Predicting pathological grade of stage I pulmonary adenocarcinoma: a CT radiomics approach.

Objectives: To investigate the value of CT radiomics combined with radiological features in predicting pathological grade of stage I invasive pulmonary adenocarcinoma (IPA) based on the International Association for the Study of Lung Cancer (IASLC) new grading system. Methods: The preoperative CT images and clinical information of 294 patients with stage I IPA were retrospectively analyzed (159 training set; 69 validation set; 66 test set). Referring to the IASLC new grading system, patients were divided into a low/intermediate-grade group and a high-grade group. Radiomic features were selected by using the least absolute shrinkage and selection operator (LASSO), the logistic regression (LR) classifier was used to establish radiomics model (RM), clinical-radiological features model (CRM) and combined rad-score with radiological features model (CRRM), and visualized CRRM by nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the performance and fitness of models. Results: In the training set, RM, CRM, and CRRM achieved AUCs of 0.825 [95% CI (0.735-0.916)], 0.849 [95% CI (0.772-0.925)], and 0.888 [95% CI (0.819-0.957)], respectively. For the validation set, the AUCs were 0.879 [95% CI (0.734-1.000)], 0.888 [95% CI (0.794-0.982)], and 0.922 [95% CI (0.835-1.000)], and for the test set, the AUCs were 0.814 [95% CI (0.674-0.954)], 0.849 [95% CI (0.750-0.948)], and 0.860 [95% CI (0.755-0.964)] for RM, CRM, and CRRM, respectively. Conclusion: All three models performed well in predicting pathological grade, especially the combined model, showing CT radiomics combined with radiological features had the potential to distinguish the pathological grade of early-stage IPA.

[Risk factors analysis and nomogram model construction of postoperative pathological upgrade of prostate cancer patients with single core positive biopsy].

OBJECTIVE: To analyze the risk factors for postoperative pathological upgrade of prostate cancer patients with single core positive biopsy, and to attempt to build a mathematical model for predicting postoperative pathological upgrade in these cancer patients with single core positive biopsy. METHODS: A retrospective analysis was conducted on 1 349 patients diagnosed with prostate cancer and undergoing radical prostatectomy at Peking University First Hospital from January 2015 to August 2020. The patients' age, body mass index, clinical stage, prostate imaging reporting and data system (PI-RADS) scores, prostate volume in magnetic resonance imaging (MRI), Gleason score of biopsy, serum prostate specific antigen (PSA) before biopsy and operation, surgical method and pathological stage were inclu-ded in the analysis. The variables with P < 0.1 in univariate analysis were included to construct multi-variate Logistic regression and the nomogram was drawn. The model was evaluated using the receiver operating curve. RESULTS: A total of 71 patients were included in this research, with 34 patients in the upgraded group and 37 patients in the non-upgraded group. There were no significant differences in the patients' age (P=0.585), body mass index (P=0.165), operation method (P=0.08), prostate volume in MRI (P=0.067), clinical stage (P=0.678), PI-RADS score (P=0.203), difference of PSA density (P=0.063), Gleason score in biopsy (P=0.068), PSA before puncture (P=0.359) and operation (P= 0.739) between the two groups. However, there were significant differences in the proportion of tumor tissue (P=0.007), postoperative pathological stage (P < 0.001) and postoperative Gleason score (P < 0.001) between the two groups. The preoperative variables with a P value of less than 0.1 (prostate volume in MRI, difference of PSA density, proportion of tumor tissue and Gleason score in biopsy) in univariate analysis were included in the Logistic regression, and the nomogram was drawn. Only the prostate volume in MRI had a P value of less than 0.05. The area under the curve of the model was 0.773. CONCLUSION: In patients with single core positive biopsy, if the prostate volume is small or the proportion of tumor in positive core is small, clinicians should be alert to the possibility of postoperative pathology upgrading, preoperative risk stratification should be carefully considered for patients with possible pathological upgrading. This model can be used to predict the pathological upgrade of patients with single core positive biopsy.

[Construction and validation of a nomogram for predicting in-hospital postoperative heart failure in elderly patients with hip fracture].

