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Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer
Open accessAbstract
PURPOSE Biomarkers on the basis of tumor-infiltrating lymphocytes (TIL) are potentially valuable in predicting the effectiveness of immune checkpoint inhibitors (ICI). However, clinical application remains challenging because of methodologic limitations and laborious process involved in spatial analysis of TIL distribution in whole-slide images (WSI). METHODS We have developed an artificial intelligence (AI)–powered WSI analyzer of TIL in the tumor microenvironment that can define three immune phenotypes (IPs): inflamed, immune-excluded, and immune-desert. These IPs were correlated with tumor response to ICI and survival in two independent cohorts of patients with advanced non–small-cell lung cancer (NSCLC). RESULTS Inflamed IP correlated with enrichment in local immune cytolytic activity, higher response rate, and prolonged progression-free survival compared with patients with immune-excluded or immune-desert phenotypes. At the WSI level, there was significant positive correlation between tumor proportion score (TPS) as determined by the AI model and control TPS analyzed by pathologists ( P < .001). Overall, 44.0% of tumors were inflamed, 37.1% were immune-excluded, and 18.9% were immune-desert. Incidence of inflamed IP in patients with programmed death ligand-1 TPS at < 1%, 1%-49%, and ≥ 50% was 31.7%, 42.5%, and 56.8%, respectively. Median progression-free survival and overall survival were, respectively, 4.1 months and 24.8 months with inflamed IP, 2.2 months and 14.0 months with immune-excluded IP, and 2.4 months and 10.6 months with immune-desert IP. CONCLUSION The AI-powered spatial analysis of TIL correlated with tumor response and progression-free survival of ICI in advanced NSCLC. This is potentially a supplementary biomarker to TPS as determined by a pathologist.
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“Numerous studies have demonstrated the potential for using AI to predict immunotherapy biomarkers from H&E images of patients with NSCLC, including tumor mutation burden (TMB; ref. 76), PD-L1 expression (105), and TILs (106, 107).”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [106]
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Cites
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
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534 · cited 1×
“1, 17 were retrospectively reviewed by investigators.”
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 15,320
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 · cited 4×
“This AI-powered spatial TIL analyzer may potentially supplement PD-L1 TPS as a supporting biomarker for ICI.”
ResultInfluentialHerbst, R. S., Baas, P., Kim, D.-W., Felip, E., Pérez-Gracia, J. L., Han, J.-Y., Molina, J., Kim, J.-H., Arvis, C. D., Ahn, M.-J., et al. (2016) · The Lancet · doi:10.1016/s0140-6736(15)01281-7 · Cited by 6,449 · cited 1×
“5%).(24,25) Distribution of IP by baseline clinical attributes and smoking history was investigated, and there was no correlation between IP and smoking status, liver metastasis, and lung immune prognostic index score(26); however, some correlation existed between IP and pleural effusion andbrainmetastasis at the poin…”
BackgroundPembrolizumab 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 · cited 2×
“8% of the PD-L1 1%-49% subgroup from the KEYNOTE 001 study.(24) In the small subset of patients (n 5 10) who received firstline ICI, the TRR of patients with inflamed versus noninflamed IP was 66.”
