Area Under Curve Research Articles

Diagnostic value and clinical significance of serum miR-4534 combined with transvaginal color Doppler ultrasound in cervical cancer

AimThe purpose of the present study was to evaluate the diagnostic performance of serum miR-4534 combined with Transvaginal Color Doppler Ultrasound (TVCDS) in cervical cancer patients.MethodsBlood samples from 126 patients with cervical cancer and 83 patients with benign uterine lesions were retrospectively analyzed. Quantitative real time polymerase chain reaction (qRT-PCR) was applied to examine the relative abundances of serum miR-4534 in cervical cancer based on a case–control study. Chi-square test was adopted to analyze the association between serum miR-4534 and other clinicopathological factors. The blood flow of cervix was examined using TVCDS, and the blood flow resistance index (RI) of cervix was summarized. Receiver operating characteristic (ROC) curves were plotted to explore the diagnostic capacity of serum miR-4534 combined with blood flow RI. Logistic regression was employed to analyze the risk factors of cervical cancer.ResultsSerum miR-4534 was distinctly increased in the study group compared with the control group (P < 0.05), while blood flow RI was dramatically decreased (P < 0.05). Moreover, increased miR-4534 was closely associated with lymph node metastasis (P = 0.010), FIGO stage (P = 0.007) and HR-HPV (P = 0.025). ROC curves demonstrated that the area under curve (AUC) of serum miR-4534 combined with the blood flow RI was 0.854, with the sensitivity and specificity of 88.9% and 73.5%, respectively, which displayed a better diagnostic capacity than serum miR-4534 and blood flow RI alone. Logistic regression analysis demonstrated that serum miR-4534 (OR = 8.805, 95% CI = 4.124–18.798; P < 0.001) was a risk factor related to the initiation and formation of cervical cancer, as well as blood flow RI (OR = 0.112; 95% CI = 0.054–0.235; P < 0.001).ConclusionSerum miR-4534 was highly expressed in cervical cancer, and associated with the development and metastasis of cervical cancer patients. MiR-4534 combined with TVCDS exhibited a considerable biomarker to detect cervical cancer patients.

A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learning.

The theory of Chinese medicine (TCM) constitution contributes to the optimisation of individualised healthcare programmes. However, at present, TCM constitution identification mainly relies on inefficient questionnaires with subjective bias. Efficient and accurate TCM constitution identification can play an important role in individualised medicine and healthcare. Building an efficient model for identifying traditional Chinese medicine constitutions using objective tongue features and machine learning techniques. The DS01-A device was applied to collect tongue images and extract features. We trained and evaluated five machine learning models: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), LightGBM (LGBM), and CatBoost (CB). Among these, we selected the model with the best performance as the base classifier for constructing our heterogeneous ensemble learning model. Using various performance metrics, including classification accuracy, precision, recall, F1 score, and area under curve (AUC), to comprehensively evaluate model performance. A total of 1149 tongue images were obtained and 45 features were extracted, forming dataset 1. RF, LGBM, and CB were selected as the base learners for the RLC-Stacking. On dataset 1, RLC-Stacking1 achieved an accuracy of 0.8122, outperforming individual classifiers. After feature selection, the classification accuracy of RLC-Stacking2 improved to 0.8287, an improvement of 0.00165 compared to RLC-Stacking1. RLC-Stacking2 achieved an accuracy exceeding 0.85 for identifying each TCM constitution type, indicating excellent identification performance. The study provides a reliable method for the accurate and rapid identification of TCM constitutions and can assist clinicians in tailoring individualized medical treatments based on personal constitution types and guide daily health care. The information extracted from tongue images serves as an effective marker for objective TCM constitution identification.

Development and validation of a nomogram for predicting advanced liver fibrosis in patients with chronic hepatitis B.

The progression of chronic hepatitis B (CHB) to liver fibrosis and even cirrhosis is often unknown to patients, but noninvasive markers capable of effectively identifying advanced liver fibrosis remains absent. Based on the results of liver biopsy, we aimed to construct a new nomogram to validate the stage of liver fibrosis in CHB patients by the basic information of CHB patients and routine laboratory tests. Patients with CHB diagnosed for the first time in the First Affiliated Hospital of Anhui Medical University from 2010 to 2018 were selected, and their basic information, laboratory tests and liver biopsy information were collected. Eventually, 974 patients were enrolled in the study, while all patients were randomized into a training cohort (n = 732) and an internal validation cohort (n = 242) according to a 3:1 ratio. In the training cohort, least absolute shrinkage and selection operator (Lasso) regression were used for predictor variable screening, and binary logistic regression analysis was used to build the diagnostic model, which was ultimately presented as a nomogram. The predictive accuracy of the nomograms was analyzed by running operating characteristic curve (ROC) to calculate area under curve (AUC), and the calibration was evaluated. Decision curve analysis (DCA) was used to determine patient benefit. In addition, we validated the built models with internal as well as external cohort (n = 771), respectively. Ultimately, the training cohort, the internal validation cohort, and the external validation cohort contained sample sizes of 188, 53, and 149, respectively, for advanced liver fibrosis. Gender, albumin (Alb), globulin (Glb), platelets (PLT), alkaline phosphatase (AKP), glutamyl transpeptidase (GGT), and prothrombin time (PT) were screened as independent predictors. Compared with the aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and King's score, the model in the training cohort (AUC = 0.834, 95% CI 0.800-0.868, p < 0.05) and internal validation cohort (AUC = 0.804, 95% CI 0.742-0.866, p < 0.05) showed the best discrimination and the best predictive performance. In addition, DCA showed that the clinical benefit of the nomogram was superior to the APRI, FIB-4 and King's scores in all cohorts. This study constructed a validated nomogram model with predictors screened from clinical variables which could be easily used for the diagnosis of advanced liver fibrosis in CHB patients.

392. MACHINE LEARNING TO PREDICT LYMPH NODE METASTASIS IN T1 ESOPHAGEAL SQUAMOUS CELL CARCINOMA: A MULTICENTER STUDY

Abstract Background Lymph node metastases (LNM) is a relatively uncommon but possible complication of T1 esophageal squamous cell carcinoma (ESCC). Existing models do poorly when it comes to quantifying this risk. As a branch of artificial intelligence, machine learning (ML) algorithms have developed and become more prominent. Up to now, no individualized ML prediction model aimed at predicting LNM in T1 esophageal squamous cell carcinoma patients is available. This study aimed to develop a machine learning model for LNM in patients with T1 esophageal squamous cell carcinoma. Methods Patients with T1 squamous cell carcinoma treated with surgery between January 2010 and September 2021 from 3 institutions were included. Machine-learning models were developed using data on patients’ age and sex, depth of tumor invasion, tumor size, tumor location, macroscopic tumor type, lymphatic and vascular invasion, and histologic grade. Elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these was generated. Use Area Under Curve (AUC) to evaluate the predictive ability of models. The contribution of each factor was calculated. To meet clinical needs, we designed the model as a user-friendly website. Results 926 and 341 patients were included in the derivation and validation cohorts. The lymph node metastasis rate was 14.8%. The models all exhibited potent discriminating power. The elastic net regression model performed best with externally validated AUC of 0.803 (AUC 0.773 for RF, 0.791 for XGB, 0.799 for ensemble) whereas the NCCN guidelines identified patients with LNM with an AUC of 0.576 and logistic model with an AUC of 0. 670. The most important features were lymphatic and vascular invasion as well as depth of tumor invasion. Conclusions The model created utilizing machine learning approaches had excellent performance estimating the likelihood of LNM in T1 ESCC, which can help patients achieve personalized treatment options.

455. SHOULD THE DIAGNOSTIC THRESHOLD FOR PH MONITORING IN GASTROESOPHAGEAL REFLUX DISEASE DIFFER BETWEEN THE EAST AND THE WEST?

