Development and Validation of a Simulation Model-Based Clinical Decision Tool: Identifying Patients Where 21-Gene Recurrence Score Testing May Change Decisions

Prediction of cognitive decline in older breast cancer survivors: the Thinking and Living with Cancer study

PURPOSE

Cancer survivors commonly report cognitive declines after cancer therapy. Due to the complex etiology of cancer-related cognitive decline (CRCD), predicting who will be at risk of CRCD remains a clinical challenge. We developed a model to predict breast cancer survivors who would experience CRCD after systematic treatment.

METHODS

We used the Thinking and Living with Cancer study, a large ongoing multisite prospective study of older breast cancer survivors with complete assessments pre-systemic therapy, 12 months and 24 months after initiation of systemic therapy. Cognition was measured using neuropsychological testing of attention, processing speed, and executive function (APE). CRCD was defined as a 0.25 SD (of observed changes from baseline to 12 months in matched controls) decline or greater in APE score from baseline to 12 months (transient) or persistent as a decline 0.25 SD or greater sustained to 24 months. We used machine learning approaches to predict CRCD using baseline demographics, tumor characteristics and treatment, genotypes, comorbidity, and self-reported physical, psychosocial, and cognitive function.

RESULTS

Thirty-two percent of survivors had transient cognitive decline, and 41% of these women experienced persistent decline. Prediction of CRCD was good: yielding an area under the curve of 0.75 and 0.79 for transient and persistent decline, respectively. Variables most informative in predicting CRCD included apolipoprotein E4 positivity, tumor HER2 positivity, obesity, cardiovascular comorbidities, more prescription medications, and higher baseline APE score.

CONCLUSIONS

Our proof-of-concept tool demonstrates our prediction models are potentially useful to predict risk of CRCD. Future research is needed to validate this approach for predicting CRCD in routine practice settings.

Benefits and Harms of Mammography Screening in 75 + Women to Inform Shared Decision-making: a Simulation Modeling Study

BACKGROUND

Guidelines recommend shared decision-making (SDM) around mammography screening for women ≥ 75 years old.

OBJECTIVE

To use microsimulation modeling to estimate the lifetime benefits and harms of screening women aged 75, 80, and 85 years based on their individual risk factors (family history, breast density, prior biopsy) and comorbidity level to support SDM in clinical practice.

DESIGN, SETTING, AND PARTICIPANTS

We adapted two established Cancer Intervention and Surveillance Modeling Network (CISNET) models to evaluate the remaining lifetime benefits and harms of screening U.S. women born in 1940, at decision ages 75, 80, and 85 years considering their individual risk factors and comorbidity levels. Results were summarized for average- and higher-risk women (defined as having breast cancer family history, heterogeneously dense breasts, and no prior biopsy, 5% of the population).

MAIN OUTCOMES AND MEASURES

Remaining lifetime breast cancers detected, deaths (breast cancer/other causes), false positives, and overdiagnoses for average- and higher-risk women by age and comorbidity level for screening (one or five screens) vs. no screening per 1000 women.

RESULTS

Compared to stopping, one additional screen at 75 years old resulted in six and eight more breast cancers detected (10% overdiagnoses), one and two fewer breast cancer deaths, and 52 and 59 false positives per 1000 average- and higher-risk women without comorbidities, respectively. Five additional screens over 10 years led to 23 and 31 additional breast cancer cases (29-31% overdiagnoses), four and 15 breast cancer deaths avoided, and 238 and 268 false positives per 1000 average- and higher-risk screened women without comorbidities, respectively. Screening women at older ages (80 and 85 years old) and high comorbidity levels led to fewer breast cancer deaths and a higher percentage of overdiagnoses.

CONCLUSIONS

Simulation models show that continuing screening in women ≥ 75 years old results in fewer breast cancer deaths but more false positive tests and overdiagnoses. Together, clinicians and 75 + women may use model output to weigh the benefits and harms of continued screening.

Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review

PURPOSE

Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research.

METHODS

We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States.

RESULTS

A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents.

CONCLUSIONS

A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.

Reassessing the Benefits and Harms of Risk-Reducing Medication Considering the Persistent Risk of Breast Cancer Mortality in Estrogen Receptor-Positive Breast Cancer

PURPOSE

Recent studies, including a meta-analysis of 88 trials, have shown higher than expected rates of recurrence and death in hormone receptor-positive breast cancer. These new findings suggest a need to re-evaluate the use of risk-reducing medication to avoid invasive breast cancer and breast cancer death in high-risk women.

METHODS

We adapted an established Cancer Intervention and Surveillance Modeling Network model to evaluate the lifetime benefits and harms of risk-reducing medication in women with a ≥ 3% 5-year risk of developing breast cancer according to the Breast Cancer Surveillance Consortium risk calculator. Model input parameters were derived from meta-analyses, clinical trials, and large observational data. We evaluated the effects of 5 years of risk-reducing medication (tamoxifen/aromatase inhibitors) with annual screening mammography ± magnetic resonance imaging (MRI) compared with no screening, MRI, or risk-reducing medication. The modeled outcomes included invasive breast cancer, breast cancer death, side effects, false positives, and overdiagnosis. We conducted subgroup analyses for individual risk factors such as age, family history, and prior biopsy.