OBJECTIVE: To construct and validate a nomogram for prediction of in-hospital postoperative heart failure (PHF) in elderly patients with hip fracture. METHODS: This was a retrospective cohort study. The patients aged ≥65 years undergoing hip fracture surgery in Peking University Third Hospital from July 2015 to December 2023 were enrolled. The patients admitted from July 2015 to December 2021 were divided into a development cohort, and the others admitted from January 2022 to December 2023 in to a validation cohort. The patients ' clinical data were collected from the electronic medical record system. Univariate and multivariate Logistic regression were employed to screen the predictors for PHF in the patients. The R software was used to construct a nomogram. Internal and external validation were performed by the Bootstrap method. The discriminatory ability of the model was determined by the area under the receiver operating characteristic curve (AUC). The calibration was evaluated by the calibration plot and Hosmer-Lemeshow goodness-of-fit test. Decision curve analysis (DCA) was performed to assess the clinical utility. RESULTS: In the study, 944 patients were eventually enrolled in the development cohort, and 469 were in the validation cohort. A total of 54 (5.7%) patients developed PHF in the deve-lopment cohort, and 18 (3.8%) patients had PHF in the validation cohort. Compared with those from non-PHF group, the patients from PHF group were older, had higher prevalence of heart disease, hypertension and pulmonary disease, had poorer American Society of Anesthesiologists (ASA) classification (Ⅲ-Ⅳ), presented with lower preoperative hemoglobin level, lower left ventricular ejection fraction, higher preoperative serum creatinine, received hip arthroplasty and general anesthesia more frequently. Multivariate Logistic regression analysis showed that age (OR=1.071, 95%CI: 1.019-1.127, P=0.008), history of heart disease (OR=5.360, 95%CI: 2.808-10.234, P < 0.001), preoperative hemoglobin level (OR=0.979, 95%CI: 0.960-0.999, P=0.041), preoperative serum creatinine (OR=1.007, 95%CI: 1.001-1.013, P=0.015), hip arthroplasty (OR=2.513, 95%CI: 1.259-5.019, P=0.009), and general anesthesia (OR=2.024, 95%CI: 1.053-3.890, P=0.034) were the independent predictors for PHF in elderly patients with hip fracture. Four preoperative predictors were incorporated to construct a preoperative nomogram for PHF in the patients. The AUC values of the nomogram in internal and external validation were 0.818 (95%CI: 0.768-0.868) and 0.873 (95%CI: 0.805-0.929), indicating its good accuracy. The calibration plots and Hosmer-Lemeshow goodness-of-fit test (internal validation: χ2=9.958, P=0.354; external validation: χ2=5.477, P=0.791) showed its satisfactory calibration. Clinical usefulness of the nomogram was confirmed by decision curve analysis. CONCLUSION: An easy-to-use nomogram for prediction of in-hospital PHF in elderly patients with hip fracture is well developed. This preoperative risk assessment tool can effectively identify patients at high risk of PHF and may be useful for perioperative management optimization.

Machine learning and explainable artificial intelligence to predict pathologic stage in men with localized prostate cancer.

BACKGROUND: Though several nomograms exist, machine learning (ML) approaches might improve prediction of pathologic stage in patients with prostate cancer. To develop ML models to predict pathologic stage that outperform existing nomograms that use readily available clinicopathologic variables. METHODS: Patients with prostate adenocarcinoma who underwent surgery were identified in the National Cancer Database. Seven ML models were trained to predict organ-confined (OC) disease, extracapsular extension, seminal vesicle invasion (SVI), and lymph node involvement (LNI). Model performance was measured using area under the curve (AUC) on a holdout testing data set. Clinical utility was evaluated using decision curve analysis (DCA). Performance metrics were confirmed on an external validation data set. RESULTS: The ML-based extreme gradient boosted trees model achieved the best performance with an AUC of 0.744, 0.749, 0.816, 0.811 for the OC, ECE, SVI, and LNI models, respectively. The MSK nomograms achieved an AUC of 0.708, 0.742, 0.806, 0.802 for the OC, ECE, SVI, and LNI models, respectively. These models also performed the best on DCA. Findings were consistent on both a holdout internal validation data set as well as an external validation data set. CONCLUSIONS: Our ML models better predicted pathologic stage relative to existing nomograms at predicting pathologic stage. Accurate prediction of pathologic stage can help oncologists and patients determine optimal definitive treatment options for patients with prostate cancer.

Crafting and Authenticating Prognostic Nomograms for Overall and Cancer-specific Survival in Pediatric Small Renal Masses: A Comprehensive Two-Decade Cohort Study.

