Delta Radiomic Analysis of Mesorectum to Predict Treatment Response and Prognosis in Locally Advanced Rectal Cancer

Giuditta Chiloiro, Davide Cusumano, Angela Romano, Luca Boldrini, Giuseppe Nicolì*, Claudio Votta*, Huong Elena Tran*, Brunella Barbaro, Davide Carano*, Vincenzo Valentini, Maria Antonietta Gambacorta

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivista

Abstract

Simple Summary Early prediction of response to cancer therapies is critical for treatment personalisation. In patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiation therapy (nCRT), delta radiomics applied to mesorectal features could potentially lead to the development of predictive models of treatment response. Pre- and post-treatment MRIs of patients treated at a single institution were analysed. Radiomic features of the mesorectum and GTV were extracted and predictive models of pathological complete response (pCR) and two-year disease-free survival (2yDFS) were developed. In 203 patients with LARC, a total of 565 variables were evaluated; the best performing 2yDFS prediction model was based on one GTV and two mesorectal features with an AUC of 0.79 in the training set and 0.70 in the validation set. The mesorectum may contain important radiomics information for predicting response to nCRT treatment in LARC patients. Background: The aim of this study is to evaluate the delta radiomics approach based on mesorectal radiomic features to develop a model for predicting pathological complete response (pCR) and 2-year disease-free survival (2yDFS) in locally advanced rectal cancer (LARC) patients undergoing neoadjuvant chemoradiotherapy (nCRT). Methods: Pre- and post-nCRT MRIs of LARC patients treated at a single institution from May 2008 to November 2016 were retrospectively collected. Radiomic features were extracted from the GTV and mesorectum. The Wilcoxon-Mann-Whitney test and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the features in predicting pCR and 2yDFS. Results: Out of 203 LARC patients, a total of 565 variables were evaluated. The best performing pCR prediction model was based on two GTV features with an AUC of 0.80 in the training set and 0.69 in the validation set. The best performing 2yDFS prediction model was based on one GTV and two mesorectal features with an AUC of 0.79 in the training set and 0.70 in the validation set. Conclusions: The results of this study suggest a possible role for delta radiomics based on mesorectal features in the prediction of 2yDFS in patients with LARC.
Lingua originaleEnglish
pagine (da-a)N/A-N/A
RivistaCancers
Volume15
DOI
Stato di pubblicazionePubblicato - 2023

Keywords

  • early regression index
  • high-risk factors
  • mesorectal fact signatures
  • predictive models
  • radiomics
  • rectal cancer

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