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Biomed Res Int. 2018 Jun 13;2018:6803971. doi: 10.1155/2018/6803971. eCollection 2018.

Use of a Radiomics Model to Predict Tumor Invasiveness of Pulmonary Adenocarcinomas Appearing as Pulmonary Ground-Glass Nodules.

Author information

1
Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University, China.
2
Department of Thoracic Surgery, Hangzhou Hospital of Traditional Chinese Medicine, China.
3
Department of Radiology, Hangzhou Hospital of Traditional Chinese Medicine, China.
4
Department of Pathology, The First Affiliated Hospital, College of Medicine, Zhejiang University, China.
5
GE Healthcare, China.
6
Department of Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.

Abstract

Background:

It is important to distinguish the classification of lung adenocarcinoma. A radiomics model was developed to predict tumor invasiveness using quantitative and qualitative features of pulmonary ground-glass nodules (GGNs) on chest CT.

Materials and Methods:

A total of 599 GGNs [including 202 preinvasive lesions and 397 minimally invasive and invasive pulmonary adenocarcinomas (IPAs)] were evaluated using univariate, multivariate, and logistic regression analyses to construct a radiomics model that predicted invasiveness of GGNs. In primary cohort (comprised of patients scanned from August 2012 to July 2016), preinvasive lesions were distinguished from IPAs based on pure or mixed density (PM), lesion shape, lobulated border, and pleural retraction and 35 other quantitative parameters (P <0.05) using univariate analysis. Multivariate analysis showed that PM, lobulated border, pleural retraction, age, and fractal dimension (FD) were significantly different between preinvasive lesions and IPAs. After logistic regression analysis, PM and FD were used to develop a prediction nomogram. The validation cohort was comprised of patients scanned after Jan 2016.

Results:

The model showed good discrimination between preinvasive lesions and IPAs with an area under curve (AUC) of 0.76 [95% CI: 0.71 to 0.80] in ROC curve for the primary cohort. The nomogram also demonstrated good discrimination in the validation cohort with an AUC of 0.79 [95% CI: 0.71 to 0.88].

Conclusions:

For GGNs, PM, lobulated border, pleural retraction, age, and FD were features discriminating preinvasive lesions from IPAs. The radiomics model based upon PM and FD may predict the invasiveness of pulmonary adenocarcinomas appearing as GGNs.

PMID:
30009172
PMCID:
PMC6020660
DOI:
10.1155/2018/6803971
[Indexed for MEDLINE]
Free PMC Article

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