Publications by authors named "Eliana Marostica"
Article Synopsis
- - Histopathology image evaluation is crucial for cancer diagnosis, but traditional AI methods struggle with generalizing across different imaging protocols and sample populations due to their specialized nature.
- - The Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model is introduced as a general-purpose, weakly supervised machine learning framework designed to systematically evaluate cancer by extracting diverse imaging features through two complementary pretraining methods.
- - CHIEF, trained on over 60,000 whole-slide images from various sites, demonstrated improved performance over existing deep learning approaches by up to 36.1%, showing its effectiveness in adapting to diverse samples and enhancing digital pathology evaluations for cancer patients.
Background: Timely and accurate intraoperative cryosection evaluations remain the gold standard for guiding surgical treatments for gliomas. However, the tissue-freezing process often generates artifacts that make histologic interpretation difficult. In addition, the 2021 WHO Classification of Tumors of the Central Nervous System incorporates molecular profiles in the diagnostic categories, so standard visual evaluation of cryosections alone cannot completely inform diagnoses based on the new classification system.
View Article and Find Full Text PDFHistopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888).
View Article and Find Full Text PDFPurpose: Histopathology evaluation is the gold standard for diagnosing clear cell (ccRCC), papillary, and chromophobe renal cell carcinoma (RCC). However, interrater variability has been reported, and the whole-slide histopathology images likely contain underutilized biological signals predictive of genomic profiles.
Experimental Design: To address this knowledge gap, we obtained whole-slide histopathology images and demographic, genomic, and clinical data from The Cancer Genome Atlas, the Clinical Proteomic Tumor Analysis Consortium, and Brigham and Women's Hospital (Boston, MA) to develop computational methods for integrating data analyses.