Non-invasive Prediction Models Research Articles

Building Dual AI Models and Nomograms Using Noninvasive Parameters for Aiding Male Bladder Outlet Obstruction Diagnosis and Minimizing the Need for Invasive Video-Urodynamic Studies: Development and Validation Study.

Diagnosing underlying causes of nonneurogenic male lower urinary tract symptoms associated with bladder outlet obstruction (BOO) is challenging. Video-urodynamic studies (VUDS) and pressure-flow studies (PFS) are both invasive diagnostic methods for BOO. VUDS can more precisely differentiate etiologies of male BOO, such as benign prostatic obstruction, primary bladder neck obstruction, and dysfunctional voiding, potentially outperforming PFS. These examinations' invasive nature highlights the need for developing noninvasive predictive models to facilitate BOO diagnosis and reduce the necessity for invasive procedures. We conducted a retrospective study with a cohort of men with medication-refractory, nonneurogenic lower urinary tract symptoms suspected of BOO who underwent VUDS from 2001 to 2022. In total, 2 BOO predictive models were developed-1 based on the International Continence Society's definition (International Continence Society-defined bladder outlet obstruction; ICS-BOO) and the other on video-urodynamic studies-diagnosed bladder outlet obstruction (VBOO). The patient cohort was randomly split into training and test sets for analysis. A total of 6 machine learning algorithms, including logistic regression, were used for model development. During model development, we first performed development validation using repeated 5-fold cross-validation on the training set and then test validation to assess the model's performance on an independent test set. Both models were implemented as paper-based nomograms and integrated into a web-based artificial intelligence prediction tool to aid clinical decision-making. Among 307 patients, 26.7% (n=82) met the ICS-BOO criteria, while 82.1% (n=252) were diagnosed with VBOO. The ICS-BOO prediction model had a mean area under the receiver operating characteristic curve (AUC) of 0.74 (SD 0.09) and mean accuracy of 0.76 (SD 0.04) in development validation and AUC and accuracy of 0.86 and 0.77, respectively, in test validation. The VBOO prediction model yielded a mean AUC of 0.71 (SD 0.06) and mean accuracy of 0.77 (SD 0.06) internally, with AUC and accuracy of 0.72 and 0.76, respectively, externally. When both models' predictions are applied to the same patient, their combined insights can significantly enhance clinical decision-making and simplify the diagnostic pathway. By the dual-model prediction approach, if both models positively predict BOO, suggesting all cases actually resulted from medication-refractory primary bladder neck obstruction or benign prostatic obstruction, surgical intervention may be considered. Thus, VUDS might be unnecessary for 100 (32.6%) patients. Conversely, when ICS-BOO predictions are negative but VBOO predictions are positive, indicating varied etiology, VUDS rather than PFS is advised for precise diagnosis and guiding subsequent therapy, accurately identifying 51.1% (47/92) of patients for VUDS. The 2 machine learning models predicting ICS-BOO and VBOO, based on 6 noninvasive clinical parameters, demonstrate commendable discrimination performance. Using the dual-model prediction approach, when both models predict positively, VUDS may be avoided, assisting in male BOO diagnosis and reducing the need for such invasive procedures.

P-565 Moving towards non – invasive euploid blastocyst prediction. Retrospective euploid blastocyst evaluation using different morphokinetics and Artificial Intelligence (AI) models

