Ziyaee Hamidreza, Cardenas Carlos E, Yeboa D Nana, Li Jing, Ferguson Sherise D, Johnson Jason, Zhou Zijian, Sanders Jeremiah, Mumme Raymond, Court Laurence, Briere Tina, Yang Jinzhong
Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas.
Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama.
Adv Radiat Oncol. 2022 Sep 28;8(1):101085. doi: 10.1016/j.adro.2022.101085. eCollection 2023 Jan-Feb.
The clinical management of brain metastases after stereotactic radiosurgery (SRS) is difficult, because a physician must review follow-up magnetic resonance imaging (MRI) scans to determine treatment outcome, which is often labor intensive. The purpose of this study was to develop an automated framework to contour brain metastases in MRI to help treatment planning for SRS and understand its limitations.
Two self-adaptive nnU-Net models trained on postcontrast 3-dimensional T1-weighted MRI scans from patients who underwent SRS were analyzed. Performance was evaluated by computing positive predictive value (PPV), sensitivity, and Dice similarity coefficient (DSC). The training and testing sets included 3482 metastases on 845 patient MRI scans and 930 metastases on 206 patient MRI scans, respectively.
In the per-patient analysis, PPV was 90.1% ± 17.7%, sensitivity 88.4% ± 18.0%, DSC 82.2% ± 9.5%, and false positive (FP) 0.4 ± 1.0. For large metastases (≥6 mm), the per-patient PPV was 95.6% ± 17.5%, sensitivity 94.5% ± 18.1%, DSC 86.8% ± 7.5%, and FP 0.1 ± 0.4. The quality of autosegmented true-positive (TP) contours was also assessed by 2 physicians using a 5-point scale for clinical acceptability. Seventy-five percent of contours were assigned scores of 4 or 5, which shows that contours could be used as-is in clinical application, and the remaining 25% were assigned a score of 3, which means they needed minor editing only. Notably, a deep dive into FPs indicated that 9% were TP metastases not identified on the original radiology review, but identified on subsequent follow-up imaging (early detection). Fifty-four percent were real metastases (TP) that were identified but purposefully not contoured for target treatment, mainly because the patient underwent whole-brain radiation therapy before/after SRS treatment.
These findings show that our tool can help radiologists and radiation oncologists detect and contour tumors from MRI, make precise decisions about suspicious lesions, and potentially find lesions at early stages.
立体定向放射外科治疗（SRS）后脑转移瘤的临床管理具有挑战性，因为医生必须查看后续的磁共振成像（MRI）扫描来确定治疗效果，这通常需要耗费大量人力。本研究的目的是开发一个自动框架，用于在MRI中勾勒脑转移瘤，以辅助SRS的治疗规划并了解其局限性。
分析了两个在接受SRS治疗患者的增强三维T1加权MRI扫描上训练的自适应nnU-Net模型。通过计算阳性预测值（PPV）、敏感性和Dice相似系数（DSC）来评估性能。训练集和测试集分别包括来自845例患者MRI扫描的3482个转移瘤和来自206例患者MRI扫描的930个转移瘤。
在患者层面分析中，PPV为90.1%±17.7%，敏感性为88.4%±18.0%，DSC为82.2%±9.5%，假阳性（FP）为0.4±1.0。对于大转移瘤（≥6mm），患者层面的PPV为95.6%±17.5%，敏感性为94.5%±18.1%，DSC为86.8%±7.5%，FP为0.1±0.4。两名医生还使用5分制对自动分割的真阳性（TP）轮廓的临床可接受性进行了评估。75%的轮廓被评为4分或5分，这表明轮廓可直接用于临床应用，其余25%被评为3分，这意味着它们仅需进行少量编辑。值得注意的是，对FP的深入分析表明，9%是在原始放射学检查中未发现但在后续随访成像中发现的TP转移瘤（早期检测）。54%是已识别但因目标治疗而特意未勾勒轮廓的真正转移瘤（TP），主要是因为患者在SRS治疗之前/之后接受了全脑放疗。
这些结果表明，我们的工具可以帮助放射科医生和放射肿瘤学家从MRI中检测和勾勒肿瘤，对可疑病变做出精确决策，并可能在早期发现病变。
