Liu Xiaofeng, Shusharina Nadya, Shih Helen A, Kuo C-C Jay, El Fakhri Georges, Woo Jonghye
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 USA.
Dept. of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12927. doi: 10.1117/12.3006897. Epub 2024 Apr 3.
In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival.
在这项工作中，我们旨在基于术前磁共振（MR）扫描预测接受不同治疗的胶质母细胞瘤（GBM）患者的生存时间（ST）。通过比较不同治疗的生存时间，可以实现个性化且精确的治疗规划。众所周知，患者的当前状态（由MR扫描表示）和治疗选择都是生存时间的成因。虽然先前基于MR的胶质母细胞瘤生存时间研究仅专注于将MR扫描直接映射到生存时间，但它们并未考虑治疗与生存时间之间的潜在因果关系。为解决这一局限性，我们提出了一种用于胶质母细胞瘤生存时间的治疗条件回归模型，该模型除了MR扫描外还纳入了治疗信息。我们的方法使我们能够以统一的方式有效利用来自所有治疗的数据，而不必为每种治疗训练单独的模型。此外，通过我们采用的自适应实例归一化，可以将治疗有效地注入到每个卷积层中。我们在BraTS20生存时间预测任务上评估我们的框架。考虑了三种治疗方案：全切除（GTR）、次全切除（STR）和不切除。评估结果证明了注入治疗对估计GBM生存的有效性。
