• Journal of Korean Neurosurgical Society
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• v.54 no.5
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• pp.399-404
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• 2013

Objective : The authors conducted a retrospective cohort study to determine prognostic factors and treatment outcomes of brain metastases (BM) from breast cancer (BC) after Gamma Knife radiosurgery (GKS). Methods : Pathologic and clinical features, and outcomes were analyzed in a cohort of 62 patients with BM from BC treated by GKS. The Kaplan- Meier method, the log-rank test, and Cox's proportional hazards model were used to assess prognostic factors. Results : Median survival after GKS was 73.0 weeks (95% confidence interval, 46.0-100.1). HER2+ [hazard ratio (HR) 0.441; p=0.045], Karnofsky performance scale (KPS) ${\geq}70$ (RR 0.416; p=0.050) and systemic chemotherapy after GKS (RR 0.282; p=0.001) were found to be a favorable prognostic factor of overall survival. Actuarial local control (LC) rate were $89.5{\pm}4.5%$ and $70.5{\pm}6.9%$ at 6 and 12 months after GKS, respectively. No prognostic factors were found to affect LC rate. Uni- and multivariate analysis revealed that the distant control (DC) rate was higher in patients with; a small number (${\leq}3$) of metastasis (HR 0.300; p=0.045), no known extracranial metastasis (p=0.013, log-rank test), or the HER2+ subtype (HR 0.267; p=0.027). Additional whole brain radiation therapy and metastasis volume were not found to be significantly associated with LC, DC, or overall survival. Conclusion : The treatment outcomes of patients with newly diagnosed BM from BC treated with GKS could be affected primarily by intrinsic subtype, KPS, and systemic chemotherapy. Therapeutic strategy and prognosis scoring system should be individualized based on considerations of intrinsic subtype in addition to traditionally known parameters related to stereotactic radiosurgery.

• Journal of the Korean Society of Radiology
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• v.81 no.3
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• pp.632-643
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• 2020

Purpose To investigate the correlation between magnetic resonance (MR) image-based radiomics features and the genomic features of breast cancer by focusing on biomolecular intrinsic subtypes and gene expression profiles based on risk scores. Materials and Methods We used the publicly available datasets from the Cancer Genome Atlas and the Cancer Imaging Archive to extract the radiomics features of 122 breast cancers on MR images. Furthermore, PAM50 intrinsic subtypes were classified and their risk scores were determined from gene expression profiles. The relationship between radiomics features and biomolecular characteristics was analyzed. A penalized generalized regression analysis was performed to build prediction models. Results The PAM50 subtype demonstrated a statistically significant association with the maximum 2D diameter (p = 0.0189), degree of correlation (p = 0.0386), and inverse difference moment normalized (p = 0.0337). Among risk score systems, GGI and GENE70 shared 8 correlated radiomic features (p = 0.0008-0.0492) that were statistically significant. Although the maximum 2D diameter was most significantly correlated to both score systems (p = 0.0139, and p = 0.0008), the overall degree of correlation of the prediction models was weak with the highest correlation coefficient of GENE70 being 0.2171. Conclusion Maximum 2D diameter, degree of correlation, and inverse difference moment normalized demonstrated significant relationships with the PAM50 intrinsic subtypes along with gene expression profile-based risk scores such as GENE70, despite weak correlations.