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

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A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

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

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

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Abstract

Background Because responses of patients with cancer to immunotherapy can vary in success, innovative predictors of response to treatment are urgently needed to improve treatment outcomes. We aimed to develop and independently validate a radiomics-based biomarker of tumour-infiltrating CD8 cells in patients included in phase 1 trials of anti-programmed cell death protein (PD)-1 or anti-programmed cell death ligand 1 (PD-L1) monotherapy. We also aimed to evaluate the association between the biomarker, and tumour immune phenotype and clinical outcomes of these patients. Methods In this retrospective multicohort study, we used four independent cohorts of patients with advanced solid tumours to develop and validate a radiomic signature predictive of immunotherapy response by combining contrast-enhanced CT images and RNA-seq genomic data from tumour biopsies to assess CD8 cell tumour infiltration. To develop the radiomic signature of CD8 cells, we used the CT images and RNA sequencing data of 135 patients with advanced solid malignant tumours who had been enrolled into the MOSCATO trial between May 1, 2012, and March 31, 2016, in France (training set). The genomic data, which are based on the CD8B gene, were used to estimate the abundance of CD8 cells in the samples and data were then aligned with the images to generate the radiomic signatures. The concordance of the radiomic signature (primary endpoint) was validated in a Cancer Genome Atlas [TGCA] database dataset including 119 patients who had available baseline preoperative imaging data and corresponding transcriptomic data on June 30, 2017. From 84 input variables used for the machine-learning method (78 radiomic features, five location variables, and one technical variable), a radiomics-based predictor of the CD8 cell expression signature was built by use of machine learning (elastic-net regularised regression method). Two other independent cohorts of patients with advanced solid tumours were used to evaluate this predictor. The immune phenotype internal cohort (n=100), were randomly selected from the Gustave Roussy Cancer Campus database of patient medical records based on previously described, extreme tumour-immune phenotypes: immune-inflamed (with dense CD8 cell infiltration) or immune-desert (with low CD8 cell infiltration), irrespective of treatment delivered; these data were used to analyse the correlation of the immune phenotype with this biomarker. Finally, the immunotherapy-treated dataset (n=137) of patients recruited from Dec 1, 2011, to Jan 31, 2014, at the Gustave Roussy Cancer Campus, who had been treated with anti-PD-1 and anti-PD-L1 monotherapy in phase 1 trials, was used to assess the predictive value of this biomarker in terms of clinical outcome. Findings We developed a radiomic signature for CD8 cells that included eight variables, which was validated with the gene expression signature of CD8 cells in the TCGA dataset (area under the curve [AUC]=0·67; 95% CI 0·57-0·77; p=0·0019). In the cohort with assumed immune phenotypes, the signature was also able to discriminate inflamed tumours from immune-desert tumours (0·76; 0·66-0·86; p<0·0001). In patients treated with anti-PD-1 and PD-L1, a high baseline radiomic score (relative to the median) was associated with a higher proportion of patients who achieved an objective response at 3 months (vs those with progressive disease or stable disease; p=0·049) and a higher proportion of patients who had an objective response (vs those with progressive disease or stable disease; p=0·025) or stable disease (vs those with progressive disease; p=0·013) at 6 months. A high baseline radiomic score was also associated with improved overall survival in univariate (median overall survival 24·3 months in the high radiomic score group, 95% CI 18·63-42·1; vs 11·5 months in the low radiomic score group, 7·98-15·6; hazard ratio 0·58, 95% CI 0·39-0·87; p=0·0081) and multivariate analyses (0·52, 0·35-0·79; p=0·0022). Interpretation The radiomic signature of CD8 cells was validated in three independent cohorts. This imaging predictor provided a promising way to predict the immune phenotype of tumours and to infer clinical outcomes for patients with cancer who had been treated with anti-PD-1 and PD-L1. Our imaging biomarker could be useful in estimating CD8 cell count and predicting clinical outcomes of patients treated with immunotherapy, when validated by further prospective randomised trials. Funding Fondation pour la Recherche Médicale, and SIRIC-SOCRATE 2.0, French Society of Radiation Oncology.

