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 701–800 of 1,003

Unlocking the therapeutic potential of immuno-radiotherapy: insights from preclinical and clinical research
Cogels, M., Caluwé, A. D., Buisseret, L., & Penninckx, S. (2026) · OncoImmunology · doi:10.1080/2162402x.2025.2608436 · Cited by 9
“203 Importantly, the spatial distribution of immune cells in tumors defines the interactions among immune cells and between immune and cancer cells, orchestrating the response to immuno-therapy.”
Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models
Zou, H.-H., Liu, J., Shi, S.-D., Ling, S.-G., Fan, Q., Huo, Y., Dong, Z., Han, G.-G., Wang, S.-J., & Wang, Y. (2025) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2025.1609639 · Cited by 5
Renal Transplant Survival Prediction From Unsupervised Deep Learning-Based Radiomics on Early Dynamic Contrast-Enhanced MRI.
Milecki, L., Bodard, S., Kalogeiton, V., Poinard, F., Tissier, A.-M., Boudhabhay, I., Correas, J., Anglicheau, D., Vakalopoulou, M., & Timsit, M. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.05.001 · Cited by 5
Self-citation
Artificial Intelligence Applications in Lymphoma Diagnosis and Management: Opportunities, Challenges, and Future Directions
Shen, M., & Jiang, Z. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s485724 · Cited by 7
“37 Lymphoma classification is a complex task that requires the integration of morphological, immunophenotypic, and molecular data.”
Deep learning to estimate response of concurrent chemoradiotherapy in non-small-cell lung carcinoma
Peng, J., Zhang, X., Hu, Y., He, T.-C., Huang, J., Zhao, M.-D., & Meng, J.-M. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05708-4 · Cited by 5
Prognostic value of enhanced cine cardiac MRI-based radiomics in dilated cardiomyopathy
Li, X., Xu, Y., Chen, X., Liu, J., He, W., Wang, S., Yin, H., Zhou, X., Song, Y., Peng, L., et al. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.132617 · Cited by 4
Metastatic Lung Adenocarcinomas: Development and Evaluation of Radiomic-Based Methods to Measure Baseline Intra-Patient Inter-Tumor Lesion Heterogeneity
Lafon, M., Cousin, S., Alame, M., Nougaret, S., Italiano, A., & Crombé, A. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01163-1 · Cited by 5
“…per patient, generally the primitive tumor, allowing for discrimination between benign and malignant lung nodules [4–7], diagnose the histologic subtype of NSCLC [8, 9], predict oncogenetic drivers [10, 11], PD-L1 status [12], response to ICI [13, 14], or chemotherapy [15], and survival [16].”
Radiomics analysis based on contrast-enhanced MRI for predicting short-term efficacy of drug-eluting beads transarterial chemoembolization in hepatocellular carcinoma
Xi, Z., Ye, Y., Yang, Y.-B., He, Y.-W., Song, Z., Ma, Q., Zeng, H., & Shao, G. (2024) · Abdominal Radiology · doi:10.1007/s00261-024-04319-3 · Cited by 5
“[16, 17].”
Prediction of hemorrhage in placenta previa: Radiomics analysis of pelvic MRI images.
Akazawa, M., & Hashimoto, K. (2024) · European Journal of Obstetrics, Gynecology, and Reproductive Biology · doi:10.1016/j.ejogrb.2024.05.033 · Cited by 5
CT‐based radiomics for differentiating peripherally located pulmonary sclerosing pneumocytoma from carcinoid
Zhang, Y., Yang, X., Bi, F., Wen, L., Niu, Y., Yang, Y., Lin, H.-S., & Yu, X.-P. (2024) · Medical Physics (Lancaster) · doi:10.1002/mp.17037 · Cited by 5
“26 In our study, all images were resampled to a voxel size of 1 × 1 × 1 mm 3 using linear interpolation.”
Exploration of a noninvasive radiomics classifier for breast cancer tumor microenvironment categorization and prognostic outcome prediction.
Han, X., Gong, Z., Guo, Y., Tang, W.-J., & Wei, X.-H. (2024) · European Journal of Radiology · doi:10.1016/j.ejrad.2024.111441 · Cited by 5
Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study
Peisen, F., Gerken, A., Hering, A., Dahm, I. C., Nikolaou, K., Gatidis, S., Eigentler, T., Amaral, T. M. S., Moltz, J. H., & Othman, A. E. (2023) · Diagnostics · doi:10.3390/diagnostics13203210 · Cited by 4
“The sample size was comparable to other radiomic studies investigating melanoma patients treated with targeted therapy [34] and immunotherapy [13,14,35,36]; however, the number of segmented lesions was larger in our cohort due to the whole-body segmentation approach [16].”
Result
A contrast-enhanced CT radiomics-based model to identify candidates for deintensified chemoradiotherapy in locoregionally advanced nasopharyngeal carcinoma patients
Lin, Y., Yang, Z., Chen, J.-C., Li, M., Cai, Z.-M., Wang, X., Zhai, T., & Lin, Z.-X. (2023) · European Radiology · doi:10.1007/s00330-023-09987-1 · Cited by 5
“…showed that heterogeneity features may be linked to aggressive tumor profile whereas heterogeneity and high gray levels might be more representative of and intertwined with inhomogeneous processes, such as necrosis and chaotic vascularization [45], even though this view remains to be proven [34].”
