{"id":9643,"date":"2025-12-09T10:55:06","date_gmt":"2025-12-09T10:55:06","guid":{"rendered":"https:\/\/kibu.ac.ke\/sgs\/?p=9643"},"modified":"2025-12-09T10:56:34","modified_gmt":"2025-12-09T10:56:34","slug":"bayesian-hierarchical-modeling-framework-for-breast-cancer-treatment-outcome-prediction-integrating-clinical-pathological-and-treatment-variables","status":"publish","type":"post","link":"https:\/\/kibu.ac.ke\/sgs\/bayesian-hierarchical-modeling-framework-for-breast-cancer-treatment-outcome-prediction-integrating-clinical-pathological-and-treatment-variables\/","title":{"rendered":"Bayesian Hierarchical Modeling Framework for Breast Cancer Treatment Outcome Prediction: Integrating Clinical, Pathological, And Treatment Variables"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"9643\" class=\"elementor elementor-9643\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-22a2d57a elementor-section-full_width elementor-section-height-min-height elementor-section-items-stretch elementor-section-height-default\" data-id=\"22a2d57a\" data-element_type=\"section\" id=\"ourgoal\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-24d1c553\" data-id=\"24d1c553\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-72332ab8 elementor-widget elementor-widget-theme-post-title elementor-page-title elementor-widget-heading\" data-id=\"72332ab8\" data-element_type=\"widget\" data-widget_type=\"theme-post-title.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h1 class=\"elementor-heading-title elementor-size-default\">Bayesian Hierarchical Modeling Framework for Breast Cancer Treatment Outcome Prediction: Integrating Clinical, Pathological, And Treatment Variables<\/h1>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-79009c87 elementor-section-height-min-height elementor-section-boxed elementor-section-height-default elementor-section-items-middle\" data-id=\"79009c87\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-no\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-20c4331\" data-id=\"20c4331\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-inner-section elementor-element elementor-element-47e7a763 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"47e7a763\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-8c33a5f elementor-invisible\" data-id=\"8c33a5f\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-149a3128 elementor-widget__width-auto elementor-widget elementor-widget-heading\" data-id=\"149a3128\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">2025<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-79d97b83 elementor-invisible\" data-id=\"79d97b83\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2622254 elementor-widget elementor-widget-heading\" data-id=\"2622254\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Authors<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c391b6 elementor-invisible elementor-widget elementor-widget-text-editor\" data-id=\"5c391b6\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Muhati Nelson Lwoyelo<\/p><p>Richard Simwa<\/p><p>Vincent Marani<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t<div class=\"elementor-column elementor-col-33 elementor-inner-column elementor-element elementor-element-43819837 elementor-invisible\" data-id=\"43819837\" data-element_type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;animation&quot;:&quot;fadeIn&quot;,&quot;animation_mobile&quot;:&quot;none&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4cd35958 elementor-tablet-align-left elementor-invisible elementor-widget elementor-widget-button\" data-id=\"4cd35958\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeInLeft&quot;}\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-shrink\" href=\"https:\/\/www.irejournals.com\/paper-details\/1709507\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">VIEW ON PUBLISHER SITE<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<div class=\"elementor-element elementor-element-59566d91 elementor-widget elementor-widget-heading\" data-id=\"59566d91\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36bda81d elementor-invisible elementor-widget elementor-widget-text-editor\" data-id=\"36bda81d\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;fadeIn&quot;}\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Breast cancer represents the most prevalent malignancy among women globally, with approximately 2.3 million new cases annually. In Kenya, it disproportionately affects younger women (35-50 years) and represents the leading cancer diagnosis. Current prediction models inadequately quantify uncertainty in treatment responses, leading to suboptimal clinical decision-making. This study developed a Bayesian hierarchical modeling framework to predict pathological complete response (pCR) in breast cancer patients by systematically integrating clinical, pathological, and treatment variables. We conducted a retrospective analysis of 5,400 patients across 12 Kenyan treatment centers using Bayesian logistic regression with random effects to model hierarchical data structure. The framework incorporated tumor stage, molecular markers (hormone receptor status, HER2), histological grade, patient demographics, and treatment protocols. Markov Chain Monte Carlo (MCMC) methods estimated posterior distributions with multiple imputation addressing missing data. The developed model demonstrated superior predictive accuracy (AUC = 0.837) compared to classical approaches, with significant effects identified for tumor stage (Stage IV OR: 3.19, 95% CrI: 1.89-4.54), hormone receptor status (OR: 0.31, 95% CrI: 0.15-0.66), and HER2 positivity (OR: 2.33, 95% CrI: 1.08-4.78). Treatment center heterogeneity accounted for 12.5% of outcome variability. This framework provides the first population-specific Bayesian approach for sub-Saharan African breast cancer prediction, enabling personalized treatment planning and improved clinical decision-making in resource-constrained settings.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>2025 Authors Muhati Nelson Lwoyelo Richard Simwa Vincent Marani VIEW ON PUBLISHER SITE Abstract Breast cancer represents the most prevalent malignancy among women globally, with approximately 2.3 million new cases annually. In Kenya, it disproportionately affects younger women (35-50 years) and represents the leading cancer diagnosis. Current prediction models inadequately quantify uncertainty in treatment responses, [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[16],"tags":[],"class_list":["post-9643","post","type-post","status-publish","format-standard","hentry","category-publications"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Bayesian Hierarchical Modeling Framework for Breast Cancer Treatment Outcome Prediction: Integrating Clinical, Pathological, And Treatment Variables - School of Graduate Studies<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/kibu.ac.ke\/sgs\/bayesian-hierarchical-modeling-framework-for-breast-cancer-treatment-outcome-prediction-integrating-clinical-pathological-and-treatment-variables\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Bayesian Hierarchical Modeling Framework for Breast Cancer Treatment Outcome Prediction: Integrating Clinical, Pathological, And Treatment Variables - School of Graduate Studies\" \/>\n<meta property=\"og:description\" content=\"2025 Authors Muhati Nelson Lwoyelo Richard Simwa Vincent Marani VIEW ON PUBLISHER SITE Abstract Breast cancer represents the most prevalent malignancy among women globally, with approximately 2.3 million new cases annually. In Kenya, it disproportionately affects younger women (35-50 years) and represents the leading cancer diagnosis. 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