TY - JOUR
T1 - Multiparametric MRI texture analysis in prediction of glioma biomarker status
T2 - Added value of MR diffusion
AU - Kihira, Shingo
AU - Tsankova, Nadejda M.
AU - Bauer, Adam
AU - Sakai, Yu
AU - Mahmoudi, Keon
AU - Zubizarreta, Nicole
AU - Houldsworth, Jane
AU - Khan, Fahad
AU - Salamon, Noriko
AU - Hormigo, Adilia
AU - Nael, Kambiz
N1 - Publisher Copyright:
© The Author(s) 2021.
PY - 2021/1/1
Y1 - 2021/1/1
N2 - Background. Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma. Methods. In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiveroperating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed. Results. From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR). Conclusion. Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.
AB - Background. Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma. Methods. In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known IDH1, EGFR, MGMT, ATRX, TP53, and PTEN status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiveroperating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed. Results. From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for IDH1, 0.99/80% for ATRX, 0.79/67% for MGMT, and 0.77/66% for EGFR. The addition of diffusion data to conventional MRI features significantly (P < .05) increased predictive performance for IDH1, MGMT, and ATRX. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (IDH1), 70% (ATRX), 70% (MGMT), and 75% (EGFR). Conclusion. Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.
KW - Glioma
KW - MR diffusion
KW - multiparametric MRI
KW - radiogenomics
KW - texture analysis
UR - http://www.scopus.com/inward/record.url?scp=85112270777&partnerID=8YFLogxK
U2 - 10.1093/noajnl/vdab051
DO - 10.1093/noajnl/vdab051
M3 - Article
AN - SCOPUS:85112270777
SN - 2632-2498
VL - 3
JO - Neuro-Oncology Advances
JF - Neuro-Oncology Advances
IS - 1
M1 - vdab051
ER -