Feasibility of contrast-enhanced MRI derived textural features to predict overall survival in locally advanced breast cancer
| Autor: | Roja Hedayati, Peter Gibbs, Steinar Lundgren, Pål Erik Goa, Jose R. Teruel, Ioanna Chronaiou, Martin D. Pickles, Guro F. Giskeødegård, Else Marie Huuse, Beathe Sitter, Tone Frost Bathen |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Gadolinium DTPA medicine.medical_specialty CONTRAST ENHANCED MRI Locally advanced Contrast Media Breast Neoplasms Breast cancer Predictive Value of Tests Image Interpretation Computer-Assisted Overall survival medicine Humans Radiology Nuclear Medicine and imaging Aged Neoplasm Staging Retrospective Studies Aged 80 and over Radiological and Ultrasound Technology medicine.diagnostic_test Norway business.industry Magnetic resonance imaging General Medicine Middle Aged Prognosis medicine.disease Magnetic Resonance Imaging Survival Rate Feasibility Studies Female Radiology Neoplasm Grading business |
| Zdroj: | Acta Radiologica |
| ISSN: | 1600-0455 0284-1851 |
| DOI: | 10.1177/0284185119885116 |
| Popis: | Background The prognosis for women with locally advanced breast cancer (LABC) is poor and there is a need for better treatment stratification. Gray-level co-occurrence matrix (GLCM) texture analysis of magnetic resonance (MR) images has been shown to predict pathological response and could become useful in stratifying patients to more targeted treatments. Purpose To evaluate the ability of GLCM textural features obtained before neoadjuvant chemotherapy to predict overall survival (OS) seven years after diagnosis of patients with LABC. Material and Methods This retrospective study includes data from 55 patients with LABC. GLCM textural features were extracted from segmented tumors in pre-treatment dynamic contrast-enhanced 3-T MR images. Prediction of OS by GLCM textural features was assessed and compared to predictions using traditional clinical variables. Results Linear mixed-effect models showed significant differences in five GLCM features (f1, f2, f5, f10, f11) between survivors and non-survivors. Using discriminant analysis for prediction of survival, GLCM features from 2 min post-contrast images achieved a classification accuracy of 73% ( P 1, f2, f10, and f11 provided significantly different survival curves in Kaplan–Meier analysis. Conclusion This study shows a clear association between textural features from post-contrast images obtained before neoadjuvant chemotherapy and OS seven years after diagnosis. Further studies in larger cohorts should be undertaken to investigate how this prognostic information can be used to benefit treatment stratification. |
| Databáze: | OpenAIRE |
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