Abstract Background The Montreal definition's reliance on troublesome typical symptoms alone is inadequate for diagnosing 'actionable' Gastroesophageal Reflux Disease (GERD). Lyon consensus 2.0 updated conclusive evidence of GERD, incorporating Los Angeles grade B or above esophagitis and Acid Exposure Time (AET) exceeding 6% in ambulatory reflux monitoring. The Seoul Consensus 2020 presents varied opinions on applying the same standards to the Asian population. A meta-analysis determined the Asian upper limit of AET as 3.2%. This study aims to compare Lyon 1.0 and 2.0 consensus compositions and assess the sensitivity and specificity of different AET thresholds in predicting Grade B or above esophagitis. Methods Patients with refractory GERD symptoms or considering anti-reflux surgery underwent objective GERD workup, including 24-hour catheter-based or 96-hour wireless ambulatory pH monitoring, High-Resolution Manometry, and upper endoscopy. This retrospective cohort study analyzed patient data from 2011-2023, excluding those with prior anti-reflux surgery, bariatric surgery, or myotomy for achalasia. Patients were categorized based on Lyon Consensus 1.0 and 2.0 as 'Non-GERD,' 'Borderline GERD,' 'Borderline GERD with evidence,' and 'Conclusive GERD.' Sensitivity, specificity, and Area-Under-Curve (AUC) were calculated using AET thresholds of 3.2%, 4%, 6%, and 7.2% to predict Grade B or above esophagitis. Results Between July 2011 and August 2023, 257 patients underwent pH monitoring and were included in the analysis, with 247 (93.8%) being ethnic Chinese. The number of patients categorized as conclusive GERD was significantly higher in Lyon 2.0 (134, 52.1%) compared to Lyon 1.0 (109, 42.4%), p &amp;lt; 0.001. Sensitivity and specificity for predicting Grade B or above esophagitis were as follows: AET 3.2% (83.3%, 48.8%), AET 4% (65.3%, 52.4%), AET 6% (73.8%, 66%), AET 7.2% (69.0, 72.1%). AET 7.2% demonstrated the highest AUC of 70.6% (Figure 1). Among patients classified as conclusive GERD, 67 (50%) underwent antireflux surgery. Conclusion Using the Asian threshold of AET 3.2% yields the highest sensitivity for detecting Grade B or above esophagitis. Additionally, the AUC is higher compared to using AET 4.0%. However, it is important to note that AET alone should not be the sole parameter for diagnosing actionable GERD, as even with the lowest AET threshold of 3.2%, 16.7% of patients with Grade B or above esophagitis would be missed.

The diagnostic value of two-dimensional ultrasound score, contrast-enhanced ultrasound score and ultrasound elastography score in prostate cancer.

Prostate cancer (PCa) is a prevalent malignancy in men, with early diagnosis being crucial for treatment and prognosis. This study evaluates the diagnostic efficacy of two-dimensional ultrasound imaging score (2DUS), contrast-enhanced ultrasound score (CEUS), and ultrasound elastography score (UES) in PCa. The PCa group consisted of patients diagnosed with primary PCa who underwent radical prostatectomy at the second hospital of Shanxi Medical University between January 2022 and December 2023. The benign lesion group consisted of patients diagnosed with benign prostatic hyperplasia (BPH)through prostate biopsy during the same period. We compared ultrasound scores at PCa and BPH patients using receiver operating characteristic (ROC) curve analysis. The study collected comprehensive patient data and analyzed variations in 2DUS, CEUS, and UES to identify relationships between preoperative ultrasound findings and the pathological states of PCa patients. PCa patients exhibited significantly higher ultrasound scores (P<0.001). Optimal cut-offs were 2.110 for 2DUS, 3.235 for CEUS, and 2.790 for UES, yielding area under curves (AUC) of 0.789, 0.817, and 0.898. The combined score provided an AUC of 0.933, with 83.33% sensitivity and 90.70% specificity. In the form of that patients with tumor stage III-IV, pathological grade III, poor differentiation, and pelvic lymph node metastasis exhibited significantly higher scores in 2DUS, CEUS, and UES compared to those with tumor stages I-II, pathological grades I-II, moderate and high differentiation without pelvic lymph node metastasis, respectively (P<0.05). The combined use of 2DUS, CEUS, and UES scores is highly effective for early PCa detection, surpassing individual scores in accuracy, and is beneficial for staging and differentiation.

