A new 3D texture feature based computer-aided diagnosis approach to differentiate pulmonary nodules

ObjectiveTo build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).MethodsWe systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis.ResultsThe AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05).ConclusionThe prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.

PurposeThe purpose of this paper is to describe innovative machine vision methods that have been employed for the capture and analysis of 3D skin textures; and the resulting potential for assisting with identification of suspicious lesions in the detection of skin cancer.Design/methodology/approachA machine vision approach has been employed for analysis of 3D skin textures. This involves an innovative application of photometric stereo for the capture of the textures, and a range of methods for analysing and quantifying them, including statistical methods and neural networks.Findings3D skin texture has been identified as a useful indicator of skin cancer. It can be used to improve realism of virtual skin reconstructions in tele‐dermatology. 3D texture features can also be combined with 2D features to obtain a more robust classifier for improving diagnostic accuracy, thereby assisting with the long‐term goal of implementing computer‐aided diagnostics for skin cancer.Originality/valueThe device developed for capturing 3D skin textures is known as the “Skin Analyser”, and as far as the authors know it is unique in the world in being able to recover 3D textures from pigmented lesions in vivo. There currently exist numerous methods for analysing lesions, including manual inspection (using established heuristics commonly known as ABCD rules), dermoscopy and SIAoscopy. The ability to capture and analyse 3D lesion textures complements these existing techniques and forms a valuable additional indicator for assisting with the early detection of dangerous skin cancers such as melanoma.

Low-dose CT images have a guiding significance in the computer-aided diagnosis of active tuberculosis. This article is based on the CT images of active tuberculosis, constructs the mask of each slice through morphology, and then performs logical operations on the CT sequence and the mask. The target area is calculated and the three-dimensional lung parenchymal data is obtained. The pyradiomics module is used to extract the three-dimensional texture feature and the classic support vector machine (support vector machine, SVM) classification algorithm is used, and the python3.7 is used for code programming. The accuracy of three-dimensional texture feature and two-dimensional texture feature in classification of active pulmonary tuberculosis was compared and analyzed. Experiments have proved that the selected three-dimensional texture features are more accurate than two-dimensional texture features in the classification of active tuberculosis, which has guiding significance for the diagnosis of active tuberculosis.

Research Type: Level 3 - Retrospective cohort study, Case-control study, Meta-analysis of Level 3 studies Introduction/Purpose: The recently proposed Progressive Collapsing Foot Deformity (PCFD) classification system categorizes different deformity patterns into five classes (A, Hindfoot Valgus; B, Midfoot/Forefoot Abduction; C, Arch Collapse; D, Peritalar Subluxation; E, Ankle Valgus Instability). Currently, the decision-making process to determine the presence or absence of each deformity class relies on the surgeon's experience and interpretation of clinical and radiographic assessments, without established cut-off threshold values in the literature for PCFD measurements. This limits the reliability and reproducibility of the classification system. The goal of the current study was to establish cut-off threshold values for the most commonly used PCFD measurements assessing Class A to D deformities, using a large cohort of PCFD patients and controls. Methods: Two patient cohorts were included: a prospective cohort consisting of normally aligned and asymptomatic control volunteers (n=197 patients, 103 females/94 males, average age 41.7 years, average BMI 28.9) and a retrospective cohort of PCFD patients (n=321 patients, 136 females/185 males, average age 50.7 years, average BMI 29.8). All patients underwent Weight-Bearing Computed Tomography (WBCT) scans of the foot and ankle. Foot bone segmentation was performed semi-automatically using specialized software, followed by automatic measurements of various parameters including: Class A (Hindoot Moment Arm – HMA), Class B (Talonavicular Coverage Angle – TCA; Talus-First Metatarsal Angle Axial – TFMA-A), Class C (Talus-First Metatarsal Angle Sagittal – TFMA-S; Forefoot Arch Angle - FAA), Class D (Coverage Maps of the Subtalar Joint Middle-MF, Posterior Facets-PF; and Sinus Tarsi-ST). Receiver Operating Characteristic (ROC) curves, Youden's Indexes and Areas Under the Curve (AUC) were calculated for each measurement. P-values of 0.05 or less were considered significant. Results: The Cut-Off Threshold Values identified by the ROC curves using Youden’s Index for different PCFD measurements were as follows (all p-values &lt; 0.0001): HMA of 13.9mm or more (AUC 85.3%) was considered diagnostic for Class A PCFD deformity. For Class B PCFD deformity, a TNCA of 38.7o or higher (AUC 84.4%) and TFMA-A of 20.3o or higher (AUC 82.1%) were considered diagnostic. For Class C PCFD deformity, a FAA of 8.7o or lower (AUC 83.9%) and TFMA-S of 18.7o or higher (AUC 82.9%) were diagnostic. For Class D Deformity, Coverage values for Middle Facet of 73.5% or lower (AUC 73.4%), Posterior Facet of 84.5% or lower (AUC 82.7%), and Sinus Tarsi of 25.7% or higher (AUC 84.4%) were diagnostic. Conclusion: In this diagnostic study involving 312 PCFD patients and 197 volunteer controls, we successfully determined the cut-off threshold values for key PCFD measurements related to Hindfoot Valgus (Class A), Midfoot/Forefoot Abduction (Class B), Arch Collapse (Class C), and Peritalar Subluxation (Class D) deformities. By utilizing a large cohort of patients and controls, we achieved high diagnostic accuracy for most measurements assessed (AUC of 80% or more) in identifying the presence or absence of these PCFD deformity classes. Our findings can potentially serve as normative threshold values to enhance the utilization, reliability, and reproducibility of the PCFD classification system. Figure 1 Scatter plots and ROC (Receiver Operating Characteristic) curves used to analyze parameters for the various deformity classifications in Progressive Collapsing Foot Deformity (PCFD). For the scatter plots, red indicates a normal control, green indicates an asymptomatic PCFD, and blue indicates a symptomatic PCFD.

