Deep learning analysis of histopathological images predicts immunotherapy prognosis and reveals tumour microenvironment features in non-small cell lung cancer
Open accessAbstract
Background Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer mortality worldwide. Immune checkpoint inhibitors (ICIs) have emerged as a crucial treatment option for patients with advanced NSCLC. However, only a subset of patients experience clinical benefit from ICIs. Therefore, identifying biomarkers that can predict response to ICIs is imperative for optimising patient selection. Methods Hematoxylin and eosin (H&E) images of NSCLC patients were obtained from the local cohort (n = 106) and The Cancer Genome Atlas (TCGA) (n = 899). We developed an ICI-related pathological prognostic signature (ir-PPS) based on H&E stained histopathology images to predict prognosis in NSCLC patients treated with ICIs using deep learning. To accomplish this, we employed a modified ResNet model (ResNet18-PG), a widely-used deep learning architecture well-known for its effectiveness in handling complex image recognition tasks. Our modifications include a progressive growing strategy to improve the stability of model training and the use of the AdamW optimiser, which enhances the optimisation process by adjusting the learning rate based on training dynamics. Results The deep learning model, ResNet18-PG, achieved an area under the receiver operating characteristic curve (AUC) of 0.918 and a recall of 0.995 on the local cohort. The ir-PPS effectively risk-stratified NSCLC patients. Patients in the low-risk group (n = 40) had significantly improved progression-free survival (PFS) after ICI treatment compared to those in the high-risk group (n = 66, log-rank P = 0.004, hazard ratio (HR) = 3.65, 95%CI: 1.75-7.60). The ir-PPS demonstrated good discriminatory power for predicting 6-month PFS (AUC = 0.750), 12-month PFS (AUC = 0.677), and 18-month PFS (AUC = 0.662). The low-risk group exhibited increased expression of immune checkpoint molecules, cytotoxicity-related genes, an elevated abundance of tumour-infiltrating lymphocytes, and enhanced activity in immune stimulatory pathways. Conclusions The ir-PPS signature derived from H&E images using deep learning could predict ICIs prognosis in NSCLC patients. The ir-PPS provides a novel imaging biomarker that may help select optimal candidates for ICIs therapy in NSCLC.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989 · see citing works
Siegel, R. L., Miller, K. D., & Jemal, A. (2020) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21590 · Cited by 20,444 · see citing works
GSVA: gene set variation analysis for microarray and RNA-Seq data
Hänzelmann, S., Castelo, R., & Guinney, J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-7 · Cited by 15,440 · see citing works
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,314 · see citing works
Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer
Reck, M., Rodríguez-Abreu, D., Robinson, A. G., Hui, R., Csőszi, T., Fülöp, A., Gottfried, M., Peled, N., Tafreshi, A., Cuffe, S., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1606774 · Cited by 10,089 · see citing works
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802 · see citing works
Pembrolizumab for the Treatment of Non–Small-Cell Lung Cancer
Garon, E. B., Rizvi, N. A., Hui, R., Leighl, N., Balmanoukian, A. S., Eder, J. P., Patnaik, A., Aggarwal, C., Gubens, M., Horn, L., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1501824 · Cited by 6,198 · see citing works
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202 · see citing works
Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response
