Oncological Outcomes After Hippocampus-Sparing Whole-Brain Radiotherapy in Cancer Patients With Newly Diagnosed Brain Oligometastases: A Single-Arm Prospective Observational Cohort Study in Taiwan

BackgroundPromisingly, the technique of hippocampus sparing during WBRT (HS-WBRT) might preserve NCFs. In this research, we examined oncological outcomes, with emphasis on neurologic/non-neurologic causes of death, CNS progression, and leptomeningeal disease (LMD) recurrence in cancer patients who underwent HS-WBRT.MethodsOne hundred and fourteen cancer patients with newly diagnosed brain oligometastases underwent HS-WBRT were consecutively enrolled. The cumulative incidence of cancer-specific deaths (neurologic or non-neurologic), LMD recurrence, and the composite endpoint of CNS progression (CNS-CE) as the first event were computed with a competing-risks approach to characterize the oncological outcomes after HS-WBRT.ResultsPatients with intact brain metastases had a significantly increased likelihood of dying from non-neurologic causes of death associated with early manifestation of progressive systemic disease (hazard ratio for non-neurologic death, 1.78; 95% CI, 1.08–2.95; p = 0.025; competing-risks Fine–Gray regression), which reciprocally rendered them unlikely to encounter LMD recurrence or any pattern of CNS progression (HR for CNS-CE as the first event, 0.13; 95% CI, 0.02–0.97; p = 0.047; competing-risks Fine–Gray regression). By contrast, patients with resection cavities post-craniotomy had reciprocally increased likelihood of CNS progression which might be associated with neurologic death eventually.ConclusionsPatterns of oncological endpoints including neurologic/non-neurologic death and cumulative incidence of CNS progression manifesting as LMD recurrence are clearly clarified and contrasted between patients with intact BMs and those with resection cavities, indicating they are clinically distinct subgroups.Trial RegistrationClinicalTrials.gov, Identifier: NCT02504788, NCT03223675.

Clinicopathologic characteristics and prognosis for male breast cancer compared to female breast cancer

Male breast cancer (MBC) is rare. Due to limited information, MBC has always been understudied. We conducted a retrospective population-based cohort study using data from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program. The clinical and biological features of female breast cancer (FBC) patients were compared with MBC patients. Cox regression models and competing risks analyses were used to identify risk factors associated with cancer-related survival in MBC and FBC groups. Results showed that MBC patients suffered from higher TNM stages, tumor grades, and a higher percentage of hormone receptor-positive tumors, compared with FBC patients (all p < 0.05). In addition, the breast tumor locations varied a lot between males and females (p < 0.05). FBC patients were associated with superior overall survival than MBC patients. Results from multivariate cox regression and competing risks analyses showed age, race, T, N, M-stages, tumor grades, estrogen receptor (ER)/progesterone receptor (PR) and human epidermal growth factor receptor-2 (HER-2) overexpression were independent prognosis factors in FBC patients (all p < 0.05). MBC patients had similar risk factors to FBC patients, but PR and HER-2 status did not independently influence survival (all p > 0.05). Tumor location was an independent prognostic factor for both gender groups.

Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples

The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.