Nomograms for Estimating Cause-Specific Death Rates of Patients With Inflammatory Breast Cancer: A Competing-Risks Analysis

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.

A nomogram for predicting survival in patients with gastrointestinal stromal tumor: a study based on the surveillance, epidemiology, and end results database

Purpose

The objective of this investigation was to construct and validate a nomogram for prognosticating cancer-specific survival (CSS) in patients afflicted with gastrointestinal stromal tumor (GIST) at 3-, 5-, and 8-years post-diagnosis.

Methods

Data pertaining to patients diagnosed with GIST were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Through random selection, a training cohort (70%) and a validation cohort (30%) were established from the patient population. Employing a backward stepwise Cox regression model, independent prognostic factors were identified. Subsequently, these factors were incorporated into the nomogram to forecast CSS rates at 3-, 5-, and 8-years following diagnosis. The nomogram's performance was assessed using indicators such as the consistency index (C-index), the area under the time-dependent receiver operating characteristic curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration curves, and decision-curve analysis (DCA).

Results

This investigation encompassed a cohort of 3,062 GIST patients. By analyzing the Cox regression model within the training cohort, nine prognostic factors were identified: age, sex, race, marital status, AJCC (American Joint Committee on Cancer) stage, surgical status, chemotherapy status, radiation status, and income status. The nomogram was subsequently developed and subjected to both internal and external validation. The nomogram exhibited favorable discrimination abilities, as evidenced by notably high C-indices and AUC values. Calibration curves confirmed the nomogram's reliability. Moreover, the nomogram outperformed the AJCC model, as demonstrated by enhanced NRI and IDI values. The DCA curves validated the clinical utility of the nomogram.

Conclusion

The present study has successfully constructed and validated the initial nomogram for predicting prognosis in GIST patients. The nomogram's performance and practicality suggest its potential utility in clinical settings. Nevertheless, further external validation is warranted.

ER+/PR- phenotype exhibits more aggressive biological features and worse outcome compared with ER+/PR+ phenotype in HER2-negative inflammatory breast cancer

The loss of progesterone receptor (PR) often predicts worse biological behavior and prognosis in estrogen receptor-positive (ER +) breast cancer. However, the impact of PR status on inflammatory breast cancer (IBC) has not been studied. Therefore, the purpose of our study was to investigate the influence of PR on IBC. Patients with ER+ and HER2-negative IBC were selected from the Surveillance, Epidemiology and End Results database. Pearson's χ2 test was used to compare the clinicopathological characteristics between patients with estrogen receptor-positive/progesterone receptor-positive (ER+/PR +) and patients with estrogen receptor-positive/progesterone receptor-negative (ER+/PR-). Univariate and multivariate analyses were performed to investigate the effects of PR status on the breast cancer-specific survival (BCSS) and overall survival (OS) in IBC. Overall, 1553 patients including 1157 (74.5%) patients with ER+/PR+ and 396 (25.5%) patients with ER+/PR- were analyzed in our study. The patients with ER+/PR- were more likely to be high histological grade (p < 0.001) and liver metastasis (p = 0.045) compared to patients with ER+/PR+. Despite higher chance of receiving chemotherapy (83.6% vs 77.3%, P = 0.008), patients with ER+/PR- showed worse BCSS (5-year BCSS rate, 34.3% vs 51.3%, P < 0.001) and OS (5-year OS rate, 31.3% vs 46.1%, P < 0.001) compared with ER+/PR+ phenotype. Multivariate survival analysis showed that patients with ER+/PR- still had worse BCSS (hazard ratios [HR]: 1.764, 95% confidence intervals [CI] 1.476-2.109, P < 0.001) and OS (HR: 1.675, 95% CI 1.411-1.975, P < 0.001) than ER+/PR+ phenotype. Furthermore, patients with ER+/PR- showed worse outcomes than ER+/PR+ phenotype in most subgroups, especially in patients with younger age (≤ 60 years), lower histological grade, lymph node involved and distant metastasis. Patients with ER+/PR- had more aggressive biological behaviors and worse outcomes than patients with ER+/PR+ in IBC. Stronger treatments maybe needed for IBC patients with ER+/PR-.

Risk-dependent conditional survival analysis and annual hazard rate of inflammatory breast cancer

PURPOSE

The real-time prognosis of patients with inflammatory breast cancer (IBC) after surviving for several years was unclear. We aimed to estimate survival over time in IBC using conditional survival (CS) and annual hazard functions.

PATIENTS AND METHODS

This study recruited 679 patients diagnosed with IBC between 2010 and 2019 from the Surveillance, Epidemiology, and End Results (SEER) database. We used the Kaplan-Meier method to estimate overall survival (OS). CS was the probability of surviving for another y years after surviving for x years after the diagnosis, and the annual hazard rate was the cumulative mortality rate of follow-up patients. Cox regression analyses were used to identify prognostic factors, and changes in real-time survival and immediate mortality in surviving patients were assessed within these prognostic factors.

RESULTS

CS analysis showed real-time improvement in survival, with 5-year OS updated annually from the initial 43.5% to 52.2%, 65.3%, 78.5%, and 89.0% (surviving 1-4 years, respectively). However, this improvement was relatively small in the first two years after diagnosis, and the smoothed annual hazard rate curve showed increasing mortality during this period. Cox regression identified seven unfavorable factors at diagnosis, but only distant metastases remained after five years of survival. Analysis of the annual hazard rate curves showed that mortality continued to decrease for most survivors, except for metastatic IBC.

CONCLUSION

Real-time survival of IBC improved dynamically over time, and the magnitude of this improvement was non-linear, depending on survival time and clinicopathological characteristics.