RESULTS

Risk-reducing tamoxifen with annual screening (± MRI) decreased the risk of invasive breast cancer by 40% and breast cancer death by 57%, compared with no tamoxifen or screening. This is equivalent to an absolute reduction of 95 invasive breast cancers, and 42 breast cancer deaths per 1,000 high-risk women. However, these drugs are associated with side effects. For example, tamoxifen could increase the number of endometrial cancers up to 11 per 1,000 high-risk women. Benefits and harms varied by individual characteristics.

CONCLUSION

The addition of risk-reducing medication to screening could further decrease the risk of breast cancer death. Clinical guidelines for high-risk women should consider integrating shared decision making for risk-reducing medication and screening on the basis of individual risk factors.

The regrouping of Luminal B (HER2 negative), a better discriminator of outcome and recurrence score

BACKGROUND

Breast cancer (BC) remains the leading cause of cancer-related deaths worldwide. High recurrence risk Luminal BC receives adjuvant chemotherapy in addition to standard hormone therapy. Considering the heterogeneity of Luminal B BC, a more accurate classification model is urgently needed.

METHODS

In this study, we retrospectively reviewed the data of 1603 patients who were diagnosed with HER2-negative breast invasive ductal carcinoma. According to the expression level of PR and Ki-67 index, the Luminal B (HER2-negative) BCs were divided into three groups: ER+PR-Ki67_low (ER-positive, PR-negative, and Ki-67 index <20%), ER+PR+Ki67_high (ER-positive, PR-positive, and Ki-67 index ≥20%), and ER+PR-Ki67_high (ER-positive, PR-negative, and Ki-67 index ≥20%). The cox proportional hazards regression model was used to evaluate the correlation between each variable and outcomes. Besides, discriminatory accuracy of the models was compared using the area under the receiver operating characteristic curve and log-rank χ² value.

RESULTS

The analysis results showed that there was a significant correlation between subtypes using this newly defined classification and overall survival (p < 0.001) and disease-free survival (DFS) (p < 0.001). Interestingly, patients in the ER+PR-Ki67_high subgroup have the worst survival outcome in Luminal B (HER2-negative) subtype, similar to Triple-negative patients. Besides, the ER+PR+Ki67_high has worse 5-year DFS compared with Luminal A group. There was a significant relationship between the regrouping subtype and the recurrence score index (RI) (p < 0.001). Moreover, the results showed that patients in ER+PR-Ki67_high subtype were more likely to have high RI for distance recurrence (RI-DR) and local recurrence (RI-LRR). Our newly defined classification has a better discrimination ability to predict survival outcome and recurrence score of Luminal B (HER2-negative) BC patients, which may help in clinical decision-making for individual treatment.

A scoping review of interactive and personalized web-based clinical tools to support treatment decision making in breast cancer

The increasing attention on personalized breast cancer care has resulted in an explosion of new interactive, tailored, web-based clinical decision tools for guiding treatment decisions in clinical practice. The goal of this study was to review, compare, and discuss the clinical implications of current tools, and highlight future directions for tools aiming to improve personalized breast cancer care. We searched PubMed, Embase, PsychInfo, Cochrane Database of Systematic Reviews, Web of Science, and Scopus to identify web-based decision tools addressing breast cancer treatment decisions. There was a total of 17 articles associated with 21 unique tools supporting decisions related to surgery, radiation therapy, hormonal therapy, bisphosphonates, HER2-targeted therapy, and chemotherapy. The quality of the tools was assessed using the International Patient Decision Aid Standard instrument. Overall, the tools considered clinical (e.g., age) and tumor characteristics (e.g., grade) to provide personalized outcomes (e.g., survival) associated with various treatment options. Fewer tools provided the adverse effects of the selected treatment. Only one tool was field-tested with patients, and none were tested with healthcare providers. Future studies need to assess the feasibility, usability, acceptability, as well as the effects of personalized web-based decision tools on communication and decision making from the patient and clinician perspectives.

The Clinical Research and Latest Application of Genomic Assays in Early-Stage Breast Cancer

Breast cancer is a kind of malignant tumor that seriously endangers women's life and health. Once diagnosed, most patients will receive a combination of treatments to achieve a cure. However, breast cancer is a heterogeneous disease. Even with the same clinical stage and pathological features, its response to treatment and postoperative recurrence risk may still be completely different. With the advent of genomic assay, some patients with early-stage breast cancer who originally needed treatment can still achieve long-term disease-free survival without adjuvant chemotherapy, so as to achieve personalized and accurate treatment mode to a certain extent. In this paper, we reviewed the 5 most widely used and studied genomic panel technologies in breast cancer, namely Oncotype DX, MammaPrint, RecurIndex, PAM50, and EndoPredict, according to accessibility and availability. Based on the results of the completed or ongoing clinical studies, we summarized the origin, applicable population, and clinical efficacy of each detection method, and discussed the potential development prospect of detection technology in the future.