cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.29964471
title: Deep learning model for predicting extraprostatic extension of prostate cancer based on H&E-stained biopsy digital images

description: To develop and validate a deep learning pipeline using prostate biopsy H&E slides to predict extraprostatic extension (EPE) in prostate cancer (PCa) patients. A total of 2592 preoperative biopsy H&E slides from 260 consecutive PCa patients who underwent radical prostatectomy were collected from January 2019 to October 2023. Whole-slide images (WSIs) were digitized, tumor regions were annotated, and 224 × 224 pixel patches were extracted. The dataset was randomly divided into training and testing sets at the patient level in a ratio of 8:2. A tumor classification model and an EPE prediction model based on multiple instance learning were trained. Subsequently, we conducted an interpretability analysis of the EPE model and further carried out a correlation analysis between the predicted probabilities of the EPE and the biochemical recurrence (BCR) of the patients. The ConvNeXt model achieved the best performance in tumor classification, with an accuracy of 0.965 and an area under the curve (AUC) of 0.981 on the test set. For EPE prediction, the model achieved AUCs of 0.943 and 0.886 in the training and test sets, respectively. Key features identified by the model, such as nuclear characteristics, were significantly associated with EPE. Predicted EPE probabilities were strongly correlated with BCR (<i>p</i> = 0.01). The AI pathology model accurately predicts postoperative EPE via biopsy slides, achieving an AUC of 0.886 on the test set, offering a novel, feasible PCa preoperative risk stratification method to aid personalized treatment. This study developed a deep learning pipeline based on biopsy H&E slides to predict extraprostatic extension (EPE) with high accuracy.The model’s predictions were based on key pathological features like nuclear characteristics and were strongly correlated with biochemical recurrence.This AI-based method offers a feasible tool for preoperative risk stratification and personalized treatment planning in prostate cancer. This study developed a deep learning pipeline based on biopsy H&E slides to predict extraprostatic extension (EPE) with high accuracy. The model’s predictions were based on key pathological features like nuclear characteristics and were strongly correlated with biochemical recurrence. This AI-based method offers a feasible tool for preoperative risk stratification and personalized treatment planning in prostate cancer.
DOI: 10.6084/m9.figshare.29964471.v1
title: Deep learning model for predicting extraprostatic extension of prostate cancer based on H&E-stained biopsy digital images

description: To develop and validate a deep learning pipeline using prostate biopsy H&E slides to predict extraprostatic extension (EPE) in prostate cancer (PCa) patients. A total of 2592 preoperative biopsy H&E slides from 260 consecutive PCa patients who underwent radical prostatectomy were collected from January 2019 to October 2023. Whole-slide images (WSIs) were digitized, tumor regions were annotated, and 224 × 224 pixel patches were extracted. The dataset was randomly divided into training and testing sets at the patient level in a ratio of 8:2. A tumor classification model and an EPE prediction model based on multiple instance learning were trained. Subsequently, we conducted an interpretability analysis of the EPE model and further carried out a correlation analysis between the predicted probabilities of the EPE and the biochemical recurrence (BCR) of the patients. The ConvNeXt model achieved the best performance in tumor classification, with an accuracy of 0.965 and an area under the curve (AUC) of 0.981 on the test set. For EPE prediction, the model achieved AUCs of 0.943 and 0.886 in the training and test sets, respectively. Key features identified by the model, such as nuclear characteristics, were significantly associated with EPE. Predicted EPE probabilities were strongly correlated with BCR (<i>p</i> = 0.01). The AI pathology model accurately predicts postoperative EPE via biopsy slides, achieving an AUC of 0.886 on the test set, offering a novel, feasible PCa preoperative risk stratification method to aid personalized treatment. This study developed a deep learning pipeline based on biopsy H&E slides to predict extraprostatic extension (EPE) with high accuracy.The model’s predictions were based on key pathological features like nuclear characteristics and were strongly correlated with biochemical recurrence.This AI-based method offers a feasible tool for preoperative risk stratification and personalized treatment planning in prostate cancer. This study developed a deep learning pipeline based on biopsy H&E slides to predict extraprostatic extension (EPE) with high accuracy. The model’s predictions were based on key pathological features like nuclear characteristics and were strongly correlated with biochemical recurrence. This AI-based method offers a feasible tool for preoperative risk stratification and personalized treatment planning in prostate cancer.
DOI: 10.6084/m9.figshare.28538196
title: Effect of adipose-related parameters on mortality in patients with liver cirrhosis: a meta-analysis