Conditional Cancer-Specific Survival for Inflammatory Breast Cancer: Analysis of SEER, 2010 to 2016

BACKGROUND

Conditional survival takes into account the time that has elapsed since diagnosis and may have additional informative value. Compared with the static traditional survival evaluation method, conditional survival predictions can be adapted to incorporate the dynamic changes during the disease and provide a more suitable way of identifying time-evolved prognoses.

METHODS

Of 3333 patients diagnosed with inflammatory breast cancer between 2010 and 2016 were extracted from the Surveillance, Epidemiology, and End Results database. The trend of the hazard rate over time was represented by the kernel density smoothing curve. The traditional cancer-specific survival (CSS) rate was estimated by the Kaplan-Meier method. Conditional CSS assessment was defined as the probability that a patient will survive y years given the x years who already survived after diagnosis, and the formula is as follows: CS(y)=CSS(x + y)/CSS(x). 3-year cancer-specific survival (CSS3) and 3-year conditional cancer-specific survival (CS3) were estimated. The Fine-Gray proportional subdistribution hazard model was constructed to screen for time-dependent risk factors associated with cancer-specific death. Subsequently, a nomogram was applied to predict a 5-year survival rate based on the number of years already survived.

RESULTS

Of 3333 patients, the cancer-specific survival (CSS) rate decreased from 57% in the 4th year to 49% in the 6th year, while the comparable 3-year CS (CS3) rate improved from 65% in the first year to 76% in the third year. Overall, the CS3 rate was superior to actuarial cancer-specific survival, which was also found in subgroup analysis, especially in patients with high-risk characteristics. The Fine-Gray's model indicated that remote organ metastasis (M stage), lymph node metastasis (N stage), and surgery all significantly impacted the prognosis for cancer-specific survival. The Fine-Gray's model-based nomogram was constructed to predict 5-year cancer-specific survival immediately after diagnosis and given survival for 1, 2, 3, and 4 years after diagnosis.

CONCLUSION

High-risk patients had a significantly improved cancer-specific survival prognosis after surviving for 1 or more years after diagnosis with inflammatory breast cancer. The probability of reaching 5-year cancer-specific survival following diagnosis improves with each additional year survived. More effective follow-up is required for patients diagnosed at an advanced N stage, remote organ metastasis, or not received surgery. Additionally, a nomogram and web-based calculator may be helpful for patients with inflammatory breast cancer during follow-up counseling (https://ibccondsurv.shinyapps.io/dynnomapp/).

A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database

Introduction

This study aimed to develop and validate a nomogram for predicting cancer-specific survival (CSS) in patients with non-keratinized large cell squamous cell carcinoma (NKLCSCC) at 3, 5, and 8 years after diagnosis.

Methods

Data on SCC patients were collected from the Surveillance, Epidemiology, and End Results database. Training (70%) and validation (30%) cohorts were generated using random selection of patients. Independent prognostic factors were selected using the backward stepwise Cox regression model. To predict the CSS rates in patients with NKLCSCC at 3, 5, and 8 years after diagnosis, all of the factors were incorporated into the nomogram. Indicators such as the concordance index (C-index), area under the time-dependent receiver operating characteristic curve (AUC), net reclassification index (NRI), integrated discrimination improvement (IDI), calibration curve, and decision-curve analysis (DCA) were then used to validate the performance of the nomogram.

Results

This study included 9,811 patients with NKLCSCC. Twelve prognostic factors were identified by Cox regression analysis in the training cohort, which were age, number of regional nodes examined, number of positive regional nodes, sex, race, marital status, American Joint Committee on Cancer (AJCC) stage, surgery status, chemotherapy status, radiotherapy status, summary stage, and income. The constructed nomogram was validated both internally and externally. The nomogram had good discrimination ability, as indicated by the comparatively high C-indices and AUC values. The nomogram was properly calibrated, as indicated by the calibration curves. Our nomogram was superior to the AJCC model, as illustrated by its superior NRI and IDI values. DCA curves indicated the clinical usability of the nomogram.

Conclusion

The first nomogram for prognosis predictions of patients with NKLCSCC has been developed and verified. Its performance and usability demonstrated that the nomogram could be utilized in clinical settings. However, additional external verification is still required.

Characteristics, Prognosis, and Competing Risk Nomograms of Cutaneous Malignant Melanoma: Evidence for Pigmentary Disorders

PURPOSE

Cutaneous malignant melanoma (CMM) always presents as a complex disease process with poor prognosis. The objective of the present study was to explore the influence of solitary or multiple cancers on the prognosis of patients with CMM to better understand the landscape of CMM.

METHODS

We reviewed the records of CMM patients between 2004 and 2015 from the Surveillance, Epidemiology, and End Results Program. The cumulative incidence function was used to represent the probabilities of death. A novel causal inference method was leveraged to explore the risk difference to death between different types of CMM, and nomograms were built based on competing risk models.

RESULTS

The analysis cohort contained 165,043 patients with CMM as the first primary malignancy. Patients with recurrent CMM and multiple primary tumors had similar overall survival status (p = 0.064), while their demographics and cause-specific death demonstrated different characteristics than those of patients with solitary CMM (p < 0.001), whose mean survival times are 75.4 and 77.3 months and 66.2 months, respectively. Causal inference was further applied to unveil the risk difference of solitary and multiple tumors in subgroups, which was significantly different from the total population (p < 0.05), and vulnerable groups with high risk of death were identified. The established competing risk nomograms had a concordance index >0.6 on predicting the probabilities of death of CMM or other cancers individually across types of CMM.

CONCLUSION

Patients with different types of CMM had different prognostic characteristics and different risk of cause-specific death. The results of this study are of great significance in identifying the high risk of cause-specific death, enabling targeted intervention in the early period at both the population and individual levels.