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

The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

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

Open this paper in CoChat — free account

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.

Integrity signals

Registry

Crossref · 10.1016/s1470-2045(18)30413-3

Cites

0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

PD-1 blockade induces responses by inhibiting adaptive immune resistance
Tumeh, P. C., Harview, C. L., Yearley, J. H., Shintaku, I. P., Taylor, E. J. M., Robert, L., Chmielowski, B., Spasic, M., Henry, G., Ciobanu, V., et al. (2014) · Nature · doi:10.1038/nature13954 · Cited by 6,609
Self-citation
Pembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer
Gandhi, L., Rodríguez-Abreu, D., Gadgeel, S., Esteban, E., Felip, E., De Angelis, F., Domine, M., Clingan, P., Hochmair, M. J., Powell, S. F., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1801005 · Cited by 6,607
Pembrolizumab versus Ipilimumab in Advanced Melanoma
Robert, C., Schachter, J., Long, G. V., Arance, A., Grob, J. J., Mortier, L., Daud, A., Carlino, M. S., McNeil, C., Lotem, M., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1503093 · Cited by 5,887
Self-citation
Elements of cancer immunity and the cancer–immune set point
Chen, D. S., & Mellman, I. (2017) · Nature · doi:10.1038/nature21349 · Cited by 5,352
Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients
Herbst, R. S., Soria, J.-C., Kowanetz, M., Fine, G. D., Hamid, O., Gordon, M. S., Sosman, J. A., McDermott, D. F., Powderly, J. D., Gettinger, S. N., et al. (2014) · Nature · doi:10.1038/nature14011 · Cited by 5,231
Self-citation
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599
Durvalumab after chemoradiotherapy in stage III non–small-cell lung cancer
Antonia, S. J., Villegas, A., Daniel, D., Vicente, D., Murakami, S., Hui, R., Yokoi, T., Chiappori, A., Lee, K. H., de Wit, M., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1709937 · Cited by 4,593
Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma
Hugo, W., Zaretsky, J. M., Sun, L., Song, C., Moreno, B. H., Hu-Lieskovan, S., Berent-Maoz, B., Pang, J., Chmielowski, B., Cherry, G., et al. (2016) · Cell · doi:10.1016/j.cell.2016.02.065 · Cited by 3,448

