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Liver fibrosis classification on trichrome histology slides using weakly supervised learning in children and young adults.
J Pathol Inform
January 2025
Cincinnati Children's AI Imaging Research (CAIIR) Center, Cincinnati, OH, United States.
Background: Traditional liver fibrosis staging via percutaneous biopsy suffers from sampling bias and variable inter-pathologist agreement, highlighting the need for more objective techniques. Deep learning models for disease staging from medical images have shown potential to decrease diagnostic variability, with recent weakly supervised learning strategies showing promising results even with limited manual annotation.
Purpose: To study the clustering-constrained attention multiple instance learning (CLAM) approach for staging liver fibrosis on trichrome whole slide images (WSIs) of children and young adults.
Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics.
PeerJ
January 2025
Departments of Clinical Pathology, The Second Affiliated Hospital of Medical College of Zhejiang University, Hangzhou, Zhejiang, China.
Objective: Breast cancer stands as the most prevalent form of cancer among women globally. This heterogeneous disease exhibits varying clinical behaviors. The stratification of breast cancer patients into risk groups, determined by their metastasis and survival outcomes, is pivotal for tailoring personalized treatments and therapeutic interventions.
View Article and Find Full Text PDFOCDet: A comprehensive ovarian cell detection model with channel attention on immunohistochemical and morphological pathology images.
Comput Biol Med
January 2025
Department of Pathology, Peking University Health Science Center, 38 College Road, Haidian, Beijing, 100191, China; Department of Pathology, School of Basic Medical Sciences, Third Hospital, Peking University Health Science Center, Beijing, 100191, China. Electronic address:
Background: Ovarian cancer is among the most lethal gynecologic malignancy that threatens women's lives. Pathological diagnosis is a key tool for early detection and diagnosis of ovarian cancer, guiding treatment strategies. The evaluation of various ovarian cancer-related cells, based on morphological and immunohistochemical pathology images, is deemed an important step.
View Article and Find Full Text PDFHistopathology and proteomics are synergistic for high-grade serous ovarian cancer platinum response prediction.
NPJ Precis Oncol
January 2025
Eötvös Loránd University, Department of Physics of Complex Systems, Budapest, Hungary.
Patients with High-Grade Serous Ovarian Cancer (HGSOC) exhibit varied responses to treatment, with 20-30% showing de novo resistance to platinum-based chemotherapy. While hematoxylin-eosin (H&E)-stained pathological slides are used for routine diagnosis of cancer type, they may also contain diagnostically useful information about treatment response. Our study demonstrates that combining H&E-stained whole slide images (WSIs) with proteomic signatures using a multimodal deep learning framework significantly improves the prediction of platinum response in both discovery and validation cohorts.
View Article and Find Full Text PDFQuantification and profiling of urine cells by integrated cytocentrifugation and infrared spectroscopy.
Spectrochim Acta A Mol Biomol Spectrosc
January 2025
Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Spain. Electronic address:
The presence of cells in urine and in particular White Blood Cells (WBCs) is often associated with Urinary Tract Infections (UTIs) and other diseases. Non-invasive screening of WBCs requires the development of cost-effective point of care diagnostic tools. Infrared (IR) spectroscopy has the potential to identify and quantify cells in urine.
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