Abstract:
Objective The tumor-stroma ratio (TSR) is considered an independent prognostic factor for gastric cancer. Traditionally, TSR assessments have relied on the visual evaluation of surgical specimens, which is a method that lacks objectivity. This study was conducted to investigate whether the TSR in preoperative biopsy specimens can be automatically quantified using deep learning methods and whether the TSR value can be used to predict the efficacy of neoadjuvant chemotherapy (NAC) in patients with gastric cancer.
Methods In total, 148 preoperative biopsy slides and 43 surgical resection slides from patients with gastric cancer who underwent NAC treatment at Yunnan Cancer Hospital between March 2013 and March 2020 were used in the study. Tumor region segmentation and epithelial-stromal segmentation models were developed. The surgical resection slides were used to trained and evaluate the model, and the biopsy slides were used to test their predictive abilities. The TSR values were determined on the basis of the intersection of predictions from both models. The postoperative pathological tumor regression grade (TRG) was used to categorize patients into good responders (TRG 0-1) and poor responders (TRG 2-3). Univariate and multivariate Logistic regression analyses were conducted to determine the correlation between the TSR value and the efficacy of NAC in gastric cancer.
Results The intersection over union (IOU) value was 0.94 for the tumor tissue segmentation model and 0.88 for the epithelial-stromal segmentation model. Using cutoff values of 44.93% and 70.22%, patients were classified into low, intermediate, and high TSR groups. The proportion of good responders was significantly different among these groups (P<0.05). Multivariate Logistic regression analysis indicated that the TSR was an independent predictor of NAC response in gastric cancer (OR=0.10, 95% CI: 0.03-0.32). When the TSR three-category classification was added as a predictor of treatment response alongside conventional clinical information, the area under curve (AUC) increased from 0.71 to 0.85.
Conclusions This deep learning model is capable of automatically segmenting tumor, epithelial, and stromal regions based on pathological slides, accurately calculating TSR value, and predicting the efficacy of NAC on the basis of the automatically computed TSR values.
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