Maria Ciolina1, Valeria Vinci1, Laura Villani1, Silvia Gigli1, Matteo Saldari1, Pierluigi Benedetti Panici2, Giorgia Perniola2, Andrea Laghi3, Carlo Catalano1, Lucia Manganaro4. 1. Department of Radiological Sciences, Oncology and Pathology, Diagnostic Imaging Unit, "Sapienza" - University of Rome, Viale Regina Margherita, 328, 00100, Rome, Italy. 2. Department of Gynecology, Obstetrics and Urology, Sapienza University of Rome, Rome, Italy. 3. Department of Radiology, Sant' Andrea Hospital, Faculty of Medicine and Psychology, "Sapienza" University of Rome, Rome, Italy. 4. Department of Radiological Sciences, Oncology and Pathology, Diagnostic Imaging Unit, "Sapienza" - University of Rome, Viale Regina Margherita, 328, 00100, Rome, Italy. lucia.manganaro@uniroma1.it.
Abstract
INTRODUCTION: To determine the performance of texture analysis and conventional MRI parameters in predicting tumoral response to neoadjuvant chemotherapy and to assess whether a relationship exists between texture tissue heterogeneity and histological type of uterine cervix cancer. METHOD AND MATERIALS: Twenty-eight patients with local advanced cervical cancer (FIGO IB2-IIIB), underwent MRI before chemotherapy. Texture analysis parameters were quantified on T2-weighted sequences, as well as the maximum diameter expressed in mm. ADC values were obtained on the ADC map. Statistical analysis included unpaired t test and ROC curve. RESULTS: No statistical correlation was found between conventional parameters and response to NACT. Mean and skewness showed a strong correlation with the histological type: Adenocarcinomas presented higher mean and skewness values (69.8 ± 10.5 and 0.55 ± 0.19) in comparison with squamous cell carcinomas. Using a cutoff value ≥ 29 for mean it was possible to differentiate the two histological types with a sensitivity of 100% and a specificity of 81%. Kurtosis showed a positive correlation with tumor response to NACT resulting higher in responders (v.m. 5.7 ± 1.1) in comparison with non-responders (2.3 ± 0.5). The optimal Kurtosis cutoff value for the identification of non-responders tumors was ≤ 3.7 with a sensitivity of 92% and a specificity of 75%. CONCLUSION: Texture analysis applied to T2-weighted images of uterine cervical cancer exceeded the role of conventional prognostic factors in predicting tumoral response; moreover, they showed a potential role to differentiate histological tumor types.
INTRODUCTION: To determine the performance of texture analysis and conventional MRI parameters in predicting tumoral response to neoadjuvant chemotherapy and to assess whether a relationship exists between texture tissue heterogeneity and histological type of uterine cervix cancer. METHOD AND MATERIALS: Twenty-eight patients with local advanced cervical cancer (FIGO IB2-IIIB), underwent MRI before chemotherapy. Texture analysis parameters were quantified on T2-weighted sequences, as well as the maximum diameter expressed in mm. ADC values were obtained on the ADC map. Statistical analysis included unpaired t test and ROC curve. RESULTS: No statistical correlation was found between conventional parameters and response to NACT. Mean and skewness showed a strong correlation with the histological type: Adenocarcinomas presented higher mean and skewness values (69.8 ± 10.5 and 0.55 ± 0.19) in comparison with squamous cell carcinomas. Using a cutoff value ≥ 29 for mean it was possible to differentiate the two histological types with a sensitivity of 100% and a specificity of 81%. Kurtosis showed a positive correlation with tumor response to NACT resulting higher in responders (v.m. 5.7 ± 1.1) in comparison with non-responders (2.3 ± 0.5). The optimal Kurtosis cutoff value for the identification of non-responders tumors was ≤ 3.7 with a sensitivity of 92% and a specificity of 75%. CONCLUSION: Texture analysis applied to T2-weighted images of uterine cervical cancer exceeded the role of conventional prognostic factors in predicting tumoral response; moreover, they showed a potential role to differentiate histological tumor types.
Authors: Thida Win; Kenneth A Miles; Sam M Janes; Balaji Ganeshan; Manu Shastry; Raymondo Endozo; Marie Meagher; Robert I Shortman; Simon Wan; Irfan Kayani; Peter J Ell; Ashley M Groves Journal: Clin Cancer Res Date: 2013-05-09 Impact factor: 12.531
Authors: Jian Z Wang; Nina A Mayr; Dongqing Zhang; Kaile Li; John C Grecula; Joseph F Montebello; Simon S Lo; William T C Yuh Journal: Cancer Date: 2010-11-01 Impact factor: 6.860
Authors: Fergus Davnall; Connie S P Yip; Gunnar Ljungqvist; Mariyah Selmi; Francesca Ng; Bal Sanghera; Balaji Ganeshan; Kenneth A Miles; Gary J Cook; Vicky Goh Journal: Insights Imaging Date: 2012-10-24