cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29606095
title: Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis

description: Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer–Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic review and meta-analysis compare the accuracy of traditional statistical models and machine learning models in breast cancer risk prediction. A total of 144 studies from 27 countries were systematically reviewed, incorporating genetic, clinical, and imaging data. Pooled C-statistics were calculated to assess model discrimination, while observed-to-expected (O/E) ratios were used to evaluate calibration. Subgroup and sensitivity analyses were conducted to examine heterogeneity and assess the influence of study bias across various populations. Machine learning-based models demonstrated superior performance, with a pooled C-statistic of 0.74, compared to 0.67 for traditional models. Models that integrated genetic and imaging data showed the highest levels of accuracy, although performance varied by population. Sensitivity analyses excluding high-bias studies showed improved discrimination in models incorporating genetic factors, with the pooled C-statistic increasing to 0.72. Traditional models, such as Gail, exhibited notably poor predictive accuracy in non-Western populations, as evidenced by a C-statistic of 0.543 in Chinese cohorts. Machine learning models provide significantly greater predictive accuracy for breast cancer risk, particularly when incorporating multidimensional data. However, issues related to model generalizability and interpretability remain, particularly in diverse populations. Future research should focus on developing more interpretable models and expanding global validation efforts to improve model applicability across different demographic groups.
DOI: 10.6084/m9.figshare.29606095.v1
title: Development and performance of female breast cancer incidence risk prediction models: a systematic review and meta-analysis

description: Accurate breast cancer risk prediction is essential for early detection and personalized prevention strategies. While traditional models, such as Gail and Tyrer–Cuzick, are widely utilized, machine learning-based approaches may offer enhanced predictive performance. This systematic review and meta-analysis compare the accuracy of traditional statistical models and machine learning models in breast cancer risk prediction. A total of 144 studies from 27 countries were systematically reviewed, incorporating genetic, clinical, and imaging data. Pooled C-statistics were calculated to assess model discrimination, while observed-to-expected (O/E) ratios were used to evaluate calibration. Subgroup and sensitivity analyses were conducted to examine heterogeneity and assess the influence of study bias across various populations. Machine learning-based models demonstrated superior performance, with a pooled C-statistic of 0.74, compared to 0.67 for traditional models. Models that integrated genetic and imaging data showed the highest levels of accuracy, although performance varied by population. Sensitivity analyses excluding high-bias studies showed improved discrimination in models incorporating genetic factors, with the pooled C-statistic increasing to 0.72. Traditional models, such as Gail, exhibited notably poor predictive accuracy in non-Western populations, as evidenced by a C-statistic of 0.543 in Chinese cohorts. Machine learning models provide significantly greater predictive accuracy for breast cancer risk, particularly when incorporating multidimensional data. However, issues related to model generalizability and interpretability remain, particularly in diverse populations. Future research should focus on developing more interpretable models and expanding global validation efforts to improve model applicability across different demographic groups.
DOI: 10.6084/m9.figshare.30312449
title: Exploring a potential novel marker for short-term mortality in decompensated cirrhosis: insights from a multicentre study

description: The derived monocyte to lymphocyte ratio (dMLR) serves as an indicator of the overall systemic inflammatory and immune status. This study seeks to investigate the correlation between dMLR and 90-day mortality in patients diagnosed with decompensated liver cirrhosis (DLC). A total of 1,342 eligible patients from three medical centres were included in this study. Multivariable Firth-corrected logistic regression analysis was conducted to explore the independent association between dMLR and 90-day mortality. Additionally, restricted cubic spline (RCS) analysis was employed to examine the relationship between dMLR and mortality risk. These findings were subsequently validated in two independent cohorts. In the total cohort, the prognostic accuracy of dMLR for predicting outcomes in DLC patients was evaluated through receiver operating characteristic (ROC) curve analysis. In the initial cohort, the observed 90-day mortality rate was 12.5%. ROC analysis showed that dMLR had comparable predictive ability to the Model for End-Stage Liver Disease. The multivariable Firth-corrected logistic regression analysis revealed that dMLR is an independent predictor of 90-day mortality. Furthermore, RCS analysis demonstrated that an increase in dMLR values corresponded with an elevated risk of mortality. These findings were corroborated in two validation cohorts. The dMLR is independently associated with 90-day mortality in patients with DLC, with an increasing risk of death corresponding to higher dMLR values. dMLR may function as a simple, cost-effective, and reliable biomarker, assisting clinicians in identifying high-risk patients and may guide risk stratification and management.
DOI: 10.6084/m9.figshare.30312449.v1
title: Exploring a potential novel marker for short-term mortality in decompensated cirrhosis: insights from a multicentre study

