Prediction of clinical risk assessment and survival in chronic obstructive pulmonary disease with pulmonary hypertension.

BACKGROUND: Patients with pulmonary hypertension (PH) and chronic obstructive pulmonary disease (COPD) have an increased risk of disease exacerbation and decreased survival. We aimed to develop and validate a non-invasive nomogram for predicting COPD associated with severe PH and a prognostic nomogram for patients with COPD and concurrent PH (COPD-PH). METHODS: This study included 535 patients with COPD-PH from six hospitals. A multivariate logistic regression analysis was used to analyse the risk factors for severe PH in patients with COPD and a multivariate Cox regression was used for the prognostic factors of COPD-PH. Performance was assessed using calibration, the area under the receiver operating characteristic curve and decision analysis curves. Kaplan-Meier curves were used for a survival analysis. The nomograms were developed as online network software. RESULTS: Tricuspid regurgitation velocity, right ventricular diameter, N-terminal pro-brain natriuretic peptide (NT-proBNP), the red blood cell count, New York Heart Association functional class and sex were non-invasive independent variables of severe PH in patients with COPD. These variables were used to construct a risk assessment nomogram with good discrimination. NT-proBNP, mean pulmonary arterial pressure, partial pressure of arterial oxygen, the platelet count and albumin were independent prognostic factors for COPD-PH and were used to create a predictive nomogram of overall survival rates. CONCLUSIONS: The proposed nomograms based on a large sample size of patients with COPD-PH could be used as non-invasive clinical tools to enhance the risk assessment of severe PH in patients with COPD and for the prognosis of COPD-PH. Additionally, the online network has the potential to provide artificial intelligence-assisted diagnosis and treatment. HIGHLIGHTS: A multicentre study with a large sample of chronic obstructive pulmonary disease (COPD) patients diagnosed with PH through right heart catheterisation. A non-invasive online clinical tool for assessing severe pulmonary hypertension (PH) in COPD. The first risk assessment tool was established for Chinese patients with COPD-PH.

Nomogram for predicting pathology upstaging in patients with EIN: is sentinel lymph node assessment useful in these patients?

OBJECTIVE: The objective of this study was to identify the risk factors for postoperative pathological escalation of endometrial cancer in patients with a pathologic diagnosis of endometrial intraepithelial neoplasia (EIN) before surgery. Some of the clues from the preoperative assessment were used to build a nomogram to predict the likely pathological escalation after surgery, and to explore the feasibility of sentinel lymph node biopsy in these patients with possible pathological escalation. METHODS: This was a retrospective analysis of patients who underwent surgical treatment for EIN diagnosed before surgery between 2018 and 2023 in The Obstetrics and Gynecology Hospital of Fudan University. parameters including clinical, radiological and histopathological factors were analyzed by univariate and multivariate logistic regression to determine the correlation with pathology upstaging. A nomogram based on the multivariate results was developed to predict the probability of pathology upstaging. A total of 729 patients were included, divided into training set and validation set. 484 patients were used to build the model. This nomogram was subsequently validated using 245 patients. RESULTS: Upstaging to endometrial carcinoma occurred in 115 (23.8 percent) of 484 women treated between 2018 and 2023 in training set. A lager endometrial thickness (at least 15 mm), menopause, hypertension, HE4, and endometrial blood were significantly associated with upstaging. A nomogram developed using these factors demonstrated good predictive performance (area under the receiver operating characteristic curve (AUC)=0.6808; 95% confidence interval [CI]=0.6246-0.7369). The nomogram showed similar predictive performance in the validation data set, based on another 245 women (AUC=0.7821; 95% CI=0.7076-0.8567). CONCLUSION: This study developed a novel nomogram based on the 5 most important factors, which can accurately predict invasive cancer. It is common for women with preoperative diagnosis of EIN to experience pathological progression to endometrial cancer. For some patients with postoperative pathological escalation, we found lymph node metastasis. This nomogram may be useful to help doctor decide whether to perform sentinel lymph node biopsy for surgical staging in these EIN patients. According to the nomogram, simultaneous sentinel lymph node biopsy in patients with high probability of postoperative pathological upgrading can provide better guidance for postoperative adjuvant treatment of endometrial cancer and avoid the occurrence of secondary surgery.

A single-center, retrospective study of hospitalized patients with lower respiratory tract infections: clinical assessment of metagenomic next-generation sequencing and identification of risk factors in patients.

