Evaluating and Balancing the Risk of Breast Cancer-Specific Death and Other Cause-Specific Death in Elderly Breast Cancer Patients

Frequency of use and characterization of frailty assessments in observational studies on older women with breast cancer: a systematic review

BACKGROUND

Breast cancer and frailty frequently co-occur in older women, and frailty status has been shown to predict negative health outcomes. However, the extent to which frailty assessments are utilized in observational research for the older breast cancer population is uncertain. Therefore, the aim of this review was to determine the frequency of use of frailty assessments in studies investigating survival or mortality, and characterize them, concentrating on literature from the past 5 years (2017-2022).

METHODS

MEDLINE, EMBASE and Cochrane Library were systematically queried to identify observational studies (case-control, cohort, cross-sectional) published from 2017-2022 that focus on older females (≥ 65 years) diagnosed with breast cancer, and which evaluate survival or mortality outcomes. Independent reviewers assessed the studies for eligibility using Covidence software. Extracted data included characteristics of each study as well as information on study design, study population, frailty assessments, and related health status assessments. Risk of bias was evaluated using the appropriate JBI tool. Information was cleaned, classified, and tabulated into review level summaries.

RESULTS

In total, 9823 studies were screened for inclusion. One-hundred and thirty studies were included in the final synthesis. Only 11 (8.5%) of these studies made use of a frailty assessment, of which 4 (3.1%) quantified frailty levels in their study population, at baseline. Characterization of frailty assessments demonstrated that there is a large variation in terms of frailty definitions and resulting patient classification (i.e., fit, pre-frail, frail). In the four studies that quantified frailty, the percentage of individuals classified as pre-frail and frail ranged from 18% to 29% and 0.7% to 21%, respectively. Identified frailty assessments included the Balducci score, the Geriatric 8 tool, the Adapted Searle Deficits Accumulation Frailty index, the Faurot Frailty index, and the Mian Deficits of Accumulation Frailty Index, among others. The Charlson Comorbidity Index was the most used alternative health status assessment, employed in 56.9% of all 130 studies. Surprisingly, 31.5% of all studies did not make use of any health status assessments.

CONCLUSION

Few observational studies examining mortality or survival outcomes in older women with breast cancer incorporate frailty assessments. Additionally, there is significant variation in definitions of frailty and classification of patients. While comorbidity assessments were more frequently included, the pivotal role of frailty for patient-centered decision-making in clinical practice, especially regarding treatment effectiveness and tolerance, necessitates more deliberate attention. Addressing this oversight more explicitly could enhance our ability to interpret observational research in older cancer patients.

Deep learning-guided adjuvant chemotherapy selection for elderly patients with breast cancer

PURPOSE

The efficacy of adjuvant chemotherapy in elderly breast cancer patients is currently controversial. This study aims to provide personalized adjuvant chemotherapy recommendations using deep learning (DL).

METHODS

Six models with various causal inference approaches were trained to make individualized chemotherapy recommendations. Patients who received actual treatment recommended by DL models were compared with those who did not. Inverse probability treatment weighting (IPTW) was used to reduce bias. Linear regression, IPTW-adjusted risk difference (RD), and SurvSHAP(t) were used to interpret the best model.

RESULTS

A total of 5352 elderly breast cancer patients were included. The median (interquartile range) follow-up time was 52 (30-80) months. Among all models, the balanced individual treatment effect for survival data (BITES) performed best. Treatment according to following BITES recommendations was associated with survival benefit, with a multivariate hazard ratio (HR) of 0.78 (95% confidence interval (CI): 0.64-0.94), IPTW-adjusted HR of 0.74 (95% CI: 0.59-0.93), RD of 12.40% (95% CI: 8.01-16.90%), IPTW-adjusted RD of 11.50% (95% CI: 7.16-15.80%), difference in restricted mean survival time (dRMST) of 12.44 (95% CI: 8.28-16.60) months, IPTW-adjusted dRMST of 7.81 (95% CI: 2.93-11.93) months, and p value of the IPTW-adjusted Log-rank test of 0.033. By interpreting BITES, the debiased impact of patient characteristics on adjuvant chemotherapy was quantified, which mainly included breast cancer subtype, tumor size, number of positive lymph nodes, TNM stages, histological grades, and surgical type.

CONCLUSION

Our results emphasize the potential of DL models in guiding adjuvant chemotherapy decisions for elderly breast cancer patients.

Use of artificial intelligence in breast surgery: a narrative review

Background and Objective

We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application.

Methods

Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded.

Key Content and Findings

AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence.

Conclusions

Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.

Prognostic Tools for Older Women with Breast Cancer: A Systematic Review

Background: Breast cancer is the most common cancer in women, and older patients comprise an increasing proportion of patients with this disease. The older breast cancer population is heterogenous with unique factors affecting clinical decision making. While many models have been developed and tested for breast cancer patients of all ages, tools specifically developed for older patients with breast cancer have not been recently reviewed. We systematically reviewed prognostic models developed and/or validated for older patients with breast cancer. Methods: We conducted a systematic search in 3 electronic databases. We identified original studies that were published prior to 8 November 2022 and presented the development and/or validation of models based mainly on clinico-pathological factors to predict response to treatment, recurrence, and/or mortality in older patients with breast cancer. The PROBAST was used to assess the ROB and applicability of each included tool. Results: We screened titles and abstracts of 7316 records. This generated 126 studies for a full text review. We identified 17 eligible articles, all of which presented tool development. The models were developed between 1996 and 2022, mostly using national registry data. The prognostic models were mainly developed in the United States (n = 7; 41%). For the derivation cohorts, the median sample size was 213 (interquartile range, 81-845). For the 17 included modes, the median number of predictive factors was 7 (4.5-10). Conclusions: There have been several studies focused on developing prognostic tools specifically for older patients with breast cancer, and the predictions made by these tools vary widely to include response to treatment, recurrence, and mortality. While external validation was rare, we found that it was typically concordant with interval validation results. Studies that were not validated or only internally validated still require external validation. However, most of the models presented in this review represent promising tools for clinical application in the care of older patients with breast cancer.

