Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer

Luigi Azario, Marco De Spirito, Vincenzo Valentini, Davide Cusumano, Nicola Dinapoli, Luca Boldrini, Giuditta Chiloiro, Roberto Gatta, Carlotta Masciocchi, Jacopo Lenkowicz, Calogero Casa', Andrea Damiani, Johan Van Soest, Andre Dekker, Philippe Lambin

Risultato della ricerca: Contributo in rivistaArticolo in rivista

42 Citazioni (Scopus)

Abstract

The aim of this study was to propose a methodology to investigate the tumour heterogeneity and evaluate its ability to predict pathologically complete response (pCR) after chemo-radiotherapy (CRT) in locally advanced rectal cancer (LARC). This approach consisted in normalising the pixel intensities of the tumour and identifying the different sub-regions using an intensity-based thresholding. The spatial organisation of these subpopulations was quantified using the fractal dimension (FD). This approach was implemented in a radiomic workflow and applied to 198 T2-weighted pre-treatment magnetic resonance (MR) images of LARC patients. Three types of features were extracted from the gross tumour volume (GTV): morphological, statistical and fractal features. Feature selection was performed using the Wilcoxon test and a logistic regression model was calculated to predict the pCR probability after CRT. The model was elaborated considering the patients treated in two institutions: Fondazione Policlinico Universitario “Agostino Gemelli” of Rome (173 cases, training set) and University Medical Centre of Maastricht (25 cases, validation set). The results obtained showed that the fractal parameters of the subpopulations have the highest performance in predicting pCR. The predictive model elaborated had an area under the curve (AUC) equal to 0.77 ± 0.07. The model reliability was confirmed by the validation set (AUC = 0.79 ± 0.09). This study suggests that the fractal analysis can play an important role in radiomics, providing valuable information not only about the GTV structure, but also about its inner subpopulations.
Lingua originaleEnglish
pagine (da-a)286-295
Numero di pagine10
RivistaLA RADIOLOGIA MEDICA
Volume123
DOI
Stato di pubblicazionePubblicato - 2018

Keywords

  • Fractals
  • Magnetic resonance imaging
  • Predictive model
  • Radiology, Nuclear Medicine and Imaging
  • Radiomics
  • Rectal cancer

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