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. 2019 Jan 17:2019:8505798.
doi: 10.1155/2019/8505798. eCollection 2019.

Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation

Valerio Nardone  1   2 , Alfonso Reginelli  3 , Fernando Scala  3 , Salvatore Francesco Carbone  4 , Maria Antonietta Mazzei  4 , Lucio Sebaste  1   2 , Tommaso Carfagno  1   2 , Giuseppe Battaglia  1   2 , Pierpaolo Pastina  1   2 , Pierpaolo Correale  5 , Paolo Tini  1   2   6 , Gianluca Pellino  7 , Salvatore Cappabianca  3 , Luigi Pirtoli  1   2   8
Affiliations

Magnetic-Resonance-Imaging Texture Analysis Predicts Early Progression in Rectal Cancer Patients Undergoing Neoadjuvant Chemoradiation

Valerio Nardone et al. Gastroenterol Res Pract. .

Abstract

Background: We hypothesized that texture analysis (TA) from the preoperative MRI can predict early disease progression (ePD), defined as the percentage of patients who relapsed or showed distant metastasis within three months from the radical surgery, in patients with locally advanced rectal cancer (LARC, stage II and III, AJCC) undergoing neoadjuvant chemoradiotherapy (C-RT).

Methods: This retrospective monoinstitutional cohort study included 49 consecutive patients in total with a newly diagnosed rectal cancer. All the patients underwent baseline abdominal MRI and CT scan of the chest and abdomen to exclude distant metastasis before C-RT. Texture parameters were extracted from MRI performed before C-RT (T1, DWI, and ADC sequences) using LifeX Software, a dedicated software for extracting texture parameters from radiological imaging. We divided the cohort in a training set of 34 patients and a validation set of 15 patients, and we tested the data sets for homogeneity, considering the clinical variables. Then we performed univariate and multivariate analysis, and a ROC curve was also generated.

Results: Thirteen patients (26.5%) showed an ePD, three of whom with lung metastases and ten with liver relapse. The model was validated based on the prediction accuracy calculated in a previously unseen set of 15 patients. The prediction accuracy of the generated model was 82% (AUC = 0.853) in the training and 80% (AUC = 0.833) in the validation cohort. The only significant features at multivariate analysis was DWI GLCM Correlation (OR: 0.239, p < 0.001).

Conclusion: Our results suggest that TA could be useful to identify patients that may develop early progression.

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Figures

Figure 1
Figure 1
The gross tumor volume (GTV) was evaluated at baseline by MRI and contoured on T2 (a), DWI (b), and ADC (c) sequences.
Figure 2
Figure 2
ROC curve for the prediction of early progression of disease (ePD) (left) and Kaplan-Meier OS curve for the two subgroups (continuous line: no ePD, 108 ± 5 months, 95% CI 98-118 line, ePD: 38 ± 4 months, 95% CI 28-48 months, p < 0.001).
Figure 3
Figure 3
BoxPlot of the TA parameters in the two subgroups of patients who develop (ePD) or not develop (no ePD) early distant progression.
See this image and copyright information in PMC

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