Abstract Study question Could Preimplantation Genetic Testing for Aneuploidies be replaced by an Aritificial Intelligence model which evaluates blastocysts and selects the most suitable ones for embryo transfer? Summary answer The use of AI for euploidy prediction has not been validated in a large scale and thus it should not be used to replace PGT-A. What is known already PGT-A is an invasive method that may affects blastocysts implantation potential. Both the biopsy protocol and the experience of the embryologist who performs the biopsy can influence the outcome. Efforts are made to develop non-invasive methods for PGT. Many AI systems have been developed in assisted reproduction.Time- Lapse systems have a specialized AI software, that assist in the evaluation of the embryos and the selection of the most suitable ones based on their morphokinetic characteristics. Other AI systems, such as ERICA, evaluate embryos and provide a measure of their euploidy, serving as an alternative method for non-invasive preimplantation genetic testing. Study design, size, duration The retrospective study was conducted between January 2018 and December 2020. A total of 585 blastocysts, derived from 144 women who had undergone an IVF – PGT-A cycle, were included. The embryos were cultured in a TL incubator and evaluated using KID Score D5 (Vitrolife), iDa Score (Vitrolife) and ERICA (ERICA). Participants/materials, setting, methods The study was conducted in a private IVF clinic. Embryos from women with an average age of 38 years who had a history of recurrent miscarriages and/or >3 failed IVF cycles were included. Biopsy was performed in all blastocysts that were cultured in TL incubators up to day 5-6. Statistical and logistic regression analysis were conducted to assess the comparison between the score of the three AI programs and the result of the biopsy. Main results and the role of chance ERICA seems uncapable to separate euploid from aneuploid embryos. T-test showed that there is no statistically significant difference between the two categories (mean_difference=0,02, p = 0,3>0,001), while the effect size (clinical value) of the difference was very low (0,085). This means that this program cannot replace PGT-A. According to the iDA Score, although there is a difference in scores between euploid and aneuploid embryos, their difference, according to t-test, is not statistically significant [t(581)=2,56, p = 0,01>0,001], while the effect size is low (0,217). According to KID Score D5, there is a statistically significant difference in scores between euploid and aneuploid embryos [t(514)=4,4, p < 0,001], which shows that this program could replace PGT-A in some cases. However, the effect size of the difference (0,37), as well as the determination coefficients are quite low. Finally, the KID Score D5 succeeds in discovering aneuploid embryos in a higher percentage than euploid ones. Limitations, reasons for caution Women under the age of 35 were not included in the study. Permission from the National Greek Authority of Medical Assisted Reproduction was needed from every patient to undergo PGT-A. The size of the sample might limit the conclusions. A larger study would help verify the results of the study. Wider implications of the findings AI models can be used as good indicators for embryo selection/prioritization and predict the euploid status of the embryos. Non-invasive euploid prediction models could be a very useful tool for embryologists especially in countries where law restrictions are strict against the cohort of patients that are interested in PGT-A cycles. Trial registration number Not Applicable

Applying machine learning in screening for Down Syndrome in both trimesters for diverse healthcare scenarios

BackgroundThis paper describes the development of low-cost, effective, non-invasive machine learning-based prediction models for Down Syndrome in the first two trimesters of pregnancy in Vietnam. These models are adaptable to different situations with limited screening capacities at community-based healthcare facilities. MethodUltrasound and biochemical testing alone and in combination, from both trimesters were employed to build prediction models based on k-Nearest Neighbor, Support Vector Machine, Random Forest, and Extreme Gradient Boosting algorithms. ResultsA total of 7,076 pregnant women from a single site in Northern Vietnam were included, and 1,035 had a fetus with Down Syndrome. Combined ultrasound and biochemical testing were required to achieve the highest accuracy in trimester 2, while models based only on biochemical testing performed as well as models based on combined testing during trimester 1. In trimester 1, Extreme Gradient Boosting produced the best model with 94% accuracy and 88% AUC, while Support Vector Machine produced the best model in trimester 2 with 89% accuracy and 84% AUC. ConclusionsThis study explored a range of machine learning models under different testing scenarios. Findings point to the potential feasibility of national screening, especially in settings without enough equipment and specialists, after additional model validation and fine tuning is performed.

Preoperative assessment of meningioma consistency using a combination of magnetic resonance elastography and diffusion tensor imaging.

