A deep-learning approach for tumor origin differentiation using cytological histology.
By Pooja Toshniwal PahariaApr 19 2024Reviewed by Lily Ramsey, LLM In a recent study published in Nature Medicine , researchers developed a deep-learning approach for tumor origin differentiation using cytological histology , recognizing malignancy and predicting tumor origin in hydrothorax and ascites using cytological pictures from 57,220 patients.
About the study In the present study, researchers present TORCH, a deep learning algorithm, to identify cancer genesis based on cytological pictures from ascites and hydrothorax. To test the generalizability and reliability of TORCH, the researchers included 4,520 consecutive patients from Tianjin Cancer Hospital and 12,467 from Yantai Hospital .
The study aims to enhance junior pathologists' diagnostic abilities using TORCH. Ablation tests assessed the advantages of including clinical characteristics in tumor origin prediction and investigated the association between clinical factors and cytological images. When coupled with five testing sets, TORCH had a top-1 accuracy of 83%, a top-2 accuracy of 96%, and a top-3 accuracy of 99%. It also produced similar micro-averaged one-versus-rest AUROC ratings in the low-certainty and high-certainty groups.
Adenocarcinoma Ascites Deep Learning Diagnostic Histology Histopathology Hospital Immunohistochemistry Malignant Medicine Micro Tumor
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