Prospective evaluation of 92 serum protein biomarkers for early detection of ovarian cancer

Background CA125 is the best available yet insufficiently sensitive biomarker for early detection of ovarian cancer. There is a need to identify novel biomarkers, which individually or in combination with CA125 can achieve adequate sensitivity and specificity for the detection of earlier-stage ovarian cancer. Methods In the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we measured serum levels of 92 preselected proteins for 91 women who had blood sampled ≤18 months prior to ovarian cancer diagnosis, and 182 matched controls. We evaluated the discriminatory performance of the proteins as potential early diagnostic biomarkers of ovarian cancer. Results Nine of the 92 markers; CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN and ADAM8 showed an area under the ROC curve (AUC) of ≥0.70 for discriminating between women diagnosed with ovarian cancer and women who remained cancer-free. All, except ADAM8, had shown at least equal discrimination in previous case-control comparisons. The discrimination of the biomarkers, however, was low for the lag-time of >9–18 months and paired combinations of CA125 with any of the 8 markers did not improve discrimination compared to CA125 alone. Conclusion Using pre-diagnostic serum samples, this study identified markers with good discrimination for the lag-time of 0–9 months. However, the discrimination was low in blood samples collected more than 9 months prior to diagnosis, and none of the markers showed major improvement in discrimination when added to CA125.

Automated Breast Volume Scanner (ABVS)-Based Radiomic Nomogram: A Potential Tool for Reducing Unnecessary Biopsies of BI-RADS 4 Lesions

Improving the assessment of breast imaging reporting and data system (BI-RADS) 4 lesions and reducing unnecessary biopsies are urgent clinical issues. In this prospective study, a radiomic nomogram based on the automated breast volume scanner (ABVS) was constructed to identify benign and malignant BI-RADS 4 lesions and evaluate its value in reducing unnecessary biopsies. A total of 223 histologically confirmed BI-RADS 4 lesions were enrolled and assigned to the training and validation cohorts. A radiomic score was generated from the axial, sagittal, and coronal ABVS images. Combining the radiomic score and clinical-ultrasound factors, a radiomic nomogram was developed by multivariate logistic regression analysis. The nomogram integrating the radiomic score, lesion size, and BI-RADS 4 subcategories showed good discrimination between malignant and benign BI-RADS 4 lesions in the training (AUC, 0.959) and validation (AUC, 0.925) cohorts. Moreover, 42.5% of unnecessary biopsies would be reduced by using the nomogram, but nine (4%) malignant BI-RADS 4 lesions were unfortunately missed, of which 4A (77.8%) and small-sized (<10 mm) lesions (66.7%) accounted for the majority. The ABVS radiomics nomogram may be a potential tool to reduce unnecessary biopsies of BI-RADS 4 lesions, but its ability to detect small BI-RADS 4A lesions needs to be improved.

Exploring the Potential Distribution of Relic Trochodendron aralioides: An Approach Using Open-Access Resources and Free Software

Trochodendron aralioides Siebold & Zucc. is a relic tree that is discontinuously scattered across the mountainous areas of Japan, Taiwan, and South Korea, but the origin of T. aralioides in South Korea is still unclear and debated. To confirm its distribution and explore its origins, we constructed a streamlined framework to examine potential species distribution using multiple open access data and free and open-source software, as well as employing maximum entropy principles to predict the potential distribution of T. aralioides. The results showed reasonably good discrimination and were used to examine and discuss the explicit distribution of T. aralioides. The potential distribution of T. aralioides in Japan extended from Iriomote Island to approximately 37° N in Honshu on the Pacific Ocean side. In Taiwan, the potential distribution of T. aralioides was more common than in Japan. It occurred at 1500–3000 m a.s.l. across the Central Mountain Range and decreased toward the northern and southern tips, correlating to the descending pattern of the cloud belt. Thermal and moisture conditions were important factors to determine the distribution of T. aralioides. The potential distribution indicated that Jeju island had high potential as a habitat for T. aralioides, and that may indirectly imply its existence and origins in South Korea, as some researchers have noted.

