Predicting survival and prognosis of postoperative breast cancer brain metastasis: a population-based retrospective analysis

Predicting the prognosis of Wilms tumor by peripheral blood cells: a real-world study of more than 30 years

BACKGROUND

Despite established excellent treatment strategies for Wilms tumor (WT), effective prognostic evaluation methods were lacking. This study aims to examine prognostic factors for WT through real-world peripheral blood cell profiling.

METHODS

Basic data and pre-treatment laboratory indices from WT and non-WT children underwent Wilcoxon test analysis. Chi-square tests assessed the correlation between blood cells and the overall survival (OS) and event-free survival (EFS) of WT. Further the Log-rank test and multivariate Cox were used to identify independent prognostic factors for OS. Traditional accepted factors were included in multi-Cox and the nomogram was constructed to further validate the outcome.

RESULTS

Blood cells significantly differed between WT and non-WT groups (P < 0.05). Univariate analysis revealed that NLR above 1.380, stage IV, M below 0.325 × 103/μL were linked with lower OS, and PLR below 94.632, LB above 3.570 × 103/μL, stage IV, M above 0.325 × 103/μL,age ≤ 3 years were meaningful for higher EFS (P < 0.05). While in the multifactorial COX, only M (HR:0.220, HR95%CI: 0.080 ~ 0.620, P = 0.004 and HR: 0.437, HR95%CI: 0.202 ~ 0.947, P = 0.036, respectively) and stage IV (HR: 7.890, HR95%CI: 1.650 ~ 37.770, P = 0.010 and HR: 3.720, HR95%CI: 1.330 ~ 10.408, P = 0.012, respectively) were independent prognostic factors for OS and EFS. These two variables also were significant after including recognized risk factors, and were demonstrated the predictability via nomogram.

CONCLUSIONS

OS and EFS were poorer in WT children with M below 0.325 × 103/μL, suggesting the potential as a prognostic predictor for WT.

Development of graded prognostic assessment for breast Cancer brain metastasis incorporating extracranial metastatic features: a retrospective analysis of 284 patients

BACKGROUND

Breast cancer brain metastasis (BCBM) is associated with poor survival outcomes and reduced quality of life. The Graded Prognostic Assessment (GPA) score model serves as a well-established tool for predicting the prognosis of BCBM. Notably, the presence of extracranial metastasis (ECM) is considered as a significant prognostic factor in the breast GPA model. This study aims to further refine other features of ECM to enhance the prognostic prediction for BCBM.

METHODS

This study included all inpatients diagnosed with BCBM at the Cancer Hospital, Chinese Academy of Medical Sciences, from January 2010 to July 2021. Baseline characteristics of patients were compared based on features of ECM, including the presence, number, location, and control status of metastases. Overall survival (OS) were compared using the Kaplan-Meier method with log-rank tests. Cox regression analyses were conducted to identify significant prognostic factors. The aforementioned ECM features were incorporated into the original Breast-GPA model to enhance its prognostic accuracy. The concordance index (C-index) and restricted mean survival time (RMST) were utilized to evaluate and compare the predictive accuracy of the updated and original survival models.

RESULTS

284 patients with BCBM were included in the study. Kaplan-Meier survival curves suggested that patients without ECM when diagnosed with BCBM showed better survival (p = 0.007). In the subgroups with ECM, more than 3 organs involved, both bone and visceral metastasis and progressive ECM portended dismal OS (p = 0.003, 0.001 and <0.001). Multivariate analysis demonstrated that molecular subtype, presence of ECM, and number of brain metastasis significantly influenced OS after BCBM. By modifying the current GPA model to include more precise characteristics of ECM, the predictive accuracy was further enhanced as indicated by the C-index and RMST curve.

CONCLUSIONS

More ECM sites, both bone and visceral invasion and uncontrolled ECM were dismal prognostic factors for survival outcomes of BCBM patients. A new Breast-GPA model with better predictive effect was constructed.

