].”
Single and multi-site CT-based radiogenomics analysis of metastatic lung adenocarcinoma and correlations with outcome
Crombé, A., Sitruk, L. A., Masson-Grehaigne, C., Lafon, M., Palussière, J., Bonhomme, B., Cousin, S., Lassau, N., & Italiano, A. (2026) · European Radiology · doi:10.1007/s00330-025-12292-8
“…kinase inhibitors, highlighting the potentially predictive value of radiomic heterogeneity, in the context of targeted therapy, consistent with literature data suggesting that tumors with greater spatial heterogeneity may harbor subclonal populations with differential therapeutic sensitivity [26].”
Immune infiltration differences underlying prognostic radiomics subtypes from matched MRI and RNA sequencing in patients with lower-grade gliomas
Hou, S., Li, W.-F., Fu, M., Li, Y.-G., Zhao, Y., & Zhang, H.-L. (2026) · Discover Oncology · doi:10.1007/s12672-026-04411-8
“Furthermore, multicenter, large-scale studies have shown that radiomics can predict tumor immunophenotypes, offering a promising approach to assess the efficacy of immune checkpoint treatments such as Programmed Cell Death Protein-1 (PD-1) and Programmed Death-Ligand 1 (PD-L1) [8, 9].”
A biology-informed radiomics model for prognostication of hepatocellular carcinoma based on AKR1B10 expression
Ying, H., Huang, L., & Hong, W. (2026) · European Journal of Radiology Open · doi:10.1016/j.ejro.2026.100725
Intelligence artificielle et imagerie thoracique
Chassagnon, G. (2025) · Encyclopédie médico-chirurgicale. Pneumologie · doi:10.1016/s1155-195x(24)46306-8
Prediction of Breast Cancer Lymph Node Metastasis by a Nomogram Model Integrating Pathomics, Radiomics, and Immunoscore
Xu, T.-M., Feng, J., Zhang, K., Gao, L., & Wang, J. (2026) · Chemical Biology and Drug Design · doi:10.1111/cbdd.70244
“Interestingly, preliminary radiomic and pathomic analyses suggest that imaging features can correlate with in situ immune cell infiltration, an emerging biomarker for predicting prognosis and immunotherapy response (Sun et al. 2018; Pagès et al. 2010; Galon and Lanzi 2020; Banna et al. 2019).”
Self-supervised learning radiomics nomogram integrating anatomical structures can identify cerebellar hypoplasia in prenatal ultrasound
He, R., Ma, Y.-L., Wu, X.-X., Liu, F., Li, L., Wu, T.-Q., Xu, G.-P., Cheng, C., Zhao, S., & Wu, X.-L. (2025) · Applied intelligence (Boston) · doi:10.1007/s10489-025-06986-1
“These features have been demonstrated to be effective in reflecting subtle features in images and can be employed to identify disease heterogeneity [31 , 32].”
放疗联合免疫治疗在不可切除III期非小细胞肺癌中的临床研究进展及未来展望
Tan, Z., Zhong, Y.-L., Wang, J.-Y., & Wu, L. (2025) · Zhongguo fei ai za zhi = Chinese journal of lung cancer · doi:10.3779/j.issn.1009-3419.2025.102.42
“…： 13.8 vs 6.2 个月， P =0.028 ； mOS ：未达到 vs 14.8 个月， P =0.005 ）。这些证据共同提示， ctDNA 阳性是复发高危标志，同时可精准筛选 ICIs 治疗获益人群。影像组学作为无创评估工具，在风险分层中展现独特价值。一项回顾性研究 [62] 显示，高影像组学评分组的 mOS 显著优于低评分组（ 24.3 vs 11.5 个月， HR=0.58 ， P =0.0081 ）。另一项研究 [63] 则证实，无论 PD-L1 水平如何，高危影像组学特征均可独立预测 PFS （ P <0.0001 ）。一系列研究 [64,65]…”
Influential
Letter to the editor on 'AI-based radiomic features predict outcomes and the added benefit of chemoimmunotherapy over chemotherapy in extensive stage small cell lung cancer: A multi-institutional study'.
Wang, G., & Wang, W. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.218107
ACGM: Attribute-Centric Graph Modeling Network for Concurrent Missing Tabular Data Imputation and COVID-19 Prognosis
Wu, Z.-R., Chen, W., Li, X.-C., Ruffini, F., Liu, S.-N., Tronchin, L., Albano, D., Faiella, E., Fazzini, D., Santucci, D., et al. (2025) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2025.3618935
“As illustrated in Table I, existing methods typically choose to use feature selection engineering [11] and traditional imputation techniques (e.g. Mean-Mode and KNN) to address the issue of missing values in tabular data.”
Tumor Microenvironment as a Key Target for Immunotherapy in Lung Cancer Patients
Ganina, A., Doskali, M., Zaripova, L., Askarov, M., Muhamedzhanova, P., Brimova, A., Kozina, L., Karimova, M., Berikbol, D., Chuvakova, E., et al. (2025) · Russian Open Medical Journal · doi:10.15275/rusomj.2025.0303
Re: Nora Sundahl, Laurence Albiges, Toni K. Choueiri, et al. Stereotactic Body Radiation Therapy Alone or in Combination with Immunotherapy in Kidney Cancer: A Systematic Review. Eur Urol 2025;88:142-54.