The clinical value of ultrasound elastography in predicting malignant thyroid nodules

In this paper, a computer-aided diagnostic (CAD) system is developed using a multiparametric magnetic resonance imaging (MPMRI) (T2-MRI and DW-MRI) to differentiate between BC staging, especially T1 and T2. The segmentation of the bladder wall (BW) and the localization of the whole BC area (A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> ) and its extent inside the wall (A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> ) is first performed. Secondly, a set of functional, texture, and morphological features are estimated. Due to the massive difference between the wall and bladder lumen cells, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> is split into nested equidistance contours (i.e., iso-contours), and features are estimated for each iso-contours. The functional features are based on the cumulative distribution function (CDF) statistical measures for the estimated apparent diffusion coefficient (ADC) from DWMRI. Texture features, namely radiomic features, are derived from T2W-MRI, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> both carcinoma intensity and gradient images for each iso-contours from A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sub> , T2-MRI. Besides, morphological features are also incorporated to describe the tumors' geometric from T2W-MRI, A <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> . Finally, the estimated iso-features are augmented and used to train and test neural networks classifier as well as a statistical machine learning (ML) classifier. The system has been tested using a leave-one-subject-out approach on 42 data sets. The overall accuracy, sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristics (ROC) are 92.86%, 97.05%, 100%, and 0.9705, respectively. We introduce the diagnostic accuracy of individual MRI modality for our proposal to highlight the advantage of fusion multiparametric iso-features that is confirmed by the ROC analysis. Furthermore, the accuracy of two different techniques statistical ML classifiers (i.e., random forest (RF) and support vector machine (SVM)) and end-to-end convolution neural networks (i.e., ResNet50) is compared against our pipeline.

Background Machine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management. Purpose To develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors. Methods We constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables. Results DL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities &amp;gt;50%) compared to only 12.7% nonsurvival patients by TIMI (score &amp;gt;5) (Figure below). Conclusions In the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1 TIMI performance on validation setDL performance on validation set

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

BackgroundMachine learning (ML) algorithm support vector machine (SVM) performed better than Thrombolysis in Myocardial Infarction (TIMI) score for ASIAN STEMI patients. However, Deep Learning (DL) effectiveness in the multiethnic ASIAN population has yet to be determined. DL has automatic learning of the feature from a given dataset without the need to conduct feature selection. However, the selected features by the algorithm is black box. Identifying features associated with mortality is essential to recognize characteristics of patients with high risk for better patient management.PurposeTo develop a DL algorithm for in-hospital mortality in multiethnic STEMI patients using predictors identified from the SVM algorithm. To investigate DL performance constructed using predictors from SVM feature extraction and expert-recommended predictors.MethodsWe constructed four algorithms; a) DL and SVM algorithms with predictors identified from the SVM variable importance b) DL and SVM using predictors based on expert recommendation. We used registry data from the National Cardiovascular Disease Database of 11397 patient's. Fifty parameters including demographics, cardiovascular risk, medications and clinical variables were considered. The Area under the curve (AUC) is the performance evaluation metric. Algorithms were validated against the TIMI and tested using the same validation data. SVM variable importance with backward elimination was used to select and rank important variables.ResultsDL algorithms outperform SVM and TIMI on the validation dataset; i) DL with SVM selected predictors (15 predictors, AUC = 0.97), ii) DL with expert-recommended predictors (16 predictors, AUC = 0.96), iii) SVM with selected predictors (15 predictors, AUC = 0.92), iv) SVM with expert-recommended predictors (AUC = 0.89) and TIMI (AUC = 0.82). Common predictors across SVM feature selection, expert-recommendation and TIMI are: age, heart rate, Killip class, fasting blood glucose, systolic blood pressure, comorbid diseases and ST-elevation. SVM feature selection also identified diuretics, PCI and pharmacotherapy drugs as predictors that improve mortality prediction in STEMI patients. Our findings suggest that the TIMI score underestimates patients risk of mortality. DL algorithm using selected predictors classified 35% of nonsurvival patients as high risk (risk probabilities >50%) compared to only 12.7% nonsurvival patients by TIMI (score >5) (Figure below).ConclusionsIn the ASIAN population, patients with STEMI can be better classified using the DL algorithm compared to the ML and TIMI score. Combining ML feature selection with DL allows the identification of distinct factors in a unique ASIAN population for better mortality prediction than relying solely on an expert recommendation as it is a very subjective approach. Continuous validation on population-specific algorithms using DL and ML is needed before implementing in a real clinical setting.Funding AcknowledgementType of funding sources: Public grant(s) – National budget only. Main funding source(s): Technology Development Fund 1TIMI performance on validation setDL performance on validation set

Iris presentation attack detection (PAD) is still an unsolved problem mainly due to the various spoof attack strategies and poor generalisation on unseen attackers. In this paper, the merits of both light field (LF) imaging and deep learning (DL) are leveraged to combine 2D texture and 3D geometry features for iris liveness detection. By exploring off‐the‐shelf deep features of planar‐oriented and sequence‐oriented deep neural networks (DNNs) on the rendered focal stack, the proposed framework excavates the differences in 3D geometric structure and 2D spatial texture between bona fide and spoofing irises captured by LF cameras. A group of pre‐trained DL models are adopted as feature extractor and the parameters of SVM classifiers are optimised on a limited number of samples. Moreover, two‐branch feature fusion further strengthens the framework's robustness and reliability against severe motion blur, noise, and other degradation factors. The results of comparative experiments indicate that variants of the proposed framework significantly surpass the PAD methods that take 2D planar images or LF focal stack as input, even recent state‐of‐the‐art (SOTA) methods fined‐tuned on the adopted database. Presentation attacks, including printed papers, printed photos, and electronic displays, can be accurately detected without fine‐tuning a bulky CNN. In addition, ablation studies validate the effectiveness of fusing geometric structure and spatial texture features. The results of multi‐class attack detection experiments also verify the good generalisation ability of the proposed framework on unseen presentation attacks.

