NCCBM, a Nomogram Prognostic Model in Breast Cancer Patients With Brain Metastasis

Exploration of anatomical distribution of brain metastasis from breast cancer at first diagnosis assisted by artificial intelligence

Objectives

This study aimed to explore the spatial distribution of brain metastases (BMs) from breast cancer (BC) and to identify the high-risk sub-structures in BMs that are involved at first diagnosis.

Methods

Magnetic resonance imaging (MRI) scans were retrospectively reviewed at our centre. The brain was divided into eight regions according to its anatomy and function, and the volume of each region was calculated. The identification and volume calculation of metastatic brain lesions were accomplished using an automatically segmented 3D BUC-Net model. The observed and expected rates of BMs were compared using 2-tailed proportional hypothesis testing.

Results

A total of 250 patients with BC who presented with 1694 BMs were retrospectively identified. The overall observed incidences of the substructures were as follows: cerebellum, 42.1 %; frontal lobe, 20.1 %; occipital lobe, 9.7 %; temporal lobe, 8.0 %; parietal lobe, 13.1 %; thalamus, 4.7 %; brainstem, 0.9 %; and hippocampus, 1.3 %. Compared with the expected rate based on the volume of different brain regions, the cerebellum, occipital lobe, and thalamus were identified as higher risk regions for BMs (P value ≤ 5.6*10-3). Sub-group analysis according to the type of BC indicated that patients with triple-negative BC had a high risk of involvement of the hippocampus and brainstem.

Conclusions

Among patients with BC, the cerebellum, occipital lobe and thalamus were identified as higher-risk regions than expected for BMs. The brainstem and hippocampus were high-risk areas of the BMs in triple negative breast cancer. However, further validation of this conclusion requires a larger sample size.

A model for predicting clinical prognosis based on brain metastasis-related genes in patients with breast cancer

Background

Brain metastasis (BM) is a clinically relevant cause of death in patients with breast cancer (BRCA). This study was designed to develop a clinical model capable of predicting BRCA patients' prognostic outcomes according to the expression of BM-related genes (BMRGs).

Methods

The public Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases served as data sources. BMRGs of BRCA were selected from previous literature. Differences among BRCA molecular subtypes were compared using R 'limma' package. The impact of BM-related differentially expressed genes (BM_DEGs) on BRCA patients' outcomes was explored with a risk score model, after which the relationship between these risk scores and immune cell infiltration was examined. Risk scores were also used to judge the predicted efficacy of immunotherapeutic interventions. The utility of risk scores in combination with clinicopathological characteristics was evaluated as a predictor of patient's survival through univariate and multivariate analyses.

Results

The R limma package was used to explore differential gene expression, after which 12 BM_DEGs were incorporated into a risk scoring model. The resultant risk scores were able to predict immunotherapeutic treatment efficacy. In addition, a nomogram incorporating risk scores, stage, and age was established. The nomogram was able to reliably predict the overall survival (OS) of BRCA patients, yielding predictive outcomes that aligned well with actual observations.

Conclusions

In summary, a predictive clinical model for BRCA patients was successfully established in this study, providing a valuable tool that may be particularly helpful for the assessment of patients facing a risk of BM development.

Prognostic value and mode selection of locoregional treatment in Stage-IV breast cancer patients

PURPOSE

This study aimed to assess the actual prognostic significance of different locoregional treatment (LRT) (surgery and radiotherapy) modalities for stage-IV  breast cancer (BC) patients and construct a competing risk nomogram to make precise predictions of the breast cancer-specific death (BCSD) risk among LRT recipients.

METHODS

A total of 9279 eligible stage-IV BC patients from the Surveillance Epidemiology and End Results (SEER) database were included in this study. Initially, we evaluated the impact of LRT on survival both before and after the propensity score matching (PSM). Then, we used the Cox hazard proportional model and competing risk model to identify the independent prognostic factors for LRT recipients. Based on the screened variables, a comprehensive nomogram was established.

RESULTS

Kaplan-Meier curves demonstrated that LRT significantly prolonged overall survival (OS) and breast cancer-specific survival (BCSS) (P < 0.001). In addition, patients treated with surgery combined with postoperative radiotherapy (PORT) possessed the optimal survival (P < 0.001). Regardless of the surgical modalities, primary tumor resection combined with radiotherapy could ameliorate the prognosis (P < 0.05). Subgroup analysis showed that in patients with T2-T4 stage, PORT had a survival benefit compared with those undergoing surgery combined with preoperative radiotherapy (PRRT) and surgery only. Based on the screened independent prognostic factors, we established a comprehensive nomogram to forecast BCSD in 1 year, 2 years and 3 years, which showed robust predictive ability.

CONCLUSION

PORT was associated with a lower BCSD in stage-IV BC patients. The practical nomogram could provide a precise prediction of BCSD for LRT recipients, which was meaningful for patients' individualized management.

Analysis of Prognostic Factors and Surgical Management of Elderly Patients with Low-Grade Gliomas

BACKGROUND

The number of elderly patients with low-grade glioma (LGG) is increasing, but their prognostic factors and surgical treatment are still controversial. This paper aims to investigate the prognostic factors of overall survival and cancer-specific survival in elderly patients with LGG and analyze the optimal surgical treatment strategy.