External validation of an ^18F-FDG-PET radiomic model predicting survival after radiotherapy for oropharyngeal cancer
Mori, M., Deantoni, C., Olivieri, M., Spezi, E., Chiara, A., Baroni, S., Picchio, M., Vecchio, A., Muzio, N. G., Fiorino, C., et al. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-022-06098-9 · Cited by 5
“CT radiomic in the field of HN cancer was also investigated in HPV classification [33] or in assessing outcome of therapies other than radiotherapy [34, 35].”
Radiomics-based prediction of response to immune checkpoint inhibitor treatment for solid cancers using computed tomography: a real-world study of two centers
Yu, Y., Bai, Y.-P., Zheng, P., Wang, N., Deng, X., Ma, H.-H., Yu, R., Ma, C., Liu, P., Xie, Y., et al. (2022) · BMC Cancer · doi:10.1186/s12885-022-10344-6 · Cited by 5
“identified an association between a radiomics-based signature and immune infiltration in solid tumors [21].”
Background
What Genetics Can Do for Oncological Imaging: A Systematic Review of the Genetic Validation Data Used in Radiomics Studies
Mombiela, R. M., Arildskov, A. R., Bruun, F. J., Hasselbalch, L., Holst, K., Rasmussen, S., & Borrás, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23126504 · Cited by 5
How to Improve SBRT Outcomes in NSCLC: From Pre-Clinical Modeling to Successful Clinical Translation
Milić, M., Mondini, M., & Deutsch, É. (2022) · Cancers · doi:10.3390/cancers14071705 · Cited by 5
Radiogenomics in personalized management of lung cancer patients: Where are we?
Araujo-Filho, J., Mayoral, M., Horvat, N., Santini, F., Gibbs, P., & Ginsberg, M. (2022) · Clinical imaging · doi:10.1016/j.clinimag.2022.01.012 · Cited by 5
Artificial Intelligence: A New Tool in Oncologist's Armamentarium
Talwar, V., Chufal, K. S., & Joga, S. (2021) · Indian Journal of Medical and Paediatric Oncology · doi:10.1055/s-0041-1735577 · Cited by 7
Evaluation of Response to Immune Checkpoint Inhibitors Using a Radiomics, Lesion-Level Approach
Song, C., Park, H., Lee, H.-Y., Lee, S.-H., Ahn, J., & Lee, S.-H. (2021) · Cancers · doi:10.3390/cancers13236050 · Cited by 5
“Recent studies have found that radiomic features have the potential to predict treatment response [19,20] and clinical outcomes [21–24].”
Background
Immunoinformatics: Pushing the boundaries of immunology research and medicine
Chatanaka, M. K., Ulndreaj, A., Sohaei, D., & Prassas, I. (2021) · ImmunoInformatics · doi:10.1016/j.immuno.2021.100007 · Cited by 10
AI in spotting high-risk characteristics of medical imaging and molecular pathology
Zhang, C., Gu, J., Zhu, Y., Meng, Z., Tong, T., Li, D., Liu, Z., Du, Y., Wang, K., & Tian, J. (2021) · Precision Clinical Medicine · doi:10.1093/pcmedi/pbab026 · Cited by 6
Current Imaging Evaluation of Tumor Response to Advanced Medical Treatment in Metastatic Renal-Cell Carcinoma: Clinical Implications
Caruso, M., Romeo, V., Stanzione, A., Buonerba, C., Lorenzo, G. D., & Maurea, S. (2021) · Applied Sciences · doi:10.3390/app11156930 · Cited by 5
“used CT images and RNA sequences of patients treated with ICIs to develop a radiomic signature of CD8+ T cells [42].”
Method
Emerging Technologies for Non-invasive Monitoring of Treatment Response to Immunotherapy for Brain Tumors
Mathios, D., Srivastava, S., Kim, T. Y., Bettegowda, C., & Lim, M. (2021) · Neuromolecular medicine · doi:10.1007/s12017-021-08677-9 · Cited by 5
Influence of inter-observer delineation variability on radiomic features of the parotid gland.