Preoperative assessment of meningioma consistency is beneficial for optimizing surgical strategy and prognosis of patients. We aim to develop a non-invasive prediction model for meningioma consistency utilizing magnetic resonance elastography (MRE) and diffusion tensor imaging (DTI). Ninety-four patients (52yr ± 22, 69 females, 25 males) diagnosed with meningioma were recruited in the study. Each patient underwent preoperative T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), DTI, and MRE. Combined MRE-DTI model was developed based on multiple logistic regression. Intraoperative tumor descriptions served as clinical criteria for evaluating meningioma consistency. The diagnostic efficacy in determining meningioma consistency was evaluated using receiver operating characteristic (ROC) curve. Further validation was conducted in twenty-seven stereotactic biopsies using indentation tests and underlying mechanism was investigated by histologic analysis. Among all the imaging modalities, MRE demonstrated the highest efficacy with the shear modulus magnitude (|G*|) achieving an area under the curve (AUC) of 0.81 (95% CI: 0.70-0.93). When combined with DTI, the diagnostic accuracy further increased (AUC: 0.88, 95% CI: 0.78-0.97), surpassing any modality alone. Indentation measurement based on stereotactic biopsies further demonstrated that the MRE-DTI model was suitable for predicting intra-tumor consistency. Histological analysis suggested that meningioma consistency may be correlated with tumor cell density and fibrous content. The MRE-DTI combined model is effective in noninvasive prediction of meningioma consistency. MRE = magnetic resonance elastography; FA = fractional anisotropy; ROC = receiver operating characteristic; AUC = area under curve.

Refining a non-invasive prediction model for neurosyphilis diagnosis by using immunoassay to detect serum anti-TP0435 (TP17) and TP0574 (TP47) IgG antibodies: two-centre cross-sectional retrospective study in China

ObjectivesInvasive lumbar puncture is the conventional method for diagnosing neurosyphilis (NS). We investigated a non-invasive alternative method to detect serum Treponema pallidum-specific antibodies against highly immunogenic antigens TP0171 (TP15), TP0435 (TP17), and TP0574 (TP47) by using luciferase immunosorbent assay. MethodsA total of 816 HIV-negative patients suspected of NS from the Beijing and Guangzhou cohorts were retrospectively selected and tested for serum anti-TP15, TP17, and TP47 IgG antibodies. Two diagnostic prediction models were developed using stepwise logistic regression in the Beijing cohort, and evaluated in the Guangzhou cohort for external validation. ResultsSerum antibodies against TP15, TP17, and TP47 showed moderate capability for NS diagnosis in the Beijing cohort and the corresponding area under the receiver operating characteristic curves (AUCs) were 0.722 [95% confidence interval (CI): 0.680–0.762)], 0.780 (95% CI: 0.741–0.817), and 0.774 (95% CI: 0.734–0.811), respectively. An expanded NS prediction model integrated with anti-TP17 and anti-TP47 antibodies showed better performance than the base NS diagnostic model without anti-TP17 and anti-TP47 antibodies with the AUC of 0.874 (95% CI: 0.841–0.906) vs. 0.845 (95% CI: 0.809–0.881) (p = 0.007) in the development cohort, and 0.934 (95% CI: 0.909–0.960) vs. 0.877 (95% CI: 0.840–0.914) (p < 0.001) in validation cohort, respectively. Decision curve analysis revealed that the net benefit of the expanded model exceeded that of the base model when the threshold probability was between 0.10 and 0.95 in both the development and external validation cohorts. DiscussionSerum antibodies against TP17 and TP47 exhibited promising diagnostic capability for NS and significantly enhanced the predictive accuracy of model for NS diagnosis. Our study highlights the potential of serum treponemal antibody detection as a non-invasive method for NS diagnosis to substitute invasive lumbar puncture in NS diagnosis.

Machine learning-based diagnostic prediction of IgA nephropathy: model development and validation study

IgA nephropathy progresses to kidney failure, making early detection important. However, definitive diagnosis depends on invasive kidney biopsy. This study aimed to develop non-invasive prediction models for IgA nephropathy using machine learning. We collected retrospective data on demographic characteristics, blood tests, and urine tests of the patients who underwent kidney biopsy. The dataset was divided into derivation and validation cohorts, with temporal validation. We employed five machine learning models—eXtreme Gradient Boosting (XGBoost), LightGBM, Random Forest, Artificial Neural Networks, and 1 Dimentional-Convolutional Neural Network (1D-CNN)—and logistic regression, evaluating performance via the area under the receiver operating characteristic curve (AUROC) and explored variable importance through SHapley Additive exPlanations method. The study included 1268 participants, with 353 (28%) diagnosed with IgA nephropathy. In the derivation cohort, LightGBM achieved the highest AUROC of 0.913 (95% CI 0.906–0.919), significantly higher than logistic regression, Artificial Neural Network, and 1D-CNN, not significantly different from XGBoost and Random Forest. In the validation cohort, XGBoost demonstrated the highest AUROC of 0.894 (95% CI 0.850–0.935), maintaining its robust performance. Key predictors identified were age, serum albumin, IgA/C3, and urine red blood cells, aligning with existing clinical insights. Machine learning can be a valuable non-invasive tool for IgA nephropathy.

