Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer

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Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review

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Predictive Biomarkers for Immunotherapy in Lung Cancer: Perspective From the International Association for the Study of Lung Cancer Pathology Committee

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Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions

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Phase III, Randomized Study of Atezolizumab Plus Bevacizumab and Chemotherapy in Patients With EGFR- or ALK-Rearranged or Translocated Non–Small-Cell Lung Cancer (ATTLAS, KCSG-LU19-04)

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Benchmarking Self-Supervised Learning on Diverse Pathology Datasets

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The artificial intelligence and machine learning in lung cancer immunotherapy

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Advances and prospects of biomarkers for immune checkpoint inhibitors

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Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions

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Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment

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Quantifying and interpreting biologically meaningful spatial signatures within tumor microenvironments

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Cutting-edge technology and automation in the pathology laboratory

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Treatment of NSCLC after chemoimmunotherapy — are we making headway?

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A Novel IoT-Enabled Healthcare Monitoring Framework and Improved Grey Wolf Optimization Algorithm-Based Deep Convolution Neural Network Model for Early Diagnosis of Lung Cancer

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Progress and challenges of artificial intelligence in lung cancer clinical translation

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Artificial intelligence in cancer: applications, challenges, and future perspectives

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Artificial intelligence: illuminating the depths of the tumor microenvironment

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OCELOT: Overlapped Cell on Tissue Dataset for Histopathology

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The Role of Immune Checkpoint Inhibitors in Cancer Therapy: Mechanism and Therapeutic Advances

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Computerized tertiary lymphoid structures density on H&E-images is a prognostic biomarker in resectable lung adenocarcinoma

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Biomarkers for Immune Checkpoint Inhibitor Response in NSCLC: Current Developments and Applicability

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Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy

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Immunotherapy and Cancer: The Multi-Omics Perspective

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High-Resolution Profiling of Lung Adenocarcinoma Identifies Expression Subtypes with Specific Biomarkers and Clinically Relevant Vulnerabilities

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Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma

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Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images

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Deep learning in digital pathology for personalized treatment plans of cancer patients

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Relationships of tumor differentiation and immune infiltration in gastric cancers revealed by single-cell RNA-seq analyses

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Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial

Rakaee, M., Andersen, S., Giannikou, K., Paulsen, E., Kilvaer, T. K., Busund, L. T., Berg, T., Richardsen, E. H., Lombardi, A. P. G., Adib, E., et al. (2023) · Annals of Oncology · doi:10.1016/j.annonc.2023.04.005 · Cited by 33 · see citing works

Deep computational image analysis of immune cell niches reveals treatment-specific outcome associations in lung cancer

Barrera, C., Corredor, G., Viswanathan, V. S., Ding, R., Toro, P., Fu, P., Buzzy, C. A., Lu, C. Y., Velu, P., Zens, P., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00403-x · Cited by 32 · see citing works

Artificial Intelligence in the Pathology of Gastric Cancer

Choi, S., & Kim, S. (2023) · Journal of the Korean Gastric Cancer Association · doi:10.5230/jgc.2023.23.e25 · Cited by 32 · see citing works

A Phase II Open-Label Randomized Clinical Trial of Preoperative Durvalumab or Durvalumab plus Tremelimumab in Resectable Head and Neck Squamous Cell Carcinoma

Kim, C. G., Hong, M. H., Kim, D., Lee, B. H., Kim, H., Ock, C., Kelly, G., Bang, Y. J., Kim, G., Lee, J. E., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-3249 · Cited by 35 · see citing works

Artificial intelligence-based digital pathology using H&E-stained whole slide images in immuno-oncology: from immune biomarker detection to immunotherapy response prediction

Zhang, J., Choi, H., Kim, Y., Park, J., Cho, S., Kim, E., Cho, A., Lee, J. Y., Choi, J., Low, C., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-011346 · Cited by 34 · see citing works

A Phase II Study of Nivolumab plus Gemcitabine in Patients with Recurrent or Metastatic Nasopharyngeal Carcinoma (KCSG HN17–11)

Jung, H. A., Park, K. U., Cho, S., Lim, J., Lee, K., Hong, M. H., Yun, T., An, H. J., Park, W., Pereira, S., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-1238 · Cited by 32 · see citing works

Intratumoral spatial heterogeneity of tumor-infiltrating lymphocytes is a significant factor for precisely stratifying prognostic immune subgroups of microsatellite instability-high colorectal carcinomas

Jung, M., Lee, J. A., Yoo, S., Bae, J. M., Kang, G. H., & Kim, J. H. (2022) · Modern Pathology · doi:10.1038/s41379-022-01137-0 · Cited by 30 · see citing works