INTRODUCTION: Lower respiratory tract infections(LRTIs) in adults are complicated by diverse pathogens that challenge traditional detection methods, which are often slow and insensitive. Metagenomic next-generation sequencing (mNGS) offers a comprehensive, high-throughput, and unbiased approach to pathogen identification. This retrospective study evaluates the diagnostic efficacy of mNGS compared to conventional microbiological testing (CMT) in LRTIs, aiming to enhance detection accuracy and enable early clinical prediction. METHODS: In our retrospective single-center analysis, 451 patients with suspected LRTIs underwent mNGS testing from July 2020 to July 2023. We assessed the pathogen spectrum and compared the diagnostic efficacy of mNGS to CMT, with clinical comprehensive diagnosis serving as the reference standard. The study analyzed mNGS performance in lung tissue biopsies and bronchoalveolar lavage fluid (BALF) from cases suspected of lung infection. Patients were stratified into two groups based on clinical outcomes (improvement or mortality), and we compared clinical data and conventional laboratory indices between groups. A predictive model and nomogram for the prognosis of LRTIs were constructed using univariate followed by multivariate logistic regression, with model predictive accuracy evaluated by the area under the ROC curve (AUC). RESULTS: (1) Comparative Analysis of mNGS versus CMT: In a comprehensive analysis of 510 specimens, where 59 cases were concurrently collected from lung tissue biopsies and BALF, the study highlights the diagnostic superiority of mNGS over CMT. Specifically, mNGS demonstrated significantly higher sensitivity and specificity in BALF samples (82.86% vs. 44.42% and 52.00% vs. 21.05%, respectively, p < 0.001) alongside greater positive and negative predictive values (96.71% vs. 79.55% and 15.12% vs. 5.19%, respectively, p < 0.01). Additionally, when comparing simultaneous testing of lung tissue biopsies and BALF, mNGS showed enhanced sensitivity in BALF (84.21% vs. 57.41%), whereas lung tissues offered higher specificity (80.00% vs. 50.00%). (2) Analysis of Infectious Species in Patients from This Study: The study also notes a concerning incidence of lung abscesses and identifies Epstein-Barr virus (EBV), Fusobacterium nucleatum, Mycoplasma pneumoniae, Chlamydia psittaci, and Haemophilus influenzae as the most common pathogens, with Klebsiella pneumoniae emerging as the predominant bacterial culprit. Among herpes viruses, EBV and herpes virus 7 (HHV-7) were most frequently detected, with HHV-7 more prevalent in immunocompromised individuals. (3) Risk Factors for Adverse Prognosis and a Mortality Risk Prediction Model in Patients with LRTIs: We identified key risk factors for poor prognosis in lower respiratory tract infection patients, with significant findings including delayed time to mNGS testing, low lymphocyte percentage, presence of chronic lung disease, multiple comorbidities, false-negative CMT results, and positive herpesvirus affecting patient outcomes. We also developed a nomogram model with good consistency and high accuracy (AUC of 0.825) for predicting mortality risk in these patients, offering a valuable clinical tool for assessing prognosis. CONCLUSION: The study underscores mNGS as a superior tool for lower respiratory tract infection diagnosis, exhibiting higher sensitivity and specificity than traditional methods.

Machine learning-based nomogram: integrating MRI radiomics and clinical indicators for prognostic assessment in acute ischemic stroke.

Background: Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis. Objective: To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS. Methods: This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model's predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes. Results: Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility. Conclusion: The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.

A new prognostic model for recurrent pregnancy loss: assessment of thyroid and thromboelastograph parameters.

