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Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

A balance between preserving urinary continence as well as sexual potency and achieving negative surgical margins is of clinical relevance while implementary difficulty. Accurate detection of extracapsular extension (ECE) of prostate cancer (PCa) is thus crucial for determining appropriate treatment options. We aimed to develop and validate an artificial intelligence (AI)–based tool for detecting ECE of PCa using multiparametric magnetic resonance imaging (mpMRI).

Methods

Eight hundred and forty nine consecutive PCa patients who underwent mpMRI and prostatectomy without previous radio- or hormonal therapy from two medical centers were retrospectively included. The AI tool was built on a ResNeXt network embedded with a spatial attention map of experts’ prior knowledge (PAGNet) from 596 training patients. Model validation was performed in 150 internal and 103 external patients. Performance comparison was made between AI, two experts using a criteria-based ECE grading system, and expert-AI interaction.

Results

An index PAGNet model using a single-slice image yielded the highest areas under the receiver operating characteristic curve (AUC) of 0.857 (95% confidence interval [CI], 0.827–0.884), 0.807 (95% CI, 0.735–0.867), and 0.728 (95% CI, 0.631–0.811) in training, internal, and external validation data, respectively. The performance of two experts (AUC, 0.632 to 0.741 vs 0.715 to 0.857) was lower (paired comparison, all p values < 0.05) than that of AI assessment. When experts’ interpretations were adjusted by AI assessments, the performance of two experts was improved.

Conclusion

Our AI tool, showing improved accuracy, offers a promising alternative to human experts for ECE staging using mpMRI.

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Availability of data and material

The imaging studies and clinical data used for algorithm development are not publicly available, because they contain private patient health information. Interested users may request access to these data, where institutional approvals along with signed data use agreements and/or material transfer agreements may be needed/negotiated. Derived result data supporting the findings of this study are available upon reasonable requests.

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Funding

Contract grant sponsor: Key research and development program of Jiangsu Province; contract grant number: BE2017756 (to Y.D.Z.). The key Project of National Natural Science Foundation of China; contract grant number: 61731009 (to G. Y.). Open Project from Shanghai Key Laboratory of Magnetic Resonance; Contract grant number: N2019001 (to G. Y.)

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Authors and Affiliations

Authors

Contributions

Y.D.Z. and Y.S. conceived, designed, and supervised the project; Y.H., Y.H.Z, J.B., M.B. H.S., and G.Y. collected and pre-processed all data and performed the research; Y.H., Y.H.Z., and J.B. performed imaging data annotation and clinical data review; Y.D.Z. and Y.S. proposed the model; Y.H. and Y.H.Z drafted the paper; all authors reviewed, edited, and approved the final version of article.

Corresponding authors

Correspondence to Yang Song or Yu-Dong Zhang.

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Conflict of interest

The authors declare that there is no conflict of interest.

Ethics approval and consent to participate

This study was retrospective and approved by the local Research Ethics Board of The First Affiliated Hospital of Nanjing Medical University (protocol 2019-SR-396), and informed patient consent was waived. All procedures performed in studies involving human participants were in accordance with the 1964 Helsinki declaration and its later amendments.

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Not applicable.

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Ying Hou and Yi-Hong Zhang are co-first authors

This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence).

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Hou, Y., Zhang, YH., Bao, J. et al. Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study. Eur J Nucl Med Mol Imaging 48, 3805–3816 (2021). https://doi.org/10.1007/s00259-021-05381-5

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