| Autor: |
Balleyguier C, S. Bockel, Sallé de Chou R, F. Bidault, Khettab M, Sylvain Reuzé, N. Lassau, Laurence M, Chouzenoux E, C Robert, Elaine Johanna Limkin, Rabiee B, A. Carré, Molecular Radiotherapy, El Haik M, Garcia Gcte, Antonios L, Ammari S, Juvina S, L. dercle |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
Austin Journal of Radiology. 8 |
| ISSN: |
2473-0637 |
| DOI: |
10.26420/austinjradiol.2021.1151 |
| Popis: |
Objective: To compare the performance of conventional and machinelearning approaches for the diagnosis of tumor recurrence after radiation therapy of brain metastases. Methods: 184 symptomatic patients with solitary metastatic brain lesions treated with radiation therapy were enrolled in a monocentric retrospective study from June 2013 to May 2018. The diagnosis was tumor recurrence (n=71) and radiation necrosis (n=113) using as reference standard expert-consensus derived from pathology and long-term follow-up. 37 potential predictors were recorded at the time of radiological progression (7-15 months after therapy): 6 clinical features and 31 imaging features including 20 radiomics features derived from standard of care 3D T1-gadolinium sequences. We compared four approaches (A, B, C, D): expert report using MRI sequences without (A) and with delayedcontrast MRI (TRAM) sequences (B), 11 non-Radiomics imaging features alone (C) and a signature combining variables selected using unsupervised machinelearning algorithms (D), training:validation sets: n=144:40 pts). Results: Overall (n=184), approaches B and C (using TRAM sequence alone) reached comparable performances with respective AUCs [95% CI] of 78.7% [72.3%-85.1%] and 76.8% [70.3%-83.3%]. Both significantly outperformed approach A with AUC [95% CI] of 57.4% [50.7%-64.1%] (DeLong’s test, p-value=10-7). In the validation set (n=40), the signature reached an AUC [95% CI] of 92% [87%-97%]. Conclusion: A quantitative analysis of TRAM sequence seems the best approach for the diagnosis of recurrent tumor after radiation therapy. It is parsimonious, objective and less time-consuming than interpreting all sequences. A signature derived from the analysis of standard of care 3D T1- gadolinium sequence showed promising results that warrant prospective validation. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