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non–Small Cell Lung Cancer

Rigamonti, A., Viatore, M., Polidori, R., Rahal, D., Erreni, M., Fumagalli, M. R., Zanini, D., Doni, A., Putignano, A. R., Bossi, P., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-1698 · Cited by 30 · see citing works

Mining the interpretable prognostic features from pathological image of intrahepatic cholangiocarcinoma using multi-modal deep learning

Ding, G., Tan, W., Lin, Y., Ling, Y., Huang, W. K., Zhang, S., Shi, J.-Y., Luo, R., Ji, Y., Wang, X., et al. (2024) · BMC Medicine · doi:10.1186/s12916-024-03482-0 · Cited by 30 · see citing works

Integrative analysis of immunogenic PANoptosis and experimental validation of cinobufagin-induced activation to enhance glioma immunotherapy

Cai, Y., Xiao, H., Xue, S., Li, P., Zhan, Z., Lin, J., Song, Z., Liu, J., Xu, W., Zhou, Q., et al. (2025) · Journal of Experimental & Clinical Cancer Research · doi:10.1186/s13046-025-03301-1 · Cited by 33 · see citing works

Circulating Histones to Detect and Monitor the Progression of Cancer

Tsoneva, D. K., Ivanov, M. N., Conev, N. V., Manev, R. R., Stoyanov, D. S., & Vinciguerra, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24020942 · Cited by 30 · see citing works

Tumor microenvironment-driven resistance to immunotherapy in non-small cell lung cancer: strategies for Cold-to-Hot tumor transformation

Yu, J., Kong, X., & Feng, Y. (2025) · Cancer Drug Resistance · doi:10.20517/cdr.2025.14 · Cited by 32 · see citing works

Immunophenotyping of pulmonary sarcomatoid carcinoma

Ma, Y., Li, W., Li, Z., Chen, J., Wang, H., Jiang, T., & Zhu, J. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.976739 · Cited by 29 · see citing works

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 · see citing works

Implications of different cell death patterns for prognosis and immunity in lung adenocarcinoma

Zhou, Y., Gao, W., Xu, Y., Wang, J., Wang, X., Shan, L., Lijuan, D., Sun, Q., Li, H., & Liu, F. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00456-y · Cited by 28 · see citing works

Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as a Potential Biomarker for Immune Checkpoint Inhibitors in Patients with Biliary Tract Cancer

Bang, Y. H., Lee, C., Bang, K., Kim, H., Kim, K., Jeong, J. H., Park, I., Ryoo, B., Lee, D. K., Choi, H. J., et al. (2024) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-24-1265 · Cited by 28 · see citing works

Bladder cancer intrinsic LRFN2 drives anticancer immunotherapy resistance by attenuating CD8 + T cell infiltration and functional transition

Yu, A., Hu, J., Fu, L., Huang, G., Deng, D., Zhang, M., Wang, Y., Shu, G., Jing, L., Li, H., et al. (2023) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007230 · Cited by 28 · see citing works

Neutrophil–lymphocyte ratio and platelet–lymphocyte ratio as potential predictive markers of treatment response in cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

Rugambwa, T. K., Abdihamid, O., Zhang, X., Peng, Y., Cai, C., Shen, H., Zeng, S., & Qiu, W. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1181248 · Cited by 27 · see citing works

Multimodal radiopathological integration for prognosis and prediction of adjuvant chemotherapy benefit in resectable lung adenocarcinoma: A multicentre study

Lin, H., Hua, J., Gong, Z., Chen, M., Qiu, B., Wu, Y., He, W., Wang, Y., Feng, Z., Liang, Y., et al. (2025) · Cancer Letters · doi:10.1016/j.canlet.2025.217557 · Cited by 27 · see citing works

A novel dataset for nuclei and tissue segmentation in melanoma with baseline nuclei segmentation and tissue segmentation benchmarks

Schuiveling, M., Liu, H., Eek, D., Breimer, G. E., Suijkerbuijk, K. P. M., Blokx, W. A. M., & Veta, M. (2025) · GigaScience · doi:10.1093/gigascience/giaf011 · Cited by 26 · see citing works

Next-Generation Cancer Phenomics: A Transformative Approach to Unraveling Lung Cancer Complexity and Advancing Precision Medicine

Dasgupta, S. (2024) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0175 · Cited by 25 · see citing works

Survival outcomes of patients with gastric cancer treated with first-line nivolumab plus chemotherapy based on claudin 18.2 expression

Kim, H., Shin, J., Hyung, J., Lee, H., Moon, M., Ma, J., Park, Y. S., & Ryu, M. (2024) · Gastric Cancer · doi:10.1007/s10120-024-01566-7 · Cited by 25 · see citing works

Exploring prognostic indicators in the pathological images of ovarian cancer based on a deep survival network

Wu, M., Zhu, C., Yang, J., Cheng, S., Yang, X., Gu, S., Xu, S., Wu, Y., Shen, W., Huang, S., et al. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2022.1069673 · Cited by 24 · see citing works

Prediction and analysis of tumor infiltrating lymphocytes across 28 cancers by TILScout using deep learning

Zhang, H., Chen, L., Li, L., Liu, Y., Das, B., Zhai, S., Tan, J., Jiang, Y., Turco, S., Yao, Y., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00866-0 · Cited by 23 · see citing works

Machine learning analysis of pathological images to predict 1-year progression-free survival of immunotherapy in patients with small-cell lung cancer

Shibaki, R., Fujimoto, D., Nozawa, T., Sano, A., Kitamura, Y., Fukuoka, J., Sato, Y., Kijima, T., Matsumoto, H., Yokoyama, T., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-007987 · Cited by 22 · see citing works

AiZynth impact on medicinal chemistry practice at AstraZeneca

Shields, J. D., Howells, R., Lamont, G. M., Leilei, Y., Madin, A., Reimann, C. E., Rezaei, H., Reuillon, T. D., Smith, B., Thomson, C., et al. (2024) · RSC Medicinal Chemistry · doi:10.1039/d3md00651d · Cited by 22 · see citing works

Clinical Validation of Artificial Intelligence–Powered PD-L1 Tumor Proportion Score Interpretation for Immune Checkpoint Inhibitor Response Prediction in Non–Small Cell Lung Cancer

Kim, H., Kim, S., Choi, S., Park, C., Park, S., Pereira, S., Ma, M., Yoo, D., Paeng, K., Jung, W., et al. (2024) · JCO Precision Oncology · doi:10.1200/po.23.00556 · Cited by 22 · see citing works

Whole slide image-based weakly supervised deep learning for predicting major pathological response in non-small cell lung cancer following neoadjuvant chemoimmunotherapy: a multicenter, retrospective, cohort study

Han, D., Li, H., Zheng, X., Fu, S., Wei, R., Zhao, Q., Liu, C., Wang, Z., Huang, W. C., & Hao, S. (2024) · Frontiers in Immunology · doi:10.3389/fimmu.2024.1453232 · Cited by 20 · see citing works

Prognostic and predictive values of a multimodal nomogram incorporating tumor and peritumor morphology with immune status in resectable lung adenocarcinoma

Lin, H., Hua, J., Wang, Y., Chen, M., Liang, Y., Yan, L., Zhao, W., Luo, S., Hong, D., Chen, X., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2024-010723 · Cited by 20 · see citing works

Immune Marker Spatial Distribution and Clinical Outcome after PD-1 Blockade in Mismatch Repair–deficient, Advanced Colorectal Carcinomas

Saberzadeh-Ardestani, B., Graham, R. P., McMahon, S., Ahanonu, E., Shi, Q., Williams, C., Hubbard, A., Zhang, W., Murányi, A., Yan, D., et al. (2023) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-1109 · Cited by 20 · see citing works

Immune checkpoint inhibitors and immunosuppressive tumor microenvironment: current challenges and strategies to overcome resistance

Gill, G. S., Kharb, S., Goyal, G., Das, P., Kurdia, K. C., Dhar, R., & Karmakar, S. (2025) · Immunopharmacology and Immunotoxicology · doi:10.1080/08923973.2025.2504906 · Cited by 19 · see citing works

Artificial intelligence for radiopharmaceutical and molecular imaging

Tao, J., Liang, L., Hao, S., Chen, Y., Yang, Z., Cai, Y., & Zhu, H. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.09.039 · Cited by 21 · see citing works

Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma

Zeng, W., Wang, J., Yang, J., Chen, Z., Cui, Y., Li, Q., Luo, G., Ding, H., Ju, S., Li, B., et al. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1217590 · Cited by 18 · see citing works

Artificial intelligence in clinical trials of lung cancer: Current and future prospects

Zhang, Y., You, P., Liu, R., Lu, Y., Li, J., Lei, Y., Wu, S., & Zhou, H. (2024) · Intelligent Oncology · doi:10.1016/j.intonc.2024.11.003 · Cited by 18 · see citing works

Cancer Prediction With Machine Learning of Thrombi From Thrombectomy in Stroke: Multicenter Development and Validation

