Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.
Purpose: To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD. Methods: 1020 patients with a prostate MRI were randomly selected to develop a DL zon...
| Published in: | Abdominal Radiology Vol. 49; no. 10; pp. 3722 - 3735 |
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| Main Authors: | , , , , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Oct2024
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179574358&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179574358 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Oct2024 vid: 49 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179574358 177959534 10.1007/s00261-024-04301-z 179574358 ppf: 3722 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer. aug: au: Kuanar, Shiba Cai, Jason Nakai, Hirotsugu Nagayama, Hiroki Takahashi, Hiroaki LeGout, Jordan Kawashima, Akira Froemming, Adam Mynderse, Lance Dora, Chandler Humphreys, Mitchell Klug, Jason Korfiatis, Panagiotis Erickson, Bradley Takahashi, Naoki affil: https://ror.org/02qp3tb03 Department of Radiology, Mayo Clinic, 55905, Rochester, MN, USA sug: ab: Purpose: To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD. Methods: 1020 patients with a prostate MRI were randomly selected to develop a DL zonal segmentation model. Test dataset included 20 cases in which 2 radiologists manually segmented both the peripheral zone (PZ) and TZ. Pair-wise Dice index was calculated for each zone. For the prediction of csPCa using PSAD and TZ-PSAD, we used 3461 consecutive MRI exams performed in patients without a history of prostate cancer, with pathological confirmation and available PSA values, but not used in the development of the segmentation model as internal test set and 1460 MRI exams from PI-CAI challenge as external test set. PSAD and TZ-PSAD were calculated from the segmentation model output. The area under the receiver operating curve (AUC) was compared between PSAD and TZ-PSAD using univariate and multivariate analysis (adjusts age) with the DeLong test. Results: Dice scores of the model against two radiologists were 0.87/0.87 and 0.74/0.72 for TZ and PZ, while those between the two radiologists were 0.88 for TZ and 0.75 for PZ. For the prediction of csPCa, the AUCs of TZPSAD were significantly higher than those of PSAD in both internal test set (univariate analysis, 0.75 vs. 0.73, p < 0.001; multivariate analysis, 0.80 vs. 0.78, p < 0.001) and external test set (univariate analysis, 0.76 vs. 0.74, p < 0.001; multivariate analysis, 0.77 vs. 0.75, p < 0.001 in external test set). Conclusion: DL model-derived zonal segmentation facilitates the practical measurement of TZ-PSAD and shows it to be a slightly better predictor of csPCa compared to the conventional PSAD. Use of TZ-PSAD may increase the sensitivity of detecting csPCa by 2–5% for a commonly used specificity level. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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