Show all 9 references

Research works printed but not linked: 41

Limkin, 2017, Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology, Ann Oncol, 28, 1191, 10.1093/annonc/mdx034 · DOI
Gillies, 2010, The biology underlying molecular imaging in oncology: from genome to anatome and back again, Clin Radiol, 65, 517, 10.1016/j.crad.2010.04.005 · DOI
Sun, 2016, In regard to Mattonen et al, Int J Radiat Oncol Biol Phys, 95, 1544, 10.1016/j.ijrobp.2016.03.038 · DOI
Ansell, 2015, Hodgkin lymphoma: MOPP chemotherapy to PD-1 blockade and beyond, Am J Hematol, 91, 109, 10.1002/ajh.24226 · DOI
Reck, 2016, Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer, N Engl J Med, 375, 1823, 10.1056/NEJMoa1606774 · DOI · Paper page
Kim, 2016, Immune escape to PD-L1/PD-1 blockade: seven steps to success (or failure), Ann Oncol, 27, 1492, 10.1093/annonc/mdw217 · DOI · Paper page
Hegde, 2016, The where, the when, and the how of immune monitoring for cancer immunotherapies in the era of checkpoint inhibition, Clin Cancer Res, 22, 1865, 10.1158/1078-0432.CCR-15-1507 · DOI · Paper page
Massard, 2017, High-throughput genomics and clinical outcome in hard-to-treat advanced cancers: results of the MOSCATO 01 trial, Cancer Discov, 7, 586, 10.1158/2159-8290.CD-16-1396 · DOI
Grabowski, 2015, Tumor infiltrating lymphocytes and PD-L1 expression differ in low and high grade neuroendocrine tumors (abstract), Neuroendocrinology, 10, 107
Crow, 1998, Pathology of uterine fibroids, Baillieres Clin Obstet Gynaecol, 12, 197, 10.1016/S0950-3552(98)80061-8 · DOI
Champiat, 2017, Hyperprogressive disease is a new pattern of progression in cancer patients treated by anti-PD-1/PD-L1, Clin Cancer Res, 23, 1920, 10.1158/1078-0432.CCR-16-1741 · DOI
Sun, 2017, Baseline lymphopenia should not be used as exclusion criteria in early clinical trials investigating immune checkpoint blockers (PD-1/PD-L1 inhibitors), Eur J Cancer, 84, 202, 10.1016/j.ejca.2017.07.033 · DOI
Becht, 2016, Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression, Genome Biol, 17, 218, 10.1186/s13059-016-1070-5 · DOI
Weinstein, 2013, The Cancer Genome Atlas Pan-Cancer analysis project, Nat Genet, 45, 1113, 10.1038/ng.2764 · DOI · Paper page
Sridharan, 2016, Immune profiling of adenoid cystic carcinoma: PD-L2 expression and associations with tumor-infiltrating lymphocytes, Cancer Immunol Res, 4, 679, 10.1158/2326-6066.CIR-16-0031 · DOI
Hatt, 2015, 18F-FDG PET uptake characterization through texture analysis: investigating the complementary nature of heterogeneity and functional tumor volume in a multi-cancer site patient cohort, J Nucl Med, 56, 38, 10.2967/jnumed.114.144055 · DOI
Vallières, 2017, Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer, Sci Rep, 7, 10117, 10.1038/s41598-017-10371-5 · DOI
Nioche, 2017, A freeware for tumor heterogeneity characterization in PET, SPECT, CT, MRI and US to accelerate advances in radiomics, J Nucl Med, 58, S1316
Leijenaar, 2015, The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis, Sci Rep, 5, 11075, 10.1038/srep11075 · DOI
Patro, 2017, Salmon provides fast and bias-aware quantification of transcript expression, Nat Methods, 14, 417, 10.1038/nmeth.4197 · DOI · Paper page
Miles, 2009, Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival, Radiology, 250, 444, 10.1148/radiol.2502071879 · DOI
Zou, 2005, Regularization and variable selection via the elastic net, J R Stat Soc Series B Stat Methodol, 67, 301, 10.1111/j.1467-9868.2005.00503.x · DOI · Paper page
Gutman, 2013, Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data, J Am Med Inform Assoc, 20, 1091, 10.1136/amiajnl-2012-001469 · DOI
Arkenau, 2009, Prospective validation of a prognostic score to improve patient selection for oncology phase I trials, J Clin Oncol, 27, 2692, 10.1200/JCO.2008.19.5081 · DOI
Jayasurya, 2000, Lymphocytic infiltration in undifferentiated nasopharyngeal cancer, Arch Otolaryngol Head Neck Surg, 126, 1329, 10.1001/archotol.126.11.1329 · DOI
Braman, 2017, Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI, Breast Cancer Res, 19, 57, 10.1186/s13058-017-0846-1 · DOI
Galloway, 1975, Texture analysis using gray level run lengths, Computer Graphics and Image Processing, 4, 172, 10.1016/S0146-664X(75)80008-6 · DOI
Fletcher, 2016, Dealing with uncertainty in CT Images, Radiology, 279, 5, 10.1148/radiol.2016152771 · DOI
Pao, 2018, Tissue-specific immunoregulation: a call for better understanding of the “immunostat” in the context of cancer, Cancer Discov, 8, 395, 10.1158/2159-8290.CD-17-1320 · DOI
Teillaud, 2017, Tertiary lymphoid structures: an anti-tumor school for adaptive immune cells and an antibody factory to fight cancer?, Front Immunol, 8, 830, 10.3389/fimmu.2017.00830 · DOI
Goc, 2014, Tertiary lymphoid structures in human lung cancers, a new driver of antitumor immune responses, Oncoimmunology, 3, e28976, 10.4161/onci.28976 · DOI
Marvel, 2015, Myeloid-derived suppressor cells in the tumor microenvironment: expect the unexpected, J Clin Invest, 125, 3356, 10.1172/JCI80005 · DOI
Lanitis, 2017, Mechanisms regulating T-cell infiltration and activity in solid tumors, Ann Oncol, 28, xii18, 10.1093/annonc/mdx238 · DOI · Paper page
Tumeh, 2017, Liver metastasis and treatment outcome with anti-PD-1 monoclonal antibody in patients with melanoma and NSCLC, Cancer Immunol Res, 5, 417, 10.1158/2326-6066.CIR-16-0325 · DOI
Tang, 2018, Development of an immune-pathology informed radiomics model for non-small cell lung cancer, Sci Rep, 8, 1922, 10.1038/s41598-018-20471-5 · DOI · Paper page
Grossmann, 2017, Defining the biological basis of radiomic phenotypes in lung cancer, Elife, 6, e23421, 10.7554/eLife.23421 · DOI · Paper page
Chen, 2018, Associations of tumor PD-1 ligands, immunohistochemical studies, and textural features in 18F-FDG PET in squamous cell carcinoma of the head and neck, Sci Rep, 8, 105, 10.1038/s41598-017-18489-2 · DOI
Thorsson, 2018, The immune landscape of cancer, Immunity, 48, 812, 10.1016/j.immuni.2018.03.023 · DOI · Paper page
Saltz, 2018, Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images, Cell Rep, 23, 181, 10.1016/j.celrep.2018.03.086 · DOI · Paper page
Davoli, 2017, Tumor aneuploidy correlates with markers of immune evasion and with reduced response to immunotherapy, Science, 355, eaaf8399, 10.1126/science.aaf8399 · DOI
Liu, 2018, Tumor-repopulating cells induce PD-1 expression in CD8+ T cells by transferring kynurenine and AhR activation, Cancer Cell, 33, 480, 10.1016/j.ccell.2018.02.005 · DOI · Paper page

