The effect of (neo)adjuvant chemotherapy on long-term survival outcomes in patients with invasive lobular breast cancer treated with endocrine therapy: A retrospective cohort study.

BACKGROUND: Despite histological and molecular differences between invasive lobular carcinoma (ILC) and invasive carcinoma of no special type, according to national treatment guidelines no distinction is made regarding the use of (neo)adjuvant chemotherapy. Studies on the long-term outcome of chemotherapy in patients with ILC are scarce and show inconclusive results. METHODS: All patients with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative ILC with an indication for chemotherapy treated with adjuvant endocrine therapy were selected from the Erasmus Medical Center Breast Cancer database. Cox proportional hazards models were used to estimate the effect of chemotherapy on recurrence-free survival (RFS), breast cancer-specific survival (BCSS), and overall survival (OS). RESULTS: A total of 520 patients were selected, of whom 379 were treated with chemotherapy and 141 were not. Patients in the chemotherapy group were younger (51 vs. 61 years old; p < .001), had a higher T status (T3+, 33% vs. 14%; p < .001), and more often had lymph node involvement (80% vs. 49%; p < .001) in comparison to the no-chemotherapy group. After adjusting for confounders, chemotherapy treatment was not associated with better RFS (hazard ratio [HR], 1.20; 95% confidence interval [CI], 0.63-2.31), BCSS (HR, 1.24; 95% CI, 0.60-2.58), or OS (HR, 0.97; 95% CI, 0.56-1.66). This was also reflected by adjusted Cox survival curves in the chemotherapy versus no-chemotherapy group for RFS (75% vs. 79%), BCSS (80% vs. 84%), and OS (72% vs. 71%). CONCLUSIONS: Chemotherapy is not associated with improved RFS, BCSS, or OS for patients with ER+/HER2- ILC treated with adjuvant endocrine therapy and with an indication for chemotherapy.

Development of a Clinical Prediction Model for 1-Year Mortality in Patients With Advanced Cancer.

Importance: To optimize palliative care in patients with cancer who are in their last year of life, timely and accurate prognostication is needed. However, available instruments for prognostication, such as the surprise question ("Would I be surprised if this patient died in the next year?") and various prediction models using clinical variables, are not well validated or lack discriminative ability. Objective: To develop and validate a prediction model to calculate the 1-year risk of death among patients with advanced cancer. Design, Setting, and Participants: This multicenter prospective prognostic study was performed in the general oncology inpatient and outpatient clinics of 6 hospitals in the Netherlands. A total of 867 patients were enrolled between June 2 and November 22, 2017, and followed up for 1 year. The primary analyses were performed from October 9 to 25, 2019, with the most recent analyses performed from June 19 to 22, 2022. Cox proportional hazards regression analysis was used to develop a prediction model including 3 categories of candidate predictors: clinician responses to the surprise question, patient clinical characteristics, and patient laboratory values. Data on race and ethnicity were not collected because most patients were expected to be of White race and Dutch ethnicity, and race and ethnicity were not considered as prognostic factors. The models' discriminative ability was assessed using internal-external validation by study hospital and measured using the C statistic. Patients 18 years and older with locally advanced or metastatic cancer were eligible. Patients with hematologic cancer were excluded. Main Outcomes and Measures: The risk of death by 1 year. Results: Among 867 patients, the median age was 66 years (IQR, 56-72 years), and 411 individuals (47.4%) were male. The 1-year mortality rate was 41.6% (361 patients). Three prediction models with increasing complexity were developed: (1) a simple model including the surprise question, (2) a clinical model including the surprise question and clinical characteristics (age, cancer type prognosis, visceral metastases, brain metastases, Eastern Cooperative Oncology Group performance status, weight loss, pain, and dyspnea), and (3) an extended model including the surprise question, clinical characteristics, and laboratory values (hemoglobin, C-reactive protein, and serum albumin). The pooled C statistic was 0.69 (95% CI, 0.67-0.71) for the simple model, 0.76 (95% CI, 0.73-0.78) for the clinical model, and 0.78 (95% CI, 0.76-0.80) for the extended model. A nomogram and web-based calculator were developed to support clinicians in adequately caring for patients with advanced cancer. Conclusions and Relevance: In this study, a prediction model including the surprise question, clinical characteristics, and laboratory values had better discriminative ability in predicting death among patients with advanced cancer than models including the surprise question, clinical characteristics, or laboratory values alone. The nomogram and web-based calculator developed for this study can be used by clinicians to identify patients who may benefit from palliative care and advance care planning. Further exploration of the feasibility and external validity of the model is needed.