Heo, J., Lee, H., Seog, Y., Kim, S., Baek, J., Park, H., Seo, K., Kim, G. S., Cho, H., Baik, M., et al. (2023) · Stroke · doi:10.1161/strokeaha.123.043127 · Cited by 17 · see citing works

Artificial Intelligence–Powered Spatial Analysis of Immune Phenotypes in Resected Pancreatic Cancer

Kim, H. M., Choi, J. H., Lim, Y., Yoon, S. J., Jang, K., Ock, C., Choi, Y. H., Joe, C., Song, S., Moon, J., et al. (2025) · JAMA Surgery · doi:10.1001/jamasurg.2025.1999 · Cited by 16 · see citing works

PD-L1 Positron Emission Tomography Imaging in Patients With Non-Small Cell Lung Cancer: Preliminary Results of the ImmunoPET Phase 0 Study

Hegi‐Johnson, F., Rudd, S. E., Wichmann, C. W., Akhurst, T. J., Roselt, P. D., Sursock, S., Trinh, J., John, T., Devereux, L., Donnelly, P. S., et al. (2023) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2023.05.019 · Cited by 16 · see citing works

A Thorough Review of the Clinical Applications of Artificial Intelligence in Lung Cancer

Kotoulas, S., Spyratos, D., Porpodis, Κ., Domvri, K., Boutou, A., Kaimakamis, E., Mouratidou, C., Alevroudis, I., Dourliou, V., Tsakiri, K., et al. (2025) · Cancers · doi:10.3390/cancers17050882 · Cited by 16 · see citing works

Optimization of cancer immunotherapy through pyroptosis: A pyroptosis-related signature predicts survival benefit and potential synergy for immunotherapy in glioma

Zeng, Y., Cai, Y., Chai, P., Mao, Y., Chen, Y., Wang, L., Zeng, K., Zhan, Z., Xie, Y., Li, C., et al. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.961933 · Cited by 15 · see citing works

Characterisation of tumour-immune phenotypes and PD-L1 positivity in squamous bladder cancer

Jung, M., Rose, M., Knuechel, R., Loeffler, C. M. L., Muti, H. S., Kather, J. N., Gaisa, N. T., & on behalf of the German Study Group of Bladder Cancer (DFBK e.V.) (2023) · BMC Cancer · doi:10.1186/s12885-023-10576-0 · Cited by 15 · see citing works

Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges

Parra, E. R., Ilié, M., Wistuba, I. I., & Hofman, P. M. (2023) · British Journal of Cancer · doi:10.1038/s41416-023-02318-7 · Cited by 15 · see citing works

Association of Baseline Tumor-Specific Neoantigens and CD8+ T-Cell Infiltration With Immune-Related Adverse Events Secondary to Immune Checkpoint Inhibitors

Kerepesi, C., Abushukair, H. M., Ricciuti, B., Nassar, A. H., Adib, E., Alessi, J. V., Pecci, F., Rakaee, M., Fadlullah, M. Z. H., Tőkés, A.-M., et al. (2024) · JCO Precision Oncology · doi:10.1200/po.23.00439 · Cited by 15 · see citing works

A Deep Learning–Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma

Hamada, K., Murakami, R., Ueda, A., Kashima, Y., Miyagawa, C., Taki, M., Yamanoi, K., Yamaguchi, K. D., Hamanishi, J., Minamiguchi, S., et al. (2024) · American Journal Of Pathology · doi:10.1016/j.ajpath.2024.02.016 · Cited by 16 · see citing works

Systemic Immune-Inflammatory Index, Tumor-Infiltrating Lymphocytes, and Clinical Outcomes in Esophageal Squamous Cell Carcinoma Receiving Concurrent Chemoradiotherapy

Yang, J., Zheng, J., Qiu, J., Zhang, M., Liu, L., Wang, Z., Zheng, Q., Liu, Y., Chen, M., & Li, J. (2023) · Journal of Immunology Research · doi:10.1155/2023/4275998 · Cited by 14 · see citing works

Heterogeneity of the tumor immune microenvironment and clinical interventions

Jin, Z., Zhou, Q., Cheng, J., Jia, Q., & Zhu, B. (2023) · Frontiers of Medicine · doi:10.1007/s11684-023-1015-9 · Cited by 14 · see citing works

Solving the puzzle of what makes immunotherapies work

Ma, X., & Chan, T. A.-T. (2022) · Trends in cancer · doi:10.1016/j.trecan.2022.06.011 · Cited by 15 · see citing works

Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor

Bang, Y. H., Park, G., Oh, J. W., Hwang, S., Jeong, J.-G., Lee, B., Joe, C. Y., Kim, H. M., Kim, J., Park, S., et al. (2025) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2025-012374 · Cited by 13 · see citing works