An improved semi-supervised segmentation of the retinal vasculature using curvelet-based contrast adjustment and generalized linear model

Diagnosis of most ophthalmic conditions, such as diabetic retinopathy, generally relies on an effective analysis of retinal blood vessels. Techniques that depend solely on the visual observation of clinicians can be tedious and prone to numerous errors. In this article, we propose a semi-supervised automated approach for segmenting blood vessels in retinal color images. Our method effectively combines some classical filters with a Generalized Linear Model (GLM). We first apply the Curvelet Transform along with the Contrast-Limited Histogram Adaptive Equalization (CLAHE) technique to significantly enhance the contrast of vessels in the retinal image during the preprocessing phase. We then use Gabor transform to extract features from the enhanced image. For retinal vasculature identification, we use a GLM learning model with a simple link identity function. Binarization is then performed using an automatic optimal threshold based on the maximum Youden index. A morphological cleaning operation is applied to remove isolated or unwanted segments from the final segmented image. The proposed model is evaluated using statistical parameters on images from three publicly available databases. We achieve average accuracies of 0.9593, 0.9553 and 0.9643, with Receiver Operating Characteristic (ROC) analysis yielding Area Under Curve (AUC) values of 0.9722, 0.9682 and 0.9767 for the CHASE_DB1, STARE and DRIVE databases, respectively. Compared to some of the best results from similar approaches published recently, our results exceed their performance on several datasets.

P070 ROLE OF NOVEL BLOOD PRESSURE METRICS IN EARLY BLOOD PRESSURE CONTROL ASSESSMENT: INSIGHTS FROM THE SPRINT TRIAL

Objective: Innovative blood pressure (BP) metrics is pivotal to expedite the BP management among individuals with hypertension. Methods and Findings: This is a post-hoc analysis of the SPRINT (Systolic Blood Pressure Intervention Trial). A total of 8369 participants, who had comprehensive BP records and experienced no adverse events during the initial 90-day follow-up, were included. Two novel BP metrics, systolic blood pressure (SBP) burden and Time-in-Target-Range (TTR), were calculated by employing three-month SBP recordings from follow-up data. SBP Burden were derived as the proportion of area under curve (AUC) over a specific SBP threshold (SBP values &gt;130 mmHg) in a short-term exposure period (from the first month to third month). SBP TTR was estimated by the proportion of time in a specified SBP target range (110 to 140mmHg) in the same exposure period. Major adverse cardiovascular events (MACE), as the primary outcome of this analysis, was a composite endpoint including myocardial infarction, stroke, heart failure, and cardiovascular death. Secondary outcomes included individual components of the primary outcome, as well as all-cause mortality. The relationship of novel BP metrics and study endpoints was accessed by using adjusted Cox proportional hazards regression models. Over a median follow-up period of 3.90 [3.88-3.94] years, 559 MACEs occurred. Both SBP burden (Hazard Ratio [HR]: 1.126 [1.039-1.221]) and SBP TTR (HR: 0.862[0.783-0.949]) were independently correlated with MACEs according to adjusted Cox models. Evaluation using C-statistic and Net Reclassification Indexes (NRI) revealed that both two novel SBP metrics outperformed mean SBP and SBP standard deviation (SD) in predicting MACEs. Furthermore, the predictive value of SBP burden and TTR was consistent across MACE(P=0.383) and secondary endpoints (all P value &gt; 0.05) in this analysis. Conclusions: Among non-diabetic patients with hypertension, both the cumulative SBP burden and TTR independently predict MACEs, surpassing the predictive power of mean SBP and SBP SD. This study substantiates the application of innovative BP measurements for early-stage assessment of BP control.

Deep neural networks and conventional machine learning classifiers to analyze thoracic survival data

Lung cancer is a prevalent global health concern and most prevalent malignancy in Indonesian hospitals. Following thoracic surgery, patients were categorized into two classes: individuals who experienced mortality within a year and those who achieved survival. Despite being about socks, the dataset for the deceased category consisted of 70 data samples, while the dataset for the final group comprised 400 samples. Data calculation involves the utilization of both deep neural networks and standard machine learning algorithms. The study use the Python programming language to evaluate the algorithms, and it measures their performance using metrics such as accuracy, F1-Score, precision, recall, receiver operating characteristic (ROC), and area under curve (AUC). The test results indicate that the deep neural network method achieves an accuracy of 95,56%, an F1 score of 79,24%, a precision of 91,96%, a recall of 85,52%, and an AUC of 85,52%. This study suggests that utilizing deep neural network data mining techniques, specifically with a cross-validation fold of 10, variations of six hidden layer encoder-decoder, relu, sigmoid activation function, optimizer Adam, and learning rate of 0,01, dropout rate of 0,2. Employing the Synthetic Minority Over-sampling Technique data preprocessing method, can effectively analyze thoracic patient survival data sets.

A three-step combination strategy for addressing outliers and class imbalance in software defect prediction

&lt;p&gt;Software defect prediction often involves datasets with imbalanced distributions where one or more classes are underrepresented, referred to as the minority class, while other classes are overrepresented, known as the majority class. This imbalance can hinder accurate predictions of the minority class, leading to misclassification. While the synthetic minority oversampling technique (SMOTE) is a widely used approach to address imbalanced learning data, it can inadvertently generate synthetic minority samples that resemble the majority class and are considered outliers. This study aims to enhance SMOTE by integrating it with an efficient algorithm designed to identify outliers among synthetic minority samples. The resulting method, called reduced outliers (RO)-SMOTE, is evaluated using an imbalanced dataset, and its performance is compared to that of SMOTE. RO-SMOTE first performs oversampling on the training data using SMOTE to balance the dataset. Next, it applies the mining outlier algorithm to detect and eliminate outliers. Finally, RO-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. The experimental results demonstrate that RO-SMOTE achieves higher accuracy, precision, recall, F1-score, and area under curve (AUC) values compared to SMOTE.&lt;/p&gt;

Serum IFN-γ Predicts the Therapeutic Effect of Belimumab in Refractory Lupus Nephritis Patients.

To evaluate belimumabf's efficacy in refractory lupus nephritis (LN) patients and identify predictive serum biomarkers for treatment response. In this single-arm retrospective study,we assessed clinical responses in LN patients at baseline and six months after initiating belimumab. Serum cytokines (IL-2, IL-4, IL-6, IL-10, TNF-α, IFN-γ) were quantified using multiplex magnetic bead flow immunoassay before and after treatment. Fourteen patients with various subtypes of refractory LN participated in the study: seven with class III and V LN, three with type V alone, two with class III, and two with class IV+V and V LN. Post six months of belimumab therapy, all participants exhibited a reduction in the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)-2K scores from their respective baseline values. Notably, most patients showed a decrease in the dosage of prednisone, levels of 24-hour urinary protein, immunoglobulins, erythrocyte sedimentation rate (ESR), and anti-double-stranded DNA antibody IgM, along with serum levels of IL-4, IL-6, IL-10, and IFN-γ. Meanwhile, levels of C3, C4, IL-2, and TNF-α were observed to increase. Of the participants, nine (64.29%) achieved a complete renal response, one (7.14%) showed a partial response, and four (28.57%) exhibited no response. Significantly, higher baseline serum IFN-γ levels were found in patients who did not achieve complete renal response (CR) compared to those who did (p = 0.009). Receiver operating characteristic (ROC) curve analysis demonstrated that baseline IFN-γ levels had an area under curve (AUC) of 0.96 (0.70-1.00), with a sensitivity of 0.89 and a specificity of 1.00 (p < 0.001). Belimumab shows potential efficacy in treating refractory LN. Baseline serum IFN-γ levels may predict response to belimumab therapy, potentially enabling more targeted treatment approaches for this challenging condition.

An ensemble learning model for predicting cancer-specific survival of muscle-invasive bladder cancer patients undergoing bladder preservation therapy.