BACKGROUND: The intricate task of diagnosing and managing small renal masses (SRMs) has become progressively convoluted within the realm of clinical practice. Contemporary clinical prediction instruments may succumb to a gradual decay in precision, coupled with an absence of unambiguous guidelines to navigate patient management. METHODS: This investigation was devised to formulate and authenticate nomograms for the overall survival (OS) and cancer- specific survival (CSS) among patients afflicted with SRMs. The study encompassed a cohort of 2558 pediatric patients diagnosed with SRMs over the period of 2000 to 2019. Independent prognostic indicators for OS and CSS, encompassing historical staging, chemotherapy regimens, surgical interventions, and pathological classifications, were ascertained through the employment of multivariate Cox proportional hazards regression analysis and backward stepwise selection. RESULTS: Through the utilization of multivariate Cox regression models, nomograms for OS and CSS were meticulously crafted, demonstrating commendable discrimination and calibration within the training set (OS C-index: 0.762, CSS C-index: 0.779). The validation set further corroborated the exemplary discrimination and calibration of the nomograms. Moreover, these nomograms adeptly differentiated between patient groups at elevated and diminished risk levels. CONCLUSION: The nomograms delineated in this research provide propitious predictive accuracy for overall survival and cancer-specific survival in patients suffering from pediatric SRMs, thereby contributing to refined risk stratification and steering the optimal therapeutic course of action. The necessity for supplementary validation prevails before the translation of these findings into clinical practice.

Coronary artery bypass grafting in octogenarians: a nomogram for predicting all-cause mortality.

BACKGROUND: The benefits and risks of coronary artery bypass grafting (CABG) in octogenarians remain unclear. This study aimed to identify the predictors of increased risk of all-cause mortality in octogenarian patients after CABG. METHODS: We retrospectively analyzed the data of 1636 octogenarians who underwent isolated elective on-pump CABG between 2007 and 2016. The primary endpoint was mortality from any cause. The Kaplan-Meier curve was generated for mortality. A univariate Cox regression was performed for preprocedural and procedural variables. The Akaike information criterion (AIC) using the Cox proportional hazard model was applied to determine the strongest predictors. We designed a nomogram based on the selected variables to calculate the mortality risk after one, five, and ten years. The bootstrap resampling based on the C-index was performed to validate the final model. Calibration plots were created at different time points. RESULTS: The mean age of the patients was 82.03 years (SD = 1.74), and 74% were male. In a median follow-up of 9.2 (95% CI 9.0,9.5) years, 626 (38.2%) patients died. After the selection of best predictors based on AIC, the multivariable Cox regression showed that ejection fraction < 40 (HR 1.41, 95% CI 1.21-1.65, P < 0.001), two-vessel disease (HR: 0.59, 95% CI 0.40-0.89, P = 0.012), peripheral vascular disease (HR 1.52, 95% CI 1.05-2.21, P = 0.027), and valvular heart disease (HR 1.45, 95% CI 1.24-1.69, P < 0.001) were the significant predictors of all-cause mortality. CONCLUSION: Octogenarians who undergo CABG have a high mortality risk, influenced by several preprocedural and procedural risk factors. The proposed nomogram can be considered for optimizing the management of this vulnerable age group. Clinical registration number IR.TUMS.THC.REC.1400.081.

Nomogram for Predicting the Risk Factors for Falls in Older People: A Secondary Data Analysis Based on the 2021 Community Health Survey.

This study aimed to identify the risk factors for falls among older individuals living at home in a community and develop a nomogram to predict falls. This study included 74 492 people aged 65 years or older who participated in the 2021 Community Health Survey conducted in Korea. The data analysis methods used included the Rao-Scott χ2 test, a complex sample t-test, and complex binary logistic regression using SPSS 26.0. Using logistic regression analysis, a fall-risk prediction nomogram was created based on regression coefficients, and the reliability of the nomogram was calculated using a receiver operating characteristic (ROC) curve and values of the area under the curve (AUC). The fall incidence rate among older adults was 16.4%. Factors affecting the subject's fall experience included being more than 85 years old (OR = 1.40); living alone (OR = 1.13); receiving basic welfare (OR = 1.18); subjective health status (OR = 1.72); number of days spent walking (OR = 0.98); obesity (OR = 1.08); severe depression (OR = 2.84); sleep duration time (OR = 1.11); experiencing cognitive decline (OR = 1.34); and diabetes (OR = 1.12). In the nomogram, the depression score exhibited the greatest discriminatory power, followed by subjective health status, gender, experience of cognitive decline, age, basic livelihood security, adequacy of sleep, living alone, diabetes, and number of days of walking. The AUC value was 0.66. An intervention plan that comprehensively considers physical, psychological, and social factors is required to prevent falls in older adults. The nomogram developed in this study will help local health institutions assess all these risk factors for falling and create and implement systematic education and intervention programs to prevent falls and fall-related injuries among older individuals.