Sorted by citations

Items 601–700 of 1,003

Radiogenomics study to predict the nuclear grade of renal clear cell carcinoma
He, X., Zhao, J.-X., He, D.-L., Ren, J.-L., Zhao, L., & Huang, G. (2023) · European Journal of Radiology Open · doi:10.1016/j.ejro.2023.100476 · Cited by 9
Improved risk stratification by PET-based intratumor heterogeneity in children with high-risk neuroblastoma
Li, C., Wang, S.-Y., Li, C., Yin, Y., Feng, F., Fu, H.-L., Wang, H., & Chen, S.-Y. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.896593 · Cited by 9
The value of AI in the Diagnosis, Treatment, and Prognosis of Malignant Lung Cancer
Wang, Y., Cai, H., Pu, Y., Li, J., Yang, F., Yang, C., Chen, L., & Hu, Z. (2022) · Frontiers in Radiology · doi:10.3389/fradi.2022.810731 · Cited by 9
“The schematic diagram of the radiomics workflow is shown in Figure 6 (52).”
Method
Machine Learning for Computed Tomography Radiomics
Bian, Y., Liu, Y. F., Li, J., Liu, F., Fang, X., Lu, J., & Shao, C. (2022) · Pancreas · doi:10.1097/mpa.0000000000002069 · Cited by 15
Radiomics signature: A potential biomarker for β-arrestin1 phosphorylation prediction in hepatocellular carcinoma
Che, F., Xu, Q., Li, Q., Huang, Z.-X., Yang, C., Wang, L. Y., Wei, Y., Shi, Y., & Song, B. (2022) · World Journal of Gastroenterology · doi:10.3748/wjg.v28.i14.1479 · Cited by 10
Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage I−III colorectal cancer
Huang, Y.-Q., He, L., Li, Z.-H., Chen, X., Han, C., Zhao, K., Zhang, Y., Qu, J., Mao, Y., Liang, C.-H., et al. (2022) · Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · doi:10.21147/j.issn.1000-9604.2022.01.04 · Cited by 9
“Remarkable advances in high-throughput technology have revolutionized the field of CT imaging interpretation, putting novel quantitative image analysis methods, particularly radiomics, under the spotlight (6-9).”
Background
Image intensity histograms as imaging biomarkers: application to immune-related colitis
Huff, D. T., Ferjančič, P., Namías, M., Emamekhoo, H., Perlman, S., & Jeraj, R. (2021) · Biomedical engineering and physics express · doi:10.1088/2057-1976/ac27c3 · Cited by 9
Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study
Wang, Z.-X., Li, N., Zheng, F., Sui, X., Han, W., Xue, F., Xu, X.-L., Yang, C.-H., Hu, Y.-D., Wang, L., et al. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-021-02849-8 · Cited by 9
“After this proof-of-concept study, we plan to apply the proposed time-dependent analysis framework to serial image data (recently termed delta-radiomics [30]) with a large sample size.”
Method
The Impact of Preoperative Radiomics Signature on the Survival of Breast Cancer Patients With Residual Tumors After NAC
Zhang, L., Jiang, X., Xie, X.-M., Wu, Y.-P., Zheng, S., Tian, W.-W., Xie, X.-H., & Li, L. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.523327 · Cited by 9
Clinicopathological implications of lncRNAs, immunotherapy and DNA methylation in lung squamous cell carcinoma: a narrative review
Sasa, G. B. K., Xuan, C., Chen, M., Jiang, Z., & Ding, X. (2021) · Translational Cancer Research · doi:10.21037/tcr-21-1607 · Cited by 9
“A recent study indicated that cancer immune invading cells are closely related to clinical outcomes (138,139).”
Background
Predictive Biomarkers for Immune Checkpoint Inhibitors in Advanced Non–Small Cell Lung Cancer
Bhalla, S., Doroshow, D. B., & Hirsch, F. R. (2020) · The Cancer Journal · doi:10.1097/ppo.0000000000000483 · Cited by 10
Pretreatment MR‐Based Radiomics Signature as Potential Imaging Biomarker for Assessing the Expression of Topoisomerase II alpha (TOPO‐IIα) in Rectal Cancer
Chen, J.-Y., Chen, Y., Zheng, D.-C., Pang, P., Lu, J.-P., & Zheng, X. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26972 · Cited by 9
Regulatory issues for artificial intelligence in radiology
Pesapane, F., Suter, M. B., Codari, M., Patella, F., Volonté, C., & Sardanelli, F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-819178-1.00052-6 · Cited by 16
Artificial intelligence-driven clinical decision support systems for precision oncology: A comprehensive review
Borat, S., & Chowdhury, S. (2026) · In Silico Research in Biomedicine · doi:10.1016/j.insi.2026.100265 · Cited by 10
Deep learning for malignant lymph node segmentation and detection: a review
Wu, W., Laville, A., Deutsch, E., & Sun, R. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1526518 · Cited by 8
Self-citation
Multiparametric MRI-based radiomics and clinical nomogram predicts the recurrence of hepatocellular carcinoma after postoperative adjuvant transarterial chemoembolization
Guo, X.-Y., Song, J.-J., Zhu, L., Liu, S., Huang, C., Zhou, L., Chen, W.-Y., Lin, G.-H., Zhao, Z.-W., Tu, J., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-14079-y · Cited by 8
“Previous research has demonstrated a correlation between tumor heterogeneity and grey-level features with tumor response and prognosis, findings that are consistent with the results of this study [24, 32, 33].”
Interplay between MRI radiomics and immune gene expression signatures in oral squamous cell carcinoma
Corti, A., Lenoci, D., Corino, V., Mattavelli, D., Ravanelli, M., Poli, T., Cavalieri, S., Licitra, L., Cecco, L. D., & Mainardi, L. T. (2025) · Scientific Reports · doi:10.1038/s41598-025-96821-x · Cited by 8
“The predictive performance of our radiomic models is in line with that obtained in similar radiogenomic studies focused on immune-related processes in HNSCC, reporting AUC ranging from 0.52 to 0.80 in the validation set 11–18 .”
Influential
Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: a multi-cohort study.
Xin, H.-J., Lai, Q., Liu, Y.-P., Liao, N., Wang, Y., Liao, B.-H., Zhou, K., Zhou, Y.-C., Bai, Y., Chen, Z., et al. (2024) · Pharmacological Research · doi:10.1016/j.phrs.2024.107535 · Cited by 8
Longitudinal Changes of CT-radiomic and Systemic Inflammatory Features Predict Survival in Advanced Non–Small Cell Lung Cancer Patients Treated With Immune Checkpoint Inhibitors