Mexican Multicenter Experience of Metastatic Spinal Disease

Background Spinal metastatic disease is a silent progressive cancer complication with an increasing prevalence worldwide. The spine is the third most common site where solid tumors metastasize. Complications involved in spinal metastasis include root or spinal cord compression, progressing to a declining quality of life as patient autonomy reduces and pain increases. The main objective of this study is to report the incidence of patients and typology of spinal metastases in three reference centers in Mexico. Methodology Retrospective cohorts of patients diagnosed with spinal metastases from January 2010 to February 2017 at the National Cancer Institute, National Rehabilitation Institute, and the Traumatology and Orthopedics Hospital "Lomas Verdes" in Mexico City were analyzed. Results A total of 326 patients (56% males) with spinal metastases were reported. The mean age was 58.06 ± 14.05 years. The main sources of spinal metastases were tumors of unknown origin in 53 (16.25%) cases, breast cancer in 67 (20.5%) cases, prostate cancer in 59 (18%) cases, myeloma in 24 (7.4%) cases, and lung cancer in 23 (7.1%) cases. Conclusions The data obtained in this analysis delivers an updated standpoint on Mexico, providing the opportunity to distinguish the current data from global references. Collecting more epidemiological information for better recording of cancer and its associated complications, as well as further studies on them, is necessary.

Assessing of case-cohort design: a case study for breast cancer patients in Xinjiang, China

Objective

To assess the effectiveness and clinical value of case-cohort design and determine prognostic factors of breast cancer patients in Xinjiang on the basis of case-cohort design.

Methods

The survival data with different sample characteristics were simulated by using Cox proportional risk models. To evaluate the effectiveness for the case-cohort, entire cohort, and simple random sampling design by comparing the mean, coefficient of variation, etc., of covariate parameters. Furthermore, the prognostic factors of breast cancer patients in Xinjiang were determined based on case-cohort sampling designs. The models were comprehensively evaluated by likelihood ratio test, the area under the receiver operating characteristic curve (AUC), and Akaike Information Criterion (AIC).

Results

In a simulations study, the case-cohort design shows better stability and improves the estimation efficiency when the censored rate is high. In the breast cancer data, molecular subtypes, T-stage, N-stage, M-stage, types of surgery, and postoperative chemotherapy were identified as the prognostic factors of patients in Xinjiang. These models based on the different sampling designs both passed the likelihood ratio test (p<0.05). Moreover, the model constructed under the case-cohort design had better fitting effect (AIC=3,999.96) and better discrimination (AUC=0.807).

Conclusion

Simulations study confirmed the effectiveness of case-cohort design and further determined the prognostic factors of breast cancer patients in Xinjiang based on this design, which presented the practicality of case-cohort design in actual data.

Prognostic factors of breast cancer brain metastasis

In this editorial we comment on the article by Chen et al published in the recent issue of the World Journal of Clinical Oncology. Brain metastasis is one of the most serious complications of breast cancer and causes high morbidity and mortality. Brain metastases may involve the brain parenchyma and/or leptomeninges. Symptomatic brain metastases develop in 10%-16% of newly recognized cases each year, and this rate increases to 30% in autopsy series. Depending on the size of the metastatic foci, it may be accompanied by extensive vasogenic edema or may occur as small tumor foci. Since brain metastases are a significant cause of morbidity and mortality, early diagnosis can have significant effects on survival and quality of life. The risk of developing brain metastases emerges progressively due to various patient and tumor characteristics. Patient variability may be particularly important in the susceptibility and distribution of brain metastases because malignant blood must cross the brain barrier and move within the brain parenchyma. Some characteristics of the tumor, such as gene expression, may increase the risk of brain metastasis. Clinical growth, tumor stage, tumor grade, growth receptor positivity, HER2 positivity, molecular subtype (such as triple negative status, luminal/nonluminal feature) increase the risk of developing breast cancer metastasis. Factors related to survival due to breast cancer brain metastasis include both tumor/patient characteristics and treatment characteristics, such as patient age, lung metastasis, surgery for brain metastasis, and HER2 positivity. If cases with a high risk of developing brain metastasis can be identified with the help of clinical procedures and artificial intelligence, survival and quality of life can be increased with early diagnosis and treatment. At the same time, it is important to predict the formation of this group in order to develop new treatment methods in cases with low survival expectancy with brain metastases.