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

Zhu, Shouqiang; Zhao, Zheng; Hu, Wenyu; Lei, Chong

doi:10.1016/j.clbc.2023.05.005

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…In recent times, CS analysis has emerged as a novel technique for evaluating cancer survival, with distinct advantages in anticipating poor prognosis cancer and determining changes in survival rates ( 23 , 24 ). Presently, numerous studies are investigating the practical application of this method in the clinical setting ( 25 , 26 ). This study initially examined the CS pattern of MCC patients and found that their survival improved dynamically with each passing year survived.…”

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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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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“…Initially, we conducted a comprehensive analysis of the CS pattern of NKLCSCC patients and found a signi cant improvement in their 10-year survival rates with each passing year. The traditional survival analysis method presents certain limitations as it fails to meet the demand for real-time dynamism [16,21,29]. Given that CS analysis enables real-time survival prediction, we then integrated this method into the nomogram and developed a novel CS-based nomogram model, with the aim of providing NKLCSCC survivors with timely and accurate survival information updates.…”

Section: Discussionmentioning

confidence: 99%

Conditional survival nomogram for dynamic monitoring of long-term survival in non- keratinizing large cell squamous cell carcinoma

Zhang,

Xiang,

Chen

et al. 2023

Preprint

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Background: The incidence of nonkeratinizing large cell squamous cell carcinoma (NKLCSCC) continues to rise. Compared to other squamous cell carcinoma subtypes, the NKLCSCC displays lower differentiation and higher malignancy, necessitating specialized analysis and research of this disease. Therefore, the aim of our study was to develop and evaluate a novel conditional survival (CS)-based prediction model for NKLCSCC patients, with the objective of offering timely and accurate updates on survival rates. Methods: The data for patients with NKLCSCC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The identified patients were randomized into the training group and the validation group, with a proportion of 7:3. The Kaplan–Meier method was used to estimate overall survival (OS). The CS rate was defined as the likelihood of a patient surviving for a specific period of time following NKLCSCC diagnosis, based on the number of years they have already survived. We firstly described the CS pattern of the NKLCSCC patients. Subsequently, a least absolute shrinkage and selection operator (LASSO) regression method with 10-fold cross-validation was employed to identify prognostic factors. A multivariate Cox regression model was used to demonstrate these predictors’ prognostic value and to develop a CS-based nomogram model. Lastly, the predictive performance of the developed model was evaluated and validated. Results: Based on the SEER database, a total of 7,252 elderly patients with NKLCSCC were identified from 2000 to 2019, with 5,076 patients allocated to the training group and 2,176 patients assigned to the validation group. Through CS analysis, we observed that these patients exhibited a remarkable improvement in 10-year survival rate with each additional year of survival. The survival rate increased from initially 56% to 62%, 70%, 74%, 79%, 82%, 86%, 90%, 93% and ultimately reached an impressive 97%. The LASSO regression analysis achieved a 10-fold cross-validation and identified 9 significant predictive factors. Then, the CS-based nomogram was successfully constructed based on these selected predictors and it could effectively stratify risk for these patients. Furthermore, this CS-based survival prediction model was successfully validated in both training and validation groups. Conclusion: This study described the CS pattern of patients with NKLCSCC, underscoring the gradual improvement in survival rates among long-term survivors over time. We have also developed the first novel CS-based nomogram model for NKLCSCC patients, which enables real-time prognostic prediction and risk stratification, facilitating personalized treatment decision-making and devising more cost-effective follow-up strategies for clinicians.

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“…Survival rates are a particularly compelling subject in many clinical research studies, as they can offer personalized prognoses, inform adjustments to follow-up schedules, and help prevent unnecessary treatments. Traditionally, survival rates have been predicted using statistical methods that take into account various factors, such as tumor stage at diagnosis, patient age, and overall health [9][10][11]. However, accurately interpreting the factors that influence these results necessitates a solid grasp of statistical evaluation, comparison, and analysis.…”

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confidence: 99%

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

Chanudom,

Tharavichitkul,

Laosiritaworn

2024

Healthc Inform Res

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Objectives: The objective of this research is to apply machine learning (ML) algorithms to predict the survival of cervical cancer patients. The aim was to address the limitations of traditional statistical methods, which often fail to provide accurate answers due to the complexity of the problem.Methods: This research employed visualization techniques for initial data understanding. Subsequently, ML algorithms were used to develop both classification and regression models for survival prediction. In the classification models, we trained the algorithms to predict the time interval between the initial diagnosis and the patient’s death. The intervals were categorized as “<6 months,” “6 months to 3 years,” “3 years to 5 years,” and “>5 years.” The regression model aimed to predict survival time (in months). We used attribute weights to gain insights into the model, highlighting features with a significant impact on predictions and offering valuable insights into the model’s behavior and decision-making process.Results: The gradient boosting trees algorithm achieved an 81.55% accuracy in the classification model, while the random forest algorithm excelled in the regression model, with a root mean square error of 22.432. Notably, radiation doses around the affected areas significantly influenced survival duration.Conclusions: Machine learning demonstrated the ability to provide high-accuracy predictions of survival periods in both classification and regression problems. This suggests its potential use as a decision-support tool in the process of treatment planning and resource allocation for each patient.

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

Cited by 4 publications

References 27 publications

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

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

Conditional survival nomogram for dynamic monitoring of long-term survival in non- keratinizing large cell squamous cell carcinoma

Prediction of Cervical Cancer Patients’ Survival Period with Machine Learning Techniques

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