TY - JOUR
T1 - Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer
AU - Dinapoli, Nicola
AU - Barbaro, Brunella
AU - Gatta, Roberto
AU - Chiloiro, Giuditta
AU - Casa, Calogero
AU - Masciocchi, Carlotta
AU - Damiani, Andrea
AU - Boldrini, Luca
AU - Gambacorta, Maria Antonietta
AU - Dezio, Michele
AU - Mattiucci, Gian Carlo
AU - Balducci, Mario
AU - van Soest, Johan
AU - Dekker, Andre
AU - Lambin, Philippe
AU - Fiorino, Claudio
AU - Sini, Carla
AU - De Cobelli, Francesco
AU - Di Muzio, Nadia
AU - Gumina, Calogero
AU - Passoni, Paolo
AU - Manfredi, Riccardo
AU - Valentini, Vincenzo
PY - 2018/11/15
Y1 - 2018/11/15
N2 - Purpose: The objective of this study is finding an intensity based histogram (IBH) signature to predict pathologic complete response (pCR) probability using only pre-treatment magnetic resonance (MR) and validate it externally in order to create a workflow for the external validation of an MR IBH signature and to apply the model out of the environment where it has been tuned. The impact of pCR and the final predictors on the survival outcome were also evaluated.Methods and Materials: Three centers using different MR scanners were involved in this retrospective study. The first center recruited 162 patients for model training, and the second and third centers provided 34 plus 25 patients for external validation. Patients provided written consent. Accrual period was from May 2008 to December 2014. After surgery pathologic response was defined. T2-weighted MR scans acquired before chemoradiation therapy (CRT) were used for analysis addressed on primary lesions. Images were pre-processed using Laplacian of Gaussian (LoG) filter with multiple s, and first order intensity histogram-based features (kurtosis, skewness, and entropy) were extracted. Features selection was performed using Mann-Whitney test. Tumor staging (cT, cN) was added to build a logistic regression model and predict pCR. Model performance was evaluated with internal and external validation using area under the curve (AUC) of the receiver operator characteristic (ROC) and calibration with Hosmer-Lemeshow test. The linear cross-correlation matrix (Pearson's coefficient) and the variance inflation factor (VIF) were used to check the correlation and the co-linearity among the final predictors. The amount of the information added through the radiomics features was estimated by using the DeLong's test, and the impact of pCR and the final predictors on survival outcomes were evaluated through the Kaplan-Meier curves by using the log-rank test and the multivariate Cox model.Results: Candidate-to-analysis features were skewness (s=0.485, P value=. 01) and entropy (s = 0.344, P valueConclusions: This MR-based, vendor-independent model can be helpful for predicting pCR probability in locally advanced rectal cancer (LARC) patients only using pre-treatment imaging. (C) 2018 Elsevier Inc. All rights reserved.
AB - Purpose: The objective of this study is finding an intensity based histogram (IBH) signature to predict pathologic complete response (pCR) probability using only pre-treatment magnetic resonance (MR) and validate it externally in order to create a workflow for the external validation of an MR IBH signature and to apply the model out of the environment where it has been tuned. The impact of pCR and the final predictors on the survival outcome were also evaluated.Methods and Materials: Three centers using different MR scanners were involved in this retrospective study. The first center recruited 162 patients for model training, and the second and third centers provided 34 plus 25 patients for external validation. Patients provided written consent. Accrual period was from May 2008 to December 2014. After surgery pathologic response was defined. T2-weighted MR scans acquired before chemoradiation therapy (CRT) were used for analysis addressed on primary lesions. Images were pre-processed using Laplacian of Gaussian (LoG) filter with multiple s, and first order intensity histogram-based features (kurtosis, skewness, and entropy) were extracted. Features selection was performed using Mann-Whitney test. Tumor staging (cT, cN) was added to build a logistic regression model and predict pCR. Model performance was evaluated with internal and external validation using area under the curve (AUC) of the receiver operator characteristic (ROC) and calibration with Hosmer-Lemeshow test. The linear cross-correlation matrix (Pearson's coefficient) and the variance inflation factor (VIF) were used to check the correlation and the co-linearity among the final predictors. The amount of the information added through the radiomics features was estimated by using the DeLong's test, and the impact of pCR and the final predictors on survival outcomes were evaluated through the Kaplan-Meier curves by using the log-rank test and the multivariate Cox model.Results: Candidate-to-analysis features were skewness (s=0.485, P value=. 01) and entropy (s = 0.344, P valueConclusions: This MR-based, vendor-independent model can be helpful for predicting pCR probability in locally advanced rectal cancer (LARC) patients only using pre-treatment imaging. (C) 2018 Elsevier Inc. All rights reserved.
KW - CT TEXTURE ANALYSIS
KW - POSTOPERATIVE CHEMORADIOTHERAPY
KW - NEOADJUVANT CHEMORADIOTHERAPY
KW - TUMOR HETEROGENEITY
KW - RADIATION-THERAPY
KW - CHEMOTHERAPY
KW - BIOMARKER
KW - SURVIVAL
U2 - 10.1016/j.ijrobp.2018.04.065
DO - 10.1016/j.ijrobp.2018.04.065
M3 - Article
C2 - 29891200
SN - 0360-3016
VL - 102
SP - 765
EP - 774
JO - International Journal of Radiation Oncology Biology Physics
JF - International Journal of Radiation Oncology Biology Physics
IS - 4
ER -