Predicting treatment response and survival in rectal cancer: insights from 18 FDG-PET/MRI post-neoadjuvant therapy

Accurate identification of patients with pathologic complete response (pCR) following neoadjuvant radiochemotherapy (RCT) for locally advanced rectal cancer (LARC) is essential. 18-FDG PET/MRI provides metabolic information that complements the morphological assessment of standard MRI, potentially enhancing the differentiation between fibrotic and tumorous tissues post-treatment. This study aims to evaluate the performance of 18-FDG PET/MRI in assessing treatment response compared to standard MRI.

A prospective study was conducted at HM Sanchinarro University Hospital, Madrid, from 2018 to 2021. Patients with LARC undergoing RCT were included and staged at diagnosis and restaged 8-12 weeks post-neoadjuvant treatment using 18-FDG PET/MRI. The primary outcome was to compare the performance of PET/MRI and standard MRI in detecting pCR and tumor regression grade (TRG) confirmed via histopathological examination. Quantitative analysis assessed the apparent diffusion coefficient (ADC) and standardized uptake value (SUV). A secondary outcome included survival analysis using the Kaplan-Meier method and Cox regression analysis for radiological and pathological prognostic markers.

Among 33 patients, pCR was observed in 45% (14/33). PET/MRI demonstrated sensitivity, specificity, and accuracy values of 0.88, 0.80, and 0.84, respectively, for detecting pCR, compared to 0.82, 0.50, and 0.67 for standard MRI (p < 0.001). PET/MRI accurately identified TRG stages in 72% of cases, compared to 50% for standard MRI. Post-SUV, post-ADC, and delta-ADC were the most precise PET/MRI predictors for pCR, with AUC values of 0.81, 0.75, and 0.55, respectively. Patients with mrEMVI and mrTRG showed worse disease-free survival (DFS).

18-FDG PET/MRI emerges as a promising imaging tool for predicting response to neoadjuvant treatment in rectal cancer, with superior diagnostic accuracy compared to standard MRI. Radiological findings, such as EMVI, can identify high-risk patients, offering valuable prognostic insights.