病理组学技术在肺癌诊疗和预后评估中的应用与挑战
The Application and Challenges of Histopathology Techniques in the Diagnosis, Treatment, and Prognostic Evaluation of Lung Cancer
DOI: 10.12677/acm.2025.152423, PDF,   
作者: 应 凤, 李升锦*：重庆医科大学附属第二医院呼吸与危重症医学科，重庆
关键词: 肺癌；病理组学；人工智能；诊断；预后；Lung Cancer； Pathomics； Artificial Intelligence； Diagnosis； Prognosis
摘要: 在精准医疗时代，肺癌患者的治疗方案制定高度依赖于精确的病理诊断和预后评估。近年来，全切片成像技术与人工智能的迅速发展极大地推动了病理组学技术的进步。病理组学在肺癌病理图像分析中，尤其是在肿瘤区域识别、预后预测、肿瘤微环境表征等方面展现出巨大的潜力。本文回顾了病理组学在肺癌诊疗及预后评估领域的最新研究进展，分析了其在当前应用中的局限，并对病理组学的未来发展方向进行了展望。
Abstract: In the era of precision medicine, the treatment plans for lung cancer patients heavily rely on accurate pathological diagnosis and prognostic evaluation. In recent years, the rapid development of whole-slide imaging technology and artificial intelligence has significantly advanced the progress of pathological histology technology. Pathomics has shown great potential in the analysis of lung cancer pathological images, especially in tumor region identification, prognostic prediction, and tumor microenvironment characterization. This article reviews the latest research developments in the field of pathomics for lung cancer diagnosis, treatment, and prognostic evaluation, analyzes its limitations in current applications, and provides an outlook on the future direction of pathomics.
文章引用：应凤, 李升锦. 病理组学技术在肺癌诊疗和预后评估中的应用与挑战[J]. 临床医学进展, 2025, 15(2): 892-899. https://doi.org/10.12677/acm.2025.152423

参考文献

[1] Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209-249. [Google Scholar] [CrossRef] [PubMed]
[2] Spigel, D.R., Faivre-Finn, C., Gray, J.E., Vicente, D., Planchard, D., Paz-Ares, L., et al. (2022) Five-Year Survival Outcomes from the PACIFIC Trial: Durvalumab after Chemoradiotherapy in Stage III Non-Small-Cell Lung Cancer. Journal of Clinical Oncology, 40, 1301-1311. [Google Scholar] [CrossRef] [PubMed]
[3] Luo, X., Zang, X., Yang, L., Huang, J., Liang, F., Rodriguez-Canales, J., et al. (2017) Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis. Journal of Thoracic Oncology, 12, 501-509. [Google Scholar] [CrossRef] [PubMed]
[4] Yu, K., Zhang, C., Berry, G.J., Altman, R.B., Ré, C., Rubin, D.L., et al. (2016) Predicting Non-Small Cell Lung Cancer Prognosis by Fully Automated Microscopic Pathology Image Features. Nature Communications, 7, Article No. 12474. [Google Scholar] [CrossRef] [PubMed]
[5] Naik, N., Madani, A., Esteva, A., Keskar, N.S., Press, M.F., Ruderman, D., et al. (2020) Deep Learning-Enabled Breast Cancer Hormonal Receptor Status Determination from Base-Level H&E Stains. Nature Communications, 11, Article No. 5727. [Google Scholar] [CrossRef] [PubMed]
[6] Jin, L., Shi, F., Chun, Q., Chen, H., Ma, Y., Wu, S., et al. (2020) Artificial Intelligence Neuropathologist for Glioma Classification Using Deep Learning on Hematoxylin and Eosin Stained Slide Images and Molecular Markers. Neuro-Oncology, 23, 44-52. [Google Scholar] [CrossRef] [PubMed]
[7] Chen, M., Zhang, B., Topatana, W., Cao, J., Zhu, H., Juengpanich, S., et al. (2020) Classification and Mutation Prediction Based on Histopathology H&E Images in Liver Cancer Using Deep Learning. NPJ Precision Oncology, 4, Article No. 14. [Google Scholar] [CrossRef] [PubMed]
[8] Kather, J.N., Heij, L.R., Grabsch, H.I., Loeffler, C., Echle, A., Muti, H.S., et al. (2020) Pan-Cancer Image-Based Detection of Clinically Actionable Genetic Alterations. Nature Cancer, 1, 789-799. [Google Scholar] [CrossRef] [PubMed]
[9] Li, F., Yang, Y., Wei, Y., He, P., Chen, J., Zheng, Z., et al. (2021) Deep Learning-Based Predictive Biomarker of Pathological Complete Response to Neoadjuvant Chemotherapy from Histological Images in Breast Cancer. Journal of Translational Medicine, 19, Article No. 348. [Google Scholar] [CrossRef] [PubMed]
[10] Farahmand, S., Fernandez, A.I., Ahmed, F.S., Rimm, D.L., Chuang, J.H., Reisenbichler, E., et al. (2022) Deep Learning Trained on Hematoxylin and Eosin Tumor Region of Interest Predicts HER2 Status and Trastuzumab Treatment Response in HER2+ Breast Cancer. Modern Pathology, 35, 44-51. [Google Scholar] [CrossRef] [PubMed]