Balbi, M., Mazzaschi, G., Leo, L., Tor, L. M. D., Milanese, G., Marrocchio, C., Silva, M., Mura, R., Favia, P., Bocchialini, G., et al. (2024) · Journal of thoracic imaging · doi:10.1097/rti.0000000000000801 · Cited by 8
Evaluation of Radiomics as a predictor of efficacy and the tumor immune microenvironment in anti-PD-1 mAb treated Recurrent/Metastatic HNSCC patients
Zandberg, D., Zenkin, Ş., Ak, M., Mamindla, P., Peddagangireddy, V., Hsieh, R., Anderson, J. L., Delgoffe, G. M., Menk, A., Skinner, H. D., et al. (2024) · Head and Neck · doi:10.1002/hed.27878 · Cited by 8
“22 In another radiomics study, Mu et al. described a multi-parametric radiomics model based on positron emission tomography (PET)/CT images to predict durable clinical benefit of immunotherapy response in patients with advanced NSCLC (AUC ranging from 0.81 to 0.86).”
Influential
Radio-immunomics in hepatocellular carcinoma: Unraveling the tumor immune microenvironment
Liu, Q., Zhu, W., Song, F., Lou, T., He, L., Zhou, W., & Feng, Z. (2024) · Meta-Radiology · doi:10.1016/j.metrad.2024.100098 · Cited by 7
Association between programmed cell death ligand-1 expression in patients with cervical cancer and apparent diffusion coefficient values: a promising tool for patient´s immunotherapy selection
Liu, K., Yang, W., Tian, H.-P., Li, Y.-X., & He, J.-L. (2024) · European Radiology · doi:10.1007/s00330-024-10759-8 · Cited by 8
“Immunotherapy targeting the programmed cell death protein 1 (PD-1)/programmed cell death ligand-1 (PD-L1) has played a pivotal role in CC and attracted increasing attention, especially in recurrent and metastatic CC [4 – 8].”
FAT1 upregulation is correlated with an immunosuppressive tumor microenvironment and predicts unfavorable outcome of immune checkpoint therapy in non-small cell lung cancer
Chen, C., Li, Y., Liu, H., Liao, M., Yang, J., & Liu, J. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28356 · Cited by 6
“Previous studies have shown that CD8 + T cell infiltration could predict the effect of ICI [46], while CAFs can prevent the infiltration of T cells and thus prevent the curative effect of ICI therapy [25,47,48].”
Background
A computed tomography-based multitask deep learning model for predicting tumour stroma ratio and treatment outcomes in patients with colorectal cancer: a multicentre cohort study
Cui, Y., Zhao, K., Meng, X., Mao, Y. J., Han, C., Shi, Z., Yang, X., Tong, T., Wu, L., & Liu, Z. (2024) · International Journal of Surgery · doi:10.1097/js9.0000000000001161 · Cited by 21
“The association between radiomics features and tumour immune microenvironment has been extensively investigated [13 – 15] .”
Background
Application of multi-modality MRI-based radiomics in the pre-treatment prediction of RPS6K expression in hepatocellular carcinoma
Yang, F., Wan, Y., Shen, X., Wu, Y., Xu, L., Meng, J., Wang, J., Liu, Z.-K., Chen, J., Lu, D., et al. (2023) · Molecular Biomedicine · doi:10.1186/s43556-023-00133-3 · Cited by 8
Genopathomic profiling identifies signatures for immunotherapy response of lung adenocarcinoma via confounder-aware representation learning
Deng, J., Yang, J., Hou, L., Wu, J., He, Y. F., Zhao, M., Ni, B., Wei, D., Pfister, H., Zhou, C., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105382 · Cited by 6
“Previous studies have shown the predictive value of artificially intelligent biomarkers based on other modalities, such as radiological (Sun et al., 2018) and genetic features (Cristescu et al., 2018), in patients with advanced NSCLC, but not using digital histology slides.”
Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics
Perrone, M., Raimondi, E., Costa, M., Rasetto, G., Rizzati, R., Lanza, G., Gafà, R., Cavallesco, G., Tamburini, N., Maniscalco, P., et al. (2022) · Cancers · doi:10.3390/cancers14143335 · Cited by 8
“analyzed both the microenvironment of solid tumors and radiomic features to evaluate CD8+ cells in the tumor periphery and found a promising way to predict immune phenotypes and clinical outcomes for solid cancer patients [30].”
Background
Computed Tomography-based Radiomics Evaluation of Postoperative Local Recurrence of Pancreatic Ductal Adenocarcinoma.
He, M., Chen, X.-Y., Wels, M., Lades, F., Li, Y.-T., Liu, Z.-Y., Jin, Z.-Y., & Xue, H.-D. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.05.019 · Cited by 8
Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents
Alqahtani, A. (2022) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2022/6201067 · Cited by 13
“[105] created a machine learning–based AI system that can effectively anticipate the therapeutic benefit of apoptosis protein 1 (PD-1) inhibitors.”
Background
Retracted
Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center “Blue Sky Radiomics” study on stage III unresectable NSCLC
Cabini, R., Brero, F., Lancia, A., Stelitano, C., Oneta, O., Ballante, E., Puppo, E., Mariani, M., Alì, E., Bartolomeo, V., et al. (2022) · Insights into Imaging · doi:10.1186/s13244-022-01171-1 · Cited by 8
“Moreover, the use of the Blue Sky dataset, which is part of an observational clinical trial [18], reduces further variability due to the different operators contouring the ROIs, and less control over a series of processes (related to image acquisition, contouring and clinical selection).”
MethodSelf-citation
A Multiomics Signature Highlights Alterations Underlying Homologous Recombination Deficiency in Triple-Negative Breast Cancer
Su, G.-H., Jiang, L., Xiao, Y., Zheng, R., Wang, H., Jiang, Y., Peng, W., Shao, Z.-M., Gu, Y., & You, C. (2022) · Annals of Surgical Oncology · doi:10.1245/s10434-022-11958-7 · Cited by 9
“Notably, CD8-positive T cells and patient response to immunotherapy were predicted by radiomics features.(36,37) Our results, together with those of previous radiomics studies, provide support that radiomics has the potential to benefit clinical index prediction and decision making.”
Result
Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models
