[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128299-en":3,"doc-seo-128299-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128299,962085570644,"Evangeline","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma","Integrative prognostic machine learning models are developed to improve pretreatment disease stratification for mantle cell lymphoma, an incurable B-cell malignancy. A curated dataset of 862 patients diagnosed from 2014 to 2022 is analyzed with a gradient-boosted machine learning framework using clinicopathologic, cytogenetic, and genomic baseline features. The model distinguishes indolent or responsive disease from aggressive disease with strong performance (AUC ROC 0.83). The same framework supports feature selection for multivariate logistic and survival modeling and produces practical indices including iMIPI and iMIPI-s, emphasizing molecular predictors such as TP53 mutational status.","The Texas Medical Center Library  \nDigitalCommons@TMC  \n\n| Faculty, Staff and Student Publications | School of Public Health |\n| --- | --- |\n\n8-1-2023  \nIntegrative Prognostic Machine Learning Models in Mantle Cell Lymphoma  \nHolly A Hill Preetesh Jain Chi Young Ok Koji Sasaki Han Chen  \nSee next page for additional authors  \nFollow this and additional works at: [https://digitalcommons.library.tmc.edu/uthsph_docs](https://digitalcommons.library.tmc.edu/uthsph_docs)  \n Part of the Oncology Commons, and the Public Health Commons  \nAuthors  \nHolly A Hill, Preetesh Jain, Chi Young Ok, Koji Sasaki, Han Chen, Michael L Wang, and Ken Chen  \nRESEARCH ARTICLE  [https://doi.org/10.1158/2767-9764.CRC-23-0083](https://doi.org/10.1158/2767-9764.CRC-23-0083)   \nIntegrative Prognostic Machine Learning Models in Mantle Cell Lymphoma  \nHolly A. Hill1 , 2 , 3 , Preetesh Jain2 , Chi Young Ok4 , Koji Sasaki5 , Han Chen3 , 6 , Michael L. Wang2 , and Ken Chen1  \nOPEN  \nACCESS  \nCheck for updates  \nABSTRACT  \nPatients with mantle cell lymphoma (MCL), an incurable B-cell malignancy, benefit from accurate pretreatment disease stratification. We curatedan extensive database of 862 patients diagnosed between 2014 and 2022 . A machine learning (ML) gradient-boosted model incorporated baseline features from clinicopathologic, cytogenetic, and genomic data with high predictive power discriminating between patients with indolent or responsive MCL and those with aggressive disease (AUC ROC = 0.83) . In addition, we utilized the gradient-boosted framework as a robust featureselection method for multivariate logistic and survival modeling. The best ML models incorporated features from clinical and genomic data types highlighting the need for correlative molecular studies in precision oncology. As proof of concept, we launched our most accurate and practical models using an application interface, which has potential for clinical implementation. We designated the 20-feature ML model–based index  \nthe “integrative MIPI” or iMIPI and a similar 10-feature ML index the“integrative simplified MIPI” or iMIPI-s. The top 10 baseline prognostic features represented in the iMIPI-s are: lactase dehydrogenase (LDH), Ki-67%, platelet count, bone marrow involvement percentage, hemoglobin levels, the total number of observed somatic mutations, TP􀀂􀀃 mutational status, Eastern Cooperative Oncology Group performance level, beta-2 microglobulin, and morphology. Our findings emphasize that prognostic applications and indices should include molecular features, especially TP􀀂􀀃 mutational status. This work demonstrates the clinical utility of complex ML models and provides further evidence for existing prognostic markers in MCL.  \nSignificance: Our model is the first to integrate a dynamic algorithm with multiple clinical and molecular features, allowing for accurate predictions of MCL disease outcomes in a large patient cohort.  \nIntroduction  \nMantle cell lymphoma (MCL) is a rare, incurable B-cell malignancy with heterogeneous clinical outcomes and molecular pathogeneses. Most patients relapse after treatment and have disease progression, while others have an indolent form of the disease or respond exceptionally to frontline therapy and  \n1 Department of Bioinformatics and Computational Biology, Division of Quantitative Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas. 2 Department of Lymphoma and Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.  \n3 Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health, Houston, Texas. 4 Department of Hematopathology, Division of Pathology-Lab Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas.  \n5 Department of Leukemia, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas. 6 Center for Precision Health, ","cbCaif6mKBhxQIhT","https://ap.wps.com/l/cbCaif6mKBhxQIhT","pdf",433510,3,1,14,"English","en",105,"# Abstract\n# Introduction\n## Clinical challenges in MCL\n## Known prognostic biomarkers and cohorts\n## Rationale for integrating molecular profiling","[{\"question\":\"What patient data were used to build the machine learning prognostic models?\",\"answer\":\"The study curated an extensive cohort of 862 mantle cell lymphoma patients diagnosed between 2014 and 2022, integrating baseline clinicopathologic, cytogenetic, and genomic information.\"},{\"question\":\"How does the gradient-boosted model perform in distinguishing indolent/responsive versus aggressive disease?\",\"answer\":\"It discriminates between indolent or responsive and aggressive disease with an AUC ROC of 0.83.\"},{\"question\":\"What are iMIPI and iMIPI-s, and what key features do they rely on?\",\"answer\":\"The 20-feature model index is iMIPI, and a related 10-feature index is iMIPI-s. The top prognostic features in iMIPI-s include LDH, Ki-67%, platelet count, bone marrow involvement percentage, hemoglobin, somatic mutation burden, TP53 mutational status, performance status, beta-2 microglobulin, and morphology.\"}]","Integrative Prognostic Machine Learning Models in Mantle Cell Lymphoma | PDF",1785946698,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"integrative-prognostic-machine-learning-models-in-mantle-cell-lymphoma","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/integrative-prognostic-machine-learning-models-in-mantle-cell-lymphoma/128299/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What patient data were used to build the machine learning prognostic models?","Question",{"text":76,"@type":77},"The study curated an extensive cohort of 862 mantle cell lymphoma patients diagnosed between 2014 and 2022, integrating baseline clinicopathologic, cytogenetic, and genomic information.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the gradient-boosted model perform in distinguishing indolent/responsive versus aggressive disease?",{"text":81,"@type":77},"It discriminates between indolent or responsive and aggressive disease with an AUC ROC of 0.83.",{"name":83,"@type":74,"acceptedAnswer":84},"What are iMIPI and iMIPI-s, and what key features do they rely on?",{"text":85,"@type":77},"The 20-feature model index is iMIPI, and a related 10-feature index is iMIPI-s. The top prognostic features in iMIPI-s include LDH, Ki-67%, platelet count, bone marrow involvement percentage, hemoglobin, somatic mutation burden, TP53 mutational status, performance status, beta-2 microglobulin, and morphology.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]