BackgroundSelf-citationTGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells
Mariathasan, S., Turley, S. J., Nickles, D., Castiglioni, A., Yuen, K., Wang, Y., Kadel III, E. E., Koeppen, H., Astarita, J. L., Cubas, R., et al. (2018) · Nature · doi:10.1038/nature25501 · Cited by 5,400
Elements of cancer immunity and the cancer–immune set point
Chen, D. S., & Mellman, I. (2017) · Nature · doi:10.1038/nature21349 · Cited by 5,352 · cited 1×
“IP was de fi ned as follows 13,14 : in fl amed as TIL density in CE area above the threshold (106/mm 2 ); immune-excluded as TIL density in CE area below the threshold and TIL density in CS area above the threshold (357/mm 2 ); and immune-desert as both TIL density in CE area and that in CS area…”
BackgroundIFN-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
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 3,357 · cited 1×
“Pembrolizumab is superior to standard chemotherapy in terms of tumor response rate (TRR) and survival in patients with tumor proportion score (TPS)$ 50%(3); however, treatment outcomes were similar between pembrolizumab and chemotherapy in patients with PD-L1 TPS 1%-49%.(4) Therefore, development of a novel biomarker…”
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Research works printed but not linked: 1
- US Food and Drug Administration, 2020, PD-L1 IHC 22C3 pharmDx—P150013/S014
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Salcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., Panizzolo, E., Martowicz, A., Trebo, M., Pall, G., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.008 · Cited by 503
Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review
Prelaj, A., Miskovic, V., Zanitti, M., Trovo, F., Genova, C., Viscardi, G., Rebuzzi, S. E., Mazzeo, L., Provenzano, L., Kosta, S., et al. (2024) · Annals of Oncology · doi:10.1016/j.annonc.2023.10.125 · Cited by 340
RETRACTED ARTICLE: Antibodies against endogenous retroviruses promote lung cancer immunotherapy
Ng, K. W., Boumelha, J., Enfield, K. S. S., Almagro, J., Cha, H., Pich, O., Karasaki, T., Moore, D. A., Salgado, R., Sivakumar, M., et al. (2023) · Nature · doi:10.1038/s41586-023-05771-9 · Cited by 252
“A For outcome analysis in the SMC LUAD cohort 34 , samples were stratified on the basis of ERVK-7 expression (the summed TPMs of any of the multiple transcripts overlapping the env ORF of this provirus), using a cut-off value of 20 TPM to define high and low ERVK-7 expression.”
InfluentialRetractedMino-Kenudson, M. A., Schalper, K. A., Cooper, W. A., Đačić, S., Hirsch, F. R., Jain, D., López‐Ríos, F., Tsao, M., Yatabe, Y., Beasley, M. B., et al. (2022) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2022.09.109 · Cited by 240
Fountzilas, E., Pearce, T. E., Baysal, M. A., Chakraborty, A., & Tsimberidou, A. M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01471-y · Cited by 233
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196
Park, S., Kim, T. M., Han, J., Lee, G., Shim, B. Y., Lee, Y., Kim, S., Kim, I. H., Lee, S., Kim, Y. J., et al. (2023) · Journal of Clinical Oncology · doi:10.1200/jco.23.01891 · Cited by 192
Benchmarking Self-Supervised Learning on Diverse Pathology Datasets
Kang, M., Song, H., Park, S., Yoo, D., & Pereira, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00326 · Cited by 187
The artificial intelligence and machine learning in lung cancer immunotherapy
Gao, Q., Yang, L., Lu, M., Jin, R., Ye, H., & Ma, T. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01456-y · Cited by 148
“[43] developed the TIL spatial analysis model Lunit SCOPE IO, which can segment and quantify tissue components in WSI images and was successfully utilized to predict the benefit of ICIs in patients with advanced NSCLC. Presently, numerous studies have used pathological image analysis to demonstrate…”
MethodRakaee, M., Adib, E., Ricciuti, B., Sholl, L. M., Shi, W., Alessi, J. V., Cortellini, A., Fulgenzi, C. A. M., Viola, P., Pinato, D. J., et al. (2023) · JAMA Oncology · doi:10.1001/jamaoncol.2022.4933 · Cited by 141
Advances and prospects of biomarkers for immune checkpoint inhibitors
Yamaguchi, H., Hsu, J.-M., Sun, L., Wang, S., & Hung, M. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2024.101621 · Cited by 152
Deep Learning Model for Predicting Immunotherapy Response in Advanced Non−Small Cell Lung Cancer
Rakaee, M., Tafavvoghi, M., Ricciuti, B., Alessi, J. V., Cortellini, A., Citarella, F., Nibid, L., Perrone, G., Adib, E., Fulgenzi, C. A. M., et al. (2024) · JAMA Oncology · doi:10.1001/jamaoncol.2024.5356 · Cited by 143
Application of Artificial Intelligence in Pathology: Trends and Challenges
Kim, I., Kang, K., Song, Y., & Kim, T. J. (2022) · Diagnostics · doi:10.3390/diagnostics12112794 · Cited by 137
Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions
Huhulea, E. N., Huang, L., Eng, S., Sumawi, B., Huang, A., Aifuwa, E., Hirani, R., Tiwari, R. K., & Etienne, M. (2025) · Biomedicines · doi:10.3390/biomedicines13040951 · Cited by 74
“Spatial analysis of tumor-infiltrating lymphocytes enables classification of immune phenotypes as either inflamed, immune excluded, or immune desert, which have been found to each separately correlate with ICI response in patients with non-small cell lung cancer [101].”
