Construction of a prediction model for the survival prognosis of bladder cancer patients based on deep learning features extracted from tumor regions in hematoxylin and eosin-stained slides

LUO Guanshui; HE Yifeng; ZHENG Zongtai; ZENG Deqin

doi:10.3969/j.issn.1674-3806.2026.01.05

Abstract：Objective To construct a prediction model for the survival prognosis of bladder cancer patients based on deep learning features extracted from tumor regions in hematoxylin and eosin(HE)-stained slides.Methods The clinical data of 379 patients with bladder cancer were collected from The Cancer Genome Atlas(TCGA)database[including 450 slices of whole-slide images(WSI)],and the clinical data of 179 patients with bladder cancer who were admitted to the Affiliated Guangdong Second Provincial General Hospital of Jinan University from September 2017 to May 2024 were collected(including 244 slices of WSI).The ResNet50 model was applied for transfer learning to identify tumor regions.The RetCCL model was applied for extracting deep learning features.The extracted deep learning features were screened via univariate Cox regression and LASSO regression,and a risk scoring model was constructed.The maximally selected rank statistics method(MSRSM)was adopted to determine of the risk scores of deep learning features.According to the optimal cut-off values,the patients were divided into high-risk group and low-risk group,and the survival prognosis was compared between the two groups by using Kaplan-Meier survival curve.The factors influencing the survival prognosis of the bladder cancer patients were analyzed by using Cox regression,and a nomogram model was constructed based on the screened risk factor indicators.The calibration accuracy and net clinical benefit of the model were comprehensively evaluated by using calibration curve and decision curve analysis(DCA).Results The RetCCL model was applied to extract the features from all the WSI,and each slice of WSI had 14336 features.Twenty-two features with prognostic predictive value were obtained by using univariate Cox regression analysis.Furthermore,LASSO regression analysis was performed on these 22 features to obtain 16 features with non-zero regression coefficients.Based on this,16 pathological deep learning features and the corresponding regression coefficients were used to construct a risk scoring model for the bladder cancer patients.The results of analyses of TCGA database and clinical data from the Affiliated Guangdong Second Provincial General Hospital of Jinan University showed that based on the risk scores of deep learning features,the overall survival of the high-risk group was significantly shorter than that of the low-risk group(P＜0.05).The results of multivariate Cox regression analysis showed that the risk level of deep learning features and M stage were independent risk factors affecting the survival prognosis of the patients with bladder cancer(P＜0.05).Based on these two indicators,a nomogram model for predicting the survival prognosis of the bladder cancer patients was constructed.The calibration curve showed that the model exhibited a good consistency between the predicted survival rates of the patients at 1 year,3 years and 5 years after surgery and the actual survival rates.The results of DCA showed that the decision-making of the prediction model could achieve good net clinical benefits in terms of survival prognosis at 1 year,3 years and 5 years after surgery.Conclusion The survival prognosis prediction model for bladder cancer patients constructed according to deep learning features extracted from tumor regions in HE-stained slides has good predictive efficacy and can provide precise individualized prognosis assessment tools for clinical practice.