Francesco Giganti1, Paolo Marra2, Alessandro Ambrosi3, Annalaura Salerno2, Sofia Antunes4, Damiano Chiari5, Elena Orsenigo6, Antonio Esposito2, Elena Mazza7, Luca Albarello8, Roberto Nicoletti9, Carlo Staudacher5, Alessandro Del Maschio2, Francesco De Cobelli2. 1. Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy. Electronic address: giganti.fra@gmail.com. 2. Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy. 3. Vita-Salute San Raffaele University, Milan, Italy. 4. Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy. 5. Vita-Salute San Raffaele University, Milan, Italy; Department of Surgery, San Raffaele Scientific Institute, Milan, Italy. 6. Department of Surgery, San Raffaele Scientific Institute, Milan, Italy. 7. Department of Oncology, San Raffaele Scientific Institute, Milan, Italy. 8. Pathology Unit, San Raffaele Scientific Institute, Milan, Italy. 9. Department of Radiology and Experimental Imaging Centre, San Raffaele Scientific Institute, Milan, Italy.
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.
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.
Authors: Valerio Nardone; Paolo Tini; Stefania Croci; Salvatore Francesco Carbone; Lucio Sebaste; Tommaso Carfagno; Giuseppe Battaglia; Pierpaolo Pastina; Giovanni Rubino; Maria Antonietta Mazzei; Luigi Pirtoli Journal: Quant Imaging Med Surg Date: 2018-02