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.

中文翻译：

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来自多参数 MRI 的 AI 衍生肿瘤体积和局限性前列腺癌的结果。


背景 一种基于人工智能 （AI） 的基于 MRI 数据测量前列腺内肿瘤体积的方法可能提供预后信息。目的 评估 AI 生成分割 （VAI） 的前列腺内肿瘤总体积是否为接受放射治疗 （RT） 或根治性前列腺切除术 （RP） 的局限性前列腺癌患者提供独立的预后信息。材料和方法 对于这项回顾性、单中心研究 （2021 年 1 月至 2023 年 8 月），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 的患者被随机分为交叉验证组和测试 RT 组。在为测试 RT 和 RP 组提供分割之前，训练 AI 分割算法以描绘交叉验证 RT 组中的前列腺影像报告和数据系统 （PI-RADS） 3-5 个病灶。Cox 回归模型用于评估 VAI 与转移时间之间的关联，并针对联合 RT （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 VAI 和美国国家综合癌症网络 （NCCN） 风险分类的受试者工作特征曲线下面积 （AUCs） 预测 5 年转移 （RP 组） 和 7 年转移 （联合 RT 组）。结果 共纳入 732 例患者 （联合放疗组 438 例患者;RP 组，294 名患者）。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）。VAI 与联合 RT 组的转移相关 （中位随访，6.9 年;调整后风险比 [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）。VAI 和 NCCN 风险类别的联合 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 5 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 分割病灶的体积是局限性前列腺癌的独立预后因素。© RSNA，2024 年本文提供补充材料。