The clinical application of artificial intelligence in cancer precision treatment
Wang, J., Zeng, Z., Li, Z., Liu, G., Zhang, S., Luo, C., Hu, S., Wan, S., & Zhao, L.-Y. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-06139-5 · Cited by 84
Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment
Kumar, K. S., Miskovic, V., Blasiak, A., Sundar, R., Pedrocchi, A., Pearson, A. T., Prelaj, A., & Ho, D. (2023) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_390084 · Cited by 68
Leivaditis, V., Maniatopoulos, A. A., Lausberg, H., Mulita, F., Papatriantafyllou, A., Liolis, E., Beltsios, E., Adamou, A., Kontodimopoulos, N., & Dahm, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14082729 · Cited by 76
“…radiomics, deep learning, and artificial neural networks (ANNs), AI improves risk assessment, treatment precision, and outcome prediction for patients undergoing stereotactic body radiation therapy (SBRT), stereotactic ablative radiotherapy (SABR), and postoperative radiotherapy (PORT) [100–104].”
Luo, D. Y., Zhou, J., Ruan, S., Zhang, B., Zhu, H., Que, Y., Ying, S., Li, X., Hu, Y., & Song, Z. (2025) · Cell Death and Disease · doi:10.1038/s41419-025-07385-7 · Cited by 73
Nardone, V., Marmorino, F., Germani, M. M., Cichowska-Cwalińska, N., Menditti, V. S., Gallo, P., Studiale, V., Taravella, A., Landi, M., Reginelli, A., et al. (2024) · Current Oncology · doi:10.3390/curroncol31090369 · Cited by 71
“In this analysis, AI was able to distinguish between three immune phenotypes (inflammatory, immunological-excluded, and immune-desert) associated with survival and response to ICI in two different cohorts with advanced NSCLC patients suggesting its potential predictive role [71].”
BackgroundShen, J., Choi, Y., Lee, T., Kim, H., Chae, Y. K., Dulken, B. W., Bogdan, S., Huang, M., Fisher, G. A., Park, S., et al. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-008339 · Cited by 65
Su, G.-H., Xiao, Y., Jiang, L., Zheng, R., Wang, H., Chen, Y., Gu, Y., You, C., & Shao, Z.-M. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03688-x · Cited by 61
“Second, recent study revealed distinct TILs infiltration phenotypes in cancer termed immune inflamed, immune desert and immune excluded, which indicated that lymphocytes infiltration pattern but not the density of TILs determined the activating status of anti-tumor immunity [54].”
BackgroundChoi, S., Cho, S. I., Jung, W., Lee, T., Choi, S. J., Song, S., Park, G., Park, S., Ma, M., Pereira, S., et al. (2023) · npj Breast Cancer · doi:10.1038/s41523-023-00577-4 · Cited by 61
Williams, H. L., Frei, A. L., Koessler, T., Berger, M. D., Dawson, H., Michielin, O. A., & Zlobec, I. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00671-1 · Cited by 61
Jing, S., Wang, H.-Q., Lin, P., Yuan, J., Tang, Z., & Li, H. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00857-1 · Cited by 65
McCaffrey, C., Jahangir, C. A., Murphy, C. L., Burke, C., Gallagher, W. M., & Rahman, A. (2024) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2024.2346545 · Cited by 60
Cutting-edge technology and automation in the pathology laboratory
Munari, E., Scarpa, A., Cima, L., Pozzi, M., Pagni, F., Vasuri, F., Marletta, S., Tos, A. P. D., & Eccher, A. (2023) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-023-03637-z · Cited by 52
Treatment of NSCLC after chemoimmunotherapy — are we making headway?
