Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity

Institution:
1Department of Radiology, HuaDong Hospital, Fudan University, Shanghai, China.
2Dana Farber Cancer Institute, Boston, MA, USA.
3Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
4Department of Radiology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
5Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Publication Date:
Dec-2017
Journal:
AJR Am J Roentgenol
Volume Number:
209
Issue Number:
6
Pages:
1216-27
Citation:
AJR Am J Roentgenol. 2017 Dec;209(6):1216-27.
PubMed ID:
29045176
PMCID:
PMC5718185
Keywords:
International Association for the Study of Lung Cancer guidelines, computer-aided diagnosis, ground-glass opacity lesions, imaging biomarkers
Appears in Collections:
NCIGT, SPL
Sponsors:
P41 EB015898/EB/NIBIB NIH HHS
Generated Citation:
Li M., Narayan V., Gill R.R., Jagannathan J.P., Barile M.F., Gao F., Bueno R., Jayender J. Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity. AJR Am J Roentgenol. 2017 Dec;209(6):1216-27. PMID: 29045176. PMCID: PMC5718185.
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OBJECTIVE: The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type. MATERIALS AND METHODS: The heterogeneity of 248 GGNs was quantified using custom software. Statistical analysis with a univariate Kruskal-Wallis test was performed to evaluate metrics for significant differences among the four GGN groups. The heterogeneity metrics were used to train a SVM to learn and predict the lesion type. RESULTS: Fifty of 57 and 51 of 57 heterogeneity metrics showed statistically significant differences among the four GGN groups on unenhanced and contrast-enhanced CT scans, respectively. The SVM predicted lesion type with greater accuracy than did three expert radiologists. The accuracy of classifying the GGNs into the four groups on the basis of the SVM algorithm was 70.9%, whereas the accuracy of the radiologists was 39.6%. The accuracy of SVM in classifying the AIS and MIA nodules was 73.1%, and the accuracy of the radiologists was 35.7%. For indolent versus invasive lesions, the accuracy of the SVM was 88.1%, and the accuracy of the radiologists was 60.8%. We found that contrast enhancement does not significantly improve the differential diagnosis of GGNs. CONCLUSION: Compared with the GGN classification done by the three radiologists, the SVM trained regarding all the heterogeneity metrics showed significantly higher accuracy in classifying the lesions into the four groups, differentiating between AIS and MIA and between indolent and invasive lesions. Contrast enhancement did not improve the differential diagnosis of GGNs.