[11] Shi, J., Wang, X., Ding, G., Dong, Z., Han, J., Guan, Z., et al. (2020) Exploring Prognostic Indicators in the Pathological Images of Hepatocellular Carcinoma Based on Deep Learning. Gut, 70, 951-961. [Google Scholar] [CrossRef] [PubMed]
[12] Wang, X., Chen, Y., Gao, Y., Zhang, H., Guan, Z., Dong, Z., et al. (2021) Predicting Gastric Cancer Outcome from Resected Lymph Node Histopathology Images Using Deep Learning. Nature Communications, 12, Article No. 1637. [Google Scholar] [CrossRef] [PubMed]
[13] Abels, E., Pantanowitz, L., Aeffner, F., Zarella, M.D., van der Laak, J., Bui, M.M., et al. (2019) Computational Pathology Definitions, Best Practices, and Recommendations for Regulatory Guidance: A White Paper from the Digital Pathology Association. The Journal of Pathology, 249, 286-294. [Google Scholar] [CrossRef] [PubMed]
[14] Beck, A.H., Sangoi, A.R., Leung, S., Marinelli, R.J., Nielsen, T.O., van de Vijver, M.J., et al. (2011) Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival. Science Translational Medicine, 3, 108ra113. [Google Scholar] [CrossRef] [PubMed]
[15] Yuan, Y., Failmezger, H., Rueda, O.M., Ali, H.R., Gräf, S., Chin, S., et al. (2012) Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling. Science Translational Medicine, 4, 157ra143. [Google Scholar] [CrossRef] [PubMed]
[16] Wang, S., Chen, A., Yang, L., Cai, L., Xie, Y., Fujimoto, J., et al. (2018) Comprehensive Analysis of Lung Cancer Pathology Images to Discover Tumor Shape and Boundary Features That Predict Survival Outcome. Scientific Reports, 8, Article No. 10393. [Google Scholar] [CrossRef] [PubMed]
[17] Alvarez-Jimenez, C., Sandino, A.A., Prasanna, P., Gupta, A., Viswanath, S.E. and Romero, E. (2020) Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers, 12, Article 3663. [Google Scholar] [CrossRef] [PubMed]
[18] Pan, X., AbdulJabbar, K., Coelho-Lima, J., Grapa, A., Zhang, H., Cheung, A.H.K., et al. (2024) The Artificial Intelligence-Based Model ANORAK Improves Histopathological Grading of Lung Adenocarcinoma. Nature Cancer, 5, 347-363. [Google Scholar] [CrossRef] [PubMed]
[19] Pan, L., Liang, Q., Zeng, W., Peng, Y., Zhao, Z., Liang, Y., et al. (2024) Feature-Interactive Siamese Graph Encoder-Based Image Analysis to Predict STAS from Histopathology Images in Lung Cancer. NPJ Precision Oncology, 8, Article No. 285. [Google Scholar] [CrossRef] [PubMed]
[20] Coudray, N., Ocampo, P.S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., et al. (2018) Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images Using Deep Learning. Nature Medicine, 24, 1559-1567. [Google Scholar] [CrossRef] [PubMed]
[21] Cao, R., Yang, F., Ma, S., Liu, L., Zhao, Y., Li, Y., et al. (2020) Development and Interpretation of a Pathomics-Based Model for the Prediction of Microsatellite Instability in Colorectal Cancer. Theranostics, 10, 11080-11091. [Google Scholar] [CrossRef] [PubMed]
[22] Ninomiya, H., Hiramatsu, M., Inamura, K., Nomura, K., Okui, M., Miyoshi, T., et al. (2009) Correlation between Morphology and EGFR Mutations in Lung Adenocarcinomas. Lung Cancer, 63, 235-240. [Google Scholar] [CrossRef] [PubMed]
[23] Nibid, L., Greco, C., Cordelli, E., Sabarese, G., Fiore, M., Liu, C.Z., et al. (2023) Deep Pathomics: A New Image-Based Tool for Predicting Response to Treatment in Stage III Non-Small Cell Lung Cancer. PLOS ONE, 18, e0294259. [Google Scholar] [CrossRef] [PubMed]
[24] Wang, X., Janowczyk, A., Zhou, Y., Thawani, R., Fu, P., Schalper, K., et al. (2017) Prediction of Recurrence in Early Stage Non-Small Cell Lung Cancer Using Computer Extracted Nuclear Features from Digital H&E Images. Scientific Reports, 7, Article No. 13543. [Google Scholar] [CrossRef] [PubMed]
[25] Wang, X., Janowczyk, A., Zhou, Y., Thawani, R., Fu, P., Schalper, K., et al. (2022) Association of Machine Learning-Based Assessment of Tumor-Infiltrating Lymphocytes on Standard Histologic Images with Outcomes of Immuno-Therapy in Patients with NSCLC. JAMA Oncology, 9, 51-60.
