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Lung adenocarcinoma H&E images and matched transcriptomic, genomic, and clinical data are retrieved from TCGA. A QuPath pipeline performs stain normalization, segmentation, and supervised cell classification; a random-forest Haralick texture plus tumor-stage model stratifies high- vs low-TIL groups. Survival uses Kaplan–Meier and Cox regression; immune modules and infiltration are characterized with multiple gene-set methods. High TIL density (135 cells/mm²) predicts improved overall survival and shows distinct immunogenomic and mutational features, with high model concordance to pathologist and RNA-seq inference. ",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-quantification-of-tumor-infiltrating-lymphocytes-by-machine-learning-reveals-prognostic-and-immunogenomic-features-in-lung-cancer/358707/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-quantification-of-tumor-infiltrating-lymphocytes-by-machine-learning-reveals-prognostic-and-immunogenomic-features-in-lung-cancer/358707.png","ImageObject",300,407,{"name":92,"@type":93},"Oliver","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address in TIL assessment for lung cancer?","Question",{"text":112,"@type":113},"Manual TIL assessment on H&E-stained slides is subjective and has limited reproducibility, leading to inconsistency across observers.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is automated TIL quantification implemented in the study?",{"text":117,"@type":113},"The pipeline is built in QuPath (v0.5.1) with stain normalization, watershed cell segmentation, and a supervised classifier to identify tumor cells, stromal cells, and TILs.",{"name":119,"@type":110,"acceptedAnswer":120},"What clinical relevance does the study find from TIL density?",{"text":121,"@type":113},"High TIL density (135 cells/mm² cut-off) is associated with improved overall survival and distinct immune-related enrichment and immunogenomic patterns.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},358707,1790194325,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},8796095461610,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nAutomated quantification of tumor-infiltrating lymphocytes by machine learning reveals prognostic and immunogenomic features in lung cancer  \nAng Li, Yutao Pang, Hongfei Zhang, Dong Wu, Liyao Lin, Zhan He, Zhu Liang, Jie Chen􀀍 & Fasheng Li􀀍  \nTumor-infiltrating lymphocytes (TILs) are key components of the tumor microenvironment (TME) and are recognized as prognostic and predictive biomarkers in non-small cell lung cancer (NSCLC) . However, manual TIL assessment on hematoxylin and eosin (H&E)-stained slides is subjective and poorly reproducible. This study aimed to develop and validate an automated, machine learning– based framework forTIL quantification and explore its associations with immunogenomic features and patient outcomes. H&E-stained slides and transcriptomic, genomic, and clinical data from lung adenocarcinoma patients were retrieved from The Cancer Genome Atlas (TCGA). An automated  \nTIL quantification pipeline was built in QuPath (v0.5.1) with stain normalization, watershed cell segmentation, and a supervised cell classifier to identify tumour cells, stromal cells, and TILs. In a separate step, a random forest model based on aggregated Haralick texture features and tumour stage was trained to classify patients into high-and low-TIL subgroups. TIL density cut-offs were defined by maximally selected rank statistics. Survival was analyzed via the Kaplan–Meier method and Cox regression. ssGSEA, ESTIMATE, GSVA, and WGCNA were applied to characterize immune infiltration and transcriptomic modules. Somatic mutations were compared between groups, and drug sensitivity was predicted via GDSC-derived ridge regression models. Model performance was evaluated via 10-fold cross-validation with SMOTE oversampling. Automated quantification achieved high concordance with the results of the pathologist review and RNA-seq inference. An optimal TIL cut-off of 135 cells/mm2 was used to stratify patients into high-and low-density groups. High-TIL tumors were enriched for adaptive immune infiltration, antigen presentation, and TCR signaling, and exhibited greater mutational diversity, whereas low-TIL tumors were enriched in ribosome biogenesis and protein translation pathways. Prognostically, high-TIL density was associated with improved overall survival (HR=0.48, 95% CI: 0.29–0.79; P = 0.004). The predicted IC50 values did not differ for standard chemotherapies but varied for the selected compounds. The Haralick-based classification model achieved anAUC of 0.87 (95% CI 0.835–0.901) in internal cross-validation, which improved to 0.892 (95% CI 0.848–0.913) when tumour stage was incorporated. This study demonstrated that automated TIL quantification is feasible and prognostically relevant in lung cancer and may provide a hypothesisgenerating marker of immune activation for future immunotherapy studies; however, direct validation in immunotherapy-treated cohorts is required before clinical implementation.  \nKeywords Tumor-infiltrating lymphocytes, Lung adenocarcinoma, Machine learning, Digital pathology, Immune microenvironment  \nLung cancer remains one of the most common malignancies worldwide and continues to be the leading cause of cancer-related death1. Over the past decade, increasing evidence has underscored the pivotal role of the tumor microenvironment (TME) in the initiation and progression of primary lung cancer. The TME encompasses  \nAffiliated Hospital of Guangdong Medical University, Xiashan District, No. 57 South Renmin Road, Zhanjiang 524001, Guangdong, China. 􀀍 email: [chen.jie13579@163.com](chen.jie13579@163.com); [lfs1020@foxmail.com](lfs1020@foxmail.com)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nboth cellular and acellular components—such as stromal and immune cells, soluble signaling molecules, and the extracellular matrix—which interact in complex ways to shape tumor growth, metastatic potential, and respon","cbCaitT6lymEDNHQ","https://ap.wps.com/l/cbCaitT6lymEDNHQ","pdf",3678368,15,"English","# Methods\n## Patients and pathological materials","[{\"question\":\"What problem does the study address in TIL assessment for lung cancer?\",\"answer\":\"Manual TIL assessment on H\\u0026E-stained slides is subjective and has limited reproducibility, leading to inconsistency across observers.\"},{\"question\":\"How is automated TIL quantification implemented in the study?\",\"answer\":\"The pipeline is built in QuPath (v0.5.1) with stain normalization, watershed cell segmentation, and a supervised classifier to identify tumor cells, stromal cells, and TILs.\"},{\"question\":\"What clinical relevance does the study find from TIL density?\",\"answer\":\"High TIL density (135 cells/mm² cut-off) is associated with improved overall survival and distinct immune-related enrichment and immunogenomic patterns.\"}]","Automated quantification of tumor-infiltrating lymphocytes by machine learning reveals prognostic and immunogenomic features in lung cancer | PDF",1790135036,38]