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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
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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“First, previous studies have primarily focused on tumor-infiltrating lymphocytes,12,26 which only provide a simplified and partial view of the TME.”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
In the citing paper
“This idea is motivated by prior work showing that either approach can extract useful information for assessing TME.12,13,33,35”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
In the citing paper
“The feasibility of this idea has been demonstrated in previous studies.12,23,24,25”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
In the citing paper
“Sun et al. developed a radiomic signature of CD8 T cells, which was correlated with clinical response and outcomes of patients treated with anti-PD1 immunotherapy.12”
From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [12]
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BackgroundArtificial intelligence and immunotherapy
Jabbari, P., & Rezaei, N. (2019) · Expert Review of Clinical Immunology · doi:10.1080/1744666x.2019.1623670 · Cited by 46
“These image-based techniques have been recruited to determine immune responses such as macrophage activation [5] and lymphocyte infiltration in breast cancer and other solid tumors [6], hence assist in determining prognosis and response to treatment.”
MethodHuang, W., Xiong, W., Tang, L., Chen, C., Yuan, Q., Zhang, C., Zhou, K., Sun, Z., Zhang, T., Han, Z., et al. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007807 · Cited by 47
Zhao, J., He, Y.-Y., Yang, X., Tian, P.-W., Zeng, L., Huang, K., Zhao, J., Zhou, J.-Q., Zhu, Y., Wang, Q., et al. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007492 · Cited by 27
“Disease stage at diagnosis was classified according to the American Joint Committee on Cancer, eighth edition, tumor, node, metastasis staging system; the best objective response was evaluated according to Response Evaluation Criteria in Solid Tumors, V.”
Kothari, G., Woon, B., Patrick, C. J., Korte, J. C., Wee, L., Hanna, G. G., Kron, T., Hardcastle, N., & Siva, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-16520-9 · Cited by 27
Machine Learning-Based Radiomics Nomogram for Detecting Extramural Venous Invasion in Rectal Cancer
Liu, S.-Y., Yu, X.-P., Yang, S., Hu, P., Hu, Y., Chen, X., Li, Y., Zhang, Z., Li, C., & Lu, Q. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.610338 · Cited by 27
“2, GE Healthcare) for image preprocessing, including resampling the image to a 1 × 1 × 1 mm(3) voxel size and standardizing the image gray level to a scale of 1 to 32 to eliminate the influence of anisotropy on the extracted features (24).”
Detection of response to tumor microenvironment–targeted cellular immunotherapy using nano-radiomics
Devkota, L., Starosolski, Z., Rivas, C. H., Stupin, I., Annapragada, A., Ghaghada, K., & Parihar, R. (2020) · Science Advances · doi:10.1126/sciadv.aba6156 · Cited by 27
Gao, X., Ma, T., Bai, S., Liu, Y., Zhang, Y., Wu, Y., Li, H., & Ye, Z. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.03.114 · Cited by 31
Advances in theranostic biomarkers for tumor immunotherapy
Bellesœur, A., Torossian, N., Amigorena, S., & Romano, E. (2020) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2020.02.005 · Cited by 34
Li, Y., Wang, P., Xu, J., Shi, X., Yin, T., & Teng, F. (2024) · OncoImmunology · doi:10.1080/2162402x.2024.2312628 · Cited by 27
“The peritumoral area plays a crucial role in the TME, 39–41 Sun et al . predicted CD8 cells with peritumoral radiomic features and used these features to predict the immunotherapy response.”
BackgroundMethodBevilacqua, R., Barbarossa, F., Fantechi, L., Fornarelli, D., Paci, E., Bolognini, S., Giammarchi, C., Lattanzio, F., Paciaroni, L., Riccardi, G. R., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12165432 · Cited by 33
Saber, R., Henault, D., Messaoudi, N., Rebolledo, R., Montagnon, E., Soucy, G., Stagg, J., Tang, A., Turcotte, S., & Kadoury, S. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04175-7 · Cited by 29
“[42] found, in a study combining several cancer sites, that a high CD8 score, indicative of inflammatory infiltrate, was associated with homogeneous lesion appearances.”
