使用扩散峰度MRI的深度学习方法预测直肠癌对新辅助放化疗的反应 ... ...

背景 评估局部进展期直肠癌对术前新辅助放化疗的反应仍具有一定的挑战性。近年来，深度学习(DL)已广泛应用于肿瘤的诊断和治疗，并获得了较好的效果。目的开发并验证基于扩散峰度和T2W MRI的DL方法，用于预测直肠癌对新辅助治疗反应。

Background Preoperative response evaluation with neoadjuvant chemoradiotherapy remains a challenge in the setting of locally advanced rectal cancer. Recently, deep learning (DL) has been widely used in tumor diagnosis and treatment and has produced exciting results. Purpose To develop and validate a DL method to predict response of rectal cancer to neoadjuvant therapy based on diffusion kurtosis and T2-weighted MRI. 

此项前瞻性研究于2015年10月—2017年12月期间招募由组织病理学及基线MRI证实具有局部晚期直肠腺癌(原位≥3期或有淋巴结转移)并计划进行新辅助放化疗的病人。将这些病人按时间顺序分为308例的训练样本和104例的测试样本。构建DL模型首先用于预测病理完全缓解(pCR)，其次是评估肿瘤消退等级(TRG)(TRG0以及TRG1与TRG2和TRG3)和T分期。其他分析包括扩散峰度MRI参数的比较和放射科医生的主观评估。

Materials and Methods In this prospective study, participants with locally advanced rectal adenocarcinoma (≥cT3 or N ) proved at histopathology and baseline MRI who were scheduled to undergo preoperative chemoradiotherapy were enrolled from October 2015 to December 2017 and were chronologically divided into 308 training samples and 104 test samples. DL models were constructed primarily to predict pathologic complete response (pCR) and secondarily to assess tumor regression grade (TRG) (TRG0 and TRG1 vs TRG2 and TRG3) and T downstaging. Other analysis included comparisons of diffusion kurtosis MRI parameters and subjective evaluation by radiologists. 

共对383名参与者[平均年龄(57±10)岁，其中男229名;290名为训练集，93名为测试集]进行了评估。在测试集的pCR模型，受试者操作特征曲线下面积(AUC)为0.99，高于评估者1和2的AUC(分别为0.66和0.72;两者均P=0.001)。DL模型中TRG的AUC为0.70，T分期AUC为0.79。使用DL模型的pCR的AUC优于单独使用表现最佳的扩散峰度MRI参数[新辅助治疗前校正非高斯效应(Dapp值)后正常扩散的扩散系数]的AUC。在预测pCR方面，放射科医生的主观评估比使用DL模型[2/99(2.2%)]会产生更高的错误率(1-准确度)[评估者1和2分别为25/93(26.9%)和23/93(24.8%)];而在DL模型的辅助下，放射科医生的错误率较低[评估者1和2分别为12/93(12.9%)和13/93(14.0%)]。

Results A total of 383 participants (mean age, 57 years ± 10 [standard deviation]; 229 men) were evaluated (290 in the training cohort, 93 in the test cohort). The area under the receiver operating characteristic curve (AUC) was 0.99 for the pCR model in the test cohort, which was higher than the AUC for raters 1 and 2 (0.66 and 0.72, respectively; P < .001 for both). AUC for the DL model was 0.70 for TRG and 0.79 for T downstaging. AUC for pCR with the DL model was better than AUC for the best-performing diffusion kurtosis MRI parameters alone (diffusion coefficient in normal diffusion after correcting the non-Gaussian effect [Dapp value] before neoadjuvant therapy, AUC = 0.76). Subjective evaluation by radiologists yielded a higher error rate (1 - accuracy) (25 of 93 [26.9%] and 23 of 93 [24.8%] for raters 1 and 2, respectively) in predicting pCR than did evaluation with the DL model (two of 93 [2.2%]); the radiologists achieved a lower error rate (12 of 93 [12.9%] and 13 of 93 [14.0%] for raters 1 and 2, respectively) when assisted by the DL model.

基于扩散峰度MRI的DL模型在预测pCR方面表现良好，并能协助放射科医生评估局部进展期直肠癌新辅助放化疗后的反应。

Conclusion A deep learning model based on diffusion kurtosis MRI showed good performance for predicting pathologic complete response and aided the radiologist in assessing response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy.