Univ Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099, F-35000, Rennes, France.
Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy; Università degli Studi di Pavia, Physics Department, Italy.
Radiother Oncol. 2020 Jun;147:40-49. doi: 10.1016/j.radonc.2020.02.028. Epub 2020 Mar 28.
To perform bladder dose-surface map (DSM) analysis for (1) identifying symptom-related sub-surfaces (Ssurf) and evaluating their prediction capability of urinary toxicity, (2) comparing DSM with dose-volume map (DVM) (method effect), and (3) assessing the reproducibility of DSM (cohort effect).
Urinary toxicities were prospectively analyzed for 254 prostate cancer patients treated with IMRT/IGRT at 78/80 Gy. DSMs were generated by unfolding bladder surfaces in a 2D plane. Pixel-by-pixel analysis was performed to identify symptom-related Ssurf. Likewise, voxel-by-voxel DVM analysis was performed to identify sub-volumes (Svol). The prediction capability of Ssurf and Svol DVHs was assessed by logistic/Cox regression using the area under the ROC curve (AUC). The Ssurf localization and prediction capability were compared to (1) the Svol obtained by DVM analysis in the same cohort and (2) the Ssurf obtained from other DSM studies.
Three Ssurf were identified in the bladder: posterior for acute retention (AUC = 0.64), posterior-superior for late retention (AUC = 0.68), and inferior-anterior-lateral for late dysuria (AUC = 0.73). Five Svol were identified: one in the urethra for acute incontinence and four in the posterior bladder part for acute and late retention, late dysuria, and hematuria. The overlap between Ssurf and Svol was moderate for acute retention, good for late retention, and bad for late dysuria, and AUCs ranged from 0.62 to 0.81. The prediction capabilities of Ssurf and Svol models were not significantly different. Among five symptoms comparable between cohorts, common Ssurf was found only for late dysuria, with a good spatial agreement.
Spatial agreement between methods is relatively good although DVM identified more sub-regions. Reproducibility of identified Ssurf between cohorts is low.
(1)通过识别与症状相关的亚表面(Ssurf)并评估其对尿毒性的预测能力,对膀胱剂量-表面图(DSM)进行分析;(2)比较 DSM 与剂量-体积图(DVM)(方法效应);(3)评估 DSM 的可重复性(队列效应)。
前瞻性分析了 254 例接受 78/80Gy 调强放疗/图像引导放疗的前列腺癌患者的尿毒性。通过在二维平面上展开膀胱表面生成 DSM。对像素进行逐点分析,以识别与症状相关的 Ssurf。同样,对体素进行逐点分析以识别亚体积(Svol)。使用 ROC 曲线下面积(AUC),通过逻辑/Cox 回归评估 Ssurf 和 SvolDVHs 的预测能力。通过与(1)同一队列中 DVM 分析获得的 Svol 和(2)来自其他 DSM 研究的 Ssurf 进行比较,评估 Ssurf 的定位和预测能力。
在膀胱中识别出三个 Ssurf:后急性潴留(AUC=0.64)、后上急性潴留(AUC=0.68)和下前外侧慢性尿潴留(AUC=0.73)。在五个 Svol 中,一个在尿道中与急性尿失禁相关,四个在后膀胱部分与急性和慢性潴留、慢性尿潴留和血尿相关。Ssurf 和 Svol 之间的重叠度适中,急性潴留,良好的晚潴留,和差的晚排尿困难,AUC 范围从 0.62 到 0.81。Ssurf 和 Svol 模型的预测能力无显著差异。在五个可比较的症状中,只有晚期排尿困难发现了共同的 Ssurf,具有良好的空间一致性。
尽管 DVM 确定了更多的亚区域,但方法之间的空间一致性相对较好。队列之间识别出的 Ssurf 的可重复性较低。