Litcius/Paper detail

18F-FDG-PET/MRI texture analysis in rectal cancer after neoadjuvant chemoradiotherapy

Giulia Capelli, Cristina Campi, Quoc Riccardo Bao, Francesco Morra, Carmelo Lacognata, Pietro Zucchetta, Diego Cecchin, Salvatore Pucciarelli, Gaya Spolverato, Filippo Crimì

2022Nuclear Medicine Communications18 citationsDOIOpen Access PDF

Abstract

OBJECTIVE: Reliable markers to predict the response to neoadjuvant chemoradiotherapy (nCRT) in locally advanced rectal cancer (LARC) are lacking. We aimed to assess the ability of 18F-FDG PET/MRI to predict response to nCRT among patients undergoing curative-intent surgery. METHODS: Patients with histological-confirmed LARC who underwent curative-intent surgery following nCRT and restaging with 18F-FDG PET/MRI were included. Statistical correlation between radiomic features extracted in PET, apparent diffusion coefficient (ADC) and T2w images and patients' histopathologic response to chemoradiotherapy using a multivariable logistic regression model ROC-analysis. RESULTS: Overall, 50 patients were included in the study. A pathological complete response was achieved in 28.0% of patients. Considering second-order textural features, nine parameters showed a statistically significant difference between the two groups in ADC images, six parameters in PET images and four parameters in T2w images. Combining all the features selected for the three techniques in the same multivariate ROC curve analysis, we obtained an area under ROC curve of 0.863 (95% CI, 0.760-0.966), showing a sensitivity, specificity and accuracy at the Youden's index of 100% (14/14), 64% (23/36) and 74% (37/50), respectively. CONCLUSION: PET/MRI texture analysis seems to represent a valuable tool in the identification of rectal cancer patients with a complete pathological response to nCRT.

Topics & Concepts

MedicineYouden's J statisticColorectal cancerReceiver operating characteristicNeoadjuvant therapyChemoradiotherapyLogistic regressionPathologicalRadiologyEffective diffusion coefficientCancerNuclear medicineMagnetic resonance imagingInternal medicineRadiation therapyBreast cancerRadiomics and Machine Learning in Medical ImagingColorectal Cancer Surgical TreatmentsColorectal and Anal Carcinomas