Singh M
An AI-assisted tool for efficient prostate cancer diagnosis in low-grade and low-volume cases
Pathologists diagnose prostate cancer by core needle biopsy. In low-grade and low-volume cases, they look for a few malignant glands out of hundreds within a core. They may miss a few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep-learning pipeline to assist pathologists in detecting malignant glands in core needle biopsies of low-grade and low-volume cases. Analyzing a gland at multiple resolutions, our model exploited morphology and neighborhood information, which were crucial in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. Besides, we made the images publicly available, becoming the first digital histopathology dataset of patients of Asian ancestry with prostatic carcinoma. Our multi-resolution classification model achieved an area under the receiver operating characteristic curve (AUROC) value of 0.992 (95% confidence interval [CI]: 0.985–0.997) in the external validation study, showing the generalizability of our multi-resolution approach.
ReadA bio-functional polymer that prevents retinal scarring through modulation of NRF2 signalling pathway
Aberrant wound healing, which leads to irreversible fibrosis, underpins many pathological diseases. In the eye, this manifests as proliferative vitreoretinopathy (PVR), a major cause of poor vision due to failed retinal detachment (RD) surgery1,2. PVR occurs due to proliferative and contractile fibrocellular scar membranes that form either in the vitreous or surrounding the retina.
ReadTumor-adjacent tissue co-expression profile analysis reveals pro-oncogenic ribosomal gene signature for prognosis of resectable hepatocellular carcinoma
Currently, molecular markers are not used when determining the prognosis and treatment strategy for patients with hepatocellular carcinoma (HCC). In the present study, we proposed that the identification of common pro‐oncogenic pathways in primary tumors (PT) and adjacent non‐malignant tissues (AT) typically used to predict HCC patient risks may result in HCC biomarker discovery.
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