自动评估脑胶质瘤负担：一种用于全自动容积和二维测量的深度学习算法。

BACKGROUND: Longitudinal measurement of glioma burden with MRI is the basis for treatment response assessment. In this study, we developed a deep learning algorithm that automatically segments abnormal fluid attenuated inversion recovery (FLAIR) hyperintensity and contrast-enhancing tumor, quantitating tumor volumes as well as the product of maximum bidimensional diameters according to the Response Assessment in Neuro-Oncology (RANO) criteria (AutoRANO). METHODS: Two cohorts of patients were used for this study. One consisted of 843 preoperative MRIs from 843 patients with low- or high-grade gliomas from 4 institutions and the second consisted of 713 longitudinal postoperative MRI visits from 54 patients with newly diagnosed glioblastomas (each with 2 pretreatment "baseline" MRIs) from 1 institution. RESULTS: The automatically generated FLAIR hyperintensity volume, contrast-enhancing tumor volume, and AutoRANO were highly repeatable for the double-baseline visits, with an intraclass correlation coefficient (ICC) of 0.986, 0.991, and 0.977, respectively, on the cohort of postoperative GBM patients. Furthermore, there was high agreement between manually and automatically measured tumor volumes, with ICC values of 0.915, 0.924, and 0.965 for preoperative FLAIR hyperintensity, postoperative FLAIR hyperintensity, and postoperative contrast-enhancing tumor volumes, respectively. Lastly, the ICCs for comparing manually and automatically derived longitudinal changes in tumor burden were 0.917, 0.966, and 0.850 for FLAIR hyperintensity volume, contrast-enhancing tumor volume, and RANO measures, respectively. CONCLUSIONS: Our automated algorithm demonstrates potential utility for evaluating tumor burden in complex posttreatment settings, although further validation in multicenter clinical trials will be needed prior to widespread implementation.

背景：通过 MRI 对脑胶质瘤瘤负荷进行纵向测量是治疗反应评估的基础。在这项研究中，我们开发了一种深度学习算法，该算法可自动分割异常的液体衰减反转恢复（FLAIR）高信号和增强肿瘤，根据神经肿瘤学反应评估（RANO）标准（AutoRANO）定量测量肿瘤体积以及最大二维直径的乘积。

方法：本研究使用了两个患者队列。一个队列由来自 4 个机构的 843 名低级别或高级别脑胶质瘤患者的 843 次术前 MRI 组成，另一个队列由来自 1 个机构的 54 名新诊断为胶质母细胞瘤患者的 713 次纵向术后 MRI 随访组成（每个患者有 2 次预处理“基线”MRI）。

结果：在接受新诊断胶质母细胞瘤治疗的患者队列中，自动生成的 FLAIR 高信号体积、增强肿瘤体积和 AutoRANO 在双基线检查中具有高度的可重复性，其组内相关系数（ICC）分别为 0.986、0.991 和 0.977。此外，手动和自动测量的肿瘤体积之间具有高度一致性，术前 FLAIR 高信号、术后 FLAIR 高信号和术后增强肿瘤体积的 ICC 值分别为 0.915、0.924 和 0.965。最后，比较手动和自动测量肿瘤负荷纵向变化的 ICC 值分别为 0.917、0.966 和 0.850，用于 FLAIR 高信号体积、增强肿瘤体积和 RANO 测量。

结论：我们的自动算法在复杂的治疗后环境中评估肿瘤负荷具有潜在的应用价值，但在广泛实施之前，还需要在多中心临床试验中进一步验证。