description: Some adipose-related parameters exhibit distinct prognostic value in patients with cirrhosis. However, the magnitude and direction of the association between individual adipose parameter and mortality in patients with cirrhosis are unclear. This study aimed to evaluate the association between individual adipose parameter and mortality in patients with cirrhosis using the meta-analysis method. The PubMed, Embase, Web of Science, China Biological Medicine, WanFang, and China National Knowledge Infrastructure databases were searched from inception through December 15, 2023, to identify eligible studies. The impact of each adipose parameter on mortality was assessed by the pooled unadjusted or adjusted hazard ratio (HR) with 95% confidence intervals (CIs) using the random effects model. A total of 33 studies involving 9626 patients were included in our analysis, with 11 adipose parameters evaluated. The pooled prevalence of sarcopenic obesity (SO) and myosteatosis in patients with cirrhosis was 15.5% and 34.4%, respectively. In adjusted analysis, each unit increase in subcutaneous adipose tissue index (SATI) (HR: 0.99, 95% CI: 0.98–1.00) or muscle attenuation (MA) (HR: 0.94, 95% CI: 0.90–0.98) and each unit decrease in visceral-to-subcutaneous adipose tissue ratio (VSR) (HR: 1.92, 95% CI: 1.45–2.54) showed an independent association with a decreased risk of mortality. However, concurrent myosteatosis (HR: 1.88, 95% CI: 1.48–2.40) or SO (HR: 2.77, 95% CI: 1.95–3.93) significantly increased the risk of mortality in patients with cirrhosis. Decreased SATI or MA, increased VSR, and concurrent myosteatosis or SO were independently associated with a higher risk of mortality in patients with cirrhosis. The associations between various adipose parameters and mortality in patients with cirrhosis remain to be determined. Therefore, we performed the first meta-analysis to determine the effect of adipose parameters on survival in patients with cirrhosis. A total of 33 studies involving 11 adipose parameters were assessed in our analysis. We found that different adipose parameters exhibited varying prognostic value: decreased subcutaneous adipose tissue index or muscle radiodensity, increased visceral-to-subcutaneous ratio, and concurrent myosteatosis or sarcopenic obesity were independently associated with a higher risk of mortality in patients with cirrhosis. Prognostic models based on these adipose parameters will contribute to the individualized management of patients with liver cirrhosis.
DOI: 10.6084/m9.figshare.28538196.v1
title: Effect of adipose-related parameters on mortality in patients with liver cirrhosis: a meta-analysis

description: Some adipose-related parameters exhibit distinct prognostic value in patients with cirrhosis. However, the magnitude and direction of the association between individual adipose parameter and mortality in patients with cirrhosis are unclear. This study aimed to evaluate the association between individual adipose parameter and mortality in patients with cirrhosis using the meta-analysis method. The PubMed, Embase, Web of Science, China Biological Medicine, WanFang, and China National Knowledge Infrastructure databases were searched from inception through December 15, 2023, to identify eligible studies. The impact of each adipose parameter on mortality was assessed by the pooled unadjusted or adjusted hazard ratio (HR) with 95% confidence intervals (CIs) using the random effects model. A total of 33 studies involving 9626 patients were included in our analysis, with 11 adipose parameters evaluated. The pooled prevalence of sarcopenic obesity (SO) and myosteatosis in patients with cirrhosis was 15.5% and 34.4%, respectively. In adjusted analysis, each unit increase in subcutaneous adipose tissue index (SATI) (HR: 0.99, 95% CI: 0.98–1.00) or muscle attenuation (MA) (HR: 0.94, 95% CI: 0.90–0.98) and each unit decrease in visceral-to-subcutaneous adipose tissue ratio (VSR) (HR: 1.92, 95% CI: 1.45–2.54) showed an independent association with a decreased risk of mortality. However, concurrent myosteatosis (HR: 1.88, 95% CI: 1.48–2.40) or SO (HR: 2.77, 95% CI: 1.95–3.93) significantly increased the risk of mortality in patients with cirrhosis. Decreased SATI or MA, increased VSR, and concurrent myosteatosis or SO were independently associated with a higher risk of mortality in patients with cirrhosis. The associations between various adipose parameters and mortality in patients with cirrhosis remain to be determined. Therefore, we performed the first meta-analysis to determine the effect of adipose parameters on survival in patients with cirrhosis. A total of 33 studies involving 11 adipose parameters were assessed in our analysis. We found that different adipose parameters exhibited varying prognostic value: decreased subcutaneous adipose tissue index or muscle radiodensity, increased visceral-to-subcutaneous ratio, and concurrent myosteatosis or sarcopenic obesity were independently associated with a higher risk of mortality in patients with cirrhosis. Prognostic models based on these adipose parameters will contribute to the individualized management of patients with liver cirrhosis.
DOI: 10.6084/m9.figshare.30500289
title: Comparison of the predictive performance of Cumulative Illness Rating Scale, Charlson Comorbidity Index and COMCOLD Index for moderate-to-severe exacerbations in elderly subjects with chronic obstructive pulmonary disease