Objective: This study aimed to identify predictors associated with thyroid function and thromboelastograph (TEG) examination parameters and establish a nomogram for predicting the risk of subsequent pregnancy loss in recurrent pregnancy loss (RPL). Methods: In this retrospective study, we analyzed the medical records of 575 RPL patients treated at Lanzhou University Second Hospital, China, between September 2020 and December 2022, as a training cohort. We also included 272 RPL patients from Ruian People's Hospital between January 2020 and July 2022 as external validation cohort. Predictors included pre-pregnancy thyroid function and TEG examination parameters. The study outcome was pregnancy loss before 24 weeks of gestation. Variable selection was performed using least absolute shrinkage and selection operator regression and stepwise regression analyses, and the prediction model was developed using multivariable logistic regression. The study evaluated the model's performance using the area under the curve (AUC), calibration curve, and decision curve analysis. Additionally, dynamic and static nomograms were constructed to provide a visual representation of the models. Results: The predictors used to develop the model were body mass index, previous pregnancy losses, triiodothyronine, free thyroxine, thyroid stimulating hormone, lysis at 30 minutes, and estimated percent lysis which were determined by the multivariable logistic regression with the minimum Akaike information criterion of 605.1. The model demonstrated good discrimination with an AUC of 0.767 (95%CI 0.725-0.808), and the Hosmer-Lemeshow test indicated good fitness of the predicting variables with a P value of 0.491. Identically, external validation confirmed that the model exhibited good performance with an AUC of 0.738. Moreover, the clinical decision curve showed a positive net benefit in the prediction model. Meanwhile, the web version we created was easy to use. The risk stratification indicated that high-risk patients with a risk score >147.9 had a higher chance of pregnancy loss (OR=6.05, 95%CI 4.09-8.97). Conclusions: This nomogram well-predicted the risk of future pregnancy loss in RPL and can be used by clinicians to identify high-risk patients and provide a reference for pregnancy management of RPL.

Assessment of the impact of residual tumors at different sites post-neoadjuvant chemotherapy on prognosis in breast cancer patients and development of a disease-free survival prediction model.

Background: Residual disease after neoadjuvant chemotherapy (NAC) in breast cancer patients predicts worse outcomes than pathological complete response. Differing prognostic impacts based on the anatomical site of residual tumors are not well studied. Objectives: The study aims to assess disease-free survival (DFS) in breast cancer patients with different residual tumor sites following NAC and to develop a nomogram for predicting 1- to 3-year DFS in these patients. Design: A retrospective cohort study. Methods: Retrospective analysis of 953 lymph node-positive breast cancer patients with residual disease post-NAC. Patients were categorized into three groups: residual disease in breast (RDB), residual disease in lymph nodes (RDN), and residual disease in both (RDBN). DFS compared among groups. Patients were divided into a training set and a validation set in a 7:3 ratio. Prognostic factors for DFS were analyzed to develop a nomogram prediction model. Results: RDB patients had superior 3-year DFS of 94.6% versus 85.2% for RDN and 81.8% for RDBN (p < 0.0001). Clinical T stage, N stage, molecular subtype, and postoperative pN stage were independently associated with DFS on both univariate and multivariate analyses. Nomogram integrating clinical tumor-node-metastasis (TNM) stage, molecular subtype, pathological response demonstrated good discrimination (C-index 0.748 training, 0.796 validation cohort), and calibration. Conclusion: The location of residual disease has prognostic implications, with nodal residuals predicting poorer DFS. The validated nomogram enables personalized DFS prediction to guide treatment decisions.

Understanding the impact of residual tumor location on prognosis after breast cancer treatment After receiving neoadjuvant chemotherapy, a treatment to shrink tumors before surgery, some breast cancer patients may still have residual tumor cells. Our study focuses on how the location of these remaining tumors ­ whether in the breast, lymph nodes, or both ­ affects the likelihood of the cancer not returning within the next 1 to 3 years. This likelihood is known as 'disease-free survival' (DFS). We analyzed data from 953 breast cancer patients who underwent neoadjuvant chemotherapy and still had residual tumors. By comparing DFS among patients with tumors remaining in different locations, we discovered that the specific location of the residual tumor significantly impacts the patient's long-term health and recovery. Additionally, we developed a predictive tool called a 'nomogram' to help doctors and patients assess the risk of cancer recurrence in the next 1 to 3 years. This tool considers various factors such as the size and type of the tumor, as well as the location and extent of the residual tumor after chemotherapy. Our research offers new insights into understanding the risk of recurrence after breast cancer treatment. This work not only enhances our comprehension of breast cancer management but also aids in devising more personalized and effective treatment strategies for patients in the future.

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). MATERIALS AND METHODS: In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. RESULTS: The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. CONCLUSION: The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

Predictive nomogram for 28-day mortality risk in mitral valve disorder patients in the intensive care unit: A comprehensive assessment from the MIMIC-III database.