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

This study aims to develop and validate a machine learning-based mortality risk prediction model for V-A ECMO patients to improve the precision of clinical decision-making. This multicenter retrospective cohort study included 280 patients receiving V-A ECMO from the Second Affiliated Hospital of Guangxi Medical University, Yulin First People’s Hospital, and the MIMIC-IV database. The data from the Second Affiliated Hospital of Guangxi Medical University and the MIMIC-IV database were merged and randomly divided in a 7:3 ratio into a training set and an internal validation set, respectively. The dataset from Yulin First People’s Hospital was reserved as an external validation cohort. The primary study outcome was defined as in-hospital mortality.Feature selection was conducted using Lasso regression, followed by the development of six machine learning models: Logistic Regression, Random Forest (RF), Deep Neural Network (DNN), Support Vector Machine (SVM), LightGBM, and CatBoost. Model performance was assessed using the Area Under the Curve (AUC), accuracy, sensitivity, specificity, and F1 score. Model validation was performed through calibration and decision curve analysis. Feature importance was evaluated using SHAP, and subgroup analysis was conducted to assess the model’s applicability across different clinical scenarios. In internal validation, the Logistic Regression model performed the best, with an AUC of 0.86 (95% CI: 0.77–0.93), accuracy of 0.76, sensitivity of 0.73, specificity of 0.79, and an F1 score of 0.73. It outperformed other models (RF: AUC = 0.79, DNN: AUC = 0.78, SVM: AUC = 0.76, LightGBM: AUC = 0.71, CatBoost: AUC = 0.77). External validation yielded consistent results, with the Logistic Regression model’s AUC at 0.75 (95% CI: 0.56–0.92), accuracy of 0.69, sensitivity of 0.64, specificity of 0.73, and an F1 score of 0.66. Calibration curve analysis revealed that the Logistic Regression model had the lowest Brier score (0.1496), indicating the most reliable predicted probabilities. Decision curve analysis demonstrated that the model provided the highest net benefit across most decision thresholds. SHAP analysis identified lactate, age, and albumin as key predictors of mortality, with lactate and age positively correlated, and albumin negatively correlated. Subgroup analysis revealed better performance in the cardiac arrest group (AUC = 0.81), non-sepsis group (AUC = 0.75), and non-diabetes group (AUC = 0.78). The Logistic Regression-based mortality risk prediction model for V-A ECMO patients demonstrated comparable or even favorable performance to more complex machine learning models, with the advantage of higher interpretability.By explicitly incorporating lactate, age, and albumin as the principal predictors, this model facilitates precise risk stratification and provides practical support for clinical decision-making in ECMO management.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-25423-4.

Objective To compare the efficacy of the CT and 18F-FDG PET/CT models in predicting the malignant risk of pulmonary nodules and to verify the predictive accuracy of model. Methods A retrospective analysis of 120 patients with pulmonary nodules confirmed by pathological diagnosis or follow-up were conducted in this study. Among these patients, 59 patients with suspected malignancy received 18F-FDG PET/CT. The corresponding receiver operating characteristic curve for each model was plotted, and the area under the curve(AUC) was calculated. The malignant risk of patients with pulmonary nodules(4 –30 mm in diameter) was measured. Model accuracy was verified based on the exclusion criteria for each model and the total cohort of all patients. MedCalc software was used for correlation analysis, and DeLong method was used for two-way comparison. Results All 120 patients with pulmonary nodules were examined. Among them, 49(40.8%) had malignant nodules(31.6% primary lung cancer and 8.2% metastatic disease). The AUC of the Brock and VA models were 0.887 and 0.758, respectively, the difference was statistically significant(Z=6.483, P=0.006). In patients receiving 18F-FDG PET/CT, the AUC of the Herder model was 0.937, which was significantly more accurate than those of the other two models. When testing the model for all patients in the cohort(i.e., patients including the original model’ s inclusion criteria), the AUC value decreased but was not significant. For the Herder model, the AUC was 0.923, and the two types of cohorts were not significant(Z=21.357, P=0.121). For subcentimeter nodules, the AUC values for the Brock and VA models were 0.846 and 0.536, respectively, and the Brock model was significantly better than the VA model(Z=8.768, P=0.0026). Conclusion The Brock model showed good accuracy and was used to predict the likelihood of malignancy in nodules detected by CT scan. The Herder model was the most accurate for patients who underwent 18F-FDG PET/CT for nodule evaluation. Key words: Solitary pulmonary nodules; Positron-emission tomography; Tomography, X-ray computed; Lung cancer risk prediction model

Clinical prediction rules could help identify children at risk of slowed growth after an episode of diarrheal illness, and these rules may be generalizable to all children, regardless of diarrhea status.

ObjectivesTo compare the performance of body mass index (BMI), waist-to-height ratio (WHtR), waist circumference (WC) and waist-to-hip ratio (WHR) in detecting type 2 diabetes among Russian, Somali and Kurdish (born...