Reck, M., Frost, N., Peters, S., Fox, B. A., Ferrara, R., Savai, R., & Barlési, F. (2025) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-025-01061-7 · Cited by 62
Irshad, R. R., Hussain, S., Sohail, S. S., Zamani, A. S., Madsen, D. Ø., Alattab, A. A., Ahmed, A. A. A., Norain, K. A. A., & Alsaiari, O. A. S. (2023) · Sensors · doi:10.3390/s23062932 · Cited by 50
“Serum tumor indicators on pathological types, staging, monitoring, and prognostication of lung cancer [6] with serum squamous cell carcinoma antigen (SCC) [7], carcinoembryonic antigen (CEA) [8], and neuron-specific enolase (NSE) [9] are regarded to be successful strategies for lung cancer detection; nevertheless, the…”
Progress and challenges of artificial intelligence in lung cancer clinical translation
Zhu, E., Muneer, A., Zhang, J., Yang, X., Li, X., Zhou, C., Heymach, J. V., Wu, J., & Le, X. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00986-7 · Cited by 59
Artificial intelligence in cancer: applications, challenges, and future perspectives
Cheng, H., & Shi, S.-S. (2025) · Molecular Cancer · doi:10.1186/s12943-025-02450-3 · Cited by 64
“For example, Park et al. [151] developed an AI-powered spatial analyzer of TILs in H&E images, which correlates with tumor response and progression-free survival in patients with advanced NSCLC undergoing ICI therapy, potentially serving as a supplementary biomarker to the tumor proportion score…”
Artificial intelligence: illuminating the depths of the tumor microenvironment
Xie, T., Huang, A., Yan, H., Ju, X., Xiang, L., & Yuan, J. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05609-6 · Cited by 55
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
Ryu, J., Puche, A. V., Shin, J., Park, S., Brattoli, B., Lee, J., Jung, W., Cho, S. I., Paeng, K., Ock, C., et al. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.02289 · Cited by 46
“It allows the quantification and analysis of different cell types, which can lead to better prognosis evaluation [34, 42] and patient”
BackgroundPrognostic and predictive biomarkers in non-small cell lung carcinoma
Odintsov, I., & Sholl, L. M. (2023) · Pathology · doi:10.1016/j.pathol.2023.11.006 · Cited by 47
The Role of Immune Checkpoint Inhibitors in Cancer Therapy: Mechanism and Therapeutic Advances
Chen, H., Yang, H., Guo, L., & Sun, Q. (2025) · MedComm · doi:10.1002/mco2.70412 · Cited by 47
“Technological innovations such as single-cell sequencing, spatial transcrip-tomics, and artificial intelligence (AI) have profoundly enhanced the understanding of tumor immune microenvironment (TIME) heterogeneity and its dynamic evolution [11, 12].”
Wang, Y., Lin, H., Yao, N., Chen, X., Qiu, B., Cui, Y., Liu, Y., Li, B., Han, C., Li, Z., et al. (2023) · iScience · doi:10.1016/j.isci.2023.107635 · Cited by 42
Biomarkers for Immune Checkpoint Inhibitor Response in NSCLC: Current Developments and Applicability
Tostes, K., Siqueira, A. P., Reis, R. M., Leal, L. F., & Arantes, L. M. R. B. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241511887 · Cited by 40
Hou, W. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1602452 · Cited by 41
Zhang, Z., & Wei, X. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.02.005 · Cited by 41
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
“Also, Yang et al. showed that the use of a model including radiomics and clinical and laboratory data of metastatic NSCLC patients was more accurate compared to traditional evaluations [92–94].”