[26] Muller, M., Schouten, R.D., De Gooijer, C.J. and Baas, P. (2017) Pembrolizumab for the Treatment of Non-Small Cell Lung Cancer. Expert Review of Anticancer Therapy, 17, 399-409. [Google Scholar] [CrossRef] [PubMed]
[27] Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports, 23, 181-193.e7. [Google Scholar] [CrossRef] [PubMed]
[28] Wang, S., Wang, T., Yang, L., Yang, D.M., Fujimoto, J., Yi, F., et al. (2019) Convpath: A Software Tool for Lung Adenocarcinoma Digital Pathological Image Analysis Aided by a Convolutional Neural Network. EBioMedicine, 50, 103-110. [Google Scholar] [CrossRef] [PubMed]
[29] Yi, F., Yang, L., Wang, S., Guo, L., Huang, C., Xie, Y., et al. (2018) Microvessel Prediction in H&E Stained Pathology Images Using Fully Convolutional Neural Networks. BMC Bioinformatics, 19, Article No. 64. [Google Scholar] [CrossRef] [PubMed]
[30] Wei, J.W., Tafe, L.J., Linnik, Y.A., Vaickus, L.J., Tomita, N. and Hassanpour, S. (2019) Pathologist-Level Classification of Histologic Patterns on Resected Lung Adenocarcinoma Slides with Deep Neural Networks. Scientific Reports, 9, Article No. 3358. [Google Scholar] [CrossRef] [PubMed]
[31] Sha, L., Osinski, B.L., Ho, I.Y., Tan, T.L., Willis, C., Weiss, H., et al. (2019) Multi-Field-of-View Deep Learning Model Predicts Nonsmall Cell Lung Cancer Programmed Death-Ligand 1 Status from Whole-Slide Hematoxylin and Eosin Images. Journal of Pathology Informatics, 10, 24. [Google Scholar] [CrossRef] [PubMed]
[32] Gertych, A., Swiderska-Chadaj, Z., Ma, Z., Ing, N., Markiewicz, T., Cierniak, S., et al. (2019) Convolutional Neural Networks Can Accurately Distinguish Four Histologic Growth Patterns of Lung Adenocarcinoma in Digital Slides. Scientific Reports, 9, Article No. 148. [Google Scholar] [CrossRef] [PubMed]
[33] Yu, K., Wang, F., Berry, G.J., Ré, C., Altman, R.B., Snyder, M., et al. (2020) Classifying Non-Small Cell Lung Cancer Types and Transcriptomic Subtypes Using Convolutional Neural Networks. Journal of the American Medical Informatics Association, 27, 757-769. [Google Scholar] [CrossRef] [PubMed]
[34] Choi, S., Cho, S.I., Ma, M., Park, S., Pereira, S., Aum, B.J., et al. (2022) Artificial Intelligence-Powered Programmed Death Ligand 1 Analyser Reduces Interobserver Variation in Tumour Proportion Score for Non-Small Cell Lung Cancer with Better Prediction of Immunotherapy Response. European Journal of Cancer, 170, 17-26. [Google Scholar] [CrossRef] [PubMed]
[35] Wang, X., Zhao, J., Marostica, E., Yuan, W., Jin, J., Zhang, J., et al. (2024) A Pathology Foundation Model for Cancer Diagnosis and Prognosis Prediction. Nature, 634, 970-978. [Google Scholar] [CrossRef] [PubMed]

为你推荐