Peripheral NK cell count predicts response and prognosis in breast cancer patients underwent neoadjuvant chemotherapy.

Purpose: The count of lymphocyte subsets in blood can reflect the immune status of the body which is closely related to the tumor immune microenvironment and the efficacy of NAT. This study aims to explore the relationship between peripheral blood lymphocyte subsets and the efficacy and prognosis of NAT in breast cancer. Methods: We retrospectively analyzed clinicopathological information and peripheral blood lymphocyte subpopulation counts of patients receiving NAT from January 2015 to November 2021 at Sun Yat-sen University Cancer Center. Kaplan-Meier curves were used to estimate the survival probability. The independent predictors of NAT response and survival prognosis were respectively analyzed by multivariate logistic regression and Cox regression, and nomograms were constructed accordingly. The prediction efficiency of three nomograms was validated separately in the training cohort and the testing cohort. Results: 230 patients were included in the study, consisting of 161 in the training cohort and 69 in the testing cohort. After a median follow-up of 1238 days, patients with higher NK cell value showed higher pCR rates and higher OS and RFS after NAT (all P < 0.001). Multivariate analyses suggested NK cell count was an independent predictor of NAT response, OS and RFS. We then built nomograms accordingly and validated the prediction performance in the testing cohort (C index for NAT response: 0.786; for OS: 0.877, for RFS: 0.794). Conclusion: Peripheral blood NK cell count is a potential predictive marker for BC patients receiving NAT. Nomograms based on it might help predict NAT response and prognosis in BC.

Electrolyte prognosis scoring system can predict overall survival in patients with osteosarcoma.

Osteosarcoma stands as the most prevalent bone tumor, characterized by a heightened tendency for local recurrence and distant metastasis, resulting in a bleak prognosis. Presently, there exists a shortage of novel markers to effectively determine the prognosis of osteosarcoma patients. Recent research indicates that hematological markers partially mirror an individual's microenvironment, offering potential insights into predicting patient prognosis. However, prior studies predominantly focused on the prognostic significance of singular hematological indices, failing to comprehensively represent the tumor microenvironment of patients. In our investigation, we meticulously gathered data on 22 hematological and electrolyte markers, utilizing LASSO Cox regression analysis to devise an Electrolyte Prognostic Scoring System (EPSS). The EPSS encompasses various indicators, including immunity, inflammation, coagulation, and electrolyte levels. Our findings indicate that the EPSS stands as an independent prognostic factor for overall survival among osteosarcoma patients. It serves as a valuable addition to clinical characteristics, adept at discerning high-risk patients from those deemed clinically low-risk. Furthermore, EPSS-based nomograms demonstrate commendable predictive capabilities.

Development and validation of a nomogram for predicting 28-day mortality in critically ill patients with acute gastrointestinal injury: prospective observational study.

Objective: Developing and validating a clinical prediction nomogram of 28-day mortality in critically ill patients with acute gastrointestinal injury (AGI). Methods: Firstly, the construction of a clinical prediction model was developed using data obtained from a prospective observational study from May 2023 to April 2024. Then, data from a prospective multicenter observational study conducted in the intensive care units of 12 teaching hospitals in 2014 were utilized to independently and externally validate the clinical prediction model developed in the first part. We first screened the covariates of the development cohort by univariate cox regression, and then carried out cox regression analysis on the development cohort by backward stepwise regression to determine the optimal fitting model. Subsequently, a nomogram was derived from this model. Results: A total of 1102 and 379 patients, 28-day mortality occurred in 20.3% and 15.8% of patients respectively, were included in the development and validation cohort, respectively. We developed a nomogram in critically ill patients with AGI and the AGI grade, APACHE II score, Mechanical ventilation (MV), Feeding intolerance (FI) and daily calorie intake (DCI) in 72 h, were independent predictors of 28-day mortality, with the OR of the AGI grade was 1.910 (95% CI, 1.588-2.298; P < 0.001), the OR of APACHE II score was 1.099 (95% CI, 1.069-1.130; P < 0.001), the OR of MV was 1.880 (95% CI, 1.215-2.911; P = 0.005), the OR of FI was 3.453 (95% CI, 2.414-4.939; P < 0.001) and the DCI > 0.7 or < 0.5 of calorie target is associated with increased 28-day mortality, with OR of 1.566 (95% CI, 1.024-2.395; P = 0.039) and 1.769 (95% CI, 1.170-2.674; P = 0.007), respectively. Independent external validation of the prediction model was performed. This model has good discrimination and calibration. The DCA and CIC also validated the good clinical utility of the nomogram. Conclusion: The prediction of 28-day mortality can be conveniently facilitated by the nomogram that integrates AGI grade, APACHE II score, MV, FI and DCI in 72 h in critically ill patients with AGI.

