Time-varying effects of prognostic factors associated with long-term survival in breast cancer

Time-varying effect in older patients with early-stage breast cancer: a model considering the competing risks based on a time scale

Background

Patients with early-stage breast cancer may have a higher risk of dying from other diseases, making a competing risks model more appropriate. Considering subdistribution hazard ratio, which is used often, limited to model assumptions and clinical interpretation, we aimed to quantify the effects of prognostic factors by an absolute indicator, the difference in restricted mean time lost (RMTL), which is more intuitive. Additionally, prognostic factors of breast cancer may have dynamic effects (time-varying effects) in long-term follow-up. However, existing competing risks regression models only provide a static view of covariate effects, leading to a distorted assessment of the prognostic factor.

Methods

To address this issue, we proposed a dynamic effect RMTL regression that can explore the between-group cumulative difference in mean life lost over a period of time and obtain the real-time effect by the speed of accumulation, as well as personalized predictions on a time scale.

Results

A simulation validated the accuracy of the coefficient estimates in the proposed regression. Applying this model to an older early-stage breast cancer cohort, it was found that 1) the protective effects of positive estrogen receptor and chemotherapy decreased over time; 2) the protective effect of breast-conserving surgery increased over time; and 3) the deleterious effects of stage T2, stage N2, and histologic grade II cancer increased over time. Moreover, from the view of prediction, the mean C-index in external validation reached 0.78.

Conclusion

Dynamic effect RMTL regression can analyze both dynamic cumulative effects and real-time effects of covariates, providing a more comprehensive prognosis and better prediction when competing risks exist.

Association of Long-term Oncologic Prognosis With Minimal Access Breast Surgery vs Conventional Breast Surgery

Importance

Minimal access breast surgery (MABS) has been used in breast cancer management. However, long-term prognostic data associated with MABS vs conventional breast surgery (CBS) are lacking.

Objective

To investigate long-term therapeutic outcomes associated with MABS vs CBS for breast cancer management.

Design, Setting, and Participants

In this single-center retrospective cohort study, 9184 individuals were assessed for inclusion. After exclusions, 2412 adult female individuals were included who were diagnosed with stage 0 to III breast cancer, underwent unilateral breast surgery between January 2004 and December 2017, and had no distant metastasis or history of severe underlying disease. Propensity score matching was performed to minimize selection bias. Data were analyzed from January 1, 2004, to December 31, 2019.

Exposures

MABS or CBS.

Main Outcomes and Measures

Data on demographic and tumor characteristics and long-term outcomes were collected and analyzed.

Results

This study included 2412 patients (100% female; median [IQR] age, 44 [40-49] years). Of these, 603 patients underwent MABS (endoscopic, endoscopy-assisted, or robot-assisted procedures in 289, 302, and 12 patients, respectively) and 1809 patients underwent CBS. The median follow-up time was 84 months (93 in the MABS group and 80 months in the CBS group). Intergroup differences were not significant for the following parameters: 10-year local recurrence-free survival (93.3% vs 96.3%; hazard ratio [HR], 1.39; 95% CI, 0.86-2.27; P = .18), regional recurrence-free survival (95.5% vs 96.7%; HR, 1.38; 95% CI, 0.81-2.36; P = .23), and distant metastasis-free survival (81.0% vs 82.0%; HR, 0.95; 95% CI, 0.74-1.23; P = .72). The 5-, 10-, and 15-year disease-free survival rates in the MABS group were 85.9%, 72.6%, and 69.1%, respectively. The corresponding rates in the CBS group were 85.0%, 76.6%, and 70.7%. The intergroup differences were not significant (HR, 1.07; 95% CI, 0.86-1.31; P = .55). The 5-, 10-, and 15-year overall survival rates in the MABS group were 92.0%, 83.7%, and 83.0%, respectively. The corresponding rates in the CBS group were 93.6%, 88.7%, and 81.0%. The intergroup differences were not significant (HR, 1.29; 95% CI, 0.97-1.72; P = .09). Post hoc subgroup analysis showed no significant intergroup differences in disease-free survival.

Conclusions and Relevance

In this cohort study, long-term outcomes following MABS were not significantly different from those following CBS in patients with early-stage breast cancer. MABS may be a safe and feasible alternative in this patient population.

Overall survival analysis of > 65-year-old patients with breast cancer based on their molecular, clinicopathological and laboratory factors

Introduction

The objective of the present study was to characterize > 65-year-old patients with breast cancer according to clinicopathological, molecular and laboratory factors.

Methods

A total of 723 breast cancer patients, who had been diagnosed and treated during 2005-2019, were retrospectively reviewed. Patients > 65 years of age (92 patients) were compared with < 50-year-old women (306 patients). We analyzed 398 women from 723 patients.

Results

Overall survival analysis was conducted for both groups, separately and combined. Patients with BC aged > 65 years were characterized by G1-2, higher lymphocyte values, lower platelet (PLT) counts and lower NLR or PLR values than patients < 50 years of age.

Conclusions

Age > 65 years is a negative prognostic factor independent of other factors.

Epidemiological features of primary breast lymphoma patients and development of a nomogram to predict survival

BACKGROUND

Studies on the epidemiology and prognosis of primary breast lymphoma (PBL) are lack for low incidence. Therefore, we aimed to investigate the epidemiological characteristics of PBL and develop nomograms to predict patient survival.