description: Chronic Obstructive Pulmonary Disease (COPD) is frequently associated with multiple comorbidities that influence clinical outcomes. This study aimed to compare the predictive performance of the Cumulative Illness Rating Scale (CIRS) with the Charlson Comorbidity Index (CCI) and COMCOLD Index for moderate-to-severe COPD exacerbations. We conducted a prospective observational study involving 200 COPD patients followed for 52 weeks. CIRS indices (Total Score, Severity Index, Comorbidity Index), CCI, and COMCOLD were calculated at baseline. The primary outcome was time-to-first moderate-to-severe exacerbation. Cox regression analyses and time-dependent receiver operating characteristic curves were used to assess prognostic performance at 12, 24, and 52 weeks. During follow-up, 66 patients (33%) experienced at least one moderate-to-severe exacerbation. All CIRS indices demonstrated significant correlations with respiratory parameters and symptom burden. In crude models, CIRS indices were significantly associated with exacerbation risk (CIRS-TS: HR 1.11, 95%CI 1.06–1.16; CIRS-SI: HR 1.16, 95%CI 1.09–1.23; CIRS-CI: HR 1.37, 95%CI 1.20–1.56; all <i>p</i> &lt; 0.001), maintaining significance after adjustment for clinical covariates. CIRS indices demonstrated superior discriminative performance compared to CCI and COMCOLD, with CIRS-SI achieving the highest time-dependent AUC values across all timepoints (0.704, 0.679, and 0.778 at 12, 24, and 52 weeks, respectively). CIRS provides superior prognostic accuracy compared to established comorbidity indices in identifying COPD patients at increased risk of exacerbations. These findings highlight the clinical relevance of incorporating a comprehensive, severity-weighted comorbidity assessment in COPD management, supporting the concept of COPD as a complex, multisystem disorder requiring an integrated approach to care. In elderly patients with COPD, CIRS provided superior prognostic accuracy for moderate-to-severe exacerbations compared with the Charlson Comorbidity Index and COMCOLD; The prognostic advantage of CIRS likely derives from its comprehensive, severity-weighted assessment of multimorbidity across multiple organ systems; Incorporating multidimensional comorbidity evaluation, such as CIRS, into clinical practice may improve risk stratification and support more personalized COPD management.
DOI: 10.6084/m9.figshare.30500289.v1
title: Comparison of the predictive performance of Cumulative Illness Rating Scale, Charlson Comorbidity Index and COMCOLD Index for moderate-to-severe exacerbations in elderly subjects with chronic obstructive pulmonary disease

