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Poster Display session 1

3449 - Radiographic Phenotyping to Identify Intracranial Disseminated Recurrence in Brain metastases Treated With Radiosurgery Using Contrast-enhanced MR Imaging


28 Sep 2019


Poster Display session 1


Tumour Site

Central Nervous System Malignancies


CheYu Hsu


Annals of Oncology (2019) 30 (suppl_5): v143-v158. 10.1093/annonc/mdz243


C. Hsu1, S. Kuo2, W. Wang3, T.W. Chen4, Y. Lee5

Author affiliations

  • 1 Radiation Oncology, National Taiwan university hostipal, 10048 - Taipei/TW
  • 2 Department Of Oncology, National Taiwan University Hospital, 10002 - Taipei City/TW
  • 3 Institute Of Applied Mathematical Sciences, National Taiwan University, 10617 - Taipei/TW
  • 4 Department Of Oncology, NTU - National Taiwan University - College of Medicine, 10051 - Taipei City/TW
  • 5 Mathematics, National Taiwan University, 10617 - Taipei/TW


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Abstract 3449


Early intracranial progression (ICP) reduce the efficacy of first line radiosurgery (SRS) for brain metastases. We aim to develop and validate a MR imaging-derived radiomic signature (RS) via deep learning approach for the prediction of 1-year disseminated ICP (DICP; more than or equal to 3 lesions and leptomeningeal carcinomatosis) in brain metastases patients treated with SRS.


A total of 1304 MRI-based radiomic features of pretreatment tumors were obtained from 208 patients with 451 lesions, who received first line SRS during August 2008 to January 2018. Variational autoencoder (VAE), trained with symmetric two encoded and decoded layers of neural network and 1,649,560 trainable parameters, was applied to reduce the dimensionality of radiomc features to 128 VAE-radiomic features. Penalized regression with 10-fold cross validation using least absolute shrinkage and selection operator performs features selection and construct RS to predict 1-year DICP events in train set of 150 patients, which was validated in test set of 58 patients. Harrell’s C-index was used to evaluate the discriminative ability of RS in both sets. The correlation of VAE-radiomic features and molecular features was analyzed by student t-test. Survival analysis was calculated using the Kaplan-Meier method.


The RS yielded 1000 times bootstrapping corrected C-index of 0.746 and 0.747 for discrimination of 1-year DICP in the train and test cohorts, respectively. As for the subgroup of patients with lung (n = 175) and breast (n = 23) origin, the RS also showed good predictive performance with C-indices of 0.735 and 0.755, respectively. EGFR-mutation (n = 113) and ER (n = 22) status were associated with selected VAE-radiomic features No. 98 (p = 0.035) and No.127 (p = 0.44), respectively. Dichotomized risk category using RS of -0.769 (Youden index) as cut-off point yielded median overall survival of 57.7 months in low risk compared to 20.5 months in high risk group (p < 0.01).


The RS model provides a novel approach to predict 1-year DICP and survival in brain metastases receiving SRS, and is warranted to be integrated into GPA for optimal selection of patients treated with first line SRS.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.


Has not received any funding.


All authors have declared no conflicts of interest.

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