Body composition parameters combined with blood biomarkers and magnetic resonance imaging predict responses to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

Abstract

AimTo investigate whether body composition parameters combined with systemic inflammatory markers and magnetic resonance imaging (MRI) can predict the pathological complete response (pCR) following neoadjuvant chemoradiotherapy (NCRT) in locally advanced rectal cancer (LARC).MethodsA retrospective analysis of data on LARC patients treated with NCTR and radical surgery between January 2013 and May 2023 was performed. Body composition parameters were assessed by measuring the skeletal muscle index (SMI), subcutaneous adipose index (SAI), and visceral adipose index (VAI) at the third lumbar vertebra level by computed tomography (CT). Inflammatory markers such as neutrophil to lymphocyte ratio (NLR) were obtained from laboratory tests performed prior to NCRT. MRI was conducted to evaluate MRI tumor regression grading (mrTRG). Logistic regression analyses were employed to identify factors affecting the pCR. The risk score of pCR was computed by a nomogram. The discrimination of the nomogram was determined using C-index and calibration curve.ResultsTwo hundred and ninety-one patients with LARC were enrolled in the study, 55 (18.9%) of whom achieved pCR after NCRT. Multivariate analysis suggested that pre-NCRT NLR≥2.6 (OR=0.378, 95% CI 0.164-0.868, P=0.022), mrTRG 3-5 (OR=0.256, 95%CI 0.121-0.54, P<0.001), and pre-NCRT L-SMI (OR=0.292, 95% CI 0.097-0.883, P=0.029) were independent risk factors for pCR. ROC curves analysis demonstrated that the performance of mrTRG combined with pre-NCRT NLR and pre-NCRT L-SMI in predicting pCR was significantly improved compared with mrTRG alone (AUC: 0.763 vs. 0.667). Additionally, mrTRG 3-5 (OR=0.375, 95% CI 0.219-0.641, P<0.001) was also an independent predictor for poor tumor regression.ConclusionThe pathological complete response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer can be effectively predicted by combining the body composition parameters with blood biomarkers and magnetic resonance imaging.