Shrimali Raj Kumar, Chakraborty Santam, Bhattacharyya Tapesh, Mallick Indranil, Achari Rimpa Basu, Prasath Sriram, Arun B, Mahata Anurupa, Vidhya Shree M, Vishnupriya E, Chatterjee Sanjoy
1 Department of Radiation Oncology, Tata Medical Center , Rajarhat, Kolkata , India.
Br J Radiol. 2019 Feb;92(1094):20180431. doi: 10.1259/bjr.20180431. Epub 2018 Nov 9.
: Radiation planning for locally-advanced non-small cell lung cancer (NSCLC) can be time-consuming and iterative. Many cases cannot be planned satisfactorily using multisegment three-dimensional conformal radiotherapy (3DCRT). We sought to develop and validate a predictive model which could estimate the probability that acceptable target volume coverage would need intensity modulated radiotherapy (IMRT).
: Variables related to the planning target volume (PTV) and topography were identified heuristically. These included the PTV, it's craniocaudal extent, the ratio of PTV to total lung volume, distance of the centroid of the PTV from the spinal canal, and the extent PTV crossed the midline. Metrics were chosen such that they could be measured objectively, quickly and reproducibly. A logistic regression model was trained and validated on 202 patients with NSCLC. A group of patients who had both complex 3DCRT and IMRT planned was then used to derive the utility of the use of such a model in the clinic based on the time taken for planning such complex 3DCRT.
: Of the 202 patients, 93 received IMRT, as they had larger volumes crossing midline. The final model showed a good rank discrimination (Harrell's C-index 0.84) and low calibration error (mean absolute error of 0.014). Predictive accuracy in an external dataset was 92%. The final model was presented as a nomogram. Using this model, the dosimetrist can save a median planning time of 168 min per case.
: We developed and validated a data-driven, decision aid which can reproducibly determine the best planning technique for locally-advanced NSCLC.
: Our validated, data-driven decision aid can help the planner to determine the need for IMRT in locally advanced NSCLC saving significant planning time in the process.
局部晚期非小细胞肺癌（NSCLC）的放射治疗计划可能耗时且需反复进行。许多病例使用多野三维适形放疗（3DCRT）无法得到满意的计划。我们试图开发并验证一种预测模型，该模型可估计获得可接受的靶区体积覆盖所需调强放疗（IMRT）的概率。
通过启发式方法确定与计划靶区（PTV）和解剖结构相关的变量。这些变量包括PTV、其颅尾径范围、PTV与全肺体积之比、PTV质心距椎管的距离以及PTV越过中线的范围。选择的指标应能客观、快速且可重复地进行测量。在202例NSCLC患者中对逻辑回归模型进行训练和验证。然后，基于计划此类复杂3DCRT所需的时间，使用一组同时进行了复杂3DCRT和IMRT计划的患者来推导该模型在临床中的实用性。
在202例患者中，93例接受了IMRT，因为他们有更大的体积越过中线。最终模型显示出良好的排序判别能力（Harrell's C指数为0.84）和较低的校准误差（平均绝对误差为0.014）。外部数据集的预测准确率为92%。最终模型以列线图的形式呈现。使用该模型，剂量师可为每个病例节省平均168分钟的计划时间。
我们开发并验证了一种数据驱动的决策辅助工具，它可以可重复地确定局部晚期NSCLC的最佳放疗计划技术。
我们经过验证的数据驱动决策辅助工具可帮助计划者确定局部晚期NSCLC患者是否需要IMRT，在此过程中节省大量计划时间。
