Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning

Fellows’ Journal Club

The authors evaluated the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma (n=35) and enhancing glioma (n=71). The mean areas under the receiver operating characteristic curve were 0.877 for the support vector machine classifier; 0.878 for reader 1; 0.899 for reader 2; and 0.845 for reader 3. They conclude that support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.

Abstract

Figure 4 from paper
A, Axial contrast-enhanced T1-weighted image of a 51-year-old woman with a grade IV glioma. All 3 radiologists incorrectly classified the tumor as PCNSL, whereas the SVM classified it correctly in 92% of the trials. B and C, Axial contrast-enhanced T1WI of a 47-year-old woman with a grade IV glioma. All 3 radiologists incorrectly classified the tumor as PCNSL, whereas the SVM classifier provided the right diagnosis in 88% of the trials.

BACKGROUND AND PURPOSE

Accurate preoperative differentiation of primary central nervous system lymphoma and enhancing glioma is essential to avoid unnecessary neurosurgical resection in patients with primary central nervous system lymphoma. The purpose of the study was to evaluate the diagnostic performance of a machine-learning algorithm by using texture analysis of contrast-enhanced T1-weighted images for differentiation of primary central nervous system lymphoma and enhancing glioma.

MATERIALS AND METHODS

Seventy-one adult patients with enhancing gliomas and 35 adult patients with primary central nervous system lymphomas were included. The tumors were manually contoured on contrast-enhanced T1WI, and the resulting volumes of interest were mined for textural features and subjected to a support vector machine–based machine-learning protocol. Three readers classified the tumors independently on contrast-enhanced T1WI. Areas under the receiver operating characteristic curves were estimated for each reader and for the support vector machine classifier. A noninferiority test for diagnostic accuracy based on paired areas under the receiver operating characteristic curve was performed with a noninferiority margin of 0.15.

RESULTS

The mean areas under the receiver operating characteristic curve were 0.877 (95% CI, 0.798–0.955) for the support vector machine classifier; 0.878 (95% CI, 0.807–0.949) for reader 1; 0.899 (95% CI, 0.833–0.966) for reader 2; and 0.845 (95% CI, 0.757–0.933) for reader 3. The mean area under the receiver operating characteristic curve of the support vector machine classifier was significantly noninferior to the mean area under the curve of reader 1 (P = .021), reader 2 (P = .035), and reader 3 (P = .007).

CONCLUSIONS

Support vector machine classification based on textural features of contrast-enhanced T1WI is noninferior to expert human evaluation in the differentiation of primary central nervous system lymphoma and enhancing glioma.

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Differentiation of Enhancing Glioma and Primary Central Nervous System Lymphoma by Texture-Based Machine Learning
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jross
Jeffrey Ross • Mayo Clinic, Phoenix

Dr. Jeffrey S. Ross is a Professor of Radiology at the Mayo Clinic College of Medicine, and practices neuroradiology at the Mayo Clinic in Phoenix, Arizona. His publications include over 100 peer-reviewed articles, nearly 60 non-refereed articles, 33 book chapters, and 10 books. He was an AJNR Senior Editor from 2006-2015, is a member of the editorial board for 3 other journals, and a manuscript reviewer for 10 journals. He became Editor-in-Chief of the AJNR in July 2015. He received the Gold Medal Award from the ASSR in 2013.

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