description: The derived monocyte to lymphocyte ratio (dMLR) serves as an indicator of the overall systemic inflammatory and immune status. This study seeks to investigate the correlation between dMLR and 90-day mortality in patients diagnosed with decompensated liver cirrhosis (DLC). A total of 1,342 eligible patients from three medical centres were included in this study. Multivariable Firth-corrected logistic regression analysis was conducted to explore the independent association between dMLR and 90-day mortality. Additionally, restricted cubic spline (RCS) analysis was employed to examine the relationship between dMLR and mortality risk. These findings were subsequently validated in two independent cohorts. In the total cohort, the prognostic accuracy of dMLR for predicting outcomes in DLC patients was evaluated through receiver operating characteristic (ROC) curve analysis. In the initial cohort, the observed 90-day mortality rate was 12.5%. ROC analysis showed that dMLR had comparable predictive ability to the Model for End-Stage Liver Disease. The multivariable Firth-corrected logistic regression analysis revealed that dMLR is an independent predictor of 90-day mortality. Furthermore, RCS analysis demonstrated that an increase in dMLR values corresponded with an elevated risk of mortality. These findings were corroborated in two validation cohorts. The dMLR is independently associated with 90-day mortality in patients with DLC, with an increasing risk of death corresponding to higher dMLR values. dMLR may function as a simple, cost-effective, and reliable biomarker, assisting clinicians in identifying high-risk patients and may guide risk stratification and management.
DOI: 10.6084/m9.figshare.28296068
title: Identification of multicohort-based predictive signature for NMIBC recurrence reveals SDCBP as a novel oncogene in bladder cancer

description: Despite surgical and intravesical chemotherapy interventions, non-muscle invasive bladder cancer (NMIBC) poses a high risk of recurrence, which significantly impacts patient survival. Traditional clinical characteristics alone are inadequate for accurately assessing the risk of NMIBC recurrence, necessitating the development of novel predictive tools. We analyzed microarray data of NMIBC samples obtained from the ArrayExpress and GEO databases. LASSO regression was utilized to develop the predictive signature. We combined gene signature and clinicopathological factors to construct a clinical nomogram for estimating NMIBC recurrence in a local cohort. Finally. the biological functions and potential mechanisms of SDCBP in bladder cancer were investigated experimentally <i>in vitro</i> and <i>in vivo</i>. An 8-gene signature was developed, and its efficiency for predicting NMIBC recurrence was evaluated using Kaplan-Meier and time-dependent ROC curves in both training and validation datasets. Immunohistochemical testing revealed elevated levels of ACTN4 and SDCBP in recurrent NMIBC tissues. We integrated the two proteins with clinical factors to develop a nomogram model, which showed superior accuracy compared to individual parameters. Gene Set Variation Analysis and Gene Set Enrichment Analysis unveiled SDCBP exerted cancer-promoting biological processes, such as angiogenesis, EMT, metastasis and proliferation. Experimental procedures demonstrated that silencing SDCBP attenuated cell growth, glucose metabolism and extracellular acidification rate, accompanied by decreased expression of p-AKT, p-ERK1/2, LDHA and Vimentin. The established 8-gene signature holds promise as a tool for predicting NMIBC recurrence, while targeting SDCBP may represent a potential strategy for delaying disease relapse.
DOI: 10.6084/m9.figshare.28296068.v1
title: Identification of multicohort-based predictive signature for NMIBC recurrence reveals SDCBP as a novel oncogene in bladder cancer

