Background: Medical students engaged in research develop general skills that are instrumental to clinical practice. The systematic review, a cornerstone of evidence-based medicine, represents an ideal starting point for student researchers. Aim and methods: To outline twelve tips on conducting a systematic review for medical students with limited research experience. Results: The tips in this article are practical, comprehensive, and informed by the authors experience as medical students. Conclusion: The twelve tips can help medical students contribute to the literature and build a valuable skillset for future research and clinical practice.

Background: Radiation therapy is a primary and cornerstone treatment modality for brain metastasis. However, it can result in complications like necrosis, which may lead to significant neurological deficits. This study aims to develop and validate an ensemble model with radiomics to predict radiation necrosis. Method: This study retrospectively collected and analyzed MRI images and clinical information from 209 stereotactic radiosurgery sessions involving 130 patients with brain metastasis. An ensemble model integrating gradient boosting, random forest, decision tree, and support vector machine was developed and validated using selected radiomic features and clinical factors to predict the likelihood of necrosis. The model performance was evaluated and compared with other machine learning algorithms using metrics, including the area under the curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). SHapley Additive exPlanations (SHAP) analysis and local interpretable model-agnostic explanations (LIME) analysis were applied to explain the model’s prediction. Results: The ensemble model achieved strong performance in the validation cohort, with the highest AUC. Compared to individual models and the stacking ensemble model, it consistently outperformed. The model demonstrated superior accuracy, generalizability, and reliability in predicting radiation necrosis. SHAP and LIME were used to interpret a complex predictive model for radiation necrosis. Both analyses highlighted similar significant factors, enhancing our understanding of prediction dynamics. Conclusions: The ensemble model using radiomic features exhibited high accuracy and robustness in predicting the occurrence of radiation necrosis. It could serve as a novel and valuable tool to facilitate radiotherapy for patients with brain metastasis.

Purpose/objective(s): Biomarkers for extracranial oligometastatic disease remain elusive and few studies have attempted to correlate genomic data to the presence of true oligometastatic disease.Methods: Patients with non-small cell lung cancer (NSCLC) and brain metastases were identified in our departmental database. Electronic medical records were used to identify patients for whom liquid biopsy-based comprehensive genomic profiling (Guardant Health) was available. Extracranial oligometastatic disease was defined as patients having ≤5 non-brain metastases without diffuse involvement of a single organ. Widespread disease was any spread beyond oligometastatic. Fisher’s exact tests were used to screen for mutations statistically associated (p<0.1) with either oligometastatic or widespread extracranial disease. A risk score for the likelihood of oligometastatic disease was generated and correlated to the likelihood of having oligometastatic disease vs widespread disease. For oligometastatic patients, a competing risk analysis was done to assess for cumulative incidence of oligometastatic progression. Cox regression was used to determine association between oligometastatic risk score and oligoprogression.Results: 130 patients met study criteria and were included in the analysis. 51 patients (39%) had extracranial oligometastatic disease. Genetic mutations included in the Guardant panel that were associated (p<0.1) with the presence of oligometastatic disease included ATM, JAK2, MAP2K2, and NTRK1, while ARID1A and CCNE1 were associated with widespread disease. Patients with a positive, neutral and negative risk score for oligometastatic disease had a 78%, 41% and 11.5% likelihood of having oligometastatic disease, respectively (p<0.0001). Overall survival for patients with positive, neutral and negative risk scores for oligometastatic disease was 86% vs 82% vs 64% at 6 months (p=0.2). Oligometastatic risk score was significantly associated with the likelihood of oligoprogression based on the Wald chi-square test. Patients with positive, neutral and negative risk scores for oligometastatic disease had a cumulative incidence of oligometastatic progression of 77% vs 35% vs 33% at 6 months (p=0.03).Conclusions: Elucidation of a genomic signature for extracranial oligometastatic disease derived from non-invasive liquid biopsy appears feasible for NSCLC patients. Patients with this signature exhibited higher rates of early oligoprogression. External validation could lead to a biomarker that has the potential to direct local therapies in oligometastatic patients.

Background/Objectives: No prior studies have attempted to identify a biomarker for initial brain metastasis velocity (iBMV), with limited studies attempting to correlate genomic data with the development of brain metastases. Methods: Patients with non-small-cell lung cancer (NSCLC) who underwent next-generation sequencing (NGS) were identified in our departmental database. iBMV was calculated by dividing the number of BMs by the interval of time between primary cancer and BM diagnosis. Two-sample t-testing was used to identify mutations statistically associated with iBMV (p < 0.1). A value of +1 was assigned to each mutation with a positive association (“deleterious genes”), and a value of −1 to each with an inverse association (“protective genes”). The sum of these values was calculated to define iBMV risk scores of −1, 0 and 1. Pearson correlation test was used to determine the association between iBMV risk score and calculated iBMV, and a competing risk analysis assessed for death as a competing risk to the development of BMs. Results: A total of 312 patients were included in the analysis, 218 of whom (70%) developed brain metastases. “Deleterious genes” included ARID1A, BRAF, CDK4, GNAQ, MLH1, MSH6, PALB2, RAD51D, RB1 and TSC1; “protective genes” included ARAF, IDH1, MYC, and PTPN11. iBMV risk scores of 1, 0 and −1, predicted an 88%, 61% and 65% likelihood of developing a BM (p < 0.01). A competing risk analysis found a significant association between iBMV risk scores of 1 vs. 0 and 1 vs. −1, and the likelihood of developing a BM using death as a competing risk. Overall survival (OS) at 1 and 2 years for patients with iBMV risk scores of 1, 0 and −1 was 72% vs. 84% vs. 85% and 46% vs. 69% vs. 70% (p < 0.02). Conclusions: Development of a genomic signature for iBMV via non-invasive liquid biopsy appears feasible in NSCLC patients. Patients with a positive iBMV risk score were more likely to develop brain metastases. Validation of this signature could lead to a biomarker with the potential to guide treatment recommendations and surveillance schedules.