Prediction model of all-cause death based on balance ability among middle-aged and older Chinese adults of overweight and obesity

Background

Advances in studies using body indicators to predict death risk. Estimating the balance ability of death risk in middle-aged and older Chinese adults with overweight and obesity is still challenging.

Methods

A retrospective analysis of the data from the China Health and Retirement Study from January 2011 to December 2018. A total of 8,632 participants were randomly divided into 7:3 a training group and a verification group, respectively. Univariable Cox analysis was used to prescreen 17 potential predictors for incorporation in the subsequent multivariable Cox analysis. Nine variables were included in the nomogram finally and validated with concordance index (C-index), calibration plots, Hosmer-Lemeshow test, and internal validation population.

Results

287 participants were death in the training group. One hundred and thirteen participants were death in the verification group. A total of nine indicators were included in the modeling group, including gender, age, marriage, hypertension, diabetes, stroke, ADL, IADL, and balance ability to establish a prediction model. The nomogram predicted death with a validated concordance index of (C-index = 0.77, 95% CI: 0.74-0.80). The inclusion of balance ability variables in the nomogram maintained predictive accuracy (C-index = 0.77, 95% CI: 0.73-0.82). The calibration curve graph and Hosmer-Lemeshow test (P > 0.05 for both the modeling group and the verification group) showed the model has a good model consistency.

Conclusion

In the present study, we provide a basis for developing a prediction model for middle-aged and older people with overweight and obesity. In most cases, balance ability is more reversible than other predictors.

Establishment and validation of a nomogram for predicting immune-related prognostic features in trunk melanoma-specific death

Background

Trunk melanoma is one of the most common and deadly types of melanomas. Multiple factors are associated with the prognosis of patients with trunk melanoma. Currently, direct, and reliable clinical tools for early assessment of individual specific risk of death are limited, and most of them are prediction models for all-cause death. Their accuracy in predicting competitiveness events, which make up a relatively large portion, may be substantially compromised. Hence, we conducted this study to investigate the risk factors of trunk melanoma-specific death to establish a comprehensive prediction model suitable for clinical application.

Methods

Patients with trunk melanoma analyzed in this study were from the SEER program [2010-2015]. The random sampling method was used to split the included cases into the training and validation cohorts at a ratio of 7:3. Univariate and multivariate competing risk models were used to screen the independent influencing factors of specific death, and then a nomogram covering these independent predictors was constructed. The concordance index (C-index) and a calibration curve were used to evaluate the calibration degree and accuracy of the nomogram.

Results

We identified 21,198 patients with trunk melanoma from the SEER database, and 3,814 of them died (17.99%). Among the death cases, deaths from other causes accounted for 66.50%The prognostic nomogram included 8 variables and 16 independent influencing factors. The overall C-index in the training set was 0.89, and the receiver operating characteristic (ROC) curve for predicting 1-, 3-, and 5-year survival was 0.928 [95% confidence interval (CI): 0.911-0.945], 0.907 (95% CI: 0.895-0.918), and 0.891 (95% CI: 0.879-0.902), respectively. The C-index of the model in the validation set was 0.89, and the area under the ROC curve (AUC) for predicting 1-, 3-, and 5-year cancer-specific death (CSD) was 0.927 (95% CI: 0.899-0.955), 0.916 (95% CI: 0.901-0.930), and 0.905 (95% CI: 0.899-0.921). Both the training set and the validation set showed the ideal calibration degree.

Conclusions

This model can be used as a potential tool for prognostic risk management of trunk melanoma in the presence of many competing events.

Effect of adjuvant chemotherapy on the survival outcomes of elderly breast cancer: A retrospective cohort study based on SEER database

BACKGROUND

Currently, the proportion of standard chemotherapy for elderly patients is much lower than that for young patients, with little evidence from clinical trials supporting the use of chemotherapy for elderly patients. The effectiveness of chemotherapy for the elderly suffering from breast cancer remains to be further verified.

METHODS

A total of 75,525 female breast cancer patients aged 70 years or older were hereby identified, all from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2010 to December 31, 2016. Kaplan-Meier analysis and multivariable Cox proportional model were performed to evaluate the effectiveness of chemotherapy on overall survival (OS) and breast cancer-specific survival (BCSS). Propensity score matching (PSM) (PSM ratio: 1:1, caliper: 0.2 standard deviation of propensity score) was applied to construct balanced cohorts with or without chemotherapy based on demographic and pathophysiological characteristics.

RESULTS

A total of 33,177 eligible patients were included, with 5273 (15.89%) receiving chemotherapy. Through PSM, 8360 patients were successfully matched, and balances between groups were almost reached. In the matched data set, multivariable Cox analysis reveals that chemotherapy was associated with a 36% and 21% risk reduction on OS (HR = 0.64, 95% CI 0.58 to 0.71) and BCSS (HR = 0.79, 95% CI 0.69 to 0.91), respectively. Furthermore, subgroups with more adjacent lymph nodes involved by tumor, or nonluminal A, were inclined to benefit more from chemotherapy. Moreover, chemotherapy did not increase the chances of dying from heart disease.

CONCLUSIONS

The present study provided evidence that chemotherapy may improve the prognosis of elderly breast cancer, especially for those subpopulations that benefit more from chemotherapy treatment.