description: Despite surgical and intravesical chemotherapy interventions, non-muscle invasive bladder cancer (NMIBC) poses a high risk of recurrence, which significantly impacts patient survival. Traditional clinical characteristics alone are inadequate for accurately assessing the risk of NMIBC recurrence, necessitating the development of novel predictive tools. We analyzed microarray data of NMIBC samples obtained from the ArrayExpress and GEO databases. LASSO regression was utilized to develop the predictive signature. We combined gene signature and clinicopathological factors to construct a clinical nomogram for estimating NMIBC recurrence in a local cohort. Finally. the biological functions and potential mechanisms of SDCBP in bladder cancer were investigated experimentally <i>in vitro</i> and <i>in vivo</i>. An 8-gene signature was developed, and its efficiency for predicting NMIBC recurrence was evaluated using Kaplan-Meier and time-dependent ROC curves in both training and validation datasets. Immunohistochemical testing revealed elevated levels of ACTN4 and SDCBP in recurrent NMIBC tissues. We integrated the two proteins with clinical factors to develop a nomogram model, which showed superior accuracy compared to individual parameters. Gene Set Variation Analysis and Gene Set Enrichment Analysis unveiled SDCBP exerted cancer-promoting biological processes, such as angiogenesis, EMT, metastasis and proliferation. Experimental procedures demonstrated that silencing SDCBP attenuated cell growth, glucose metabolism and extracellular acidification rate, accompanied by decreased expression of p-AKT, p-ERK1/2, LDHA and Vimentin. The established 8-gene signature holds promise as a tool for predicting NMIBC recurrence, while targeting SDCBP may represent a potential strategy for delaying disease relapse.
DOI: 10.6084/m9.figshare.28684764
title: Establishing and validating models integrated with hematological biomarkers and clinical characteristics for the prognosis of non-esophageal squamous cell carcinoma patients

description: This study aimed to construct a novel model and validate its predictive power in non-esophageal squamous cell carcinoma (NESCC) patients. This retrospective study included 151 patients between October 2006 and September 2016. The LASSO Cox and Random Survival Forest (RSF) models were developed with the help of hematological biomarkers and clinical characteristics. The concordance index (C-index) was used to assess the prognostic power of the LASSO Cox model, RSF model, and TNM staging. Based on the risk scores of the LASSO Cox and RSF models, we divided patients into low-risk and high-risk subgroups. We constructed two models in NESCC patients according to LASSO Cox regression and RSF models. The RSF model reached a C-index of 0.841 (95% CI: 0.792–0.889) in the primary cohort and 0.880 (95% CI: 0.830–0.930) in the validation cohort, which was higher than the C-index of the LASSO Cox model 0.656 (95% CI: 0.580–0.732) and 0.632 (95% CI: 0.542–0.720) in the two cohorts. The integrated C/D area under the ROC curve (AUC) values for the LASSO Cox and RSF models were 0.701 and 0.861, respectively. In both two models, Kaplan-Meier survival analysis and the estimated restricted mean survival time (RMST) values indicated that the low-risk subgroup had a better prognostic outcome than the high-risk subgroup (<i>p</i> &lt; 0.05). The RSF model has better prediction power than the LASSO Cox and the TNM staging models. It has a guiding value for the choice of individualized treatment in patients with NESCC.
DOI: 10.6084/m9.figshare.28684764.v1
title: Establishing and validating models integrated with hematological biomarkers and clinical characteristics for the prognosis of non-esophageal squamous cell carcinoma patients

description: This study aimed to construct a novel model and validate its predictive power in non-esophageal squamous cell carcinoma (NESCC) patients. This retrospective study included 151 patients between October 2006 and September 2016. The LASSO Cox and Random Survival Forest (RSF) models were developed with the help of hematological biomarkers and clinical characteristics. The concordance index (C-index) was used to assess the prognostic power of the LASSO Cox model, RSF model, and TNM staging. Based on the risk scores of the LASSO Cox and RSF models, we divided patients into low-risk and high-risk subgroups. We constructed two models in NESCC patients according to LASSO Cox regression and RSF models. The RSF model reached a C-index of 0.841 (95% CI: 0.792–0.889) in the primary cohort and 0.880 (95% CI: 0.830–0.930) in the validation cohort, which was higher than the C-index of the LASSO Cox model 0.656 (95% CI: 0.580–0.732) and 0.632 (95% CI: 0.542–0.720) in the two cohorts. The integrated C/D area under the ROC curve (AUC) values for the LASSO Cox and RSF models were 0.701 and 0.861, respectively. In both two models, Kaplan-Meier survival analysis and the estimated restricted mean survival time (RMST) values indicated that the low-risk subgroup had a better prognostic outcome than the high-risk subgroup (<i>p</i> &lt; 0.05). The RSF model has better prediction power than the LASSO Cox and the TNM staging models. It has a guiding value for the choice of individualized treatment in patients with NESCC.
DOI: 10.6084/m9.figshare.28643279
title: Association of thrombocytopenia and D-dimer elevation with in-hospital mortality in acute aortic dissection