ResultPartial tumor irradiation plus pembrolizumab in treating large advanced solid tumor metastases
Korpics, M., Onderdonk, B., Dadey, R., Hara, J., Karapetyan, L., Zha, Y., Karrison, T., Olson, A., Fleming, G., Weichselbaum, R., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci162260 · Cited by 26
“RSs were calculated using previously reported methods from pretreatment CT simulation images acquired from a Philips CT scanner (20).”
Tumor Microenvironment, Radiology, and Artificial Intelligence: Should We Consider Tumor Periphery?
Mohammadi, A., Mirza-Aghazadeh-Attari, M., Faeghi, F., Homayoun, H., Abolghasemi, J., Vogl, T., Bureau, N., Bakhshandeh, M., Acharya, R., & Ardakani, A. A. (2022) · Journal of ultrasound in medicine · doi:10.1002/jum.16086 · Cited by 26
“Noteworthy, their proxy indicator was also associated with specific immunopathologic observations, such as the abundance of tumor-associated neutrophils.(26) The association between tumor margin morphometric features (such as roundness factor, convexity, and LoG kurtosis 3) and the clinical profile of the tumor was al…”
BackgroundRole of radiomics in predicting immunotherapy response
Kothari, G. (2022) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13426 · Cited by 33
“Some of the significant strengths, however, of this study included the use of an openaccess radiomics platform, details provided regarding image pre-processing, as well as explicit reporting of the radiomics model used and features included, allowing this model to be subsequently also externally validated in patients…”
MethodInfluentialYe, Z., Zeng, D., Zhou, R., Shi, M., & Liao, W. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.819807 · Cited by 25
Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients
de la Pinta, C., Castillo, M. E., Collado, M., Galindo-Pumariño, C., & Peña, C. (2021) · Cancers · doi:10.3390/cancers13215547 · Cited by 32
“Moreover, calculation of CD8 infiltration by a radiogenomics signature using CT images and RNA sequencing data was proposed, in order to predict the immune phenotype of advanced solid malignant tumors and clinical outcomes of immunotherapy-treated patients [49].”
MethodHyperprogressive Disease: Main Features and Key Controversies
Arasanz, H., Zuazo, M., Bocanegra, A., Chocarro, L., Blanco, E., Martínez, M., Morilla, I., Fernandez, G., Teijeira, L., Morente, P., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22073736 · Cited by 26
Xu, N., Zhou, J.-J., He, X., Ye, S., Miao, H., Liu, H., Chen, Z., Zhao, Y., Pan, Z., & Wang, M. (2020) · Clinical Breast Cancer · doi:10.1016/j.clbc.2020.12.008 · Cited by 26
Feng, Q., Wang, M., Song, Q., Wu, Z.-W., Jiang, H.-Y., Pang, P., Liao, Z.-L., Yu, E.-Y., & Ding, Z. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00435 · Cited by 26
Anderson, K. G., Braun, D. A., Buqué, A., Gitto, S. B., Guerriero, J. L., Horton, B. L., Keenan, B. P., Kim, T. S., Overacre-Delgoffe, A. E., Ruella, M., et al. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2022-006533 · Cited by 22
“145 AI-based histopathology analyses have shown predictive value in determining response to immunotherapy in MMR-defective tumors.”
Deep radiomic signature with immune cell markers predicts the survival of glioma patients
Chaddad, A., Daniel, P. M., Zhang, M., Rathore, S., Sargos, P., Desrosiers, C., & Niazi, T. M. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.10.117 · Cited by 26
“While standard radiomic features have shown promising results [9, 13, 16, 18, 49], the use of multiscale features from different 3D CNN layers as learnable radiomics descriptors remains limited [37].”
