Abdominal radiology (New York) · 2019
Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer.
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Wan Lijuan, Zhang Chongda, Zhao Qing, Meng Yankai, Zou Shuangmei, Yang Yang, Liu Yuan, Jiang Jun, Ye Feng, Ouyang Han, Zhao Xinming, Zhang Hongmei
Аннотация
PURPOSE: The aim of this study was to build an appropriate diagnostic model for predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC), by combining magnetic resonance imaging (MRI) parameters with clinical factors.
METHODS: Eighty-four patients with LARC who underwent MR examination before and after nCRT were enrolled in this study. MRI parameters including cylindrical approximated tumor volume (CATV) and relative signal intensity of tumor (rT2wSI) were measured; corresponding reduction rates (RR) were calculated; and MR tumor regression grade (mrTRG) and other conventional MRI parameters were assessed. Logistic regression with lasso regularization was performed and the appropriate prediction model for pCR was built up. An external cohort of thirty-six patients was used as the validation group for testing the model. Receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performance.
RESULTS: In the development and the validation group, 17 patients (20.2%) and 11 patients (30.6%), respectively, achieved pCR. Two CATV-related parameters (CATVpost, which is the CATV measured after nCRT and CATVRR), one rT2wSI-related parameter (rT2wSIRR), and mrTRG were the most important parameters for predicting pCR and were retained in the diagnostic model. In the development group, the area under the receiver-operating characteristic curve (AUC) for predicting pCR is 0.88 [95% confidence interval (CI) 0.78-0.97, p < 0.001], with a sensitivity of 82.4% and a specificity of 83.6%. In the validation group, the AUC is 0.84 (95% CI 0.70-0.98, p = 0.001), with a sensitivity of 81.8% and a specificity of 76.0%.
CONCLUSION: A diagnostic model including CATVpost, CATVRR, rT2wSIRR, and mrTRG was useful for predicting pCR after nCRT in patients with LARC and may be used as an effective organ-preservation strategy.
METHODS: Eighty-four patients with LARC who underwent MR examination before and after nCRT were enrolled in this study. MRI parameters including cylindrical approximated tumor volume (CATV) and relative signal intensity of tumor (rT2wSI) were measured; corresponding reduction rates (RR) were calculated; and MR tumor regression grade (mrTRG) and other conventional MRI parameters were assessed. Logistic regression with lasso regularization was performed and the appropriate prediction model for pCR was built up. An external cohort of thirty-six patients was used as the validation group for testing the model. Receiver-operating characteristic (ROC) analysis was used to assess the diagnostic performance.
RESULTS: In the development and the validation group, 17 patients (20.2%) and 11 patients (30.6%), respectively, achieved pCR. Two CATV-related parameters (CATVpost, which is the CATV measured after nCRT and CATVRR), one rT2wSI-related parameter (rT2wSIRR), and mrTRG were the most important parameters for predicting pCR and were retained in the diagnostic model. In the development group, the area under the receiver-operating characteristic curve (AUC) for predicting pCR is 0.88 [95% confidence interval (CI) 0.78-0.97, p < 0.001], with a sensitivity of 82.4% and a specificity of 83.6%. In the validation group, the AUC is 0.84 (95% CI 0.70-0.98, p = 0.001), with a sensitivity of 81.8% and a specificity of 76.0%.
CONCLUSION: A diagnostic model including CATVpost, CATVRR, rT2wSIRR, and mrTRG was useful for predicting pCR after nCRT in patients with LARC and may be used as an effective organ-preservation strategy.