Jiang, P., Gu, S., Pan, D., Fu, J., Sahu, A., Hu, X., Li, Z., Traugh, N., Bu, X., Li, B., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0136-1 · Cited by 5,179 · see citing works
The immune contexture in human tumours: impact on clinical outcome
Fridman, W. H., Pagès, F., Sautès-Fridman, C., & Galon, J. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3245 · Cited by 4,981 · see citing works
IFN-γ–related mRNA profile predicts clinical response to PD-1 blockade
Ayers, M., Lunceford, J., Nebozhyn, M., Murphy, E., Loboda, A., Kaufman, D. R., Albright, A., Cheng, J. D., Kang, S. P., Shankaran, V., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci91190 · Cited by 3,788 · see citing works
Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge
Tomczak, K. J., Czerwińska, P., & Wiznerowicz, M. (2015) · Współczesna Onkologia · doi:10.5114/wo.2014.47136 · Cited by 3,503 · see citing works
Mok, T. S. K., Wu, Y.-L. L. C., Kudaba, I., Kowalski, D. M., Cho, B. C., Turna, H. Z., de Castro, G., Srimuninnimit, V., Лактионов, К. К., Bondarenko, I., et al. (2019) · The Lancet · doi:10.1016/s0140-6736(18)32409-7 · Cited by 3,319 · see citing works
B cells and tertiary lymphoid structures promote immunotherapy response
Helmink, B. A., Reddy, S. M., Gao, J., Zhang, S., Basar, R., Thakur, R., Yizhak, K., Sade-Feldman, M., Blando, J., Han, G., et al. (2020) · Nature · doi:10.1038/s41586-019-1922-8 · Cited by 2,731 · see citing works
Tertiary lymphoid structures improve immunotherapy and survival in melanoma
Cabrita, R., Lauss, M., Sanna, A., Donia, M., Skaarup Larsen, M., Mitra, S., Johansson, I., Phung, B., Harbst, K., Vallon-Christersson, J., et al. (2020) · Nature · doi:10.1038/s41586-019-1914-8 · Cited by 2,374 · see citing works
Mao, X., Xu, J., Wang, W., Liang, C., Hua, J., Liu, J., Zhang, B., Meng, Q., Yu, X., & Shi, S. (2021) · Molecular Cancer · doi:10.1186/s12943-021-01428-1 · Cited by 2,290 · see citing works
The transcription factor Myc controls metabolic reprogramming upon T lymphocyte activation
Wang, R., Dillon, C. P., Shi, L. Z., Milasta, S., Carter, R., Finkelstein, D., McCormick, L. L., Fitzgerald, P., Chi, H., Munger, J., et al. (2011) · Immunity · doi:10.1016/j.immuni.2011.09.021 · Cited by 2,140 · cited 1× · see citing works
“In summary, our results demonstrate that the application of deep learning to routine histology may provide prognostic imaging biomarkers to advance personalised immunotherapy for lung cancer.”
Activation of a metabolic gene regulatory network downstream of mTOR complex 1
Düvel, K., Yecies, J. L., Menon, S., Raman, P., Lipovsky, A. I., Souza, A. L., Triantafellow, E., Ma, Q., Gorski, R., Cleaver, S., et al. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.06.022 · Cited by 2,020 · see citing works
Turning foes to friends: targeting cancer-associated fibroblasts
Chen, X., & Song, E. (2018) · Nature Reviews Drug Discovery · doi:10.1038/s41573-018-0004-1 · Cited by 1,884 · see citing works
The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers
Sondka, Z., Bamford, S., Cole, C. G., Ward, S. A., Dunham, I., & Forbes, S. A. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0060-1 · Cited by 1,597 · see citing works
Glutathione metabolism in cancer progression and treatment resistance
Bansal, A., & Simon, M. C. (2018) · The Journal of Cell Biology · doi:10.1083/jcb.201804161 · Cited by 1,234 · see citing works
Garon, E. B., Hellmann, M. D., Rizvi, N. A., Carcereny, E., Leighl, N. B., Ahn, M.-J., Eder, J. P., Balmanoukian, A. S., Aggarwal, C., Horn, L., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.19.00934 · Cited by 1,231 · see citing works
