[Construction and Validation of Prediction Models of Risk Factors for Early Death in Patients With Metastatic Melanoma]. / è½¬ç§»æ€§é»‘è‰²ç´ ç˜¤æ‚£è€ æ—©æœŸæ­»äº¡é£Žé™©çš„é¢„æµ‹æ¨¡åž‹æž„å»ºåŠéªŒè¯.

Objective: To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma (MM). Methods: The study covered 2138 cases from the Surveillance, Epidemiology, and End Results Program (SEER) database and all these patients were diagnosed with MM between 2010 and 2015. Logistic regression was performed to identify independent risk factors affecting early death in MM patients. These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death. The efficacy of the model was assessed with receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). In addition, external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020. Results: According to the results of logistic regression, marital status, the primary site, N staging, surgery, chemotherapy, bone metastases, liver metastases, lung metastases, and brain metastases could be defined as independent predictive factors for early death. Based on these factors, 2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death, respectively. For the models for all-cause and cancer-specific early death, the areas under the curve (AUCs) for the training group were 0.751 (95% confidence interval [CI]: 0.726-0.776) and 0.740 (95% CI: 0.714-0.765), respectively. The AUCs for the internal validation group were 0.759 (95% CI: 0.722-0.797) and 0.757 (95% CI: 0.718-0.780), respectively, while the AUCs for the external validation group were 0.750 (95% CI: 0.649-0.850) and 0.741 (95% CI: 0.644-0.838), respectively. The calibration curves showed high agreement between the predicted and the observed probabilities. DCA analysis indicated high clinical application value of the models. Conclusion: The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.

Using Apparent Diffusion Coefficient (ADC) of Endometrial Cancer MRI to Determine P53 Molecular Subtypes.

BACKGROUND: Endometrial Cancer (EC) is a highly heterogeneous cancer comprising both histological and molecular subtypes. Using a non-invasive modality method to trigger these subtypes as early as possible can aid clinicians in establishing individualized treatment. PURPOSE: The study aimed to clarify the value of the Apparent Diffusion Coefficient (ADC) of EC MRI in determining molecular subtypes. MATERIAL AND METHODS: We retrospectively recruited 109 patients with pathologically proven EC (78 endometrioid cancers and 31 non-endometrioid cancers) with available molecular classification from a tertiary centre. MRI was prospectively performed a month prior to surgery; images were blindly interpreted by two experienced radiologists with consensus reading. The ADC value was measured by an experienced radiologist on the commercially available processing workstation. Interoperator measurement consistency was calculated. RESULTS: Our sample comprised 17 PLOE, 32 MSI-H, 31 NSMP, and 29 P53abn ECs. Clinical information did not differ significantly among the groups. The maximum diameter and volume of the lesions differed among the groups. The ADC value in the maximal area (ADCarea) or region of interest (ROI, ADCroi) in the P53abn group was higher than that in the other groups (894.0 ±12.6 and 817.5 ± 83.3 x10-6 mm2/s). The ADC mean values were significantly different between the P53abn group and the other groups (P = 0.000). The nomogram showed the highest discriminative ability to distinguish P53abn EC from other types (AUC: 0.859). CONCLUSION: Our results have suggested the quantitative MR characteristics (ADC values) derived from preoperative EC MRI to provide useful information in preoperatively determining P53abn cancer.

A nomogram for predicting the infectious disease-specific health literacy of older adults in China.

PURPOSE: To identify the predictors of infectious disease-specific health literacy (IDSHL), and establish an easy-to-apply nomogram to predict the IDSHL of older adults. METHODS: This cross-sectional study included 380 older adults who completed the IDSHL, self-rated health, sociodemographic and other questionnaires. Logistic regression was used to identify the IDSHL predictors. Nomogram was used to construct a predictive model. RESULTS: Up to 70.1% of older adults had limited IDSHL. Age, education, place of residence, self-rated health, and Internet access were the important influencing factors of IDSHL. The established nomogram model showed high accuracy (receiver operating characteristic curve: 0.848). CONCLUSIONS: The IDSHL of Chinese older adults was significantly deficient. The constructed nomogram is an intuitive tool for IDSHL prediction that can not only contribute towards rapid screening of high-risk older adults with limited IDSHL but also provide guidance for healthcare providers to develop prevention strategies for infectious diseases.

A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer.

OBJECTIVES: To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI. MATERIALS AND METHODS: A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications. RESULTS: The constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model. CONCLUSION: A combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer.

Association between increased systemic immune-inflammation index and postoperative delirium in older intertrochanteric fracture patients.

BACKGROUND AND PURPOSE: The systemic immune-inflammation index (SII), a novel inflammation index derived from the counts of circulating platelets, neutrophils and lymphocytes, has been studied in the treatment of acute cancer and ischemic stroke (AIS). However, the clinical value of the SII in postoperative delirium patients has not been further investigated. The purpose of our research was to study the incidence and preoperative risk factors for postoperative delirium (POD) and verify whether the SII could serve as a potential marker for POD in older intertrochanteric fracture patients. Finally, we created a novel nomogram for predicting POD in older patients with intertrochanteric fractures. METHODS: We enrolled elderly patients with intertrochanteric fractures who underwent proximal femoral nail antirotation (PFNA) between February 2021 and April 2023. Univariate and multivariate logistic analyses were subsequently performed to confirm the risk factors and construct a nomogram model.Calibration curve and clinical decision curve analysis (DCA) were used to assess the model's fitting performance. The performance of the nomogram was evaluated for discrimination, calibration, and clinical utility. RESULTS: A total of 293 patients were eligible for inclusion in the study, 25.6% (75/293) of whom had POD. The POD patients had higher SII values than the non-POD patients. The SII was strongly correlated with POD in older intertrochanteric fracture patients, and the optimal cutoff value was 752.6 × 109. Multivariate analysis revealed that age, diabetes, total albumin, SII > 752.6 × 109 and a CRP > 20.25 mg/L were independent risk factors for POD patients. By incorporating these 5 factors, the model achieved a concordance index of 0.745 (95% CI, 0.683-0.808) and had a well-fitted calibration curve and good clinical application value. CONCLUSION: The SII is a simple and valuable biomarker for POD, and the new nomogram model can be used to accurately predict the occurrence of POD. They can be utilized in clinical practice to identify those at high risk of POD in older intertrochanteric fracture patients.

Development and validation of nomograms for predicting prognosis in patients with solitary HCC: A TRIPOD-Compliant study.

Objective: To develop and validate nomograms for predicting the OS and CSS of patients with Solitary Hepatocellular Carcinoma (HCC). Methods: Using the TRIPOD guidelines, this study identified 5206 patients in the Surveillance, Epidemiology, and End Results (SEER) 17 registry database. All patients were randomly divided in a ratio of 7:3 into a training cohort (n = 3646) and a validation cohort (n = 1560), and the Chinese independent cohort (n = 307) constituted the external validation group. The prognosis-related risk factors were selected using univariate Cox regression analysis, and the independent prognostic factors of OS and CSS were identified using the Lasso-Cox regression model. The nomograms for predicting the OS and CSS of the patients were constructed based on the identified prognostic factors. Their prediction ability was evaluated using the concordance index (C-index), receiver operating characteristic (ROC) curve, and calibration curve in both the training and validation cohorts. Results: We identified factors that predict OS and CSS and constructed two nomograms based on the data. The ROC analysis, C-index analysis, and calibration analysis indicated that the two nomograms performed well over the 1, 3, and 5-year OS and CSS periods in both the training and validation cohorts. Additionally, these results were confirmed in the external validation group. Decision curve analysis (DCA) demonstrated that the two nomograms were clinically valuable and superior to the TNM stage system. Conclusion: We established and validated nomograms to predict 1,3, and 5-year OS and CSS in solitary HCC patients, and our results may also be helpful for clinical decision-making.

A New Risk Prediction Model for Detecting Endoscopic Activity of Ulcerative Colitis.

Background/Aims: : Ulcerative colitis (UC) is an incurable, relapsing-remitting inflammatory disease that increases steadily. Mucosal healing has become the primary therapeutic objective for UC. Nevertheless, endoscopic assessments are invasive, expensive, time-consuming, and inconvenient. Therefore, it is crucial to develop a noninvasive predictive model to monitor endoscopic activity in patients with UC. Methods: : Clinical data of 198 adult patients with UC were collected from January 2016 to August 2022 at Huadong Hospital, China. Results: : Patients with UC were randomly divided into the training cohort (70%, n=138) and the validation cohort (30%, n=60). The receiver operating characteristic curve value for the training group was 0.858 (95% confidence interval [CI], 0.781 to 0.936), whereas it was 0.845 (95% CI, 0.731 to 0.960) for the validation group. The calibration curve employed the Hosmer-Lemeshow test (p>0.05) to demonstrate the consistency between the predicted and the actual probabilities in the nomogram of these two groups. The decision curve analysis validated that the nomogram had clinical usefulness. Conclusions: : The nomogram, which incorporated activated partial thromboplastin time, fecal occult blood test, ß2-globulin level, and fibrinogen degradation products, served as a prospective tool for evaluating UC activity in clinical practices.

Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients.

Purpose: To establish nomograms integrating serum lactate levels and traditional risk factors for predicting diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients. Patients and methods: A total of 570 T2DM patients and 100 healthy subjects were enrolled. T2DM patients were categorized into normal and high lactate groups. Univariate and multivariate logistic regression analyses were employed to identify independent predictors for DKD. Then, nomograms for predicting DKD were established, and the model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Results: T2DM patients exhibited higher lactate levels compared to those in healthy subjects. Glucose, platelet, uric acid, creatinine, and hypertension were independent factors for DKD in T2DM patients with normal lactate levels, while diabetes duration, creatinine, total cholesterol, and hypertension were indicators in high lactate levels group (P<0.05). The AUC values were 0.834 (95% CI, 0.776 to 0.891) and 0.741 (95% CI, 0.688 to 0.795) for nomograms in both normal lactate and high lactate groups, respectively. The calibration curve demonstrated excellent agreement of fit. Furthermore, the DCA revealed that the threshold probability and highest Net Yield were 17-99% and 0.36, and 24-99% and 0.24 for the models in normal lactate and high lactate groups, respectively. Conclusion: The serum lactate level-based nomogram models, combined with traditional risk factors, offer an effective tool for predicting DKD probability in T2DM patients. This approach holds promise for early risk assessment and tailored intervention strategies.

Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging.

BACKGROUND: Gastric cancer (GC) is prevalent and aggressive, especially when patients have distant lung metastases, which often places patients into advanced stages. By identifying prognostic variables for lung metastasis in GC patients, it may be possible to construct a good prediction model for both overall survival (OS) and the cumulative incidence prediction (CIP) plot of the tumour. AIM: To investigate the predictors of GC with lung metastasis (GCLM) to produce nomograms for OS and generate CIP by using cancer-specific survival (CSS) data. METHODS: Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance, epidemiology, and end results program database. The major observational endpoint was OS; hence, patients were separated into training and validation groups. Correlation analysis determined various connections. Univariate and multivariate Cox analyses validated the independent predictive factors. Nomogram distinction and calibration were performed with the time-dependent area under the curve (AUC) and calibration curves. To evaluate the accuracy and clinical usefulness of the nomograms, decision curve analysis (DCA) was performed. The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer (AJCC) staging system by utilizing Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). Finally, the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared. RESULTS: For the purpose of creating the OS nomogram, a CIP plot based on CSS was generated. Cox multivariate regression analysis identified eleven significant prognostic factors (P < 0.05) related to liver metastasis, bone metastasis, primary site, surgery, regional surgery, treatment sequence, chemotherapy, radiotherapy, positive lymph node count, N staging, and time from diagnosis to treatment. It was clear from the DCA (net benefit > 0), time-dependent ROC curve (training/validation set AUC > 0.7), and calibration curve (reliability slope closer to 45 degrees) results that the OS nomogram demonstrated a high level of predictive efficiency. The OS prediction model (New Model AUC = 0.83) also performed much better than the old Cox-AJCC model (AUC difference between the new model and the old model greater than 0) in terms of risk stratification (P < 0.0001) and verification using the IDI and NRI. CONCLUSION: The OS nomogram for GCLM successfully predicts 1- and 3-year OS. Moreover, this approach can help to appropriately classify patients into high-risk and low-risk groups, thereby guiding treatment.

BioPrev-C - development and validation of a contemporary prostate cancer risk calculator.

Objectives: To develop a novel biopsy prostate cancer (PCa) prevention calculator (BioPrev-C) using data from a prospective cohort all undergoing mpMRI targeted and transperineal template saturation biopsy. Materials and methods: Data of all men who underwent prostate biopsy in our academic tertiary care center between 11/2016 and 10/2019 was prospectively collected. We developed a clinical prediction model for the detection of high-grade PCa (Gleason score ≥7) based on a multivariable logistic regression model incorporating age, PSA, prostate volume, digital rectal examination, family history, previous negative biopsy, 5-alpha-reductase inhibitor use and MRI PI-RADS score. BioPrev-C performance was externally validated in another prospective Swiss cohort and compared with two other PCa risk-calculators (SWOP-RC and PBCG-RC). Results: Of 391 men in the development cohort, 157 (40.2%) were diagnosed with high-grade PCa. Validation of the BioPrev C revealed good discrimination with an area under the curve for high-grade PCa of 0.88 (95% Confidence Interval 0.82-0.93), which was higher compared to the other two risk calculators (0.71 for PBCG and 0.84 for SWOP). The BioPrev-C revealed good calibration in the low-risk range (0 - 0.25) and moderate overestimation in the intermediate risk range (0.25 - 0.75). The PBCG-RC showed good calibration and the SWOP-RC constant underestimation of high-grade PCa over the whole prediction range. Decision curve analyses revealed a clinical net benefit for the BioPrev-C at a clinical meaningful threshold probability range (≥4%), whereas PBCG and SWOP calculators only showed clinical net benefit above a 30% threshold probability. Conclusion: BiopPrev-C is a novel contemporary risk calculator for the prediction of high-grade PCa. External validation of the BioPrev-C revealed relevant clinical benefit, which was superior compared to other well-known risk calculators. The BioPrev-C has the potential to significantly and safely reduce the number of men who should undergo a prostate biopsy.

The crucial role of age and site in incidence and prognosis of female neuroendocrine neoplasms in the United States: a population-based study from 2000 to 2018.

BACKGROUND: As the incidence continues to rise, global concern about neuroendocrine neoplasms (NENs) is mounting. However, little is known about how NENs affect women patients. METHODS: The annual percentage change (APC) was calculated to describe the incidence. Cox proportional hazards multivariable regression was used to identify risk factors. The nomograms were employed to estimate prognosis. RESULTS: A total of 39,237 female NENs (fNENs) cases were identified. The incidence of fNENs increased annually (APC = 4.5, 95% CI 4.1-4.8, P < 0.05), and the incidence pattern and survival outcomes showed age and site-specificity. Appendiceal, rectal, and pulmonary fNENs were major contributors to the incidence of patients younger than 40, between 40-59, and over 60 years old, respectively. The Cox proportional hazards regression model revealed that age, tumor size, grade, stage, and primary sites were closely related to survival. The worst survival outcomes appeared in breast, reproductive system, and liver fNENs for patients under 40, between 40-49, and over 50 years old, respectively. A nomogram based on these developed with higher predictive accuracy of prognosis, with a C index of 0.906 in the training cohort and 0.901 in the validation cohort. CONCLUSIONS: Our findings revealed distinct site-specific tendencies in the incidence and survival patterns among fNEN patients across various age groups. Thus, reasonable patient screening and stratification strategies should be implemented, especially for young patients.

Prediction of the benign and malignant nature of masses in COPD background based on Habitat-based enhanced CT radiomics modeling: A preliminary study.

BACKGROUND: It is difficult to differentiate between chronic obstructive pulmonary disease (COPD)-peripheral bronchogenic carcinoma (COPD-PBC) and inflammatory masses. OBJECTIVE: This study aims to predict COPD-PBC based on clinical data and preoperative Habitat-based enhanced CT radiomics (HECT radiomics) modeling. METHODS: A retrospective analysis was conducted on clinical imaging data of 232 cases of postoperative pathological confirmed PBC or inflammatory masses. The PBC group consisted of 82 cases, while the non-PBC group consisted of 150 cases. A training set and a testing set were established using a 7:3 ratio and a time cutoff point. In the training set, multiple models were established using clinical data and radiomics texture changes within different enhanced areas of the CT mass (HECT radiomics). The AUC values of each model were compared using Delong's test, and the clinical net benefit of the models was tested using decision curve analysis (DCA). The models were then externally validated in the testing set, and a nomogram of predicting COPD-PBC was created. RESULTS: Univariate analysis confirmed that female gender, tumor morphology, CEA, Cyfra21-1, CT enhancement pattern, and Habitat-Radscore B/C were predictive factors for COPD-PBC (P< 0.05). The combination model based on these factors had significantly higher predictive performance [AUC: 0.894, 95% CI (0.836-0.936)] than the clinical data model [AUC: 0.758, 95% CI (0.685-0.822)] and radiomics model [AUC: 0.828, 95% CI (0.761-0.882)]. DCA also confirmed the higher clinical net benefit of the combination model, which was validated in the testing set. The nomogram developed based on the combination model helped predict COPD-PBC. CONCLUSION: The combination model based on clinical data and Habitat-based enhanced CT radiomics can help differentiate COPD-PBC, providing a new non-invasive and efficient method for its diagnosis, treatment, and clinical decision-making.

Determination of p53abn endometrial cancer: a multitask analysis using Radiological-Clinical nomogram on MR imaging.

OBJECTIVES: We aimed to differentiate endometrial cancer (EC) between TP53mutation (P53abn) and Non-P53abn subtypes using radiological-clinical nomogram on EC body volume MR imaging. MATERIALS AND METHODS: We retrospectively recruited two hundred twenty-seven patients with pathologically proven EC from our institution. All these patients have undergone molecular pathology diagnosis based on the cancer genome atlas (TCGA). Clinical characteristics and histological diagnosis were recorded from the hospital information system. Radiomics features were extracted from online Pyradiomics processors. The diagnostic performance across different acquisition protocols was calculated and compared. The radiological-clinical nomogram was established to determine the non-endometrioid, high-risk, and P53abn EC group. RESULTS: The best MRI sequence for differentiation P53abn from the non-P53abn group was contrast-enhanced T1WI (test AUC: 0.8). The best MRI sequence both for differentiation endometrioid cancer from non-endometrioid cancer and high risk from low-and intermediate-risk groups was apparent diffusion coefficient map (test AUC: 0.665 and 0.690). For all three tasks, the combined model incorporating all the best discriminative features from each sequence yielded the best performance. The combined model achieved an AUC of 0.845 in the testing cohorts for P53abn cancer identification. The MR-based radiomics diagnostic model performed better than the clinical-based model in determining P53abn EC (AUC : 0.834 versus 0.682). CONCLUSION: In the present study, the diagnostic model based on the combination of both radiomics and clinical features yielded a higher performance in differentiating non-endometrioid and P53abn cancer from other EC molecular subgroups, which might help design a tailed treatment, especially for patients with high-risk EC.

Age-related curves of AMH using the Gen II, the picoAMH and the Elecsys assays in women with polycystic ovary syndrome.