Forde, E., Leech, M., Robert, C., Herron, E., & Marignol, L. (2021) · Physica medica (Testo stampato) · doi:10.1016/j.ejmp.2021.01.084 · Cited by 5
Self-citation
A New Model to Improve the Prediction of Prognosis of Endometrial Carcinoma by Combining Traditional Classification With the Presence of Tumor-infiltrating Lymphocytes
Miyamoto, M., Hada, T., Ishibashi, H., Iwahashi, H., Kakimoto, S., Suzuki, R., Sakamoto, T., Matsuura, H., Tsuda, H., & Takano, M. (2021) · Anticancer Research · doi:10.21873/anticanres.14861 · Cited by 5
Beyond Palliation: The Rationale for Metastasis-Directed Therapy for Metastatic Non–Small Cell Lung Cancer
Foster, C. C., Pitroda, S. P., & Weichselbaum, R. R. (2019) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2019.05.025 · Cited by 7
AI applications in prevalent diseases and disorders
Catania, L. J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-824477-7.00007-9 · Cited by 11
Radiomics and Imaging Genomics for Evaluation of Tumor Response
Lee, G., Bak, S., Lee, H., Choi, J., & Park, H. (2020) · Medical radiology · doi:10.1007/978-3-030-31171-1_13 · Cited by 5
The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer
Chen, Y.-L., Qin, Y., Yang, M., Li, W., Cheng, M., Huang, Y., Zhu, T., & Wang, K. (2026) · Cancer Biology and Medicine · doi:10.20892/j.issn.2095-3941.2025.0655 · Cited by 4
“Integrating quantitative imaging signatures with clinical factors, molecular biomarkers (Ki-67, HER2, and PD-L1) and features of the tumor immune microenvironment allows for deeper biological contextualization of imaging phenotypes 66 .”
Interpretable fusion deep learning on super-resolution MRI for perineural invasion prediction in pancreatic ductal adenocarcinoma: a multicenter study.
Li, J., Xu, J., Shen, T., Zhou, J.-J., Wang, P., Lu, T.-Y., Li, D.-L., Zeng, M.-S., Tang, Q.-Y., Zhu, Q.-Y., et al. (2025) · European Journal of Radiology · doi:10.1016/j.ejrad.2025.112610 · Cited by 4
Application of Radiomics in Melanoma: A Systematic Review and Meta-Analysis
Falcone, R., Verkhovskaia, S., Pietro, F. R. D., Scianni, C., Poti, G., Morelli, M. F., Marchetti, P., Galitiis, F. D., Sammarra, M., & Cavallo, A. U. (2025) · Cancers · doi:10.3390/cancers17193130 · Cited by 4
“For example, tumors with high mean positive pixel (MPP) values on CT scans were associated with worse survival outcomes, and with lower levels of CD8+ T cells, suggesting an “immune-cold” phenotype, which tends to respond poorly to ICIs [13].”
68Ga-NODAGA-SNA006 PET/CT Imaging of Tumor CD8+ T-Cell Infiltration Predicts Immunotherapy Response in Lung Cancer.
Zhou, J.-X., Zhou, M., Chen, W., Liu, Y., Hai, W., Huang, J.-W., Wang, X.-F., Zhang, Y., & Zhang, Q.-R. (2025) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000006098 · Cited by 4
Early Tumor Shrinkage and Depth of Response as Predictive Markers of Treatment Response and Prognosis in Solid Tumors
Cao, P., & Yang, X.-J. (2025) · Cancer Medicine · doi:10.1002/cam4.71251 · Cited by 4
“Radiomics, by extracting multidimensional textural and spatial features from CT, MRI, or PET, can reveal tumor heterogeneity and spatial patterns that are beyond the resolution of RECIST, thereby complementing ETS and DpR [82–84].”
Association of PD-1, LAG-3 and TIM-3 expression on intratumoral CD8 T-cells with response to atezolizumab in a Real-World-Evidence biomarker study for advanced urothelial carcinoma patients
Andrea, C. D., Abengozar-Muela, M., Arranz, J., Climent, M., Puente, J., Vizcay, Á., de la Fuente, L. M., Jurado, J. M., Bonfill, T., Santander, C., et al. (2025) · Oncoimmunology · doi:10.1080/2162402x.2025.2538687 · Cited by 4
“8 Biomarkers based on high-throughput molecular techniques, 26 or other strategies, such as radiomics, 27 provide relevant insights about the mechanism of action of checkpoint inhibitors, but are not ready to guide oncology practice.”
MRI-Based Radiomics for Outcome Stratification in Pediatric Osteosarcoma
Ngan, E., Mullikin, D., Theruvath, A., Annapragada, A., Ghaghada, K. B., Heczey, A., & Starosolski, Z. (2025) · Cancers · doi:10.3390/cancers17152586 · Cited by 4
“Radiomics has been applied to analyze different diseases, including cancers [7–12].”
Decoding Immunodeficiencies with Artificial Intelligence: A New Era of Precision Medicine
Sciaccotta, R., Barone, P., Murdaca, G., Fazio, M., Stagno, F., Gangemi, S., Genovese, S., & Allegra, A. (2025) · Biomedicines · doi:10.3390/biomedicines13081836 · Cited by 4
“Mass spectrometry (MS) is the most precise method for determining the immunopeptidome [39,59].”
Emerging Techniques of Translational Research in Immuno-Oncology: A Focus on Non-Small Cell Lung Cancer
Guardamagna, M., Zamorano, E., Albarrán-Artahona, V., Mesas, A., & Benítez, J. C. (2025) · Cancers · doi:10.3390/cancers17132244 · Cited by 5
“Machine learning (ML) programs—a subset of AI—can learn from data to identify patterns and generate predictive models, thus providing a comprehensive analysis of histopathological samples through imaging features to assist in diagnosis and prognosis [16,17].”