More muscle-invasive bladder cancer (MIBC) patients are now eligible for bladder-preserving therapy (BPT), underscoring the need for precision medicine. This study aimed to identify prognostic predictors and construct a predictive model among MIBC patients who undergo BPT. Data relating to MIBC patients were obtained from the Surveillance, Epidemiology and End Results (SEER) database from 2004 to 2016. Eleven features were included to establish multiple models. The predictive effectiveness was assessed using receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA) and clinical impact curve (CIC). SHapley Additive exPlanations (SHAP) were used to explain the impact of features on the predicted targets. The ROC showed that Catboost and Random Forest (RF) obtained better predictive discrimination in both 3- and 5-year models [test set area under curves (AUC) =0.80 and 0.83, respectively]. Furthermore, Catboost showed better performance in calibration plots, DCA and CIC. SHAP analysis indicated that age, M stage, tumor size, chemotherapy, T stage and gender were the most important features in the model for predicting the 3-year cancer-specific survival (CSS). In contrast, M stage, age, tumor size and gender as well as the N and T stages were the most important features for predicting the 5-year CSS. The Catboost model exhibits the highest predictive performance and clinical utility, potentially aiding clinicians in making optimal individualized decisions for MIBC patients with BPT.

Predictors of Medical and Dental Clinic Closure by Machine Learning Methods: Cross-Sectional Study Using Empirical Data.

Small clinics are important in providing health care in local communities. Accurately predicting their closure would help manage health care resource allocation. There have been few studies on the prediction of clinic closure using machine learning techniques. This study aims to test the feasibility of predicting the closure of medical and dental clinics (MCs and DCs, respectively) and investigate important factors associated with their closure using machine running techniques. The units of analysis were MCs and DCs. This study used health insurance administrative data. The participants of this study ran and closed clinics between January 1, 2020, and December 31, 2021. Using all closed clinics, closed and run clinics were selected at a ratio of 1:2 based on the locality of study participants using the propensity matching score of logistic regression. This study used 23 and 19 variables to predict the closure of MCs and DCs, respectively. Key variables were extracted using permutation importance and the sequential feature selection technique. Finally, this study used 5 and 6 variables of MCs and DCs, respectively, for model learning. Furthermore, four machine learning techniques were used: (1) logistic regression, (2) support vector machine, (3) random forest (RF), and (4) Extreme Gradient Boost. This study evaluated the modeling accuracy using the area under curve (AUC) method and presented important factors critically affecting closures. This study used SAS (version 9.4; SAS Institute Inc) and Python (version 3.7.9; Python Software Foundation). The best-fit model for the closure of MCs with cross-validation was the support vector machine (AUC 0.762, 95% CI 0.746-0.777; P<.001) followed by RF (AUC 0.736, 95% CI 0.720-0.752; P<.001). The best-fit model for DCs was Extreme Gradient Boost (AUC 0.700, 95% CI 0.675-0.725; P<.001) followed by RF (AUC 0.687, 95% CI 0.661-0.712; P<.001). The most significant factor associated with the closure of MCs was years of operation, followed by population growth, population, and percentage of medical specialties. In contrast, the main factor affecting the closure of DCs was the number of patients, followed by annual variation in the number of patients, year of operation, and percentage of dental specialists. This study showed that machine running methods are useful tools for predicting the closure of small medical facilities with a moderate level of accuracy. Essential factors affecting medical facility closure also differed between MCs and DCs. Developing good models would prevent unnecessary medical facility closures at the national level.

Comparative study of machine-and deep-learning based classification algorithms for biomedical Raman spectroscopy (RS): case study of RS based pathogenic microbe identification.

One key aspect pushing the frontiers of biomedical RS is dedicated machine- or deep- learning (ML or DL) algorithms. Yet, systematic comparative study between ML and DL algorithms has not been conducted for biomedical RS, largely due to the limited availability of open-source and large Raman spectra dataset. Therefore we compared typical ML partial least square-discriminant analysis (PLS-DA) and DL one dimensional convolution neural network (1D-CNN) based pathogenic microbe identification on 12,000 Raman spectra from six species of microbe (i.e., K. aerogenes (Klebsiella aerogenes), C. albicans (Candida albicans), C. glabrata (Candida glabrata), Group A Strep. (Group A Streptococcus), E. coli1 (Escherichia coli1), E. coli2 (Escherichia coli2)) when 100%, 75%, 50% and 25% of the 12,000 Raman spectra were retained. The total Raman dataset was analyzed with 80% split for training and 20% for testing. The 100% retained testing dataset accuracy, area under curve (AUC) of the receiver operating characteristic (ROC) curve were 95.25% and 0.997 for 1D-CNN, which are higher than those (89.42% and 0.979) of PLS-DA. Yet, PLS-DA outperforms 1D-CNN for 75%, 50% and 25% retained testing dataset. The resultant accuracies and AUCs demonstrated the performance reliance of PLS-DA and 1D-CNN on Raman spectra number. Besides, both loadings on the latent variables of PLS-DA and the saliency maps of 1D-CNN largely captured Raman peaks arising from DNA and proteins with comparable interpretability. The results of the current work indicated that both ML and DL algorithms should be explored for application-wise Raman spectra identification to select whichever with higher accuracies and AUCs.