Targeting natural killer cells in cancer immunotherapy
Guillerey, C., Huntington, N. D., & Smyth, M. J. (2016) · Nature Immunology · doi:10.1038/ni.3518 · Cited by 1,061 · see citing works
Monitoring immune-checkpoint blockade: response evaluation and biomarker development
Nishino, M., Ramaiya, N. H., Hatabu, H., & Hodi, F. S. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.88 · Cited by 1,023 · see citing works
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972 · see citing works
Germain, C., Gnjatic, S., Tamzalit, F., Knockaert, S., Remark, R., Goc, J., Lepelley, A., Becht, E., Katsahian, S., Bizouard, G., et al. (2014) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201309-1611oc · Cited by 833 · see citing works
Gene expression markers of Tumor Infiltrating Leukocytes
Danaher, P. J., Warren, S. E., Dennis, L. M., D’Amico, L., White, A. M., Disis, M. L., Geller, M. A., Odunsi, K. O., Beechem, J., & Fling, S. P. (2017) · Journal for ImmunoTherapy of Cancer · doi:10.1186/s40425-017-0215-8 · Cited by 791 · see citing works
Gettinger, S. N., Horn, L., Jackman, D. M., Spigel, D. R., Antonia, S. J., Hellmann, M. D., Powderly, J. D., Heist, R. S., Sequist, L. V. D., Smith, D. C., et al. (2018) · Journal of Clinical Oncology · doi:10.1200/jco.2017.77.0412 · Cited by 740 · see citing works
TIP: A Web Server for Resolving Tumor Immunophenotype Profiling
Xu, L., Deng, C., Pang, B., Zhang, X., Liu, W., Liao, G., Yuan, H., Peng, C., Li, F., Long, Z., et al. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0689 · Cited by 698 · see citing works
Self-citationB cells, plasma cells and antibody repertoires in the tumour microenvironment
Sharonov, G. V., Serebrovskaya, E. O., Yuzhakova, D. V., Britanova, O. V., & Chudakov, D. M. (2020) · Nature reviews. Immunology · doi:10.1038/s41577-019-0257-x · Cited by 630 · see citing works
Interferon-γ Drives Treg Fragility to Promote Anti-tumor Immunity
Overacre-Delgoffe, A. E., Chikina, M., Dadey, R. E., Yano, H., Brunazzi, E. A., Shayan, G., Horne, W., Moskovitz, J. M., Kolls, J. K., Sander, C., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.005 · Cited by 627 · see citing works
Cytokines in the Treatment of Cancer
Conlon, K. C., Miljković, M. D., & Waldmann, T. A. (2018) · Journal of Interferon & Cytokine Research · doi:10.1089/jir.2018.0019 · Cited by 523 · see citing works
Pan-cancer characterization of immune-related lncRNAs identifies potential oncogenic biomarkers
Li, Y., Jiang, T., Zhou, W., Li, J., Li, X., Wang, Q., Yin, J., Yin, J., Chen, L., Zhang, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14802-2 · Cited by 415 · see citing works
Self-citationDigital pathology: current status and future perspectives
Al‐Janabi, S., Huisman, A., & van Diest, P. J. (2011) · Histopathology · doi:10.1111/j.1365-2559.2011.03814.x · Cited by 363 · see citing works
Prognostic Effect of Tumor Lymphocytic Infiltration in Resectable Non–Small-Cell Lung Cancer
Brambilla, E. M., Teuff, G. L., Marguet, S., Lantuéjoul, S., Dunant, A., Graziano, S. L., Pirker, R., Douillard, J.-Y., Chevalier, T. L., Filipits, M., et al. (2016) · Journal of Clinical Oncology · doi:10.1200/jco.2015.63.0970 · Cited by 351 · see citing works
Jiang, M., Jia, K., Wang, L., Li, W., Chen, B., Liu, Y., Wang, H., Zhao, S., He, Y., & Zhou, C. (2021) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2021.01.003 · Cited by 334 · see citing works
Bruno, T. C., Ebner, P. J., Moore, B. L., Squalls, O., Waugh, K. A., Eruslanov, E. B., Singhal, S. K., Mitchell, J. D., Franklin, W. A., Merrick, D. T., et al. (2017) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-17-0075 · Cited by 302 · see citing works