Molecular markers for the efficacy of neoadjuvant immunotherapy for head and neck squamous cell carcinoma
Zhu, J., & Ning, Y. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1586130 · Cited by 4
Computed tomography-based deep learning radiomics model for preoperative prediction of tumor immune microenvironment in colorectal cancer
Zhou, C., Zhang, Y., Yang, Z.-J., Huang, Y.-Q., & Da, M.-X. (2025) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v17.i5.106103 · Cited by 4
“Sun et al [37] developed a radiomics signature consisting of eight variables to identify CD8+ T cells, which was subsequently validated against the gene expression signature of CD8+ T cells in the Cancer Genome Atlas dataset, resulting in an AUC value of 0.67.”
Influential
Investigating tumor immunogenicity as a determinant of differential abscopal effects
Jeong, Y., Lee, K.-J., Kim, Y. J., Lee, S.-J., Koom, W., Lee, I. J., & Kim, K. (2025) · Journal of Radiation Research · doi:10.1093/jrr/rraf021 · Cited by 4
“Assessing tumor immunogenicity has historically been challenging; however, recent advancements have shown that radiomic features derived from computed tomography imaging can predict CD8 + T-cell infiltration [20].”
Radiomic markers associated with clinical benefit in patients with radiographic progression of advanced uveal melanoma on tebentafusp
Beylergil, V., Collins, L. E., Schwartz, L. H., Eche, T., Zhao, B., Champiat, S., Carvajal, R. D., Abedin, S. E., & Dercle, L. (2025) · European Journal of Cancer · doi:10.1016/j.ejca.2025.115386 · Cited by 3
Self-citation
Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma
Peng, K., Zhang, X., Li, Z.-L., Wang, Y.-C., Sun, H.-W., Zhao, W., Pan, J., Zhang, X.-Y., Wu, X., Yu, X.-R., et al. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1493735 · Cited by 4
Radiation induced liver injury (RILI) evaluation using longitudinal computed tomography (CT) in image‐guided precision murine radiotherapy
Wang, Y.-N., Guan, F.-D., Wang, S.-Y., Jian, W.-W., & Khan, M. K. (2024) · Precision Radiation Oncology · doi:10.1002/pro6.1244 · Cited by 4
“Sun et al. 17 correlated noninvasive radiomics features to quantitative tumor infiltration of CD8 T cells.”
Natural Killer Cell-Associated Radiogenomics Model for Hepatocellular Carcinoma: Integrating CD2 and Enhanced CT-Derived Radiomics Signatures.
Chen, Y.-Z., Meng, Z.-S., Zhang, Y.-N., & Xiang, Z.-L. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.10.043 · Cited by 4
Radiomic profiling of high-risk aneurysms with blebs: an exploratory study
Dier, C., Sanchez, S., Sagues, E., Gudino, A., Jaramillo, R., Wendt, L., & Samaniego, E. (2024) · Journal of NeuroInterventional Surgery · doi:10.1136/jnis-2024-022133 · Cited by 4
“40 Previous studies have demonstrated that wall enhancement correlates with histological inflammatory changes, with blebs showing a tendency to capture more contrast.”
A study predicting long-term survival capacity in postoperative advanced gastric cancer patients based on MAOA and subcutaneous muscle fat characteristics
Han, Y., Chang, Y., Wang, J., Li, N., Yu, Y., Yang, Z.-B., Lv, W., Liu, W., Yin, J., & Wu, J. (2024) · World Journal of Surgical Oncology · doi:10.1186/s12957-024-03466-7 · Cited by 4
“CT-based radiomics has been demonstrated to aid in predicting treatment responses and outcomes for various cancers [14–16].”
Background
Automatic prediction of non-iodine-avid status in lung metastases for radioactive I131 treatment in differentiated thyroid cancer patients
Gao, X.-Y., Chen, H.-Y., Wang, Y., Xu, F., Zhang, A.-N., Yang, Y., & Gu, Y.-J. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1429115 · Cited by 4
Multi-institutional validation of a radiomics signature for identification of postoperative progression of soft tissue sarcoma
Yu, Y., Guo, H.-W., Zhang, M., Hou, F., Yang, S.-F., Huang, C.-C., Duan, L., & Wang, H.-X. (2024) · Cancer Imaging · doi:10.1186/s40644-024-00705-8 · Cited by 4
“Radiomics can be regarded as a “digital biopsy” that allows in-depth analysis of spatial heterogeneity and tumor phenotypes in various clinical scenarios [29, 30].”
Background
A Glimpse Into the Future for Unresectable Stage III Non-Small Cell Lung Cancer
Bartolomeo, V., Cortiula, F., Hendriks, L. E. L., Ruysscher, D. D., & Filippi, A. R. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.11.005 · Cited by 4
Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer.
