Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation

Meng, Xiangdi; Hao, Furong; Ju, Zhuojun; Chang, Xiaolong; Guo, Yinghua

doi:10.3389/fpubh.2022.953992

Cited by 11 publications

(6 citation statements)

References 31 publications

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“…Traditional survival analysis methods may exaggerate the risk of CSM because they fail to consider the effect of competing risk factor on patients’ death. [ 10 ] To analyze of survival data in the presence of competing risks, Austin et al [ 11 ] proposed a statistical method called competition risk model. As the competing risk model considers the influence of other risk factors on CSM, it is more in line with clinical practice to study the prognosis of patients.…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of nomogram for the cancer-specific mortality for HER2-positive breast cancer patients

2024

Medicine

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The cancer-specific mortality (CSM) of patients with human epidermal growth factor receptor 2 positive (HER2+) breast cancer remains dismal and varies widely from person to person. Therefore, we aim to construct a nomogram to predict CSM in HER2+ breast cancer using data from the surveillance, epidemiology, and end results (SEER) database. The clinicopathological data of patients diagnosed with HER2+ breast cancer from 2000 to 2019 were selected from the SEER database. Independent prognostic factors for CSM of patients were identified by competing risk model. Subsequently, we constructed a new predicting nomogram. Calibration curves, receiver operating characteristic curve, and decision curve were used to evaluate the efficiency of the nomogram. A total of 45,362 breast cancer patients in the SEER database were selected for study and randomly separated into training (n = 31,753) and validation (n = 13,609) cohorts. Univariate and multivariate analysis showed that age, race, tumor grade, clinical stage, T stage, surgery status, radiotherapy, chemotherapy, and regional nodes examined were independent risk factors for CSM of HER2+ breast cancer patients. Receiver operating characteristic curves for the prediction nomogram of the CSM for breast cancer patients indicated that the 1-, 3- and 5-year AUCs were 0.874, 0.843, and 0.820 in the training cohort and 0.861, 0.845, and 0.825 in the validation cohort, respectively. The c-index was 0.817 and 0.821 in training cohort and validation cohort, respectively. Moreover, a good agreement was seen between the observed outcome and the predicted probabilities in the calibration curves of the nomogram in training cohort and validation cohort. Further decision curve analysis demonstrated good clinical utilities of the nomogram in training cohort and validation cohort. The nomogram shows good accuracy and reliability in predicting the CSM of breast cancer patients, and it could provide some theoretical support for clinicians to make decisions.

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Section: Discussionmentioning

confidence: 99%

Construction and validation of nomogram for the cancer-specific mortality for HER2-positive breast cancer patients

2024

Medicine

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“…Therefore, the precise and timely forecasting of survival is paramount. CS analysis can aid physicians and patients in gaining a better understanding of the patient's prognosis and treatment process, while also providing a foundation for designing treatment plans and follow-up procedures (27). Traditional methods for survival analysis have limitations when applied to real-time data (10, 20, 28).…”

Section: Discussionmentioning

confidence: 99%

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Zhang,

Xiang,

Chen

et al. 2024

Front. Med.

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BackgroundMerkel cell carcinoma (MCC) is a rare type of invasive neuroendocrine skin malignancy with high mortality. However, with years of follow-up, what is the actual survival rate and how can we continually assess an individual’s prognosis? The purpose of this study was to estimate conditional survival (CS) for MCC patients and establish a novel CS-based nomogram model.MethodsThis study collected MCC patients from the Surveillance, Epidemiology, and End Results (SEER) database and divided these patients into training and validation groups at the ratio of 7:3. CS refers to the probability of survival for a specific timeframe (y years), based on the patient’s survival after the initial diagnosis (x years). Then, we attempted to describe the CS pattern of MCCs. The Least absolute shrinkage and selection operator (LASSO) regression was employed to screen predictive factors. The Multivariate Cox regression analysis was applied to demonstrate these predictors’ effect on overall survival and establish a novel CS-based nomogram.ResultsA total of 3,843 MCC patients were extracted from the SEER database. Analysis of the CS revealed that the 7-year survival rate of MCC patients progressively increased with each subsequent year of survival. The rates progressed from an initial 41–50%, 61, 70, 78, 85%, and finally to 93%. And the improvement of survival rate was nonlinear. The LASSO regression identified five predictors including patient age, sex, AJCC stage, surgery and radiotherapy as predictors for CS-nomogram development. And this novel survival prediction model was successfully validated with good predictive performance.ConclusionCS of MCC patients was dynamic and increased with time since the initial diagnosis. Our newly established CS-based nomogram can provide a dynamic estimate of survival, which has implications for follow-up guidelines and survivorship planning, enabling clinicians to guide treatment for these patients better.

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“…The chi-square test was employed to compare the categorical variables between the two groups. Subsequently, three methodologies including univariate Cox regression (P<0.05 as screening criteria), the least absolute shrinkage and selection operator (LASSO) regression (lambda.min is used as a screening criterion) and the best subset regression (BSR) (adjusted R-squared maximum as screening criteria) were utilized for predictor selection in training cohort, as described in the previous article ( 19 ). The subset of variables initially identified through these three distinct methodologies was then subjected to a multivariate Cox regression in conjunction with a gradual stepwise backward regression procedure, aimed at refining and enhancing our final model selection.…”

Section: Methodsmentioning

confidence: 99%

Conditional survival of patients with primary bone lymphoma of the spine: how survival changes after initial diagnosis

Zheng,

Yang,

Qian

et al. 2024

Front. Oncol.

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BackgroundThe current survival prediction methodologies for primary bone lymphoma (PBL) of the spine are deficient. This study represents the inaugural utilization of conditional survival (CS) to assess the outcome of this disease. Moreover, our objective was to devise a CS-based nomogram for predicting overall survival (OS) in real-time for spinal PBL.MethodsPatients with PBL of the spine diagnosed between January 2000 and December 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The OS was determined through the Kaplan–Meier method. The CS characteristic of patients with spinal PBL was delineated, with the CS being estimated utilizing the formula: CS(α|β) = OS(α+β)/OS(β). CS(α|β) denotes the probability of additional α-year survivorship, assuming the patient has already survived β years after the time of observation. Three methods including univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) regression were used to identify predictors for CS-based nomogram construction.ResultsKaplan-Meier analysis was executed to determine the OS rate for these patients, revealing a survival rate of 68% and subsequently 63% at the 3-year and 5-year mark respectively. We then investigated the CS patterning exhibited by these patients and discovered the survival of PBL in the spine progressively improved with time. Meanwhile, through three different prognostic factor selection methods, we identified the best predicter subset including age, tumor histology, tumor stage, chemotherapy and marital status, for survival prediction model construction. Finally, we successfully established and validated a novel CS-based nomogram model for real-time and dynamic survival estimation. Moreover, we further designed a risk stratification system to facilitate the identification of high-risk patients.ConclusionsThis is the first study to analyze the CS pattern of PBL of the spine. And we have also developed a CS-based nomogram that provide dynamic prognostic data in real-time, thereby aiding in the formulation of personalized treatment strategies in clinical practice.

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Conditional survival nomogram predicting real-time prognosis of locally advanced breast cancer: Analysis of population-based cohort with external validation

Cited by 11 publications

References 31 publications

Construction and validation of nomogram for the cancer-specific mortality for HER2-positive breast cancer patients

Construction and validation of nomogram for the cancer-specific mortality for HER2-positive breast cancer patients

Conditional survival analysis and dynamic prediction of long-term survival in Merkel cell carcinoma patients

Conditional survival of patients with primary bone lymphoma of the spine: how survival changes after initial diagnosis

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