CONTEXT: Several challenges still exist to adopt the anti-Müllerian hormone (AMH) as a marker of polycystic ovary morphology (PCOM), as included in the recently updated international guideline. Although different evaluations of age- and assay-specific reference ranges have been published in the last years, these studies have mainly been conducted in normo-ovulatory or infertile women. OBJECTIVE: To develop an age-specific percentile distribution of AMH in patients with polycystic ovary syndrome (PCOS) measured by three different assays. DESIGN: Retrospective cross-sectional study. PATIENTS: 2,725 women aged 20 to 40 years with PCOS diagnosis were included. INTERVENTION (S): Serum AMH measurement by the Gen II (Beckman Coulter), the picoAMH (Ansh Labs), and the Elecsys (Roche) assays. MAIN OUTCOME MEAUSRE (S): Age-specific centile curves for all the assays and correlations between AMH, clinical, hormonal, and ultrasound characteristics. RESULTS: Age-related nomograms for the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles of AMH were calculated using the LMS method for all the assays. AMH levels were significantly different between PCOS phenotypes. AMH levels were positive correlated to luteinizing hormone (LH), LH/follicular stimulating hormone (FSH) ratio, testosterone, androstenedione, free androgen index, mean follicular number, and mean ovarian volume. CONCLUSIONS: To our knowledge this is the first study reporting age specific percentile nomograms of serum AMH levels measured by the Gen II, the picoAMH and the Elecsys assays in a large population of PCOS women. These findings may help to interpret AMH levels in PCOS patients and facilitate the use of AMH as a diagnostic tool across age ranges.

Nomogram for preoperative differentiation of benign and malignant breast tumors using contrast-enhanced cone-beam breast CT (CE CB-BCT) quantitative imaging and assessment features.

PURPOSE: Breast cancer's impact necessitates refined diagnostic approaches. This study develops a nomogram using radiology quantitative features from contrast-enhanced cone-beam breast CT for accurate preoperative classification of benign and malignant breast tumors. MATERIAL AND METHODS: A retrospective study enrolled 234 females with breast tumors, split into training and test sets. Contrast-enhanced cone-beam breast CT-images were acquired using Koning Breast CT-1000. Quantitative assessment features were extracted via 3D-slicer software, identifying independent predictors. The nomogram was constructed to preoperative differentiation benign and malignant breast tumors. Calibration curve was used to assess whether the model showed favorable correspondence with pathological confirmation. Decision curve analysis confirmed the model's superiority. RESULTS: The study enrolled 234 female patients with a mean age of 50.2 years (SD ± 9.2). The training set had 164 patients (89 benign, 75 malignant), and the test set had 70 patients (29 benign, 41 malignant). The nomogram achieved excellent predictive performance in distinguishing benign and malignant breast lesions with an AUC of 0.940 (95% CI 0.900-0.940) in the training set and 0.970 (95% CI 0.940-0.970) in the test set. CONCLUSION: This study illustrates the effectiveness of quantitative radiology features derived from contrast-enhanced cone-beam breast CT in distinguishing between benign and malignant breast tumors. Incorporating these features into a nomogram-based diagnostic model allows for breast tumor diagnoses that are objective and possess good accuracy. The application of these insights could substantially increase reliability and efficacy in the management of breast tumors, offering enhanced diagnostic capability.

Radiomics-based nomogram guides adaptive de-intensification in locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy.

OBJECTIVES: This study aimed to construct a radiomics-based model for prognosis and benefit prediction of concurrent chemoradiotherapy (CCRT) versus intensity-modulated radiotherapy (IMRT) in locoregionally advanced nasopharyngeal carcinoma (LANPC) following induction chemotherapy (IC). MATERIALS AND METHODS: A cohort of 718 LANPC patients treated with IC + IMRT or IC + CCRT were retrospectively enrolled and assigned to a training set (n = 503) and a validation set (n = 215). Radiomic features were extracted from pre-IC and post-IC MRI. After feature selection, a delta-radiomics signature was built with LASSO-Cox regression. A nomogram incorporating independent clinical indicators and the delta-radiomics signature was then developed and evaluated for calibration and discrimination. Risk stratification by the nomogram was evaluated with Kaplan-Meier methods. RESULTS: The delta-radiomics signature, which comprised 19 selected features, was independently associated with prognosis. The nomogram, composed of the delta-radiomics signature, age, T category, N category, treatment, and pre-treatment EBV DNA, showed great calibration and discrimination with an area under the receiver operator characteristic curve of 0.80 (95% CI 0.75-0.85) and 0.75 (95% CI 0.64-0.85) in the training and validation sets. Risk stratification by the nomogram, excluding the treatment factor, resulted in two groups with distinct overall survival. Significantly better outcomes were observed in the high-risk patients with IC + CCRT compared to those with IC + IMRT, while comparable outcomes between IC + IMRT and IC + CCRT were shown for low-risk patients. CONCLUSION: The radiomics-based nomogram can predict prognosis and survival benefits from concurrent chemotherapy for LANPC following IC. Low-risk patients determined by the nomogram may be potential candidates for omitting concurrent chemotherapy during IMRT. CLINICAL RELEVANCE STATEMENT: The radiomics-based nomogram was constructed for risk stratification and patient selection. It can help guide clinical decision-making for patients with locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy, and avoid unnecessary toxicity caused by overtreatment. KEY POINTS: • The benefits from concurrent chemotherapy remained controversial for locoregionally advanced nasopharyngeal carcinoma following induction chemotherapy. • Radiomics-based nomogram achieved prognosis and benefits prediction of concurrent chemotherapy. • Low-risk patients defined by the nomogram were candidates for de-intensification.

A Web-Based Dynamic Nomogram to Predict the Risk of Methicillin-Resistant Staphylococcal Infection in Patients with Pneumonia.

The aim of this study was to create a dynamic web-based tool to predict the risks of methicillin-resistant Staphylococcus spp. (MRS) infection in patients with pneumonia. We conducted an observational study of patients with pneumonia at Cho Ray Hospital from March 2021 to March 2023. The Bayesian model averaging method and stepwise selection were applied to identify different sets of independent predictors. The final model was internally validated using the bootstrap method. We used receiver operator characteristic (ROC) curve, calibration, and decision curve analyses to assess the nomogram model's predictive performance. Based on the American Thoracic Society, British Thoracic Society recommendations, and our data, we developed a model with significant risk factors, including tracheostomies or endotracheal tubes, skin infections, pleural effusions, and pneumatoceles, and used 0.3 as the optimal cut-off point. ROC curve analysis indicated an area under the curve of 0.7 (0.63-0.77) in the dataset and 0.71 (0.64-0.78) in 1000 bootstrap samples, with sensitivities of 92.39% and 91.11%, respectively. Calibration analysis demonstrated good agreement between the observed and predicted probability curves. When the threshold is above 0.3, we recommend empiric antibiotic therapy for MRS. The web-based dynamic interface also makes our model easier to use.

Risk factors and an early predictive model for Kawasaki disease shock syndrome in Chinese children.

BACKGROUND: Kawasaki disease shock syndrome (KDSS), though rare, has increased risk for cardiovascular complications. Early diagnosis is crucial to improve the prognosis of KDSS patients. Our study aimed to identify risk factors and construct a predictive model for KDSS. METHODS: This case-control study was conducted from June, 2015 to July, 2023 in two children's hospitals in China. Children initially diagnosed with KDSS and children with Kawasaki disease (KD) without shock were matched at a ratio of 1:4 by using the propensity score method. Laboratory results obtained prior to shock syndrome and treatment with intravenous immunoglobulin were recorded to predict the onset of KDSS. Univariable logistic regression and forward stepwise logistic regression were used to select significant and independent risk factors associated with KDSS. RESULTS: After matching by age and gender, 73 KDSS and 292 KD patients without shock formed the development dataset; 40 KDSS and 160 KD patients without shock formed the validation dataset. Interleukin-10 (IL-10) > reference value, platelet counts (PLT) < 260 × 109/L, C-reactive protein (CRP) > 80 mg/ml, procalcitonin (PCT) > 1ng/ml, and albumin (Alb) < 35 g/L were independent risk factors for KDSS. The nomogram model including the above five indicators had area under the curves (AUCs) of 0.91(95% CI: 0.87-0.94) and 0.90 (95% CI: 0.71-0.86) in the development and validation datasets, with a specificity and sensitivity of 80% and 86%, 66% and 77%, respectively. Calibration curves showed good predictive accuracy of the nomogram. Decision curve analyses revealed the predictive model has application value. CONCLUSIONS: This study identified IL-10, PLT, CRP, PCT and Alb as risk factors for KDSS. The nomogram model can effectively predict the occurrence of KDSS in Chinese children. It will facilitate pediatricians in early diagnosis, which is essential to the prevention of cardiovascular complications.

Effects of surgical management for gastrointestinal stromal tumor patients with liver metastasis on survival outcomes.

Purpose: To investigate the effect of surgical resection on survival in gastrointestinal stromal tumors synchronous liver metastasis (GIST-SLM) and to develop clinically usable predictive models for overall survival (OS) and cancer-specific survival (CSS) in patients. Methods: We identified patients in the SEER database diagnosed with GISTs from 2010 to 2019. We used propensity score matching (PSM) to balance the bias between the Surgery and No surgery groups. Kaplan-Meier(K-M) analysis was used to detect differences in OS and CSS between the two groups. The nomogram to predict 1, 3, and 5-year OS and CSS were developed and evaluated. Results: After PSM, 228 patients were included in this study. There were significant differences in 1, 3, and 5-year OS and CSS between the two groups (OS: 93.5% vs. 84.4%, 73.2% vs. 55.3%, 60.9% vs. 36.9%, P=0.014; CSS: 3.5% vs.86.2%,75.3% vs.57.9%, 62.6% vs. 42.9%, P=0.02). We also found that patients who received surgery combined with targeted therapy had better OS and CSS at 1, 3, and 5 years than those who received surgery only (OS: 96.6% vs.90.9%, 74.9% vs. 56.8%, 61.7% vs. 35.5%, P=0.022; CSS: 96.6% vs. 92.1%, 77.4% vs.59.2%,63.8% vs. 42.0%, P=0.023). The area under the curve (AUC) was 0.774, 0.737, and 0.741 for 1, 3, and 5-year OS, respectively, with 0.782 and 0.742 for 1, 3, and 5-year CSS. In the model, C-index was 0.703 for OS and 0.705 for CSS and showed good consistency. Conclusion: Surgical treatment can improve the OS and CSS of patients with GIST-SLM. In addition, the combination with chemotherapy may be more favorable for the long-term survival of patients. Meanwhile, we constructed the nomograms for predicting OS and CSS at 1, 3, and 5-year, and validated them internally. Our model can contribute to clinical management and treatment strategy optimization.

Predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer: The establishment of a spectral CT-based nomogram from prospective datasets.

BACKGROUND: To establish a spectral CT-based nomogram for predicting early neoadjuvant chemotherapy (NAC) response for locally advanced gastric cancer (LAGC). METHODS: This study prospectively recruited 222 cases (177 male and 45 female patients, 9.59 ± 9.54 years) receiving NAC and radical gastrectomy. Triple enhanced spectral CT scans were performed before NAC initiation. According to post-operative tumor regression grade (TRG), patients were classified into responders (TRG = 0 + 1) or non-responders (TRG = 2 + 3), and split into a primary (156) and validation (66) dataset at 7:3 ratio chronologically. We compared clinicopathological data, follow-up information, iodine concentration (IC), normalized ICs (nICs) in arterial/venous/delayed phases (AP/VP/DP) between responders and non-responders. Independent risk factors of response were screened by multivariable logistic regression and adopted for model construction. Model was visualized by nomograms and its capability was determined through receiver operating characteristic (ROC) curves. Log-rank survival analysis was conducted to explore associations between TRG, nomogram and patients' survival. RESULTS: This work identified Borrmann classification, ICDP, and nICDP were independent risk factors of response outcomes. A spectral CT-based nomogram was built accordingly and achieved an area under the curve (AUC) of 0.797 (0.692-0.879) and 0.741(0.661-0.811) for the primary and validation dataset, respectively, higher than AUC of individual parameters alone. The nomogram was related to disease-free survival in the validation dataset (Hazard ratio (HR): 5.19 [1.18-12.93], P = 0.02). CONCLUSIONS: The spectral CT-based nomogram provides an efficient tool for predicting the pathologic response outcomes of GC after NAC and disease-free survival risk stratification.