Individualized Nomogram for Predicting Survival in Patients with Brain Metastases After Stereotactic Radiosurgery Utilizing Driver Gene Mutations and Volumetric Surrogates

A nomogram for predicting subsequent liver metastasis in patients with metastatic breast cancer

Background

To investigate the clinical characteristics of liver metastasis from metastatic breast cancer and construct a competing risk nomogram for predicting the probability of liver metastasis.

Methods

Clinical data of patients with metastatic breast cancer from Tianjin Medical University Cancer Institute during 2008-2018 were retrospectively collected. Independent prognostic factors were assessed by the Fine-Gray competing risk model. A competing risk nomogram was constructed by integrating those independent prognostic factors and evaluated with concordance index (C-index) and calibration curves.

Results

A total of 1406 patients were retrospectively analyzed, and randomly divided into the training set (n=986) and the validation set (n=420). Multivariate analysis showed that menopausal status, HER-2 status, bone metastasis and lung metastasis were identified as independent prognostic factors in the nomogram. The C-index in the training set was 0.719 (95% CI: 0.706-0.732), and in the validation set was 0.740 (95% CI: 0.720-0.732). The calibration curves in the training set and validation set showed that the nomogram had a sufficient level of calibration. A risk stratification was further established to divide all the patients into three prognostic groups.

Conclusion

We had developed a tool that can predict subsequent liver metastasis from metastatic breast cancer, which may be useful for identifying the patients at risk of liver metastasis and guiding the individualized treatment. It had been verified that the nomogram has good discrimination and calibration, and had certain potential clinical value. This nomogram can be used to screen patients with low, intermediate and high risk of liver metastasis from metastatic breast cancer, so as to develop a more complete follow-up plan.

Predefined and data-driven CT radiomics predict recurrence-free and overall survival in patients with pulmonary metastases treated with stereotactic body radiotherapy

BACKGROUND

This retrospective study explores two radiomics methods combined with other clinical variables for predicting recurrence free survival (RFS) and overall survival (OS) in patients with pulmonary metastases treated with stereotactic body radiotherapy (SBRT).

METHODS

111 patients with 163 metastases treated with SBRT were included with a median follow-up time of 927 days. First-order radiomic features were extracted using two methods: 2D CT texture analysis (CTTA) using TexRAD software, and a data-driven technique: functional principal components analysis (FPCA) using segmented tumoral and peri-tumoural 3D regions.

RESULTS

Using both Kaplan-Meier analysis with its log-rank tests and multivariate Cox regression analysis, the best radiomic features of both methods were selected: CTTA-based "entropy" and the FPCA-based first mode of variation of tumoural CT density histogram: "F1." Predictive models combining radiomic variables and age showed a C-index of 0.62 95% with a CI of (0.57-0.67). "Clinical indication for SBRT" and "lung primary cancer origin" were strongly associated with RFS and improved the RFS C-index: 0.67 (0.62-0.72) when combined with the best radiomic features. The best multivariate Cox model for predicting OS combined CTTA-based features-skewness and kurtosis-with size and "lung primary cancer origin" with a C-index of 0.67 (0.61-0.74).

CONCLUSION

In conclusion, concise predictive models including CT density-radiomics of metastases, age, clinical indication, and lung primary cancer origin can help identify those patients with probable earlier recurrence or death prior to SBRT treatment so that more aggressive treatment can be applied.

Comparative efficacy and safety of olanzapine and risperidone in the treatment of psychiatric and behavioral symptoms of Alzheimer's disease: Systematic review and meta-analysis

OBJECTIVES

Olanzapine and risperidone have emerged as the most widely used drugs as short-term prescription in the treatment of behavioral disturbances in dementia. The present systematic review and meta-analysis was hence performed to investigate the effectiveness and safety profile of olanzapine and risperidone in the treatment of behavioral and psychological symptoms of dementia (BPSD), aiming to provide updated suggestion for clinical physicians and caregivers.

DESIGN

Prospective controlled clinical studies were included, of which available data was extracted. Outcomes of BEHAVE-AD scores with the variation of grades, specific behaviors variables, as well as safety signals were pooled for the analysis by odds rates and weighted mean differences, respectively.

DATA SOURCES

Medline, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), and WanFang.

ELIGIBILITY CRITERIA

Prospective, controlled clinical studies, conducted to compare the effectiveness and safety profile of olanzapine and risperidone in the treatment of BPSD.

DATA EXTRACTION AND SYNTHESIS

Interested data including baseline characteristics and necessary outcomes from the included studies were extracted independently by 2 investigators. BEHAVE-AD scale was adopted to assess the efficacy in the present study. All behaviors were evaluated at the time of the initiation of the treatment, as well as the completion of drugs courses. Adverse events were assessed with the criteria of Treatment Emergent Symptom Scale, or Coding Symbols for a Thesaurus of Adverse Reaction Terms dictionary. Weighted mean difference was used for the pooled analysis.

RESULTS

A total of 2427 participants were included in the present meta-analysis. Comparative OR on response rate, and remarkable response rate between olanzapine and risperidone was 0.65 (95% CI: 0.51-0.84; P = .0008), and 0.62 (95% CI: 0.50-0.78; P < .0001), respectively. There were statistical differences observed by olanzapine on the improvement of variables including delusions (WMD, -1.83, 95% CI, -3.20, -0.47), and nighttime behavior disturbances (WMD, -1.99, 95% CI, -3.60, -0.38) when compared to risperidone.

CONCLUSION

Our results suggested that olanzapine might be statistically superior to risperidone on the reduction of BPSD of Alzheimer's disease, especially in the relief of delusions and nighttime behavior disturbances. In addition, olanzapine was shown statistically lower risks of agitation, sleep disturbance, and extrapyramidal signs.

Efficacy and safety of osimertinib for leptomeningeal metastases from EGFR-mutant non-small cell lung cancer: a pooled analysis

BACKGROUND

The aim of this study was to evaluate the efficacy and safety of osimertinib for the treatment of leptomeningeal metastases (LM) from epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer (NSCLC).

METHODS

We conducted a systematic review and meta-analysis to aggregate the clinical outcomes of patients with LM from EGFR-mutant NSCLC treated with osimertinib. A comprehensive literature search for published and unpublished studies was implemented in April 2021 of PubMed, EMBASE, the Cochrane Library, and several international conference databases, in accordance with the PRISMA guidelines. Meta-analysis of proportions was conducted to calculate the pooled rate of overall response rate (ORR), disease control rate (DCR), one-year overall survival (OS), and adverse events (AEs).

RESULTS

A total of eleven studies (five prospective and six retrospective) including 353 patients were included. The majority of patients (346/353, 98.0%) received osimertinib as ≥ 2nd-line treatment for LM, either at a dosage of 80 mg (161/353, 45.6%) or 160 mg (191/353, 54.1%). The pooled rates of ORR and DCR were 42% (95% CI 24% to 59%) and 93% (95% CI 88% to 97%), respectively. The pooled one-year OS rate was 59% (95% CI 53% to 65%) in 233 patients from five studies. The highest incidence of AEs of all grades was rash (53%), followed by diarrhea (45%), paronychia (35%), decreased appetite (35%), and dry skin (27%), based on data from four studies.

CONCLUSIONS

Our study highlighted and confirmed the meaningful efficacy and a manageable safety profile of osimertinib for the treatment of LM from EGFR-mutant advanced NSCLC.

Clinical characteristics and prognosis of non-small cell lung cancer patients with liver metastasis: A population-based study

BACKGROUND

The presence of liver metastasis (LM) is an independent prognostic factor for shorter survival in non-small cell lung cancer (NSCLC) patients. The median overall survival of patients with involvement of the liver is less than 5 mo. At present, identifying prognostic factors and constructing survival prediction nomogram for NSCLC patients with LM (NSCLC-LM) are highly desirable.

AIM

To build a forecasting model to predict the survival time of NSCLC-LM patients.

METHODS

Data on NSCLC-LM patients were collected from the Surveillance, Epidemiology, and End Results database between 2010 and 2018. Joinpoint analysis was used to estimate the incidence trend of NSCLC-LM. Kaplan-Meier curves were constructed to assess survival time. Cox regression was applied to select the independent prognostic predictors of cancer-specific survival (CSS). A nomogram was established and its prognostic performance was evaluated.

RESULTS

The age-adjusted incidence of NSCLC-LM increased from 22.7 per 1000000 in 2010 to 25.2 in 2013, and then declined to 22.1 in 2018. According to the multivariable Cox regression analysis of the training set, age, marital status, sex, race, histological type, T stage, metastatic pattern, and whether the patient received chemotherapy or not were identified as independent prognostic factors for CSS (P < 0.05) and were further used to construct a nomogram. The C-indices of the training and validation sets were 0.726 and 0.722, respectively. The results of decision curve analyses (DCAs) and calibration curves showed that the nomogram was well-discriminated and had great clinical utility.

CONCLUSION

We designed a nomogram model and further constructed a novel risk classification system based on easily accessible clinical factors which demonstrated excellent performance to predict the individual CSS of NSCLC-LM patients.

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.

Transcription Factors Leading to High Expression of Neuropeptide L1CAM in Brain Metastases from Lung Adenocarcinoma and Clinical Prognostic Analysis

Background

There is a lack of understanding of the development of metastasis in lung adenocarcinoma (LUAD). This study is aimed at exploring the upstream regulatory transcription factors of L1 cell adhesion molecule (L1CAM) and to construct a prognostic model to predict the risk of brain metastasis in LUAD.

Methods

Differences in gene expression between LUAD and brain metastatic LUAD were analyzed using the Wilcoxon rank-sum test. The GRNdb (http://www.grndb.com) was used to reveal the upstream regulatory transcription factors of L1CAM in LUAD. Single-cell expression profile data (GSE131907) were obtained from the transcriptome data of 10 metastatic brain tissue samples. LUAD prognostic nomogram prediction models were constructed based on the identified significant transcription factors and L1CAM.

Results

Survival analysis suggested that high L1CAM expression was negatively significantly associated with overall survival, disease-specific survival, and prognosis in the progression-free interval (p < 0.05). The box plot indicates that high expression of L1CAM was associated with distant metastases in LUAD, while ROC curves suggested that high expression of L1CAM was associated with poor prognosis. FOSL2, HOXA9, IRF4, IKZF1, STAT1, FLI1, ETS1, E2F7, and ADARB1 are potential upstream transcriptional regulators of L1CAM. Single-cell data analysis revealed that the expression of L1CAM was found significantly and positively correlated with the expression of ETS1, FOSL2, and STAT1 in brain metastases. L1CAM, ETS1, FOSL2, and STAT1 were used to construct the LUAD prognostic nomogram prediction model, and the ROC curves suggest that the constructed nomogram possesses good predictive power.

Conclusion

By bioinformatics methods, ETS1, FOSL2, and STAT1 were identified as potential transcriptional regulators of L1CAM in this study. This will help to facilitate the early identification of patients at high risk of metastasis.