A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features.

The most common route of breast cancer metastasis is through the mammary lymphatic network. An accurate assessment of the axillary lymph node (ALN) burden before surgery can avoid unnecessary axillary surgery, consequently preventing surgical complications. In this study, we aimed to develop a non-invasive prediction model incorporating breast specific gamma image (BSGI) features and ultrasonographic parameters to assess axillary lymph node status. Cohorts of breast cancer patients who underwent surgery between 2012 and 2021 were created (The training set included 1104 ultrasound images and 940 BSGI images from 235 patients, the test set included 568 ultrasound images and 296 BSGI images from 99 patients) for the development of the prediction model. six machine learning (ML) methods and recursive feature elimination were trained in the training set to create a strong prediction model. Based on the best-performing model, we created an online calculator that can make a linear predictor in patients easily accessible to clinicians. The receiver operating characteristic (ROC) and calibration curve are used to verify the model performance respectively and evaluate the clinical effectiveness of the model. Six ultrasonographic parameters (transverse diameter of tumour, longitudinal diameter of tumour, lymphatic echogenicity, transverse diameter of lymph nodes, longitudinal diameter of lymph nodes, lymphatic color Doppler flow imaging grade) and one BSGI features (axillary mass status) were selected based on the best-performing model. In the test set, the support vector machines' model showed the best predictive ability (AUC = 0.794, sensitivity = 0.641, specificity = 0.8, PPV = 0.676, NPV = 0.774 and accuracy = 0.737). An online calculator was established for clinicians to predict patients' risk of ALN metastasis ( https://wuqian.shinyapps.io/shinybsgi/ ). The result in ROC showed the model could benefit from incorporating BSGI feature. This study developed a non-invasive prediction model that incorporates variables using ML method and serves to clinically predict ALN metastasis and help in selection of the appropriate treatment option.

A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma

ObjectivesAt present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomics was used to accurately predict the lymph node status of lung adenocarcinoma patients based on contrast-enhanced CT.MethodsA total of 503 cases that fulfilled the analysis requirements were gathered from two distinct hospitals. Among these, 287 patients exhibited lymph node metastasis (LNM +) while 216 patients were confirmed to be without lymph node metastasis (LNM-). Using both traditional and deep learning methods, 22,318 features were extracted from the segmented images of each patient's enhanced CT. Then, the spearman test and the least absolute shrinkage and selection operator were used to effectively reduce the dimension of the feature data, enabling us to focus on the most pertinent features and enhance the overall analysis. Finally, the classification model of lung adenocarcinoma lymph node metastasis was constructed by machine learning algorithm. The Accuracy, AUC, Specificity, Precision, Recall and F1 were used to evaluate the efficiency of the model.ResultsBy incorporating a comprehensively selected set of features, the extreme gradient boosting method (XGBoost) effectively distinguished the status of lymph nodes in patients with lung adenocarcinoma. The Accuracy, AUC, Specificity, Precision, Recall and F1 of the prediction model performance on the external test set were 0.765, 0.845, 0.705, 0.784, 0.811 and 0.797, respectively. Moreover, the decision curve analysis, calibration curve and confusion matrix of the model on the external test set all indicated the stability and accuracy of the model.ConclusionsLeveraging enhanced CT images, our study introduces a noninvasive classification prediction model based on the extreme gradient boosting method. This approach exhibits remarkable precision in identifying the lymph node status of lung adenocarcinoma patients, offering a safe and accurate alternative to invasive procedures. By providing clinicians with a reliable tool for diagnosing and assessing disease progression, our method holds the potential to significantly improve patient outcomes and enhance the overall quality of clinical practice.

A Continuous Non-Invasive Blood Pressure Prediction Method Based on Deep Sparse Residual U-Net Combined with Improved Squeeze and Excitation Skip Connections.

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network's representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life.

