Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer

By F. Tixier, C. L. Le Rest, Mathieu Hatt, N. Albarghach, O. Pradier, J. Metges, L. Corcos, Dimitris Visvikis. Journal of Nuclear Medicine, 2011

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18F-FDG PET is often used in clinical routine for diagnosis, staging, and response to therapy assessment or prediction. The standardized uptake value (SUV) in the primary or regional area is the most common quantitative measurement derived from PET images used for those purposes. The aim of this study was to propose and evaluate new parameters obtained by textural analysis of baseline PET scans for the prediction of therapy response in esophageal cancer. Methods: Forty-one patients with newly diagnosed esophageal cancer treated with combined radiochemotherapy were included in this study. All patients underwent pretreatment whole-body 18F-FDG PET. Patients were treated with radiotherapy and alkylatinlike agents (5-fluorouracil-cisplatin or 5-fluorouracil-carboplatin). Patients were classified as nonresponders (progressive or stable disease), partial responders, or complete responders according to the Response Evaluation Criteria in Solid Tumors. Different image-derived indices obtained from the pretreatment PET tumor images were considered. These included usual indices such as maximum SUV, peak SUV, and mean SUV and a total of 38 features (such as entropy, size, and magnitude of local and global heterogeneous and homogeneous tumor regions) extracted from the 5 different textures considered. The capacity of each parameter to classify patients with respect to response to therapy was assessed using the Kruskal–Wallis test (P < 0.05). Specificity and sensitivity (including 95% confidence intervals) for each of the studied parameters were derived using receiver-operating-characteristic curves. Results: Relationships between pairs of voxels, characterizing local tumor metabolic nonuniformities, were able to significantly differentiate all 3 patient groups (P < 0.0006). Regional measures of tumor characteristics, such as size of nonuniform metabolic regions and corresponding intensity nonuniformities within these regions, were also significant factors for prediction of response to therapy (P = 0.0002). Receiver-operating-characteristic curve analysis showed that tumor textural analysis can provide nonresponder, partial-responder, and complete-responder patient identification with higher sensitivity (76%–92%) than any SUV measurement. Conclusion: Textural features of tumor metabolic distribution extracted from baseline 18F-FDG PET images allow for the best stratification of esophageal carcinoma patients in the context of therapy-response prediction.

Subfields: Radiology, Nuclear Medicine and Imaging, Surgery

View Paper: https://www.semanticscholar.org/paper/1cacf3b37b71fddd4cbed4338a05aa670d1062ce

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Characterization of PET/CT images using texture analysis: the past, the present… any future? (2016, methodology): “The use of TA has beenwidespread inMR and CT imaging since the early 1990s [22, 23] and more recently (end of the 2000s) for PET intratumour heterogeneity characterization [15, 24, 25].”
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, methodology): “Notably, given the heterogeneity in acquisition and reconstruction of positron emission tomography images, caution has been urged in developing quantitative imaging models using positron emission tomography (60-63).”
The role of quantitative PET in predicting cancer treatment outcomes (2014, background): “Textural features were shown to predict response in cancers of the cervix [38], esophagus [65], head and neck [66], and lung [67].”
Radiomics: Extracting more information from medical images using advanced feature analysis (2012, background): “also explored the potential of SUV based, shape and texture features extracted from baseline FDG-PET, images, to assess response to therapy and prognosis in order to predict response to combined chemo-radiation treatment in oesophageal cancer.(16) Also, textural features in FDG PET images exhibited ”