Smyczyńska, U., Grabia, S., Nowicka, Z., Papis-Ubych, A., Bibik, R., Latusek, T., Rutkowski, T., Fijuth, J., Fendler, W., & Tomasik, B. (2021) · medRxiv · doi:10.3390/cancers13215584 · Cited by 8
“Radiomic features were shown to reflect cancer biology in terms of histologic type [12], overall and progression-free survival [12–14], probability of recurrence [15], activity of biological pathways [12,16], human papillomavirus infection [17], probability lymph node [18] or distant metastases [19], CD8 cells infiltr…”
Background
Multi-Omics and Its Clinical Application in Hepatocellular Carcinoma: Current Progress and Future Opportunities.
Yang, W., Jiang, H.-Y., Liu, C., Wei, J.-W., Zhou, Y., Gong, P., Song, B., & Tian, J. (2021) · Chinese medical sciences journal = Chung-kuo i hsueh k'o hsueh tsa chih · doi:10.24920/003984 · Cited by 8
CEUS-Based Radiomics Can Show Changes in Protein Levels in Liver Metastases After Incomplete Thermal Ablation
Bao, H., Chen, T., Zhu, J., Xie, H.-Y., & Chen, F. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.694102 · Cited by 8
“In theory, imaging has the potential to reveal the tumor phenotype and molecular changes from the level of macroscopic characteristics of organ tissues to the level of local cell and molecular characteristics (12).”
Background
A CT-Based Radiomic Signature Can Be Prognostic for 10-Months Overall Survival in Metastatic Tumors Treated with Nivolumab: An Exploratory Study
Corino, V., Bologna, M., Calareso, G., Licitra, L., Ghi, M., Rinaldi, G., Caponigro, F., Morelli, F., Airoldi, M., Allegrini, G., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11060979 · Cited by 8
“Strengths and weaknesses in relation to other studies, discussing particularly any differences in results: This was not the first study in which radiomics has been applied to define the prognosis of patients treated with immunotherapy [5,7–9,12–26], even though most of the studies refer to lung cancers [5,7,8,12].”
MethodResult
Biomarker Technologies to Support Early Clinical Immuno-oncology Development: Advances and Interpretation
Cannarile, M. A., Gomes, B., Cañamero, M., Reis, B., Byrd, A. L., Charo, J., Yadav, M., & Karanikas, V. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2345 · Cited by 14
Validation of a radiomic approach to decipher NSCLC immune microenvironment in surgically resected patients
Trentini, F., Mazzaschi, G., Milanese, G., Pavone, C., Madeddu, D., Gnetti, L., Frati, C., Lorusso, B., Lagrasta, C., Minari, R., et al. (2021) · Tumori · doi:10.1177/03008916211000808 · Cited by 8
Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies
Foley, K., Pearson, B., Riddell, Z. C., & Taylor, S. A. (2021) · Clinical Radiology · doi:10.1016/j.crad.2021.01.001 · Cited by 9
A Deep Learning-Facilitated Radiomics Solution for the Prediction of Lung Lesion Shrinkage in Non-Small Cell Lung Cancer Trials
Chen, A., Saouaf, J., Zhou, B., Crawford, R., Yuan, J.-D., Ma, J., Baumgartner, R., Wang, S.-B., & Goldmacher, G. (2020) · IEEE International Symposium on Biomedical Imaging · doi:10.1109/isbi45749.2020.9098561 · Cited by 8
“[6] introduced a multicohort study where they correlated radiomic signatures extracted from CT scans with CD8β gene expression to indicate the abundance of tumor-infiltrating cytotoxic T-cells in 135 patients.”
Background
Design and Conduct of Early Clinical Studies of Immunotherapy: Recommendations from the Task Force on Methodology for the Development of Innovative Cancer Therapies 2019 (MDICT)
Smoragiewicz, M., Adjei, A. A., Calvo, E., Tabernero, J. M., Marabelle, A., Massard, C., Tang, J., de Vries, E. G. E., Douillard, J.-Y., & Seymour, L. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-3136 · Cited by 11
“A radiomics approach, the high-throughput extraction of large amounts of image features from radiographic images (34), using features from chemotherapy images may be easier and less costly to identify hot and cold tumors (35).”
Self-citation
First-line checkpoint inhibitors for wild-type advanced non-small-cell cancer: a pair-wise and network meta-analysis.
Wang, X.-J., Lin, J.-Z., Yu, S.-H., Wu, S.-X., Luo, H.-S., Du, Z., & Li, X.-Y. (2019) · Immunotherapy · doi:10.2217/imt-2018-0107 · Cited by 8
Improving prediction of treatment response and prognosis in colorectal cancer with AI-based medical image analysis
Liu, X., Zhang, S., Shao, L., Sun, C., Li, B., Wei, W., Ying, Z., Liu, Z., & Tian, J. (2024) · The Innovation Medicine · doi:10.59717/j.xinn-med.2024.100069 · Cited by 8
Adaptive resistance in cancer immunotherapy
Yang, K. E., Yang, C., Xiong, K., Hao, J., & Ye, L. (2026) · Cellular and Molecular Immunology · doi:10.1038/s41423-026-01424-9 · Cited by 11
Definitive Radiotherapy to the Primary Tumor in Stage IV NSCLC: A Consensus Statement From the International Association for the Study of Lung Cancer Advanced Radiation Technology Subcommittee.
McMahon, R., Chua, K. L. M., Faivre-Finn, C., Filippi, A., Hendriks, L., John, T., Liu, S. V., McDonald, F., Popat, S., Saw, S. P., et al. (2026) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2026.103643 · Cited by 7
Radiogenomics: transforming lung cancer care through non-invasive imaging and genomic integration
Shariaty, F., & Pavlov, V. A. (2025) · Medical Oncology · doi:10.1007/s12032-025-03118-0 · Cited by 6
“PET/CT and CT models can estimate PD-L1 status and capture intratumoral CD8⁺ T-cell contexture, predicting durable benefit and survival in external/prospective cohorts; com - bined with clinical factors, performance approaches IHC-based triage in some studies [55, 56].”
Prospects and challenges of deep learning in gynecologic malignancies
Zhang, Y., & Zeng, L. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1592078 · Cited by 8
Advances in Multimodal Imaging Techniques for Evaluating and Predicting the Efficacy of Immunotherapy for NSCLC
Liu, J.-Y., Xie, M., Shen, J., Yao, J., Lin, X.-W., Bao, X.-Y., Zhang, X., Liang, Y.-R., Yang, Y., Jiang, G.-G., et al. (2025) · Cancer Management and Research · doi:10.2147/cmar.s522136 · Cited by 7
Deep Learning Radiomics Nomogram Based on MRI for Differentiating between Borderline Ovarian Tumors and Stage I Ovarian Cancer: A Multicenter Study
Wang, X., Tao, Q., Chu, X., Gao, M., Zhang, Y., Chen, Y., Bai, G., Chen, S., & Wei, M. (2025) · Academic Radiology · doi:10.1016/j.acra.2024.12.067 · Cited by 5
Machine Learning and Computed Tomography Radiomics to Predict Disease Progression to Upfront Pembrolizumab Monotherapy in Advanced Non-Small-Cell Lung Cancer: A Pilot Study
Janzen, I., Ho, C., Melosky, B., Ye, Q.-W., Li, J. L., Wang, G., Lam, S., MacAulay, C., & Yuan, R. (2024) · Cancers · doi:10.3390/cancers17010058 · Cited by 7
Intranodular and perinodular ultrasound radiomics distinguishes benign and malignant thyroid nodules: a multicenter study
Zhu, X., Li, J., Li, H., Wang, K.-F., Zhang, J., Meng, J., Wu, R., Zhang, M.-L., & Du, H. (2024) · Gland surgery · doi:10.21037/gs-24-416 · Cited by 7
“Recently, multiple studies have confirmed the diagnostic and predictive ability of radiomics in the peritumoral region in disease assessment (19-21).”
The Emerging Role of Immune Checkpoint Blockade for the Treatment of Lung Cancer Brain Metastases
Wu, X., Stabile, L. P., & Burns, T. F. (2024) · Clinical Lung Cancer · doi:10.1016/j.cllc.2024.06.004 · Cited by 7
Interpretable multiphasic CT-based radiomic analysis for preoperatively differentiating benign and malignant solid renal tumors: a multicenter study
Wu, Y.-H., Cao, F., Lei, H.-Q., Zhang, S., Mei, H.-B., Ni, L., & Pang, J. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04351-3 · Cited by 7
The Art of Bioimmunogenomics (BIGs) 5.0 in CAR-T Cell Therapy for Lymphoma Management
Anurogo, D., Luthfiana, D., Anripa, N., Fauziah, A. I., Soleha, M., Rahmah, L., Ratnawati, H., Wargasetia, T. L., Pratiwi, S. E., Siregar, R. N., et al. (2024) · Advanced Pharmaceutical Bulletin · doi:10.34172/apb.2024.034 · Cited by 9
Radiomics in stereotactic body radiotherapy for non-small cell lung cancer: a systematic review and radiomic quality score study
Cheung, B. (2024) · Radiation Oncology Journal · doi:10.3857/roj.2023.00612 · Cited by 7
“Recently, treatment response towards immunotherapy has been predicted with good accuracy using radiomic features [16].”
Background
Development and validation of a radiomics nomogram for diagnosis of malignant pleural effusion
Wei, M.-Z., Zhang, Y.-P., Zhao, L., & Zhao, Z.-H. (2023) · Discover Oncology · doi:10.1007/s12672-023-00835-8 · Cited by 7
“It is crucial to select features and build models based on disease categories, and to correlate radiomics features with more biological information [43, 44].”
Background
Updates on radiotherapy-immunotherapy combinations: Proceedings of 6 th annual ImmunoRad conference
Gregucci, F., Spada, S., Barcellos‐Hoff, M. H., Bhardwaj, N., Hak, C. C. W., Fiorentino, A., Guha, C., Guzmán, M. L., Harrington, K. J., Herrera, F. G., et al. (2023) · OncoImmunology · doi:10.1080/2162402x.2023.2222560 · Cited by 12
“Radiomics has indeed been successfully applied to develop an imaging biomarker of tumor-infiltrating CD8 + T cells in patients receiving IT 93,94 .”
Self-citation
A CT-based radiomics nomogram for predicting histopathologic growth patterns of colorectal liver metastases
Sun, C., Liu, X.-H., Sun, J., Dong, L.-C., Wei, F., Bao, C., Zhong, J., & Li, Y.-M. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-023-04852-6 · Cited by 7
“It develops rapidly in cancer detection, diagnosis, selection of treatment strategies, prognosis, treatment response and monitoring (Sun et al. 2018; Mayerhoefer et al. 2020; Park et al. 2018).”
Background
Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma
Tabari, A., Cox, M., D’Amore, B., Mansur, A., Dabbara, H., Boland, G. M., Gee, M. S., & Daye, D. (2023) · Cancers · doi:10.3390/cancers15102700 · Cited by 7
“, an MRI-based radiomics model correlated with response to immunotherapy was developed and validated on 3 independent cohorts with diverse image acquisition parameters [47].”
Method
Application of radiomics in lung immuno‐oncology
Yan, W., Chen, Q., Mourad, W. F., Yuan, J. D., Shi, Z., Yang, J., Lu, Q., & Zhang, J. (2023) · Precision Radiation Oncology · doi:10.1002/pro6.1191 · Cited by 8
Post-Surgical Imaging Assessment in Rectal Cancer: Normal Findings and Complications
Muzio, F. D., Fusco, R., Cutolo, C., Giacobbe, G., Bruno, F., Palumbo, P., Danti, G., Grazzini, G., Flammia, F., Borgheresi, A., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12041489 · Cited by 11
Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy
Han, X.-Y., Wang, Y.-J., Jia, X., Zheng, Y.-T., Ding, C., Zhang, X., Zhang, K., Cao, Y.-K., Li, Y.-M., Xia, L., et al. (2023) · Translational Lung Cancer Research · doi:10.21037/tlcr-24-7 · Cited by 7
“Furthermore, the radiomic signature of CD8 + T cells has been proven to predict the immunophenotype of solid tumors and clinical outcomes in patients with cancer who had been treated with ICI therapy (33).”
CNPY4 is a potential promising prognostic-related biomarker and correlated with immune infiltrates in gliomas
Li, J., Huang, Q., & Mo, L. (2022) · Medicine · doi:10.1097/md.0000000000030044 · Cited by 7
DNA Methylation Regulator-Meditated Modification Patterns Define the Distinct Tumor Microenvironment in Lung Adenocarcinoma
Yuan, D.-D., Wei, Z., Wang, Y., Cheng, F., Zeng, Y.-J., Yang, L., Zhang, S., Li, J.-B., & Tang, R. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.734873 · Cited by 7
A Radiomic Approach to Access Tumor Immune Status by CD8+TRMs on Surgically Resected Non-Small-Cell Lung Cancer
Min, J., Dong, F., Wu, P., Xu, X., Wu, Y., Tan, Y., Yang, F., & Chai, Y. (2021) · OncoTargets and Therapy · doi:10.2147/ott.s316994 · Cited by 7
“Previous study use radiomics approach to assess tumor-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy showed that imaging biomarker could be useful in estimating CD8 cell count and predicting clinical outcomes of patients treated with immunotherapy, when validated by further prospective ra…”
