Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features.

Affiliation

Imaging Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale 142 km 3.95, 10060, Candiolo, TO, Italy. [Email]

Abstract

OBJECTIVE : Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15-30% of cases, therefore it would be useful to assess if pretreatment of 18F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC.
METHODS : Fifty-two patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumor regression grade (TRG) as follows: 22 as pR+ (nine with TRG = 1, 13 with TRG = 2) and 30 as pR- (16 with TRG = 3, 13 with TRG = 4 and 1 with TRG = 5). First-order parameters and 21 second-order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semi-automatically segmented tumors on T2w MRI, ADC maps, and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models.
RESULTS : In the mono-parametric approach, PET homogeneity reached the maximum AUC (0.77; sensitivity = 72.7% and specificity = 76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing six second-order texture features (five from PET and one from T2w MRI) yields the highest predictivity in distinguish between pR+ and pR- patients (AUC = 0.86; sensitivity = 86%, and specificity = 83% at the Youden index).
CONCLUSIONS : If preliminary results of this study are confirmed, pretreatment PET and MRI could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.

Keywords

18F-FDG PET/CT imaging,Locally advanced rectal cancer,Magnetic resonance imaging,Prediction of treatment response,Radiomics,Texture features,