Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.

IF 2.3 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Abdominal Radiology Pub Date : 2024-10-01 Epub Date: 2024-06-19 DOI:10.1007/s00261-024-04301-z

Shiba Kuanar, Jason Cai, Hirotsugu Nakai, Hiroki Nagayama, Hiroaki Takahashi, Jordan LeGout, Akira Kawashima, Adam Froemming, Lance Mynderse, Chandler Dora, Mitchell Humphreys, Jason Klug, Panagiotis Korfiatis, Bradley Erickson, Naoki Takahashi

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引用次数: 0

Abstract

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.

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通过磁共振成像深度学习前列腺分区分割模型计算出的过渡区 PSA 密度，用于预测有临床意义的前列腺癌。

目的：从T2加权图像中开发前列腺MR的深度学习（DL）分区分割模型，并评估TZ-PSAD与PSAD相比在预测csPCa（Gleason评分7分或更高）存在方面的效果。方法：随机选取1020例前列腺MRI患者，开发DL分区分割模型。测试数据集包括 20 个病例，由 2 名放射科医生手动分割外周区（PZ）和 TZ。计算每个区域的配对 Dice 指数。在使用 PSAD 和 TZ-PSAD 预测 csPCa 时，我们使用了 3461 例无前列腺癌病史、有病理证实和 PSA 值但未用于开发分割模型的连续 MRI 检查作为内部测试集，并使用了来自 PI-CAI 挑战赛的 1460 例 MRI 检查作为外部测试集。PSAD 和 TZ-PSAD 由分割模型输出计算得出。利用单变量和多变量分析（调整年龄）以及 DeLong 检验比较了 PSAD 和 TZ-PSAD 的接收器操作曲线下面积（AUC）：TZ和PZ模型对两位放射科医生的Dice评分分别为0.87/0.87和0.74/0.72，而两位放射科医生对TZ和PZ的Dice评分分别为0.88和0.75。对于 csPCa 的预测，在两个内部测试集中，TZPSAD 的 AUCs 都明显高于 PSAD（单变量分析，0.75 vs. 0.73，p 结论：TZPSAD 的 AUCs 明显高于 PSAD）：DL 模型衍生的区域分割有助于 TZ-PSAD 的实际测量，与传统的 PSAD 相比，TZ-PSAD 对 csPCa 的预测效果略好。就常用的特异性水平而言，使用 TZ-PSAD 可将检测 csPCa 的灵敏度提高 2-5%。

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来源期刊

Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

5.20

自引率

8.30%

发文量

334

期刊介绍： Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits