Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement
Automatic assessment of glioma burden: a deep learning algorithm for fully automated volumetric and bidimensional measurement
复制标题
神经胶质瘤负担的自动评估：用于全自动体积和二维测量的深度学习算法
DOI：
10.1093/neuonc/noz106
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发表时间：
2019-11-01
期刊：
影响因子：
15.9
通讯作者：
Kalpathy-Cramer, Jayashree
中科院分区：
文献类型：
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作者：
Chang, Ken;Beers, Andrew L.;Kalpathy-Cramer, Jayashree
摘要 背景 MRI 纵向测量神经胶质瘤负荷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，可以自动分割异常液体衰减反转恢复（FLAIR）高信号和对比增强肿瘤，根据神经肿瘤学反应评估（RANO）标准（AutoRANO）定量肿瘤体积以及最大二维直径的乘积。方法 本研究采用两组患者。第一项包括来自 4 个机构的 843 名低级别或高级别胶质瘤患者的 843 次术前 MRI，第二项包括来自 1 个机构的 54 名新诊断的胶质母细胞瘤患者的 713 次纵向术后 MRI 就诊（每人有 2 次治疗前“基线”MRI）。结果 自动生成的 FLAIR 高信号体积、对比增强肿瘤体积和 AutoRANO 对于双基线就诊具有高度可重复性，术后 GBM 患者队列的组内相关系数 (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 高信号和术后对比增强肿瘤体积的 ICC 值分别为 0.915、0.924 和 0.965。最后，对于 FLAIR 高信号体积、对比增强肿瘤体积和 RANO 测量，用于比较手动和自动得出的肿瘤负荷纵向变化的 ICC 分别为 0.917、0.966 和 0.850。结论 我们的自动化算法展示了在复杂的治疗后环境中评估肿瘤负荷的潜在实用性，尽管在广泛实施之前还需要在多中心临床试验中进行进一步验证。
Abstract 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.