Decision Curve Analysis Research Articles

Validation and Evaluation of 5 Scoring Systems for Predicting Metastatic Risk in Pheochromocytoma and Paraganglioma.

Currently, 5 scoring systems have been proposed in the literature for predicting metastatic risk in pheochromocytoma and paraganglioma (PPGL): Pheochromocytoma of the Adrenal Gland Scaled Score (PASS), Grading System for Adrenal Pheochromocytoma and Paraganglioma (GAPP), Composite Pheochromocytoma/paraganglioma Prognostic Score (COPPS), Age, Size, Extra-adrenal location, Secretion type (ASES) score, and Size, Genetic, Age, and PASS (SGAP) model. To validate and evaluate these 5 scoring systems, we conducted a retrospective review of cases diagnosed as PPGL at the Department of Pathology, West China Hospital of Sichuan University, between January 2012 and December 2019. A total of 185 PPGL cases were included, comprising 35 cases with metastasis and 150 cases remained metastasis-free for over 8 years after surgery. The criteria of the 5 scoring systems were used for scoring and risk classification. The predictive performance of the 5 scoring systems was validated, compared, and evaluated using concordance index (C-index) and decision curve analysis (DCA). The C-indices for PASS, GAPP, and SGAP were 0.600, 0.547, and 0.547, respectively, indicating low discriminative ability. In contrast, COPPS and ASES had C-indices of 0.740 and 0.706, respectively, indicating better discriminative performance. DCA also showed that the predictive capability of COPPS was superior to that of ASES, with both outperformed PASS, while PASS had better predictive ability than GAPP and SGAP. Our analysis indicated that pathology-based scoring systems cannot accurately predict metastatic risk of PPGL. Establishing a precise prediction system requires integrating clinical, pathologic, and molecular information, using a scientific methodology for predictive factor selection and weight assessment.

Development and Validation of a Prognostic Nomogram for Breast Cancer Patients With Multi-Organ Metastases: An Analysis of the Surveillance, Epidemiology, and End Results Program Database.

Multi-organ metastases represent a substantial life-threatening risk for breast cancer (BC) patients. Nonetheless, the current dearth of assessment tools for patients with multi-organ metastatic BC adversely impacts their evaluation. We conducted a retrospective analysis of BC patients with multi-organ metastases using data from the SEER database from 2010 to 2019. The patients were randomly allocated into a training cohort and a validation cohort in a 7:3 ratio. Univariate COX regression analysis, the LASSO, and multivariate Cox regression analyses were performed to identify independent prognostic factors in the training set. Based on these factors, a nomogram was constructed to estimate overall survival (OS) probability for BC patients with multi-organ metastases. The performance of the nomogram was evaluated using C-indexes, ROC curves, calibration curves, decision curve analysis (DCA) curves, and the risk classification system for validation. A total of 3626 BC patients with multi-organ metastases were included in the study, with 2538 patients in the training cohort and 1088 patients in the validation cohort. Age, grade, metastasis location, surgery, chemotherapy, and subtype were identified as significant independent prognostic factors for OS in BC patients with multi-organ metastases. A nomogram for predicting 1-year, 3-year, and 5-year OS was constructed. The evaluation metrics, including C-indexes, ROC curves, calibration curves, and DCA curves, demonstrated the excellent predictive performance of the nomogram. Additionally, the risk grouping system effectively stratified BC patients with multi-organ metastases into distinct prognostic categories. The developed nomogram showed high accuracy in predicting the survival probability of BC patients with multi-organ metastases, providing valuable information for patient counseling and treatment decision making.

GALAD score as a prognostic model for recurrence of hepatocellular carcinoma after local ablation.

Currently, thehighrecurrenceratestill forms severe challenges inhepatocellular carcinoma (HCC) treatment. The GALAD score, including age, gender, alpha-fetoprotein (AFP), lens culinaris agglutinin-reactive AFP (AFP-L3), and des-gamma-carboxyprothrombin (DCP) was developed as a diagnostic model. However, evidence isstilllacking to confirm the capability of the GALAD score to predict the recurrence of HCC. This study included 390 HCC patients after local ablation at Beijing You'an Hospital from January 1, 2018, to December 31, 2022. Firstly, the area under the receiver operating characteristic (ROC) curve (AUC) was calculated to assess the predictive capability of the GALAD score. Then, the Kaplan-Meier (KM) curve and log-rank test were used to compare the prognosis between two groups classified by GALAD score. Finally, a nomogram for high-risk patients was established by Lasso-Cox regression. It was assessed by ROC curves, calibration curves, and decision curve analysis (DCA). The ROC curve (AUC: 0.749) and KM curve showed the GALAD score had good predictive ability and could clearly stratify patients into two groups through the risk of recurrence. Prognostic factors selected by Lasso-Cox regression contained tumor number, tumor size, and globulin. The nomogram for high-risk patients showed reliable discrimination, calibration, and clinical utility. This research displayed that the GALAD score is an effective model for predicting the recurrence of HCC. Meanwhile, we found the poor prognosis of the high-risk group and created a nomogram for these patients.