BACKGROUND: Mitral valve disorder (MVD) stands as the most prevalent valvular heart disease. Presently, a comprehensive clinical index to predict mortality in MVD remains elusive. The aim of our study is to construct and assess a nomogram for predicting the 28-day mortality risk of MVD patients. METHODS: Patients diagnosed with MVD were identified via ICD-9 code from the MIMIC-III database. Independent risk factors were identified utilizing the LASSO method and multivariate logistic regression to construct a nomogram model aimed at predicting the 28-day mortality risk. The nomogram's performance was assessed through various metrics including the area under the curve (AUC), calibration curves, Hosmer-Lemeshow test, integrated discriminant improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA). RESULTS: The study encompassed a total of 2771 patients diagnosed with MVD. Logistic regression analysis identified several independent risk factors: age, anion gap, creatinine, glucose, blood urea nitrogen level (BUN), urine output, systolic blood pressure (SBP), respiratory rate, saturation of peripheral oxygen (SpO2), Glasgow Coma Scale score (GCS), and metastatic cancer. These factors were found to independently influence the 28-day mortality risk among patients with MVD. The calibration curve demonstrated adequate calibration of the nomogram. Furthermore, the nomogram exhibited favorable discrimination in both the training and validation cohorts. The calculations of IDI, NRI, and DCA analyses demonstrate that the nomogram model provides a greater net benefit compared to the Simplified Acute Physiology Score II (SAPSII), Acute Physiology Score III (APSIII), and Sequential Organ Failure Assessment (SOFA) scoring systems. CONCLUSION: This study successfully identified independent risk factors for 28-day mortality in patients with MVD. Additionally, a nomogram model was developed to predict mortality, offering potential assistance in enhancing the prognosis for MVD patients. It's helpful in persuading patients to receive early interventional catheterization treatment, for example, transcatheter mitral valve replacement (TMVR), transcatheter mitral valve implantation (TMVI).

Acute Pancreatitis in Pregnancy: A Propensity Score Matching Analysis and Dynamic Nomogram for Risk Assessment.

BACKGROUND: Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment. OBJECTIVE: The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients. STUDY DESIGN: Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated. RESULTS: A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability. CONCLUSION: The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.

MH-STRALP: A scoring system for prognostication in patients with upper gastrointestinal bleeding.

BACKGROUND: Upper gastrointestinal bleeding (UGIB) is a common medical emergency and early assessment of its outcomes is vital for treatment decisions. AIM: To develop a new scoring system to predict its prognosis. METHODS: In this retrospective study, 692 patients with UGIB were enrolled from two centers and divided into a training (n = 591) and a validation cohort (n = 101). The clinical data were collected to develop new prognostic prediction models. The endpoint was compound outcome defined as (1) demand for emergency surgery or vascular intervention, (2) being transferred to the intensive care unit, or (3) death during hospitalization. The models' predictive ability was compared with previously established scores by receiver operating characteristic (ROC) curves. RESULTS: Totally 22.2% (131/591) patients in the training cohort and 22.8% (23/101) in the validation cohort presented poor outcomes. Based on the stepwise-forward Logistic regression analysis, eight predictors were integrated to determine a new post-endoscopic prognostic scoring system (MH-STRALP); a nomogram was determined to present the model. Compared with the previous scores (GBS, Rockall, ABC, AIMS65, and PNED score), MH-STRALP showed the best prognostic prediction ability with area under the ROC curves (AUROCs) of 0.899 and 0.826 in the training and validation cohorts, respectively. According to the calibration curve, decision curve analysis, and internal cross-validation, the nomogram showed good calibration ability and net clinical benefit in both cohorts. After removing the endoscopic indicators, the pre-endoscopic model (pre-MH-STRALP score) was conducted. Similarly, the pre-MH-STRALP score showed better predictive value (AUROCs of 0.868 and 0.767 in the training and validation cohorts, respectively) than the other pre-endoscopic scores. CONCLUSION: The MH-STRALP score and pre-MH-STRALP score are simple, convenient, and accurate tools for prognosis prediction of UGIB, and may be applied for early decision on its management strategies.

Development of a haematological indices-based nomogram for prognostic prediction and immunotherapy response assessment in primary pulmonary lymphoepithelioma-like carcinoma patients.

Background: Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare yet aggressive malignancy. This study aims to investigate a deep learning model based on hematological indices, referred to as haematological indices-based signature (HIBS), and propose multivariable predictive models for accurate prognosis prediction and assessment of therapeutic response to immunotherapy in PPLELC. Methods: This retrospective study included 117 patients with PPLELC who received immunotherapy and were randomly divided into a training (n=82) and a validation (n=35) cohort. A total of 41 hematological features were extracted from routine laboratory tests and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to establish the HIBS. Additionally, we developed a nomogram using the HIBS and clinical characteristics through multivariate Cox regression analysis. To evaluate the nomogram's predictive performance, we used calibration curves and calculated the time-dependent area under the curve (AUC). Kaplan-Meier survival analysis was performed to estimate progression-free survival (PFS) in both cohorts. Results: The proposed HIBS comprised 14 hematological features and showed that patients who experienced disease progression had significantly higher HIBS scores compared to those who did not progress (P<0.001). Five prognostic factors, including HIBS, tumor-node-metastasis (TNM) stage, presence of bone metastasis and the specific immunotherapy regimen, were found to be independent factors and were used to construct a nomogram, which effectively categorized PPLELC patients into a high-risk and a low-risk group, with patients in the high-risk patients demonstrating worse PFS (7.0 vs. 18.0 months, P<0.001) and lower overall response rates (22.2% vs. 52.7%, P<0.001). The nomogram showed satisfactory discrimination for PFS, with AUC values of 0.837 and 0.855 in the training and validation cohorts, respectively. Conclusions: The HIBS-based nomogram could effectively predict the PFS and response of patients with PPLELC regarding immunotherapy and serve as a valuable tool for clinical decision making.

Nomogram for personalized prognostic assessment of children with favorable histology Wilms tumor: A retrospective analysis.

OBJECTIVE: This retrospective study aimed to construct and validate a nomogram for personalized prognostic assessment of favorable histology Wilms tumor (FHWT) based on clinical and pathological variables. METHODS AND MATERIALS: This was a retrospective study collected data from patients who underwent surgery for FHWT between March 2007 and November 2022 at Beijing Children's Hospital. Univariate and multivariate Cox proportional hazards regression analyses were conducted to determine the significance variables and constructed the nomogram in predicting event-free survival (EFS) in FHWT patients. RESULTS: A total of 401 FHWT patients were included in the study, with the median age of the patients was 3.4 years. The overall 1-, 3-, and 5-year OS rates were 98.2%, 96.3%, and 93.9%. The 1-, 3-, and 5-year EFS rates were 91.2%, 88.2%, and 86.6%. Subgroup analysis revealed age greater than 2 years was associated with a worse prognosis than age less than or equal to 2 years (P < 0.001), and patients with high-risk Wilms tumors were associated with a higher rate of recurrence and death (P < 0.001). Multivariate analysis showed that age (HR: 2.449, 95%CI: 1.004-5.973), stage (HR: 1.970, 95% CI:1.408-2.756), and histological risk (HR:9.414, 95% CI: 4.318-20.525) were identified as independent predictors of EFS (P < 0.05) and used to construct the nomogram. The prognostic nomogram demonstrated good calibration, great clinical utility, and the time-dependent receiver operating curve analysis showed that the nomogram had precise predictability, with area under the curve values of 0.85(95CI:0.796-0.913), 0.85(95CI:0.80-0.91), and 0.88(95CI:0.839-0.937) for 1-,3-year and 5-year EFS. CONCLUSION: This study provides valuable insights into the clinical characteristics and outcomes of FHWT patients. Accurate staging and histological risk assessment are important in predicting outcomes, and the prognostic nomogram we developed can be a useful tool for clinicians to assess patient prognosis and make informed treatment decisions.

Cost-effective prognostic evaluation of breast cancer: using a STAR nomogram model based on routine blood tests.

Background: Breast cancer (BC) is the most common and prominent deadly disease among women. Predicting BC survival mainly relies on TNM staging, molecular profiling and imaging, hampered by subjectivity and expenses. This study aimed to establish an economical and reliable model using the most common preoperative routine blood tests (RT) data for survival and surveillance strategy management. Methods: We examined 2863 BC patients, dividing them into training and validation cohorts (7:3). We collected demographic features, pathomics characteristics and preoperative 24-item RT data. BC risk factors were identified through Cox regression, and a predictive nomogram was established. Its performance was assessed using C-index, area under curves (AUC), calibration curve and decision curve analysis. Kaplan-Meier curves stratified patients into different risk groups. We further compared the STAR model (utilizing HE and RT methodologies) with alternative nomograms grounded in molecular profiling (employing second-generation short-read sequencing methodologies) and imaging (utilizing PET-CT methodologies). Results: The STAR nomogram, incorporating subtype, TNM stage, age and preoperative RT data (LYM, LYM%, EOSO%, RDW-SD, P-LCR), achieved a C-index of 0.828 in the training cohort and impressive AUCs (0.847, 0.823 and 0.780) for 3-, 5- and 7-year OS rates, outperforming other nomograms. The validation cohort showed similar impressive results. The nomogram calculates a patient's total score by assigning values to each risk factor, higher scores indicating a poor prognosis. STAR promises potential cost savings by enabling less intensive surveillance in around 90% of BC patients. Compared to nomograms based on molecular profiling and imaging, STAR presents a more cost-effective, with potential savings of approximately $700-800 per breast cancer patient. Conclusion: Combining appropriate RT parameters, STAR nomogram could help in the detection of patient anemia, coagulation function, inflammation and immune status. Practical implementation of the STAR nomogram in a clinical setting is feasible, and its potential clinical impact lies in its ability to provide an early, economical and reliable tool for survival prediction and surveillance strategy management. However, our model still has limitations and requires external data validation. In subsequent studies, we plan to mitigate the potential impact on model robustness by further updating and adjusting the data and model.

