Establishment and Clinical Application of the Nomogram Related to Risk or Prognosis of Hepatocellular Carcinoma: A Review

Gender impact on pancreatic neuroendocrine neoplasm (PanNEN) prognosis according to survival nomograms

PURPOSE

Personalizing care and outcome evaluation are important aims in the field of NEN and nomograms may represent useful tools for clinicians. Of note, gender difference is being progressively more considered in NEN care, as it may also impact on survival. This systematic review aims to describe and analyze the available nomograms on pancreatic NENs (PanNENs) to identify if gender differences are evaluated and if they could impact on patients' management and prognosis.

METHODS

We performed an electronic-based search using PubMed updated until June 2024, summarizing the available evidence of gender impact on PanNEN survival outcomes as emerges from published nomograms.

RESULTS

34 articles were identified regarding prognostic nomograms in PanNEN fields. The most included variables were age, tumor grade, tumor stage, while only 5 papers (14.7%) included sex as one of the key model variables with a significant impact on patients' prognosis. These 5 studies analyzed a total of 18,920 PanNENs. 3 studies found a significant impact of sex on overall survival (OS), whereas the remaining 2 studies showed no significant impact of sex on OS.

CONCLUSIONS

Gender difference is being progressively more considered in PanNEN diagnosis, care and survival. Nomograms represent a potentially useful tool in patients' management and in outcomes prediction in the field of PanNENs. A key role of sex in the prognosis of PanNENs has been found in few models, while definitive conclusions couldn't be drawn. Future studies are needed to finally establish gender impact on PanNEN prognosis.

Identification and validation of PANoptosis-related LncRNAs prognosis system in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common solid malignancies in the world. Due to the limited effectiveness of current drug treatments, further research on HCC is necessary. PANoptosis is defined as an inflammatory RCD whose main features combine pyroptosis, apoptosis and necroptosis which cannot be explained by any of these three RCDs alone. In HCC, risk stratification based on PANoptosis-associated lncRNAs has clinical application potential. In this study, we explored HCC related PANoptosis-related lncRNAs (PRLs) by analyzing significantly differentially expressed genes in HCC. HCC-associated PRL scores were established by WGCNA, LASSO analysis and multivariate Cox assessment. Subsequently, we verified the prognostic analysis ability of PRL score for HCC patients, and on this basis established a prognostic risk assessment model for HCC and verified its reliability. The relationship between PRL score and immune infiltration as well as drug sensitivity was further analyzed to evaluate the clinical reference value of this model. Western blot analysis and PCR further verified the reliability of bioinformatics results. The observed suppression of HCC progression and invasiveness following selected PRL knockdown further validated the reliability of our bioinformatics analysis results. Our results provide new evidence for the role of PANoptosis-associated lncRNAs in HCC.

Development of a machine learning-based predictive model for transitional cell carcinoma of the renal pelvis in White Americans: a SEER-based study

Background

Transitional cell carcinoma (TCC) of the renal pelvis is a rare cancer within the urinary system. However, the prognosis is not entirely satisfactory. This study aims to develop a clinical model for predicting cancer-specific survival (CSS) at 1-, 3-, and 5-year for White Americans with renal pelvic TCC.

Methods

Data of all White American patients diagnosed with TCC of the renal pelvis from 2010 to 2015 were extracted and analyzed from the Surveillance, Epidemiology, and End Results (SEER) database in this retrospective study. Subsequently, after excluding the metastatic group, a subgroup analysis was performed on the data of 1,715 White Americans with non-metastatic renal pelvic TCC. Patients included in this study were randomly divided into the training and validation sets in a ratio of 7:3. In addition, the features in the training set were extracted by the Boruta algorithm. The importance of these features was visualized using the eXtreme Gradient Boosting (XGBoost)-based SHapley Additive exPlanation (SHAP) tool. To improve predictive accuracy, a nomogram model with these identified independent prognostic variables was developed.

