Melerowitz L, Sreenivasa S, Nachbar M, Stsefanenka A, Beck M, Senger C, Predescu N, Ullah Akram S, Budach V, Zips D, Heiland M, Nahles S, Stromberger C
Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Radiation Oncology, Augustenburger Platz 1, 13353, Berlin, Germany.
MVision AI, Paciuksenkatu 29 00270 Helsinki, Finland.
Clin Transl Radiat Oncol. 2024 Apr 18;47:100780. doi: 10.1016/j.ctro.2024.100780. eCollection 2024 Jul.
Current segmentation approaches for radiation treatment planning in head and neck cancer patients (HNCP) typically consider the entire mandible as an organ at risk, whereas segmentation of the maxilla remains uncommon. Accurate risk assessment for osteoradionecrosis (ORN) or implant-based dental rehabilitation after radiation therapy may require a nuanced analysis of dose distribution in specific mandibular and maxillary segments. Manual segmentation is time-consuming and inconsistent, and there is no definition of jaw subsections.
The mandible and maxilla were divided into 12 substructures. The model was developed from 82 computed tomography (CT) scans of HNCP and adopts an encoder-decoder three-dimensional (3D) U-Net structure. The efficiency and accuracy of the automated method were compared against manual segmentation on an additional set of 20 independent CT scans. The evaluation metrics used were the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and surface DSC (sDSC).
Automated segmentations were performed in a median of 86 s, compared to manual segmentations, which took a median of 53.5 min. The median DSC per substructure ranged from 0.81 to 0.91, and the median HD95 ranged from 1.61 to 4.22. The number of artifacts did not affect these scores. The maxillary substructures showed lower metrics than the mandibular substructures.
The jaw substructure segmentation demonstrated high accuracy, time efficiency, and promising results in CT scans with and without metal artifacts. This novel model could provide further investigation into dose relationships with ORN or dental implant failure in normal tissue complication prediction models.
目前头颈部癌症患者（HNCP）放射治疗计划的分割方法通常将整个下颌骨视为危险器官，而上颌骨的分割仍不常见。放疗后对放射性骨坏死（ORN）或基于种植体的牙齿修复进行准确的风险评估可能需要对特定下颌骨和上颌骨节段的剂量分布进行细致分析。手动分割既耗时又不一致，而且没有下颌骨亚节段的定义。
将下颌骨和上颌骨分为12个子结构。该模型由82例HNCP的计算机断层扫描（CT）图像构建而成，采用编码器 - 解码器三维（3D）U - Net结构。在另外一组20例独立的CT扫描图像上，将自动分割方法的效率和准确性与手动分割进行比较。使用的评估指标包括骰子相似系数（DSC）、95%豪斯多夫距离（HD95）和表面DSC（sDSC）。
自动分割的中位时间为86秒，而手动分割的中位时间为53.5分钟。每个子结构的中位DSC范围为0.81至0.91，中位HD95范围为1.61至4.22。伪影数量不影响这些分数。上颌骨子结构的指标低于下颌骨子结构。
下颌骨亚结构分割在有和没有金属伪影的CT扫描中均显示出高精度、时间效率和良好的结果。这种新型模型可为正常组织并发症预测模型中与ORN或牙种植体失败的剂量关系提供进一步研究。