Optimizing predictions: improved performance of preoperative gadobenate-enhanced MRI hepatobiliary phase features in predicting vessels encapsulating tumor clusters in hepatocellular carcinoma-a multicenter study.

Vessels Encapsulating Tumor Clusters (VETC) are now recognized as independent indicators of recurrence and overall survival in hepatocellular carcinoma (HCC) patients. However, there has been limited investigation into predicting the VETC pattern using hepatobiliary phase (HBP) features from preoperative gadobenate-enhanced MRI. This study involved 252 HCC patients with confirmed VETC status from three different hospitals (Hospital 1: training set with 142 patients; Hospital 2: test set with 64 patients; Hospital 3: validation set with 46 patients). Independent predictive factors for VETC status were determined through univariate and multivariate logistic analyses. Subsequently, these factors were used to construct two distinct VETC prediction models. Model 1 included all independent predictive factors, while Model 2 excluded HBP features. The performance of both models was assessed using the Area Under the Curve (AUC), Decision Curve Analysis, and Calibration Curve. Prediction accuracy between the two models was compared using Net Reclassification Improvement (NRI) and Integrated Discriminant Improvement (IDI). CA199, IBIL, shape, peritumoral hyperintensity on HBP, and arterial peritumoral enhancement were independent predictors of VETC. Model 1 showed robust predictive performance, with AUCs of 0.836 (training), 0.811 (test), and 0.802 (validation). Model 2 exhibited moderate performance, with AUCs of 0.813, 0.773, and 0.783 in the respective sets. Calibration and decision curves for both models indicated consistent predictions between predicted and actual VETC, benefiting HCC patients. NRI showed Model 1 increased by 0.326, 0.389, and 0.478 in the training, test, and validation sets compared to Model 2. IDI indicated Model 1 increased by 0.036, 0.028, and 0.025 in the training, test, and validation sets compared to Model 2. HBP features from preoperative gadobenate-enhanced MRI can enhance the predictive performance of VETC in HCC.

Identifying Optimal Candidates for Primary Tumor Resection Among Metastatic Pancreatic Cancer Patients: A Population-Based Predictive Model.

There is a controversy about whether surgery should proceed among metastatic pancreatic cancer (mPC) patients. A survival benefit was observed in mPC patients who underwent primary tumor resection; however, determining which patients would benefit from surgery is complex. For this purpose, we created a model to identify mPC patients who may benefit from primary tumor excision. Patients with mPC were extracted from the Surveillance, Epidemiology, and End Results database, and separated into surgery and nonsurgery groups based on whether the primary tumor was resected. Propensity score matching (PSM) was applied to balance confounding factors between the two groups. A nomogram was developed using multivariable logistic regression to estimate surgical benefit. Our model is evaluated using multiple methods. About 662 of 14,183 mPC patients had primary tumor surgery. Kaplan-Meier analyses showed that the surgery group had a better prognosis. After PSM, a survival benefit was still observed in the surgery group. Among the surgery cohort, 202 patients survived longer than 4months (surgery-beneficial group). The nomogram discriminated better in training and validation sets under the receiver operating characteristic (ROC) curve (AUC), and calibration curves were consistent. Decision curve analysis (DCA) revealed that it was clinically valuable. This model is better at identifying candidates for primary tumor excision. A helpful prediction model was developed and validated to identify ideal candidates who may benefit from primary tumor resection in mPC.

Development and validation of a nomogram to predicting the efficacy of PD-1/PD-L1 inhibitors in patients with nasopharyngeal carcinoma.

With the treatment of nasopharyngeal carcinoma (NPC) by PD-1/PD-L1 inhibitors used widely in clinic, it becomes very necessary to anticipate whether patients would benefit from it. We aimed to develop a nomogram to evaluate the efficacy of anti-PD-1/PD-L1 in NPC patients. Totally 160 NPC patients were enrolled in the study. Patients were measured before the first PD-1/PD-L1 inhibitors treatment and after 8-12weeks of immunotherapy by radiological examinations to estimate the effect. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to screen hematological markers and establish a predictive model. The nomogram was internally validated by bootstrap resampling and externally validated. Performance of the model was evaluated using concordance index, calibration curve, decision curve analysis and receiver operation characteristic curve. Patients involved were randomly split into training cohort ang validation cohort. Based on Lasso logistic regression, systemic immune-inflammation index (SII) and ALT to AST ratio (LSR) were selected to establish a predictive model. The C-index of training cohort and validating cohort was 0.745 and 0.760. The calibration curves and decision curves showed the precise predictive ability of this nomogram. The benefit of the model showed in decision curve was better than TNM stage. The area under the curve (AUC) value of training cohort and validation cohort was 0.745 and 0.878, respectively. The predictive model helped evaluating efficacy with high accuracy in NPC patients treated with PD-1/PD-L1 inhibitors.

A novel nomogram for the prediction of perforation during endoscopic submucosal dissection for colorectal neoplasms.

High perforation risk hinders the widespread adoption of ESD for colorectal neoplasms. This study was performed to determine the risk factors of colorectal endoscopic submucosal dissection (ESD)-induced perforation and develop a predictive model. A total of 1046 colorectal neoplasms in 1011 patients were retrospectively enrolled from January 2011 to December 2021, in a single tertiary center as the derivation cohort. We identified independent risk factors for perforation using univariate analysis and multi-variate logistic regression. A nomogram was developed based on the logistic regression model and prospectively applied to 266 colorectal neoplasms as the validation cohort. The performance of the predictive model was evaluated with the receiver operating characteristic curve, calibration plot, and decision curve analysis. Independent pre-operative factors for colorectal ESD-induced perforation were tumor located in the left colon [odds ratio (OR) 2.39, P = 0.040], size ≥ 40 mm (OR 3.36, P < 0.001), ≥2/3 circumference (OR 7.55, P = 0.004), located across folds (OR 6.26, P < 0.001), and laterally spreading tumor (non-granular type, OR 2.34, P = 0.029; granular type, OR 2.46, P = 0.021). The nomogram model incorporating the pre-operative factors performed well in both the derivation and validation cohorts (areas under the curve of 0.750 and 0.806, respectively). Decision curve analysis demonstrated that the clinical benefit of the nomogram was favorable. The novel nomogram, developed and prospectively validated, incorporating tumor size, location, and morphology can successfully predict perforation during ESD for colorectal neoplasms.

Abnormal coagulation after hepatectomy in patients with normal preoperative coagulation function

BackgroundTo explore the risk factors for postoperative abnormal coagulation (PAC) and establish a predictive model for patients with normal preoperative coagulation function who underwent hepatectomy.Materials and MethodsA total of 661 patients with normal preoperative coagulation function who underwent hepatectomy between January 2015 and December 2021 at the First Affiliated Hospital of Sun Yat-sen University were divided into two groups: the postoperative abnormal coagulation group (PAC group, n = 362) and the normal coagulation group (non-PAC group, n = 299). Univariate and multivariate logistic analyses were used to identify the risk factors for PAC.ResultsThe incidence of PAC in 661 patients who underwent hepatectomy was 54.8% (362/661). The least absolute shrinkage and selection operator (LASSO) method was used for multivariate logistic regression analysis. The preoperative international normalized ratio (INR), intraoperative succinyl gelatin infusion and major hepatectomy were found to be independent risk factors for PAC. A nomogram for predicting the PAC after hepatectomy was constructed. The model presented a receiver operating characteristic (ROC) curve of 0.742 (95% confidence interval (CI): 0.697–0.786) in the training cohort. The validation set demonstrated a promising ROC of 0.711 (95% CI: 0.639–0.783), and the calibration curve closely approximated the true incidence. Decision curve analysis (DCA) was performed to assess the clinical usefulness of the predictive model. The risk of PAC increased when the preoperative international normalized ratio (INR) was greater than 1.025 and the volume of intraoperative succinyl gelatin infusion was greater than 1500 ml.ConclusionThe PAC is closely related to the preoperative INR, intraoperative succinyl gelatin infusion and major hepatectomy. A three-factor prediction model was successfully established for predicting the PAC after hepatectomy.

Establishment and evaluation of a prognostic model for patients with unresectable gastric cancer liver metastases

