Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients.

Autor:	McKenney AS; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.; Department of Radiology, New York-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York., Weg E; Department of Radiation Oncology, University of Washington, Seattle, Washington., Bale TA; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York.; Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York., Wild AT; Department Southeast Radiation Oncology, Levine Cancer Institute, Charlotte, North Carolina., Um H; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York., Fox MJ; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York., Lin A; Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York.; Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York., Yang JT; Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, New York.; Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, New York., Yao P; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York., Birger ML; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York., Tixier F; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York., Sellitti M; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York., Moss NS; Department of Neurosurgery, Memorial Sloan Kettering Cancer Center, New York, New York.; Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York., Young RJ; Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York.; Department of Radiology, New York-Presbyterian Hospital/Weill Cornell Medical Center, New York, New York.; Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, New York., Veeraraghavan H; Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York.
Jazyk:	angličtina
Zdroj:	Advances in radiation oncology [Adv Radiat Oncol] 2022 Feb 06; Vol. 8 (1), pp. 100916. Date of Electronic Publication: 2022 Feb 06 (Print Publication: 2023).
DOI:	10.1016/j.adro.2022.100916
Abstrakt:	Purpose: Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma. Methods and Materials: We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression. A recursive feature elimination random forest classifier was built using nested cross-validation without and with O 6 -methylguanine-DNA methyltransferase status to predict pseudoprogression. Results: A classifier constructed with cross-validation on the training cohort achieved an area under the receiver operating curve of 81% for predicting pseudoprogression. This was further improved to 89% with the addition of O 6 -methylguanine-DNA methyltransferase status into the classifier. Conclusions: Our results suggest that radiomic analysis of contrast T1-weighted images and magnetic resonance imaging perfusion images can assist the prompt diagnosis of pseudoprogression. Validation on external and independent data sets is necessary to verify these advanced analyses, which can be performed on routinely acquired clinical images and may help inform clinical treatment decisions. (© 2022 The Authors.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu