Background. In patients with locally recurrent brain metastases (LRBMs), the role of (repeat) craniotomy is controversial. This study aimed to analyze long-term oncological outcomes in this... Show moreBackground. In patients with locally recurrent brain metastases (LRBMs), the role of (repeat) craniotomy is controversial. This study aimed to analyze long-term oncological outcomes in this heterogeneous population.Methods. Craniotomies for LRBM were identified from a tertiary neuro-oncological institution. First, we assessed overall survival (OS) and intracranial control (ICC) stratified by molecular profile, prognostic indices, and multimodality treatment. Second, we compared LRBMs to propensity score-matched patients who underwent craniotomy for newly diagnosed brain metastases (NDBM).Results. Across 180 patients, median survival after LRBM resection was 13.8 months and varied by molecular profile, with >24 months survival in ALK/EGFR+ lung adenocarcinoma and HER2+ breast cancer. Furthermore, 102 patients (56.7%) experienced intracranial recurrence; median time to recurrence was 5.6 months. Compared to NDBMs (n = 898), LRBM patients were younger, more likely to harbor a targetable mutation and less likely to receive adjuvant radiation (P < 0.05). After 1:3 propensity matching stratified by molecular profile, LRBM patients generally experienced shorter OS (hazard ratio 1.67 and 1.36 for patients with or without a mutation, P < 0.05) but similar ICC (hazard ratio 1.11 in both groups, P > 0.20) compared to NDBM patients with similar baseline. Results across specific molecular subgroups suggested comparable effect directions of varying sizes.Conclusions. In our data, patients with LRBMs undergoing craniotomy comprised a subgroup of brain metastasis patients with relatively favorable clinical characteristics and good survival outcomes. Recurrent status predicted shorter OS but did not impact ICC. Craniotomy could be considered in selected, prognostically favorable patients. Show less
PURPOSE The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight... Show morePURPOSE The aim of this study was to develop an open-source natural language processing (NLP) pipeline for text mining of medical information from clinical reports. We also aimed to provide insight into why certain variables or reports are more suitable for clinical text mining than others.MATERIALS AND METHODS Various NLP models were developed to extract 15 radiologic characteristics from free-text radiology reports for patients with glioblastoma. Ten-fold cross-validation was used to optimize the hyperparameter settings and estimate model performance. We examined how model performance was associated with quantitative attributes of the radiologic characteristics and reports.RESULTS In total, 562 unique brain magnetic resonance imaging reports were retrieved. NLP extracted 15 radiologic characteristics with high to excellent discrimination (area under the curve, 0.82 to 0.98) and accuracy (78.6% to 96.6%). Model performance was correlated with the inter-rater agreement of the manually provided labels (rho = 0.904; P<.001) but not with the frequency distribution of the variables of interest (rho = 0.179; P =.52). All variables labeled with a near perfect inter-rater agreement were classified with excellent performance (area under the curve > 0.95). Excellent performance could be achieved for variables with only 50 to 100 observations in the minority group and class imbalances up to a 9:1 ratio. Report-level classification accuracy was not associated with the number of words or the vocabulary size in the distinct text documents.CONCLUSION This study provides an open-source NLP pipeline that allows for text mining of narratively written clinical reports. Small sample sizes and class imbalance should not be considered as absolute contraindications for text mining in clinical research. However, future studies should report measures of inter-rater agreement whenever ground truth is based on a consensus label and use this measure to identify clinical variables eligible for text mining. Show less
Purpose To describe practice patterns and patient outcomes with respect to the use of postoperative systemic therapy (ST) after resection of a solitary breast cancer brain metastasis (BCBM).... Show morePurpose To describe practice patterns and patient outcomes with respect to the use of postoperative systemic therapy (ST) after resection of a solitary breast cancer brain metastasis (BCBM). Methods A multi-institutional retrospective review of consecutive patients undergoing resection of a single BCBM without extracranial metastases was performed to describe subtype-specific postoperative outcomes and assess the impact of types of ST on site of recurrence, progression-free survival (PFS), and overall survival (OS). Results Forty-four patients were identified. Stratified estimated survival was 15, 24, and 23 months for patients with triple negative, estrogen receptor positive (ER+), and HER2+ BCBMs, respectively. Patients receiving postoperative ST had a longer median PFS (8 versus 4 months, adjusted p-value 0.01) and OS (32 versus 15 months, adjusted p-value 0.21). Nine patients (20%) had extracranial progression, 23 (52%) had intracranial progression, three (8%) had both, and nine (20%) did not experience progression at last follow-up. Multivariate analysis showed that postoperative hormonal therapy was associated with longer OS (HR 0.26; 95% CI 0.08-0.89; p = 0.03) but not PFS (HR 0.35, 95% CI 0.08-1.47, p = 0.15) in ER+ patients. Postoperative HER2-targeted therapy was not associated with longer OS or PFS in HER2+ patients. Conclusions Disease progression occurred intracranially more often than extracranially following resection of a solitary BCBM. In ER+ patients, postoperative hormonal therapy was associated with longer OS. Postoperative HER2-targeted therapy did not show survival benefit in HER2+ patients. These results should be validated in larger cohorts. Show less
