Publications by authors named "A Taylor-Weiner"
Article Synopsis
- - Clinical trials for metabolic dysfunction-associated steatohepatitis (MASH) need accurate histologic scoring to assess participants and outcomes, but varying interpretations have affected results.
- - The AI-based tool AIM-MASH showed strong consistency and agreement with expert pathologists in scoring MASH histology, achieving accuracy comparable to that of average pathologists.
- - AIM-MASH demonstrated a strong ability to predict patient outcomes, correlating well with pathologist scores and noninvasive biomarkers, indicating it could enhance the efficiency and reliability of clinical trials for MASH.
Article Synopsis
- The study focuses on creating a deep learning digital pathology tool for accurately detecting, segmenting, and classifying nuclei in cancer tissues, addressing challenges in quantifying nuclear morphology in histologic images.
- This tool was trained on nucleus annotations to analyze H&E-stained slides from various cancer cohorts (BRCA, LUAD, PRAD), revealing significant differences in nuclear features like shape and size linked to genomic instability and cancer prognosis.
- Results highlighted that certain nuclear characteristics, particularly in fibroblasts, were associated with patient survival outcomes and gene expression related to tumor biology, paving the way for better understanding of cancer biomarkers.
Introduction: Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate.
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- Clinical trials for nonalcoholic steatohepatitis (NASH) rely on consistent histologic scoring, but variability in these interpretations has affected trial results.* -
- An AI tool called AIM-NASH was developed to provide standardized scoring for NASH histology, showing strong correlation with expert consensus scores and improving predictive accuracy for patient outcomes.* -
- In a retrospective analysis, AIM-NASH helped meet previously unmet pathological endpoints in the ATLAS trial, suggesting it could reduce variability in scoring and enhance the assessment of treatment responses in clinical trials.*
Article Synopsis
- * Researchers used machine learning and advanced analysis of tissue samples from NASH clinical trials to identify a 5-gene expression signature that could predict disease progression in patients with severe liver fibrosis (F3 and F4 stages).
- * This study found that the Notch signaling pathway, linked to liver diseases, was significantly present in the gene signature, and in a validation cohort, drugs that improved liver conditions also reduced levels of various Notch signaling components.