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Exploring technical issues in personalized medicine: NSCLC survival prediction by quantitative image analysis-usefulness of density correction of volumetric CT data
- Publication Year :
- 2020
-
Abstract
- The aim of this study was to apply density correction method to the quantitative image analysis of non-small cell lung cancer (NSCLC) computed tomography (CT) images, determining its influence on overall survival (OS) prediction of surgically treated patients. Clinicopathological (CP) data and preoperative CT scans, pre- and post-contrast medium (CM) administration, of 57 surgically treated NSCLC patients, were retrospectively collected. After CT volumetric density measurement of primary gross tumour volume (GTV), aorta and tracheal air, density correction was conducted on GTV (reference values: aortic blood and tracheal air). For each resulting data set (combining CM administration and normalization), first-order statistical and textural features were extracted. CP and imaging data were correlated with patients 1-, 3- and 5-year OS, alone and combined (uni-/multivariate logistic regression and Akaike information criterion). Predictive performance was evaluated using the ROC curves and AUC values and compared among non-normalized/normalized data sets (DeLong test). The best predictive values were obtained when combining CP and imaging parameters (AUC values: 1 year 0.72; 3 years 0.82; 5 years 0.78). After normalization resulted an improvement in predicting 1-year OS for some of the grey level size zonebased features (large zone low grey level emphasis) and for the combined CP-imaging model, a worse performance for grey level co-occurrence matrix (cluster prominence and shade) and first-order statistical (range) parameters for 1- and 5-year OS, respectively. The negative performance of cluster prominence in predicting 1-year OS was the only statistically significant result (p value 0.05). Density corrections of volumetric CT data showed an opposite influence on the performance of imaging quantitative features in predicting OS of surgically treated NSCLC patients, even if no statistically significant for almost all predictors.
- Subjects :
- Normalization (statistics)
Adult
Male
Cone beam computed tomography
Lung Neoplasms
Biotechnology innovation
Correction methods
NSCLC
Personalized medicine
Radiomics
Textural analysis
Aged
Aged, 80 and over
Carcinoma, Non-Small-Cell Lung
Cone-Beam Computed Tomography
Contrast Media
Female
Humans
Middle Aged
Neoplasm Staging
Predictive Value of Tests
Radiographic Image Interpretation, Computer-Assisted
Retrospective Studies
Tumor Burden
Precision Medicine
Logistic regression
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Computer-Assisted
80 and over
Medicine
Radiology, Nuclear Medicine and imaging
Non-Small-Cell Lung
Neuroradiology
Settore MED/36 - DIAGNOSTICA PER IMMAGINI E RADIOTERAPIA
Receiver operating characteristic
business.industry
Carcinoma
Radiographic Image Interpretation
General Medicine
Data set
030220 oncology & carcinogenesis
Predictive value of tests
Akaike information criterion
business
Nuclear medicine
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....4cc6843195b2d97fe1c3fd5a04684dc4