CT-Based AI model for predicting therapeutic outcomes in ureteral stones after single extracorporeal shock wave lithotripsy through a cohort study.

OBJECTIVES: Exploring the efficacy of an artificial intelligence (AI) model derived from the analysis of CT images to precisely forecast the therapeutic outcomes of singular-session extracorporeal shock wave lithotripsy (ESWL) in the management of ureteral stones. METHODS: A total of 317 patients diagnosed clinically with ureteral stones were included in this investigation. Unenhanced CT was administered to the participants within the initial fortnight preceding the inaugural ESWL. The internal cohort consisted of 250 individuals from a local healthcare facility, whereas the external cohort comprised 67 participants from another local medical institution. The proposed framework comprises three main components: an automated semantic segmentation model developed using 3D U-Net, a feature extractor that integrates radiomics and autoencoder techniques, and an ESWL efficacy prediction model trained with various machine learning algorithms. All participants underwent thorough postoperative follow-up examinations four weeks hence. The efficacy of ESWL was defined by the absence of stones or residual fragments measuring ≤2 mm in KUB X-ray assessments. Model stability and generalizability were judiciously validated through a fivefold cross-validation approach and a multi-center external test strategy. Moreover, Shapley Additive Explanations (SHAP) values for individual features were computed to elucidate the nuanced contributions of each feature to the model's decision-making process. RESULTS: The semantic segmentation model we constructed exhibited an average Dice coefficient of 0.88 ± 0.08 on the external testing set. ESWL classifiers built using Support Vector Machine (SVM), Random Forest (RF), XGBoost (XB), and CatBoost (CB) achieved AUROC values of 0.78, 0.84, 0.85, and 0.90, respectively, on the internal validation set. For the external testing set, SVM, RF, XB, and CB predicted ESWL with AUROC values of 0.68, 0.79, 0.80, and 0.83, respectively, with the last one being the optimal algorithm. The radiomics features and auto-encoder features made significant contributions to the decision-making process of the classification model. CONCLUSIONS: This investigation unmistakably underscores the remarkable predictive prowess exhibited by a scrupulously crafted AI model using CT images to precisely anticipate the therapeutic results of a singular session of extracorporeal shock wave lithotripsy for ureteral stones.

Associations of the triglyceride-glucose index and atherogenic index of plasma with the severity of new-onset coronary artery disease in different glucose metabolic states.

BACKGROUND: The triglyceride-glucose (TyG) index is considered a dependable biomarker for gauging insulin resistance. The atherogenic index of plasma (AIP) represents a marker reflecting atherosclerosis. However, there is currently no study specifically exploring the associations of these two biomarkers with the severity of new-onset coronary artery disease (CAD) under different glucose metabolic states. Therefore, this study aims to evaluate the correlations of these two biomarkers with CAD severity in patients newly diagnosed with CAD under various glucose metabolism conditions. METHOD: Totally 570 subjects first administered coronary angiography were enrolled, including 431 first diagnosed CAD patients and 139 non-CAD patients. CAD severity  was gauged by the quantity of narrowed arteries (single-vessel and multi-vessel CAD). According to WHO diabetes guidelines, glucose metabolic states were divided into normal glucose regulation (NGR), pre-diabetes mellitus (Pre-DM), and diabetes mellitus (DM). The relationships of the TyG index and AIP with CAD severity were validated by logistic regression analysis, including adjustment for traditional cardiovascular risk elements and medical treatments. Their predictive efficacy for CAD was evaluated by receiver operating characteristic (ROC) curves. RESULT: The TyG index and AIP were independently correlated with CAD in accordance with logistic regression analysis (both P < 0.05). Regardless of the glucose metabolic states, there was no statistical correlation between the TyG index and CAD severity. However, AIP in NGR patients was significantly related to CAD severity (P < 0.05). The areas under the curve of the TyG index and AIP for predicting CAD were 0.682 and 0.642 (both P < 0.001), respectively, and their optimal cut-off values were 3.210 (Youden index: 0.305) and 0.095 (Youden index:0.246), respectively. CONCLUSION: The TyG index and AIP have significant associations with CAD. The TyG index had no association with CAD severity, regardless of glucose metabolic states. AIP exhibited a discernible link with CAD severity in NGR patients, but not in the pre-DM or DM populations. The TyG index and AIP have similar predictive values for new-onset CAD.

An automatic texture feature analysis framework of renal tumor: surgical, pathological, and molecular evaluation based on multi-phase abdominal CT.

