Kawula Maria, Hadi Indrawati, Nierer Lukas, Vagni Marica, Cusumano Davide, Boldrini Luca, Placidi Lorenzo, Corradini Stefanie, Belka Claus, Landry Guillaume, Kurz Christopher
Department of Radiation Oncology, University Hospital, LMU Munich, Munich, Germany.
Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Rome, Italy.
Med Phys. 2023 Mar;50(3):1573-1585. doi: 10.1002/mp.16056. Epub 2022 Nov 7.
Online adaptive radiation therapy (RT) using hybrid magnetic resonance linear accelerators (MR-Linacs) can administer a tailored radiation dose at each treatment fraction. Daily MR imaging followed by organ and target segmentation adjustments allow to capture anatomical changes, improve target volume coverage, and reduce the risk of side effects. The introduction of automatic segmentation techniques could help to further improve the online adaptive workflow by shortening the re-contouring time and reducing intra- and inter-observer variability. In fractionated RT, prior knowledge, such as planning images and manual expert contours, is usually available before irradiation, but not used by current artificial intelligence-based autocontouring approaches.
The goal of this study was to train convolutional neural networks (CNNs) for automatic segmentation of bladder, rectum (organs at risk, OARs), and clinical target volume (CTV) for prostate cancer patients treated at 0.35 T MR-Linacs. Furthermore, we tested the CNNs generalization on data from independent facilities and compared them with the MR-Linac treatment planning system (TPS) propagated structures currently used in clinics. Finally, expert planning delineations were utilized for patient- (PS) and facility-specific (FS) transfer learning to improve auto-segmentation of CTV and OARs on fraction images.
In this study, data from fractionated treatments at 0.35 T MR-Linacs were leveraged to develop a 3D U-Net-based automatic segmentation. Cohort C1 had 73 planning images and cohort C2 had 19 planning and 240 fraction images. The baseline models (BMs) were trained solely on C1 planning data using 53 MRIs for training and 10 for validation. To assess their accuracy, the models were tested on three data subsets: (i) 10 C1 planning images not used for training, (ii) 19 C2 planning, and (iii) 240 C2 fraction images. BMs also served as a starting point for FS and PS transfer learning, where the planning images from C2 were used for network parameter fine tuning. The segmentation output of the different trained models was compared against expert ground truth by means of geometric metrics. Moreover, a trained physician graded the network segmentations as well as the segmentations propagated by the clinical TPS.
The BMs showed dice similarity coefficients (DSC) of 0.88(4) and 0.93(3) for the rectum and the bladder, respectively, independent of the facility. CTV segmentation with the BM was the best for intermediate- and high-risk cancer patients from C1 with DSC=0.84(5) and worst for C2 with DSC=0.74(7). The PS transfer learning brought a significant improvement in the CTV segmentation, yielding DSC=0.72(4) for post-prostatectomy and low-risk patients and DSC=0.88(5) for intermediate- and high-risk patients. The FS training did not improve the segmentation accuracy considerably. The physician's assessment of the TPS-propagated versus network-generated structures showed a clear advantage of the latter.
The obtained results showed that the presented segmentation technique has potential to improve automatic segmentation for MR-guided RT.
使用混合磁共振直线加速器（MR-Linacs）的在线自适应放射治疗（RT）可以在每个治疗分次中给予定制的放射剂量。每日进行磁共振成像，随后进行器官和靶区分割调整，能够捕捉解剖结构变化，改善靶区体积覆盖，并降低副作用风险。自动分割技术的引入有助于通过缩短重新勾画轮廓的时间并减少观察者内和观察者间的变异性，进一步改善在线自适应工作流程。在分次放射治疗中，诸如计划图像和专家手动勾画轮廓等先验知识通常在照射前就已具备，但当前基于人工智能的自动轮廓勾画方法并未加以利用。
本研究的目的是训练卷积神经网络（CNNs），用于对在0.35 T MR-Linacs上接受治疗的前列腺癌患者的膀胱、直肠（危及器官，OARs）和临床靶区体积（CTV）进行自动分割。此外，我们测试了卷积神经网络在来自独立机构的数据上的泛化能力，并将其与目前临床中使用的MR-Linac治疗计划系统（TPS）传播的结构进行比较。最后，利用专家计划轮廓进行患者特异性（PS）和机构特异性（FS）迁移学习，以改善分次图像上CTV和OARs的自动分割。
在本研究中，利用在0.35 T MR-Linacs上进行分次治疗的数据来开发基于3D U-Net的自动分割。队列C1有73幅计划图像，队列C2有19幅计划图像和240幅分次图像。基线模型（BMs）仅使用53幅磁共振图像进行训练、10幅进行验证，基于C1计划数据进行训练。为评估其准确性，模型在三个数据子集上进行测试：（i）10幅未用于训练的C1计划图像，（ii）19幅C2计划图像，以及（iii）240幅C2分次图像。BMs还作为FS和PS迁移学习的起点，其中来自C2的计划图像用于网络参数微调。通过几何指标将不同训练模型的分割输出与专家真值进行比较。此外，一名经过培训的医生对网络分割以及临床TPS传播的分割进行评分。
BMs对直肠和膀胱的骰子相似系数（DSC）分别为0.88(4)和0.93(3)，与机构无关。BM对C1中、高危癌症患者CTV分割效果最佳，DSC = 0.84(5)，对C2患者最差，DSC = 0.74(7)。PS迁移学习使CTV分割有显著改善，前列腺切除术后和低危患者的DSC为0.72(4)，中、高危患者的DSC为0.88(5)。FS训练并未显著提高分割准确性。医生对TPS传播结构与网络生成结构的评估显示后者具有明显优势。
所得结果表明，所提出的分割技术有潜力改善磁共振引导放射治疗的自动分割。