Lin, A., Wei, T., Meng, H., Luo, P., & Zhang, J. (2019) · Molecular Cancer · doi:10.1186/s12943-019-1062-7 · Cited by 263 · see citing works
Chen, C., Chen, C., Yu, W.-H., Chen, S. S., Chang, Y., Hsu, T., Hsiao, M., Yeh, C., & Chen, C. (2021) · Nature Communications · doi:10.1038/s41467-021-21467-y · Cited by 221 · see citing works
Negative Regulation of Cytokine Signaling in ImmunityPreprint
Yoshimura, A., Ito, M., Chikuma, S., Akanuma, T., & Nakatsukasa, H. (2017) · Cold Spring Harbor Perspectives in Biology · doi:10.1101/cshperspect.a028571 · Cited by 199 · see citing works
Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis
Luo, X., Zang, X., Yang, L., Huang, J., Liang, F., Rodriguez‐Canales, J., Wistuba, I. I., Gazdar, A. F., Xie, Y., & Xiao, G. (2016) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2016.10.017 · Cited by 189 · see citing works
Altered cancer metabolism in mechanisms of immunotherapy resistance
Ramapriyan, R., Caetano, M. S., Barsoumian, H. B., Mafra, A. C. P., Zambalde, É. P., Menon, H., Tsouko, E., Welsh, J. W., & Cortez, M. A. (2018) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2018.11.004 · Cited by 171 · see citing works
CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer
Lin, A., Qi, C., Wei, T., Li, M., Cheng, Q., Liu, Z., Luo, P., & Zhang, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac129 · Cited by 152 · see citing works
GSK3 Inhibition Drives Maturation of NK Cells and Enhances Their Antitumor Activity
Cichocki, F. M., Valamehr, B. B., Bjordahl, R., Zhang, B., Rezner, B., Rogers, P., Gaidarova, S., Moreno, S. K., Tuininga, K., Dougherty, P., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0799 · Cited by 151 · see citing works
Wang, S., Chen, A., Yang, L., Cai, L., Xie, Y., Fujimoto, J., Gazdar, A. F., & Xiao, G. (2018) · Scientific Reports · doi:10.1038/s41598-018-27707-4 · Cited by 143 · see citing works
Gene Set Enrichment Analysis (GSEA) for Interpreting Gene Expression Profiles
Shi, J., & Walker, M. (2007) · Current Bioinformatics · doi:10.2174/157489307780618231 · Cited by 141 · see citing works
Cao, D., Xu, H., Xu, X., Guo, T., & Ge, W. (2019) · OncoImmunology · doi:10.1080/2162402x.2019.1629258 · Cited by 140 · see citing works
mTOR, linking metabolism and immunity
Xu, X., Ye, L., Araki, K., & Ahmed, R. (2012) · Seminars in Immunology · doi:10.1016/j.smim.2012.12.005 · Cited by 96 · see citing works
mTOR Signaling in Metabolism and Cancer
Huang, S. (2020) · Cells · doi:10.3390/cells9102278 · Cited by 79 · see citing works
Lung adenocarcinoma-intrinsic GBE1 signaling inhibits anti-tumor immunity
Li, L., Li, Y., Cheng, S., Fan, Z., Shen, Z., Xue, W., Zheng, Y., Li, F., Wang, D., Zhang, K., et al. (2019) · Molecular Cancer · doi:10.1186/s12943-019-1027-x · Cited by 76 · see citing works
Ren, Q., Zhang, P., Zhang, X., Feng, Y., Li, L., Lin, H., & Yu, Y. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1199040 · Cited by 63 · see citing works
Cheng, G., Zhang, F., Xing, Y., Hu, X., Zhang, H., Chen, S., Li, M., Peng, C., Ding, G., Zhang, D., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.893198 · Cited by 61 · see citing works
MAPK pathway mutations in head and neck cancer affect immune microenvironments and ErbB3 signaling
Ngan, H.-L., Liu, Y., Fong, A., Poon, P. H. Y., Yeung, C. K., Chan, S. S. M., Lau, A., Piao, W., Li, H., Tse, J. S. W., et al. (2020) · Life Science Alliance · doi:10.26508/lsa.201900545 · Cited by 60 · see citing works
Automated tumor analysis for molecular profiling in lung cancer
Hamilton, P. W., Wang, Y., Boyd, C., James, J. A., Loughrey, M. B., Hougton, J. P., Boyle, D. P., Kelly, P., Maxwell, P., McCleary, D. F., et al. (2015) · Oncotarget · doi:10.18632/oncotarget.4391 · Cited by 58 · see citing works
