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A multimodal deep learning model (MMLM) was developed by fusing features from a modified ResNet152 for pathological images, standardized RNA sequencing, methylation microarray data, and clinical information via feature-level fusion. Class Activation Mapping and UMAP improve interpretability. On an independent test set, MMLM outperforms monomodal ResNet with markedly higher AUC and average precision, and shows promising risk staging prediction while highlighting data access and privacy limits.",{"@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":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-deep-learning-model-for-multiclass-lung-cancer-classification-using-multimodal-data-fusion/450384/",{"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/a-deep-learning-model-for-multiclass-lung-cancer-classification-using-multimodal-data-fusion/450384.png","ImageObject",300,407,{"name":92,"@type":93},"Jake","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-01","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What inputs does the multimodal model use for lung cancer classification?","Question",{"text":112,"@type":113},"It integrates pathological images modeled with a modified ResNet152, standardized RNA sequencing data, methylation microarray data, and clinical information using feature-level fusion.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the proposed method improve interpretability?",{"text":117,"@type":113},"Class Activation Mapping (CAM) and UMAP mapping are incorporated to enhance interpretability, transparency, and trust in the model’s decisions.",{"name":119,"@type":110,"acceptedAnswer":120},"How did the multimodal model perform compared with the monomodal ResNet model?",{"text":121,"@type":113},"On an independent test set, the MMLM achieved substantially higher AUC and average precision values than the monomodality ResNet, and it also showed encouraging results for risk staging prediction.","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},450384,1790816624,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":56,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":143,"read_time":144},962084928904,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Lin et al. Discover Oncology (2026) 17:28 [https://doi.org/10.1007/s12672-025-04168-6](https://doi.org/10.1007/s12672-025-04168-6)  \nDiscover Oncology  \nRESEARCH Open Access  \nA deep learning model for multiclass lung cancer classification using multimodal data fusion  \nWeixuan Lin 1,2†, Xuchen Cheng2†, Yongxuan Wang2, Siqi Zhou2, Wenchao Zhong2, Wenya Chen2, Shuping Song2, Siyuan Gan2, Ying Cheng2, Dingke Chen 1,2* andYanqin Sun 1,2*  \n† Weixuan Lin and Xuchen Cheng have contributed equally to this work.  \n*Correspondence:  \nDingke Chen [714266013@qq.com](714266013@qq.com)  \nYanqin Sun [sunyanqin@gdmu.edu.cn](sunyanqin@gdmu.edu.cn)[ ](sunyanqin@gdmu.edu.cn)1The Affiliated Dongguan Songshan Lake Central Hospital, Guangdong Medical University, Dongguan, China 2Guangdong Medical University, Zhanjiang, China  \nAbstract  \nIntroduction Lung cancer is the most prevalent malignant tumour in terms of morbidity and mortality. Accurate classification and risk staging are essential for developing appropriate treatment plans and achieving precision in lung cancer diagnosis and treatment.  \nMethod A lung cancer pathological image classification model was trained using ResNet152 . The image input structure of ResNet152 was modified to support custom input image block sizes. A multimodal deep learning model (MMLM) was developed that integrated features extracted from the trained ResNet152 (RNet), standardized RNA sequencing data, methylation microarray data, and clinical information through a feature-level fusion method. This process emulates the way in which pathologists integrate multimodal information to make decisions. The incorporation of Class Activation Mapping (CAM) and UMAP mapping enhanced the interpretability of the model, thus increasing its transparency and fostering greater trust in its decisionmaking process.  \nResults In the context of lung cancer classification on an independent test set, compared with monomodality RNet, the MMLM demonstrated superior performance. The areas under the curve (AUC) values of the MMLM were 0 . 999, 1. 000, and 0. 980, which significantly surpassed RNet’s values of 0 . 822, 0 . 732, and 0. 781, respectively. Similarly, the average precision (AP) values of the MMLM were 0 . 999, 1. 000, and 0. 909, which surpassed those of RNet (0 . 793, 0 . 606, and 0.471) . In addition, supplementary validation of the ability of the MMLM to predict lung cancer risk staging yielded encouraging results.  \nConclusions The MMLM integrates pathological images, RNA sequencing data, methylation data, and clinical information to improve lung cancer classification and prognosis. However, challenges remain, such as limited access to comprehensive clinical data, patient privacy concerns, and computational resource demands. The MMLM holds significant potential to become a powerful tool for personalized lung cancer diagnosis and treatment with continued advancements in data integration and privacy.  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/l](http://creativecomm","cbCaijaYBBPJslmS","https://ap.wps.com/l/cbCaijaYBBPJslmS","pdf",4455245,"English","# Introduction\n## Background and clinical need\n# Method\n## ResNet152-based pathological modeling\n## Multimodal feature-level fusion\n## Interpretability via CAM and UMAP\n# Results\n## Independent test set performance vs monomodality\n## Risk staging prediction validation\n# Conclusions","[{\"question\":\"What inputs does the multimodal model use for lung cancer classification?\",\"answer\":\"It integrates pathological images modeled with a modified ResNet152, standardized RNA sequencing data, methylation microarray data, and clinical information using feature-level fusion.\"},{\"question\":\"How does the proposed method improve interpretability?\",\"answer\":\"Class Activation Mapping (CAM) and UMAP mapping are incorporated to enhance interpretability, transparency, and trust in the model’s decisions.\"},{\"question\":\"How did the multimodal model perform compared with the monomodal ResNet model?\",\"answer\":\"On an independent test set, the MMLM achieved substantially higher AUC and average precision values than the monomodality ResNet, and it also showed encouraging results for risk staging prediction.\"}]","A deep learning model for multiclass lung cancer classification using multimodal data fusion | PDF",1790733043,48]