NSCLC tumor shrinkage prediction using quantitative image features

Highlights • Lung tumors shrink during radiotherapy, with much variation between patients. • Pre-treatment CT images can be used to predict tumor shrinkage after treatment. • Potential uses include identifying patients who will benefit from adaptive radiation therapy.

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Bibliographische Detailangaben
Veröffentlicht in:	Computerized medical imaging and graphics 2016-04, Vol.49, p.29-36
Hauptverfasser:	Hunter, Luke A, Chen, Yi Pei, Zhang, Lifei, Matney, Jason E, Choi, Haesun, Kry, Stephen F, Martel, Mary K, Stingo, Francesco, Liao, Zhongxing, Gomez, Daniel, Yang, Jinzhong, Court, Laurence E
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Carcinoma, Non-Small-Cell Lung - diagnostic imaging Carcinoma, Non-Small-Cell Lung - radiotherapy Feature extraction Humans Internal Medicine Lung Neoplasms - diagnostic imaging Lung Neoplasms - radiotherapy Mathematical models Medical services NSCLC Other Patients Pattern Recognition, Automated - methods prediction Pretreatment Prognosis Quantitative image feature Radiographic Image Interpretation, Computer-Assisted - methods Radiotherapy, Image-Guided - methods Reproducibility of Results Sensitivity and Specificity Shrinkage Subtraction Technique texture Tomography, X-Ray Computed - methods Treatment Outcome Tumor Burden tumor shrinkage Tumors
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Beschreibung
Zusammenfassung:	Highlights • Lung tumors shrink during radiotherapy, with much variation between patients. • Pre-treatment CT images can be used to predict tumor shrinkage after treatment. • Potential uses include identifying patients who will benefit from adaptive radiation therapy.
ISSN:	0895-6111 1879-0771
DOI:	10.1016/j.compmedimag.2015.11.004