Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [F]PSMA-1007 PET/CT and multiparametric MRI: A multicenter study.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 3/4)
유사 논문P · Population 대상 환자/모집단
388 patients who underwent radical prostatectomy were enrolled from centers A, B and C.
I · Intervention 중재 / 시술
radical prostatectomy were enrolled from centers A, B and C
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[CONCLUSIONS] The MPC-M model demonstrated strong predictive performance for PCa ECE across internal and external cohorts, while the E-MPC-M model retained much of this performance with enhanced clinical practicality. However, these models should be considered as preliminary, and larger prospective multicenter studies are required to confirm their robustness and generalizability.
[BACKGROUND] This study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [F]PSMA-1007 PET/CT to predict extracapsular extension (ECE) in prostate cancer
APA
Yao F, Zhu D, et al. (2025). Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [F]PSMA-1007 PET/CT and multiparametric MRI: A multicenter study.. Prostate cancer and prostatic diseases. https://doi.org/10.1038/s41391-025-01063-7
MLA
Yao F, et al.. "Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [F]PSMA-1007 PET/CT and multiparametric MRI: A multicenter study.." Prostate cancer and prostatic diseases, 2025.
PMID
41361534 ↗
Abstract 한글 요약
[BACKGROUND] This study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [F]PSMA-1007 PET/CT to predict extracapsular extension (ECE) in prostate cancer (PCa), and to explore easy models integrating DL with clinical expertise.
[METHODS] A total of 388 patients who underwent radical prostatectomy were enrolled from centers A, B and C. Three DL models based on mpMRI, PET/CT, and a combined MPC model were developed and compared with a manual model based on the ECE grading system. Additionally, three combined models (mpMRI-M, PET/CT-M, and MPC-M) were constructed by integrating the DL models with the Manual model. To enhance clinical applicability, an easy model (E-MPC-M) was developed. Model performance was evaluated using the area under the receiver-operating-characteristic curve (AUC) and metrics derived from the confusion matrix. Gradient-weighted class-activation-mapping (Grad-CAM) was employed to visualize model interpretability.
[RESULTS] In the internal cohort, the Manual, MPC, and MPC-M models achieved AUCs of 0.752, 0.897, and 0.907, respectively; corresponding sensitivities were 0.616, 0.896, and 0.915, and specificities were 0.791, 0.740, and 0.802. In the external validation cohort, these models achieved AUCs of 0.665, 0.824, and 0.849; sensitivities of 0.318, 0.955, and 0.955; and specificities of 0.960, 0.600, and 0.640, respectively. The E-MPC-M model also showed robust performance, with an AUC of 0.862 in the internal cohort and 0.775 in the external cohort. Grad-CAM visualizations highlighted the model's focus on tumor-relevant regions, confirming effective learning of tumor features.
[CONCLUSIONS] The MPC-M model demonstrated strong predictive performance for PCa ECE across internal and external cohorts, while the E-MPC-M model retained much of this performance with enhanced clinical practicality. However, these models should be considered as preliminary, and larger prospective multicenter studies are required to confirm their robustness and generalizability.
[METHODS] A total of 388 patients who underwent radical prostatectomy were enrolled from centers A, B and C. Three DL models based on mpMRI, PET/CT, and a combined MPC model were developed and compared with a manual model based on the ECE grading system. Additionally, three combined models (mpMRI-M, PET/CT-M, and MPC-M) were constructed by integrating the DL models with the Manual model. To enhance clinical applicability, an easy model (E-MPC-M) was developed. Model performance was evaluated using the area under the receiver-operating-characteristic curve (AUC) and metrics derived from the confusion matrix. Gradient-weighted class-activation-mapping (Grad-CAM) was employed to visualize model interpretability.
[RESULTS] In the internal cohort, the Manual, MPC, and MPC-M models achieved AUCs of 0.752, 0.897, and 0.907, respectively; corresponding sensitivities were 0.616, 0.896, and 0.915, and specificities were 0.791, 0.740, and 0.802. In the external validation cohort, these models achieved AUCs of 0.665, 0.824, and 0.849; sensitivities of 0.318, 0.955, and 0.955; and specificities of 0.960, 0.600, and 0.640, respectively. The E-MPC-M model also showed robust performance, with an AUC of 0.862 in the internal cohort and 0.775 in the external cohort. Grad-CAM visualizations highlighted the model's focus on tumor-relevant regions, confirming effective learning of tumor features.
[CONCLUSIONS] The MPC-M model demonstrated strong predictive performance for PCa ECE across internal and external cohorts, while the E-MPC-M model retained much of this performance with enhanced clinical practicality. However, these models should be considered as preliminary, and larger prospective multicenter studies are required to confirm their robustness and generalizability.