Shin Jaeseung, Seo Nieun, Baek Song-Ee, Son Nak-Hoon, Lim Joon Seok, Kim Nam Kyu, Koom Woong Sub, Kim Sungwon
From the Department of Radiology and Research Institute of Radiological Science, Severance Hospital, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul 03722, South Korea (J.S., N.S., S.E.B., J.S.L., S.K.); Data Science Team, Center for Digital Health, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, South Korea (N.H.S.); and Departments of Surgical Oncology (N.K.K.) and Radiation Oncology (W.S.K.), Yonsei University College of Medicine, Seoul, South Korea.
Radiology. 2022 May;303(2):351-358. doi: 10.1148/radiol.211986. Epub 2022 Feb 8.
Background Preoperative assessment of pathologic complete response (pCR) in locally advanced rectal cancer (LARC) after neoadjuvant chemoradiotherapy (nCRT) is increasingly needed for organ preservation, but large-scale validation of an MRI radiomics model remains lacking. Purpose To evaluate radiomics models based on T2-weighted imaging and diffusion-weighted MRI for predicting pCR after nCRT in LARC and compare their performance with visual assessment by radiologists. Materials and Methods This retrospective study included patients with LARC (clinical stage T3 or higher, positive nodal status, or both) who underwent post-nCRT MRI and elective resection between January 2009 and December 2018. Surgical histopathologic analysis was the reference standard for pCR. Radiomic features were extracted from the volume of interest on T2-weighted images and apparent diffusion coefficient (ADC) maps from post-nCRT MRI to generate three models: T2 weighted, ADC, and both T2 weighted and ADC (merged). Radiomics signatures were generated using the least absolute shrinkage and selection operator with tenfold cross-validation. Three experienced radiologists independently rated tumor regression grades at MRI and compared these with the radiomics models' diagnostic outcomes. Areas under the curve (AUCs) of the radiomics models and pooled readers were compared by using the DeLong method. Results Among 898 patients, 189 (21%) achieved pCR. The patients were chronologically divided into training ( = 592; mean age ± standard deviation, 59 years ± 12; 388 men) and test ( = 306; mean age, 59 years ± 12; 190 men) sets. The radiomics signatures of the T2-weighted, ADC, and merged models demonstrated AUCs of 0.82, 0.79, and 0.82, respectively, with no evidence of a difference found between the T2-weighted and merged models ( = .49), while the ADC model performed worse than the merged model ( = .02). The T2-weighted model had higher classification performance (AUC, 0.82 vs 0.74 [ = .009]) and sensitivity (80.0% vs 15.6% [ < .001]), but lower specificity (68.4% vs 98.6% [ < .001]) than the pooled performance of the three radiologists. Conclusion An MRI-based radiomics model showed better classification performance than experienced radiologists for diagnosing pathologic complete response in patients with locally advanced rectal cancer after neoadjuvant chemoradiotherapy. © RSNA, 2022 See also the editorial by Taylor in this issue.
背景 新辅助放化疗（nCRT）后局部晚期直肠癌（LARC）的病理完全缓解（pCR）的术前评估对于器官保留的需求日益增加，但MRI放射组学模型的大规模验证仍然缺乏。目的 评估基于T2加权成像和扩散加权MRI的放射组学模型，以预测LARC患者nCRT后的pCR，并将其性能与放射科医生的视觉评估进行比较。材料与方法 这项回顾性研究纳入了2009年1月至2018年12月期间接受nCRT后MRI检查和择期切除的LARC患者（临床分期为T3或更高、淋巴结阳性或两者皆有）。手术组织病理学分析是pCR的参考标准。从nCRT后MRI的T2加权图像和表观扩散系数（ADC）图上的感兴趣体积中提取放射组学特征，以生成三个模型：T2加权、ADC以及T2加权和ADC两者合并（合并）。使用最小绝对收缩和选择算子及十折交叉验证生成放射组学特征。三名经验丰富的放射科医生在MRI上独立评定肿瘤退缩分级，并将其与放射组学模型的诊断结果进行比较。使用DeLong方法比较放射组学模型和汇总读者的曲线下面积（AUC）。结果 在898例患者中，189例（21%）实现了pCR。患者按时间顺序分为训练组（n = 592；平均年龄±标准差，59岁±12岁；388名男性）和测试组（n = 306；平均年龄，59岁±12岁；190名男性）。T2加权、ADC和合并模型的放射组学特征的AUC分别为0.82、0.79和0.82，T2加权模型和合并模型之间未发现差异证据（P = 0.49），而ADC模型的表现比合并模型差（P = 0.02）。T2加权模型比三名放射科医生的汇总表现具有更高的分类性能（AUC，0.82对0.74 [P = 0.009]）和敏感性（80.0%对15.6% [P < 0.001]），但特异性较低（68.4%对98.6% [P < 0.001]）。结论 基于MRI的放射组学模型在诊断新辅助放化疗后局部晚期直肠癌患者的病理完全缓解方面显示出比经验丰富的放射科医生更好的分类性能。© RSNA，2022 另见本期Taylor的社论。