Development and validation of nomogram-based models for personalized survival assessment in pediatric hepatoblastoma patients.

Background: Hepatoblastoma (HB) is a prevalent form of liver cancer in pediatric patients, characterized by an embryonal malignant tumor. In the current study, a clinical prediction model was developed; that can effectively assess the likelihood of a patient's survival with HB. Methods: Data from the Surveillance, Epidemiology, and End Results (SEER) database for cases of HB between 2010 and 2019 were used in this retrospective research. Information on clinicopathologic characteristics, therapeutic interventions, and survival outcomes were included in the data. The HB patients were randomly assigned to the training or validation cohort in a 7:3 ratio. Using univariate and multivariate Cox proportional hazards regression models, the prognostic indicators for overall survival (OS) and cancer-specific survival (CSS) were identified. The area under the receiver operating characteristic curve (AUC-ROC), calibration plots, and concordance index (C-index) were used to evaluate the accuracy and calibration of these models. The clinical utility of the models was examined using decision curve analysis (DCA). Results: The multivariate Cox regression analysis revealed multiple autonomous prognostic determinants for the OS and CSS, including age, surgical interventions, and chemotherapy administration. Significantly, tumor size was found to be a strong predictor of OS. AUC values of 0.915, 0.846, and 0.847 for 1-, 3-, and 5-year OS, respectively, indicated that the nomogram-based models were highly accurate at predicting outcomes. Similarly, the AUC values for CSS were 0.871, 0.814, and 0.825. The C-index measurements, which quantify the discriminatory performance of the models, produced CSS values of 0.836 and OS values of 0.864. Furthermore, the calibration plots accurately represented the actual survival rates. Concurrently, the DCA had validated the clinical relevance of the nomogram-based models. Conclusions: The present study successfully developed and validated user-friendly nomogram-based models, allowing for accurate assessment of OS and CSS in pediatric HB patients. These tools enable personalized survival predictions, enhance risk stratification, and strengthen clinical decision-making for managing HB.

An ultrasound-based nomogram model in the assessment of pathological complete response of neoadjuvant chemotherapy in breast cancer.

Introduction: We aim to predict the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) in breast cancer patients by constructing a Nomogram based on radiomics models, clinicopathological features, and ultrasound features. Methods: Ultrasound images of 464 breast cancer patients undergoing NAC were retrospectively analyzed. The patients were further divided into the training cohort and the validation cohort. The radiomics signatures (RS) before NAC treatment (RS1), after 2 cycles of NAC (RS2), and the different signatures between RS2 and RS1 (Delta-RS/RS1) were obtained. LASSO regression and random forest analysis were used for feature screening and model development, respectively. The independent predictors of pCR were screened from clinicopathological features, ultrasound features, and radiomics models by using univariate and multivariate analysis. The Nomogram model was constructed based on the optimal radiomics model and clinicopathological and ultrasound features. The predictive performance was evaluated with the receiver operating characteristic (ROC) curve. Results: We found that RS2 had better predictive performance for pCR. In the validation cohort, the area under the ROC curve was 0.817 (95%CI: 0.734-0.900), which was higher than RS1 and Delta-RS/RS1. The Nomogram based on clinicopathological features, ultrasound features, and RS2 could accurately predict the pCR value, and had the area under the ROC curve of 0.897 (95%CI: 0.866-0.929) in the validation cohort. The decision curve analysis showed that the Nomogram model had certain clinical practical value. Discussion: The Nomogram based on radiomics signatures after two cycles of NAC, and clinicopathological and ultrasound features have good performance in predicting the NAC efficacy of breast cancer.

