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Apparent Diffusion Coefficient Predicts Pathology Complete Response of Rectal Cancer Treated with Neoadjuvant
Yuan-Gui Chen1, Ming-Qiu Chen1, Yu-Yan Guo1
1Department of Radiation Oncology, Fujian Medical University Union Hospital, Fuzhou City, Fujian Province, P.R. China.
Objective:
To evaluate the predictive value of the apparent diffusion coefficient (ADC) for pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer.
Methods:
A total of 265 patients with rectal adenocarcinoma, whole Diffusion-Weighted MRI (DWI-MRI) images, clinically stage II to III (cT3-4 and/or cN+) and treated with NCRT followed by TME were screened. Fifty patients with pCR and another 50 patients without pCR with similar clinical charcacters and treatment regimens were selected for statistical analysis. All the patients' pre-CRT and post-CRT average ADC values were calculated from the coefficient maps created by DWI-MRI and recorded independently. The difference in the ADC values between the pCR and non-pCR was analyzed by the Mann-Whitney U test. The cut-off ADC value of the receiver operating characteristic (ROC) curve with pCR was then established.
Results:
The mean pre- and post-ADC values in all patients, and in pCR patients and non-pCR patients were 0.879±0.06 and 1.383±0.11, 0.859±0.04 and 1.440±0.10, 0.899±0.07 and 1.325±0.09 (×10(-3) mm(2)/s), respectively. The difference between the pre- and post-ADC values in all patients, pCR patients, and non-pCR patients were considered to be statistically significant. The pre-ADC value was significantly lower in the pCR patients than in the non-pCR patients (p = 0.003), whereas the post-ADC values were significantly higher in the pCR patients than in the non-pCR patients. The percentage increase of the ADC value (ΔADC%) in the pCR and non-pCR patients were 68% and 48% respectively (p<0.001). The ROC curves of the cut-off value of the pre-CRT patient ADC value was 0.866×10(-3) mm(2)/s. The AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.670 (95% CI 0.563-0.777), 0.600, 0.640, 60%, 60%, and 60%, respectively. The cut-off value of ΔADC% was 58%. The corresponding AUC, sensitivity, specificity, PPV, NPV, and accuracy of diagnosing pCR were 0.856 (95% CI 0.783-0.930), 0.800, 0.760, 76.9%, 79.2%, and 78%, respectively.
Conclusions:
DWI-MRI technology can be efficient for predicting pCR for LARC after NCRT. Although the mean pre-CRT ADC value and the ΔADC% are moderate predictors for pCR, the latter would be more accurate.
