AUTHOR=Zang Shiming , Jiang Cuiping , Zhang Lele , Fu Jingjing , Meng Qingle , Wu Wenyu , Shao Guoqiang , Sun Hongbin , Jia Ruipeng , Wang Feng TITLE=Deep learning based on 68Ga-PSMA-11 PET/CT for predicting pathological upgrading in patients with prostate cancer JOURNAL=Frontiers in Oncology VOLUME=13 YEAR=2024 URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1273414 DOI=10.3389/fonc.2023.1273414 ISSN=2234-943X ABSTRACT=Objectives

To explore the feasibility and importance of deep learning (DL) based on 68Ga-prostate-specific membrane antigen (PSMA)-11 PET/CT in predicting pathological upgrading from biopsy to radical prostatectomy (RP) in patients with prostate cancer (PCa).

Methods

In this retrospective study, all patients underwent 68Ga-PSMA-11 PET/CT, transrectal ultrasound (TRUS)-guided systematic biopsy, and RP for PCa sequentially between January 2017 and December 2022. Two DL models (three-dimensional [3D] ResNet-18 and 3D DenseNet-121) based on 68Ga-PSMA-11 PET and support vector machine (SVM) models integrating clinical data with DL signature were constructed. The model performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

Results

Of 109 patients, 87 (44 upgrading, 43 non-upgrading) were included in the training set and 22 (11 upgrading, 11 non-upgrading) in the test set. The combined SVM model, incorporating clinical features and signature of 3D ResNet-18 model, demonstrated satisfactory prediction in the test set with an AUC value of 0.628 (95% confidence interval [CI]: 0.365, 0.891) and accuracy of 0.727 (95% CI: 0.498, 0.893).

Conclusion

A DL method based on 68Ga-PSMA-11 PET may have a role in predicting pathological upgrading from biopsy to RP in patients with PCa.