Deep learning-based computed tomography urography image analysis for prediction of HER2 status in bladder cancer.

Abstract

Purpose: Bladder cancer (BCa) is one of the most common malignant tumors in the urinary system. BCa with HER2 overexpression can benefit from more precise treatments, but HER2 testing is costly and subjective. This study aimed to detect HER2 expression using computed tomography urography (CTU) images. Method: We gathered CTU images from 97 patients with BCa during the excretion phase in Renmin Hospital of Wuhan University, manually delineated the BCa regions, extracted radiomic features using the Pyradiomics package, conducted data dimensionality reduction via principal component analysis (PCA), and trained three models (Logistic Regression [LR], Random Forest [RF] and Multilayer Perceptron [MLP]) to discern the HER2 expression status. Results: Pyradiomics package was used to extract 975 radiological features and the cumulative interpretation area under the variance curve was 90.964 by PCA. Using an MLP-based deep learning model for identifying HER2 overexpression, the area under the curve (AUC) reached 0.79 (95% confidence interval [CI] 0.74-0.86) in the training set and 0.73 (95% CI 0.66-0.77) in the validation set. LR and RF had AUC of 0.69 (95% CI 0.63-0.75) and 0.66 (95% CI 0.61-0.70) in the training set and 0.61 (95% CI 0.55-0.67) and 0.59 (95% CI 0.55-0.63) in the test set, respectively. Conclusion: The study firstly presents a non-invasive method for identifying and detecting HER2 expression in BCa CTU images. It might not only reduce the cost and subjectivity of traditional HER2 testing but also provide a new technical method for the precise treatment of BCa.