背景:全骨髓照射(TMI)和全骨髓和淋巴照射(TMLI)具有优势。然而,根据TMI和TMLI计划勾画靶病变是一项费力且耗时的工作.此外,尽管TMI和TMLI之间的靶病变的描绘不同,临床区别不明确,TMI期间的淋巴结(LN)面积覆盖率仍不确定。因此,本研究根据TMI计划计算LN区域覆盖率。Further,训练并评估了用于描绘LN区域的基于深度学习的模型。
方法:在根据TMI计划治疗的患者中,对全身区域LN区域进行手动轮廓绘制。估算了TMI计划中划定的LN区域的剂量覆盖率。为了训练用于自动分割的深度学习模型,我们从其他患者获得了其他全身计算机断层扫描数据.将患者和数据分为训练/验证和测试组,并使用“nnU-NET”框架开发模型。使用Dice相似系数(DSC)评估训练后的模型,精度,召回,和Hausdorff距离95(HD95)。测量并比较了使用深度学习模型手动绘制和修剪预测结果所需的时间。
结果:TMI计划对LN区域的剂量覆盖率为V100%(接受100%处方剂量的体积百分比),V95%,V90%的中值为46.0%,62.1%,73.5%,分别。最低的V100%值在腹股沟(14.7%),髂外(21.8%),和主动脉旁(42.8%)LN。DSC的中值,精度,召回,训练模型的HD95分别为0.79、0.83、0.76和2.63。手动轮廓绘制和简单修改的预测轮廓绘制的时间在统计学上有显着差异。
结论:腹股沟的剂量覆盖率,外髂关节,根据TMI计划进行治疗时,主动脉旁LN区域次优.这项研究表明,使用深度学习自动划定LN区域可以促进TMLI的实现。
BACKGROUND: Total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI) have the advantages. However, delineating target lesions according to TMI and TMLI plans is labor-intensive and time-consuming. In addition, although the delineation of target lesions between TMI and TMLI differs, the clinical distinction is not clear, and the lymph node (LN) area coverage during TMI remains uncertain. Accordingly, this study calculates the LN area coverage according to the TMI plan. Further, a deep learning-based model for delineating LN areas is trained and evaluated.
METHODS: Whole-body regional LN areas were manually contoured in patients treated according to a TMI plan. The dose coverage of the delineated LN areas in the TMI plan was estimated. To train the deep learning model for automatic segmentation, additional whole-body computed tomography data were obtained from other patients. The patients and data were divided into training/validation and test groups and models were developed using the \"nnU-NET\" framework. The trained models were evaluated using Dice similarity coefficient (DSC), precision, recall, and Hausdorff distance 95 (HD95). The time required to contour and trim predicted results manually using the deep learning model was measured and compared.
RESULTS: The dose coverage for LN areas by TMI plan had V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% median values of 46.0%, 62.1%, and 73.5%, respectively. The lowest V100% values were identified in the inguinal (14.7%), external iliac (21.8%), and para-aortic (42.8%) LNs. The median values of DSC, precision, recall, and HD95 of the trained model were 0.79, 0.83, 0.76, and 2.63, respectively. The time for manual contouring and simply modified predicted contouring were statistically significantly different.
CONCLUSIONS: The dose coverage in the inguinal, external iliac, and para-aortic LN areas was suboptimal when treatment is administered according to the TMI plan. This research demonstrates that the automatic delineation of LN areas using deep learning can facilitate the implementation of TMLI.