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Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [18F]PSMA-1007 PET/CT and multiparametric MRI: A multicenter study

Prostate Cancer and Prostatic Diseases (2025)Cite this article

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Abstract

Background

This study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [18F]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.

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Fig. 1: The flowchart of patient selection.
Fig. 2: Deep learning network construction and data augmentation.
Fig. 3: Development of the easy models (E-mpMRI-M, E-PET/CT-M, E-MPC-M) with two illustrative examples.
Fig. 4: Receiver operating characteristic (ROC) curves for the different models.
Fig. 5: Decision-curves analysis (DCA) for different models.
Fig. 6: Grad-CAM heatmaps for different modalities.

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Data availability

The datasets generated and analysed during the current study are available from the corresponding author on reasonable request.

Code availability

The custom Python code used to generate the results and central conclusions of this study is available from the corresponding author upon reasonable request.

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Funding

This study has received funding by the Key Laboratory of Novel Nuclide Technologies on Precision Diagnosis and Treatment & clinical Transformation of Wenzhou (Grant No. 2023HZSY0012), the Discipline Cluster of Oncology, Wenzhou Medical University, China (Grant No. z1-2023008), the Summit Advancement Disciplines of Zhejiang Province (Wenzhou Medical University – Pharmaceutics).

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Author notes

  1. These authors contributed equally: Fei Yao, Dongqin Zhu.

Authors and Affiliations

  1. The Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Fei Yao, Yuandi Zhuang, Shuying Bian, Yunjun Yang & Kehua Pan

  2. The Department of Nuclear Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Dongqin Zhu, Yunjun Yang & Jie Lin

  3. The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Heng Lin & Cunke Miao

  4. The Department of Urology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China

    Qi Lin

  5. The Department of Nuclear Medicine, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, 310000, China

    Tiancheng Li

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Contributions

Conception and design: FY and KHP. Acquisition of data: QL, TCL, SYB, and YDZ. Analysis and interpretation of data: DQZ, HL, and CKM. Drafting of the manuscript: YF and DQZ. Critical revision of the manuscript: DQZ and KHP. Statistical analysis: YF, HL, and CKM. Supervision: YYJ and JL.

Corresponding author

Correspondence to Kehua Pan.

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The author declares no competing interests.

Ethics approval and consent to participate

All methods were performed in accordance with the relevant guidelines and regulations. This study was approved by our hospital’s Institutional Review Board (Reference Number: KY2022-R012). Since this study was a retrospective study, the requirement for informed consent was waived.

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Yao, F., Zhu, D., Lin, H. et al. Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [18F]PSMA-1007 PET/CT and multiparametric MRI: A multicenter study. Prostate Cancer Prostatic Dis (2025). https://doi.org/10.1038/s41391-025-01063-7

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