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Self-citation
A novel sub-regional radiomics model to predict immunotherapy response in non-small cell lung carcinoma
Peng, J., Zou, D., Zhang, X., Ma, H., Han, L.-J., & Yao, B. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-04904-6 · Cited by 38
“Previous studies have investigated the use of radiomics in NSCLC, including the assessment of the immune-inflammatory status of tumors, which is thought to play a key role in distinguishing potential responders to ICIs from non-responders [13–15].”
Background
Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy
Zhang, Z., & Wei, X. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.005 · Cited by 41
A tumor vasculature–based imaging biomarker for predicting response and survival in patients with lung cancer treated with checkpoint inhibitors
Alilou, M., Khorrami, M., Prasanna, P., Bera, K., Gupta, A. B., Viswanathan, V. S., Patil, P. D., Velu, P., Fu, P., Velcheti, V., et al. (2022) · Science Advances · doi:10.1126/sciadv.abq4609 · Cited by 41
FDG PET/CT for Evaluation of Immunotherapy Response in Lung Cancer Patients
Leger, M. C., Routy, B., & Juneau, D. (2022) · Seminars in Nuclear Medicine · doi:10.1053/j.semnuclmed.2022.04.010 · Cited by 37
Absolute quantification of tumor-infiltrating immune cells in high-grade glioma identifies prognostic and radiomics values
Kim, A. R., Choi, K. S., Kim, S. K., Kim, K., Kang, H. J., Kim, S., Chowdhury, T., Yu, H. J., Lee, C. E., Lee, J. H., et al. (2021) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-020-02836-w · Cited by 44
“[36], however, with ‘real’ cell counts of immune cells, not the ‘assumed’ immune phenotype using estimated cell counts.”
Background
Optimizing Radiation Therapy to Boost Systemic Immune Responses in Breast Cancer: A Critical Review for Breast Radiation Oncologists
Ho, A. Y., Wright, J. L., Blitzblau, R. C., Mutter, R. W., Duda, D. G., Norton, L., Bardia, A., Spring, L. M., Isakoff, S. J., Chen, J. H., et al. (2020) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2020.05.011 · Cited by 45
Do we need to see to believe?—radiomics for lung nodule classification and lung cancer risk stratification
Khawaja, A. I., Bartholmai, B. J., Rajagopalan, S., Karwoski, R. A., Varghese, C., Maldonado, F., & Peikert, T. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd.2020.03.105 · Cited by 43
“They reported higher baseline radiomics score for patients who responded to immunotherapy which was in turn associated with a higher survival rate (55).”
Background
Immunotherapy and Cancer: The Multi-Omics Perspective
Donisi, C., Pretta, A., Pusceddu, V., Ziranu, P., Lai, E., Puzzoni, M., Mariani, S., Massa, E., Madeddu, C., & Scartozzi, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25063563 · Cited by 41
“Sun and colleagues led a study establishing a predictive imaging model of patients’ outcomes after immunotherapy, combining CT scans with RNA-seq data obtained from cancer biopsies [85].”
Background
Interventional Radiology for Local Immunotherapy in Oncology
Tselikas, L., Champiat, S., Sheth, R. A., Yevich, S., Ammari, S., Deschamps, F., Farhane, S., Roux, C., Susini, S., Mouraud, S., et al. (2021) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-4073 · Cited by 47
“Another potential imaging tool that can be applied include MRI sequences (40) or imaging analysis using artificial intelligence and radiomics analysis to guide tumor selection (41, 42).”
