ArticleQuantitative imaging in medicine and surgery2017
Quantitative texture analysis on pre-treatment computed tomography predicts local recurrence in stage I non-small cell lung cancer following stereotactic radiation therapy.
Article in Quantitative imaging in medicine and surgery, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 13 citations in OpenAlex.
- A Novel Radiomics Nomogram for the Prediction of Secondary Loss of Response to Infliximab in Crohn's Disease.Journal of inflammation research · 2021Article
- Three dimensional texture analysis of noncontrast chest CT in differentiating solitary solid lung squamous cell carcinoma from adenocarcinoma and correlation to immunohistochemical markers.Thoracic cancer · 2020Article
- Assessment of Clinical Stage IA Lung Adenocarcinoma with pN1/N2 Metastasis Using CT Quantitative Texture Analysis.Cancer management and research · 2020Article
- Article
- Quantitative features can predict further growth of persistent pure ground-glass nodule.Quantitative imaging in medicine and surgery · 2019Article
- Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.Quantitative imaging in medicine and surgery · 2019Article
- Prognostic Role Of Computed Tomography Textural Features In Early-Stage Non-Small Cell Lung Cancer Patients Receiving Stereotactic Body Radiotherapy.Cancer management and research · 2019Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe prediction of local recurrence (LR) of stage I non-small cell lung cancer (NSCLC) after definitive stereotactic body radiotherapy (SBRT) remains elusive. The purpose of this study was to assess whether quantitative imaging features on pre-treatment computed tomography (CT) can predict LR beyond 18 (
methodsThis retrospective study evaluated 36 patients with 37 stage I NSCLC who had local tumor control (LC; n=19) and (LR; n=18). Textural features were extracted on pre-treatment CT. Mann-Whitney U tests were used to compare LC and LR groups. Receiver-operating characteristic (ROC) curves were constructed and the area under the curve (AUC) calculated with LR as outcome.
resultsGray-level correlation and sum variance were greater in the LR group, compared with the LC group (P=0.02 and P=0.04, respectively). Gray-level difference variance was lower in the LR group (P=0.004). The logistic regression model generated using gray-level correlation and difference variance features resulted in AUC (SE) 0.77 (0.08) (P=0.0007). The addition of 18F-FDG PET/CT SUV
conclusionsCT textural features were found to be predictors of LR of early stage NSCLC on baseline CT prior to SBRT.
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