Lesion attention guided neural network for contrast-enhanced mammography-based biomarker status prediction in breast cancer

Background and objectiveAccurate identification of molecular biomarker statuses is crucial in cancer diagnosis, treatment, and prognosis. Studies have demonstrated that medical images could be utilized for non-invasive prediction of biomarker statues. The biomarker status-associated features extracted from medical images are essential in developing medical image-based non-invasive prediction models. Contrast-enhanced mammography (CEM) is a promising imaging technique for breast cancer diagnosis. This study aims to develop a neural network-based method to extract biomarker-related image features from CEM images and evaluate the potential of CEM in non-invasive biomarker status prediction. MethodsAn end-to-end learning convolutional neural network with the whole breast images as inputs was proposed to extract CEM features for biomarker status prediction in breast cancer. The network focused on lesion regions and flexibly extracted image features from lesion and peri‑tumor regions by employing supervised learning with a smooth L1-based consistency constraint. An image-level weakly supervised segmentation network based on Vision Transformer with cross attention to contrast images of breasts with lesions against the contralateral breast images was developed for automatic lesion segmentation. Finally, prediction models were developed following further selection of significant features and the implementation of random forest-based classification. Results were reported using the area under the curve (AUC), accuracy, sensitivity, and specificity. ResultsA dataset from 1203 breast cancer patients was utilized to develop and evaluate the proposed method. Compared to the method without lesion attention and with only lesion regions as inputs, the proposed method performed better at biomarker status prediction. Specifically, it achieved an AUC of 0.71 (95 % confidence interval [CI]: 0.65, 0.77) for Ki-67 and 0.73 (95 % CI: 0.65, 0.80) for human epidermal growth factor receptor 2 (HER2). ConclusionsA lesion attention-guided neural network was proposed in this work to extract CEM image features for biomarker status prediction in breast cancer. The promising results demonstrated the potential of CEM in non-invasively predicting the biomarker statuses in breast cancer.

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

: 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. : Clinical data of 198 adult patients with UC were collected from January 2016 to August 2022 at Huadong Hospital, China. : 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. : The nomogram, which incorporated activated partial thromboplastin time, fecal occult blood test, β2-globulin level, and fibrinogen degradation products, served as a prospective tool for evaluating UC activity in clinical practices.

Construction of a nomogram for predicting compensated cirrhosis with Wilson’s disease based on non-invasive indicators

BackgroundWilson’s disease (WD) often leads to liver fibrosis and cirrhosis, and early diagnosis of WD cirrhosis is essential. Currently, there are few non-invasive prediction models for WD cirrhosis. The purpose of this study is to non-invasively predict the occurrence risk of compensated WD cirrhosis based on ultrasound imaging features and clinical characteristics.MethodsA retrospective analysis of the clinical characteristics and ultrasound examination data of 102 WD patients from November 2018 to November 2020 was conducted. According to the staging system for WD liver involvement, the patients were divided into a cirrhosis group (n = 43) and a non-cirrhosis group (n = 59). Multivariable logistic regression analysis was used to identify independent influencing factors for WD cirrhosis. A nomogram for predicting WD cirrhosis was constructed using R analysis software, and validation of the model’s discrimination, calibration, and clinical applicability was completed. Due to the low incidence of WD and the small sample size, bootstrap internal sampling with 500 iterations was adopted for validation to prevent overfitting of the model.ResultsAcoustic Radiation Force Impulse (ARFI), portal vein diameter (PVD), and serum albumin (ALB) are independent factors affecting WD cirrhosis. A nomogram for WD cirrhosis was constructed based on these factors. The area under the ROC curve (AUC) of the model’s predictive ability is 0.927 (95% CI: 0.88–0.978). As demonstrated by 500 Bootstrap internal sampling validations, the model has high discrimination and calibration. Clinical decision curve analysis shows that the model has high clinical practical value. ROC curve analysis of the model’s rationality indicates that the model’s AUC is greater than the AUC of using ALB, ARFI, and PVD alone.ConclusionThe nomogram model constructed based on ARFI, PVD, and ALB can serve as a non-invasive tool to effectively predict the risk of developing WD cirrhosis.

