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

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.

Automated summary

Peritoneal recurrence is the most common and lethal type of recurrence in gastric cancer with serosal invasion after surgery. Current evaluation methods are not sufficient for predicting peritoneal recurrence in this type of gastric cancer. Pathomics analyses, consisting of multiple pathomics features extracted from stained images, have shown potential for risk stratification and outcome prediction. A pathomics signature was found to be significantly associated with peritoneal recurrence, and a pathomics nomogram was developed for more accurate prediction.