Evaluation results of predicted lymph node areas.

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.

研究背景
全骨髓照射（Total marrow irradiation, TMI）与全骨髓淋巴照射（Total marrow and lymphoid irradiation, TMLI）具备临床应用优势。然而，依据TMI与TMLI计划勾画靶区不仅工作量繁重且耗时良久。此外，尽管TMI与TMLI的靶区勾画存在差异，但二者的临床界定尚不明确，且TMI实施过程中淋巴结（lymph node, LN）区域的照射覆盖范围仍未明确。为此，本研究依据TMI计划计算淋巴结区域的照射覆盖范围，并训练、评估一款用于淋巴结区域勾画的深度学习模型。

研究方法
本研究对接受TMI计划治疗的患者，手动勾画其全身区域性淋巴结区域，并评估TMI计划中已勾画淋巴结区域的剂量覆盖情况。为训练用于自动分割的深度学习模型，本研究从其他患者处获取额外的全身计算机断层扫描（computed tomography, CT）数据。将患者及数据划分为训练/验证集与测试集，并基于nnU-NET框架开发模型。采用戴斯相似系数（Dice similarity coefficient, DSC）、精确率、召回率以及95%豪斯多夫距离（Hausdorff distance 95, HD95）对训练完成的模型进行评估。同时测量并对比人工勾画靶区与基于深度学习模型预测结果的手动修整所需耗时。

研究结果
TMI计划对淋巴结区域的剂量覆盖中，接受处方剂量100%的体积占比（V100%）、V95%及V90%的中位数分别为46.0%、62.1%与73.5%。腹股沟淋巴结（14.7%）、髂外淋巴结（21.8%）以及腹主动脉旁淋巴结（42.8%）的V100%值最低。训练完成的模型，其戴斯相似系数、精确率、召回率及95%豪斯多夫距离的中位数分别为0.79、0.83、0.76与2.63。人工勾画靶区与基于预测结果的简易修整所需耗时，二者差异具有统计学意义。

研究结论
按照TMI计划实施治疗时，腹股沟、髂外及腹主动脉旁淋巴结区域的剂量覆盖情况未达最优。本研究证实，采用深度学习实现淋巴结区域的自动勾画，可助力全骨髓淋巴照射（TMLI）方案的临床实施。