Development and Validation of Nomogram to Predict Very Early Recurrence of Combined Hepatocellular-Cholangiocarcinoma After Hepatic Resection: A Multi-Institutional Study

Background and Objectives Combined hepatocellular cholangiocarcinoma (cHCC) has a high incidence of early recurrence. The objective of this study is to construct a model predicting very early recurrence (VER)(ie, recurrence within 6 months after surgery) of cHCC. Methods 131 consecutive patients from Eastern Hepatobiliary Surgery Hospital served as a development cohort to construct a nomogram predicting VER by using multivariable logistic regression analysis. The model was internally and externally validated in an validation cohort of 90 patients from Mengchao Hepatobiliary Hospital using the C concordance statistic, calibration analysis and decision curve analysis (DCA). Results The VER nomogram contains microvascular invasion(MiVI), macrovascular invasion(MaVI) and CA19-9>25mAU/mL. The model shows good discrimination with C-indexes of 0.77 (95%CI: 0.69 - 0.85 ) and 0.76 (95%CI:0.66 - 0.86) in the development cohort and validation cohort respectively. Decision curve analysis demonstrated that the model are clinically useful and the calibration of our model was favorable. Our model stratified patients into two different risk groups, which exhibited significantly different VER. Conclusions Our model demonstrated favorable performance in predicting VER in cHCC patients.

O-OGC01 Development and validation of multivariate prediction model of long-term survival after oesophagectomy in patients with oesophageal cancer

Background Long-term survival after oesophagectomy remains poor, with recurrence a feared common outcome. Prediction tools can help clinicians identify high-risk patients and optimise treatment decisions based on their prognostic factors. This study developed and evaluated a prediction model to predict long-term survival and time-to-recurrence following surgery for oesophageal cancer. Methods Patients who underwent curative surgery between June 2009-2015 from the European iNvestigation of SUrveillance After Resection for Esophageal Cancer study were included. Prediction models were developed for overall survival (OS) and disease-free survival (DFS) using Cox proportional hazards (CPH) and Random Survival Forest (RSF). Model performance was evaluated using discrimination (time-dependent area under the curve (tAUC)) and calibration (visual comparison of predicted and observed survival probabilities). Results This study included 4719 patients with an OS of 47.7% and DFS of 48.4% at 5 years. Sixteen variables were included in the final model. CPH and RSF demonstrated good discrimination with a tAUC of 78.2% (95% CI 77.4-79.1%) and 77.1% (95% CI 76.1-78.1%) for OS and a tAUC of 79.4% (95% CI 78.5-80.2%) and 78.6% (95% CI 77.5-79.5%) respectively for DFS at 5 years. CPH showed good agreement between predicted and observed probabilities in all quintiles. RSF showed good agreement for patients with survival probabilities between 20-80% and moderate agreement in the &lt;20% and &gt;80% quintile groups. Conclusions This study demonstrated the ability of a statistical model to accurately predict long-term survival and time-to-recurrence after surgery for oesophageal cancer, with CPH and RSF models showing good discrimination and calibration. Identification of patient groups at risk of recurrence and poor long-term survival can improve patient outcomes by enhancing selection of treatment methods and surveillance strategies. Future work evaluating prediction-based decisions against standard decision-making is required to improve understanding of the clinical utility derived from prognostic model use.

Profiling of Myositis Specific Antibodies and Composite Scores as an Aid in the Differential Diagnosis of Autoimmune Myopathies

(1) Background: Myositis specific antibodies (MSA) represent important diagnostic and stratification tools in idiopathic inflammatory myositis (IIM) patients. Here we aimed to evaluate the clinical performance of MSA profiled by a novel particle based multi-analyte technology (PMAT) in IIM and subsets thereof. (2) Methods: 264 IIM patients and 200 controls were tested for MSA using PMAT (Inova Diagnostics, research use only). Diagnostic performance was analyzed and composite scores were generated. (3) Results: The sensitivity/specificity of the individual MSA were: 19.7%/100% (Jo-1), 7.2%/100.0% (Mi-2), 3.0%/99.0% (NXP2), 3.8%/100.0% (SAE), 2.7%/100.0% (PL-7), 1.9%/99.5 (PL-12), 1.1%/100.0% (EJ), 15.5%/99.5% (TIF1ƴ), 8.3%/98.5% (MDA5), 6.1%/99.0% (HMGCR) and 1.9%/98.5% (SRP). Of all IIM patients, 180/264 tested positive for at least one of the MSAs. In the individual control group, 12/200 (6.0%) tested positive for at least one MSA, most of which had levels close to the cut-off (except one SRP and one PL-12). Only 6/264 (2.3%) IIM patients were positive for more than one antibody (MDA5/HMGCR, EJ/PL-7, 2 x MDA5/TIF1ƴ, EJ/SAE, SAE/TIF1ƴ). The overall sensitivity was 68.2% paired with a specificity of 94.0%, leading to an odds ratio of 33.8. The composite scores showed good discrimination between subgroups (e.g., anti-synthetase syndrome). (4) Conclusion: MSA, especially when combined in composite scores (here measured by PMAT), provide value in stratification of patients with IIM.

Importance of the Spatial Distribution of Rare Earth Elements in the Bottom Sediments of Reservoirs as a Potential Proxy for Tracing Sediments Sources. A Case Study in the Dominican Republic

The geochemical composition of rare earth elements (REE) in the bottom sediments of two Dominican reservoirs and in soils from their catchments was studied to identify possible sources of the deposited materials. Knowledge of the origin of the sediments will serve to control the excessive rates of erosion and sedimentation that occur annually due to periodic extreme climatic events that promote excessive silting of the lakes, followed by loss of storage capacity and degradation of water quality. The REE contents of sediments and soils were normalized to the North American Shale Composite (NASC) and the ratio of light/heavy rare earths (LREE/HREE ratio), Ce and Eu anomalies, and some fractionation parameters were determined. The REE patterns are more homogeneous in the sediments, indicating uniform sedimentation in both deposits. The sediment data reflect depletion of REE from the sources, enrichment of light REE (LREE) and some middle REE (MREE), and positive Eu and Ce anomalies. All data were plotted in correlation diagrams between some fractionation parameters of light–middle–heavy REE and anomalies of Ce and Eu. The similarity of the ratios between these parameters in all samples and the overlap of data from soils and rocks on the sediment projection in the diagrams allowed a good discrimination of the main sources of the materials.

Machine-Learning Based Early Warning System for Prediction for Disseminated Intravascular Coagulation after Allogeneic Hematopoietic Stem Cell Transplantation: A Nationwide Multicenter Study

Introduction Allogeneic haematopoietic stem cell transplantation (allo-HSCT) has been demonstrated to be the most effective therapy for various malignant as well as nonmalignant haematological diseases. The wide use of allo-HSCT has inevitably led to a variety of complications after transplantation, with bleeding complications such as disseminated intravascular coagulation (DIC). DIC accounts for a significant proportion of life-threatening bleeding cases occurring after allo-HSCT. However, information on markers for early identification remains limited, and no predictive tools for DIC after allo-HSCT are available. This research aimed to identify the risk factors for DIC after allo-HSCT and establish prediction models to predict the occurrence of DIC after allo-HSCT. Methods The definition of DIC was based on the International Society of Thrombosis and Hemostasis (ISTH) scoring system. Overall, 197 patients with DIC after allo-HSCT at Peking University People's Hospital and other 7 centers in China from January 2010 to June 2021 were retrospectively identified. Each patient was randomly matched to 3 controls based on the time of allo-HSCT (±3 months) and length of follow-up (±6 months). A lasso regression model was used for data dimension reduction, feature selection, and risk factor building. Multivariable logistic regression analysis was used to develop the prediction model. We incorporated the clinical risk factors, and this was presented with a nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal and external validation was assessed. Various machine learning models were further used to perform machine learning modeling by attempting to complete the data sample classification task, including XGBClassifier, LogisticRegression, MLPClassifier, RandomForestClassifier, and AdaBoostClassifier. Results A total of 7280 patients received allo-HSCT from January 2010 to June 2021, and DIC occurred in 197 of these patients (incidence of 2.7%). The derivation cohort included 120 DIC patients received allo-HSCT and 360 patients received allo-HSCT from Peking University People's Hospital, and the validation cohort included the remaining 77 patients received allo-HSCT and 231 patients received allo-HSCT from the other 7 centers. The median time for DIC events was 99.0 (IQR, 46.8-220) days after allo-HSCT. The overall survival of patients with DIC was significantly reduced (P ＜ 0.0001). By Lasso regression, the 10 variables with the highest importance were found to be prothrombin time activity (PTA), shock, C-reactive protein, internationalization normalized ratio, bacterial infection, oxygenation, fibrinogen, blood creatinine, white blood cell count, and acute respiratory distress syndrome (from highest to lowest). In the multivariate analysis, the independent risk factors for DIC included PTA, bacterial infection and shock (P &lt;0.001), and these predictors were included in the clinical prediction nomogram. The model showed good discrimination, with a C-index of 0.975 (95%CI, 0.939 to 0.987 through internal validation) and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 [95% CI, 0.759 to 0.766]) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. The predictive value ROC curves of different machine learning models show that XGBClassifier is the best performing model for this dataset, with an area under the curve of 0.86. Conclusions Risk factors for DIC after allo-HSCT were identified, and a nomogram model and various machine learning models were established to predict the occurrence of DIC after allo-HSCT. Combined, these can help recognize high-risk patients and provide timely treatment. In the future, we will further refine the prognostic model utilizing nationwide multicenter data and conduct prospective clinical trials to reduce the incidence of DIC after allo-HSCT and improve the prognosis. Disclosures No relevant conflicts of interest to declare.

Development and Validation of Prediction Model for High Ovarian Response in In Vitro Fertilization-Embryo Transfer: A Longitudinal Study

Objective. To develop and validate a prediction model for high ovarian response in in vitro fertilization-embryo transfer (IVF-ET) cycles. Methods. Totally, 480 eligible outpatients with infertility who underwent IVF-ET were selected and randomly divided into the training set for developing the prediction model and the testing set for validating the model. Univariate and multivariate logistic regressions were carried out to explore the predictive factors of high ovarian response, and then, the prediction model was constructed. Nomogram was plotted for visualizing the model. Area under the receiver-operating characteristic (ROC) curve, Hosmer-Lemeshow test and calibration curve were used to evaluate the performance of the prediction model. Results. Antral follicle count (AFC), anti-Müllerian hormone (AMH) at menstrual cycle day 3 (MC3), and progesterone (P) level on human chorionic gonadotropin (HCG) day were identified as the independent predictors of high ovarian response. The value of area under the curve (AUC) for our multivariate model reached 0.958 (95% CI: 0.936-0.981) with the sensitivity of 0.916 (95% CI: 0.863-0.953) and the specificity of 0.911 (95% CI: 0.858-0.949), suggesting the good discrimination of the prediction model. The Hosmer-Lemeshow test and the calibration curve both suggested model’s good calibration. Conclusion. The developed prediction model had good discrimination and accuracy via internal validation, which could help clinicians efficiently identify patients with high ovarian response, thereby improving the pregnancy rates and clinical outcomes in IVF-ET cycles. However, the conclusion needs to be confirmed by more related studies.

Predicting the nature of pleural effusion in patients with lung adenocarcinoma based on 18F-FDG PET/CT

Background This study aims to establish a predictive model on the basis of 18F-FDG PET/CT for diagnosing the nature of pleural effusion (PE) in patients with lung adenocarcinoma. Methods Lung adenocarcinoma patients with PE who underwent 18F-FDG PET/CT were collected and divided into training and test cohorts. PET/CT parameters and clinical information in the training cohort were collected to estimate the independent predictive factors of malignant pleural effusion (MPE) and to establish a predictive model. This model was then applied to the test cohort to evaluate the diagnostic efficacy. Results A total of 413 lung adenocarcinoma patients with PE were enrolled in this study, including 245 patients with MPE and 168 patients with benign PE (BPE). The patients were divided into training (289 patients) and test (124 patients) cohorts. CEA, SUVmax of tumor and attachment to the pleura, obstructive atelectasis or pneumonia, SUVmax of pleura, and SUVmax of PE were identified as independent significant factors of MPE and were used to construct a predictive model, which was graphically represented as a nomogram. This predictive model showed good discrimination with the area under the curve (AUC) of 0.970 (95% CI 0.954–0.986) and good calibration. Application of the nomogram in the test cohort still gave good discrimination with AUC of 0.979 (95% CI 0.961–0.998) and good calibration. Decision curve analysis demonstrated that this nomogram was clinically useful. Conclusions Our predictive model based on 18F-FDG PET/CT showed good diagnostic performance for PE, which was helpful to differentiate MPE from BPE in patients with lung adenocarcinoma.