Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?
Abstract Background To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging...
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doaj-f3ab845e3ddf4f8bb8ffb3b36c6f3a752021-04-02T14:02:03ZengBMCCancer Imaging1470-73302019-06-011911810.1186/s40644-019-0223-7Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT?Subba R. Digumarthy0Atul M. Padole1Shivam Rastogi2Melissa Price3Meghan J. Mooradian4Lecia V. Sequist5Mannudeep K. Kalra6Department of Radiology, Massachusetts General HospitalDepartment of Radiology, Massachusetts General HospitalDepartment of Radiology, Massachusetts General HospitalDepartment of Radiology, Massachusetts General HospitalDepartment of Medicine, Massachusetts General HospitalDepartment of Medicine, Massachusetts General HospitalDepartment of Radiology, Massachusetts General HospitalAbstract Background To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Materials and methods The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software. Results Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT (P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505–0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605–0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001). Conclusions Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT.http://link.springer.com/article/10.1186/s40644-019-0223-7RadiomicsLung cancerSubsolid nodulesBenign and malignant lung nodulesChest CTFollow up CT |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Subba R. Digumarthy Atul M. Padole Shivam Rastogi Melissa Price Meghan J. Mooradian Lecia V. Sequist Mannudeep K. Kalra |
spellingShingle |
Subba R. Digumarthy Atul M. Padole Shivam Rastogi Melissa Price Meghan J. Mooradian Lecia V. Sequist Mannudeep K. Kalra Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? Cancer Imaging Radiomics Lung cancer Subsolid nodules Benign and malignant lung nodules Chest CT Follow up CT |
author_facet |
Subba R. Digumarthy Atul M. Padole Shivam Rastogi Melissa Price Meghan J. Mooradian Lecia V. Sequist Mannudeep K. Kalra |
author_sort |
Subba R. Digumarthy |
title |
Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? |
title_short |
Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? |
title_full |
Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? |
title_fullStr |
Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? |
title_full_unstemmed |
Predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up CT? |
title_sort |
predicting malignant potential of subsolid nodules: can radiomics preempt longitudinal follow up ct? |
publisher |
BMC |
series |
Cancer Imaging |
issn |
1470-7330 |
publishDate |
2019-06-01 |
description |
Abstract Background To assess if radiomics can differentiate benign and malignant subsolid lung nodules (SSNs) on baseline or follow up chest CT examinations. If radiomics can differentiate between benign and malignant subsolid lung nodules, the clinical implications are shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Materials and methods The IRB approved retrospective study included 36 patients (mean age 69 ± 8 years; 5 males, 31 females) with 108 SSNs (31benign, 77 malignant) who underwent follow up chest CT for evaluation of indeterminate SSN. All SSNs were identified on both baseline and follow up chest CT. DICOM CT images were deidentified and exported into the open access 3D Slicer software (version 4.7) to obtain radiomic features. Logistic regression analyses and receiver operating characteristic (ROC) curves for various quantitative parameters were generated with SPSS statistical software. Results Only 2/92 radiomic features (cluster shade and surface volume ratio) enabled differentiation between malignant and benign SSN on baseline chest CT (P = 0.01 and 0.03) with moderate accuracy [AUC 0.624 (0.505–0.743)]. On follow-up CT, 52/92 radiomic features were significantly different between benign and malignant SSN (P: 0.04 - < 0.0001) with improved accuracy [AUC: 0.708 (0.605–0.811), P = 0.04 - < 0.0001]. Radiomics of benign SSN were stable over time, whereas 63/92 radiomic features of malignant SSNs changed significantly between the baseline and follow up chest CT (P: 0.04 - < 0.0001). Conclusions Temporal changes in radiomic features of subsolid lung nodules favor malignant etiology over benign. The change in radiomics features of subsolid lung nodules can allow shorter follow up CT imaging and early recognition of lung adenocarcinoma on imaging. Radiomic features have limited application in differentiating benign and early malignant SSN on baseline chest CT. |
topic |
Radiomics Lung cancer Subsolid nodules Benign and malignant lung nodules Chest CT Follow up CT |
url |
http://link.springer.com/article/10.1186/s40644-019-0223-7 |
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