Pre-Treatment T2-WI Based Radiomics Features for Prediction of Locally Advanced Rectal Cancer Non-Response to Neoadjuvant Chemoradiotherapy: A Preliminary Study

Abstract

Locally advanced rectal cancer (LARC) response to neoadjuvant chemoradiotherapy (nCRT) is very heterogeneous and up to 30% of patients are considered non-responders, presenting no tumor regression after nCRT. This study aimed to determine the ability of pre-treatment T2-weighted based radiomics features to predict LARC non-responders. A total of 67 LARC patients who underwent a pre-treatment MRI followed by nCRT and total mesorectal excision were assigned into training (n = 44) and validation (n = 23) groups. In both datasets, the patients were categorized according to the Ryan tumor regression grade (TRG) system into non-responders (TRG = 3) and responders (TRG 1 and 2). We extracted 960 radiomic features/patient from pre-treatment T2-weighted images. After a three-step feature selection process, including LASSO regression analysis, we built a radiomics score with seven radiomics features. This score was significantly higher among non-responders in both training and validation sets (p < 0.001 and p = 0.03) and it showed good predictive performance for LARC non-response, achieving an area under the curve (AUC) = 0.94 (95% CI: 0.82–0.99) in the training set and AUC = 0.80 (95% CI: 0.58–0.94) in the validation group. The multivariate analysis identified the radiomics score as an independent predictor for the tumor non-response (OR = 6.52, 95% CI: 1.87–22.72). Our results indicate that MRI radiomics features could be considered as potential imaging biomarkers for early prediction of LARC non-response to neoadjuvant treatment.

The Heterogeneity of Skewness in T2W-Based Radiomics Predicts the Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

“…The highest ratio (r = 9.4) can be found in the study of Cusumano et al, which, however, achieved a worse separation (AUC = 0.77), and the remaining studies had ratios ranging from r = 5.5 to r = 0.87—namely, more features than patients [ 7 , 28 ]. Though their results were sometimes a bit better than ours (AUC = 0.92 [ 30 ] and AUC = 0.94 [ 28 , 31 , 33 ]), some others were much worse (AUC = 0.72 [ 7 ], AUC = 0.75 [ 34 ] and AUC = 0.82 [ 32 ]). Although the majority of these studies carried out an external validation of the predictive model on a holdout test-set, their very low r values made the generalisability of their models questionable.…”

“…It follows that the use of radiomics in the evaluation of LARC, as described above, might also allow identifying the responsive patients and providing them with targeted therapies while differentiating the non-responsive patients who could beneficiate of intensified neoadjuvant treatment regimen as neoadjuvant mFOLFIRINOX before nCRT [ 9 ]. More specifically, various authors have recently addressed the prediction of nCRT responses based on clinical assessments or different TRG staging systems [ 7 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. However, the first relevant distinction of the present study is that in all those studies, the ratio r was far smaller than that in the present study.…”

Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment

“…Interestingly, the largest majority of the published MRI radiomics studies takes into account histogram features (considered alone or in more advanced models based also on textural, shape, and filtered ones), supporting systematic investigations in this direction [ 28 , 145 , 146 , 147 , 148 , 149 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 , 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 ].…”

A radiomics-based nomogram for preoperative T staging prediction of rectal cancer

“…Contrast-enhanced T1-weighted images are obtained by scanning after intravenous injection of a contrast agent to determine angiogenesis inside tumors. However, for staging of rectal cancer, contrast-enhanced T1-weighted imaging is not recommended as a routine sequence [39]. In our study, we extracted features from original and filtered images, including wavelet filter and LoG filter images.…”