Prostate cancer classification using 3D deep learning and ultrasound video clips: a multicenter study

Abstract

Objective: This study aimed to evaluate the effectiveness of deep-learning models using transrectal ultrasound (TRUS) video clips in predicting prostate cancer.

Methods: We manually segmented TRUS video clips from consecutive men who underwent examination with EsaoteMyLab™ Class C ultrasonic diagnostic machines between January 2021 and October 2022. The deep learning-inflated 3D ConvNet (I3D) model was internally validated using split-sample validation on the development set through cross-validation. The final performance was evaluated on two external test sets using geographic validation. We compared the results obtained from a ResNet 50 model, four ML models, and the diagnosis provided by five senior sonologists.

Results: A total of 815 men (median age: 71 years; IQR: 67-77 years) were included. The development set comprised 552 men (median age: 71 years; IQR: 67-77 years), the internal test set included 93 men (median age: 71 years; IQR: 67-77 years), external test set 1 consisted of 96 men (median age: 70 years; IQR: 65-77 years), and external test set 2 had 74 men (median age: 72 years; IQR: 68-78 years). The I3D model achieved diagnostic classification AUCs greater than 0.86 in the internal test set as well as in the independent external test sets 1 and 2. Moreover, it demonstrated greater consistency in sensitivity, specificity, and accuracy compared to pathological diagnosis (kappa > 0.62, p < 0.05). It exhibited a statistically significant superior ability to classify and predict prostate cancer when compared to other AI models, and the diagnoses provided by sonologists (p<0.05).

Conclusion: The I3D model, utilizing TRUS prostate video clips, proved to be valuable for classifying and predicting prostate cancer.

Keywords: I3D model; deep learning; multicenter study; prostate cancer; ultrasound.

Figure 1

Flowchart (a, b) shows inclusion of patients into study. The data from Center 1 and Center 2 were randomly divided into a development set and an internal test set. The data from Center 3 and Center 4 were used as external test sets 1 and 2, respectively. PCa, prostate cancer; TRUS, transrectal ultrasonography.

Figure 2

Diagram (a) shows overview of the ML (ML) and DL (DL) classification process. Diagram (b) shows ResNet 50 and I3D workflow.

Figure 3

ROC of all models in the internal test, External test 1, and External test 2 sets. Graph (a) shows areas under the receiver operating characteristic (ROC) curve; the AUC values of the I3D model were superior to those of the other models. Graph (b) shows the ROC curve (95%CI, blue area), the optimal threshold, True Positive Rate (TPR) and True Negative Rate (TNR) of the I3D model in the three test sets.

Figure 4

The upper graphs display the confusion matrices, while the lower graphs show the violin plots for the I3D model’s classification of benign and malignant tumors in the internal test set, the external test set 1, and the external test set 2.

Figure 5

Images depict heatmap examples from TRUS videos of four patients in the external test set 2. In positive cases (c, d), the I3D model exhibited a relatively focused heatmap indicating the presence of prostate cancer. However, in negative cases (a, b), the attention was more diffuse, and there was no distinct focus area observed. (a) a 66-year-old man with a prostate-specific antigen level of 4.52 ng/mL and a biopsy pathology result indicating benign prostatic hyperplasia, (b) a 70-year-old man with a prostate-specific antigen level of 6.56 ng/m and a biopsy pathology result indicating benign prostatic hyperplasia, (c) a 74-year-old man with prostate-specific antigen level of 6.8 ng/mL and a biopsy pathology result indicating Gleason grade group 7, (d) a 78-year-old man with a prostate-specific antigen level of 17.81 ng/mL and a biopsy pathology result indicating Gleason grade group 6.