Details

Autor(en) / Beteiligte

• Zhang, Shun
• Chiang, Gloria Chia-Yi
• Magge, Rajiv S.
• Fine, Howard Alan
• Ramakrishna, Rohan
• Chang, Eileen Wang
• Pulisetty, Tejas
• Wang, Yi
• Zhu, Wenzhen
• Kovanlikaya, Ilhami

Titel

Texture analysis on conventional MRI images accurately predicts early malignant transformation of low-grade gliomas

Ist Teil von

• European radiology, 2019-06, Vol.29 (6), p.2751-2759

Ort / Verlag

Berlin/Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2019

Link zum Volltext

Quelle

SpringerLink (Online service)

Beschreibungen/Notizen

• Objectives Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. This study aimed to evaluate the feasibility of texture analysis on preoperative conventional MRI images in predicting early malignant transformation from low- to high-grade glioma and compare its utility to histogram analysis alone. Methods A total of 68 patients with low-grade glioma (LGG) were included in this study, 15 of which showed malignant transformation. Patients were randomly divided into training (60%) and testing (40%) sets. Texture analyses were performed to obtain the most discriminant factor (MDF) values for both training and testing data. Receiver operating characteristic (ROC) curve analyses were performed on MDF values and 9 histogram parameters in the training data to obtain cutoff values for determining the correct rates of discrimination between two groups in the testing data. Results The ROC analyses on MDF values resulted in an area under the curve (AUC) of 0.90 (sensitivity 85%, specificity 84%) for T2w FLAIR, 0.92 (86%, 94%) for ADC, 0.96 (97%, 84%) for T1w, and 0.82 (78%, 75%) for T1w + Gd and correctly discriminated between the two groups in 93%, 100%, 93%, and 92% of cases in testing data, respectively. In the astrocytoma subgroup, AUCs were 0.92 (88%, 83%) for T2w FLAIR and 0.90 (92%, 74%) for T1w + Gd and correctly discriminated two groups in 100% and 92% of cases. The MDF outperformed all 9 of the histogram parameters. Conclusion Texture analysis on conventional preoperative MRI images can accurately predict early malignant transformation of LGGs, which may guide therapeutic planning. Key Points • Texture analysis performed on MRI images can provide additional quantitative information that is invisible to human assessment. • Texture analysis based on conventional preoperative MR images can accurately predict early malignant transformation from low- to high-grade glioma. • Texture analysis is a clinically feasible technique that may provide an alternative and effective way of determining the likelihood of early malignant transformation and help guide therapeutic decisions.