Results

A total of 1,887 White American patients with renal pelvic TCC were included in this study. In the training set, the area under the curve (AUC) for CSS nomograms at 1-, 3-, and 5-year were 0.813 [95% confidence interval (CI): 0.774-0.852], 0.738 (95% CI: 0.702-0.774), and 0.733 (95% CI: 0.698-0.768), respectively. Correspondingly, the AUCs for CSS nomograms at the above time points were 0.781 (95% CI: 0.732-0.830), 0.785 (95% CI: 0.741-0.829), and 0.775 (95% CI: 0.729-0.820) in the validation set, respectively. The subgroup analysis results revealed that the AUCs for CSS nomograms at 1-, 3-, and 5-year were 0.788, 0.725, and 0.726 in the training set, respectively, while the AUCs for CSS nomograms at 1-, 3-, and 5-year were 0.831, 0.786, and 0.754 in the training set, respectively.

Conclusions

In this study, a nomogram that predicts CSS in White American patients diagnosed with renal pelvic TCC was efficiently constructed. The application of the nomogram may enhance patient care and assist clinicians in choosing the optimal treatment strategies.

Nomogram-based prognostic stratification for patients with large hepatocellular carcinoma: a population study of SEER database and a Chinese cohort

Background

Large hepatocellular carcinoma (HCC) with a diameter ≥5 cm remains a significant challenge of poor survival and raises the need for prognosis evaluation. This study aimed to develop and validate a nomogram-based prognostic stratification to assess overall survival (OS) of patients with large HCC.

Methods

Data of patients with large HCC were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database and our hospital, and were divided into the training cohort, internal validation cohort and external validation cohort. Cox analysis was performed to identify independent prognostic factors for the construction of nomogram in training cohort. The predictive ability of the nomogram was validated compared with the tumor node metastasis (TNM) classification staging system. Furthermore, prognostic stratification system based on nomogram was developed.

Results

Independent prognostic factors including histological grade, T stage, M stage, alpha fetoprotein (AFP), fibrosis score and surgery, were incorporated to construct nomogram. C-indexes of nomogram were 0.730, 0.726 and 0.724 in the training, internal and external validation cohorts, respectively. Importantly, nomogram harbored a superior discrimination and clinical benefit than the TNM staging system. Nomogram-based prognostic stratification divided patients into three groups: 345-414 (low-risk group), 415-460 (medium-risk group) and 461-513 (high-risk group). As shown in Kaplan-Meier curves, there were significant differences in OS among low-, medium- and high-risk groups (P<0.01).

Conclusions

Nomogram showed a superior prognostic predictive ability compared with the TNM staging system. The prognostic stratification serves as a valuable tool to assist clinicians on the selection of optimal treatment method and follow-up plan, particularly for the high-risk population.

Establishment and validation of apnea risk prediction models in preterm infants: a retrospective case control study

BACKGROUND

Apnea is common in preterm infants and can be accompanied with severe hypoxic damage. Early assessment of apnea risk can impact the prognosis of preterm infants. We constructed a prediction model to assess apnea risk in premature infants for identifying high-risk groups.

METHODS

A total of 162 and 324 preterm infants with and without apnea who were admitted to the neonatal intensive care unit of Xiamen University between January 2018 and December 2021 were selected as the case and control groups, respectively. Demographic characteristics, laboratory indicators, complications of the patients, pregnancy-related factors, and perinatal risk factors of the mother were collected retrospectively. The participants were randomly divided into modeling (n = 388) and validation (n = 98) sets in an 8:2 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate logistic regression analyses were used to independently filter variables from the modeling set and build a model. A nomogram was used to visualize models. The calibration and clinical utility of the model was evaluated using consistency index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve, and the model was verified using the validation set.

RESULTS

Results of LASSO combined with multivariate logistic regression analysis showed that gestational age at birth, birth length, Apgar score, and neonatal respiratory distress syndrome were predictors of apnea development in preterm infants. The model was presented as a nomogram and the Hosmer-Lemeshow goodness of fit test showed a good model fit (χ2=5.192, df=8, P=0.737), with Nagelkerke R2 of 0.410 and C-index of 0.831. The area under the ROC curve and 95% CI were 0.831 (0.787-0.874) and 0.829 (0.722-0.935), respectively. Delong's test comparing the AUC of the two data sets showed no significant difference (P=0.976). The calibration curve showed good agreement between the predicted and actual observations. The decision curve results showed that the threshold probability range of the model was 0.07-1.00, the net benefit was high, and the constructed clinical prediction model had clinical utility.