BackgroundChen, Y., Wei, K., Liu, D., Xiang, J., Wang, G., Meng, X., & Peng, J. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.675458 · Cited by 31
“In certain cancers, radiomic features have been demonstrated to be an effective predictor for responses to anticancer therapy (14, 15).”
BackgroundHe, B., Zhong, Y., Zhu, Y., Deng, J., Fang, M., She, Y., Wang, T., Yang, Y., Sun, X., Belluomini, L., et al. (2022) · Translational Lung Cancer Research · doi:10.21037/tlcr-22-244 · Cited by 29
“Regarding the visualization results, we found that whether a dual-task network or survival network was used, the tumor microenvironment played an irreplaceable role in predicting tumor progression and patient OS, which was consistent with the conclusions of previous studies (32-34).”
Deep Multi-Instance Learning Using Multi-Modal Data for Diagnosis of Lymphocytosis
Sahasrabudhe, M., Sujobert, P., Zacharaki, E., Maurin, E., Grange, B., Jallades, L., Paragios, N., & Vakalopoulou, M. (2020) · IEEE journal of biomedical and health informatics · doi:10.1109/jbhi.2020.3038889 · Cited by 25
“In the last decade radiomics has emerged in oncology as a way to extract imaging features for diagnosis or prediction of treatment outcome or to be used as a surrogate of oncogenic processes that are difficult to explore by contextual biopsies [1]–[3].”
BackgroundSelf-citationRole of image-guided biopsy and radiomics in the age of precision medicine.
Tselikas, L., Sun, R., Ammari, S., Dercle, L., Yevich, S., Hollebecque, A., Ngo-Camus, M., Nicotra, C., Deutsch, E., Deschamps, F., et al. (2019) · Chinese Clinical Oncology · doi:10.21037/cco.2019.12.02 · Cited by 25
“For example, radiomics may be use to predict CD8 T cell genes expression, which might serve as a prognostic factor for patients treated with immunotherapy (32).”
BackgroundMethodSelf-citationMalhotra, J. D., Mambetsariev, I., Gilmore, G., Fricke, J., Nam, A., Gallego, N., Chen, B. T., Chen, M. Y., Amini, A., Lukas, R. V., et al. (2024) · JAMA Oncology · doi:10.1001/jamaoncol.2024.5218 · Cited by 22
Inglese, M., Patel, N., Linton‐Reid, K., Loreto, F., Win, Z., Perry, R. J., Carswell, C. J., Grech‐Sollars, M., Crum, W. R., Lu, H., et al. (2022) · Communications Medicine · doi:10.1038/s43856-022-00133-4 · Cited by 30
Bian, Y., Liu, C., Li, Q., Meng, Y., Liu, F., Zhang, H., Fang, X., Li, J., Yu, J.-Y., Feng, X.-C., et al. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27871 · Cited by 24
Shu, Z.-Y., Pang, P., Wu, X., Cui, S., Xu, Y.-Y., & Zhang, M.-M. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.548616 · Cited by 24
Sun, F., Chen, Y.-C., Chen, X., Sun, X.-R., & Xing, L. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109411 · Cited by 24
Guerrisi, A., Loi, E., Ungania, S., Russillo, M., Bruzzaniti, V., Elia, F., Desiderio, F., Marconi, R., Solivetti, F. M., & Strigari, L. (2020) · Cancer Medicine · doi:10.1002/cam4.2709 · Cited by 23
Sang, J., & Ye, X. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1268331 · Cited by 22
Song, R., Liu, F., Yu, P., Zhang, Y., & Wang, L. (2023) · Biomarker Research · doi:10.1186/s40364-023-00498-1 · Cited by 27
“Researchers explored and independently validated a radiomic biomarker for CD8 + TILs to estimate the antiPD-L1 monotherapy efficacy in a phase 1 trial in multiple solid tumors [24].”
