AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer.
IF 12.1
1区 医学
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
David D Yang, Leslie K Lee, James M G Tsui, Jonathan E Leeman, Heather M McClure, Atchar Sudhyadhom, Christian V Guthier, Mary-Ellen Taplin, Quoc-Dien Trinh, Kent W Mouw, Neil E Martin, Peter F Orio, Paul L Nguyen, Anthony V D'Amico, Kee-Young Shin, Katie N Lee, Martin T King
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Abstract
Background An artificial intelligence (AI)-based method for measuring intraprostatic tumor volume based on data from MRI may provide prognostic information. Purpose To evaluate whether the total volume of intraprostatic tumor from AI-generated segmentations (VAI ) provides independent prognostic information in patients with localized prostate cancer treated with radiation therapy (RT) or radical prostatectomy (RP). Materials and Methods For this retrospective, single-center study (January 2021 to August 2023), patients with cT1-3N0M0 prostate cancer who underwent MRI and were treated with RT or RP were identified. Patients who underwent RT were randomly divided into cross-validation and test RT groups. An AI segmentation algorithm was trained to delineate Prostate Imaging Reporting and Data System (PI-RADS) 3-5 lesions in the cross-validation RT group before providing segmentations for the test RT and RP groups. Cox regression models were used to evaluate the association between VAI and time to metastasis and adjusted for clinical and radiologic factors for combined RT (ie, cross-validation RT and test RT) and RP groups. Areas under the receiver operating characteristic curve (AUCs) were calculated for VAI and National Comprehensive Cancer Network (NCCN) risk categorization for prediction of 5-year metastasis (RP group) and 7-year metastasis (combined RT group). Results Overall, 732 patients were included (combined RT group, 438 patients; RP group, 294 patients). Median ages were 68 years (IQR, 62-73 years) and 61 years (IQR, 56-66 years) for the combined RT group and the RP group, respectively. VAI was associated with metastasis in the combined RT group (median follow-up, 6.9 years; adjusted hazard ratio [AHR], 1.09 per milliliter increase; 95% CI: 1.04, 1.15; P = .001) and the RP group (median follow-up, 5.5 years; AHR, 1.22; 95% CI: 1.08, 1.39; P = .001). AUCs for 7-year metastasis for the combined RT group for VAI and NCCN risk category were 0.84 (95% CI: 0.74, 0.94) and 0.74 (95% CI: 0.80, 0.98), respectively (P = .02). Five-year AUCs for the RP group for VAI and NCCN risk category were 0.89 (95% CI: 0.80, 0.98) and 0.79 (95% CI: 0.64, 0.94), respectively (P = .25). Conclusion The volume of AI-segmented lesions was an independent, prognostic factor for localized prostate cancer. © RSNA, 2024 Supplemental material is available for this article.
多参数磁共振成像的 AI 导出肿瘤体积与局部前列腺癌的预后
背景 基于核磁共振成像数据的人工智能(AI)测量前列腺内肿瘤体积的方法可提供预后信息。目的 评估根据人工智能生成的分割(VAI)得出的前列腺内肿瘤总体积是否能为接受放疗(RT)或根治性前列腺切除术(RP)治疗的局部前列腺癌患者提供独立的预后信息。材料与方法 在这项回顾性单中心研究(2021 年 1 月至 2023 年 8 月)中,确定了接受 MRI 并接受 RT 或 RP 治疗的 cT1-3N0M0 前列腺癌患者。接受 RT 治疗的患者被随机分为交叉验证组和 RT 测试组。在为测试 RT 组和 RP 组提供分割之前,对人工智能分割算法进行了训练,以在交叉验证 RT 组中划分出前列腺成像报告和数据系统(PI-RADS)3-5 病灶。Cox回归模型用于评估VAI与转移时间之间的关系,并对联合RT组(即交叉验证RT和测试RT)和RP组的临床和放射学因素进行调整。计算了预测 5 年转移(RP 组)和 7 年转移(联合 RT 组)的 VAI 和美国国立综合癌症网络(NCCN)风险分类的接收者操作特征曲线下面积(AUC)。结果 共纳入 732 例患者(联合 RT 组,438 例;RP 组,294 例)。联合 RT 组和 RP 组的中位年龄分别为 68 岁(IQR,62-73 岁)和 61 岁(IQR,56-66 岁)。联合 RT 组(中位随访 6.9 年;调整后危险比 [AHR],每毫升增加 1.09;95% CI:1.04,1.15;P = .001)和 RP 组(中位随访 5.5 年;AHR,1.22;95% CI:1.08,1.39;P = .001)的 VAI 与转移相关。VAI和NCCN风险类别联合RT组7年转移的AUC分别为0.84(95% CI:0.74,0.94)和0.74(95% CI:0.80,0.98)(P = .02)。RP组VAI和NCCN风险类别的五年AUC分别为0.89(95% CI:0.80,0.98)和0.79(95% CI:0.64,0.94)(P = .25)。结论 AI 分区病灶的体积是局部前列腺癌的一个独立预后因素。© RSNA, 2024 这篇文章有补充材料。
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