{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T11:34:25Z","timestamp":1786361665707,"version":"build-2736575974"},"reference-count":48,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Cancer Inform"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The transformation from benign to malignant breast tissue represents a complex biological continuum rather than a discrete pathological switch. Current diagnostic paradigms rely heavily on binary classification systems that may inadequately capture the nuanced morphometric signatures underlying this transformation process. While machine learning classifiers have demonstrated high classification accuracy on morphometric datasets, they typically function as predictive black boxes that offer limited biological insight into the transformation mechanisms themselves.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To establish a comprehensive morphometric framework for characterizing the architectural signatures that distinguish benign from malignant breast tissue through quantitative analysis of cellular features and develop a clinically translatable diagnostic algorithm for breast cancer detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 569 breast tissue samples from the Wisconsin Diagnostic Breast Cancer Dataset, comprising 212 malignant and 357 benign cases. Thirty morphometric parameters were evaluated across three analytical dimensions: central tendency measures, variability indices, and extreme value characterizations. A weighted diagnostic algorithm was developed using the eight most discriminative parameters, with performance validated through receiver operating characteristic analysis and ten-fold cross-validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Our analysis revealed distinct morphometric transformation signatures characterized by progressive architectural disruption. Concavity measurements demonstrated the most pronounced differentiation (3.49-fold increase in malignant tissues), followed by concave point density (3.42-fold increase) and cellular area expansion (2.11-fold increase). Categorical analysis demonstrated clear hierarchical organization of discriminative features, with boundary architecture parameters showing superior diagnostic utility compared to size or regularity measures. The developed diagnostic algorithm achieved excellent performance with 94.3% sensitivity, 91.6% specificity, and 92.6% overall accuracy. Receiver operating characteristic analysis yielded an area under the curve of 0.974, demonstrating superior discriminative capability. The algorithm employs weighted scoring of morphometric parameters, with concavity-related features receiving highest priority (49% combined weighting) due to their biological significance in malignant transformation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Contextualized malignantization represents a morphometrically quantifiable process characterized by specific architectural transformation patterns, particularly boundary disruption signatures. The developed diagnostic algorithm successfully translates morphometric research into clinical practice, providing objective, quantitative diagnostic criteria that could enhance breast cancer detection while reducing diagnostic variability. This framework provides a foundation for developing more nuanced diagnostic approaches that capture the biological complexity of breast tissue transformation while establishing a conceptual advance from binary classification toward continuous characterization of transformation processes with promising clinical translation potential pending prospective multi-center validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/11769351261476174","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T23:23:56Z","timestamp":1786145036000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Contextualized Malignantization: Morphometric Signatures of Breast Tissue Transformation and a Clinically Translatable Diagnostic Algorithm"],"prefix":"10.1177","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8229-0408","authenticated-orcid":false,"given":"Oluwaseun Adebayo","family":"Bamodu","sequence":"first","affiliation":[{"name":"Department of Research and Implementation, Building Research and Implementation to Drive Global Equality Africa (BRIDGEAfrica)"},{"name":"Bethesda Achievers Consultancy LLC"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"e_1_3_8_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2011.02.013"},{"issue":"5","key":"e_1_3_8_3_2","first-page":"1016","article-title":"Revisiting the hallmarks of cancer","volume":"7","author":"Fouad YA","year":"2017","unstructured":"Fouad YA, Aanei C. 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