Cai, C., Hu, T., Rong, Z., Gong, J., & Tong, T. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111254 · Cited by 4
Research status and progress of radiomics in bone and soft tissue tumors: A review
Zhang, X., Peng, J., Ji, G.-H., Li, T., Li, B., & Xiong, H. (2023) · Medicine · doi:10.1097/md.0000000000036198 · Cited by 4
Radiomics Based on Contrast-Enhanced CT for Recognizing c-Met-Positive Hepatocellular Carcinoma: a Noninvasive Approach to Predict the Outcome of Sorafenib Resistance
Gu, J., Bao, S., Akemuhan, R., Jia, Z., Zhang, Y., & Huang, C. (2023) · Molecular Imaging and Biology · doi:10.1007/s11307-023-01870-1 · Cited by 5
“All selected images were resampled to a voxel size of 1 × 1 × 1 mm 3 to meet standardization; then, a fixed bin width of 25 HU helped reduce image noise, gaining all tumour images with isotropic resolution [24, 25].”
PMSG-Net: A priori-guided multilevel graph transformer fusion network for immunotherapy efficacy prediction
Yang, W., Wu, W., Wang, L., Zhang, S., Zhao, J.-J., & Qiang, Y. (2023) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2023.107371 · Cited by 4
Radiotherapy Dose in Patients Receiving Immunotherapy
Fitzgerald, K., & Schoenfeld, J. D. (2023) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2023.03.012 · Cited by 4
Multivariable Risk Modelling and Survival Analysis with Machine Learning in SARS-CoV-2 Infection
Ciarmiello, A., Tutino, F., Giovannini, E., Milano, A., Barattini, M., Yosifov, N., Calvi, D., Setti, M., Sívori, M., Sani, C., et al. (2023) · medRxiv · doi:10.3390/jcm12227164 · Cited by 4
Construction of an enhanced computed tomography radiomics model for non-invasively predicting granzyme A in head and neck squamous cell carcinoma by machine learning
Hang, R., Bai, G., Sun, B., Xu, P., Sun, X., Yan, G., Zhang, W., & Wang, F. (2023) · European Archives of Oto-Rhino-Laryngology · doi:10.1007/s00405-023-07909-x · Cited by 6
“It is very important to evaluate the immune status; at the same time, a high-quality article suggests that GZMA can cause tumor cell death through GSDMB, which is a form of immunogenic cell death, and has important value in turning cold tumors into hot tumors [24].”
Background
Chest computed tomography radiomics to predict the outcome for patients with COVID-19 at an early stage
Wu, S., Zhang, R., Wan, X.-J., Yao, T., Zhang, Q.-W., Chen, X., & Fan, X.-B. (2023) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2022.21576 · Cited by 4
Inferring FDG-PET-positivity of lymph node metastases in proven lung cancer from contrast-enhanced CT using radiomics and machine learning
Gorodetski, B., Becker, P., Baur, A., Hartenstein, A., Rogasch, J., Furth, C., Amthauer, H., Hamm, B., Makowski, M., & Penzkofer, T. (2022) · European Radiology Experimental · doi:10.1186/s41747-022-00296-8 · Cited by 4
“…focused on the primary tumour, e.g. , for differentiating between benign and malignant lung lesions [23, 24] or between primary and secondary tumours [25], survival prediction [26–28], treatment response [29, 30], or predicting lymph node (LN) metastases on the morphology of the primary tumour [7].”
Background
Differential diagnosis of hepatocellular carcinoma and hepatic hemangioma based on maximum wavelet-coefficient statistics: Novel radiomics features from plain CT
Qiu, J.-J., Yin, J., Ji, L., Lu, C.-Y., Li, K.-S., Zhang, Y.-G., & Lin, Y.-X. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103046 · Cited by 4
Development and validation of a computed tomography–based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer
He, L., Li, Z.-H., Yan, L.-X., Chen, X., Sanduleanu, S., Zhong, W., Lambin, P., Ye, Z., Sun, Y.-S., Liu, Y.-L., et al. (2022) · European Radiology · doi:10.1007/s00330-022-08873-6 · Cited by 4
“Notably, this study is one of the few investigations utilising an immune-informed approach to establish a link between underlying pathology and clinical outcomes [11, 19, 30, 32].”
BackgroundMethodInfluential
Evaluation of Microscopic Tumour Extension in Localized Stage Non-Small-Cell Lung Cancer for Stereotactic Radiotherapy Planning
Schmitt, M., Aussenac, L., Seitlinger, J., Lindner, V., Noël, G., & Antoni, D. (2022) · Cancers · doi:10.3390/cancers14051282 · Cited by 4
The TILs-US score on ultrasonography can predict the pathological response to neoadjuvant chemotherapy for human epidermal growth factor receptor 2-positive and triple-negative breast cancer.