BACKGROUND Liver metastases (LM) is the primary factor contributing to unfavorable outcomes in patients diagnosed with gastric cancer (GC). The objective of this study is to analyze significant prognostic risk factors for patients with GCLM and develop a reliable nomogram model that can accurately predict individualized prognosis, thereby enhancing the ability to evaluate patient outcomes. AIM To analyze prognostic risk factors for GCLM and develop a reliable nomogram model to accurately predict individualized prognosis, thereby enhancing patient outcome assessment. METHODS Retrospective analysis was conducted on clinical data pertaining to GCLM (type III), admitted to the Department of General Surgery across multiple centers of the Chinese PLA General Hospital from January 2010 to January 2018. The dataset was divided into a development cohort and validation cohort in a ratio of 2:1. In the development cohort, we utilized univariate and multivariate Cox regression analyses to identify independent risk factors associated with overall survival in GCLM patients. Subsequently, we established a prediction model based on these findings and evaluated its performance using receiver operator characteristic curve analysis, calibration curves, and clinical decision curves. A nomogram was created to visually represent the prediction model, which was then externally validated using the validation cohort. RESULTS A total of 372 patients were included in this study, comprising 248 individuals in the development cohort and 124 individuals in the validation cohort. Based on Cox analysis results, our final prediction model incorporated five independent risk factors including albumin levels, primary tumor size, presence of extrahepatic metastases, surgical treatment status, and chemotherapy administration. The 1-, 3-, and 5-years Area Under the Curve values in the development cohort are 0.753, 0.859, and 0.909, respectively; whereas in the validation cohort, they are observed to be 0.772, 0.848, and 0.923. Furthermore, the calibration curves demonstrated excellent consistency between observed values and actual values. Finally, the decision curve analysis curve indicated substantial net clinical benefit. CONCLUSION Our study identified significant prognostic risk factors for GCLM and developed a reliable nomogram model, demonstrating promising predictive accuracy and potential clinical benefit in evaluating patient outcomes.

Development and Validation of a Novel Machine Learning Model to predict the Survival of Patients with Gastrointestinal Neuroendocrine Neoplasms.

Well-calibrated models for personalized prognostication of patients with gastrointestinal neuroendocrine neoplasms (GINENs) are limited. This study aimed to develop and validate a machine-learning model to predict the survival of patients with GINENs. oblique random survival forest (ORSF) model, Cox proportional hazard risk model, Cox model with least absolute shrinkage and selection operator penalization, CoxBoost, Survival Gradient Boosting Machine, Extreme Gradient Boosting survival regression, DeepHit, DeepSurv, DNNSurv, Logistic-Hazard model, and PC-Hazard model were compared. We further tuned hyperparameters and selected variables for the best-performing ORSF. Then, the final ORSF model was validated. 43444 patients with GINENs were included. The median (interquartile range) survival time was 53 (19-102) months. The ORSF model performed best, in which age, histology, M stage, tumor size, primary tumor site, sex, tumor number, surgery, lymph nodes removed, N stage, race, and grade were ranked as important variables. However, chemotherapy and radiotherapy were not necessary for the ORSF model. The ORSF model had an overall C-index of 0.86 (95% confidence interval, 0.85-0.87). The area under the receiver operation curves at 1-, 3-, 5-, and 10-year were 0.91, 0.89, 0.87, and 0.80, respectively. The decision curve analysis showed superior clinical usefulness of the ORSF model than the American Joint Committee on Cancer Stage. A nomogram and an online tool were given. The machine-learning ORSF model could precisely predict the survival of patients with GINENs, with the ability to identify patients at high risk for death and probably guide clinical practice.

A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.

Differentiating intestinal tuberculosis (ITB) from Crohn's disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD. Eighty-two patients including 25 patients with ITB and 57 patients with CD were retrospectively recruited (54 in training cohort and 28 in testing cohort). The region of interest (ROI) for the lesion was delineated on magnetic resonance enterography (MRE) and colonoscopy images. Radiomic features were extracted by least absolute shrinkage and selection operator regression. Pathological feature was extracted automatically by deep-learning method. Clinical features were filtered by logistic regression analysis. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Delong's test was applied to compare the efficiency between the multidisciplinary-based model and the other four single-disciplinary-based models. The radiomics model based on MRE features yielded an AUC of 0.87 (95% confidence interval [CI] 0.68-0.96) on the test data set, which was similar to the clinical model (AUC, 0.90 [95% CI 0.71-0.98]) and higher than the colonoscopy radiomics model (AUC, 0.68 [95% CI 0.48-0.84]) and pathology deep-learning model (AUC, 0.70 [95% CI 0.49-0.85]). Multidisciplinary model, integrating 3 clinical, 21 MRE radiomic, 5 colonoscopy radiomic, and 4 pathology deep-learning features, could significantly improve the diagnostic performance (AUC of 0.94, 95% CI 0.78-1.00) on the bases of single-disciplinary-based models. DCA confirmed the clinical utility. Multidisciplinary-based model integrating clinical, MRE, colonoscopy, and pathology features was useful in distinguishing ITB from CD.

Nomogram for predicting the risk of postoperative delirium in elderly patients undergoing orthopedic surgery

ObjectiveTo retrospectively analyze the risk factors for postoperative delirium (POD) after orthopedic surgery in elderly patients and establish an individualized nomogram to predict the risk of POD.MethodsThe data of 1011 patients who underwent orthopedic surgery from January 2019 to January 2022 were retrospectively analyzed. Univariate and multivariate logistic analyses were used to screen for independent risk factors. Stepwise regression was conducted to screen risk factors to construct a nomogram to predict the risk of POD after orthopedic surgery in elderly individuals, and nomogram validation analyses were performed.ResultsThe logistic regression results showed that age (≥ 75 years old vs. < 75 years old; odds ratio (OR) = 2.889; 95% confidence interval (CI), 1.149, 7.264), sex (male vs. female, OR = 2.368; 95% CI, 1.066, 5.261), and preoperative cognitive impairment (yes vs. no, OR = 13.587; 95% CI, 4.360, 42.338) were independent risk factors for POD in elderly patients who underwent orthopedic surgery (P < 0.05). A nomogram was constructed using 7 risk factors, i.e., age, American Society of Anesthesiologists (ASA) classification, sex, preoperative hemoglobin (Hb), preoperative pulmonary disease, cognitive impairment, and intraoperative infusion volume. The area under the curve (AUC) showed good discrimination (0.867), the slope of the calibration curve was 1.0, and the optimal net benefit of the nomogram from the decision curve analysis (DCA) was 0.01–0.58.ConclusionThis study used 7 risk factors to construct a nomogram to predict the risk of POD after major orthopedic surgery in elderly individuals, and the nomogram had good discrimination ability, accuracy, and clinical practicability.

Interpretable machine learning based on CT-derived extracellular volume fraction to predict pathological grading of hepatocellular carcinoma.

To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC). The study retrospectively analyzed 238 patients who underwent HCC resection surgery between January 2013 and April 2023. Six machine learning algorithms were employed to construct predictive models for HCC PG: logistic regression, extreme gradient boosting, Light Gradient Boosting Machine (LightGBM), random forest, adaptive boosting, and Gaussian naive Bayes. Model performance was evaluated using receiver operating characteristic curve analysis, including area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1 score. Calibration plots were used for visual evaluation of model calibration. Clinical decision curve analysis was performed to assess potential clinical utility by calculating net benefit. 166 patients from Hospital A were allocated to the training set, while 72 patients from Hospital B (constituting 30.25% of the total sample) were assigned to the test set. The model achieved an AUC of 1.000 (95%CI: 1.000-1.000) in the training set and 0.927 (95%CI: 0.837-0.999) in the validation set, respectively. Ultimately, the model achieved an AUC of 0.909 (95%CI: 0.837-0.980) in the test set, with an accuracy of 0.778, sensitivity of 0.906, specificity of 0.789, negative predictive value of 0.556, and F1 score of 0.908. This study successfully developed and validated a non-invasive auxiliary assessment method based on CT-derived ECV to predict the HCC PG, providing important supplementary information for clinical decision-making.

Establishment of a mortality risk nomogram for predicting in-hospital mortality of sepsis: cohort study from a Chinese single center

ObjectiveTo establish a mortality risk nomogram for predicting in-hospital mortality of sepsis patients in the Chinese population.MethodsData were obtained from the medical records of sepsis patients enrolled at the Affiliated Huadu Hospital, Southern Medical University, between 2019 and 2021. A total of 696 sepsis patients were initially included in our research, and 582 cases were finally enrolled after screening and divided into the survival group (n = 400) and the non-survival group (n = 182) according to the incidence of mortality during hospitalization. Twenty-eight potential sepsis-related risk factors for mortality were identified. Least absolute shrinkage and selection operator (LASSO) regression was used to optimize variable selection by running cyclic coordinate descent with k-fold (tenfold in this case) cross-validation. We used binary logistic regression to build a model for predicting mortality from the variables based on LASSO regression selection. Binary logistic regression was used to establish a nomogram based on independent mortality risk factors. To validate the prediction accuracy of the nomogram, receiver operating characteristic curve (ROC) analysis, decision curve analysis (DCA) and restricted cubic spline (RCS) analysis were employed. Eventually, the Hosmer-Lemeshow test and calibration curve were used for nomogram calibration.ResultsLASSO regression identified a total of ten factors, namely, chronic heart disease (CHD), lymphocyte count (LYMP), neutrophil-lymphocyte ratio (NLR), red blood cell distribution width (RDW), C reactive protein (CRP), Procalcitonin (PCT), lactic acid, prothrombin time (PT), alanine aminotransferase (ALT), total bilirubin (Tbil), interleukin-6 (IL6), that were incorporated into the multivariable analysis. Finally, a nomogram including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6 was established by multivariable logistic regression. The ROC curves of the nomogram in the training and validation sets were 0.9836 and 0.9502, respectively. DCA showed that the nomogram could be applied clinically if the risk threshold was between 29.52 and 99.61% in the training set and between 31.32 and 98.49% in the testing set. RCS showed that when the value of independent risk factors from the predicted model exceeded the median, the mortality hazard ratio increased sharply. The results of the Hosmer–Lemeshow test (χ2 = 0.1901, df = 2, p = 0.9091) and the calibration curves of the training and validation sets showed good agreement with the actual results, which indicated good stability of the model.ConclusionOur nomogram, including CHD, LYMP, NLR, RDW, lactic acid, PT, CRP, PCT, Tbil, ALT, and IL6, exhibits good performance for predicting mortality risk in adult sepsis patients.

External Validation of Updated Prediction Models for Neurological Outcomes at 90 Days in Patients With Out-of-Hospital Cardiac Arrest.

Few prediction models for individuals with early-stage out-of-hospital cardiac arrest (OHCA) have undergone external validation. This study aimed to externally validate updated prediction models for OHCA outcomes using a large nationwide dataset. We performed a secondary analysis of the JAAM-OHCA (Comprehensive Registry of In-Hospital Intensive Care for Out-of-Hospital Cardiac Arrest Survival and the Japanese Association for Acute Medicine Out-of-Hospital Cardiac Arrest) registry. Previously developed prediction models for patients with cardiac arrest who achieved the return of spontaneous circulation were updated. External validation was conducted using data from 56 institutions from the JAAM-OHCA registry. The primary outcome was a dichotomized 90-day cerebral performance category score. Two models were updated using the derivation set (n=3337). Model 1 included patient demographics, prehospital information, and the initial rhythm upon hospital admission; Model 2 included information obtained in the hospital immediately after the return of spontaneous circulation. In the validation set (n=4250), Models 1 and 2 exhibited a C-statistic of 0.945 (95% CI, 0.935-0.955) and 0.958 (95% CI, 0.951-0.960), respectively. Both models were well-calibrated to the observed outcomes. The decision curve analysis showed that Model 2 demonstrated higher net benefits at all risk thresholds than Model 1. A web-based calculator was developed to estimate the probability of poor outcomes (https://pcas-prediction.shinyapps.io/90d_lasso/). The updated models offer valuable information to medical professionals in the prediction of long-term neurological outcomes for patients with OHCA, potentially playing a vital role in clinical decision-making processes.

A PSMA PET/CT-based risk model for prediction of concordance between targeted biopsy and combined biopsy in detecting prostate cancer.

This study is to investigate the diagnostic value of 68Ga-PSMA-11 in improving the concordance between mpMRI-TB and combined biopsy (CB) in detecting PCa. 115 consecutive men with 68Ga-PSMA-11 PET/CT prior to prostate biopsy were included for analysis. PSMA intensity, quantified as maximum standard uptake value (SUVmax), minimum apparent diffusion coefficient (ADCmin) and other clinical characteristics were evaluated relative to biopsy concordance using univariate and multivariate logistic regression analyses. A prediction model was developed based on the identified parameters, and a dynamic online diagnostic nomogram was constructed, with its discrimination evaluated through the area under the ROC curve (AUC) and consistency assessed using calibration plots. To assess its clinical applicability, a decision curve analysis (DCA) was performed, while internal validation was conducted using bootstrapping methods. Concordance between mpMRI-TB and CB occurred in 76.5% (88/115) of the patients. Multivariate logistic regression analyses performed that SUVmax (OR= 0.952; 95% CI 0.917-0.988; P= 0.010) and ADCmin (OR= 1.006; 95% CI 1.003-1.010; P= 0.001) were independent risk factors for biopsy concordance. The developed model showed a sensitivity, specificity, accuracy and AUC of 0.67, 0.78, 0.81 and 0.78 in the full sample. The calibration curve demonstrated that the nomogram's predicted outcomes closely resembled the ideal curve, indicating consistency between predicted and actual outcomes. Furthermore, the decision curve analysis (DCA) highlighted the clinical net benefit achievable across various risk thresholds. These findings were reinforced by internal validation. The developed prediction model based on SUVmax and ADCmin showed practical value in guiding the optimization of prostate biopsy pattern. Lower SUVmax and Higher ADCmin values are associated with greater confidence in implementing mono-TB and safely avoiding SB, effectively balancing benefits and risks.

Abstract PO3-02-08: Risk factors and a new predictive nomogram for predicting the Non-sentinel Lymph Node Metastasis in 708 early-stage Breast Cancer Patients with Positive Sentinel Nodes in China

Abstract Objective: The management of positive sentinel lymph node biopsies (SLNB) in patients with early-stage breast cancer remains a matter of controversy. Some studies have shown that not all breast cancer patients with positive sentinel lymph nodes (SLNs) benefit from further axillary lymph node dissection (ALND). Our study aims to analyse the risk factors and construct a new nomogram to predict non-sentinel lymph node (NSLN) metastasis in Chinese early-stage breast cancer patients with positive sentinel lymph nodes. Methods: The clinical data of 708 patients with early-stage breast cancer who attended The Fourth Hospital of Hebei Medical University for surgical treatment from January 2016 to December 2021 was retrospectively analyzed. All of them were female breast cancer patients with positive sentinel lymph nodes and underwent ALND. The patients were divided into modeling group and validation group according to the time of treatment. The patients in the modeling group were divided into a positive NSLN group and a negative NSLN group. The characteristics: age, maximum tumor diameter, the number of positive SLNs, proportion of positive SLNs, vessel carcinoma embolus, nerve invasion, status of ER, PR, HER-2 and Ki-67 were included in the analysis. And the independent risk factors of NSLN metastasis were screened by univariate (chi-square test) and multivariate (logistic regression) analysis. A nomogram model was established according to the screened independent risk factors, and the model was externally validated by the validation group. The ROC curve was plotted to calculate the area under the curve (AUC) and evaluate the predictive power of the nomogram model. A calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the model. Results: Among the 708 patients, (modeling group n=410, validation group n=298), the average age, average number of positive SLNs, and NSLN positive rate were 50.9±11.1, 1.55±0.87 and 25.71%. The univariate analysis revealed statistically significant differences in the number of positive SLNs (P&amp;lt; 0.001), the proportion of positive SLNs (P &amp;lt; 0.001), vessel carcinoma embolus (P=0.001), nerve invasion (P=0.012), and ER status (P=0.029) in the NSLN-positive group compared with the negative group. The multifactorial logistic regression analysis revealed that the number of positive SLN (P=0.002; OR: 1.700; 95% CI: 1.219-2.370), the proportion of positive SLN (P=0.048; OR: 3.214; 95% CI: 1.001-10.490), vessel carcinoma embolus (P=0.010; OR: 2.076; 95% CI: 1.194-3.612), nerve invasion (P=0.024; OR: 2.119; 95% CI: 1.221-3.678), and ER status (P=0.045; OR: 2.978; 95% CI: 1.022-8.675) were independent risk factors for NSLN positivity. Furthermore, the nomogram was built using these 5 factors and validated on 298 patients in the validation group. The AUC was 0.728 (P&amp;lt; 0.001; 95%CI: 0.673-0.784) in the modeling group and 0.810 (P&amp;lt;0.001; 95%CI: 0.735-0.886) in the validation group. The calibration curves of the modeling and validation groups were close to the ideal curve, and DCA also showed that the model could be applied in clinical practice. Conclusion: For early-stage breast cancer with positive SLNs, these factors as the positive number of SLNs &amp;gt; 2, the ratio of positive SLNs &amp;gt; 50%, vessel carcinoma embolus, nerve invasion and ER-positive could predict NSLN metastasis well. The proposed nomogram appears to accurately estimate the likelihood of positive NSLNs and may greatly help the surgeon to decide intraoperatively whether to perform further ALND and avoid non-essential ALND as well as postoperative complications. Citation Format: Zhenchuan Song, Xueyi Zhao, Liu Yang. Risk factors and a new predictive nomogram for predicting the Non-sentinel Lymph Node Metastasis in 708 early-stage Breast Cancer Patients with Positive Sentinel Nodes in China [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-02-08.

Predictive value of PD-L1 and TMB for short-term efficacy prognosis in non-small cell lung cancer and construction of prediction models

