A MRI-based radiomics model for predicting the response to anlotinb combined with temozolomide in recurrent malignant glioma patients.
Anlotinb
Radiomics mode
Recurrent malignant glioma
Temozolomide
Journal
Discover. Oncology
ISSN: 2730-6011
Titre abrégé: Discov Oncol
Pays: United States
ID NLM: 101775142
Informations de publication
Date de publication:
23 Aug 2023
23 Aug 2023
Historique:
received:
24
03
2023
accepted:
08
07
2023
medline:
24
8
2023
pubmed:
24
8
2023
entrez:
23
8
2023
Statut:
epublish
Résumé
Anlotinib is a multitarget anti-angiogenic drug that combined with temozolomide (TMZ) can effectively prolongs the overall survival (OS) of recurrent malignant glioma(rMG),but some patients do not respond to anlotinib combined with TMZ. These patients were associated with a worse prognosis and lack effective identification methods. Therefore, it is necessary to differentiate patients who may have good response to anlotinb in combination with TMZ from those who are not, in order to provide personalized targeted therapies. Fifty three rMG patients (42 in training cohort and 11 in testing cohort) receiving anlotinib combined with TMZ were enrolled. A total of 3668 radiomics features were extracted from the recurrent MRI images. Radiomics features are reduced and filtered by hypothesis testing and Least Absolute Shrinkage And Selection (LASSO) regression. Eight machine learning models construct the radiomics model, and then screen out the optimal model. The performance of the model was assessed by its discrimination, calibration, and clinical usefulness with validation. Fifty three patients with rMG were enrolled in our study. Thirty four patients displayed effective treatment response, showed a higher survival benefits than non-response group, the median progression-free survival(PFS) was 8.53 months versus 5.33 months (p = 0.06) and the median OS was 19.9 months and 7.33 months (p = 0.029), respectively. Three radiomics features were incorporated into the model construction as final variables after LASSO regression analysis. In testing cohort, Logistic Regression (LR) model has the best performance with an Area Under the Curve (AUC) of 0.93 compared with other models, which can effectively predict the response of rMG patients to anlotinib in combination with TMZ. The calibration curve confirmed the agreement between the observed actual and prediction probability. Within the reasonable threshold probability range (0.38-0.88), the radiomics model shows good clinical utility. The above-described radiomics model performed well, which can serve as a clinical tool for individualized prediction of the response to anlotinb combined with TMZ in rMG patients.
Identifiants
pubmed: 37612579
doi: 10.1007/s12672-023-00751-x
pii: 10.1007/s12672-023-00751-x
pmc: PMC10447352
doi:
Types de publication
Journal Article
Langues
eng
Pagination
154Informations de copyright
© 2023. Springer Science+Business Media, LLC.
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