CONCLUSIONS

Our risk prediction model based on gestational age, birth length, Apgar score 10 min post-birth, and neonatal respiratory distress syndrome was validated in many aspects and had good predictive efficacy and clinical utility.

Risk factors influencing postoperative outcome of arthroscopic rotator cuff repair and construction of a nomogram prediction model

OBJECTIVE

To investigate the risk factors influencing the postoperative outcome of arthroscopic rotator cuff repair (ARCR) and develop a nomogram prediction model.

METHODS

A retrospective study was conducted on 302 patients who underwent ARCR from January 2019 to August 2023. Patients were categorized into two groups: a control group with 150 patients showing good recovery and an observation group with 152 patients exhibiting poor recovery. Relevant clinical data were collected and statistically analyzed. A nomogram model was constructed based on the results of multivariate logistic regression analysis. The model's accuracy, discrimination, and clinical utility were evaluated using calibration charts, AUC, c-index, and decision curve analysis. Internal validation was performed through self-random sampling.

RESULTS

Univariate and multivariate regression analysis identified having a frozen shoulder, large rotator cuff tear, increased intraoperative rivet use, diabetes, and traumatic tear as predictive risk factors for poor postoperative outcomes. These factors were utilized to develop a clinical predictive nomogram. The nomogram model demonstrated excellent predictive accuracy for poor postoperative outcomes, both internally and externally. The unadjusted concordance index (C-index) was 0.793 [95% confidence interval (CI), 0.825-0.995]. The AUC for the nomogram was 0.788. Decision curve analysis revealed that the predictive model was clinically useful when the threshold probability ranged from 20 to 60%.

CONCLUSION

The presence of a frozen shoulder, large rotator cuff tear, increased intraoperative rivet use, diabetes, and traumatic tear elevate the risk of suboptimal outcomes following ARCR. Conversely, having a higher preoperative University of California at Los Angeles Shoulder Rating Scale score mitigates this risk. This study introduces a novel nomogram model, exhibiting relatively high accuracy, which enables clinicians to precisely assess the postoperative adverse risk among patients with rotator cuff injuries requiring arthroscopic repair at the outset of treatment.

Development of a nomogram to predict 30-day mortality of sepsis patients with gastrointestinal bleeding: An analysis of the MIMIC-IV database

Background

We aimed to establish and validate a prognostic nomogram model for improving the prediction of 30-day mortality of gastrointestinal bleeding (GIB) in critically ill patients with severe sepsis.

Methods

In this retrospective study, the current retrospective cohort study extracted data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, then partitioned the cohort randomly into training and validation subsets. The cohort was partitioned into training and validation subsets randomly. Our primary endpoint was 30-day all-cause mortality. To reduce data dimensionality and identify predictive variables, the least absolute shrinkage and selection operator (LASSO) regression was employed. A prediction model was constructed by multivariate logistic regression. Model performance was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA).

Results

The analysis included 1435 total patients, comprising 1005 in the training cohort and 430 in the validation cohort. We found that age, smoking status, glucose, (BUN), lactate, Sequential Organ Failure Assessment (SOFA) score, mechanical ventilation≥48h (MV), parenteral nutrition (PN), and chronic obstructive pulmonary disease (COPD) independently influenced mortality in sepsis patients with concomitant GIB. The C-indices were 0.746 (0.700-0.792) and 0.716 (0.663-0.769) in the training and validation sets, respectively. Based on the area under the curve (AUC) and DCA, the nomogram exhibited good discrimination for 30-day all-cause mortality in sepsis with GIB.

Conclusions

For sepsis patients complicated with GIB, we created a unique nomogram model to predict the 30-day all-cause mortality. This model could be a significant therapeutic tool for clinicians in terms of personalized treatment and prognosis prediction.

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy

Introduction

This study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).

Methods

The research included 881 early-stage HCC patients treated at Beijing You'an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results

The study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram's predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.

Conclusions

The established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.