A Nomogram for Predicting Cancer-Associated Venous Thromboembolism in Hospitalized Patients Receiving Chemoradiotherapy for Cancer.

PURPOSE: The aim of this study was to develop a novel nomogram to predict cancer-associated venous thromboembolism (CAT) in hospitalized patients with cancer who receive chemoradiotherapy. METHODS: This was a retrospective cohort study of hospitalized patients with cancer who received chemoradiotherapy between January 2010 and December 2022. Predictive factors for CAT were determined using univariate and multivariate logistic regression analyses, and a risk prediction model based on the nomogram was constructed and validated internally. Nomogram performance was assessed using receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: A total of 778 patients were eligible for inclusion in this study. The nomogram incorporated 5 independent risk factors: age, cancer stage, use of nonsteroidal anti-inflammatory drugs, D-dimer levels, and history of diabetes mellitus. The area under the curve (AUC) of the nomogram for the training and validation cohorts was 0.816 and 0.781, respectively, with 95% confidence intervals (CIs) of 0.770-0.861 and 0.703-0.860, respectively. The calibration and DCA curves also displayed good agreement and clinical applicability of the nomogram model. CONCLUSIONS: The incidence of CAT was relatively high among patients with cancer receiving chemoradiotherapy. The nomogram risk model developed in this study has good prediction efficiency and can provide a reference for the clinical evaluation of the risk of adverse outcomes in patients with cancer receiving chemoradiotherapy.

Risk stratification of thymic epithelial tumors based on peritumor CT radiomics and semantic features.

OBJECTIVES: To develop and validate nomograms combining radiomics and semantic features to identify the invasiveness and histopathological risk stratification of thymic epithelial tumors (TET) using contrast-enhanced CT. METHODS: This retrospective multi-center study included 224 consecutive cases. For each case, 6764 intratumor and peritumor radiomics features and 31 semantic features were collected. Multi-feature selections and decision tree models were performed on radiomics features and semantic features separately to select the most important features for Masaoka-Koga staging and WHO classification. The selected features were then combined to create nomograms for the two systems. The performance of the radiomics model, semantic model, and combined model was evaluated using the area under the receiver operating characteristic curves (AUCs). RESULTS: One hundred eighty-seven cases (56.5 years ± 12.3, 101 men) were included, with 62 cases as the external test set. For Masaoka-Koga staging, the combined model, which incorporated five peritumor radiomics features and four semantic features, showed an AUC of 0.958 (95% CI: 0.912-1.000) in distinguishing between early-stage (stage I/II) and advanced-stage (III/IV) TET in the external test set. For WHO classification, the combined model incorporating five peritumor radiomics features and two semantic features showed an AUC of 0.857 (0.760-0.955) in differentiating low-risk (type A/AB/B1) and high-risk (B2/B3/C) TET. The combined models showed the most effective predictive performance, while the semantic models exhibited comparable performance to the radiomics models in both systems (p > 0.05). CONCLUSION: The nomograms combining peritumor radiomics features and semantic features could help in increasing the accuracy of grading invasiveness and risk stratification of TET. CRITICAL RELEVANCE STATEMENT: Peripheral invasion and histopathological type are major determinants of treatment and prognosis of TET. The integration of peritumoral radiomics features and semantic features into nomograms may enhance the accuracy of grading invasiveness and risk stratification of TET. KEY POINTS: Peritumor region of TET may suggest histopathological and invasive risk. Peritumor radiomic and semantic features allow classification by Masaoka-Koga staging (AUC: 0.958). Peritumor radiomic and semantic features enable the classification of histopathological risk (AUC: 0.857).

Nomograms to predict occult contralateral central lymph node metastases in unilateral papillary thyroid carcinoma with ipsilateral clinical lymph node metastasis.