Pathology diagnosis of intraoperative frozen thyroid lesions assisted by deep learning

BackgroundThyroid cancer is a common thyroid malignancy. The majority of thyroid lesion needs intraoperative frozen pathology diagnosis, which provides important information for precision operation. As digital whole slide images (WSIs) develop, deep learning methods for histopathological classification of the thyroid gland (paraffin sections) have achieved outstanding results. Our current study is to clarify whether deep learning assists pathology diagnosis for intraoperative frozen thyroid lesions or not.MethodsWe propose an artificial intelligence-assisted diagnostic system for frozen thyroid lesions that applies prior knowledge in tandem with a dichotomous judgment of whether the lesion is cancerous or not and a quadratic judgment of the type of cancerous lesion to categorize the frozen thyroid lesions into five categories: papillary thyroid carcinoma, medullary thyroid carcinoma, anaplastic thyroid carcinoma, follicular thyroid tumor, and non-cancerous lesion. We obtained 4409 frozen digital pathology sections (WSI) of thyroid from the First Affiliated Hospital of Sun Yat-sen University (SYSUFH) to train and test the model, and the performance was validated by a six-fold cross validation, 101 papillary microcarcinoma sections of thyroid were used to validate the system’s sensitivity, and 1388 WSIs of thyroid were used for the evaluation of the external dataset. The deep learning models were compared in terms of several metrics such as accuracy, F1 score, recall, precision and AUC (Area Under Curve).ResultsWe developed the first deep learning-based frozen thyroid diagnostic classifier for histopathological WSI classification of papillary carcinoma, medullary carcinoma, follicular tumor, anaplastic carcinoma, and non-carcinoma lesion. On test slides, the system had an accuracy of 0.9459, a precision of 0.9475, and an AUC of 0.9955. In the papillary carcinoma test slides, the system was able to accurately predict even lesions as small as 2 mm in diameter. Tested with the acceleration component, the cut processing can be performed in 346.12 s and the visual inference prediction results can be obtained in 98.61 s, thus meeting the time requirements for intraoperative diagnosis. Our study employs a deep learning approach for high-precision classification of intraoperative frozen thyroid lesion distribution in the clinical setting, which has potential clinical implications for assisting pathologists and precision surgery of thyroid lesions.

Vaginal and endometrial microbiome dysbiosis associated with adverse embryo transfer outcomes

BackgroundAssisted reproductive technology (ART) is the most effective method to treat infertility and the pathogenesis of implantation failure after in vitro fertilization-embryo transfer (IVF-ET) is a challenging filed in infertility. Microbes in the female reproductive tract are considered to be associated with gynecological and obstetric diseases. However, its effects on embryo implantation failure are unsured.PurposeThis study aimed to investigate reproductive tract dysbiosis, identify different bacteria in reproductive tract as potential biomarkers of embryo implantation failure and demonstrate the pathogenesis through metabolites analysis.MethodsWe compared the data from 16S rRNA gene and metagenome in reproductive tracts through QIIME2 and HUMAnN2 by the times of embryo implantation failure on 239 infertile patients and 17 healthy women.ResultsOur study revealed a strong positive correlation between Lactobacillus abundance and embryo implantation success (IS) after IVF-ET. The microbial community composition and structure in reproductive tract showed substantially difference between the embryo implantation failure (IF) and healthy control. Moreover, we established a diagnostic model through receiver operating characteristic (ROC) with 0.913 area under curve (AUC) in IS and multiple implantation failures (MIF), verified its effectiveness with an AUC = 0.784 demonstrating microbial community alterations could efficiently discriminate MIF patients. Metagenome functional analyses of vaginal samples from another independent infertile patients after IVF-ET revealed the L-lysine synthesis pathway enriched in IF patients, along with ascended vaginal pH and decreased Lactobacillus abundance.ConclusionsThis study clarifies several independent relationships of bacteria in vagina and endometrial fluid on embryo implantation failure and undoubtedly broadens the understanding about female reproductive health.

