• Diagnostic performance of MRI for EMVI after preoperative chemoradiotherapy is good. • The mean DFS was lower in yMR-EMVI-positive than yMR-EMVI-negative patients. • MRI can facilitate prognosis prediction of rectal cancer patients with preoperative chemoradiotherapy.
Objective: To provide an evidence-based guide for the MRI interpretation of complete tumor response after neoadjuvant chemoradiation therapy (CRT) for rectal cancer using visual assessment on T2-weighted imaging (T2) and diffusion-weighted imaging (DWI). Materials and Methods: PubMed MEDLINE, EMBASE, and Cochrane Library were searched on November 28, 2019 to identify articles on the following issues: 1) sensitivity and specificity of T2 or DWI for diagnosing pathologic complete response (pCR) and the criteria for MRI diagnosis; 2) MRI alone vs. MRI combined with other test(s) in sensitivity and specificity for pCR; and 3) tests to select patients for the watch-and-wait management. Eligible articles were selected according to meticulous criteria and were synthesized. Results: Of 1615 article candidates, 55 eligible articles (for all three issues combined) were identified. Combined T2 and DWI performed better than T2 alone, with a meta-analytic summary sensitivity of 0.62 (95% confidence interval [CI], 0.43-0.77; I 2 = 80.60) and summary specificity of 0.89 (95% CI, 0.80-0.94; I 2 = 92.61) for diagnosing pCR. The criteria for the complete response on T2 in most studies had the commonality of remarkable tumor decrease to the absence of mass-like or nodular intermediate signal, although somewhat varied, as follows: (near) normalization of the wall; regular, thin, hypointense scar in the luminal side with (near) normal-appearance or homogeneous intermediate signal in the underlying wall; and hypointense thickening of the wall. The criteria on DWI were the absence of a hyperintense signal at high b-value (≥ 800 sec/mm 2 ) in most studies. The specific algorithm to combine T2 and DWI was obscure in half of the studies. MRI combined with endoscopy was the most utilized means to select patients for the watch-and-wait management despite a lack of strong evidence to guide and support a multi-test approach. Conclusion: This systematic review and meta-analysis provide an evidence-based practical guide for MRI assessment of complete tumor response after CRT for rectal cancer.
In preoperative imaging, the demarcation of rectal cancer with magnetic resonance images provides an important basis for cancer staging and treatment planning. Recently, deep learning has greatly improved the state-of-the-art method in automatic segmentation. However, limitations in data availability in the medical field can cause large variance and consequent overfitting to medical image segmentation networks. In this study, we propose methods to reduce the model variance of a rectal cancer segmentation network by adding a rectum segmentation task and performing data augmentation; the geometric correlation between the rectum and rectal cancer motivated the former approach. Moreover, we propose a method to perform a bias-variance analysis within an arbitrary region-of-interest (ROI) of a segmentation network, which we applied to assess the efficacy of our approaches in reducing model variance. As a result, adding a rectum segmentation task reduced the model variance of the rectal cancer segmentation network within tumor regions by a factor of 0.90; data augmentation further reduced the variance by a factor of 0.89. These approaches also reduced the training duration by a factor of 0.96 and a further factor of 0.78, respectively. Our approaches will improve the quality of rectal cancer staging by increasing the accuracy of its automatic demarcation and by providing rectum boundary information since rectal cancer staging requires the demarcation of both rectum and rectal cancer. Besides such clinical benefits, our method also enables segmentation networks to be assessed with bias-variance analysis within an arbitrary ROI, such as a cancerous region.
INDEX TERMSBias-variance analysis, data augmentation, image segmentation, magnetic resonance imaging (MRI), multi-task learning, neural networks, rectal cancer segmentation, rectum segmentation.
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