Method
Delta-volume radiomics of induction chemotherapy to predict outcome of subsequent chemoradiotherapy for locally advanced hypopharyngeal cancer
Su, C., Lee, J.-C., Chang, Y.-F., Su, N., Lee, P.-H., Dai, K., Tai, H., Leu, Y., & Chen, Y.-J. (2021) · Tumori · doi:10.1177/03008916211039018 · Cited by 7
Local Therapies and Modulation of Tumor Surrounding Stroma in Malignant Pleural Mesothelioma: A Translational Approach
Lisini, D., Lettieri, S., Nava, S., Accordino, G., Frigerio, S., Bortolotto, C., Lancia, A., Filippi, A., Agustoni, F., Pandolfi, L., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22169014 · Cited by 7
“Diffusion-weighted imaging (DWI) allows patient-tailored care in MPM: it has already been used to discriminate between long- and short-term overall survivors [50].”
BackgroundSelf-citation
Uncertainty measurement of radiomics features against inherent quantum noise in computed tomography imaging
Tu, S., Chen, W.-Y., & Wu, C. (2021) · European Radiology · doi:10.1007/s00330-021-07943-5 · Cited by 7
“Features SRHGE, SRLGE, LGRE, and LRLGE were identified useful in one of recent immunotherapy studies based on the CT imaging on different disease sites [1].”
BackgroundInfluential
A multidisciplinary consensus on the morphological and functional responses to immunotherapy treatment
León-Mateos, L., García-Velloso, M., García-Figueiras, R., Rodríguez-Moreno, J., Vercher-Conejero, J., Sanchez, M., Gracia, J. L. P., Simó-Perdigó, M., & Gorospe, L. (2020) · Clinical and Translational Oncology · doi:10.1007/s12094-020-02442-3 · Cited by 7
Artificial intelligence in health care: value for whom?
Zeitoun, J., & Ravaud, P. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30141-2 · Cited by 7
Understanding Response to Immunotherapy Using Standard of Care and Experimental Imaging Approaches
Flavell, R. R., Evans, M. J., Villanueva-Meyer, J. E., & Yom, S. S. (2020) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2020.06.025 · Cited by 10
State of the Future: Translational Approaches in Renal Cell Carcinoma in the Immunotherapy Era
Bakouny, Z., Flippot, R., Braun, D. A., Lalani, A. A., & Choueiri, T. K. (2019) · European Urology Focus · doi:10.1016/j.euf.2019.02.014 · Cited by 8
Is there any correlation between spectral CT imaging parameters and PD‐L1 expression of lung adenocarcinoma?
Chen, M.-L., Shi, A., Li, X.-T., Wei, Y.-Y., Qi, L.-P., & Sun, Y.-S. (2019) · Thoracic Cancer · doi:10.1111/1759-7714.13273 · Cited by 7
Companion diagnostic tests for treatment of lung cancer patients: what are the current and future challenges?
Hofman, P. M., & Barlési, F. (2019) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2019.1611426 · Cited by 9
Clinical applications of immunogenomics in hepatocellular carcinoma
Carter, J. K., Cameron, D. C., & Villanueva, A. (2026) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2025.1323 · Cited by 6
“They generated a radiomic signature that could discriminate inflamed vs immune-exhausted molecular classes, levels of CD8+ T cell infiltration, and predict OS and progression-free survival[223].”
A CT-based interpretable deep learning signature for predicting PD-L1 expression in bladder cancer: a two-center study
Han, X., Guan, J., Guo, L., Jiao, Q., Wang, K., Hou, F., Liu, S.-L., Yang, S.-F., Huang, C.-C., Cong, W., et al. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00849-1 · Cited by 6
Application of Intravoxel Incoherent Motion in the Prediction of Intra-Tumoral Tertiary Lymphoid Structures in Hepatocellular Carcinoma
Ma, L., Liao, S., Zhang, X., Zhou, F., Geng, Z., Hu, J. R., Zhang, Y., Zhang, C., Meng, T., Wang, S., et al. (2025) · Journal of Hepatocellular Carcinoma · doi:10.2147/jhc.s508357 · Cited by 7
“11,12 A previous study demonstrated that qualitative imaging features on pre-operative computed tomography (CT) may be predictors of intra-tumor TLSs in HCC, suggesting a possibility to infer TLS status through image-derived morphometrics.”
Radiomics-based biomarker for PD-1 status and prognosis analysis in patients with HCC
Hapaer, G., Che, F., Xu, Q., Li, Q., Liang, A., Wang, Z., Ziluo, J., Zhang, X., Wei, Y., Yuan, Y., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1435668 · Cited by 9
Computed tomography-based radiomic model for the prediction of neoadjuvant immunochemotherapy response in patients with advanced gastric cancer
Zhang, J., Wang, Q., Guo, T., Gao, W., Yu, Y., Wang, R., Yu, H., Chen, J.-J., Sun, L.-L., Zhang, B.-Y., et al. (2024) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v16.i10.4115 · Cited by 6
Prognostic Significance of the Immune Microenvironment in Endometrial Cancer
Lee, M., Jung, W., Kang, J., Lee, K. H., Lee, S. J., Hong, S. H., Kang, J. K., & Lee, A. (2024) · Laboratory Investigation · doi:10.1016/j.labinv.2024.102126 · Cited by 6
Multimodal Imaging Approach for Tumor Treatment Response Evaluation in the Era of Immunotherapy.
Lee, G., Moon, S., Kim, J. H., Jeong, D. Y., Choi, J., Choi, J. Y., & Lee, H.-Y. (2024) · Investigative Radiology · doi:10.1097/rli.0000000000001096 · Cited by 6
Assessing the stability and discriminative ability of radiomics features in the tumor microenvironment: Leveraging peri-tumoral regions in vestibular schwannoma.
Hosseini, M., Aghamiri, S. R., Ardekani, A. F., & Bagherimofidi, S. M. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111654 · Cited by 6
Advancing neoadjuvant therapies in resectable non-small cell lung cancer: implications for novel treatment strategies and biomarker discovery
Jeon, H., Gor, R., D’Aiello, A., Stiles, B. M., Illei, P. B., & Halmos, B. (2024) · Pathology & Oncology Research · doi:10.3389/pore.2024.1611817 · Cited by 7
Radiomics signature for dynamic changes of tumor-infiltrating CD8+ T cells and macrophages in cervical cancer during chemoradiotherapy
Huang, K., Huang, X.-H., Zeng, C., Wang, S.-Y., Zhan, Y.-Z., Cai, Q., Peng, G., Yang, Z., Zhou, L., Chen, J.-Z., et al. (2024) · Cancer Imaging · doi:10.1186/s40644-024-00680-0 · Cited by 6
