Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images
Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images
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DOI：
10.1186/s12883-020-1613-y
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发表时间：
2020-02-07
期刊：
影响因子：
2.6
通讯作者：
Wang, Wen
中科院分区：
文献类型：
--
作者：
Zhao, Sha-Sha;Feng, Xiu-Long;Wang, Wen
背景医学影像学鉴别世界卫生组织（WHO）分级II级（ODG2）和III级（ODG3）少突胶质细胞瘤仍然是一个挑战。我们研究了机器学习与传统T1对比增强（T1 CE）和流体衰减反转恢复（FLAIR）磁共振成像（MRI）放射组学的结合是否具有更好的疗效。方法本研究回顾性招募2015年1月至2017年7月36例组织学证实的odg患者在任何干预前接受T1 CE检查，其中33例接受FLAIR MR检查。使用ITK-SNAP在T1 CE和FLAIR切片上逐层手工绘制覆盖整个肿瘤增强的感兴趣体积（volume of interest， VOI），并使用3d切片软件从VOI中提取共1072个特征。采用随机森林（RF）算法对ODG2和ODG3进行区分，并进行5倍交叉验证。比较基于放射组学的机器学习和放射科医师评估的诊断效果。结果本组ODG2 19例，ODG3 17例，ODG3多表现为明显坏死及结节/环状强化（P < 0.05）。T1 CE的AUC、ACC、敏感性和特异性分别为0.798、0.735、0.672、0.789，FLAIR为0.774、0.689、0.700、0.683，两者联合的AUC、ACC、敏感性和特异性分别为0.861、0.781、0.778、0.783。放射科医师1、2、3的auc分别为0.700、0.687、0.714。基于放射组学的机器学习的疗效优于放射科医生的评估。结论基于T1 CE和FLAIR放射组学的机器学习在鉴别ODG2和ODG3方面优于放射科医生。
Background The medical imaging to differentiate World Health Organization (WHO) grade II (ODG2) from III (ODG3) oligodendrogliomas still remains a challenge. We investigated whether combination of machine leaning with radiomics from conventional T1 contrast-enhanced (T1 CE) and fluid attenuated inversion recovery (FLAIR) magnetic resonance imaging (MRI) offered superior efficacy. Methods Thirty-six patients with histologically confirmed ODGs underwent T1 CE and 33 of them underwent FLAIR MR examination before any intervention from January 2015 to July 2017 were retrospectively recruited in the current study. The volume of interest (VOI) covering the whole tumor enhancement were manually drawn on the T1 CE and FLAIR slice by slice using ITK-SNAP and a total of 1072 features were extracted from the VOI using 3-D slicer software. Random forest (RF) algorithm was applied to differentiate ODG2 from ODG3 and the efficacy was tested with 5-fold cross validation. The diagnostic efficacy of radiomics-based machine learning and radiologist's assessment were also compared. Results Nineteen ODG2 and 17 ODG3 were included in this study and ODG3 tended to present with prominent necrosis and nodular/ring-like enhancement (P < 0.05). The AUC, ACC, sensitivity, and specificity of radiomics were 0.798, 0.735, 0.672, 0.789 for T1 CE, 0.774, 0.689, 0.700, 0.683 for FLAIR, as well as 0.861, 0.781, 0.778, 0.783 for the combination, respectively. The AUCs of radiologists 1, 2 and 3 were 0.700, 0.687, and 0.714, respectively. The efficacy of machine learning based on radiomics was superior to the radiologists' assessment. Conclusions Machine-learning based on radiomics of T1 CE and FLAIR offered superior efficacy to that of radiologists in differentiating ODG2 from ODG3.