Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI.
Purpose: To present fully automated DL-based prostate cancer detection system for prostate MRI. Methods: MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-...
| Published in: | Abdominal Radiology Vol. 47; no. 4; pp. 1425 - 1435 |
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| Main Authors: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Apr2022
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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=156445491&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156445491 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Apr2022 vid: 47 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 156445491 154977537 10.1007/s00261-022-03419-2 156445491 ppf: 1425 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep learning-based artificial intelligence for prostate cancer detection at biparametric MRI. aug: au: Mehralivand, Sherif Yang, Dong Harmon, Stephanie A. Xu, Daguang Xu, Ziyue Roth, Holger Masoudi, Samira Kesani, Deepak Lay, Nathan Merino, Maria J. Wood, Bradford J. Pinto, Peter A. Choyke, Peter L. Turkbey, Baris affil: Molecular Imaging Branch, NCI, NIH, Bethesda, MD, USA sug: ab: Purpose: To present fully automated DL-based prostate cancer detection system for prostate MRI. Methods: MRI scans from two institutions, were used for algorithm training, validation, testing. MRI-visible lesions were contoured by an experienced radiologist. All lesions were biopsied using MRI-TRUS-guidance. Lesions masks, histopathological results were used as ground truth labels to train UNet, AH-Net architectures for prostate cancer lesion detection, segmentation. Algorithm was trained to detect any prostate cancer ≥ ISUP1. Detection sensitivity, positive predictive values, mean number of false positive lesions per patient were used as performance metrics. Results: 525 patients were included for training, validation, testing of the algorithm. Dataset was split into training (n = 368, 70%), validation (n = 79, 15%), test (n = 78, 15%) cohorts. Dice coefficients in training, validation sets were 0.403, 0.307, respectively, for AHNet model compared to 0.372, 0.287, respectively, for UNet model. In validation set, detection sensitivity was 70.9%, PPV was 35.5%, mean number of false positive lesions/patient was 1.41 (range 0–6) for UNet model compared to 74.4% detection sensitivity, 47.8% PPV, mean number of false positive lesions/patient was 0.87 (range 0–5) for AHNet model. In test set, detection sensitivity for UNet was 72.8% compared to 63.0% for AHNet, mean number of false positive lesions/patient was 1.90 (range 0–7), 1.40 (range 0–6) in UNet, AHNet models, respectively. Conclusion: We developed a DL-based AI approach which predicts prostate cancer lesions at biparametric MRI with reasonable performance metrics. While false positive lesion calls remain as a challenge of AI-assisted detection algorithms, this system can be utilized as an adjunct tool by radiologists. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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