MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy
MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy
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DOI：
10.1148/radiol.2018172300
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发表时间：
2018-06-01
期刊：
影响因子：
19.7
通讯作者：
Petkovska, Iva
中科院分区：
文献类型：
--
作者：
Horvat, Natally;Veeraraghavan, Harini;Petkovska, Iva
目的：探讨基于t2加权的放射组学与t2加权和扩散加权（DW）成像定性评价在直肠癌新辅助化疗-放疗（CRT）后临床完全缓解诊断中的价值。材料与方法：本回顾性研究纳入2012年3月至2016年2月期间114例直肠癌患者在CRT后行磁共振（MR）成像。女性的中位年龄（114人中有47人，占41%）为55.9岁（四分位数范围为45.4-66.7岁），男性的中位年龄（114人中有67人，占59%）为55岁（四分位数范围为48-67岁）。手术组织病理学分析是病理完全缓解（pCR）的参考标准。在定性评估方面，两位放射科医生达成了共识。对于放射组学，一位放射科医生在高空间分辨率的t2加权图像上分割感兴趣的体积。随机森林分类器通过使用合成少数过采样技术平衡每个响应类别中的患者数量后，根据患者的结果进行分类。统计学分析采用Wilcoxon秩和检验、McNemar检验和Benjamini-Hochberg法。结果：114例患者中有21例（18%）实现pCR。放射组分类器的曲线下面积为0.93(95%可信区间[CI]: 0.87, 0.96)，灵敏度为100% (95% CI: 0.84, 1)，特异性为91% (95% CI: 0.84, 0.96)，阳性预测值为72% (95% CI: 0.53, 0.87)，阴性预测值为100% （95% CI: 0.96, 1）。放射组学的诊断效能显著高于单纯t2加权成像或DW成像的定性评估（P < 0.02）。放射组学的特异性和阳性预测值明显高于t2加权和DW联合成像（P < 0.0001）。结论：相对于t2加权和DW成像的定性评价，基于t2加权的放射组学对局部晚期直肠癌CRT术后pCR诊断具有更好的分类效果。(c) rsna, 2018。
Purpose: To investigate the value of T2-weighted-based radiomics compared with qualitative assessment at T2-weighted imaging and diffusion-weighted (DW) imaging for diagnosis of clinical complete response in patients with rectal cancer after neoadjuvant chemotherapy-radiation therapy (CRT).Materials and Methods: This retrospective study included 114 patients with rectal cancer who underwent magnetic resonance (MR) imaging after CRT between March 2012 and February 2016. Median age among women (47 of 114, 41%) was 55.9 years (interquartile range, 45.4-66.7 years) and median age among men (67 of 114, 59%) was 55 years (interquartile range, 48-67 years). Surgical histopathologic analysis was the reference standard for pathologic complete response (pCR). For qualitative assessment, two radiologists reached a consensus. For radiomics, one radiologist segmented the volume of interest on high-spatial-resolution T2-weighted images. A random forest classifier was trained to separate the patients by their outcomes after balancing the number of patients in each response category by using the synthetic minority oversampling technique. Statistical analysis was performed by using the Wilcoxon rank-sum test, McNemar test, and Benjamini-Hochberg method.Results: Twenty-one of 114 patients (18%) achieved pCR. The radiomic classifier demonstrated an area under the curve of 0.93 (95% confidence interval [CI]: 0.87, 0.96), sensitivity of 100% (95% CI: 0.84, 1), specificity of 91% (95% CI: 0.84, 0.96), positive predictive value of 72% (95% CI: 0.53, 0.87), and negative predictive value of 100% (95% CI: 0.96, 1). The diagnostic performance of radiomics was significantly higher than was qualitative assessment at T2-weighted imaging or DW imaging alone (P < .02). The specificity and positive predictive values were significantly higher in radiomics than were at combined T2-weighted and DW imaging (P < .0001).Conclusion: T2-weighted-based radiomics showed better classification performance compared with qualitative assessment at T2-weighted and DW imaging for diagnosing pCR in patients with locally advanced rectal cancer after CRT. (C) RSNA, 2018.