Development and validation of nomograms for predicting survival in patients with de novo metastatic triple-negative breast cancer

Chen, Mao-Shan; Liu, Pengcheng; Yi, Jin-Zhi; Xu, Li‐Yan; He, Tao; Wu, Hao; Yang, Jiqiao; Lv, Qing

doi:10.1038/s41598-022-18727-2

Cited by 2 publications

(1 citation statement)

References 48 publications

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“…The main outcomes in our study were cancer-specific survival (CSS) and overall survival (OS), the specific definition of which referred to studies [ 13 , 15 , 16 ]. Data with continuous variables with nonnormal distributions were presented as M (P25, P75) and those with categorical variables as percentages.…”

Section: Methodsmentioning

confidence: 99%

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning

Huang,

Yuan,

Zhao

et al. 2024

PLoS ONE

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Background Primary pancreatic signet ring cell carcinoma (PSRCC), an extremely rare histologic variant of pancreatic cancer, has a poor prognosis. This study aimed to investigate the prognostic value of chemotherapy in PSRCC. Methods Patients with PSRCC between 2000 and 2019 were identified using the Surveillance Epidemiology and End Results (SEER) database. The main outcomes in this study were cancer-specific survival (CSS) and overall survival (OS). The baseline characteristics of patients were compared using Pearson’s Chi-square test. Kaplan-Meier analysis was used to generate the survival curves. Least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression models, and Random Survival Forest model were used to analyze the prognostic variables for OS and CSS. The variance inflation factors (VIFs) were used to analyze whether there was an overfitting problem. Results A total of 588 patients were identified. Chemotherapy was an independent prognostic factor for OS and CSS, and significantly associated with OS (HR = 0.33, 95% CI = 0.27–0.40, P <0.001) and CSS (HR = 0.32, 95% CI = 0.26–0.39, P <0.001). Conclusions Chemotherapy showed beneficial effects on OS and CSS in patients with PSRCC and should be recommended in clinical practice.

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Section: Methodsmentioning

confidence: 99%

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning

Huang,

Yuan,

Zhao

et al. 2024

PLoS ONE

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Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset

Adam,

Wieder

2024

Cancers

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Importance: Treatment of women with stage IV breast cancer (BC) extends population-averaged survival by only a few months. Here, we develop a model for identifying individual circumstances where appropriate therapy will extend survival while minimizing adverse events. Objective: Our goal is to develop high-confidence deep learning (DL) models to predict survival in individual stage IV breast cancer patients based on their unique circumstances generated by patient, cancer, treatment, and adverse event variables. We previously showed that predictive DL survival modeling of potentially curable stage I–III patients can be improved by combining time-fixed and time-varying covariates. Here, we demonstrate that DL-based predictive survival modeling in stage IV patients, where treatment does not offer a cure, can generate accurate individual survival predictions by considering subsequent lines of potential treatment to guide therapy. This guidance is rarely obtainable in the nearly limitless scenarios of metastatic disease. Design, Setting, and Participants: We applied the SEER-Medicare linked dataset from 1991 to 2016 to investigate 14,312 unique stage IV patients with 1,880,153 entries. We used DeepSurv- and DeepHit-, Nnet-survival- and Cox-Time DL-based predictive models to consider the combination of time-fixed and time-varying covariates at each visit for each patient. We adopted random sampling to divide the input dataset into training, validation, and testing sets. We verified the models’ implementation using the pycox package and fine-tuned the models using the open-source library Amazon SageMaker Python SDK 2.232.2 (software development kit). Our results demonstrated the proof of principle of the models by generating individual patients’ survival curves. Conclusions and Relevance: By extending the survival prediction models to consider stage IV BC patients’ time-fixed and time-varying covariates, we achieved a prediction error below 10%. Based on their circumstance-specific situations, these models can predict survival in individual stage IV patients with high confidence. The models will serve as an important adjunct to treatment decisions in patients with stage IV BC and test what-if scenarios of treatment or no treatment options to optimize therapy for extending patient lives and minimizing adverse events.

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Development and validation of nomograms for predicting survival in patients with de novo metastatic triple-negative breast cancer

Cited by 2 publications

References 48 publications

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning

Effect of chemotherapy on prognosis in patients with primary pancreatic signet ring cell carcinoma: A large real-world study based on machine learning

Predictive Modeling of Long-Term Survivors with Stage IV Breast Cancer Using the SEER-Medicare Dataset

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