OBJECTIVES: To determine whether the texture feature analysis of multi-phase abdominal CT can provide a robust prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. METHODS: A total of 1051 participants with renal tumor were split into the internal cohort (850 patients from four different hospitals) and the external testing cohort (201 patients from another local hospital). The proposed framework comprised a 3D-kidney and tumor segmentation model by 3D-UNet, a feature extractor for the regions of interest based on radiomics and image dimension reduction, and the six classifiers by XGBoost. A quantitative model interpretation method called SHAP was used to explore the contribution of each feature. RESULTS: The proposed multi-phase abdominal CT model provides robust prediction for benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in the internal validation set, with the AUROC values of 0.88 ± 0.1, 0.90 ± 0.1, 0.91 ± 0.1, 0.89 ± 0.1, 0.84 ± 0.1, and 0.88 ± 0.1, respectively. The external testing set also showed impressive results, with AUROC values of 0.83 ± 0.1, 0.83 ± 0.1, 0.85 ± 0.1, 0.81 ± 0.1, 0.79 ± 0.1, and 0.81 ± 0.1, respectively. The radiomics feature including the first-order statistics, the tumor size-related morphology, and the shape-related tumor features contributed most to the model predictions. CONCLUSIONS: Automatic texture feature analysis of abdominal multi-phase CT provides reliable predictions for multi-tasks, suggesting the potential usage of clinical application. CLINICAL RELEVANCE STATEMENT: The automatic texture feature analysis framework, based on multi-phase abdominal CT, provides robust and reliable predictions for multi-tasks. These valuable insights can serve as a guiding tool for clinical diagnosis and treatment, making medical imaging an essential component in the process. KEY POINTS: • The automatic texture feature analysis framework based on multi-phase abdominal CT can provide more accurate prediction of benign and malignant, histological subtype, pathological stage, nephrectomy risk, pathological grade, and Ki67 index in renal tumor. • The quantitative decomposition of the prediction model was conducted to explore the contribution of the extracted feature. • The study involving 1051 patients from 5 medical centers, along with a heterogeneous external data testing strategy, can be seamlessly transferred to various tasks involving new datasets.

Emergence of Weyl fermions by ferrimagnetism in a noncentrosymmetric magnetic Weyl semimetal.

Condensed matter physics has often provided a platform for investigating the interplay between particles and fields in cases that have not been observed in high-energy physics. Here, using angle-resolved photoemission spectroscopy, we provide an example of this by visualizing the electronic structure of a noncentrosymmetric magnetic Weyl semimetal candidate NdAlSi in both the paramagnetic and ferrimagnetic states. We observe surface Fermi arcs and bulk Weyl fermion dispersion as well as the emergence of new Weyl fermions in the ferrimagnetic state. Our results establish NdAlSi as a magnetic Weyl semimetal and provide an experimental observation of ferrimagnetic regulation of Weyl fermions in condensed matter.

A quantitative analysis framework of placenta accreta spectrum: placenta subtype, intraoperative bleeding, and hysterectomy risk evaluation based on magnetic resonance imaging-anatomical-clinical features.

Background: Placenta accreta spectrum (PAS) is a significant contributor to maternal morbidity and mortality. Our objective was to develop a quantitative analysis framework utilizing magnetic resonance imaging (MRI)-anatomical-clinical features to predict 3 clinically significant parameters in patients with PAS: placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy). Methods: A total of 125 pregnant women with PAS from 2 medical centers were enrolled into an internal training set and an external testing set. Some 21 MRI-anatomical-clinical features were integrated as input into the framework. The proposed quantitative analytic framework contains mainly 3 classifiers built by extreme gradient boosting (XGBoost) and their testing in external datasets. We also further compared the accuracy of placenta subtype prediction between the proposed model and 4 radiologists. A quantitative model interpretation method called SHapley Additive exPlanations (SHAP) was conducted to explore the contribution of each feature. Results: The placenta subtype (invasive vs. non-invasive), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) demonstrated impressive area under the receiver operating characteristic curve (AUROC) values of 0.93, 0.88, and 0.90, respectively, in the internal validation set. Even in the external testing set, these metrics maintained their strength, achieving AUROC values of 0.91, 0.82, and 0.82, respectively. Comparing our proposed framework to the 4 radiologists, our model exhibited superior accuracy, specificity, and sensitivity in predicting placental subtypes within the external testing cohort. The features associated with intraplacental dark T2 bands played a crucial role in the decision-making process of all 3 prediction models. Conclusions: The quantitative analysis framework can provide a robust method for classification of placenta subtype (invasive vs. non-invasive placenta), intraoperative bleeding (≥1,500 vs. <1,500 mL), and hysterectomy risk (hysterectomy vs. non-hysterectomy) based on MRI-anatomical-clinical features in PAS.

Automatic analysis framework based on 3D-CT multi-scale features for accurate prediction of Ki67 expression levels in substantial renal cell carcinoma.

PURPOSE: To investigate the effectiveness of an automatic analysis framework based on 3D-CT multi-scale features in predicting Ki67 expression levels in substantial renal cell carcinoma (RCC). METHODS: This retrospective study was conducted using multi-center cohorts consisting of 588 participants with pathologically confirmed RCC. The participants were divided into an internal training set (n = 485) and an external testing set (n = 103) from four and one local hospitals, respectively. The proposed automatic analytic framework comprised a 3D kidney and tumor segmentation model constructed by 3D UNet, a 3D-CT multi-scale features extractor based on the renal-tumor feature, and a low or high Ki67 prediction classifier using XGBoost. The framework was validated using a fivefold cross-validation strategy. The Shapley additive explanation (SHAP) method was used to determine the contribution of each feature. RESULTS: In the prediction of low or high Ki67, the combination of renal and tumor features achieved better performance than any single features. Internal validation using a fivefold cross-validation strategy yielded AUROC values of 0.75 ± 0.1, 0.75 ± 0.1, 0.83 ± 0.1, 0.77 ± 0.1, and 0.87 ± 0.1, respectively. The optimal model achieved an AUROC of 0.87 ± 0.1 and 0.82 ± 0.1 for low vs. high Ki67 prediction in the internal validation and external testing sets, respectively. Notably, the tumor first-order-10P was identified as the most influential feature in the model decision. CONCLUSIONS: Our study suggests that the proposed automatic analysis framework based on 3D-CT multi-scale features has great potential for accurately predicting Ki67 expression levels in substantial RCC. CRITICAL RELEVANCE STATEMENT: Automatic analysis framework based on 3D-CT multi-scale features provides reliable predictions for Ki67 expression levels in substantial RCC, indicating the potential usage of clinical applications.

An automated surgical decision-making framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features in renal cell carcinoma.

OBJECTIVES: To determine whether 3D-CT multi-level anatomical features can provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. METHODS: This is a retrospective study based on multi-center cohorts. A total of 473 participants with pathologically proved renal cell carcinoma were split into the internal training and the external testing set. The training set contains 412 cases from five open-source cohorts and two local hospitals. The external testing set includes 61 participants from another local hospital. The proposed automatic analytic framework contains the following modules: a 3D kidney and tumor segmentation model constructed by 3D-UNet, a multi-level feature extractor based on the region of interest, and a partial or radical nephrectomy prediction classifier by XGBoost. The fivefold cross-validation strategy was used to get a robust model. A quantitative model interpretation method called the Shapley Additive Explanations was conducted to explore the contribution of each feature. RESULTS: In the prediction of partial versus radical nephrectomy, the combination of multi-level features achieved better performance than any single-level feature. For the internal validation, the AUROC was 0.93 ± 0.1, 0.94 ± 0.1, 0.93 ± 0.1, 0.93 ± 0.1, and 0.93 ± 0.1, respectively, as determined by the fivefold cross-validation. The AUROC from the optimal model was 0.82 ± 0.1 in the external testing set. The tumor shape Maximum 3D Diameter plays the most vital role in the model decision. CONCLUSIONS: The automated surgical decision framework for partial or radical nephrectomy based on 3D-CT multi-level anatomical features exhibits robust performance in renal cell carcinoma. The framework points the way towards guiding surgery through medical images and machine learning. CLINICAL RELEVANCE STATEMENT: We proposed an automated analytic framework that can assist surgeons in partial or radical nephrectomy decision-making. The framework points the way towards guiding surgery through medical images and machine learning. KEY POINTS: • The 3D-CT multi-level anatomical features provide a more accurate prediction of surgical decision-making for partial or radical nephrectomy in renal cell carcinoma. • The data from multicenter study and a strict fivefold cross-validation strategy, both internal validation set and external testing set, can be easily transferred to different tasks of new datasets. • The quantitative decomposition of the prediction model was conducted to explore the contribution of each extracted feature.