METHODS

Data of patients who were diagnosed with PBL from 1975 to 2011 and incidence rate of PBL from 1975 to 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Time-varying multivariable Cox regression analysis was performed to identify independent prognostic factors for overall survival (OS) and disease-specific survival (DSS). Nomograms were constructed based on the independent prognostic factors identified in multivariate Cox regression analysis.

RESULTS

A total of 1427 patients diagnosed with PBL were identified with the average age of 67.1 years. The overall incidence of PBL is 1.35/1,000,000 (adjusted to the United States standard population in 2000) from 1975 to 2017, with a significant upward trend by an annual percentage change (APC) of 2.91 (95%CI 2.29-3.94, P < 0.05). Age, sex, race, year of diagnosis, marital status, histological subtype, Ann Arbor Stage, and treatment modality were assessed as independent prognostic factors for OS and DSS by multivariable Cox regression (P < 0.05). Nomograms were constructed to predict the 1-, 3-, 5-, and 10- year OS and DSS. The concordance index (C-index) and calibration plots showed robustness and accuracy of the nomogram.

CONCLUSION

The overall incidence of PBL was steadily increasing over the past four decades. Nomograms constructed can predicting 1-, 3-, 5-, and 10-year OS and identify patients with high-risk PBL.

Cancer progression and the invisible phase of metastatic colonization

Metastatic dissemination occurs very early in the malignant progression of a cancer but the clinical manifestation of metastases often takes years. In recent decades, 5-year survival of patients with many solid cancers has increased due to earlier detection, local disease control and adjuvant therapies. As a consequence, we are confronted with an increase in late relapses as more antiproliferative cancer therapies prolong disease courses, raising questions about how cancer cells survive, evolve or stop growing and finally expand during periods of clinical latency. I argue here that the understanding of early metastasis formation, particularly of the currently invisible phase of metastatic colonization, will be essential for the next stage in adjuvant therapy development that reliably prevents metachronous metastasis.

Machine Learning techniques in breast cancer prognosis prediction: A primary evaluation

More than 750 000 women in Italy are surviving a diagnosis of breast cancer. A large body of literature tells us which characteristics impact the most on their prognosis. However, the prediction of each disease course and then the establishment of a therapeutic plan and follow‐up tailored to the patient is still very complicated. In order to address this issue, a multidisciplinary approach has become widely accepted, while the Multigene Signature Panels and the Nottingham Prognostic Index are still discussed options. The current technological resources permit to gather many data for each patient. Machine Learning (ML) allows us to draw on these data, to discover their mutual relations and to esteem the prognosis for the new instances. This study provides a primary evaluation of the application of ML to predict breast cancer prognosis. We analyzed 1021 patients who underwent surgery for breast cancer in our Institute and we included 610 of them. Three outcomes were chosen: cancer recurrence (both loco‐regional and systemic) and death from the disease within 32 months. We developed two types of ML models for every outcome (Artificial Neural Network and Support Vector Machine). Each ML algorithm was tested in accuracy (=95.29%‐96.86%), sensitivity (=0.35‐0.64), specificity (=0.97‐0.99), and AUC (=0.804‐0.916). These models might become an additional resource to evaluate the prognosis of breast cancer patients in our daily clinical practice. Before that, we should increase their sensitivity, according to literature, by considering a wider population sample with a longer period of follow‐up. However, specificity, accuracy, minimal additional costs, and reproducibility are already encouraging.

Neoadjuvant Chemoradiotherapy and Tumor Recurrence in Patients with Early T-Stage Cancer of the Lower Rectum

BACKGROUND

The role neoadjuvant chemoradiotherapy (nCRT) plays in oncological outcomes in early T-stage rectal cancer is uncertain. The present work aims to clarify prognostic outcomes by estimating the effect of nCRT on tumor recurrence prior to major surgery compared with major surgery alone.

PATIENTS AND METHODS

Prospectively collected data were retrospectively analyzed for patients diagnosed with localized rectal adenocarcinoma ≤ 8 cm from the anal verge, with final histopathology ≤ T2 (≤ ypT2/≤ pT2), regardless of magnetic resonance imaging staging, between 1990 and 2017. As the effect of nCRT on recurrence varied over time, thereby violating the Cox proportional hazards assumption, the effect of nCRT on recurrence hazards was estimated using a time-varying multivariate Cox model over two separate time intervals (≤ 1 year and > 1 year postsurgery) by nCRT.

RESULTS

Long-course nCRT was associated with a 5.6-fold increase in the hazard of recurrence ≤ 1 year postsurgery [hazard ratio (HR) 5.6; 95% confidence interval (CI) 1.2-24.9; P = 0.02], but there was no increase in recurrence hazards > 1 year (HR 0.84; 95% CI 0.4-2.0; P = 0.70). In subgroup analysis restricted to ≤ mrT2/≤ ypT2 and ≤ pT2 tumors (omitting > mrT2 tumors), the effect of nCRT on recurrence no longer varied over time, indicating that tumor heterogeneity was responsible for the observed increased recurrence hazards ≤ 1 year postsurgery; That is, > mrT2 tumors that were downstaged to ≤ ypT2 after nCRT were responsible for the time-varying effects of nCRT and increased recurrence hazards ≤ 1 year postsurgery. Subsequently, no difference was found in prognostic outcomes either with or without nCRT before surgery in the homogeneous population of ≤ mrT2/≤ ypT2 and ≤ pT2 tumors.

CONCLUSIONS

No evidence was found to indicate that nCRT prior to surgery reduces tumor recurrence in early T-stage lower rectal cancer compared with surgery alone.