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study

Background and Aim This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD. Methods This cross-sectional study was conducted among 7701 participants with MAFLD from August 2018 to December 2023. All participants were divided into a training cohort and a validation cohort. The study compared different subgroups’ demographic, anthropometric, and laboratory examination indicators and conducted logistic regression analysis to assess the correlation between independent variables and liver fibrosis. Nomograms were created using the logistic regression model. The predictive values of noninvasive models and nomograms were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). Results Four nomograms were developed for the quantitative analysis of significant liver fibrosis risk based on the multivariate logistic regression analysis results. The nomogram’s area under ROC curves (AUC) was 0.710, 0.714, 0.748, and 0.715 in overall MAFLD, OW-MAFLD, Lean-MAFLD, and T2DM-MAFLD, respectively. The nomogram had a higher AUC in all MAFLD participants and OW-MAFLD than the other non-invasive scores. The DCA curve showed that the net benefit of each nomogram was higher than that of APRI and FIB-4. In the validation cohort, the AUCs of the nomograms were 0.722, 0.750, 0.719, and 0.705, respectively. Conclusion APRI, FIB-4, and NFS performed poorly predicting significant fibrosis in patients with MAFLD. The new model demonstrated improved diagnostic accuracy and clinical applicability in identifying significant fibrosis in MAFLD.

Non-invasive prediction model of histologic chorioamnionitis with preterm prelabour rupture of membranes

BackgroundThe aim of this study is to identify risk factors associated with histological chorioamnionitis (HCA) and develop a predictive model for antepartum assessment of the risk of PPROM with HCA. MethodsThis study retrospectively analyzed pregnant women who experienced PPROM between 25 + 0 and 35 + 0 weeks of gestational age. The women were divided into two groups based on the presence or absence of HCA. Univariate and multivariate logistic regression analyses were conducted to identify maternal risk factors and develop a clinical prediction model for HCA. The model's discrimination and consistency were evaluated using receiver operating characteristic (ROC) and calibration curves. ResultsSeventeen thousand one hundred forty-six (17,146) pregnant women were screened, and 726 (4.23 %) had PPROM. Out of the 286 subjects with PPROM, 160 developed HCA. The maternal age of these subjects ranged from 18 to 43 years (30.0 ± 5.4), while their gestational age (GA) ranged from 25 + 0 to 35 + 0 weeks (31.6 ± 2.0). The average GA at delivery was 32.2 ± 2.0 (weeks).Compared with the non-HCA group, the expectant time > 48 h, GA at delivery > 32 weeks, twin pregnancy, HGB (<110 g/Lg/L), degree of LGB (IIb-III), and WBC (>9.5 × 109 /L) were significantly more than in the PPROM with HCA group. The results show that the best model was obtained by leave-one-out logistic regression (AUC = 0.785, CA = 0.741, F1 = 0.739, Precision = 0.740, Recall = 0.741). In the validation set, logistic regression also achieved good results (AUC = 0.710, CA = 0.671, F1 = 0.654, Precision = 0.683, Recall = 0.671). Combining the previous analysis, we found that the prognostic model constructed using the core six features had the best predictive effect. ConclusionsSix features were associated with the occurrence of chorioamnionitis. These features were used to construct a diagnostic model that can accurately predict the probability of chorioamnionitis occurrence and provide a beneficial tool for the prevention and management of PPROM with HCA.

Computed tomography-based body composition parameters can predict short-term prognosis in ulcerative colitis patients.