Pathological Response and Immune Biomarker Assessment in Non-Small-Cell Lung Carcinoma Receiving Neoadjuvant Immune Checkpoint Inhibitors

Rojas, F. R., Parra, E. R., Wistuba, I. I., Haymaker, C., & Soto, L. M. S. (2022) · Cancers · doi:10.3390/cancers14112775 · Cited by 14 · see citing works

Integrating artificial intelligence in osteosarcoma prognosis: the prognostic significance of SERPINE2 and CPT1B biomarkers

Qu, H., Jiang, J., Zhan, X., Liang, Y., Guo, Q., Liu, P., Lü, L., Yang, Y., Xu, W., Zhang, Y., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-54222-6 · Cited by 13 · see citing works

Automated tumor immunophenotyping predicts clinical benefit from anti‐PD‐L1 immunotherapy

Xiao, L., Eastham, J., Giltnane, J. M., Zou, W., Zijlstra, A., Tabatsky, E., Banchereau, R., Chang, C., Nabet, B. Y., Patil, N. S., et al. (2024) · The Journal of Pathology · doi:10.1002/path.6274 · Cited by 13 · see citing works

A real‐world study of immune checkpoint inhibitors in advanced triple‐negative breast cancer

Zhang, Z., Zhang, Y., Liu, C., Shao, J., Chen, Y., Zhu, Y., Zhang, L., Qin, B., Kong, Z., Wang, X., et al. (2023) · Cancer Innovation · doi:10.1002/cai2.70 · Cited by 13 · see citing works

Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases

Alshorman, J., Mehran, M. J., Bahrami, Y., Mohammadzadeh, S., Barzigar, R., Morshedi, M., Haider, K. H., Tembo, K. M., Rong, S.-J., Jadgal, N., et al. (2026) · Clinical and Experimental Medicine · doi:10.1007/s10238-026-02107-5 · Cited by 14 · see citing works

Pembrolizumab for Patients with Relapsed or Refractory Extranodal NK/T-Cell Lymphoma in Korea

Lee, J. Y., Kwon, J. H., Hur, J. Y., Yi, J. H., Lee, J., Cho, H., Do, Y. R., Jo, J., Kang, H. J., Koh, Y., et al. (2023) · Cancer Research and Treatment · doi:10.4143/crt.2023.1042 · Cited by 12 · see citing works

Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes and tertiary lymphoid structures in non-small cell lung cancer patients treated with immune-checkpoint inhibitors±chemotherapy.

Bang, Y. H., Jeong, J.-G., Hwang, S., Park, G., Lee, B., Joe, C., Kim, H. M., Kim, J., Jung, H. A., Park, S., et al. (2025) · Journal of Clinical Oncology · doi:10.1200/jco.2025.43.16_suppl.8572 · Cited by 1 · see citing works

1912P Inflamed immune phenotype as a novel predictive marker of immune checkpoint inhibitors for non-small cell lung cancer

Zenke, Y., Yoh, K., Furuya, N., Nishino, K., Miyamoto, S., Oizumi, S., Suzuki, H., Tanaka, Y., Yamamoto, G., Sakai, T., et al. (2025) · Annals of Oncology · doi:10.1016/j.annonc.2025.08.2536

Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.

Shen, J., Lee, T., Hwang, J., Choi, Y., Lee, S., Kim, H., Chung, J.-H., Bogdan, S., Huang, M., Raclin, T., et al. (2021) · Journal of Clinical Oncology · doi:10.1200/jco.2021.39.15_suppl.2607 · Cited by 6 · see citing works

202 Spatial analysis of tumor-infiltrating lymphocytes in tumor microenvironment in non-small cell lung cancer patients who have resistance after EGFR tyrosine kinase inhibitors

Bang, Y. H., Ahn, C. H., Joe, C., Park, S., Jung, H. A., Choi, Y., Ahn, J. S., Ahn, M., Shin, S., Ock, C., et al. (2023) · Regular and Young Investigator Award Abstracts · doi:10.1136/jitc-2023-sitc2023.0202

Digital pathology–based AI spatial biomarker to predict outcomes for immune checkpoint inhibitors in advanced non-small cell lung cancer.

Eweje, F. R., Li, Z., Li, Y., Bergstrom, C. P., Kim, T., Olguin, F., Hewett, S., Gopaulchan, M., Nirschl, J., Neal, J. W., et al. (2025) · Journal of Clinical Oncology · doi:10.1200/jco.2025.43.16_suppl.8569 · Cited by 1 · see citing works