515P - A MRI-based radiomics model for predicting the response to anlotinb combined with temozolomide in recurrent malignant glioma patients

Background

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, there is a great need to differentiate patients who are likely to have a good response to anlotinb in combination with TMZ from those who are not, so as to provide more personalized targeted therapy and free patients who might have a poor treatment response from risk of those severe side effects and high cost.

Methods

A total of 53 rMG patients (42 in training cohort and 11 in validation 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 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.

Results

53 patients with rMG were enrolled in our study. 34 patients displayed effective treatment response, showed a higher survival benefits than non-response group, the median 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. Compared with other models, LR model has the best performance with an AUC of 0.929 in validation cohort, 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.

Conclusions

The above-described radiomics modle performed well, which can serve as a clinical tool for individualized prediction of the response to anlotinb combined with TMZ in rMG patients.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

S. Zhou.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.