Emerging evidence suggests a potential relationship between body composition and short-term prognosis of ulcerative colitis (UC). Early and accurate assessment of rapid remission based on conventional therapy via abdominal computed tomography (CT) images has rarely been investigated. This study aimed to build a prediction model using CT-based body composition parameters for UC risk stratification. In total, 138 patients with abdominal CT images were enrolled. Eleven quantitative parameters related to body composition involving skeletal muscle mass, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) were measured and calculated using a semi-automated segmentation method. A prediction model was established with significant parameters using a multivariable logistic regression. The receiver operating characteristic (ROC) curve was plotted to evaluate prediction performance. Subgroup analyses were implemented to evaluate the diagnostic efficiency of the prediction model between different disease locations, centers, and CT scanners. The Delong test was used for statistical comparison of ROC curves. VAT density, SAT density, gender, and visceral obesity were significantly statistically different between remission and invalidation groups (all p < 0.05). The accuracy, sensitivity, specificity, and area under the ROC curve (AUC) of the prediction model were 82.61%, 95.45%, 69.89%, and 0.855 (0.792-0.917), respectively. The positive predictive value and negative predictive value were 70.79% and 93.88%, respectively. No significant differences in the AUC of the prediction model were found in different subgroups (all p > 0.05). The predicting model constructed with CT-based body composition parameters is a potential non-invasive approach for short-term prognosis identification and risk stratification. Additionally, VAT density was an independent predictor for escalating therapeutic regimens in UC cohorts. The CT images were used for evaluating body composition and risk stratification of ulcerative colitis patients, and a potential non-invasive prediction model was constructed to identify non-responders with conventional therapy for making therapeutic regimens timely and accurately. • CT-based prediction models help divide patients into invalidation and remission groups in UC. • Results of the subgroup analysis confirmed the stability of the prediction model with a high AUC (all > 0.820). • The visceral adipose tissue density was an independent predictor of bad short-term prognosis in UC.

A nomogram based on clinicopathological and ultrasound characteristics to predict central neck lymph node metastases in papillary thyroid cancer.

Papillary thyroid cancer (PTC) has grown rapidly in prevalence over the past few decades, and central neck lymph node metastasis (CNLNM) is associated with poor prognoses. However, whether to carry out preventive central neck lymph node dissection (CNLND) is still controversial. We aimed to construct a prediction model of CNLNM to facilitate making clinical surgical regimens. A total of 691 patients with PTC between November 2018 and December 2021 were included in our study. Univariate and multivariate analyses were performed on basic information and clinicopathological characteristics, as well as ultrasound characteristics (American College of Radiology (ACR) scores). The prediction model was constructed and performed using a nomogram, and then discriminability, calibrations, and clinical applicability were evaluated. Five variables, namely, male, age >55 years, clinical lymph node positivity, tumor size ≥1 cm, and ACR scores ≥6, were independent predictors of CNLNM in the multivariate analysis, which were eventually included to construct a nomogram model. The area under the curve (AUC) of the model was 0.717, demonstrating great discriminability. A calibration curve was developed to validate the calibration of the present model by bootstrap resampling, which indicated that the predicted and actual values were in good agreement and had no differentiation from the ideal model. The decision curve analysis (DCA) indicated that the prediction model has good clinical applicability. Our non-invasive prediction model combines ACR scores with clinicopathological features presented through nomogram and has shown good performance and application prospects for the prediction of CNLNM in PTCs.

Predictive potential of various plasma inflammation-, angiogenesis-, and extracellular matrix remodeling-associated mediators for intra-amniotic inflammation and/or microbial invasion of the amniotic cavity in preterm labor.

To determine whether various inflammatory-, angiogenic/anti-angiogenic-, and extracellular matrix remodeling-associated proteins in plasma, alone or in combination with conventionalblood-based markers, can predict intra-amniotic inflammation and/or microbial invasion of the amniotic cavity (IAI/MIAC) in women with spontaneous preterm labor (PTL). A total of 193 singleton pregnant women with PTL (23-33weeks) were included in this retrospective cohort study. Plasma samples were obtained at the time of amniocentesis. Amniotic fluid (AF) was cultured for microorganism detection and consequent MIAC diagnosis. IL-6 levels were determined in AF and used to identify IAI (AF IL-6 ≥ 2.6ng/mL). Endostatin, haptoglobin, IGFBP-2/3, LBP, M-CSF, MMP-2/8, pentraxin 3, PlGF, S100A8/A9, and VEGFR-1 levels were assayed in plasma samples by ELISA. CRP levels and neutrophil-to-lymphocyte ratio (NLR) were measured. Plasma LBP, MMP-8, and S100A8/A9 levels, CRP levels, and NLR were significantly higher, and plasma IGFBP-2 and MMP-2 levels were significantly lower in women with IAI/MIAC than in those without this condition, whereas no baseline variables differed significantly between the two groups. Using a stepwise regression analysis, a noninvasive prediction model for IAI/MIAC was developed, which included plasma LBP, MMP-2, and MMP-8 levels (area under the curve [AUC], 0.785). The AUC for this prediction model was significantly or borderline greater than that of any single factor included in the model. IGFBP-2, LBP, MMP-2, MMP-8, and S100A8/A9 may represent valuable plasma biomarkers for predicting IAI/MIAC in women with PTL. Combination of LBP, MMP-2, and MMP-8 expression data can significantly improve the predictive potential for IAI/MIAC.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome.