Towards the Development of a Learning-Based Intention Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs.

There has been a growth in the design and use of power assist devices for manual wheelchairs (MWCs) to alleviate the physical load of MWC use. A pushrim-activated power-assisted wheel (PAPAW) is an example of a power assist device that replaces the conventional wheel of a MWC. Although the use of PAPAWs provides some benefits to MWC users, it can also cause difficulties in maneuvering the wheelchair. In this research, we examined the characteristics of wheelchair propulsion when using manual and powered wheels. We used the left and right wheels' angular velocity to calculate the linear and angular velocity of the wheelchair. Results of this analysis revealed that the powered wheel's controller is not optimally designed to reflect the intentions of a wheelchair user. To address some of the challenges with coordinating the pushes on PAPAWs, we proposed the design of a user-intention detection framework. We used the kinematic data of MWC experiments and tested six supervised learning algorithms to classify one of four movements: "not moving", "moving straight forward", "turning left", and "turning right". We found that all the classification algorithms determined the type of movement with high accuracy and low computation time. The proposed intention detection framework can be used in the design of learning-based controllers for PAPAWs that take into account the individualized characteristics of wheelchair users. Such a system may improve the experience of PAPAW users.

Quasi-degenerate magnetic states in α-RuCl3.

Exploring quantum spin liquid (QSL) state has both fundamental scientific value and realistic application potential. Recently, α-RuCl3 was experimentally observed to hold in-plane zigzag antiferromagnetic (AFM) order at low temperature, which was further proposed to be proximate to a Kitaev QSL ground state. We have studied the magnetic properties of α-RuCl3 in the framework of electronic structure calculation based on density functional theory (DFT) with Hubbard U correction (DFT+U) and spin-orbit coupling. When the intra-orbital Hubbard interaction U and the inter-orbital Hund's coupling J adopt the commonly accepted values of U = 2.0 eV and J = 0.4 eV, the zigzag AFM order indeed owns the minimum energy, consistent with the experimental observation. More importantly, we find that compared with the ferromagnetic order in the previous theoretical studies, there exist a series of magnetic configurations energetically even closer to the zigzag AFM ground state. The further calculations and analysis indicate that these low-energy magnetic states are closely related to the electronic correlation effect of Ru 4d orbitals. By decreasing U and increasing J with just about 0.2 eV, they become energetically degenerate with the zigzag AFM order, inducing strong magnetic frustration and then yielding a state without long-range magnetic order but with nonzero local moments. Considering the facts that theoretically the pressure usually reduces the intra-orbital Hubbard interaction and meanwhile enhances the inter-orbital Hund's coupling, while experimentally the pressure drives α-RuCl3 into a quantum disordered phase, our results provide a perspective to understand the exotic magnetic behaviors of α-RuCl3.

Factors influencing message dissemination through social media.

Online social networks strongly impact our daily lives. An internet user (a "Netizen") wants messages to be efficiently disseminated. The susceptible-infected-recovered (SIR) dissemination model is the traditional tool for exploring the spreading mechanism of information diffusion. We here test our SIR-based dissemination model on open and real-world data collected from Twitter. We locate and identify phase transitions in the message dissemination process. We find that message content is a stronger factor than the popularity of the sender. We also find that the probability that a message will be forwarded has a threshold that affects its ability to spread, and when the probability is above the threshold the message quickly achieves mass dissemination.

The melilite-type compound [Formula: see text] (A = K, La) being a room temperature ferromagnetic semiconductor.

The seeking of room temperature ferromagnetic semiconductors, which take advantages of both the charge and spin degrees of freedom of electrons to realize a variety of functionalities in devices integrated with electronic, optical, and magnetic storage properties, has been a long-term goal of scientists and engineers. Here, by using the spin-polarized density functional theory calculations, we predict a new series of high temperature ferromagnetic semiconductors based on the melilite-type oxysulfide Sr2MnGe2S6O through hole (K) and electron (La) doping. Due to the lack of strong antiferromagnetic superexchange between Mn ions, the weak antiferromagnetic order in the parent compound Sr2MnGe2S6O can be suppressed easily by charge doping with either p-type or n-type carriers, giving rise to the expected ferromagnetic order. At a doping concentration of 25%, both the hole-doped and electron-doped compounds can achieve a Curie temperature (Tc) above 300 K. The underlying mechanism is analyzed. Our study provides an effective approach for exploring new types of high temperature ferromagnetic semiconductors.