Kimura, Y., Masumoto, N., Kanou, A., Fukui, K., Sasada, S., Emi, A., Kadoya, T., Arihiro, K., & Okada, M. (2022) · Surgical oncology · doi:10.1016/j.suronc.2022.101725 · Cited by 4
Longitudinal CT Imaging to Explore the Predictive Power of 3D Radiomic Tumour Heterogeneity in Precise Imaging of Mantle Cell Lymphoma (MCL)
Lisson, C., Lisson, C., Achilles, S., Mezger, M. F., Wolf, D., Schmidt, S., Thaiss, W. M., Bloehdorn, J., Beer, A., Stilgenbauer, S., et al. (2022) · Cancers · doi:10.3390/cancers14020393 · Cited by 4
(Chemo)Radiotherapy–Immunotherapy Combinations: Time to Get Tailored?
Mondini, M., & Deutsch, E. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-21-1173 · Cited by 4
Self-citation
Prognostic Modeling of Patients Undergoing Surgery Alone for Esophageal Squamous Cell Carcinoma: A Histopathological and Computed Tomography Based Quantitative Analysis
Wu, L.-L., Wang, J., Huang, W., Liu, X., Huang, Y.-Y., Zeng, J., Cui, C., Lu, J.-B., Lin, P., Long, H., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.565755 · Cited by 4
“In previous studies, deep learning of radiomics has shown unique value in diagnosis and prognosis in the assessment of liver fibrosis in chronic hepatitis B (24), access to tumor-infiltrating CD8 cells, and responses to anti-PD-1 or anti-PD-L1 immunotherapy (25) that could predict lymph node metastasis in ESCC and col…”
Background
A Quantitative Assessment of Pre-Operative MRI Reports in Glioma Patients: Report Metrics and IDH Prediction Ability
Cao, H., Erson-Omay, E., Gunel, M., Moliterno, J., & Fulbright, R. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.600327 · Cited by 4
“Through advanced MR imaging method and radiomics analysis, the pre-operative genotype (3), immune phenotype (4), and prognosis prediction (5) become possible.”
Method
Editorial: Multi-omic Data Integration in Oncology
Finotello, F., Calura, E., Risso, D., Hautaniemi, S., & Romualdi, C. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.01768 · Cited by 4
“In addition, important information can be exploited by integrating omics data and biomedical images (9), a field that is experiencing new advances in terms of sensitivity and resolution.”
Background
Radiomics: A Path Forward to Predict Immunotherapy Response in Non-Small Cell Lung Cancer.
Yousefi, B., Katz, S., & Roshkovan, L. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2020200075 · Cited by 4
Radiomics to predict response to immunotherapy: an imminent reality?
Limkin, E., & Sun, R. (2020) · Future Oncology · doi:10.2217/fon-2020-0015 · Cited by 4
Self-citation
Are radiomics ready for clinical prime-time in PET/CT imaging?
Rest, C. C. L., & Hustinx, R. (2019) · The Quarterly Journal of Nuclear Medicine and Molecular Imaging · doi:10.23736/s1824-4785.19.03210-2 · Cited by 5
Gene Expression High-Dimensional Clustering Towards a Novel, Robust, Clinically Relevant and Highly Compact Cancer Signature
Battistella, E., Vakalopoulou, M., Estienne, T., Lerousseau, M., Sun, R., Robert, C., Nikos Paragios, A., & Deutsch, É. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-17938-0_41 · Cited by 5
Self-citation
Tertiary lymphoid structures as biomarkers and therapeutic targets in neoadjuvant cancer immunotherapy
Wang, T., Xu, W., & Wu, X. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1882195 · Cited by 3
“PD-L1 expression re ﬂ ects one axis of immune regulation, TMB estimates antigenic potential, CD8 + T-cell density indicates cytotoxic in ﬁ ltra-tion, and ctDNA re ﬂ ects tumor burden or molecular residual disease (37).”
AGPAT3 reshapes tumor cell vulnerability to IFNγ-mediated ferroptosis and enhances immunotherapy efficacy through lipid remodeling
Liu, C., Hu, C., Cheng, J., Xin, D., Jin, S., Tian, W., Li, S., Jin, Y., Liu, Y., Wu, W., et al. (2026) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-013305 · Cited by 2
“Recent studies have used omics data and machine learning methods to identify novel biomarkers and immune cell markers, 4 5 thereby effectively predicting patient responses to ICI therapy.”
Influence of CT harmonization in longitudinal radiomics for NSCLC immunotherapy response prediction
Farina, B., Vegas-Sánchez-Ferrero, G., Ramos-Guerra, A. D., Miras, C. P., Peral, A. A., Merino, J. A., Zugazagoitia, J., Peces-Barba, G., Maceiras, L., Paz-Ares, L., et al. (2026) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2026.111501 · Cited by 3
Identification of symptomatic carotid plaque by CTA-based radiomics: a multicenter study
Zhang, W., Wang, Z., Xu, Q., Wang, X., Zhang, L., Chen, R., Liao, M., & Zhong, J.-Q. (2026) · Frontiers in Neurology · doi:10.3389/fneur.2026.1750076 · Cited by 3
Artificial Intelligence in Histopathological Analysis for Predicting Immunotherapy Response in Cutaneous Melanoma
Yoo, S. A., & Lee, J. H. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262110729 · Cited by 3
Survival Prediction for Postoperative Patients With Kidney Cancer Based on Computed Tomography Radiomics: Retrospective Cohort Study
He, G.-Z., Lai, L.-R., Yu, G., Pang, H.-W., Yu, X., Zhang, H., Zhu, Q., & Lv, Y. (2025) · JMIR Medical Informatics · doi:10.2196/73162 · Cited by 3
Predictions of Response in Non-small Cell Lung Cancer Patients Treated with Immune Checkpoint Inhibitors Using Clinical Data, Deep Learning, and Radiomics.
Wang, C., Li, Y.-X., Ji, Y., Yu, K., Qin, C., Liu, L., Shuai, Y.-J., Chen, J.-H., Li, A., & Zhang, T. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.09.037 · Cited by 3
Integrating CT Radiomics and Clinical Information to Predict Prognosis of Advanced NSCLC Patients Receiving Chemoimmunotherapy
Zhong, H., Zhang, H.-H., Wu, J., Zhao, X., Dan, Y.-C., Li, J., Li, L., Luo, M.-P., Xu, Y., Xu, B., et al. (2025) · Current Medical Science · doi:10.1007/s11596-025-00117-0 · Cited by 3
“Concurrently, CT imaging has been widely adopted to predict immune markers such as PD-L1 expression [17, 18] and CD8+ tumor-infiltrating lymphocytes (TILs) [19].”
QVT score, a radiomic biomarker of vascular complexity, enables prognostication and monitoring of NSCLC immunotherapy
Chae, Y. K., Velcheti, V., Zhang, K., Hiremath, A., Chung, L. I., Maghsoudi, O. H., Chitalia, R., Lee, J., Li, H., Lee, S., et al. (2026) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-013391 · Cited by 4
Early and hereditary breast cancer: advances in risk stratification and imaging approaches
Cortiana, V., Kannan, S., Vallabhaneni, H., Gambill, J., Nadar, S., Rangrej, V. J. K., Park, C., & Leyfman, Y. (2025) · Therapeutic Advances in Medical Oncology · doi:10.1177/17588359251349677 · Cited by 3
“141 The reliance on small, homogeneous datasets for training radiomic and AI models limits their applicability to diverse patient populations.”
Radiomics‐based prediction of T cell‐inflamed gene expression profile and prognosis in head and neck squamous cell carcinoma
Jiang, H., Zhou, L.-J., Zhang, Q., Yu, T., & Yu, Z. (2025) · Medical Physics (Lancaster) · doi:10.1002/mp.18028 · Cited by 3
“44 These results illustrate how radiomic features can capture both the physical characteristics and underlying biological processes of tumors.”
Unraveling the tumor-microenvironment through a radiogenomic-based multiomic approach to predict outcomes of immunotherapy in non-small cell lung cancer
Jeong, D. Y., Joe, C., Lee, S.-M., Park, S., Moon, S. H., Choi, J. Y., Kim, J., Lee, S.-H., & Lee, H.-Y. (2025) · Comput. Methods Programs Biomed. · doi:10.1016/j.cmpb.2025.108915 · Cited by 3
Using Cancer-Associated Fibroblasts as a Shear-Wave Elastography Imaging Biomarker to Predict Anti-PD-1 Efficacy of Triple-Negative Breast Cancer
Zhang, Z., Liang, S.-Y., Zheng, D., Wang, S., Zhou, J., Wang, Z., Huang, Y., Chang, C., Wang, Y.-Y., Guo, Y., et al. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26083525 · Cited by 3
“Recently, radiomics has emerged as an innovative method to explore the relationship between imaging features of tumors and their gene expression to predict immunotherapy response [11,12].”
Artificial Intelligence for Drug Discovery: An Update and Future Prospects
Howell, H. J., McGale, J., Choucair, A., Shirini, D., Aide, N., Postow, M. A., Wang, L., Tordjman, M., Lopci, E., Lecler, A., et al. (2025) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2025.01.004 · Cited by 7
Self-citation
Advances in radiomics applications for intrahepatic cholangiocarcinoma: a comprehensive review and future directions
Xu, J.-W., Li, B., & Yu, D. (2024) · Hepatoma Research · doi:10.20517/2394-5079.2024.79 · Cited by 1
Prediction of CD8+T lymphocyte infiltration levels in gastric cancer from contrast‐enhanced CT and clinical factors using machine learning
Xie, W.-T., Jiang, S., Xin, F.-J., Jiang, Z.-N., Pan, W., Zhou, X.-M., Xiang, S., Xu, Z., Lu, Y., & Wang, D.-S. (2024) · Medical Physics (Lancaster) · doi:10.1002/mp.17350 · Cited by 3
“25 As the diversity of cancer types will add difﬁculty to model training, we hypothesized that radiological pre-CT-BASED dictors focused on a single kind of cancer may have a better performance.”
Prognostic value of clinical and radiomic parameters in patients with liver metastases from uveal melanoma
Lever, M., Bogner, S., Giousmas, M., Mairinger, F., Baba, H., Richly, H., Gromke, T., Schuler, M., Bechrakis, N., & Kalkavan, H. (2024) · Pigment Cell & Melanoma Research · doi:10.1111/pcmr.13184 · Cited by 3
“Successful applications were reported in many clinical settings, in particular high incidence oncological entities, for example, for lung cancer (Aerts et al., 2014; Sun et al., 2018).”