Integrating Multi-Organ Imaging-Derived Phenotypes and Genomic Information for Predicting the Occurrence of Common Diseases.

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.

Establishment and verification of a TME prognosis scoring model based on the acute myeloid leukemia single-cell transcriptome

The tumor microenvironment (TME) plays an important role in the occurrence and progression of Acute Myeloid Leukemia (AML). Single-cell sequencing has enabled researchers to explore the correlation between TME subgroups and tumor prognosis, distinguish the existence of drug-resistant subgroups of tumor cells, and unravel the complexity of the AML cellular heterogeneity. We used bone marrow immune cell enrichment analysis from public databases to screen prognostic genes, construct prognostic models, and validate their prognostic significance on independent external datasets and patient samples. A total of 18,251 single cells were obtained to establish prognostic scoring models for 10 key genes including CCL5, ETLS2, and IL2RA.The AML cases were divided into two groups: high-risk and low-risk. The low-risk group exhibited a higher survival rate than the high-risk group. The areas under curves (AUC) of 1-, 3- and 5-year survival curves in the TCGA and GEO training sets were greater than 0.8 and 0.6, respectively, indicating effective prediction. The model’s prognostic efficacy was confirmed across multiple validation sets. It demonstrated increased expression of ETS2, CCL5, and IL2RA in AML samples compared to controls, which was associated with decreased overall survival (OS). This prognostic scoring model based on tumor immune infiltration provides a reference for developing novel treatment strategies for recurrent/refractory AML.

Modified Beer-Lambert algorithm to measure pulsatile blood flow, critical closing pressure, and intracranial hypertension.

We introduce a frequency-domain modified Beer-Lambert algorithm for diffuse correlation spectroscopy to non-invasively measure flow pulsatility and thus critical closing pressure (CrCP). Using the same optical measurements, CrCP was obtained with the new algorithm and with traditional nonlinear diffusion fitting. Results were compared to invasive determination of intracranial pressure (ICP) in piglets (n = 18). The new algorithm better predicted ICP elevations; the area under curve (AUC) from logistic regression analysis was 0.85 for ICP ≥ 20 mmHg. The corresponding AUC for traditional analysis was 0.60. Improved diagnostic performance likely results from better filtering of extra-cerebral tissue contamination and measurement noise.

Integrating cat boost algorithm with triangulating feature importance to predict survival outcome in recurrent cervical cancer

Cervical cancer is one of the most dangerous malignancies in women. Prolonged survival times are made possible by breakthroughs in early recognition and efficient treatment of a disease.The existing methods are lagging on finding the important attributes to predict the survival outcome. The main objective of this study is to find individuals with cervical cancer who are at greater risk of death from recurrence by predicting the survival.A novel approach in a proposed technique is Triangulating feature importance to find the important risk factors through which the treatment may vary to improve the survival outcome.Five algorithms Support vector machine, Naive Bayes, supervised logistic regression, decision tree algorithm, Gradient boosting, and random forest are used to build the concept. Conventional attribute selection methods like information gain (IG), FCBF, and ReliefFare employed. The recommended classifier is evaluated for Precision, Recall, F1, Mathews Correlation Coefficient (MCC), Classification Accuracy (CA), and Area under curve (AUC) using various methods. Gradient boosting algorithm (CAT BOOST) attains the highest accuracy value of 0.99 to predict survival outcome of recurrence cervical cancer patients. The proposed outcome of the research is to identify the important risk factors through which the survival outcome of the patients improved.