Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction
Prostate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low–intermediate prostate-specific antigen (PSA), which often includes aggressive cancers, is imperative. This study evaluated the predictive accuracy of deep learning analysis using multimodal me...
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MDPI AG
2024-11-01
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| Series: | Current Oncology |
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| Online Access: | https://www.mdpi.com/1718-7729/31/11/530 |
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| author | Hayato Takeda Jun Akatsuka Tomonari Kiriyama Yuka Toyama Yasushi Numata Hiromu Morikawa Kotaro Tsutsumi Mami Takadate Hiroya Hasegawa Hikaru Mikami Kotaro Obayashi Yuki Endo Takayuki Takahashi Manabu Fukumoto Ryuji Ohashi Akira Shimizu Go Kimura Yukihiro Kondo Yoichiro Yamamoto |
| author_facet | Hayato Takeda Jun Akatsuka Tomonari Kiriyama Yuka Toyama Yasushi Numata Hiromu Morikawa Kotaro Tsutsumi Mami Takadate Hiroya Hasegawa Hikaru Mikami Kotaro Obayashi Yuki Endo Takayuki Takahashi Manabu Fukumoto Ryuji Ohashi Akira Shimizu Go Kimura Yukihiro Kondo Yoichiro Yamamoto |
| author_sort | Hayato Takeda |
| collection | DOAJ |
| description | Prostate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low–intermediate prostate-specific antigen (PSA), which often includes aggressive cancers, is imperative. This study evaluated the predictive accuracy of deep learning analysis using multimodal medical data focused on clinically significant PCa in patients with PSA ≤ 20 ng/mL. Our cohort study included 178 consecutive patients who underwent ultrasound-guided prostate biopsy. Deep learning analyses were applied to predict clinically significant PCa. We generated receiver operating characteristic curves and calculated the corresponding area under the curve (AUC) to assess the prediction. The AUC of the integrated medical data using our multimodal deep learning approach was 0.878 (95% confidence interval [CI]: 0.772–0.984) in all patients without PSA restriction. Despite the reduced predictive ability of PSA when restricted to PSA ≤ 20 ng/mL (<i>n</i> = 122), the AUC was 0.862 (95% CI: 0.723–1.000), complemented by imaging data. In addition, we assessed clinical presentations and images belonging to representative false-negative and false-positive cases. Our multimodal deep learning approach assists physicians in determining treatment strategies by predicting clinically significant PCa in patients with PSA ≤ 20 ng/mL before biopsy, contributing to personalized medical workflows for PCa management. |
| format | Article |
| id | doaj-art-24f19a96097940b18699af9ddd9e7f52 |
| institution | Kabale University |
| issn | 1198-0052 1718-7729 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Current Oncology |
| spelling | doaj-art-24f19a96097940b18699af9ddd9e7f522024-11-26T17:59:02ZengMDPI AGCurrent Oncology1198-00521718-77292024-11-0131117180718910.3390/curroncol31110530Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen RestrictionHayato Takeda0Jun Akatsuka1Tomonari Kiriyama2Yuka Toyama3Yasushi Numata4Hiromu Morikawa5Kotaro Tsutsumi6Mami Takadate7Hiroya Hasegawa8Hikaru Mikami9Kotaro Obayashi10Yuki Endo11Takayuki Takahashi12Manabu Fukumoto13Ryuji Ohashi14Akira Shimizu15Go Kimura16Yukihiro Kondo17Yoichiro Yamamoto18Department of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Radiology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanPathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, JapanPathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, JapanPathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanPathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, JapanPathology Informatics Team, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, JapanDepartment of Integrated Diagnostic Pathology, Nippon Medical School, Tokyo 113-8603, JapanDepartment of Analytic Human Pathology, Nippon Medical School, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanDepartment of Urology, Nippon Medical School Hospital, Tokyo 113-8603, JapanProstate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low–intermediate prostate-specific antigen (PSA), which often includes aggressive cancers, is imperative. This study evaluated the predictive accuracy of deep learning analysis using multimodal medical data focused on clinically significant PCa in patients with PSA ≤ 20 ng/mL. Our cohort study included 178 consecutive patients who underwent ultrasound-guided prostate biopsy. Deep learning analyses were applied to predict clinically significant PCa. We generated receiver operating characteristic curves and calculated the corresponding area under the curve (AUC) to assess the prediction. The AUC of the integrated medical data using our multimodal deep learning approach was 0.878 (95% confidence interval [CI]: 0.772–0.984) in all patients without PSA restriction. Despite the reduced predictive ability of PSA when restricted to PSA ≤ 20 ng/mL (<i>n</i> = 122), the AUC was 0.862 (95% CI: 0.723–1.000), complemented by imaging data. In addition, we assessed clinical presentations and images belonging to representative false-negative and false-positive cases. Our multimodal deep learning approach assists physicians in determining treatment strategies by predicting clinically significant PCa in patients with PSA ≤ 20 ng/mL before biopsy, contributing to personalized medical workflows for PCa management.https://www.mdpi.com/1718-7729/31/11/530deep learningprostate cancerclinically significant prostate cancermultimodal dataPSA |
| spellingShingle | Hayato Takeda Jun Akatsuka Tomonari Kiriyama Yuka Toyama Yasushi Numata Hiromu Morikawa Kotaro Tsutsumi Mami Takadate Hiroya Hasegawa Hikaru Mikami Kotaro Obayashi Yuki Endo Takayuki Takahashi Manabu Fukumoto Ryuji Ohashi Akira Shimizu Go Kimura Yukihiro Kondo Yoichiro Yamamoto Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction Current Oncology deep learning prostate cancer clinically significant prostate cancer multimodal data PSA |
| title | Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction |
| title_full | Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction |
| title_fullStr | Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction |
| title_full_unstemmed | Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction |
| title_short | Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction |
| title_sort | clinically significant prostate cancer prediction using multimodal deep learning with prostate specific antigen restriction |
| topic | deep learning prostate cancer clinically significant prostate cancer multimodal data PSA |
| url | https://www.mdpi.com/1718-7729/31/11/530 |
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