Background: Neoadjuvant chemotherapy (NAC) has been recognized as an effective therapeutic option for locally advanced gastric cancer as it is expected to reduce tumor size, increase the resection rate, and improve overall survival. However, for patients who are not responsive to NAC, the best operation timing may be missed together with suffering from side effects. Therefore, it is paramount to differentiate potential respondents from non-respondents. Histopathological images contain rich and complex data that can be exploited to study cancers. We assessed the ability of a novel deep learning (DL)-based biomarker to predict pathological responses from images of hematoxylin and eosin (H&E)-stained tissue.
Methods: In this multicentre observational study, H&E-stained biopsy sections of patients with gastric cancer were collected from four hospitals. All patients underwent NAC followed by gastrectomy. The Becker tumor regression grading (TRG) system was used to evaluate the pathologic chemotherapy response. Based on H&E-stained slides of biopsies, DL methods (Inception-V3, Xception, EfficientNet-B5, and ensemble CRSNet models) were employed to predict the pathological response by scoring the tumor tissue to obtain a histopathological biomarker, the chemotherapy response score (CRS). The predictive performance of the CRSNet was evaluated.
Results: 69,564 patches from 230 whole-slide images of 213 patients with gastric cancer were obtained in this study. Based on the F1 score and area under the curve (AUC), an optimal model was finally chosen, named the CRSNet model. Using the ensemble CRSNet model, the response score derived from H&E staining images reached an AUC of 0.936 in the internal test cohort and 0.923 in the external validation cohort for predicting pathological response. The CRS of major responders was significantly higher than that of minor responders in both internal and external test cohorts (both p < 0.001).
Conclusion: In this study, the proposed DL-based biomarker (CRSNet model) derived from histopathological images of the biopsy showed potential as a clinical aid for predicting the response to NAC in patients with locally advanced GC. Therefore, the CRSNet model provides a novel tool for the individualized management of locally advanced gastric cancer.
Keywords: Biopsy; Deep learning; Gastric cancer; Histopathological images; Neoadjuvant chemotherapy; Pathological response.
© 2023. The Author(s) under exclusive licence to The International Gastric Cancer Association and The Japanese Gastric Cancer Association.