Contextualized Malignantization: Morphometric Signatures of Breast Tissue Transformation and a Clinically...

Area of Science:

• Biomedical Engineering
• Computational Pathology
• Oncology

Background:

• Breast tissue transformation is a continuum, not a discrete switch.
• Current diagnostics may miss nuanced morphometric signatures.
• Machine learning offers accuracy but lacks biological insight.

Purpose of the Study:

• Establish a morphometric framework for benign vs. malignant breast tissue.
• Develop a clinically translatable algorithm for breast cancer detection.

Main Methods:

• Analyzed 569 breast tissue samples (212 malignant, 357 benign).
• Evaluated 30 morphometric parameters across central tendency, variability, and extreme values.
• Developed a weighted diagnostic algorithm using 8 discriminative parameters, validated by ROC analysis and cross-validation.

Main Results:

• Distinct morphometric signatures of transformation revealed progressive architectural disruption.
• Concavity measurements, concave point density, and cellular area showed significant differentiation.
• The algorithm achieved 94.3% sensitivity, 91.6% specificity, and 0.974 AUC.
• Concavity-related features were prioritized in the algorithm due to biological significance.

Conclusions:

• Malignantization is a quantifiable morphometric process with specific architectural patterns.
• The diagnostic algorithm translates morphometric research into clinical practice, enhancing detection and reducing variability.
• This framework advances breast cancer diagnostics from binary classification towards continuous characterization.