We have located links that may give you full text access.
Multiparametric radiomic tissue signature and machine learning for distinguishing radiation necrosis from tumor progression after stereotactic radiosurgery.
Neuro-oncology advances. 2021 January
BACKGROUND: Stereotactic radiosurgery (SRS) may cause radiation necrosis (RN) that is difficult to distinguish from tumor progression (TP) by conventional MRI. We hypothesize that MRI-based multiparametric radiomics (mpRad) and machine learning (ML) can differentiate TP from RN in a multi-institutional cohort.
METHODS: Patients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T1 post-contrast (T1 c) and T2 fluid-attenuated inversion recovery (T2 -FLAIR) radiomic features. Feature selection and supervised ML were performed in a randomly selected training cohort ( N = 95) and validated in the remaining cases ( N = 40) using surgical pathology as the gold standard.
RESULTS: One hundred and thirty-five discrete lesions (37 RN, 98 TP) from 109 patients were included. Radiographic diagnoses by an experienced neuroradiologist were concordant with histopathology in 67% of cases (sensitivity 69%, specificity 59% for TP). Radiomic analysis indicated institutional origin as a significant confounding factor for diagnosis. A random forest model incorporating 1 mpRad, 4 T1 c, and 4 T2 -FLAIR features had an AUC of 0.77 (95% confidence interval [CI]: 0.66-0.88), sensitivity of 67% and specificity of 86% in the training cohort, and AUC of 0.71 (95% CI: 0.51-0.91), sensitivity of 52% and specificity of 90% in the validation cohort.
CONCLUSIONS: MRI-based mpRad and ML can distinguish TP from RN with high specificity, which may facilitate the triage of patients with growing brain metastases after SRS for repeat radiation versus surgical intervention.
METHODS: Patients with growing brain metastases after SRS at 2 institutions underwent surgery, and RN or TP were confirmed by histopathology. A radiomic tissue signature (RTS) was selected from mpRad, as well as single T1 post-contrast (T1 c) and T2 fluid-attenuated inversion recovery (T2 -FLAIR) radiomic features. Feature selection and supervised ML were performed in a randomly selected training cohort ( N = 95) and validated in the remaining cases ( N = 40) using surgical pathology as the gold standard.
RESULTS: One hundred and thirty-five discrete lesions (37 RN, 98 TP) from 109 patients were included. Radiographic diagnoses by an experienced neuroradiologist were concordant with histopathology in 67% of cases (sensitivity 69%, specificity 59% for TP). Radiomic analysis indicated institutional origin as a significant confounding factor for diagnosis. A random forest model incorporating 1 mpRad, 4 T1 c, and 4 T2 -FLAIR features had an AUC of 0.77 (95% confidence interval [CI]: 0.66-0.88), sensitivity of 67% and specificity of 86% in the training cohort, and AUC of 0.71 (95% CI: 0.51-0.91), sensitivity of 52% and specificity of 90% in the validation cohort.
CONCLUSIONS: MRI-based mpRad and ML can distinguish TP from RN with high specificity, which may facilitate the triage of patients with growing brain metastases after SRS for repeat radiation versus surgical intervention.
Full text links
Related Resources
Trending Papers
Renin-Angiotensin-Aldosterone System: From History to Practice of a Secular Topic.International Journal of Molecular Sciences 2024 April 5
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app