Predicting individual outcomes after radical cystectomy in urothelial variants with Cancer of the Bladder Risk Assessment (COBRA) score.

OBJECTIVE: To validate the Cancer of the Bladder Risk Assessment (COBRA) score in patients with urothelial variants. METHODS: Epidemiological, clinical, radiological, and anatomopathological data were collected from patients with urothelial carcinoma who underwent radical cystectomy at the Institute of Cancer of São Paulo between May 2008 and December 2022. Patients with the presence of at least 10% of any urothelial variants in the radical cystectomy specimens' anatomopathological exam were included in the study. The COBRA score and derivatives were applied and correlated with oncological outcomes. RESULTS: A total of 680 patients [482 men (70.9%) and 198 women (29.1%)]; 66 years (IQR 59-73) underwent radical cystectomy for bladder tumor, and of these patients, a total of 167 patients presented any type of urothelial variant. The median follow-up time was 28.77 months (IQR 12-85). The three most prevalent UV were squamous differentiation (50.8%), glandular differentiation (31.3%), and micropapillary differentiation (11.3%). The subtypes with the worst prognosis were sarcomatoid with a median survival of 8 months (HR 1.161; 95% CI 0.555-2.432) and plasmacytoid with 14 months (HR 1.466; 95% CI 0.528-4.070). The COBRA score for patients with micropapillary variants demonstrated good predictive accuracy for OS (log-rank P = 0.009; 95% IC 6.78-29.21) and CSS (log-rank P = 0.002; 95% IC 13.06-26.93). CONCLUSIONS: In our study, the COBRA score proved an effective risk stratification tool for urothelial histological variants, especially for the micropapillary urothelial variant. It may be helpful in the prognosis evaluation of UV patients after radical cystectomy.

Comparative prognosis and risk assessment in gallbladder neuroendocrine neoplasms versus adenocarcinomas.

Background: Gallbladder neuroendocrine neoplasms (GB-NENs) are a rare malignant disease, with most cases diagnosed at advanced stages, often resulting in poor prognosis. However, studies regarding the prognosis of this condition and its comparison with gallbladder adenocarcinomas (GB-ADCs) have yet to yield convincing conclusions. Methods: We extracted cases of GB-NENs and GB-ADCs from the Surveillance, Epidemiology, and End Results (SEER) database in the United States. Firstly, we corrected differences in clinical characteristics between the two groups using propensity score matching (PSM). Subsequently, we visualized and compared the survival outcomes of the two groups using the Kaplan-Meier method. Next, we employed the least absolute shrinkage and selection operator (LASSO) regression and Cox regression to identify prognostic factors for GB-NENs and constructed two nomograms for predicting prognosis. These nomograms were validated with an internal validation dataset from the SEER database and an external validation dataset from a hospital. Finally, we categorized patients into high-risk and low-risk groups based on their overall survival (OS) scores. Results: A total of 7,105 patients were enrolled in the study, comprising 287 GB-NENs patients and, 6,818 GB-ADCs patients. There were substantial differences in clinical characteristics between patients, and GB-NENs exhibited a significantly better prognosis. Even after balancing these differences using PSM, the superior prognosis of GB-NENs remained evident. Independent prognostic factors selected through LASSO and Cox regression were age, histology type, first primary malignancy, tumor size, and surgery. Two nomograms for prognosis were developed based on these factors, and their performance was verified from three perspectives: discrimination, calibration, and clinical applicability using training, internal validation, and external validation datasets, all of which exhibited excellent validation results. Using a cutoff value of 166.5 for the OS nomogram score, patient mortality risk can be identified effectively. Conclusion: Patients with GB-NENs have a better overall prognosis compared to those with GB-ADCs. Nomograms for GB-NENs prognosis have been effectively established and validated, making them a valuable tool for assessing the risk of mortality in clinical practice.

Risk assessment of severe adult tetanus using the NLR and AST level and construction of a nomogram prediction model.

