Artificial Intelligence for T classification of TNM breast cancer in MRI imaging: Enabling Precision in Treatment Decisions.
Authors
Affiliations (2)
Affiliations (2)
- BIOPHYSIC, UTM, Tunis, Tunis, Tunis, 1007, Tunisia.
- Universite de Tunis El Manar Faculte de Medecine de Tunis, Tunis, Tunis, Tunis, Tunis, 1007, Tunisia.
Abstract
The integration of artificial intelligence (AI) into breast cancer management presents transformative potential for both diagnosis and treatment planning. This study introduces a resilient AI framework designed to accomplish, from breast MRI images, two critical tasks: (1) accurate and automated segmentation of breast tumors, and (2) T-stage classification of breast cancer in accordance with the 2018 eighth edition of TNM staging system.The dataset comprises sagittal MRI scans utilized for tumor segmentation through a U-Net architecture, which yielded high precision and specificity. The generated 3D reconstructions from segmented slices significantly improved visualization of tumor margins, providing crucial spatial insights that support more effective therapeutic planning.Segmented images were utilized as input to a ResNet-50 convolutional neural network, which demonstrated robust classification performance across all T stage categories (T1mi, T1a, T1b, T1c, T2, T3, T4a, T4b, T4c, and T4d), highlighting its high precision, specificity, and F1-scores in accurately distinguishing tumor progression.The T-stage serves as a critical determinant in selecting appropriate treatment modalities, ranging from surgery and chemotherapy to radiotherapy or palliative care, and in estimating prognosis. Our classification results underscore the clinical significance of tumor size progression in early stages (T1mi-T2), where each incremental increase in diameter is associated with poorer outcomes. For advanced categories (T3-T4a-T4d), our model consistently highlighted a uniformly poor prognosis, irrespective of tumor dimensions, reinforcing the pivotal role of anatomical invasion in staging and therapeutic decisions.This AI framework represents a significant advancement in breast cancer automation, enabling more precise staging and fostering improved clinical decision-making and patient outcomes.