MethodSholl, L. M., Awad, M., Basu Roy, U., Beasley, M. B., Cartun, R. W., Hwang, D. M., Kalemkerian, G., Lopez-Rios, F., Mino-Kenudson, M., Paintal, A., et al. (2024) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2023-0536-cp · Cited by 38
Roh, W., Geffen, Y., Cha, H., Miller, M. L., Anand, S. K., Kim, J., Heiman, D. I., Gainor, J. F., Laird, P. W., Cherniack, A. D., et al. (2022) · Cancer Research · doi:10.1158/0008-5472.can-22-0432 · Cited by 37
Pan, X., Lin, H., Han, C., Feng, Z., Wang, Y., Lin, J., Qiu, B., Yan, L., Li, B., Xu, Z., et al. (2022) · iScience · doi:10.1016/j.isci.2022.105605 · Cited by 37
“Recently, some research work advocated considering the importance of TILs in cancer epithelium region.(6,21) Park et al.”
Patkar, S., Chen, A., Basnet, A., Bixby, A., Rajendran, R., Chernet, R., Faso, S., Kumar, P. A., Desai, D., El-Zammar, O., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00765-w · Cited by 41
Deep learning in digital pathology for personalized treatment plans of cancer patients
Wen, Z., Wang, S., Yang, D. M., Xie, Y., Chen, M., Bishop, J., & Xiao, G. (2023) · Seminars in Diagnostic Pathology · doi:10.1053/j.semdp.2023.02.003 · Cited by 34
Lim, Y., Choi, S., Oh, H. J., Kim, C., Song, S., Kim, S., Song, H., Park, S., Kim, J. W., Kim, J. H., et al. (2023) · npj Precision Oncology · doi:10.1038/s41698-023-00470-0 · Cited by 33
Tumour organoids and assembloids: Patient‐derived cancer avatars for immunotherapy
Mei, J., Liu, X., Tian, H., Chen, Y., Cao, Y., Zeng, J., Liu, Y., Chen, Y., Gao, Y., Yin, J., et al. (2024) · Clinical and Translational Medicine · doi:10.1002/ctm2.1656 · Cited by 34
Zhou, X., Yang, J., Lu, Y., Ma, Y., Meng, Y., Li, Q., Gao, J., Jiang, Z., Guo, L., Wang, W., et al. (2023) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-023-04702-1 · Cited by 36
“Next, to study the relationship between the differentiation signature of the cancer cells and characteristics of the corresponding immune microenvironments, we divided the patients into the immune-poor type and immune-rich type according to the density of tumor-infiltrating CD8+ T cells [39].”
BackgroundRakaee, 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
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 34
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
“Park et al. [79] developed an AI-powered WSI analyzer of TIL in the TME to classify immune phenotypes and demonstrated that the spatial analysis of TIL was significantly correlated with tumor response and patient prognosis in advanced non-small cell lung cancer patients.”
BackgroundKim, 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 37
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
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
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
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
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
“Deep learning has invoked tremendous insights into cancer prognostication and treatment efficacy prediction based on whole-slide images (WSIs) [1–3].”
BackgroundCai, 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
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
“Similarly, AI-mediated analysis of tumor-infiltrating lymphocytes in advanced non–small-cell lung cancer (NSCLC) identified three immune phenotypes that correlated with response to immune checkpoint inhibitors and progression-free survival, which is challenging by manual quantification [98].”
BackgroundYu, J., Kong, X., & Feng, Y. (2025) · Cancer Drug Resistance · doi:10.20517/cdr.2025.14 · Cited by 32
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
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
“Sun et al. (11) published the results of a radiomic signature model that predicts CD8 cells based on CD8B geneassociated signatures in NSCLC tumors and correlated this with ICI treatment outcomes.”
BackgroundMethodInfluentialImplications 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
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
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 30
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
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 30
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
Dasgupta, S. (2024) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2024.0175 · Cited by 25
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
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
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 25
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
“15 16 Therefore, to develop an objective biomarker for assessing the efficacy of immunotherapy using the TIME, the precise and objective spatial analysis of the complex positioning of inflammatory and tumor cells should be performed using machine learning.”
MethodAiZynth 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
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
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 21
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 25
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
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
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 23
“Based on pathomics, Park et al. 138 developed Lunit SCOPE IO, a spatial analysis model for tumor-infiltrating lymphocytes (TIL).”
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
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
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
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 17
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
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
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 16
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 16
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
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
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
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
“better OS than TIL-negative cases when compared with melanoma, breast, lung, liver, and colorectal cancers [6, 7].”
