Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram.

Dexin Chen, Jianbo Lai,Jiaxin Cheng,Meiting Fu,Liyan Lin,Feng Chen, Rong Huang,Jun Chen,Jianping Lu,Yuning Chen, Guangyao Huang, Miaojia Yan, Xiaodan Ma,Guoxin Li,Gang Chen,Jun Yan

iScience(2023)

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摘要
Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual's risk in peritoneal recurrence of GC with serosal invasion.
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关键词
Data processing in systems biology,Experimental models in systems biology,Oncology,Pathology
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