Abstract
Objective
We aimed to develop deep learning classifiers for assessing therapeutic response on bone scans of patients with prostate cancer.
Methods
A set of 3791 consecutive bone scans coupled with their last previous scan (1528 patients) was evaluated. Bone scans were labeled as “progression” or “nonprogression” on the basis of clinical reports and image review. A 2D-convolutional neural network architecture was trained with three different preprocessing methods: 1) no preprocessing (Raw), 2) spatial normalization (SN), and 3) spatial and count normalization (SCN). Data were allocated into training, validation, and test sets in the ratio of 72:8:20, with the 20% independent test set rotating all scans over a five-fold testing procedure. A Grad-CAM algorithm was employed to generate class activation maps to visualize the lesions contributing to the decision. Diagnostic performance was compared using area under the receiver operating characteristics curves (AUCs).
Results
The data consisted of 791 scans labeled as “progression” and 3000 scans labeled as “nonprogression.” The AUCs of the classifiers were 0.632–0.710 on the Raw dataset, were significantly higher with the use of SN at 0.784–0.854 (p < 0.001 for Raw versus SN), and higher still with SCN at 0.954–0.979 (p < 0.001 for SN versus SCN). Class activation maps of the SCN model visualized lesions contributing to the model’s decision of progression.
Conclusion
With preprocessing of spatial and count normalization, our deep learning model achieved excellent performance in classifying the therapeutic response of bone scans in patients with prostate cancer.
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Funding
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT; No. NRF-2020M2D9A1094074; 2021R1A2C3009056), and by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HR18C0016).
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SH, JSO, and JJL contributed to the study conceptualization, data acquisition, data analysis, data interpretation, writing, and editing of the manuscript. SYS contributed to data analysis and writing of the manuscript.
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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and the principles of the 1964 Declaration of Helsinki and its subsequent amendments or comparable ethical standards.
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This retrospective study was approved by local institutional review board (IRB No. 2022–0672). The needs for informed consent were waived by the committee.
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Han, S., Oh, J.S., Seo, S.Y. et al. Performance of deep learning models for response evaluation on whole-body bone scans in prostate cancer. Ann Nucl Med 37, 685–694 (2023). https://doi.org/10.1007/s12149-023-01872-7
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DOI: https://doi.org/10.1007/s12149-023-01872-7