Abstract：
OBJECTIVE: To assess impact of image quality on prostate cancer extraprostatic extension (EPE) detection on MRI using a deep learning-based AI algorithm.
METHODS: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher\'s exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.
RESULTS: A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).
CONCLUSIONS: Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.
METHODS: This retrospective, single institution study included patients who were imaged with mpMRI and subsequently underwent radical prostatectomy from June 2007 to August 2022. One genitourinary radiologist prospectively evaluated each patient using the NCI EPE grading system. Each T2WI was classified as low- or high-quality by a previously developed AI algorithm. Fisher\'s exact tests were performed to compare EPE detection metrics between low- and high-quality images. Univariable and multivariable analyses were conducted to assess the predictive value of image quality for pathological EPE.
RESULTS: A total of 773 consecutive patients (median age 61 [IQR 56-67] years) were evaluated. At radical prostatectomy, 23% (180/773) of patients had EPE at pathology, and 41% (131/318) of positive EPE calls on mpMRI were confirmed to have EPE. The AI algorithm classified 36% (280/773) of T2WIs as low-quality and 64% (493/773) as high-quality. For EPE grade ≥ 1, high-quality T2WI significantly improved specificity for EPE detection (72% [95% CI 67-76%] vs. 63% [95% CI 56-69%], P = 0.03), but did not significantly affect sensitivity (72% [95% CI 62-80%] vs. 75% [95% CI 63-85%]), positive predictive value (44% [95% CI 39-49%] vs. 38% [95% CI 32-43%]), or negative predictive value (89% [95% CI 86-92%] vs. 89% [95% CI 85-93%]). Sensitivity, specificity, PPV, and NPV for EPE grades ≥ 2 and ≥ 3 did not show significant differences attributable to imaging quality. For NCI EPE grade 1, high-quality images (OR 3.05, 95% CI 1.54-5.86; P < 0.001) demonstrated a stronger association with pathologic EPE than low-quality images (OR 1.76, 95% CI 0.63-4.24; P = 0.24).
CONCLUSIONS: Our study successfully employed a deep learning-based AI algorithm to classify image quality of prostate MRI and demonstrated that better quality T2WI was associated with more accurate prediction of EPE at final pathology.
摘要：
目的：使用基于深度学习的AI算法评估图像质量对MRI上前列腺癌前列腺外延伸（EPE）检测的影响。
方法：本回顾性研究，单机构研究纳入了2007年6月至2022年8月接受了mpMRI成像并随后接受根治性前列腺切除术的患者.一名泌尿生殖系统放射科医生使用NCIEPE分级系统对每位患者进行了前瞻性评估。每个T2WI被以前开发的AI算法分类为低质量或高质量。进行Fisher精确检验以比较低质量和高质量图像之间的EPE检测指标。进行单变量和多变量分析以评估图像质量对病理性EPE的预测价值。
结果：共评估了773名连续患者（中位年龄61[IQR56-67]岁）。在根治性前列腺切除术中，23%(180/773)的患者在病理上有EPE，并且在mpMRI上有41％（131/318）的EPE阳性呼叫被证实患有EPE。AI算法将36%（280/773）的T2WI分类为低质量，将64%（493/773）分类为高质量。对于EPE等级≥1，高质量T2WI显着提高了EPE检测的特异性（72％[95％CI67-76％]与63%[95%CI56-69%]，P=0.03),但没有显著影响敏感性(72%[95%CI62-80%]与75%[95%CI63-85%])，阳性预测值（44%[95%CI39-49%]与38%[95%CI32-43%])，或阴性预测值(89%[95%CI86-92%]与89%[95%CI85-93%]）。灵敏度,特异性，PPV,EPE≥2级和≥3级的NPV未显示出归因于成像质量的显着差异。对于NCI1级EPE，高质量图像（OR3.05，95％CI1.54-5.86；P<0.001）显示与病理性EPE的相关性强于低质量图像（OR1.76，95％CI0.63-4.24；P=0.24）。
结论：我们的研究成功地采用了基于深度学习的AI算法对前列腺MRI的图像质量进行分类，并证明了更好的T2WI质量与最终病理时更准确的EPE预测相关。
方法：本回顾性研究，单机构研究纳入了2007年6月至2022年8月接受了mpMRI成像并随后接受根治性前列腺切除术的患者.一名泌尿生殖系统放射科医生使用NCIEPE分级系统对每位患者进行了前瞻性评估。每个T2WI被以前开发的AI算法分类为低质量或高质量。进行Fisher精确检验以比较低质量和高质量图像之间的EPE检测指标。进行单变量和多变量分析以评估图像质量对病理性EPE的预测价值。
结果：共评估了773名连续患者（中位年龄61[IQR56-67]岁）。在根治性前列腺切除术中，23%(180/773)的患者在病理上有EPE，并且在mpMRI上有41％（131/318）的EPE阳性呼叫被证实患有EPE。AI算法将36%（280/773）的T2WI分类为低质量，将64%（493/773）分类为高质量。对于EPE等级≥1，高质量T2WI显着提高了EPE检测的特异性（72％[95％CI67-76％]与63%[95%CI56-69%]，P=0.03),但没有显著影响敏感性(72%[95%CI62-80%]与75%[95%CI63-85%])，阳性预测值（44%[95%CI39-49%]与38%[95%CI32-43%])，或阴性预测值(89%[95%CI86-92%]与89%[95%CI85-93%]）。灵敏度,特异性，PPV,EPE≥2级和≥3级的NPV未显示出归因于成像质量的显着差异。对于NCI1级EPE，高质量图像（OR3.05，95％CI1.54-5.86；P<0.001）显示与病理性EPE的相关性强于低质量图像（OR1.76，95％CI0.63-4.24；P=0.24）。
结论：我们的研究成功地采用了基于深度学习的AI算法对前列腺MRI的图像质量进行分类，并证明了更好的T2WI质量与最终病理时更准确的EPE预测相关。