MethodSelf-citation
A multiparametric approach to improve the prediction of response to immunotherapy in patients with metastatic NSCLC
Re, M. D., Cucchiara, F., Rofi, E., Fontanelli, L., Petrini, I., Gri, N., Pasquini, G., Rizzo, M., Gabelloni, M., Belluomini, L., et al. (2020) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-020-02810-6 · Cited by 38
Predicting Chronic Myocardial Ischemia Using CCTA-Based Radiomics Machine Learning Nomogram
Shu, Z.-Y., Cui, S., Zhang, Y.-Q., Xu, Y.-Y., Hung, S.-C., Fu, L., Pang, P., Gong, X.-Y., & Jin, Q.-Y. (2020) · Journal of Nuclear Cardiology · doi:10.1007/s12350-020-02204-2 · Cited by 37
DRD-UNet, a UNet-Like Architecture for Multi-Class Breast Cancer Semantic Segmentation
Ortega-Ruíz, M. A., Karabağ, C., Román-Rangel, E., & Reyes‐Aldasoro, C. C. (2024) · IEEE Access · doi:10.1109/access.2024.3377428 · Cited by 39
“Spatial weighted UNet is used for 3D CT brain images [61] with residual-inception blocks densely connected that reduces trainable parameters over the MRI dataset.”
Method
Machine Learning for Prediction of Immunotherapy Efficacy in Non-Small Cell Lung Cancer from Simple Clinical and Biological Data
Benzekry, S., Grangeon, M., Karlsen, M., Alexa, M., Bicalho-Frazeto, I., Chaléat, S., Tomasini, P., Barbolosi, D., Barlesi, F., & Greillier, L. (2021) · medRxiv · doi:10.3390/cancers13246210 · Cited by 36
“Based on the combination of quantitative imaging and machine learning, radiomics study have also started to emerge for prediction of ICIs efficacy from radiological images [24,45].”
Background
Hypoxia-Related Radiomics and Immunotherapy Response: A Multicohort Study of Non-Small Cell Lung Cancer
Tunali, I., Tan, Y. Q., Gray, J. E., Katsoulakis, E., Eschrich, S. A., Saller, J. J., Aerts, H. J. W. L., Boyle, T. A., Qi, J., Güveniş, A., et al. (2021) · JNCI Cancer Spectrum · doi:10.1093/jncics/pkab048 · Cited by 54
“(42) developed a radiomic signature that predicts CD8 cell infiltration and used”
The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up
Ayachy, R. E., Giraud, N., Giraud, P., Durdux, C., Giraud, P., Burgun, A., & Bibault, J. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.603595 · Cited by 39
“Radiomics signature was successfully validated to discriminate immune phenotype and predict survival and response to anti-PD-1 or PD-L1 immunotherapy (65, 84, 85).”
Background
Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature
Xie, C., Pang, C. L., Chan, B., Wong, E. Y.-Y., Dou, Q., & Vardhanabhuti, V. (2021) · Cancers · doi:10.3390/cancers13102469 · Cited by 39
“Semi-automatic segmentation is usually realized by thresholding and region-growing techniques, which is more efficient as it requires less manual intervention [28,29].”
Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma
Li, W., Lv, X.-Z., Zheng, X., Ruan, S., Hu, H.-T., Chen, L.-D., Huang, Y., Li, X., Zhang, C.-Q., Xie, X.-Y., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.544979 · Cited by 36
Charged Particle and Conventional Radiotherapy: Current Implications as Partner for Immunotherapy
Marcus, D., Lieverse, R. I. Y., Klein, C., Abdollahi, A., Lambin, P., Dubois, L. J., & Yaromina, A. (2021) · Cancers · doi:10.3390/cancers13061468 · Cited by 43
“In recent years, attention has been paid to the identification and development of predictive biomarkers for the efficacy of ICIs, including biomarkers of the tumor genome and neoantigens, the tumor immune microenvironment phenotype, liquid biopsy biomarkers and host-related factors [181,182].”
Background
Prediction of the progression from mild cognitive impairment to Alzheimer’s disease using a radiomics-integrated model
Shu, Z., Mao, D., Xu, Y., Shao, Y., Pang, P., & Gong, X. (2021) · Therapeutic Advances in Neurological Disorders · doi:10.1177/17562864211029551 · Cited by 39
Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer
Roisman, L. C., Kian, W., Anoze, A., Fuchs, V., Spector, M., Steiner, R., Kassel, L., Rechnitzer, G., Fried, I., Peled, N., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00473-x · Cited by 43
“Sun et al. 69 determined the radiomic signature of CD8 cells using RNA-seq data combined with a radiomic analysis of solid tumors in a variety of cancers (including lung cancer) and validated it using two independent cohorts of patients.”
