{"paper_id":"3fde1fa1-7cb5-4aca-9ea0-11a35840e3ed","body_text":"Leveraging Artificial Intelligence for Effective Breast Cancer Management in Low-Resource Centers: A Pilot Project of Tnm Staging Machine | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Leveraging Artificial Intelligence for Effective Breast Cancer Management in Low-Resource Centers: A Pilot Project of Tnm Staging Machine Agodirin Olayide, Chijioke Chijindu, Rahman Ganiyu, Oluwadiya Kehinde, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6718906/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Leveraging artificial intelligence in breast cancer management offers opportunities to address a major public health challenge in low-resource settings. Yet the challenges of implementing AI in developing regions may contribute to global disparities due to the considerable cost of infrastructure, data acquisition, data cleaning, and modeling. Aim: To lay the foundation for AI integration into a clinical workflow in a low-resource center by training and evaluating a machine learning model for breast cancer staging. Methods: In a Python Jupyter notebook environment, using Pandas, Numpy, and Sklearn (Scikit-learn), we trained a decision tree classifier (DTC) model to learn the rules of the 8th edition of the AJCC Breast Cancer Staging for Breast Cancer. The model was trained on 158 samples derived from the AJCC 8th Edition staging guidelines and validated via 39 real-world anonymized records. The performance evaluation was performed with metrics derived from a confusion matrix and 3-fold cross validation. Result: Starting with a high dissimilarity in the global datasets with a Gini impurity of 0.807 in the input data, the model achieved pure classifications with a Gini impurity of 0.0, indicating no misclassifications. The decision tree classifier achieved 100% accuracy, precision, and recall on all the test and real-world datasets. Conclusion: We trained a decision classifier AI model to learn and implement the AJCC breast cancer staging guidelines with perfect performance, demonstrating the feasibility of cost-effective AI solutions for breast cancer staging in low-resource settings and paving way for broader clinical integration, such as integration, into treatment recommendation systems. breast cancer machine learning AI in healthcare low income Full Text Additional Declarations No competing interests reported. Supplementary Files 3.SupplementaryFIle.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Dec, 2025 Reviews received at journal 12 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviews received at journal 05 Jul, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviewers invited by journal 24 Jun, 2025 Editor invited by journal 28 May, 2025 Editor assigned by journal 23 May, 2025 Submission checks completed at journal 23 May, 2025 First submitted to journal 21 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6718906\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":476496967,\"identity\":\"e5ae870b-1f79-4037-b14b-9c833e598771\",\"order_by\":0,\"name\":\"Agodirin 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Yet the challenges of implementing AI in developing regions may contribute to global disparities due to the considerable cost of infrastructure, data acquisition, data cleaning, and modeling.\\u003c/p\\u003e \\u003cp\\u003eAim: To lay the foundation for AI integration into a clinical workflow in a low-resource center by training and evaluating a machine learning model for breast cancer staging.\\u003c/p\\u003e \\u003cp\\u003eMethods: In a Python Jupyter notebook environment, using Pandas, Numpy, and Sklearn (Scikit-learn), we trained a decision tree classifier (DTC) model to learn the rules of the 8th edition of the AJCC Breast Cancer Staging for Breast Cancer. The model was trained on 158 samples derived from the AJCC 8th Edition staging guidelines and validated via 39 real-world anonymized records. The performance evaluation was performed with metrics derived from a confusion matrix and 3-fold cross validation.\\u003c/p\\u003e \\u003cp\\u003eResult: Starting with a high dissimilarity in the global datasets with a Gini impurity of 0.807 in the input data, the model achieved pure classifications with a Gini impurity of 0.0, indicating no misclassifications. 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