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Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis.
- Source :
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Radiological physics and technology [Radiol Phys Technol] 2018 Mar; Vol. 11 (1), pp. 27-35. Date of Electronic Publication: 2017 Dec 05. - Publication Year :
- 2018
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Abstract
- Radiomics, which involves the extraction of large numbers of quantitative features from medical images, has attracted attention in cancer research. In radiomics analysis, tumor segmentation is a crucial step. In this study, we evaluated the potential application of radiomics for predicting the histology of early stage non-small cell lung cancer (NSCLC) by analyzing interobserver variability in tumor delineation. Forty patient datasets were included in this study, 21 involving adenocarcinomas and 19 involving squamous cell carcinomas. All patients underwent stereotactic body radiotherapy treatment. In total, 476 features were extracted from each dataset, representing treatment planning, computed tomography images, and gross tumor volume (GTV). The definition of GTV can significantly affect the histology prediction. Therefore, in the present study, the effect of interobserver tumor delineation variability on radiomic features was evaluated by preparing 4 volumes of interest (VOIs) for each patient, as follows: the original GTV (which was delineated at treatment planning); two GTVs delineated retrospectively by radiation oncologists; and a semi-automatic GTV contoured by a medical physicist. Radiomic features extracted from each VOI were then analyzed using a naïve Bayesian model. Area-under-the-curve (AUC) analysis showed that interobserver variability in delineation is a significant factor in radiomics performance. Nevertheless, with 8 selected features, AUC values averaged over the VOIs were high (0.725 ± 0.070). The present study indicated that radiomics has potential for predicting early stage NSCLC histology despite variability in delineation. The high prediction accuracy implies that noninvasive histology evaluation by radiomics is a promising clinical application.
- Subjects :
- Adenocarcinoma classification
Adenocarcinoma diagnostic imaging
Aged
Aged, 80 and over
Bayes Theorem
Carcinoma, Non-Small-Cell Lung classification
Carcinoma, Non-Small-Cell Lung diagnostic imaging
Carcinoma, Squamous Cell classification
Carcinoma, Squamous Cell diagnostic imaging
Female
Humans
Lung Neoplasms classification
Lung Neoplasms diagnostic imaging
Male
Middle Aged
Prognosis
Tumor Burden
Adenocarcinoma pathology
Carcinoma, Non-Small-Cell Lung pathology
Carcinoma, Squamous Cell pathology
Lung Neoplasms pathology
Observer Variation
Radiometry methods
Tomography, X-Ray Computed methods
Subjects
Details
- Language :
- English
- ISSN :
- 1865-0341
- Volume :
- 11
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Radiological physics and technology
- Publication Type :
- Academic Journal
- Accession number :
- 29209915
- Full Text :
- https://doi.org/10.1007/s12194-017-0433-2