ObjectiveTo investigate the correlation between programmed death ligand 1(PD-L1), tumor mutation burden (TMB) and the short-term efficacy and clinical characteristics of anti-PD-1 immune checkpoint inhibitor combination chemotherapy in NSCLC patients. The efficacy of the prediction model was evaluated.MethodsA total of 220 NSCLC patients receiving first-line treatment with anti-PD-1 immune checkpoint inhibitor combined with chemotherapy were retrospectively collected. The primary endpoint was short-term efficacy ORR. The correlation between short-term efficacy, PD-L1, TMB, and clinical characteristics using χ2 test or t-test was evaluated. Screen the independent prognostic factors using univariate and multivariate logistic regression analyses, and construct a nomogram prediction model using the “rms” package in R software. Using receiver operating characteristic (ROC) curve analysis to evaluate the independent Prognostic factors and the prediction model. Using decision curve analysis (DCA) to verify the superiority of the prediction model.ResultsThe mean values of PD-L1, TMB, neutrophils, lymphocytes, neutrophil-to-lymphocyte ratio, and albumin were the highest in the ORR group, PD-L1 expression and TMB correlated with epidermal growth factor receptor expression. Multivariate analyses showed that PD-L1, TMB, and neutrophil were independent prognostic factors for ORR. The area under the ROC curve (AUC) values of the ROC constructed based on these three indicators were 0.7104, 0.7139, and 0.7131, respectively. The AUC value under the ROC of the nomogram model was 0.813. The DCA of the model showed that all three indicators used together to build the prediction model of the net return were higher than those of the single indicator prediction model.ConclusionPD-L1, TMB, and neutrophils are independent prognostic factors for short-term efficacy. The nomogram prediction model constructed using these three indicators can further improve predictive efficacy of ICIs in patients with NSCLC.

Association between biological aging and diabetic retinopathy

The impact of aging on diabetic retinopathy (DR) remains underestimated. The current study aimed to investigate the association between biological aging and DR, in contrast to chronological age (CA). Using the National Health and Nutrition Survey data from 2005 to 2008. Biological aging was evaluated through the biological age (BA) and phenotypic age (PA), which were calculated from clinical markers. DR was identified in participants with diabetes mellitus (DM) when they exhibited one or more retinal microaneurysms or retinal blot hemorrhages under retinal imaging, with or without the presence of more severe lesions. Survey-weighted multivariable logistic regression was performed, and the regression model was further fitted using restricted cubic splines. The discriminatory capability and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Based on weighted analyses, of the 3100 participants included in this study, of which 162 had DR. In the adjusted model, BA (odds ratio [OR] = 1.12, 95% CI, 1.06–1.18) and PA (OR = 1.11, 95% CI, 1.07–1.14) were associated with DR, while CA was not significantly (OR = 1.01, 95% CI, 0.99–1.03). Narrowing the analysis to DM participants and adjusting for factors like insulin showed similar results. ROC and DCA analyses indicate that BA/PA predicted DR better than CA and offer greater clinical utility. The positive association between BA/PA and DR was consistent across subgroups despite potential interactions. Biological aging heightens DR risk, with BA/PA showing a stronger association than CA. Our findings underscored the importance of timely anti-aging interventions for preventing DR.

Abstract PO1-07-07: Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving treatment

Abstract Background Despite modern surgical and irradiation techniques, ipsilateral breast tumor recurrence (IBTR) accounts for 5-15% of all cancer recurrences in women treated with breast conservative treatment. Historically, the methods to define true recurrence (TR) and new primary tumor (NP) for IBTR mainly rely on clinical and pathological criteria, limiting the accuracy of the discerption and causing misclassification. This study aimed to develop a preoperative, noninvasive model for distinguishing IBTR by integrating clinicopathological review with paired dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) at diagnosis and following in-breast recurrence. MethodsWe retrospectively extracted radiomics features from MRI to develop a radiomic cohort of IBTR (n =46), among which all patients underwent paired preoperative DCE-MRI. This radiomic cohort was divided into a training cohort (n =27) and a validation cohort (n =19) with stratified random sampling. Classification of IBTR as TR or NP on the basis of tumor location, histologic subtype, estrogen receptor, and HER2 status was set as the gold standard. The least absolute shrinkage and selection operator (LASSO) regression and logistic regression were utilized to perform radiomics feature selection and model training, respectively. The clinical utility of the model was determined via decision curve analysis (DCA). Results We selected three radiomics features (first-order feature Kurtosis from the first postcontrast phase on IBTR DCE-MRI, GLCM feature Imc2 from the first postcontrast phase on IBTR DCE-MRI, and the delta feature Correlation from the precontrast phase of the change between IBTR and primary tumor DCE-MRI) to develop an IBTR-classification predicting radiomic model, which performed well in the validation cohort (AUC 0.867, 95% confidence interval (CI) 0.694-1). Further investigation for sensitivity (73.3%) and specificity (100%) verified a favorable concordance between the radiomic classification and the conventional standard, with a diagnostic accuracy of 79%. Conclusions: Our study demonstrated the feasibility of the radiomics model in predicting IBTR classification and provided preoperative information about the nature of “recurrence”. This might have important implications in surgical approaches and multidisciplinary care for IBTR. Further efforts are needed to improve the reproducibility of radiomics features and models in multiple centers. Citation Format: Feilin Qu, Guan-Hua Su, Jin-Hui Li, Chao You, Zhi-Ming Shao. Radiomics features for distinguishing true recurrence versus new primary tumor following breast-conserving treatment [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-07.