BackgroundMethod
The diagnosis, classification, and treatment of sarcoma in this era of artificial intelligence and immunotherapy
Crombé, A., Dugage, M. R., & Italiano, A. (2022) · 癌症：英文版 · doi:10.1002/cac2.12373 · Cited by 38
“…CE-CT scans and response to ICIs, including multiple solid tumors (AUC = 0.83 for predicting ICI response in a non-small cell lung cancer cohort in the study by Trebeschi et al. [105] and multivariate hazard ratio for high radiomics score group = 0.52 [0.34–0.79] in the study by Sun et al. [107]).”
BackgroundInfluential
Artificial intelligence in oncologic imaging
Chen, M., Terzic, A., Becker, A. S., Johnson, J. M., Wu, C. C., Wintermark, M., Wald, C., & Wu, J. (2022) · European Journal of Radiology Open · doi:10.1016/j.ejro.2022.100441 · Cited by 41
“Radiomics based on extraction of CT features has shown to predict response to immuno-therapy in solid tumors including non-small cell lung cancer and melanoma [52].”
Background
An MRI-based radiomics-clinical nomogram for the overall survival prediction in patients with hypopharyngeal squamous cell carcinoma: a multi-cohort study
Chen, J., Lu, S.-H., Mao, Y.-T., Tan, L., Li, G., Gao, Y., Tan, P., Huang, D.-H., Zhang, X., Qiu, Y.-Z., et al. (2021) · European Radiology · doi:10.1007/s00330-021-08292-z · Cited by 35
“To date, radiomics has been extensively used in tumor diagnosis [8, 9], metastasis [10, 11], treatment efficacy [12, 13], and prognosis [14, 15] in diverse human malignancies.”
Imaging features of gadoxetic acid-enhanced MR imaging for evaluation of tumor-infiltrating CD8 cells and PD-L1 expression in hepatocellular carcinoma
Sun, L., Mu, L., Zhou, J. W., Tang, W., Zhang, L., Xie, S., Chen, J., & Wang, J. (2021) · Cancer Immunology Immunotherapy · doi:10.1007/s00262-021-02957-w · Cited by 37
“Imaging features are complementary to biopsy and have the advantage of being noninvasive and can be used for serial monitoring [11, 15].”
Background
CT based radiomic approach on first line pembrolizumab in lung cancer
Zerunian, M., Caruso, D., Zucchelli, A., Polici, M., Capalbo, C., Filetti, M., Mazzuca, F., Marchetti, P., & Laghi, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-86113-5 · Cited by 35
Systematic review of radiomic biomarkers for predicting immune checkpoint inhibitor treatment outcomes.
Zhang, C., Fonseca, L. D. A. F., Shi, Z.-W., Zhu, C., Dekker, A., Bermejo, I., & Wee, L. (2020) · Methods · doi:10.1016/j.ymeth.2020.11.005 · Cited by 35
Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC
Mazzaschi, G., Milanese, G., Pagano, P., Madeddu, D., Gnetti, L., Trentini, F., Falco, A., Frati, C., Lorusso, B., Lagrasta, C. A., et al. (2020) · Lung Cancer · doi:10.1016/j.lungcan.2020.04.006 · Cited by 38
Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer
Li, J., Zhang, C., Wei, J., Zheng, P., Zhang, H., Xie, Y., Bai, J., Zhu, Z., Zhou, K., Liang, X., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.552270 · Cited by 37
Primary pulmonary lymphoepithelioma-like carcinoma: a rare type of lung cancer with a favorable outcome in comparison to squamous carcinoma
Chen, B., Chen, X.-P., Zhou, P., Yang, L., Ren, J., Yang, X.-D., & Li, W.-M. (2019) · Respiratory Research · doi:10.1186/s12931-019-1236-2 · Cited by 35
The Discovery of Biomarkers in Cancer Immunotherapy
George, A. P., Kuzel, T. M., Zhang, Y., & Zhang, B. (2019) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.03.015 · Cited by 41
“The radiomics biomarker, or radiomics signature, is comprised of contrast-enhanced CT images and RNA-seq genomic data acquired from biopsies of patients with metastatic solid tumors to quantify tumor infiltration of CD8 cells [123].”
