Purpose

We aim to develop and validate a new adaptive method for prostate cancer radiation therapy (RT), using an offline strategy to improve treatment personalization by modeling the internal target volume on individual basis and account for the residual set-up uncertainties by robust optimization.

Methods and Materials

Twenty patients with intermediate-high prostate cancer treated with radical radiation therapy were enrolled. The first step of the offline adaptive RT strategy is the identification of a patient-specific internal target volume based on the kV cone beam computed tomography (kV-CBCT) data sets acquired during the first 5 fractions. The deformable image registration algorithm ANACONDA was used to propagate the clinical target volumes (CTVs) from the reference-planning computed tomography to the CBCTs; these contours were assessed by a radiation oncologist. In the second step, the internal target volume was used to replan the treatment using a min-max robust algorithm based on the worst scenario optimization. The CTV coverage and organs-at-risk sparing achieved with the robust plan (RP) were analyzed and compared with the original standard plan, calculating the dose distributions on the residual CBCTs.

Results

The RP was shown to achieve optimal coverage of the CTV even in the worst scenario, with significantly lower doses to the rectum and bladder. CTV coverage of the RP was statistically better than the standard plan in terms of D99 (P = .008) and D98 (P = .02). Statistically significant mean dose reduction and D2 reduction were noted for the rectum (P < .05) and bladder (P < .009). Moreover, the RP appeared to be less sensitive to bladder and rectal filling.

Conclusions

This adaptive strategy in prostate cancer radiation therapy is feasible and safe; it may be used to adapt the treatment with better target coverage and organs-at-risk sparing than standard planning target volume–based planning.

中文翻译：

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前列腺癌外束放射治疗的适应性策略：通过稳健优化管理几何不确定性。

目的

我们的目标是开发和验证一种新的前列腺癌放射治疗 (RT) 自适应方法，使用离线策略通过基于个体对内部目标体积进行建模来改善治疗个性化，并通过稳健的优化来解决剩余设置的不确定性。

方法和材料

招募了 20 名接受根治性放射治疗的中高前列腺癌患者。离线自适应 RT 策略的第一步是根据前 5 个部分期间获得的 kV 锥形束计算机断层扫描 (kV-CBCT) 数据集识别患者特定的内部目标体积。使用可变形图像配准算法 ANACONDA 将临床目标体积 (CTV) 从参考计划计算机断层扫描传播到 CBCT；这些轮廓由放射肿瘤学家评估。在第二步中，使用基于最坏场景优化的最小-最大稳健算法，使用内部目标体积重新规划治疗。分析了稳健计划 (RP) 实现的 CTV 覆盖率和风险器官保留率，并与原始标准计划进行了比较，

结果

即使在最坏的情况下，RP 也能实现 CTV 的最佳覆盖，直肠和膀胱的剂量显着降低。就 D99 ( P = .008) 和 D98 ( P = .02)而言，RP 的 CTV 覆盖率在统计学上优于标准计划。直肠 ( P < .05) 和膀胱 ( P < .009) 的平均剂量减少和 D2 减少具有统计学意义。此外，RP 似乎对膀胱和直肠充盈不太敏感。

结论

这种前列腺癌放射治疗的适应性策略是可行且安全的；与基于目标体积的标准计划相比，它可用于调整治疗，使其具有更好的目标覆盖范围和风险器官保留率。