Pattern of heterogeneity in normal brain ageing: screening for mild cognitive impairment and its risk of progression with a radiomics model
Yan, Y., He, X., Xu, Y., Zhao, F., Peng, J., & Shao, Y. (2024) · Age and Ageing · doi:10.1093/ageing/afae140 · Cited by 4
Radiomic Features at Contrast-Enhanced CT Predict Virus-Driven Liver Fibrosis: A Multi-Institutional Study
Wang, J., Tang, S., Wu, J., Xu, S., Sun, Q., Zhou, Z., Xu, X., Liu, Y., Liu, Q., Mao, Y., et al. (2024) · Clinical and Translational Gastroenterology · doi:10.14309/ctg.0000000000000712 · Cited by 2
“Image preprocessing included standardizing voxel spacing by resampling images to a voxel size of 1 3 1 3 1 mm and reducing image noise and normalizing intensities by discretizing voxel intensity values with a ﬁ xed-width bin of 25 Houns ﬁ eld units (22,23).”
Method
Cell cycle associated protein 1 associates with immune infiltration and ferroptosis in gastrointestinal cancer
Gao, Y., Wu, R.-M., Pei, Z.-J., Ke, C.-B., Zeng, D.-B., Li, X., & Zhang, Y. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e28794 · Cited by 3
α‐SMA+ cancer‐associated fibroblasts increased tumor enhancement ratio on contrast‐enhanced multidetector‐row computed tomography in stages I–III colon cancer
Wang, Z., Han, S.-G., Xu, K.-L., Yang, Q., Wang, X.-H., Tang, Y., Shao, Y.-K., & Ye, Y. (2023) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.16366 · Cited by 3
“10 Cheng et al . reported that an enhanced rim of colorectal cancer liver metastasis lesion on the portal venous phase on CE-MDCT could predict the response to bevacizumab-based chemotherapy.”
BackgroundSelf-citation
High expression of centromere protein N as novel biomarkers for gastric adenocarcinoma
Wang, X.-J., Zhang, K., Fu, C., Wu, F., Zhang, J., Han, B., Pan, H., & Luan, L. (2023) · Cancer Reports · doi:10.1002/cnr2.1798 · Cited by 3
“Immune cell infiltration has been linked to the development and spread of cancer.(31,32) In this study, we explored the association between CENP-N expression and immune cell infiltration in STAD.”
Background
Immune checkpoint inhibition: a future guided by radiology
Khan, F., Jones, K. I., & Lyon, P. C. (2023) · British Journal of Radiology · doi:10.1259/bjr.20220565 · Cited by 4
Radiomic analysis of enhanced CMR cine images predicts left ventricular remodeling after TAVR in patients with symptomatic severe aortic stenosis
He, W.-Z., Huang, H.-S., Chen, X.-Y., Yu, J.-Q., Liu, J., Li, X., Yin, H., Zhang, K., & Peng, L.-Q. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.1096422 · Cited by 3
Basic premises: searching for new targets and strategies in diffuse gliomas
Sansone, G., Vivori, N., Vivori, C., Stefano, A. L. D., & Pïcca, A. (2022) · Clinical and Translational Imaging · doi:10.1007/s40336-022-00507-7 · Cited by 3
“Seminal works on solid tumours demonstrated that radiomic analyses may be leveraged to predict immune microenvironment features relevant to clinical practice (as the level of tumour infiltrating CD8 lymphocytes [189]).”
Background
Predictors of Toxicity Among Older Adults with Cancer.
Extermann, M., Chetty, I., Brown, S., Al-Jumayli, M., & Movsas, B. (2022) · Seminars in Radiation Oncology · doi:10.1016/j.semradonc.2021.11.004 · Cited by 3
Radiological patterns of tumour progression in patients treated with a combination of immune checkpoint blockers and antiangiogenic drugs.
Guiard, E., Baldini, C., Pobel, C., Assi, T., Bernard-Tessier, A., Martin-Romano, P., Hollebecque, A., Verlingue, L., Géraud, A., Michot, J., et al. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.02.024 · Cited by 3
Self-citation
Radiographical Evaluation of Tumor Immunosuppressive Microenvironment and Treatment Outcomes in Gastric Cancer: A Retrospective, Multicohort Study
Lin, J., Lin, J.-P., Weng, Y., Lv, C.-B., Chen, J., Zhan, C., Li, P., Xie, J., Wang, J.-B., Lü, J., et al. (2022) · Annals of Surgical Oncology · doi:10.1245/s10434-022-11499-z · Cited by 4
“Radiomics can achieve a comprehensive assessment of the tumor phenotype by using high-throughput imaging features that can provide more internal tumor information than TNM stage, such as changes in cellular and genetic levels.(11) In this study, we confirmed that the prognostic value of the RISS was independent of TNM…”
Result

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