BackgroundMethodInfluential
Recent advances and challenges in colorectal cancer: From molecular research to treatment
Qi, G., Zhao, R., Gao, C., Ma, Z., Wang, S., & Xu, J. (2025) · World Journal of Gastroenterology · doi:10.3748/wjg.v31.i21.106964 · Cited by 40
“Sun et al [160] developed and validated a radiomics-based biomarker for tumor-infiltrating CD8 cells using ML, predicting responses to PD-1/PD-L1 therapies based on computed tomography features.”
Pretreatment radiomic biomarker for immunotherapy responder prediction in stage IB–IV NSCLC (LCDigital-IO Study): a multicenter retrospective study
Wu, S., Zhan, W., Liu, L., Xie, D., Yao, L., Yao, H., Liao, G., Huang, L., Zhou, Y., You, P., et al. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007369 · Cited by 38
Radiomic and clinical data integration using machine learning predict the efficacy of anti-PD-1 antibodies-based combinational treatment in advanced breast cancer: a multicentered study
Zhao, J., Sun, Z., Yu, Y., Yuan, Z., Lin, Y., Tan, Y., Duan, X., Yao, H., Wang, Y., & Liu, J. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-006514 · Cited by 40
Role of Machine Learning in Precision Oncology: Applications in Gastrointestinal Cancers
Tabari, A., Chan, S. M., Omar, O. M., Iqbal, S. I., Gee, M. S., & Daye, D. (2022) · Cancers · doi:10.3390/cancers15010063 · Cited by 37
Prediction of Histologic Neoadjuvant Chemotherapy Response in Osteosarcoma Using Pretherapeutic MRI Radiomics
Bouhamama, A., Leporq, B., Khaled, W., Nemeth, A., Brahmi, M., Dufau, J., Marec‐Bérard, P., Drapé, J., Gouin, F., Bertrand-Vasseur, A., et al. (2022) · Radiology Imaging Cancer · doi:10.1148/rycan.210107 · Cited by 35
Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients
Sugai, Y., Kadoya, N., Tanaka, S., Tanabe, S., Umeda, M., Yamamoto, T., Takeda, K., Dobashi, S., Ohashi, H., Takeda, K., et al. (2021) · Radiation Oncology · doi:10.1186/s13014-021-01810-9 · Cited by 37
Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram
Li, H.-M., Zhang, R., Li, R., Xia, W., Chen, X.-J., Zhang, J., Cai, S.-Q., Li, Y., Zhao, S.-H., Qiang, J., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07902-0 · Cited by 34
“The statistical differences between ROC curves were tested with the DeLong method [29].”
Method
The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland
Xu, Y.-Y., Shu, Z.-Y., Song, G., Liu, Y., Pang, P., Wen, X.-H., & Gong, X.-Y. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.634452 · Cited by 34
“To eliminate the potential impact of different imaging parameters on the extracted features, we preprocessed the segmented images, including resampling the images to 1×1 ×1 mm(3) voxel size, intensity normalizing, and standardizing the gray levels to range from 1 to 32 (17).”
