Pre-treatment MDCT-based texture analysis for therapy response prediction in gastric cancer: Comparison with tumour regression grade at final histology. Issue 90 (May 2017)

• Record Type: Journal Article
• Authors: Giganti, Francesco; Marra, Paolo; Ambrosi, Alessandro; Salerno, Annalaura; Antunes, Sofia; Chiari, Damiano; Orsenigo, Elena; Esposito, Antonio; Mazza, Elena; Albarello, Luca; Nicoletti, Roberto; Staudacher, Carlo; Del Maschio, Alessandro; De Cobelli, Francesco
• Is Part Of: European journal of radiology. Issue 90(2017)
• Page Start: 129
• Page End: 137
• Publication Date: 2017-05
• Subjects: DW-MRI Diffusion-weighted Magnetic Resonance Imaging -- MDCT Multi-detector computed tomography -- TRG tumour regression grade -- TNM tumour/node/metastasis -- ROC receiver operating characteristic -- AUC area under the curve -- LOOCV Leave One Out Cross Validation -- AIC Akaike Information Criteria -- ADC apparent diffusion coefficient; Gastric cancer -- Multi-detector computed tomography -- Texture analysis -- Neo-adjuvant therapy -- Tumour regression grade; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2017.02.043
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Texture analysis from MDCT can predict treatment response in gastric cancer. Texture analysis can improve the clinical workup of gastric cancer. Further studies are necessary before the application in clinical practice. Abstract: Purpose: An accurate prediction of tumour response to therapy is fundamental in oncology, so as to prompt personalised treatment options if needed. The aim of this study was to investigate the ability of preoperative texture analysis from multi-detector computed tomography (MDCT) in the prediction of the response rate to neo-adjuvant therapy in patients with gastric cancer. Material and methods: Thirty-four patients with biopsy-proven gastric cancer were examined by MDCT before neo-adjuvant therapy, and treated with radical surgery after treatment completion. Tumour regression grade (TRG) at final histology was also assessed. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. Patients with TRG 1–3 were considered responders while TRG 4–5 as non- responders . The response rate to neo-adjuvant therapy was assessed both at univariate and multivariate analysis. Results: Fourteen parameters were significantly different between the two subgroups at univariate analysis; in particular, entropy and compactness (higher in responders ) and uniformity (lower in responders ). According to our model, the following parameters could identify non-responders at multivariate analysis: entropy (≤6.86 with a logarithm of Odds Ratio − Log OR −: 4.11; p = 0.003); range (>158.72; Log OR: 3.67; p = 0.010) and root mean square (≤3.71; Log OR: 4.57; p = 0.005). Entropy and three-dimensional volume were not significantly correlated ( r = 0.06; p = 0.735). Conclusion: Pre-treatment texture analysis can potentially provide important information regarding the response rate to neo-adjuvant therapy for gastric cancer, improving risk stratification.