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.

Development and validation of a noninvasive prediction model for significant hepatic liver fibrosis in Chinese patients with autoimmune hepatitis

Introduction and ObjectivesAutoimmune hepatitis (AIH) is a prevalent noninfectious liver disease. However, there is currently a lack of noninvasive tests appropriate for evaluating liver fibrosis in AIH patients. The objective of this study was to develop and validate a predictive model for noninvasive assessment of significant liver fibrosis (S ≥ 2) in patients to provide a reliable method for evaluating liver fibrosis in individuals with AIH. Materials and MethodsThe clinical data of 374 AIH patients were analyzed. A prediction model was established through logistic regression in the training set, and bootstrap method was used to validate the models internally. In addition, the clinical data of 109 AIH patients were collected for external verification of the model.The model was expressed as a nomogram, and area under the curve (AUC) of the receiver operating characteristic (ROC), calibration curve, and decision curve analysis were used to evaluate the accuracy of the prediction model. ResultsLogistic regression analysis revealed that age, platelet count (PLT), and the A/G ratio were identified as independent risk factors for liver fibrosis in AIH patients (P < 0.05). The diagnostic model that was composed of age, PLT and A/G was superior to APRI and FIB-4 in both the internal validation (0.872, 95%CI: 0.819–0.924) and external validation (0.829, 95%CI: 0.753–0.904). ConclusionsOur predictive model can predict significant liver fibrosis in AIH patients more accurately, simply, and noninvasively.

Beyond invasive biopsies: using VASARI MRI features to predict grade and molecular parameters in gliomas

BackgroundGliomas present a significant economic burden and patient management challenge. The 2021 WHO classification incorporates molecular parameters, which guide treatment decisions. However, acquiring these molecular data involves invasive biopsies, prompting a need for non-invasive diagnostic methods. This study aims to assess the potential of Visually AcceSAble Rembrandt Images (VASARI) MRI features to predict glioma characteristics such as grade, IDH mutation, and MGMT methylation status.MethodsThis study enrolled 107 glioma patients treated between 2017 and 2022, meeting specific criteria including the absence of prior chemotherapy/radiation therapy, and the presence of molecular and MRI data. Images were assessed using the 27 VASARI MRI features by two blinded radiologists. Pathological and molecular assessments were conducted according to WHO 2021 CNS Tumor classification. Cross-validation Least Absolute Shrinkage and Selection Operator (CV-LASSO) logistic regression was applied for statistical analysis to identify significant VASARI features in determining glioma grade, IDH mutation, and MGMT methylation status.ResultsThe study demonstrated substantial observer agreement in VASARI feature evaluation (inter- and intra-observer κ = 0.714 - 0.831 and 0.910, respectively). Patient imaging characteristics varied significantly with glioma grade, IDH mutation, and MGMT methylation. A predictive model was established using VASARI features for glioma grade prediction, exhibiting an AUC of 0.995 (95% CI = 0.986 – 0.998), 100% sensitivity, and 92.86% specificity. IDH mutation status was predicted with AUC 0.930 (95% CI = 0.882 - 0.977), and improved slightly to 0.933 with 'age-at-diagnosis' added. A model predicting MGMT methylation had a satisfactory performance (AUC 0.757, 95% CI = 0.645 - 0.868), improving to 0.791 when 'age-at-diagnosis' was added.ConclusionsThe T1/FLAIR ratio, enhancement quality, hemorrhage, and proportion enhancing predict glioma grade with excellent accuracy. The proportion enhancing, thickness of enhancing margin, and T1/FLAIR ratio are significant predictors for IDH mutation status. Lastly, MGMT methylation is related to the longest diameter of the lesion, edema crossing the midline, and the proportion of the non-enhancing lesion. VASARI MRI features offer non-invasive and accurate predictive models for glioma grade, IDH mutation, and MGMT methylation status, enhancing glioma patient management.