Method
The Importance of STK11/LKB1 Assessment in Non-Small Cell Lung Carcinomas
Mograbi, B., Heeke, S., & Hofman, P. M. (2021) · Diagnostics · doi:10.3390/diagnostics11020196 · Cited by 42
Clinical utility of the C‐reactive protein:albumin ratio in non‐small cell lung cancer patients treated with nivolumab
Araki, T., Tateishi, K., Sonehara, K., Hirota, S., Komatsu, M., Yamamoto, M., Kanda, S., Kuraishi, H., Hanaoka, M., & Koizumi, T. (2021) · Thoracic Cancer · doi:10.1111/1759-7714.13788 · Cited by 34
Radiomics features as predictive and prognostic biomarkers in NSCLC
Bortolotto, C., Lancia, A., Stelitano, C., Montesano, M., Merizzoli, E., Agustoni, F., Stella, G. M., Preda, L., & Filippi, A. R. (2020) · Expert Review of Anticancer Therapy · doi:10.1080/14737140.2021.1852935 · Cited by 38
Self-citation
Computational modelling of modern cancer immunotherapy
Valentinuzzi, D., & Jeraj, R. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/abc3fc · Cited by 34
Radiomics and “radi-…omics” in cancer immunotherapy: a guide for clinicians
Porcu, M., Solinas, C., Mannelli, L., Micheletti, G., Lambertini, M., Willard-Gallo, K. E., Neri, E., Flanders, A. E., & Saba, L. (2020) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2020.103068 · Cited by 43
The progress and confusion of anti-PD1/PD-L1 immunotherapy for patients with advanced non-small cell lung cancer
Zhang, S., Bai, X., & Shan, F. (2020) · International Immunopharmacology · doi:10.1016/j.intimp.2020.106247 · Cited by 40
Circulating biomarkers predictive of tumor response to cancer immunotherapy
Lee, E. Y., & Kulkarni, R. P. (2019) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2019.1659728 · Cited by 33
“tumor response, termed ‘radiomics’ [99,100].”
Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer
Sun, Z.-P., Zhang, T., Ahmad, M. U., Zhou, Z.-X., Qiu, L., Zhou, K., Xiong, W.-J., Xie, J., Zhang, Z., Chen, C.-L., et al. (2024) · Journal of Clinical Investigation · doi:10.1172/jci175834 · Cited by 33
“Previous studies have shown an association between radiomics and CD8 + T cells (36, 41).”
Influential
Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study
Wu, T., Duan, Y., Zhang, T., Tian, W., Liu, H., & Deng, Y. (2022) · Frontiers in Bioscience-Landmark · doi:10.31083/j.fbl2709254 · Cited by 37
“[70] employed an AI technique to analyze RNA sequencing data and CT images from patients with malignant tumors.”
Method
A Novel TGF-β Risk Score Predicts the Clinical Outcomes and Tumour Microenvironment Phenotypes in Bladder Cancer
Liu, Z., Qi, T., Li, X., Yao, Y., Othmane, B., Chen, J., Zu, X., Ou, Z., & Hu, J. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.791924 · Cited by 35
“Despite the prominent role of TGF-b in regulating multiple cancer processes, therapies targeting the TGF-b pathway have not been well explored (10).”
Background
Multi-center evaluation of artificial intelligent imaging and clinical models for predicting neoadjuvant chemotherapy response in breast cancer
Qi, T., Hian, O. H., Kumaran, A., Tan, T. J., Tan, R. S. Y. C., Su-Xin, G. L., Lim, E. H., Ng, R. C. H., Yeo, M. C. R., Lim, F. L. W. T., et al. (2022) · Breast Cancer Research and Treatment · doi:10.1007/s10549-022-06521-7 · Cited by 38
“Similar conclusion on the importance of peri-tumoral region is found in a separate radiomics study conducted by Sun et al. [27].”
Result
Artificial intelligence-based radiomics models in endometrial cancer: A systematic review
Lecointre, L., Dana, J., Lodi, M., Akladios, C. Y., & Gallix, B. (2021) · European Journal of Surgical Oncology · doi:10.1016/j.ejso.2021.06.023 · Cited by 46
Machine-Learning and Stochastic Tumor Growth Models for Predicting Outcomes in Patients With Advanced Non–Small-Cell Lung Cancer
Siah, K. W., Khozin, S., Wong, C. H., & Lo, A. W. (2019) · JCO Clinical Cancer Informatics · doi:10.1200/cci.19.00046 · Cited by 38
“For example, radiomic features from deep learning models have shown immense potential in NSCLC prognostication.(25-31) Such variables will inevitably become more important as we gradually reach the limits of biomedical reductionism—that is, the target-based treatment paradigm—and shift toward systems biology approache…”
Background
MRI-derived radiomics assessing tumor-infiltrating macrophages enable prediction of immune-phenotype, immunotherapy response and survival in glioma
Chen, D., Zhang, R., Huang, X., Ji, C., Xia, W., Qi, Y., Yang, X., Lin, L., Wang, J., Cheng, H., et al. (2024) · Biomarker Research · doi:10.1186/s40364-024-00560-6 · Cited by 34
“Although TIM has been determined to be an important factor in immunotherapy, noninvasive predictors based on radiographs have only recently been developed as biomarkers for immunotherapy in solid tumors [10].”
BackgroundMethod
Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme
Luan, J.-X., Zhang, D., Liu, B., Yang, A.-C., Lv, K., Hu, P., Yu, H.-W., Shmuel, A., Zhang, C.-C., & Ma, G.-L. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-023-04823-y · Cited by 32
“The results of a study showed that the CD8 + T-cell infiltration level in four different cohorts of solid tumor patients could effectively predict the efficacy of immunotherapy based on the CD8 + cell score [20].”
Result
Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment
Granata, V., Fusco, R., Muzio, F. D., Brunese, M. C., Setola, S. V., Ottaiano, A., Cardone, C., Avallone, A., Patrone, R., Pradella, S., et al. (2023) · La radiologia medica · doi:10.1007/s11547-023-01710-w · Cited by 32
Cancer immunotherapy response prediction from multi-modal clinical and image data using semi-supervised deep learning
Wang, X., Jiang, Y., Chen, H., Zhang, T., Han, Z., Chen, C., Yuan, Q., Xiong, W., Wang, W., Li, G., et al. (2023) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2023.109793 · Cited by 33
Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors
Park, C., Jeong, D. Y., Choi, Y., Oh, Y. J., Kim, J., Ryu, J., Paeng, K., Lee, S., Ock, C., & Lee, H. Y. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1038089 · Cited by 30
Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy
Sun, R., Lerousseau, M., Briend-Diop, J., Routier, É., Roy, S., Henry, T., Ka, K., Jiang, R., Temar, N., Carré, A., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004867 · Cited by 32
“Multivariate models of overall survival and PFS when using the cutoff from Sun et al, 2018 for the minimal value of radiomicsscore”
MethodSelf-citation
Radiomics Prediction of EGFR Status in Lung Cancer—Our Experience in Using Multiple Feature Extractors and The Cancer Imaging Archive Data
Lü, L., Sun, S. H., Yang, H., Linning, E., Guo, P., Schwartz, L. H., & Zhao, B. (2020) · Tomography · doi:10.18383/j.tom.2020.00017 · Cited by 36
Radiomic signature: A novel magnetic resonance imaging-based prognostic biomarker in patients with skull base chordoma
Wei, W., Wang, K., Liu, Z., Tian, K., Wang, L., Du, J., Ma, J., Wang, S., Li, L., Zhao, R., et al. (2019) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2019.10.002 · Cited by 37
Imaging-based Biomarkers for Predicting and Evaluating Cancer Immunotherapy Response
Wu, M., Zhang, Y., Zhang, Y., Liu, Y., Wu, M., & Ye, Z. (2019) · Radiology Imaging Cancer · doi:10.1148/rycan.2019190031 · Cited by 47
“To overcome these limitations, Sun et al developed a radiomic signature of immune infiltration of tumors to predict response to anti-PD-1 or antiPD-L1 immunotherapy (77).”
Background
Uncontrolled confounders may lead to false or overvalued radiomics signature: a proof of concept using survival analysis in a multicenter cohort of kidney cancer
Lu, L., Ahmed, F. S., Akin, O., Luk, L., Guo, X., Yang, H., Yoon, J., Hakimi, A. A., Schwartz, L. H., & Zhao, B. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.638185 · Cited by 31
A Comprehensive Review of Multimodal Large Language Models for Medical Imaging and Omics Data
Vavekanand, R. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10504-y · Cited by 29
“Cancer Genome Atlas Program (TCGA) [ 13, 41, 42 ].”
Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC
Fiste, O., Gkiozos, I., Charpidou, A. G., & Syrigos, N. K. (2024) · Cancers · doi:10.3390/cancers16040831 · Cited by 39
“Indicatively, Sun et al. developed an AI model based on CT radiomics in order to predict CD8 infiltration and its predictive value of response to ICI, with rather modest results (AUC of 0.63–0.76) [60,61].”