BACKGROUND: No significant difference in disease-specific survival and recurrence-free survival exists between papillary thyroid cancer (PTC) patients with high-risk features subjected to lobectomy and thyroidectomy. However, it is unclear which type of patients with unilateral PTC combined with ipsilateral clinical involved lymph nodes (cN1) can receive a less aggressive treatment. METHODS: We collected the medical records of 631 patients diagnosed with unilateral PTC and ipsilateral cN1. These patients initially underwent total thyroidectomy and bilateral central lymph node dissection (LND), with or without lateral LND. We conducted an analysis to investigate the associations between contralateral occult central lymph node metastasis (CLNM) and clinicopathologic factors. RESULTS: The proportion of contralateral occult CLNM was 38.9 %. Age ≤45 years, tumor diameter >1 cm, obesity, and involvement of lymph node regions ≥2 were independent risk factors for contralateral occult CLNM. Multifocality and ipsilateral neck high-volume lymph node metastases were independent risk factors among the postoperative pathological factors. A predicting model was developed to quantify the risk of each factor, which revealed that patients without any of the risk factors mentioned above had a 20-30 % probability of contralateral occult CLNM, whereas the probability was greater than 60 % when all factors were present. CONCLUSION: Based on the predictive nomograms, we proposed a risk stratification scheme based on different nomogram scores. In the debate about prophylactic central LND among contralateral central lymph node in unilateral PTC with ipsilateral clinical LNM, our nomograms provide the balance to avoid overtreatment and undertreatment through personal risk assessment.

Radiotherapy is recommended for hormone receptor-negative older breast cancer patients after breast conserving surgery.

In this study, the necessity of radiotherapy (RT) for hormone receptor-negative older breast cancer patients after breast-conserving surgery (BCS) was investigated. The data of hormone receptor-negative invasive breast cancer patients who underwent BCS were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. All patients were separated into two groups, namely, the RT group and the no radiotherapy (No RT) group. The 3- and 5-year overall survival (OS) and cancer-specific survival (CSS) rates were compared between the No RT and RT groups after propensity score matching (PSM). The nomograms for predicting the survival of patients were constructed from variables identified by univariate or multivariate Cox regression analysis. A total of 2504 patients were enrolled in the training cohort, and 630 patients were included in the validation cohort. After PSM, 738 patients were enrolled in the No RT group and RT group. We noted that RT can improve survival in hormone receptor-negative older breast cancer patients who undergo BCS. Based on the results of multivariate Cox analysis, age, race, tumour grade, receipt of RT and chemotherapy, pathological T stage, N status, M status and HER2 status were linked to OS and CSS for these patients, and nomograms for predicting OS and CSS were constructed and validated. Moreover, RT improved OS and CSS in hormone receptor-negative older breast cancer patients who underwent BCS. In addition, the proposed nomograms more accurately predicted OS and CSS for hormone receptor-negative older breast cancer patients after BCS.

Preoperative CT and Radiomics Nomograms for Distinguishing Bronchiolar Adenoma and Early-Stage Lung Adenocarcinoma.

RATIONALE AND OBJECTIVES: Evaluating the capability of CT nomograms and CT-based radiomics nomograms to differentiate between Bronchiolar Adenoma (BA) and Early-stage Lung Adenocarcinoma (LUAD). MATERIALS AND METHODS: In this retrospective study; we analyzed data from 226 patients who were treated at our institution and pathologically confirmed to have either BA or Early-stage LUAD. Patients were randomly divided into a training cohort (n=158) and a testing cohort (n=68). All CT images were independently analyzed and measured by two radiologists using conventional computed tomography. Clinical predictive factors were identified using logistic regression. Multivariable logistic regression analysis was used to construct differential diagnostic models for BA and early-stage LUAD, including traditional CT and radiomics models. The performance of the models was determined based on the area under the receiver operating characteristic curve, discrimination ability, and decision curve analysis (DCA). RESULTS: Lesion shape, tumor-lung interface, and pleural retraction signs were identified as independent clinical predictors. The areas under the curve for the CT nomogram, radiomic features, and radiomics nomogram were 0.854, 0.769, and 0.901, respectively. Both the CT nomogram and the radiomics nomogram demonstrated good generalizability in distinguishing between the two entities. DCA indicated that the nomograms achieved a higher net benefit compared to the use of radiomic features alone. CONCLUSION: The two preoperative nomograms hold significant value in differentiating between patients with BA and those with Early-stage LUAD, and they contribute to informed clinical treatment decision-making.

Analysis of the Relationship Between Primary Tumor Site and Clinicopathological Characteristics and Survival Prognosis of Breast Cancer Patients Based on SEER Database.