description: Data on the association between the degree of platelet and coagulative-fibrinolytic variables abnormalities and the risk of in-hospital mortality in acute aortic dissection (AAD) are limited. This multicentre retrospective cohort study included patients diagnosed with AAD by aortic computed tomographic angiography between 2010 and 2021 in five tertiary hospitals in China. The primary outcome was defined as all-cause mortality during hospitalization. Associations between platelet counts, coagulation-fibrinolytic parameters and all-cause in-hospital mortality were assessed using Cox proportional hazards regression models. Among the 2567 participants, the median age was 54 years (interquartile range, IQR: 47–63); 531 (20.7%) were female, and the in-hospital mortality rate was 589 (23.0%). The Cox proportional hazards regression model indicated that lower platelet count, prothrombin activity (PTA), and fibrinogen levels and longer prothrombin time (PT) and thrombin time (TT) were linearly positively associated with an increased risk of in-hospital mortality (<i>p</i> &lt; 0.05). A non-linear and positive association was confirmed between D-dimer levels and in-hospital mortality risk (<i>p</i> &lt; 0.05). Additionally, a significant interaction between platelet counts and D-dimer levels was observed (<i>p</i> = 0.029). According to the subgroup analysis, compared to those in the reference group, those with thrombocytopenia (&lt;140 × 10<sup>9</sup>/L) and high D-dimer levels (&gt;14.6 µg/mL) had a 3.59-fold increased risk of in-hospital mortality (HR, 3.59; 95% CI, 2.00–6.42). Our analysis revealed associations between changes in platelet count, PT, PTA, TT, fibrinogen and D-dimer levels and outcomes in patients with AAD. Furthermore, the combined effect of thrombocytopenia and high D-dimer levels significantly increased the risk of in-hospital mortality. Platelet count and coagulation-fibrinolysis system variables, including PT, PTA, TT, fibrinogen and D-dimer levels, are significantly associated with in-hospital mortality risk in acute aortic dissection (AAD) patients. A significant interaction between platelet count and D-dimer level (<i>p</i> = 0.029) highlights that the combined effect of thrombocytopenia and elevated D-dimer levels significantly increases the risk of in-hospital mortality in AAD patients. Prompt and repeated assessment of platelet counts and coagulation-fibrinolysis system variables upon admission can serve as pivotal biomarkers for risk stratification in AAD patients, facilitating timely and targeted interventions.
DOI: 10.6084/m9.figshare.28643279.v1
title: Association of thrombocytopenia and D-dimer elevation with in-hospital mortality in acute aortic dissection

