부산대학교 도서관

Aims: Desmoplastic reaction (DR) categorisation has been shown to be a promising prognostic factor in oesophageal squamous cell carcinoma (ESCC). The usual DR evaluation is performed using semiquantitative scores, which can be subjective. This study aimed to investigate whether a deep‐learning classifier could be used for DR classification. We further assessed the prognostic significance of the deep‐learning classifier and compared it to that of manual DR reporting and other pathological factors currently used in the clinic. Methods and results: From 222 surgically resected ESCC cases, 31 randomly selected haematoxylin–eosin‐digitised whole slides of patients with immature DR were used to train and develop a deep‐learning classifier. The classifier was trained for 89 370 iterations. The accuracy of the deep‐learning classifier was assessed to 30 unseen cases, and the results revealed a Dice coefficient score of 0.81. For survival analysis, the classifier was then applied to the entire cohort of patients, which was split into a training (n = 156) and a test (n = 66) cohort. The automated DR classification had a higher prognostic significance for disease‐specific survival than the manually classified DR in both the training and test cohorts. In addition, the automated DR classification outperformed the prognostic accuracy of the gold‐standard factors of tumour depth and lymph node metastasis. Conclusions: This study demonstrated that DR can be objectively and quantitatively assessed in ESCC using a deep‐learning classifier and that automatically classed DR has a higher prognostic significance than manual DR and other features currently used in the clinic. [ABSTRACT FROM AUTHOR]