Breast cancer diagnosis commonly considers individual nuclear morphological characteristics, whereas their joint organization is less frequently evaluated. We developed the Morphological Network Disruption Index (MNDI), a patient-level measure of morphological abnormality relative to a benign reference state. Using the...
INTRODUCTION
Breast cancer prognosis prediction remains challenging due to the heterogeneous nature of clinical and multiomics data and the complex interactions among diverse biological mechanisms. This study proposes a multimodal deeplearning framework integrating clinical variables, gene expression profiles, and copy...
Reza Bozorgpour, Mohammadreza Soltany Sadrabadi· Clinical Breast Cancer· 0 citations
Overall, the reviewed evidence indicates that AI has considerable potential to support the detection, risk assessment, and management of cerebral aneurysms, while emphasizing the need for standardized datasets, prospective multicenter validation, and interpretable models to enable reliable clinical adoption.
Reza Bozorgpour, P. Kim, Jacob Rammer· Journal of imaging informati...· 0 citations
It is demonstrated that assigning complementary roles to stacking multimodal information integration and bagging for prediction stabilization provides an effective and robust framework for multi-omics breast cancer prognosis, supporting the use of complementary ensemble paradigms for multimodal prediction in precision...
Reza Bozorgpour, Mohammadreza Soltany Sadrabadi· Clinical Cancer Bulletin· 0 citations
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