BackgroundHeterogeneity 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
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
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
“46 Conversely, patients with L858R mutations had preserved TIL levels and demonstrated increased EC abundance, possibly because of the differential modulation of vascular remodeling or immune signaling pathways post-TKI.”
InfluentialRojas, F. R., Parra, E. R., Wistuba, I. I., Haymaker, C., & Soto, L. M. S. (2022) · Cancers · doi:10.3390/cancers14112775 · Cited by 14
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
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
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 12
“Previous studies have found that the PD ‐ L1 expression levels, percentage of CD8 + T cells, TIL density, and tumor mutational burden (TMB) may be related to the efficacy of immunotherapy [12 – 14].”
BackgroundAlshorman, 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 15
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
“AI-powered spatial analysis of IP was found to correlate with tumor response and PFS with ICIs in advanced non–small cell lung cancer, demonstrating the therapeutic applicability of IP as a biomarker of ICIs [30].”
Background
Metadata
- Authors
- Park, S., Ock, C.-Y., Kim, H., Pereira, S., Park, S., Ma, M., Choi, S., Kim, S., Shin, S., Aum, B. J., Paeng, K., Yoo, D., Cha, H., Park, S., Suh, K. J., Jung, H. A., Kim, S. H., Kim, Y. J., Sun, J.-M., Chung, J.-H., Ahn, J. S., Ahn, M.-J., Lee, J. S., Park, K., Song, S. Y., Bang, Y.-J., Choi, Y.-L., Mok, T. S., & Lee, S.-H.
- Affiliations
- Samsung Medical Center (KR)
- Lunit (South Korea) (KR)
- Seoul National University Bundang Hospital (KR)
- Lunit, Seoul, Republic of Korea
- Ajou University (KR)
- Seoul National University (KR)
- Chinese University of Hong Kong (HK)
- Corresponding author
- Lee, Se-Hoon
- ORCID
- Park, Sehhoon
- Ock, Chan-Young
- Kim, Hyojin
- Park, Seonwook
- Ma, Minuk
- Choi, Sangjoon
- Kim, Seokhwi
- Shin, Seunghwan
- Aum, Brian Jaehong
- Cha, Hongui
- Park, Sunyoung
- Suh, Koung Jin
- Jung, Hyun Ae
- Kim, Se Hyun
- Kim, Yu Jung
- Sun, Jong-Mu
- Chung, Jin-Haeng
- Ahn, Jin Seok
- Ahn, Myung-Ju
- Lee, Jong Seok
- Park, Keunchil
- Bang, Yung-Jue
- Choi, Yoon-La
- Mok, Tony S.
- Lee, Se-Hoon
- Venue
- Journal of Clinical Oncology
- Published
- 2022-06-10
- Volume
- 40
- Issue
- 17
- Pages
- 1916-1928
- ISSN
- 0732-183X (ISSN-L)
- 1527-7755
- PMC
- PMC9177249
- OpenAlex
- W4220803499
- Cited by
- 280
- References
- 29
- Citations by year
- 2026: 46 · 2025: 76 · 2024: 75 · 2023: 56 · 2022: 27
- FWCI
- 24.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Journal of Clinical Oncology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- AJOU Open Repository (Ajou University) (submittedVersion, cc-by-nc-nd)
- Seoul National University Open Repository (Seoul National University) (submittedVersion)
- Funders
- Lunit
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- 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
- Keywords
- tumor-infiltrating lymphocytes, immune checkpoint inhibitors, non-small cell lung cancer, tumor microenvironment, immunophenotype, immune-excluded tumors, tumor proportion score, PD-L1 expression, whole slide image analysis, artificial intelligence, predictive biomarkers, progression-free survival, overall survival
- MeSH terms
- Immune Checkpoint Inhibitors, Artificial Intelligence, Carcinoma, Non-Small-Cell Lung, Humans, Lung Neoplasms, Biomarkers, Lymphocytes, Tumor-Infiltrating, Tumor Microenvironment, B7-H1 Antigen, Spatial Analysis
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