Self-citationMukherji, D., Jabbour, M. N., Saroufim, M., Temraz, S. N., Nasr, R. W., Charafeddine, M., Assi, R. E., Shamseddine, A. I., & Tawil, A. N. (2015) · Clinical Genitourinary Cancer · doi:10.1016/j.clgc.2015.12.002 · Cited by 48 · see citing works
Shi, Y., Liu, X., Du, J., Zhang, D., Liu, J., Chen, M., Zhao, J., Zhong, W., Xu, Y., & Wang, M. (2021) · Thoracic Cancer · doi:10.1111/1759-7714.14248 · Cited by 38 · see citing works
Zhang, W., Quan, Y., Ma, X., Zeng, L., Li, J., Chen, S., Su, M., Hong, L., Li, P., Wang, H., et al. (2023) · Redox Biology · doi:10.1016/j.redox.2023.102608 · Cited by 32 · see citing works
Wang, J., Xiu, J., Baca, Y., Arai, H., Battaglin, F., Kawanishi, N. K., Soni, S., Zhang, W., Millstein, J., Shields, A. F., et al. (2021) · Annals of Oncology · doi:10.1016/j.annonc.2021.03.203 · Cited by 27 · see citing works
Sheng, L., Gao, J., Xu, Q., Zhang, X., Huang, M., Dai, X., Li, S., & Liu, L. (2021) · Therapeutic Advances in Medical Oncology · doi:10.1177/17588359211018537 · Cited by 24 · see citing works
Self-citationmTOR Pathway Gene Mutations Predict Response to Immune Checkpoint Inhibitors in Multiple Cancers
Cheng, L., Wang, Y., Qiu, L., Chang, Y., Lu, H., Liu, C., Zhang, B., Zhou, Y., Bai, H., Xiong, L., et al. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03436-1 · Cited by 17 · see citing works
Self-citationMED12 mutation as a potential predictive biomarker for immune checkpoint inhibitors in pan-cancer
Zhou, Y. C., Tan, Y., Zhang, Q., Duan, Q., & Chen, J. (2022) · European journal of medical research · doi:10.1186/s40001-022-00856-z · Cited by 9 · see citing works
Self-citationGlycogen Metabolism Predicts the Efficacy of Immunotherapy for Urothelial Carcinoma
Zhang, Y., Li, X., Zhou, R., Lin, A., Cao, M., Lyu, Q., Luo, P., & Zhang, J. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.723066 · Cited by 6 · see citing works
Self-citationTang, B., Zhao, X., Liu, H., Zhang, Q., Liu, K., Yang, X., & Huang, Y. (2023) · PubMed Central · doi:10.30498/ijb.2022.307202.3168 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Luo X, Zang X, Yang L, Huang J, Liang F, Rodriguez-Canales J, et al. Compre- hensive computational pathological image analysis predicts lung cancer prog- nosis. J Thorac Oncol. 2017;12:501-9.
Cited by
Sorted by citations
Geng, Z., Li, K., Mei, P., Gong, Z., Yan, R., Huang, Y., Zhang, C., Zhao, B., Lu, M., Yang, R., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002821 · Cited by 16 · see citing works
Machine learning predictions of tumor progression: How reliable are we?
Sakkal, M., & Hajal, A. A. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110156 · Cited by 8 · see citing works
Image-based inference of tumor cell trajectories enables large-scale cancer progression analysis
Liu, Y., Cai, L., Rong, R., Wang, S., Jia, L., Quan, P., Zhou, Q., Xiao, G., & Xie, Y. (2025) · Science Advances · doi:10.1126/sciadv.adv9466 · Cited by 7 · see citing works
Yu, C., Zhang, D., Chen, C., Xing, S., Li, P., Zhang, Y., Shang, L., Ma, H., & Zhou, J. (2026) · Critical Reviews in Oncology/Hematology · doi:10.1016/j.critrevonc.2026.105191 · Cited by 5 · see citing works
Peng, S., Jia, K., Peng, M., Chen, Y., Lin, A., Luo, P., Wang, J., Wang, Y., & Bai, Y. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000002526 · Cited by 4 · see citing works
Self-citationZhang, Y., Meng, F., Gu, Z., Deng, Y., Liu, T., Jiang, H., Chen, T., Huang, L., & Wang, J. (2025) · Nature Communications · doi:10.1038/s41467-025-65075-6 · Cited by 4 · see citing works
A six-tiered framework for evaluating AI models from repeatability to replaceability
