Gloe Jacob N, Borisch Eric A, Froemming Adam T, Kawashima Akira, LeGout Jordan D, Nakai Hirotsugu, Takahashi Naoki, Riederer Stephen J
Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Department of Radiology, Mayo Clinic, Phoenix, AZ, USA.
Eur Radiol Exp. 2025 Apr 29;9(1):44. doi: 10.1186/s41747-025-00584-z.
T2-weighted images are a critical component of prostate magnetic resonance imaging (MRI), and it would be useful to automatically assess image quality (IQ) on a patient-specific basis without radiologist oversight.
This retrospective study comprised 1,412 axial T2-weighted prostate scans. Four experienced uroradiologists graded IQ using a 0-to-3 scale (0 = uninterpretable; 1 = marginally interpretable; 2 = adequately diagnostic; 3 = more than adequately diagnostic), binarized into nondiagnostic (IQ0 or IQ1), requiring rescanning, and diagnostic (IQ2 or IQ3), not requiring rescanning. The deep learning (DL) model was trained on 1,006 scans; 203 other scans were used for validation of multiple convolutional neural networks; the remaining 203 exams were used as a test set. 3D-DenseNet_169 was chosen among 11 models based on multiple evaluation criteria. The rescan predictions were compared to the number of rescans performed on a subset of 174 exams.
The model accurately predicts radiologist IQ scores (Cohen κ = 0.658), similar to the human inter-rater reliability (κ = 0.688-0.791). The model also predicts rescanning necessity similarly to radiologists: model κ = 0.537; reviewer κ = 0.577-0.703. The rescan model prediction area under the curve was 0.867.
The DL model showed a strong ability to differentiate diagnostic from nondiagnostic axial T2-weighted prostate images, accurately mimicking expert radiologists' IQ scores. Using the model, the clinical unnecessary rescan rate could be reduced from over 50% to less than 30%.
DL assessment of T2-weighted prostate MRI scans can accurately assess IQ, determining the need to repeat inadequate scans as well as avoiding repeat scans of those with adequate diagnostic quality, resulting in reduced unnecessary rescanning.
Artificial intelligence assessment of prostate MRI T2-weighted image quality can improve exam time management. The model showed over 75% accuracy in assessing prostate MRI T2-weighted image quality. Expert radiologists have a substantial agreement in evaluating prostate MRI T2-weighted image quality.
T2加权图像是前列腺磁共振成像(MRI)的关键组成部分,在无需放射科医生监督的情况下,基于患者个体自动评估图像质量(IQ)将很有用。
这项回顾性研究包括1412例轴向T2加权前列腺扫描。四位经验丰富的泌尿放射科医生使用0至3级评分(0 =无法解读;1 =勉强可解读;2 =诊断充分;3 =诊断非常充分)对IQ进行分级,分为需要重新扫描的非诊断性(IQ0或IQ1)和不需要重新扫描的诊断性(IQ2或IQ3)。深度学习(DL)模型在1006次扫描上进行训练;另外203次扫描用于验证多个卷积神经网络;其余203次检查用作测试集。基于多个评估标准,从11个模型中选择了3D-DenseNet_169。将重新扫描预测结果与174例检查子集中实际进行的重新扫描次数进行比较。
该模型能准确预测放射科医生的IQ评分(Cohen κ = 0.658),与人类阅片者间的可靠性相似(κ = 0.688 - 0.791)。该模型在预测是否需要重新扫描方面也与放射科医生相似:模型κ = 0.537;阅片者κ = 0.577 - 0.703。重新扫描模型预测的曲线下面积为0.867。
DL模型显示出很强的区分诊断性和非诊断性轴向T2加权前列腺图像的能力,能准确模拟专家放射科医生的IQ评分。使用该模型,临床不必要的重新扫描率可从超过50%降至不到30%。
对T2加权前列腺MRI扫描进行DL评估可准确评估IQ,确定是否需要重复质量不佳的扫描,并避免对诊断质量足够的扫描进行重复扫描,从而减少不必要的重新扫描。
人工智能对前列腺MRI T2加权图像质量的评估可改善检查时间管理。该模型在评估前列腺MRI T2加权图像质量方面的准确率超过75%。专家放射科医生在评估前列腺MRI T2加权图像质量方面有很高的一致性。