description: Chronic Obstructive Pulmonary Disease (COPD) is frequently associated with multiple comorbidities that influence clinical outcomes. This study aimed to compare the predictive performance of the Cumulative Illness Rating Scale (CIRS) with the Charlson Comorbidity Index (CCI) and COMCOLD Index for moderate-to-severe COPD exacerbations. We conducted a prospective observational study involving 200 COPD patients followed for 52 weeks. CIRS indices (Total Score, Severity Index, Comorbidity Index), CCI, and COMCOLD were calculated at baseline. The primary outcome was time-to-first moderate-to-severe exacerbation. Cox regression analyses and time-dependent receiver operating characteristic curves were used to assess prognostic performance at 12, 24, and 52 weeks. During follow-up, 66 patients (33%) experienced at least one moderate-to-severe exacerbation. All CIRS indices demonstrated significant correlations with respiratory parameters and symptom burden. In crude models, CIRS indices were significantly associated with exacerbation risk (CIRS-TS: HR 1.11, 95%CI 1.06–1.16; CIRS-SI: HR 1.16, 95%CI 1.09–1.23; CIRS-CI: HR 1.37, 95%CI 1.20–1.56; all <i>p</i> &lt; 0.001), maintaining significance after adjustment for clinical covariates. CIRS indices demonstrated superior discriminative performance compared to CCI and COMCOLD, with CIRS-SI achieving the highest time-dependent AUC values across all timepoints (0.704, 0.679, and 0.778 at 12, 24, and 52 weeks, respectively). CIRS provides superior prognostic accuracy compared to established comorbidity indices in identifying COPD patients at increased risk of exacerbations. These findings highlight the clinical relevance of incorporating a comprehensive, severity-weighted comorbidity assessment in COPD management, supporting the concept of COPD as a complex, multisystem disorder requiring an integrated approach to care. In elderly patients with COPD, CIRS provided superior prognostic accuracy for moderate-to-severe exacerbations compared with the Charlson Comorbidity Index and COMCOLD; The prognostic advantage of CIRS likely derives from its comprehensive, severity-weighted assessment of multimorbidity across multiple organ systems; Incorporating multidimensional comorbidity evaluation, such as CIRS, into clinical practice may improve risk stratification and support more personalized COPD management.
DOI: 10.6084/m9.figshare.29933847
title: Organ involvement and laboratory abnormalities associated with tuberculosis disease in systemic lupus erythematosus patients: a systematic review and meta-analysis

description: Tuberculosis (TB) is one of the most common infectious diseases in patients with systemic lupus erythematosus (SLE) and often has severe and devastating manifestations. This study aimed to systematically investigate the association between organ involvement and laboratory abnormalities in patients with systemic lupus erythematosus (SLE) associated with tuberculosis disease (TB). Relevant studies were obtained from electronic databases, including PubMed, Scopus, Science Direct, and EBSCO from inception to November 2024. The primary outcome was the odds ratio (OR) of TB incidence associated with organ involvement and laboratory abnormalities. Quality of studies was assessed using the Newcastle–Ottawa Scale (NOS). A meta-analysis was performed using R. Fourteen studies with a total of 5,626 adult SLE patients were included, of whom 514 (9.14%) were male. Renal involvement was the only organ involvement that was significantly associated with TB (OR = 1.53; 95%CI:1.01-2.32; <i>I<sup>2</sup></i>=76%). Among laboratory parameters, lymphopenia (OR = 2.21; 95%CI:1.42-3.42; <i>I</i><sup>2</sup>=26%) and anemia (OR = 1.73; 95%CI:1.16-2.58; <i>I</i><sup>2</sup>=21%) showed a significant association with TB. No significant associations were found for other organs or laboratory abnormalities. Renal involvement, lymphopenia, and anemia could be notable risk factors for TB in patients with SLE, suggesting enhanced awareness of these indicators to minimize the risk of TB in patients with SLE.
DOI: 10.6084/m9.figshare.29933847.v1
title: Organ involvement and laboratory abnormalities associated with tuberculosis disease in systemic lupus erythematosus patients: a systematic review and meta-analysis

description: Tuberculosis (TB) is one of the most common infectious diseases in patients with systemic lupus erythematosus (SLE) and often has severe and devastating manifestations. This study aimed to systematically investigate the association between organ involvement and laboratory abnormalities in patients with systemic lupus erythematosus (SLE) associated with tuberculosis disease (TB). Relevant studies were obtained from electronic databases, including PubMed, Scopus, Science Direct, and EBSCO from inception to November 2024. The primary outcome was the odds ratio (OR) of TB incidence associated with organ involvement and laboratory abnormalities. Quality of studies was assessed using the Newcastle–Ottawa Scale (NOS). A meta-analysis was performed using R. Fourteen studies with a total of 5,626 adult SLE patients were included, of whom 514 (9.14%) were male. Renal involvement was the only organ involvement that was significantly associated with TB (OR = 1.53; 95%CI:1.01-2.32; <i>I<sup>2</sup></i>=76%). Among laboratory parameters, lymphopenia (OR = 2.21; 95%CI:1.42-3.42; <i>I</i><sup>2</sup>=26%) and anemia (OR = 1.73; 95%CI:1.16-2.58; <i>I</i><sup>2</sup>=21%) showed a significant association with TB. No significant associations were found for other organs or laboratory abnormalities. Renal involvement, lymphopenia, and anemia could be notable risk factors for TB in patients with SLE, suggesting enhanced awareness of these indicators to minimize the risk of TB in patients with SLE.
DOI: 10.6084/m9.figshare.29446498
title: Platelet transfusion response in critically ill patients with thrombocytopenia: a retrospective study and predictive nomogram in a general ICU population

description: Although suboptimal platelet transfusion (PT) response in critically ill patients with thrombocytopenia remains a challenge in clinical practice. This study aimed to investigate PT response during intensive care unit (ICU) stay among thrombocytopenic patients without underlying hematologic disease. This retrospective single-center analysis included thrombocytopenic patients without primary hematologic disorders who received PT in ICU between June 2021 and December 2023. Clinical and laboratory variables were analyzed using a generalized linear mixed-effects model (GLMM), with the results visualized through a nomogram. The 28-day survival curves, stratified by receiving single or multiple PT episodes, were established using the Kaplan–Meier method. Suboptimal PT response was observed in 522 episodes (77.9%, 522/670) and in 291 patients (79.9%, 291/364). The GLMM identified sepsis, splenomegaly, mechanical ventilation, higher APACHE II score, and longer time interval of post-PT platelet count as independent predictors of suboptimal response, while higher white blood cell count at ICU admission and the PT episode number in ICU were independently protective. A nomogram based on these seven variables demonstrated good predictive performance. Suboptimal PT episodes were associated with higher red blood cell and fresh frozen plasma requirements. The 28-day survival probability was significantly higher in the single transfusion group with optimal response versus the suboptimal response. Repeat PT may enhance the PT response and survival. Suboptimal PT response was associated with increased RBC and FFP transfusion requirements. The established nomogram demonstrated strong predictive accuracy and may provide a practical tool for optimizing PT practices in the ICU.
DOI: 10.6084/m9.figshare.29446498.v1
title: Platelet transfusion response in critically ill patients with thrombocytopenia: a retrospective study and predictive nomogram in a general ICU population

description: Although suboptimal platelet transfusion (PT) response in critically ill patients with thrombocytopenia remains a challenge in clinical practice. This study aimed to investigate PT response during intensive care unit (ICU) stay among thrombocytopenic patients without underlying hematologic disease. This retrospective single-center analysis included thrombocytopenic patients without primary hematologic disorders who received PT in ICU between June 2021 and December 2023. Clinical and laboratory variables were analyzed using a generalized linear mixed-effects model (GLMM), with the results visualized through a nomogram. The 28-day survival curves, stratified by receiving single or multiple PT episodes, were established using the Kaplan–Meier method. Suboptimal PT response was observed in 522 episodes (77.9%, 522/670) and in 291 patients (79.9%, 291/364). The GLMM identified sepsis, splenomegaly, mechanical ventilation, higher APACHE II score, and longer time interval of post-PT platelet count as independent predictors of suboptimal response, while higher white blood cell count at ICU admission and the PT episode number in ICU were independently protective. A nomogram based on these seven variables demonstrated good predictive performance. Suboptimal PT episodes were associated with higher red blood cell and fresh frozen plasma requirements. The 28-day survival probability was significantly higher in the single transfusion group with optimal response versus the suboptimal response. Repeat PT may enhance the PT response and survival. Suboptimal PT response was associated with increased RBC and FFP transfusion requirements. The established nomogram demonstrated strong predictive accuracy and may provide a practical tool for optimizing PT practices in the ICU.