Tian, S., Lam, A. W. Y., Sung, J. J. �., & Goh, W. W. B. (2025) · Trends in biotechnology · doi:10.1016/j.tibtech.2025.07.015 · Cited by 4 · see citing works
Artificial Intelligence for Predicting Immunotherapy Efficacy in Non-Small Cell Lung Cancer
Cao, P., Jia, X., Yang, Y., Wang, X., Zhu, J., Li, X., Yang, Z., & Wen, Q. (2026) · Journal of Inflammation Research · doi:10.2147/jir.s581764 · Cited by 2 · see citing works
Self-citationPark, S., Kim, S., & Jiang, P. (2025) · Scientific Reports · doi:10.1038/s41598-025-14166-x · Cited by 1 · see citing works
The consensus molecular subtypes of esophageal squamous cell carcinoma
Cui, H., Zhu, Z., Xu, E., Qi, L., Cheng, Y., Zhang, Y., Zhang, L., Cheng, M. Y., Yang, B., Sun, R., et al. (2026) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-026-02577-9 · Cited by 1 · see citing works
Genomic innovations in cancer prevention, diagnosis, prognosis and precision therapeutics
Ananda, H., Sahana, S. R., Murthy, S. R., Kunnath, A. N., Prashant, A., Kaifi, J. T., Kiran, P. K., & Suvilesh, K. N. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1828450 · Cited by 1 · see citing works
Li, X., He, Y., Wu, J., Wang, Z., Zhao, X., Zhang, Y., Pan, B., Tang, Y., & Han, J. (2026) · PubMed · doi:10.1126/sciadv.aed6373 · Cited by 1 · see citing works
“Deep learning architectures have demonstrated substantial potential across diverse computational frameworks and data modalities ( 22 , 23 ).”
Self-citationMaitra, C., Das, V., Seal, D. B., & De, R. K. (2026) · medRxiv · doi:10.64898/2026.02.28.26347335
Miao, R.-Z., Hao-rui, Z., Li, T.-Y., Zhou, J., & Yang, X.-R. (2026) · Hepatoma Research · doi:10.20517/2394-5079.2025.92
Kong, L., Wu, S., Cai, C., Tsui, F. W., Zhang, X., Zhan, W., Yao, L., Pei, X., Lv, X., Zhou, H., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.104154
Zhang, W., Luo, Z., Liu, K., Wang, Y., Hou, Y., Su, X., Wu, X., Miao, C., Liu, Q., He, Z., et al. (2026) · 中国科学通报：英文版 · doi:10.1016/j.scib.2026.09.016
Self-citation
- Authors
- Wang, Y., Ju, X., Hua, R., Chen, J., Dai, X., Liu, L., Wang, G., Bai, Y., Hu, H., & Li, X.
- Affiliations
- University of Electronic Science and Technology of China (CN)
- Chengdu Seventh People's Hospital (CN)
- Sichuan University (CN)
- Corresponding author
- Wang, Guifang
- Bai, Yifeng
- Hu, Honglin
- Li, Xiaohua
- Venue
- British Journal of Cancer
- Published
- 2024-10-25
- Volume
- 131
- Issue
- 11
- Pages
- 1833-1845
- ISSN
- 0007-0920 (ISSN-L)
- 1532-1827
- PMC
- PMC11589918
- OpenAlex
- W4403768133
- Cited by
- 18
- References
- 64
- Citations by year
- 2026: 11 · 2025: 7
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- British Journal of Cancer (publishedVersion)
- SSRN Electronic Journal (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Funders
- Department of Science and Technology of Sichuan Province
- Sichuan Provincial Administration of Traditional Chinese Medicine (2023ZD021, 2021MS276)
- Health Commission of Sichuan Province
- Sichuan Province Science and Technology Support Program
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Keywords
- non-small cell lung cancer, immune checkpoint inhibitors, immunotherapy response prediction, tumor microenvironment, H&E histopathology, deep learning, tumor-infiltrating lymphocytes, immune checkpoint molecules, progression-free survival, biomarkers, risk stratification, TCGA
- MeSH terms
- Deep Learning, Immune Checkpoint Inhibitors, Aged, Carcinoma, Non-Small-Cell Lung, Female, Humans, Immunotherapy, Lung Neoplasms, Male, Middle Aged, Prognosis, Biomarkers, Tumor, Tumor Microenvironment
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