METHODS

Patients in the study were obtained from the Surveillance, Epidemiology, and End Results database and patients were randomized into a training and a test set (7:3). Clinical variables were analyzed by univariate and multivariate Cox regression analysis to screen for significant prognostic factors, and nomograms visualized the prognosis. In addition, survival analysis of elderly patients regarding different surgical management was also analyzed by Kaplan-Meier curves.

RESULTS

Six prognostic factors were screened by univariate and multivariate Cox regression analysis on the training set: tumor site, laterality, histological type, the extent of surgery, radiotherapy, and chemotherapy, and all factors were visualized by nomogram. And we evaluated the accuracy of the nomogram model using consistency index, calibration plots, receiver operator characteristic curves, and decision curve analysis, showing that the nomogram has strong accuracy and applicability. We also found that gross total resection improved overall survival and cancer-specific survival in patients with LGG aged ≥65 years relative to those who did not undergo surgery (P < 0.001).

CONCLUSIONS

Based on the Surveillance, Epidemiology, and End Results database, we created and validated prognostic nomograms for elderly patients with LGG, which can help clinicians to provide personalized treatment services and clinical decisions for their patients. More importantly, we found that older age alone should not preclude aggressive surgery for LGGs.

Novel models by machine learning to predict prognosis of breast cancer brain metastases

BACKGROUND

Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What's more, primary surgery for BCBM patients is still controversial.

METHODS

The data used for analysis in this study was obtained from the SEER database (2010-2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K-M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes.

RESULTS

The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95%CI 0.273-0.999, P < 0.05) than those with less than USD$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157-2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219-2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR-/HER2- subtype. In contrast, surgery could not help BCBM patients with HR + /HER2- subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608-1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604-1.368, P = 0.630).

CONCLUSION

We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes.

The Usefulness of Prognostic Tools in Breast Cancer Patients with Brain Metastases

Simple Summary

Due to the variability of an individual’s prognosis and the variety of treatment options available to breast cancer (BC) patients with brain metastases (BM), establishing the proper therapy is challenging. Since 1997, several prognostic tools for BC patients with BM have been proposed with variable precision in determining the overall survival. The majority of prognostic tools include the performance status, the age at BM diagnosis, the number of BM, the primary tumor phenotype/genotype and the extracranial metastases status as an outcome of systemic therapy efficacy. It is necessary to update the prognostic indices used by physicians as advances in local and systemic treatments develop and change the parameters of survival. Free access to prognostic tools online may increase their routine adoption in clinical practice. Clinical trials on BC patients with BM remains a broad field for the application of prognostic tools.

Abstract

Background: Determining the proper therapy is challenging in breast cancer (BC) patients with brain metastases (BM) due to the variability of an individual’s prognosis and the variety of treatment options available. Several prognostic tools for BC patients with BM have been proposed. Our review summarizes the current knowledge on this topic. Methods: We searched PubMed for prognostic tools concerning BC patients with BM, published from January 1997 (since the Radiation Therapy Oncology Group developed) to December 2021. Our criteria were limited to adults with newly diagnosed BM regardless of the presence or absence of any leptomeningeal metastases. Results: 31 prognostic tools were selected: 13 analyzed mixed cohorts with some BC cases and 18 exclusively analyzed BC prognostic tools. The majority of prognostic tools in BC patients with BM included: the performance status, the age at BM diagnosis, the number of BM (rarely the volume), the primary tumor phenotype/genotype and the extracranial metastasis status as a result of systemic therapy. The prognostic tools differed in their specific cut-off values. Conclusion: Prognostic tools have variable precision in determining the survival of BC patients with BM. Advances in local and systemic treatment significantly affect survival, therefore, it is necessary to update the survival indices used depending on the type and period of treatment.

Brain metastases in de novo breast cancer: An updated population-level study from SEER database

PURPOSE

Although there are current studies on breast cancer brain metastasis, population-level analysis is still lacking. As treatment for metastatic breast cancer has improved, an updated population-level analysis is necessary. Our aim was to use the SEER database to characterize the incidence and survival of patients with brain metastases at the initial diagnosis of breast cancer.

PATIENTS AND METHODS

Patients with breast cancer from 2010 to 2018 were identified using the SEER database. The stratified incidence and median survival of patients with BM at diagnosis were described. Multivariate logistic and Cox regression were performed to determine the covariates associated with brain metastasis and survival outcomes, respectively. Multiple comparisons based on Cox proportional hazards model were performed for the analysis of interactive effects on overall survival.

RESULTS

A total of 2,248 patients with brain metastases at the initial diagnosis of breast cancer were identified, accounting for 0.40% of all patients with breast cancer, and 7.26% of patients with metastatic disease. Incidence proportions were highest, and survival outcomes were worst among patients with hormone receptor (HR)-negative human epidermal growth factor receptor 2 (HER2)-positive and triple-negative subtypes. For patients with brain metastases, the prognostic differences among different molecular subtypes have been gradually narrowing, and the survival benefits from various treatment methods have been all increased over time.

CONCLUSION

Our study provides an updated population-level estimate of the incidence and survival for patients with brain metastases at the diagnosis of breast cancer, thus may help early identification, prognostic stratification and treatment planning for such patients.