Wavelets-Based Texture Analysis of Post Neoadjuvant Chemoradiotherapy Magnetic Resonance Imaging as a Tool for...

Background: Patients with locally advanced rectal cancer (LARC) treated by neoadjuvant chemoradiotherapy (nCRT) may experience pathological complete response (pCR). Tools that can identify pCR are required to define candidates suitable for the watch and wait (WW) strategy. Automated image analysis is used for predicting clinical aspects of diseases. Texture analysis of magnetic resonance imaging (MRI) wavelets algorithms provides a novel way to identify pCR. We aimed to evaluate wavelets-based image analysis of MRI for predicting pCR. Methods: MRI images of rectal cancer from 22 patients who underwent nCRT were captured at best representative views of the tumor. The MRI images were digitized and their texture was analyzed using different mother wavelets. Each mother wavelet was used to scan the image repeatedly at different frequencies. Based on these analyses, coefficients of similarity were calculated providing a variety of textural variables that were subsequently correlated with histopathology in each case. This allowed for proper identification of the best mother wavelets able to predict pCR. The predictive formula of complete response was computed using the independent statistical variables that were singled out by the multivariate regression model. Results: The statistical model used four wavelet variables to predict pCR with an accuracy of 100%, sensitivity of 100%, specificity of 100%, and PPV and NPV of 100%. Conclusions: Wavelet-transformed texture analysis of radiomic MRI can predict pCR in patients with LARC. It may provide a potential accurate surrogate method for the prediction of clinical outcomes of nCRT, resulting in an effective selection of patients amenable to WW.