“The above-mentioned study by Sun et al [32], and Jiang et al [50] found that radiomics signatures could reflect the immune state and the characteristics of the TME.”
BackgroundMethod
Prediction of transformation in the histopathological growth pattern of colorectal liver metastases after chemotherapy using CT-based radiomics
Wei, S., Gou, X.-Y., Zhang, Y.-L., Cui, J., Liu, X., Hong, N., Sheng, W., Cheng, J., & Wang, Y. (2024) · Clinical and Experimental Metastasis · doi:10.1007/s10585-024-10275-5 · Cited by 6
“Radiomics is a noninvasive method that complements biopsy and can be used to evaluate the entire lesion and microenvironment and to longitudinally assess disease progression [18].”
Cancer immunotherapy efficacy and machine learning
Fang, Y., Chen, X., & Cao, C. (2024) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2024.2311684 · Cited by 7
“Radiology is a noninvasive tool which complements biopsies, allows assessment of the tumor and its micro-environment, and visualizes the disease evolution [27].”
Background
Early acquired resistance to EGFR-TKIs in lung adenocarcinomas before radiographic advanced identified by CT radiomic delta model based on two central studies
Li, X., Zhang, C.-X., Li, T.-T., Lin, X., Wu, D.-M., Yang, G., & Cao, D.-R. (2023) · Scientific Reports · doi:10.1038/s41598-023-42916-2 · Cited by 6
Application of radiomics and artificial intelligence in lung cancer immunotherapy: a guide and hurdles from clinical trials
Wu, X.-R., & Polychronis, A. (2023) · Journal of Cancer Metastasis and Treatment · doi:10.20517/2394-4722.2023.10 · Cited by 6
Multimodality deep learning radiomics nomogram for preoperative prediction of malignancy of breast cancer: a multicenter study
Wu, P., Jiang, Y., Xing, H., Song, W., Cui, X., Wu, X.-L., & Xu, G. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acec2d · Cited by 6
Radiomics combined with transcriptomics to predict response to immunotherapy from patients treated with PD-1/PD-L1 inhibitors for advanced NSCLC
Bouhamama, A., Leporq, B., Faraz, K., Foy, J., Boussageon, M., Pérol, M., Ortiz‐Cuaran, S., Ghiringhelli, F., Saintigny, P., Beuf, O., et al. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1168448 · Cited by 7
“Radiomics has been used to predict response to anti-PD-L1 immunotherapy and assess tumor-infiltrating CD8 cells or CD3 cells (18).”
BackgroundMethodResultInfluential
Immunotherapy Assessment: A New Paradigm for Radiologists
Granata, V., Fusco, R., Setola, S. V., Simonetti, I., Picone, C., Simeone, E., Festino, L., Vanella, V., Vitale, M. G., Montanino, A., et al. (2023) · Diagnostics · doi:10.3390/diagnostics13020302 · Cited by 7
Genome-wide DNA methylation profile analysis identifies an individualized predictive signature for melanoma immune response
Yan, J., Wu, X.-W., Zhu, Y.-Y., & Cang, S. (2023) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04566-1 · Cited by 6
Non-invasive measurement of tumor immune microenvironment and prediction of survival and chemotherapeutic benefits from 18F fluorodeoxyglucose PET/CT images in gastric cancer
Li, J., Zhang, C., Guo, H., Li, S., You, Y., Zheng, P., Zhang, H., Wang, H., & Bai, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1019386 · Cited by 6
“Recently, several studies showed that the tumor imaging biomarkers could provide a promising way to predict the immune phenotype of tumors and to infer clinical outcomes for patients with cancer who had been treated with PD-1 and PD-L1 inhibitor (15, 16).”
Background
Monitoring of Current Cancer Therapy by Positron Emission Tomography and Possible Role of Radiomics Assessment
Oriuchi, N., Endoh, H., & Kaira, K. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23169394 · Cited by 8
Intra-scan inter-tissue variability can help harmonize radiomics features in CT
Beaumont, H., Iannessi, A., Cucchi, J., Bertrand, A., & Lucidarme, O. (2021) · European Radiology · doi:10.1007/s00330-021-08154-8 · Cited by 6
“of quantitative measurements from clinical images, combining them with the aim of identifying characteristics that quantify patients’ conditions or monitor therapeutic responses [1].”
Background
The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning
Angus, L., Starmans, M., Rajicic, A., Odink, A., Jalving, M., Niessen, W., Visser, J. J., Sleijfer, S., Klein, S., & van der Veldt, A. V. D. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11040257 · Cited by 6
“Similar to our study, previous radiomics models, either to predict therapy response or survival, had a low to moderate performance in metastatic melanoma [26,38,39].”
Result
Correction to: Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers
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/s00330-021-07721-3 · Cited by 60
A novel immunogenomic prognostic signature in lung squamous carcinoma
Hou, J., & Zhong, Q. (2021) · Medicine · doi:10.1097/md.0000000000024073 · Cited by 9
“Emerging evidence has revealed that cancer immune infiltrating cells are closely correlated with clinical outcomes.([33,34]) In this study, we investigated the correlation between risk signature and immune infiltrating cells.”
Background
Radiomic Model for Distinguishing Dissecting Aneurysms from Complicated Saccular Aneurysms on high-Resolution Magnetic Resonance Imaging
Cao, X., Xia, W., Tang, Y., Zhang, B., Yang, J., Zeng, Y., Geng, D., & Zhang, J. (2020) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2020.105268 · Cited by 6
Methodological studies
Azam, F., Phillips, R., Newport, R., Johnson, R. L., Midgley, R., & Kerr, D. J. (2021) · Principles of Translational Science in Medicine · doi:10.1016/b978-0-12-820493-1.00002-7 · Cited by 6
AI-driven radiogenomics in gynecologic oncology: from radiological digital biopsy to a new paradigm in precision therapy
Qiqi, K., & Ban, Y. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1745519 · Cited by 7

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Metadata

Record

About these checks