We sought to examine high-risk factors for severe tetanus, construct a nomogram model, and predict the risk probability of severe tetanus in adult patients to provide a theoretical basis for clinical intervention. Methods: A retrospective analysis was employed in this study, which enrolled 65 adult patients with tetanus diagnosed at the Second Affiliated Hospital of Hainan Medical University from January 2017 to September 2022. Study participants were divided into severe and mild groups based on the Ablett classification. The general data and laboratory markers of both groups were compared, and logistic regression analysis was used to screen for independent risk factors for severe tetanus. A nomogram prediction model was constructed, and receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were constructed and used to assess discrimination, calibration, and net benefit. Results: Of the 65 adults patients with tetanus, 28 were placed in the severe group and 37 were placed in the mild group. Univariate logistic regression analysis showed that there were statistically significant differences in the incubation period, time from disease onset to treatment, white blood cell count (WBC), neutrophil count (NEU), lymphocyte count (LYM), platelet count (PLT), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lactate dehydrogenase level (LDH), myoglobin level (Mb), and aspartate aminotransferase (AST) level between the two groups (P < 0.05). while the differences in age; sex; and creatine kinase, creatine kinase isoenzyme, and alanine aminotransferase levels were not statistically significant (P > 0.05). Multivariate analysis showed that NLR (odds ratio [OR] = 4.998, 95 % confidence interval [CI] = 1.154-21.649, P = 0.031), AST (OR = 1.074, 95 % CI = 1.007-1.146, P = 0.031), PLT (OR = 1.055, 95 % CI = 1.006-1.106, P = 0.027), and incubation period (OR = 0.597, 95 % CI = 0.423-0.843, P = 0.003) are independent risk factor for severe tetanus. A Nomogram for predicting Severe Tetanus (N-ST) prediction model was constructed based on variables in the multivariate analysis with P < 0.05. The ROC curve showed that the optimal cutoff point was 108.044 points. At this point, the sensitivity was 86.5 %, the specificity was 89.3 %, the area under the ROC curve was 0.936, and model discrimination was good. The calibration curve overlapped with the ideal curve, and the DCA curve showed that the model can provide clinical benefits. Conclusion: NLR, AST, PLT, and incubation period are predictors of severe tetanus. The constructed N-ST model can provide a new, convenient, and rapid method to predict the risk probability of severe tetanus in adults and guide early clinical intervention.

Intra- and Peritumoral Based Radiomics for Assessment of Lymphovascular Invasion in Invasive Breast Cancer.

BACKGROUND: Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated. PURPOSE: To investigate the value of intra- and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions. STUDY TYPE: Retrospective. POPULATION: Three hundred and sixteen patients were enrolled from two centers and divided into training (N = 165), internal validation (N = 83), and external validation (N = 68) cohorts. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T/dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI). ASSESSMENT: Radiomics features were extracted and selected based on intra- and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS-DCE plus DWI). The clinical model was built with MRI-axillary lymph nodes (MRI ALN), MRI-reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS-DCE plus DWI, MRI ALN, MPE, and ADC. STATISTICAL TESTS: Intra- and interclass correlation coefficient analysis, Mann-Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS-DCE plus DWI, clinical model, and nomogram. RESULTS: A total of 10 features were found to be associated with LVI, 3 from intra- and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts. DATA CONCLUSION: The constructed preoperative nomogram might effectively assess LVI. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer.

RATIONALE AND OBJECTIVES: This study aimed to create a nomogram model that combines clinical factors with radiomics analysis of both intra- and peritumoral regions extracted from preoperative digital breast tomosynthesis (DBT) images, in order to develop a reliable method for predicting the lymphovascular invasion (LVI) status in invasive breast cancer (IBC) patients. MATERIALS AND METHODS: A total of 178 patients were randomly split into a training dataset (N = 124) and a validation dataset (N = 54). Comprehensive clinical data, encompassing DBT features, were gathered for all cases. Radiomics features were extracted and selected from intra- and peritumoral region to establish radiomics signature (Radscore). To construct the clinical model and nomogram model, univariate and multivariate logistic regression analyses were utilized to identify independent risk factors. To assess and validate these models, various analytical methods were employed, including receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI). RESULTS: The clinical model is constructed based on two independent risk factors: tumor margin and the DBT-reported lymph node metastasis (DBT_reported_LNM). Incorporating Radscore_Combine (utilizing both intra- and peritumoral radiomics features), tumor margin, and DBT_reported_LNM into the nomogram achieved a reliable predictive performance, with area under the curve (AUC) values of 0.906 and 0.905 in both datasets, respectively. The significant improvement demonstrated by the NRI and IDI indicates that the Radscore_Combine could be a valuable biomarker for effectively predicting the status of LVI. CONCLUSION: The nomogram demonstrated a reliable ability to predict LVI in IBC patients.