Luo, W., Zhao, B., & Shi, Z.-Y. (2025) · European Urology · doi:10.1016/j.eururo.2025.09.4132
Artificial Intelligence in Cancer Immunotherapy: A Leap towards Precision Medicine
Akbari, M., Sadigh‐Eteghad, S., Bahadori, A., Ghassemi-moghaddam, H., & Ziaee, M. (2025) · مجله دانشگاه علوم پزشکی اردبیل · doi:10.61882/jarums.25.1.7
Predicting Durable Clinical Benefits of Postoperative Adjuvant Chemotherapy in Non-small Cell Lung Cancer: A Nomogram Based on CT Imaging and Immune Type.
Deng, L., Zhang, M., Zhu, K., Ren, J.-L., Zhang, P., Zhang, Y.-T., Jing, M., Han, T., Zhang, B., & Zhou, J.-L. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.07.004
A contrastive learning-based neural network to synthesize cell subpopulation features from DCE-MRI for predicting prognosis in breast cancer
Ge, Y., Fan, M., Li, X., Liu, Y.-Y., & Li, L.-H. (2024) · Medical Imaging · doi:10.1117/12.3006685
Noninvasive identification of SOX9 status using radiomics signatures may help construct personalized treatment strategy in hepatocellular carcinoma
Che, F., Wei, Y., Xu, Q., Li, Q., Zhang, T., Wang, L., Li, M., Yuan, F., & Song, B. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04190-2
“In another study, Ferté et al. explored the correlation of radiomics features with the expression of CD8 in solid tumors and showed that the imaging signature may be useful in estimating CD8 cell count and may further help the clinician improve treat-ment selection [30].”
In Response.
Chen, M., & Aboagye, E. (2024) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2023.10.019
Characteristics of long-survivor metastatic melanoma after polychemotherapy and interferon: a retrospective study.
Py, C., Vito, C. D., Tsantoulis, P. K., Kaya, G., Labidi-Galy, S., & Dietrich, P.-Y. (2023) · Swiss medical weekly · doi:10.57187/s.3504
“This pattern has been associated with a better response to immune checkpoint blockade [32, 33] and chemotherapy [34] in metastatic cancer.”
Background
Les outils d’imagerie en oncologie thoracique en 2023
Assié, J., Leleu, O., Olivier, P., & Revel, M. (2023) · Revue des Maladies Respiratoires Actualités · doi:10.1016/s1877-1203(23)00015-0
Translating Data Science Results into Precision Oncology Decisions: A Mini Review
Capobianco, E., & Dominietto, M. D. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12020438
« Adaptation of the tumour and its ecosystem to radiotherapies: Mechanisms, imaging and therapeutic approaches » XIVe édition du workshop organisé par le réseau « Vectorisation, Imagerie, Radiothérapies » du Cancéropôle Grand-Ouest, 22–25 septembre 2021, Le Bono, France
Léost, F., Delpon, G., Garcion, E., Gestin, J., Hatt, M., Potiron, V. A., Rbah‐Vidal, L., & Supiot, S. (2022) · Bulletin du Cancer · doi:10.1016/j.bulcan.2022.06.005
Stereotactic radiosurgery of radioresistant glioblastomas. The ways of overcoming radioresistance of hypoxic tumors
Griazov, A., Griazov, A., Grydina, N., & Stuley, V. (2022) · Український радіологічний та онкологічний журнал · doi:10.46879/ukroj.2.2022.25-40
The Next-Level Precision Medicine in Cancer Management Using Artificial Intelligence.
Tian, J. (2021) · Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih · doi:10.24920/004013
Médecine de précision et immunoradiothérapie
Chargari, C., Robert, C., Genestie, C., & Deutsch, É. (2021) · Cancer/Radiothérapie · doi:10.1016/j.canrad.2021.06.032
Self-citation
Early-stage lung cancer detection from radiomics to deep learning in thoracic CT images: a narrative review with contemporary clinical recommendations
Yang, M.-L., She, Y., Ren, Y.-J., Deng, J.-J., Xie, D., Sun, X.-W., Shi, J., & Chen, C. (2021) · Shanghai Chest · doi:10.21037/shc-20-81
“(52) established a model to predict CD8 expression, which predicted an AUC of 0.”
Background
Imaging Biomarkers to Assess Response to Immune Checkpoint Inhibitors in Solid Tumors to Tailor Therapy.
Summers, R. (2021) · Radiology · doi:10.1148/radiol.2021204407
“Sun et al (7) developed a radiomics score that predicted the abundance of CD8 immune cells in tumors.”
Background
PET Beyond Pictures
Wolsztynski, E., & Eary, J. (2020) · PET/CT and PET/MR in Melanoma and Sarcoma · doi:10.1007/978-3-030-60429-5_6

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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