4.3 Article

Developing a prediction model based on MRI for pathological complete response after neoadjuvant chemoradiotherapy in locally advanced rectal cancer

期刊

ABDOMINAL RADIOLOGY
卷 44, 期 9, 页码 2978-2987

出版社

SPRINGER
DOI: 10.1007/s00261-019-02129-6

关键词

Rectal neoplasms; Magnetic resonance imaging; Magnetic resonance tumor regression grading; Neoadjuvant chemoradiotherapy; Pathological complete response

资金

  1. Special scientific research projects of Beijing science and technology project [Z16110000051610]
  2. Beijing hope marathon special fund [LC2016A05]
  3. Peking Union Medical College Youth Fund
  4. Fundamental Research Funds for the Central Universities [3332018078]
  5. Beijing Hope Run Special Fund of the Cancer Foundation of China [LC2017B18]

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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.

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