MethodInfluential
Prediction of recurrence-free survival and adjuvant therapy benefit in patients with gastrointestinal stromal tumors based on radiomics features
Wang, F., Zheng, H.-L., Li, J.-T., Li, P., Zheng, C.-H., Chen, Q.-Y., Huang, C.-M., & Xie, J.-W. (2022) · La radiologia medica · doi:10.1007/s11547-022-01549-7 · Cited by 31
Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?
Sun, R., Henry, T., Laville, A., Carré, A., Hamaoui, A., Bockel, S., Chaffai, I., Lévy, A., Chargari, C., Robert, C., et al. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-004848 · Cited by 47
Self-citation
A comprehensive texture feature analysis framework of renal cell carcinoma: pathological, prognostic, and genomic evaluation based on CT images
Wu, K., Wu, P. S., Yang, K. G., Li, Z., Kong, S., Yu, L., Zhang, E., Liu, H., Guo, Q., & Wu, S. (2021) · European Radiology · doi:10.1007/s00330-021-08353-3 · Cited by 30
“There is evidence of the connection between CT texture features and tissue microenvironment, such as immune infiltration and mutation status [36–39].”
Background
Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer
Onozato, Y., Nakajima, T., Yokota, H., Morimoto, J., Nishiyama, A., Toyoda, T., Inage, T., Tanaka, K., Sakairi, Y., Suzuki, H., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-93002-4 · Cited by 31

Authors: Sun, R., Limkin, E. J., Vakalopoulou, M., Dercle, L., Champiat, S., Han, S. R., Verlingue, L., Brandao, D., Lancia, A., Ammari, S., Hollebecque, A., Scoazec, J.-Y., Marabelle, A., Massard, C., Soria, J.-C., Robert, C., Paragios, N., Deutsch, E., & Ferté, C.
Affiliations: Inserm (FR)
Université Paris-Saclay (FR)
Institut Gustave Roussy (FR)
L'Hôpital Nord Franche-Comté (FR)
Université Paris-Sud (FR)
ORCID: Sun, Roger
Limkin, Elaine Johanna
Vakalopoulou, Maria
Dercle, Laurent
Champiat, Stéphane
Verlingue, Loïc
Lancia, Andrea
Ammari, Samy
Hollebecque, Antoine
Scoazec, Jean-Yves
Marabelle, Aurélien
Massard, Christophe
Soria, Jean-Charles
Robert, Charlotte
Ferté, Charles
Venue: The Lancet Oncology
Publisher: Elsevier BV
Published: 2018-09
Volume: 19
Issue: 9
Pages: 1180-1191
ISSN: 1470-2045 (ISSN-L)
DOI: 10.1016/s1470-2045(18)30413-3
OpenAlex: W2885326465
Cited by: 1,236
References: 48
Citations by year: 2026: 109 · 2025: 118 · 2024: 186 · 2023: 180 · 2022: 165 · 2021: 221 · 2020: 155 · 2019: 89 · 2018: 9
FWCI: 81.87
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: The Lancet Oncology (publishedVersion)
PubMed (publishedVersion)
HAL (submittedVersion)
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Keywords: radiomics, anti-PD-1 therapy, PD-L1 blockade, immunotherapy response prediction, imaging biomarkers, advanced solid tumors, contrast-enhanced CT, radiomics signature, elastic net
MeSH terms: Antineoplastic Agents, Immunological, Adult, Aged, Female, Humans, Male, Middle Aged, Neoplasms, Phenotype, Predictive Value of Tests, Retrospective Studies, RNA, Neoplasm, Time Factors, Tomography, X-Ray Computed, Biomarkers, Tumor, Reproducibility of Results, Lymphocytes, Tumor-Infiltrating, Treatment Outcome, Sequence Analysis, RNA, CD8-Positive T-Lymphocytes, Gene Expression Profiling, Molecular Imaging, Transcriptome, B7-H1 Antigen, Programmed Cell Death 1 Receptor

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A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study

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