PURPOSE: This study aimed to analyze the association between the primary tumor site and clinicopathological characteristics and survival prognosis of breast cancer (BC) patients using a large population database. METHODS: BC patients screened in the Surveillance, Epidemiology, and End Results (SEER) database were categorized into 6 groups based on primary sites. Descriptive statistics, Kaplan-Meier curves, Cox regression models, forest plots were used to assess the effect of primary sites on overall survival (OS) and breast cancer-specific survival (BCSS). Multivariate Cox proportional analyses were conducted to calculate hazard ratios (HRs) and adjusted subgroups' hazard ratios (AHRs). Nomograms were utilized to predict OS and BCSS. RESULTS: Among 193,043 BC patients, the highest incidence was found in the upper outer quadrant (52.60%). Central portion patients are associated with more clinical features indicating a poor prognosis, and had worse OS and BCSS than other sites. Univariate and multifactorial Cox analyses showed associations between OS/BCSS and various factors. Subgroup analyses revealed differences in OS and BCSS between central portion and upper outer quadrant varied among age, T and N stage. The nomogram was established to predict the survival of central portion BC patients. CONCLUSIONS: Primary tumor site is associated with clinicopathological features and prognosis of BC, may be influenced by age at diagnosis and T and N stage. Central portion BC patients have worse prognosis due to older age at diagnosis, higher T stage and higher likelihood of lymph node metastasis. Early diagnosis and treatment may help to improve survival of central portion BC.

Clinical multi-dimensional prognostic nomogram for predicting the efficacy of immunotherapy in NSCLC.

The advent of immunotherapy has greatly improved the prognosis of non-small cell lung (NSCLC) patients. However, given its low response rate and high cost of treatment, the search for valuable predictive markers of treatment efficacy is necessary. Considering the complexity and heterogeneity of the tumour and tumour microenvironment, the construction of a multi-dimensional prediction model is necessary. Therefore, we aimed to integrate clinical parameters, radiomic features, and immune signature data from NSCLC patients receiving immunotherapy to construct a multi-dimensional prediction model to better predict the efficacy of immunotherapy. The current study enrolled 137 NSCLC patients who received immunotherapy. We collected baseline clinical information, CT images, and tumour tissue specimens. Using 3D-Slicer software, radiomic features were extracted from patient CT images, and tumor tissue samples obtained before immunotherapy were subjected to immunohistochemical staining. Then, the least absolute shrinkage and selection operator (LASSO) Cox regression analysis was applied to downscale the data, and the radiomic features and immune signatures associated with the prognosis of immunotherapy patients were identified. The modified lung immune predictive index (mLIPI), radiomics score (Radioscore), immune score and multi-dimensional model nomogram were constructed. The C-index and area under the curve (AUC) were applied to evaluate the predictive efficacy of the models. Three radiomic features and three immune signatures that could predict the efficacy of immunotherapy were eventually screened. Multivariate analysis showed that the mLIPI, Radioscore, and immune score were independent predictive factors for PFS and OS (P < 0.05 for all models). The multi-dimensional model combining the three models showed better predictive efficacy than the mLIPI, Radioscore, and immune score (PFS: 0.721 vs. 0.662 vs. 0.610 vs. 0.610; OS: 0.727 vs. 0.661 vs. 0.601 vs. 0.602 respectively). The multi-dimensional model showed the best predictive efficacy, with C-index for PFS and OS higher than mLIPI, radioscore and immune score: 0.721 vs. 0.662 vs. 0.610 vs. 0.610 for PFS and 0.727 vs. 0.661 vs. 0.601 vs. 0.602 for OS, respectively. The AUC for the multi-dimensional model also performed better than those of the individual models: 0.771 vs. 0.684 vs. 0.715 vs. 0.711 for PFS and 0.768 vs. 0.662 vs. 0.661 vs. 0.658 for OS, respectively. The multi-dimensional model combining the three models had better predictive efficacy than any single model and was more likely to help provide patients personalized and precision medicine.

A Retrospective Study Establishing a Nomogram Predictive Model for Postoperative High-Activity Delirium After Non-Cardiac Surgery.

