Metastasis patterns and prognosis in breast cancer patients aged ≥ 80 years: a SEER database analysis

Role of sex in liver tumor occurrence and clinical outcomes: A comprehensive review

Clinical research on sex-based differences in the manifestations, pathophysiology, and prevalence of several diseases, including those affecting the liver, has expanded considerably in recent years. Increasing evidence suggests that liver diseases develop, progress, and respond to treatment differently depending on the sex. These observations support the concept that the liver is a sexually dimorphic organ in which estrogen and androgen receptors are present, which results in disparities between men and women in liver gene expression patterns, immune responses, and the progression of liver damage, including the propensity to develop liver malignancies. Sex hormones play protective or deleterious roles depending on the patient's sex, the severity of the underlying disease, and the nature of precipitating factors. Moreover, obesity, alcohol consumption, and active smoking, as well as social determinants of liver diseases leading to sex-related inequalities, may interact strongly with hormone-related mechanisms of liver damage. Drug-induced liver injury, viral hepatitis, and metabolic liver diseases are influenced by the status of sex hormones. Available data on the roles of sex hormones and gender differences in liver tumor occurrence and clinical outcomes are conflicting. Here, we critically review the main gender-based differences in the molecular mechanisms associated with liver carcinogenesis and the prevalence, prognosis, and treatment of primary and metastatic liver tumors.

Machine Learning Algorithm to Estimate Distant Breast Cancer Recurrence at the Population Level with Administrative Data

Purpose

High-quality population-based cancer recurrence data are scarcely available, mainly due to complexity and cost of registration. For the first time in Belgium, we developed a tool to estimate distant recurrence after a breast cancer diagnosis at the population level, based on real-world cancer registration and administrative data.

Methods

Data on distant cancer recurrence (including progression) from patients diagnosed with breast cancer between 2009-2014 were collected from medical files at 9 Belgian centers to train, test and externally validate an algorithm (i.e., gold standard). Distant recurrence was defined as the occurrence of distant metastases between 120 days and within 10 years after the primary diagnosis, with follow-up until December 31, 2018. Data from the gold standard were linked to population-based data from the Belgian Cancer Registry (BCR) and administrative data sources. Potential features to detect recurrences in administrative data were defined based on expert opinion from breast oncologists, and subsequently selected using bootstrap aggregation. Based on the selected features, classification and regression tree (CART) analysis was performed to construct an algorithm for classifying patients as having a distant recurrence or not.

Results

A total of 2507 patients were included of whom 216 had a distant recurrence in the clinical data set. The performance of the algorithm showed sensitivity of 79.5% (95% CI 68.8-87.8%), positive predictive value (PPV) of 79.5% (95% CI 68.8-87.8%), and accuracy of 96.7% (95% CI 95.4-97.7%). The external validation resulted in a sensitivity of 84.1% (95% CI 74.4-91.3%), PPV of 84.1% (95% CI 74.4-91.3%), and an accuracy of 96.8% (95% CI 95.4-97.9%).

Conclusion

Our algorithm detected distant breast cancer recurrences with an overall good accuracy of 96.8% for patients with breast cancer, as observed in the first multi-centric external validation exercise.

Metastatic patterns and prognosis of patients with primary malignant cardiac tumor

Background

Distant metastases are independent negative prognostic factors for patients with primary malignant cardiac tumors (PMCT). This study aims to further investigate metastatic patterns and their prognostic effects in patients with PMCT.

Materials and methods

This multicenter retrospective study included 218 patients with PMCT diagnosed between 2010 and 2017 from Surveillance, Epidemiology, and End Results (SEER) database. Logistic regression was utilized to identify metastatic risk factors. A Chi-square test was performed to assess the metastatic rate. Kaplan-Meier methods and Cox regression analysis were used to analyze the prognostic effects of metastatic patterns.

Results

Sarcoma (p = 0.002) and tumor size¿4 cm (p = 0.006) were independent risk factors of distant metastases in patients with PMCT. Single lung metastasis (about 34%) was the most common of all metastatic patterns, and lung metastases occurred more frequently (17.9%) than bone, liver, and brain. Brain metastases had worst overall survival (OS) and cancer-specific survival (CSS) among other metastases, like lung, bone, liver, and brain (OS: HR = 3.20, 95% CI: 1.02-10.00, p = 0.046; CSS: HR = 3.53, 95% CI: 1.09-11.47, p = 0.036).

Conclusion

Patients with PMCT who had sarcoma or a tumor larger than 4 cm had a higher risk of distant metastases. Lung was the most common metastatic site, and brain metastases had worst survival among others, such as lung, bone, liver, and brain. The results of this study provide insight for early detection, diagnosis, and treatment of distant metastases associated with PMCT.

A Nomogram for Predicting the Risk of Bone Metastasis in Newly Diagnosed Head and Neck Cancer Patients: A Real-World Data Retrospective Cohort Study From SEER Database

Background: Bone metastasis (BM) is one of the typical metastatic types of head and neck cancer (HNC). The occurrence of BM prevents the HNC patients from obtaining a long survival period. Early assessment of the possibility of BM could bring more therapy options for HNC patients, as well as a longer overall survival time. This study aims to identify independent BM risk factors and develop a diagnostic nomogram to predict BM risk in HNC patients.

Methods: Patients diagnosed with HNC between 2010 and 2015 were retrospectively evaluated in the Surveillance, Epidemiology, and End Results (SEER) database, and then eligible patients were enrolled in our study. First, those patients were randomly assigned to training and validation sets in a 7:3 ratio. Second, univariate and multivariate logistic regression analyses were used to determine the HNC patients’ independent BM risk factors. Finally, the diagnostic nomogram’s risk prediction capacity and clinical application value were assessed using calibration curves, receiver operating characteristic (ROC), and decision curve analysis (DCA) curves.

Results: 39,561 HNC patients were enrolled in the study, and they were randomly divided into two sets: training (n = 27,693) and validation (n = 11,868). According to multivariate logistic regression analysis, race, primary site, tumor grade, T stage, N stage, and distant metastases (brain, liver, and lung) were all independent risk predictors of BM in HNC patients. The diagnostic nomogram was created using the above independent risk factors and had a high predictive capacity. The training and validation sets’ area under the curves (AUC) were 0.893 and 0.850, respectively. The AUC values of independent risk predictors were all smaller than that of the constructed diagnostic nomogram. Meanwhile, the calibration curve and DCA also proved the reliability and accuracy of the diagnostic nomogram.

Conclusion: The diagnostic nomogram can quickly assess the probability of BM in HNC patients, help doctors allocate medical resources more reasonably, and achieve personalized management, especially for HNC patients with a potentially high BM risk, thus acquiring better early education, early detection, and early diagnosis and treatment to maximize the benefits of patients.