Time Interval to Initiation of Whole-Brain Radiation Therapy in Patients With Small Cell Lung Cancer With Brain Metastasis

Artificial Intelligence and Precision Medicine: A New Frontier for the Treatment of Brain Tumors

Brain tumors are a widespread and serious neurological phenomenon that can be life- threatening. The computing field has allowed for the development of artificial intelligence (AI), which can mimic the neural network of the human brain. One use of this technology has been to help researchers capture hidden, high-dimensional images of brain tumors. These images can provide new insights into the nature of brain tumors and help to improve treatment options. AI and precision medicine (PM) are converging to revolutionize healthcare. AI has the potential to improve cancer imaging interpretation in several ways, including more accurate tumor genotyping, more precise delineation of tumor volume, and better prediction of clinical outcomes. AI-assisted brain surgery can be an effective and safe option for treating brain tumors. This review discusses various AI and PM techniques that can be used in brain tumor treatment. These new techniques for the treatment of brain tumors, i.e., genomic profiling, microRNA panels, quantitative imaging, and radiomics, hold great promise for the future. However, there are challenges that must be overcome for these technologies to reach their full potential and improve healthcare.

21-Gene recurrence score predictive for prognostic benefit of radiotherapy in patients age ≥ 70 with T1N0 ER/PR + HER2- breast cancer treated with breast conserving surgery and endocrine therapy

PURPOSE

Based on the results of the Cancer and Leukemia Group B (CALGB) 9343 trial, patients age ≥70 with T1N0 hormone receptor positive (ER/PR+), human epidermal growth factor receptor-2 negative (HER2-) breast cancer who are treated with breast conserving surgery (BCS) and endocrine therapy (ET) are candidates for omission of radiotherapy (RT). Because the CALGB 9343 trial did not stratify based on recurrence score (RS) test (Oncotype Dx), we conducted the present retrospective study to determine whether RS is predictive of who may benefit from RT following BCS in this cohort.

MATERIALS AND METHODS

The National Cancer Database (NCDB) was queried (2004-2017) for patients age ≥ 70 with pT1N0 ER+/PR + HER2- breast cancer treated with BCS and ET. Patients were stratified based on their RS (low risk [LR] = 1-10, intermediate risk [IR] = 11-25, high risk [HR] = 26-99). Propensity score matching (PSM) created 1:1 matched cohorts of patients who received radiotherapy and those who did not. Kaplan-Meier analysis evaluated overall survival (OS). Univariable (UVA) and multivariable (MVA) Cox proportional hazard analyses identified clinical and treatment factors prognostic for OS.

RESULTS

A total of 11,891 patients met the selection criteria: 3364 in the LR cohort, 7305 in the IR cohort, and 1222 in the HR cohort. A total of 79 % received RT: 77 % in the LR cohort, 79 % in the IR cohort, and 85 % in the HR cohort. Because PSM could not be efficiently performed in the HR cohort alone, the IR and HR cohort were merged (IRHR) for matching. After PSM, the 5-year OS in the LR cohort was 91 % for those who received RT and 89 % for those who did not (p = 0.605). In the IRHR cohort, the 5-year OS was 91 % for those who received RT and 87 % for those who did not (p = 0.003). On MVA in the LR cohort, RT (p = 0.727) was not predictive of improved OS. On MVA in the IRHR cohort, RT (p = 0.010) was a positive prognostic factor for OS.

CONCLUSION

In this older cohort of patients, there is an OS benefit with the use of RT in patients with IRHR RS but not in patients with LR RS. Pending prospective evaluation, assessment of RS in this older subset of patients is recommended with consideration of RT when RS is ≥11.