Background: Postoperative high-activity delirium (PDHA) manifests as a high alertness, restlessness, hallucinations, and delusions. Occurrence of PDHA represents an increased risk of poor prognosis for patients. Objective: To establish and validate a nomogram prediction model for high-activity delirium after non-cardiac surgery in a post-anesthesia care unit (PACU). Methods: This study retrospectively enrolled adult patients who underwent non-cardiac surgery and were observed in the PACU as training data. Patients were divided into PDHA (199 patients) and non-PDHA (396 patients) groups. Patients' general data, preoperative indicators, intraoperative conditions, and postoperative PACU conditions were collected. The risk factors for PDHA were identified using univariate and multivariate logistic regression analyses. A predictive column chart was created using R language. Adult patients who underwent non-cardiac surgery and entered the PACU for observation were randomly selected as the validation set data (198 cases) for model performance validation. Results: The incidence rate of adult PDHA in the PACU was 0.275%. Sex, age, smoking history, low preoperative albumin level, Society of Anesthesiologists (ASA) classification, anesthesia duration, and postoperative PACU pain score were independent risk factors for hyperactive delirium in PACU adults. In this study, an adult PACU PDHA nomogram prediction model was developed. The training dataset verified that the ROC curve (area under the curve) and 95% confidence interval (95% CI) were 0.936 (0.917-0.955). The ROC curve of the validation data row showed that the area under the curve and 95% CI were 0.926 (0.885-0.967). Conclusion: The nomogram predictive model for PACU adult high-activity delirium constructed in this study showed good predictive performance. This model could enable the visualization and graphical prediction of adult high-activity delirium occurrence after PACU, which has clinical value.

Prediction Model of Delayed Hemothorax in Patients with Traumatic Occult Hemothorax Using a Novel Nomogram.

Background: Delayed hemothorax (dHTX) can occur unexpectedly, even in patients who initially present without signs of hemothorax (HTX), potentially leading to death. We aimed to develop a predictive model for dHTX requiring intervention, specifically targeting those with no or occult HTX. Methods: This retrospective study was conducted at a level 1 trauma center. The primary outcome was the occurrence of dHTX requiring intervention in patients who had no HTX or occult HTX and did not undergo closed thoracostomy post-injury. To minimize overfitting, we employed the least absolute shrinkage and selection operator (LASSO) logistic regression model for feature selection. Thereafter, we developed a multivariable logistic regression (MLR) model and a nomogram. Results: In total, 688 patients were included in the study, with 64 cases of dHTX (9.3%). The LASSO and MLR analyses revealed that the depth of HTX (adjusted odds ratio [aOR], 3.79; 95% confidence interval [CI], 2.10-6.85; p<0.001) and the number of totally displaced rib fractures (RFX) (aOR, 1.90; 95% CI, 1.56-2.32; p<0.001) were significant predictors. Based on these parameters, we developed a nomogram to predict dHTX, with a sensitivity of 78.1%, a specificity of 76.0%, a positive predictive value of 25.0%, and a negative predictive value of 97.1% at the optimal cut-off value. The area under the receiver operating characteristic curve was 0.832. Conclusion: The depth of HTX on initial chest computed tomography and the number of totally displaced RFX emerged as significant risk factors for dHTX. We propose a novel nomogram that is easily applicable in clinical settings.

Development and validation of a novel nomogram to avoid unnecessary biopsy in patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml.

OBJECTIVES: To develop and validate a prediction model for identifying non-prostate cancer (non-PCa) in biopsy-naive patients with PI-RADS category ≥ 4 lesions and PSA ≤ 20 ng/ml to avoid unnecessary biopsy. PATIENTS AND METHODS: Eligible patients who underwent transperineal biopsies at West China Hospital between 2018 and 2022 were included. The patients were randomly divided into training cohort (70%) and validation cohort (30%). Logistic regression was used to screen for independent predictors of non-PCa, and a nomogram was constructed based on the regression coefficients. The discrimination and calibration were assessed by the C-index and calibration plots, respectively. Decision curve analysis (DCA) and clinical impact curves (CIC) were applied to measure the clinical net benefit. RESULTS: A total of 1580 patients were included, with 634 non-PCa. Age, prostate volume, prostate-specific antigen density (PSAD), apparent diffusion coefficient (ADC) and lesion zone were independent predictors incorporated into the optimal prediction model, and a corresponding nomogram was constructed ( https://nomogramscu.shinyapps.io/PI-RADS-4-5/ ). The model achieved a C-index of 0.931 (95% CI, 0.910-0.953) in the validation cohort. The DCA and CIC demonstrated an increased net benefit over a wide range of threshold probabilities. At biopsy-free thresholds of 60%, 70%, and 80%, the nomogram was able to avoid 74.0%, 65.8%, and 55.6% of unnecessary biopsies against 9.0%, 5.0%, and 3.6% of missed PCa (or 35.9%, 30.2% and 25.1% of foregone biopsies, respectively). CONCLUSION: The developed nomogram has favorable predictive capability and clinical utility can help identify non-PCa to support clinical decision-making and reduce unnecessary prostate biopsies.