BackgroundLin, J.-X., Wang, F., Wang, Z.-K., Wang, J.-B., Zheng, C.-H., Li, P., Huang, C.-M., & Xie, J.-W. (2023) · La radiologia medica · doi:10.1007/s11547-023-01637-2 · Cited by 23
Lin, P., Lin, Y.-Q., Gao, R.-Z., Wan, W., He, Y., & Yang, H. (2023) · European Radiology · doi:10.1007/s00330-023-09503-5 · Cited by 23
Zhang, L., Li, X., Yang, L., Tang, Y., Guo, J.-T., Li, D., Li, S., Li, Y., Wang, L., Lei, Y., et al. (2022) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.28498 · Cited by 23
Biomarkers for response to immunotherapy in hepatobiliary malignancies
Lin, Z., Qin, L., & Chen, J. (2022) · Hepatobiliary & pancreatic diseases international · doi:10.1016/j.hbpd.2022.08.002 · Cited by 31
Liu, Z.-M., Zhang, H., Ge, M., Hao, X., An, X., & Tian, Y.-S. (2022) · Child's Nervous System · doi:10.1007/s00381-022-05507-6 · Cited by 23
T cell subtype profiling measures exhaustion and predicts anti-PD-1 response
Schillebeeckx, I., Earls, J., Flanagan, K. C., Hiken, J., Bode, A., Armstrong, J. R., Messina, D. N., Adkins, D. R., Ley, J. C., Alborelli, I., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-05474-7 · Cited by 24
Liu, H., Ye, Z., Yang, T., Xie, H., Duan, T., Li, M., Wu, M., & Song, B. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.724464 · Cited by 23
Han, S., Wang, P., Cai, H., Wan, J., Li, S., Lin, Z.-H., Yu, C., & Yan, C. (2021) · Aging · doi:10.18632/aging.203102 · Cited by 38
“Immune-inflamed tumors are characterized by increased TMB, checkpoint expression, interferon-γ signaling, and CD8+ T cell infiltration and are responsive to immunotherapy [26, 27].”
BackgroundSelf-citationRadiogenomics in Interventional Oncology
Moussa, A. M., & Ziv, E. (2021) · Current Oncology Reports · doi:10.1007/s11912-020-00994-9 · Cited by 26
Martins, P. R., Machado, C. M. T., Coxir, S. A., de Oliveira, A. J., Moreira, T. B., Campos, L. S., Alcântara, R., de Paula, S. D., Salles, P. G. D. O., Gollob, K., et al. (2019) · Experimental and molecular pathology (Print) · doi:10.1016/j.yexmp.2019.104314 · Cited by 23
Noninvasive imaging in cancer immunotherapy: The way to precision medicine
Du, Y., Qi, Y., Jin, Z., & Tian, J. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.08.009 · Cited by 30
Himoto, Y., Veeraraghavan, H., Zheng, J.-T., Zamarin, D., Snyder, A., Capanu, M., Nougaret, S., Vargas, H., Shitano, F., Callahan, M., et al. (2019) · JCO Precision Oncology · doi:10.1200/po.19.00038 · Cited by 23
“Few studies to date have evaluated the role of radiomics to model spatial TH and capture immune TME.(33,35) Vargas et al(35) proposed a novel method to compute interTH on CE-CT and found higher inter-TH to associate with shorter overall survival and greater risk of incomplete surgical resection in 38 patients with new…”
BackgroundMethodInfluentialImage-Guided Radiooncology: The Potential of Radiomics in Clinical Application
Peeken, J. C., Wiestler, B., & Combs, S. E. (2020) · Recent results in cancer research/Recent Results in Cancer Research · doi:10.1007/978-3-030-42618-7_24 · Cited by 29
The artificial intelligence revolution in gastric cancer management: clinical applications
Li, R., Li, J., Wang, Y., Liu, X., Xu, W., Sun, R., Xue, B., Zhang, X., Ai, Y., Du, Y., et al. (2025) · Cancer Cell International · doi:10.1186/s12935-025-03756-4 · Cited by 25
“Sun et al.used four separate cohorts of patients with advanced solid tumors to develop and validate radiomic signatures that predict response to immunotherapy by combining contrast-enhanced CT images and RNA-seq genomic data from tumor biopsies to assess CD8 cell tumor infiltration [122].”
