BackgroundLocally advanced breast cancer (LABC) is generally considered to have a relatively poor prognosis. However, with years of follow-up, what is its real-time survival and how to dynamically estimate an individualized prognosis? This study aimed to determine the conditional survival (CS) of LABC and develop a CS-nomogram to estimate overall survival (OS) in real-time.MethodsLABC patients were recruited from the Surveillance, Epidemiology, and End Results (SEER) database (training and validation groups, n = 32,493) and our institution (testing group, n = 119). The Kaplan–Meier method estimated OS and calculated the CS at year (x+y) after giving x years of survival according to the formula CS(y|x) = OS(y+x)/OS(x). y represented the number of years of continued survival under the condition that the patient was determined to have survived for x years. Cox regression, best subset regression, and the least absolute shrinkage and selection operator (LASSO) regression were used to screen predictors, respectively, to determine the best model to develop the CS-nomogram and its network version. Risk stratification was constructed based on this model.ResultsCS analysis revealed a dynamic improvement in survival occurred with increasing follow-up time (7 year survival was adjusted from 63.0% at the time of initial diagnosis to 66.4, 72.0, 77.7, 83.5, 89.0, and 94.7% year by year [after surviving for 1–6 years, respectively]). In addition, this improvement was non-linear, with a relatively slow increase in the second year after diagnosis. The predictors identified were age, T and N status, grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER 2), surgery, radiotherapy and chemotherapy. A CS-nomogram developed by these predictors and the CS formula was used to predict OS in real-time. The model's concordance indexes (C-indexes) in the training, validation and testing groups were 0.761, 0.768 and 0.810, which were well-calibrated according to the reality. In addition, the web version was easy to use and risk stratification facilitated the identification of high-risk patients.ConclusionsThe real-time prognosis of LABC improves dynamically and non-linearly over time, and the novel CS-nomogram can provide real-time and personalized prognostic information with satisfactory clinical utility.
BackgroundSurvival prediction for cervical cancer is usually based on its stage at diagnosis or a multivariate nomogram. However, few studies cared whether long-term survival improved after they survived for several years. Meanwhile, traditional survival analysis could not calculate this dynamic outcome. We aimed to assess the improvement of survival over time using conditional survival (CS) analysis and developed a novel conditional survival nomogram (CS-nomogram) to provide individualized and real-time prognostic information.MethodsCervical cancer patients were collected from the Surveillance, Epidemiology, and End Results (SEER) database. The Kaplan–Meier method estimated cancer-specific survival (CSS) and calculated the conditional CSS (C-CSS) at year y+x after giving x years of survival based on the formula C-CSS(y|x) =CSS(y+x)/CSS(x). y indicated the number of years of further survival under the condition that the patient was determined to have survived for x years. The study identified predictors by the least absolute shrinkage and selection operator (LASSO) regression and used multivariate Cox regression to demonstrate these predictors’ effect on CSS and to develop a nomogram. Finally, the CSS possibilities predicted by the nomogram were brought into the C-CSS formula to create the CS-nomogram.ResultsA total of 18,511 patients aged <65 years with cervical cancer from 2004 to 2019 were included in this study. CS analysis revealed that the 15-year CSS increased year by year from the initial 72.6% to 77.8%, 84.5%, 88.8%, 91.5%, 93.5%, 94.8%, 95.7%, 96.4%, 97.3%, 98.0%, 98.5%, 99.1%, and 99.4% (after surviving for 1-13 years, respectively), and found that when survival exceeded 5-6 years, the risk of death from cervical cancer would be less than 5% in 10-15 years. The CS-nomogram constructed using tumor size, lymph node status, distant metastasis status, and histological grade showed strong predictive performance with a concordance index (C-index) of 0.805 and a stable area under the curve (AUC) between 0.795 and 0.816 over 15 years.ConclusionsCS analysis in this study revealed the gradual improvement of CSS over time in long-term survived cervical cancer patients. We applied CS to the nomogram and developed a CS-nomogram successfully predicting individualized and real-time prognosis.
BackgroundAcute hematologic toxicity (HT) is a common complication during radiotherapy of cervical cancer which may lead to treatment delay or interruption. Despite the use of intensity-modulated radiation therapy (IMRT) with the pelvic bone marrow (PBM) sparing, some patients still suffer from acute HT. We aimed to identify predictors associated with HT and develop a nomogram for predicting grade 2 or higher (G2+) acute HT in cervical cancer following the PBM sparing strategy.MethodsThis study retrospectively analyzed 125 patients with cervical cancer who underwent IMRT with the PBM sparing strategy at our institution. Univariate and multivariate logistic regression, best subset regression, and least absolute shrinkage and selection operator (LASSO) regression, respectively, were used for predictor screening, and Akaike information criterion (AIC) was used to determine the best model for developing the nomogram. Finally, we quantified the risk of G2+ acute HT based on this model to establish a risk stratification.ResultsThe independent predictors used to develop the nomogram were histological grade, pre-radiotherapy chemotherapy, pre-radiotherapy HT, and radiotherapy [IMRT alone vs. concurrent chemoradiotherapy (CCRT)] which were determined by the univariate and multivariate logistic regression with the minimum AIC of 125.49. Meanwhile, the heat map showed that there is no multicollinearity among the predictors. The nomogram was well-calibrated to reality, with a Brier score of 0.15. The AUC value was 0.82, and the median Brier score and AUC in 1000 five-fold cross-validation were 0.16 and 0.80, respectively. The web version developed together was very easy to use. The risk stratification indicated that high-risk patients (risk point > 195.67) were more likely to develop G2+ acute HT [odds ratio (OR) = 2.17, 95% confidence interval (CI): 1.30–3.05].ConclusionThis nomogram well-predicted the risk of G2+ acute HT during IMRT in cervical cancer after the PBM sparing strategy, and the constructed risk stratification could assist physicians in screening high-risk patients and provide a useful reference for future prevention and treatment strategies for acute HT.
BackgroundConditional survival (CS) is defined as the possibility of further survival after patients have survived for several years since diagnosis. This may be highly valuable for real-time prognostic monitoring, especially when considering individualized factors. Such prediction tools were lacking for non-metastatic triple-negative breast cancer (TNBC). Therefore, this study estimated CS and developed a novel CS-nomogram for real-time prediction of 10-year survival.MethodsWe recruited 32,836 non-metastatic TNBC patients from the Surveillance, Epidemiology, and End Results (SEER) database (2010-2019), who were divided into training and validation groups according to a 7:3 ratio. The Kaplan-Meier method estimated overall survival (OS), and the CS was calculated using the formula CS(y|x) =OS(y+x)/OS(x), where OS(x) and OS(y+x) were the survival of x- and (x+y)-years, respectively. The least absolute shrinkage and selection operator (LASSO) regression identified predictors to develop the CS-nomogram.ResultsCS analysis reported gradual improvement in real-time survival over time since diagnosis, with 10-year OS updated annually from an initial 69.9% to 72.8%, 78.1%, 83.0%, 87.0%, 90.3%, 93.0%, 95.0%, 97.0%, and 98.9% (after 1-9 years of survival, respectively). The LASSO regression identified age, marriage, race, T status, N status, chemotherapy, surgery, and radiotherapy as predictors of CS-nomogram development. This model had a satisfactory predictive performance with a stable 10-year time-dependent area under the curves (AUCs) between 0.75 and 0.86.ConclusionsSurvival of non-metastatic TNBC survivors improved dynamically and non-linearly with survival time. The study developed a CS-nomogram that provided more accurate prognostic data than traditional nomograms, aiding clinical decision-making and reducing patient anxiety.
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