description: Data on the association between the degree of platelet and coagulative-fibrinolytic variables abnormalities and the risk of in-hospital mortality in acute aortic dissection (AAD) are limited. This multicentre retrospective cohort study included patients diagnosed with AAD by aortic computed tomographic angiography between 2010 and 2021 in five tertiary hospitals in China. The primary outcome was defined as all-cause mortality during hospitalization. Associations between platelet counts, coagulation-fibrinolytic parameters and all-cause in-hospital mortality were assessed using Cox proportional hazards regression models. Among the 2567 participants, the median age was 54 years (interquartile range, IQR: 47–63); 531 (20.7%) were female, and the in-hospital mortality rate was 589 (23.0%). The Cox proportional hazards regression model indicated that lower platelet count, prothrombin activity (PTA), and fibrinogen levels and longer prothrombin time (PT) and thrombin time (TT) were linearly positively associated with an increased risk of in-hospital mortality (<i>p</i> &lt; 0.05). A non-linear and positive association was confirmed between D-dimer levels and in-hospital mortality risk (<i>p</i> &lt; 0.05). Additionally, a significant interaction between platelet counts and D-dimer levels was observed (<i>p</i> = 0.029). According to the subgroup analysis, compared to those in the reference group, those with thrombocytopenia (&lt;140 × 10<sup>9</sup>/L) and high D-dimer levels (&gt;14.6 µg/mL) had a 3.59-fold increased risk of in-hospital mortality (HR, 3.59; 95% CI, 2.00–6.42). Our analysis revealed associations between changes in platelet count, PT, PTA, TT, fibrinogen and D-dimer levels and outcomes in patients with AAD. Furthermore, the combined effect of thrombocytopenia and high D-dimer levels significantly increased the risk of in-hospital mortality. Platelet count and coagulation-fibrinolysis system variables, including PT, PTA, TT, fibrinogen and D-dimer levels, are significantly associated with in-hospital mortality risk in acute aortic dissection (AAD) patients. A significant interaction between platelet count and D-dimer level (<i>p</i> = 0.029) highlights that the combined effect of thrombocytopenia and elevated D-dimer levels significantly increases the risk of in-hospital mortality in AAD patients. Prompt and repeated assessment of platelet counts and coagulation-fibrinolysis system variables upon admission can serve as pivotal biomarkers for risk stratification in AAD patients, facilitating timely and targeted interventions.
DOI: 10.6084/m9.figshare.30476831
title: Effect of pressure-controlled ventilation-volume guaranteed mode on intraoperative respiratory mechanics, oxygenation and postoperative pulmonary complications: a systematic review and network meta-analysis

description: Ventilation strategies during general anesthesia are crucial for optimizing respiratory mechanics, ensuring adequate oxygenation, and minimizing postoperative pulmonary complications (PPCs) in surgical patients. This systematic review and network meta-analysis (NMA) aim to evaluate the effect of pressure controlled volume guaranteed ventilation (PCV-VG) in patients undergoing non-cardiothoracic surgery. We selected randomized controlled trials (RCTs) to explore the effect of PCV-VG mode on intraoperative respiratory mechanics, oxygenation and PPCs in surgical patients published in English by searching PubMed, Embase, Web of Science, and the Cochrane from inception to May 2025. The primary outcome was dynamic lung compliance (Cdyn). Fourteen studies with 2263 patients were included. Compared to volume-controlled ventilation (VCV), PCV-VG had higher Cdyn and lower airway peak pressure (Ppeak). For tidal volume (Vt), partial pressure of carbon dioxide in arterial blood (PaCO<sub>2</sub>), partial pressure of oxygen in arterial blood (PaO<sub>2</sub>), oxygenation index (OI), airway mean pressure (Pmean), and PPCs, there were no significant difference among pressure-controlled ventilation (PCV), VCV, and PCV-VG. Compared to VCV, PCV-VG did not significantly affect the values of potential of hydrogen (pH) and partial pressure of end-tidal carbon dioxide (PetCO<sub>2</sub>). The subgroup analysis only showed that PCV had higher Cdyn than VCV in prone position. PCV-VG was superior to VCV in Cdyn and Ppeak, PCV only achieved higher Cdyn than VCV for prone position. The three modes did not differ in Vt, Pmean, PetCO<sub>2,</sub> pH, PaCO<sub>2</sub>, PaO<sub>2</sub>, OI, and PPCs for patients undergoing non-cardiothoracic surgery. We evaluate the effect of pressure controlled volume guaranteed ventilation (PCV-VG) on on intraoperative respiratory mechanics, oxygenation and postoperative pulmonary complications.PCV-VG results in significantly higher Cdyn and lower Ppeak compared to VCV.PCV-VG, PCV, and VCV did not have significant impacts on Vt, PaCO<sub>2</sub>, PaO<sub>2</sub>, OI, Pmean, and PPCs. We evaluate the effect of pressure controlled volume guaranteed ventilation (PCV-VG) on on intraoperative respiratory mechanics, oxygenation and postoperative pulmonary complications. PCV-VG results in significantly higher Cdyn and lower Ppeak compared to VCV. PCV-VG, PCV, and VCV did not have significant impacts on Vt, PaCO<sub>2</sub>, PaO<sub>2</sub>, OI, Pmean, and PPCs.