Self-citationWang, X., Wei, M.-T., Chen, Y., Jia, J., Zhang, Y., Dai, Y., Qin, C., Bai, G.-J., & Chen, S.-Q. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01855-w · Cited by 22
Radiomics Biomarkers to Predict Checkpoint Inhibitor Pneumonitis in Non-small Cell Lung Cancer.
Du, Y.-H., Zhang, S., Jia, X.-H., Zhang, X., Li, X.-Q., Pan, L.-B., Li, Z.-H., Niu, G., Liang, T., & Guo, H. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.09.053 · Cited by 22
Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review
ter Maat, L. S., van Duin, I. A. J., Elias, S. G., van Diest, P. J., Pluim, J. P. W., Verhoeff, J. J. C., de Jong, P. A., Leiner, T., Veta, M., & Suijkerbuijk, K. P. M. (2022) · European Journal of Cancer · doi:10.1016/j.ejca.2022.07.034 · Cited by 21
Prospective Clinical Research of Radiomics and Deep Learning in Oncology: A Translational Review.
Zhang, X.-P., Zhang, Y., Zhang, G., Qiu, X.-T., Tan, W., Yin, X., & Liao, L. (2022) · Critical reviews in oncology/hematology · doi:10.1016/j.critrevonc.2022.103823 · Cited by 22
Cella, E., Zullo, L., Marconi, S., Rossi, G., Coco, S., Dellepiane, C., Alama, A., Rozeboom, L. M., Bennicelli, E., Parisi, F., et al. (2022) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2022.2116273 · Cited by 25
“Indeed, the association of a radiomic signature and genomic data, both related to CD8+ abundance in tumor tissue, has demonstrated association with objective response in patients treated with immunotherapy [71].”
BackgroundNovel Insights Into Mesothelioma Therapy: Emerging Avenues and Future Prospects
Kuryk, Ł., Rodella, G., Staniszewska, M., Pancer, K. W., Wieczorek, M., Salmaso, S., Caliceti, P., & Garofalo, M. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.916839 · Cited by 26
“In fact, it is wellestablished that immune-inflamed tumors are characterized by greater CD8+ T-cell infiltration and higher PD-L1 expression, leading to a better response to immunotherapy (68).”
BackgroundLi, J., Shi, Z., Liu, F., Fang, X., Cao, K., Meng, Y., Zhang, H., Yu, J., Feng, X., Li, Q., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.671333 · Cited by 29
Huang, X., Mai, J., Huang, Y.-Q., He, L., Chen, X., Wu, X., Li, Y.-X., Yang, X., Dong, M.-Y., Huang, J., et al. (2020) · Clinical Breast Cancer · doi:10.1016/j.clbc.2020.12.004 · Cited by 22
Future of Radiation and Immunotherapy
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Application of molecular imaging technology in tumor immunotherapy.
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Robust radiomics: a review of guidelines for radiomics in medical imaging
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Discovery of predictive biomarkers for cancer therapy through computational approaches
Wang, X., Nguyen, J., Nader, K. M., Miihkinen, M., Wall, P., Singhal, A., Bedard, P. L., Ideker, T., Aittokallio, T. A., & Haibe‐Kains, B. (2026) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01109-8 · Cited by 24
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“To date, whole-slide imaging systems have been necessary tools for hospitals, disease control centres, new drug development, and scientific research institutions [111–114].”
Method
Metadata
- 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
- Venue
- The Lancet Oncology
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 19
- Issue
- 9
- Pages
- 1180-1191
- ISSN
- 1470-2045 (ISSN-L)
- 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
- Locations
- The Lancet Oncology (publishedVersion)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Fondation pour la Recherche Médicale
- American Society for Radiation Oncology
- 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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