Copenhagen, Denmark
Onsite/Online

ESTRO 2022

Session Item

Radiomics, modelling and statistical methods
7011
Poster (digital)
Physics
Delta Radiomics can predict complete pathological response in rectal cancer patients
Antonio Angrisani, Italy
PO-1759

Abstract

Delta Radiomics can predict complete pathological response in rectal cancer patients
Authors:

Antonio Angrisani1, Teresa Di Pietro1, Emma D’Ippolito1, Valerio Nardone1, Angelo Sangiovanni1, Alfonso Reginelli2, Cesare Guida3, Salvatore Cappabianca1

1"L. Vanvitelli" University of Campania, Precision Medicine - Radiotherapy Unit, Naples, Italy; 2"L. Vanvitelli" University of Campania, Precision Medicine - Radiology Unit, Naples, Italy; 3Ospedale del Mare, Radiotherapy, Naples, Italy

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Purpose or Objective

The present study was designed to evaluate MRI delta texture analysis (D-TA) in predicting the outcome in terms of the complete pathological response of patients with locally advanced rectal cancer undergoing neoadjuvant chemoradiotherapy (C-RT) followed by surgery.

Material and Methods

We performed a retrospective analysis on 100 patients with locally advanced rectal adenocarcinoma undergoing C-RT and radical surgery in three different centers between January 2013 and December 2019. The gross tumor volume (GTV) was evaluated at both baselines and after C-RT MRI and contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted with LifeX Software, and D-TA was calculated as the percentage variations in the two-time points. By performing univariate analysis and multivariate analysis (logistic regression), these TA parameters were then correlated with patients' pathological outcomes. Complete pathological response (pCR, with no viable cancer cells: TRG 0) was chosen as the statistical end-point. ROC Curves were calculated on the three different datasets.

Results

In the whole cohort, 21 patients (21%) showed a pCR. At univariate analysis and binary logistic analysis, the only parameter that resulted significantly correlated with pCR in the Training dataset was ADC GLCM-Entropy. The binary logistic regression was repeated in the two Validation Dataset. AUC for pCR was 0.87 in the Training Dataset and respectively 0.92 and 0.88 in the two Validation Datasets.

Conclusion

Our results suggest that D-TA has a significant role in the prediction of pCR, thus this method may lead to select patients who may potentially avoid surgery. However, further analysis with prospective and multicenter trials is warranted.