Search

Background: Heart failure with preserved ejection fraction (HFpEF) is associated with high hospitalization and mortality rates, representing a significant healthcare burden. This study aims to utilize various information including echocardiogram and phonocardiogram to construct and validate a nomogram, assisting in clinical decision‐making. Methods: This study analyzed 204 patients (68 HFpEF and 136 non‐HFpEF) from the First Affiliated Hospital of Chongqing Medical University. A total of 49 features were integrated and used, including phonocardiogram, echocardiogram features, and clinical parameters. The least absolute shrinkage and selection operator (LASSO) regression was used to select the optimal matching factors, and a stepwise logistic regression was employed to determine independent risk factors and develop a nomogram. Model performance was evaluated by the area under receiver operating characteristic (ROC) curve (AUC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results: The nomogram was constructed using five significant indicators, including NT‐proBNP (OR = 4.689, p = 0.015), E/e′ (OR = 1.219, p = 0.032), LAVI (OR = 1.088, p < 0.01), D/S (OR = 0.014, p < 0.01), and QM1 (OR = 1.058, p < 0.01), and showed a better AUC of 0.945 (95% CI = 0.908–0.982) in the training set and 0.933 (95% CI = 0.873–0.992) in the testing set compared to conventional nomogram without phonocardiogram features. The calibration curve and Hosmer–Lemeshow test demonstrated no statistical significance in the training and testing sets (p = 0.814 and p = 0.736), indicating the nomogram was well‐calibrated. The DCA and CIC results confirmed favorable clinical usefulness. Conclusion: The nomogram, integrating phonocardiogram and echocardiogram features, enhances HFpEF diagnostic efficiency, offering a valuable tool for clinical decision‐making. [ABSTRACT FROM AUTHOR]

Background: Gastrointestinal stromal tumors (GISTs) are clinically heterogeneous with various malignant potential in different individuals. It is crucial to explore a reliable method for preoperative risk stratification of gastric GISTs noninvasively. Purpose: To establish and evaluate a machine learning model using the combination of computed tomography (CT) morphology, radiomics, and deep learning features to predict the risk stratification of primary gastric GISTs preoperatively. Methods: The 193 gastric GISTs lesions were randomly divided into training set, validation set, and test set in a ratio of 6:2:2. The qualitative and quantitative CT morphological features were assessed by two radiologists. The tumors were segmented manually, and then radiomic features were extracted using PyRadiomics and the deep learning features were extracted using pre‐trained Resnet50 from arterial phase and venous phase CT images, respectively. Pearson correlation analysis and recursive feature elimination were used for feature selection. Support vector machines were employed to build a classifier for predicting the risk stratification of GISTs. This study compared the performance of models using different pre‐trained convolutional neural networks (CNNs) to extract deep features for classification, as well as the performance of modeling features from single‐phase and dual‐phase images. The arterial phase, venous phase and dual‐phase machine learning models were built, respectively, and the morphological features were added to the dual‐phase machine learning model to construct a combined model. Receiver operating characteristic (ROC) curves were used to evaluate the efficacy of each model. The clinical application value of the combined model was determined through the decision curve analysis (DCA) and the net reclassification index (NRI) was analyzed. Results: The area under the curve (AUC) of the dual‐phase machine learning model was 0.876, which was higher than that of the arterial phase model or venous phase model (0.813, 0.838, respectively). The combined model had best predictive performance than the above models with an AUC of 0.941 (95% CI: 0.887–0.974) (p = 0.012, Delong test). DCA demonstrated that the combined model had good clinical application value with an NRI of 0.575 (95% CI: 0.357–0.891). Conclusion: In this study, we established a combined model that incorporated dual‐phase morphology, radiomics, and deep learning characteristics, which can be used to predict the preoperative risk stratification of gastric GISTs. [ABSTRACT FROM AUTHOR]

Objective To evaluate the diagnostic efficacy of modified DIXON magnetic resonance angiography (mDIXON-MRA) in the possibility of axillary lymph node dissection for patients with breast cancer. Methods A retrospective case-control trial was conducted on 77 patients with breast cancer who underwent MRI examination in the Affiliated Tumor Hospital of Chongqing University from December 2018 to January 2020. All the patients preoperatively received mDIXON-MRA, T1 weighted imaging (T1WI), T2 weighted imaging (T2WI), fat-suppressed T2-weighted imaging (T2WI-FS), and contrast-enhanced T1 weighted imaging (T1WI-CE) on the axillary region. After image post-processing and reconstruction, the obtained images were analyzed and scored by 2 radiologists respectively according to the film reviewing criteria. The resectability of lymph nodes was evaluated by judging the relationship between lymph nodes and peripheral blood vessels, and the difference of scoring the images from different sequences was compared and analyzed. Receiver operating characteristic (ROC) curve was plotted based on the intraoperative judgment of the surgeon, and then the effectiveness of different sequences was analyzed in diagnosis of the resectability of enlarged lymph nodes in breast cancer patients. Results Mean score of the images from mDIXON-MRA, T1WI, T2WI, T2WI-FS and T1WI-CE were 2.01±0.97, 2.22±1.05, 2.22±1.07, 2.42±1.06, and 2.53±1.02, respectively. And significant differences were observed in the diagnostic effectiveness of axillary lymph nodes resectability based on the 5 sequences (P < 0.05). The area under the curve (AUC) of mDIXON-MRA sequence took the highest (0.854) among the 5 sequences, which was equivalent to the area after the combination of multiple sequences, showing the best evaluation efficiency. Conclusion mDIXON-MRA presents the highest clinical diagnostic value among various MRI sequences in the evaluation of the resectability of axillary lymph nodes in breast cancer patients, with its diagnostic efficiency similar to that of combined application of multiple sequences.

Objective: This study aimed to investigate iron deposition and thickness and signal changes in optic radiation (OR) by enhanced T2*-weighted angiography imaging (ESWAN) in patients with relapsing-remitting multiple sclerosis (RRMS) with unilateral and bilateral lesions or no lesions.Methods: Fifty-one RRMS patients (42 patients with a disease duration [DD] ≥ 2 years [group Mor], nine patients with a DD < 2 years [group Les]) and 51 healthy controls (group Con) underwent conventional magnetic resonance imaging (MRI) and ESWAN at 3.0 T. The mean phase value (MPV) of the OR was measured on the phase image, and thickness and signal changes of the OR were observed on the magnitude image.Results: The average MPVs for the OR were 1,981.55 ± 7.75 in group Mor, 1,998.45 ± 2.01 in group Les, and 2,000.48 ± 5.53 in group Con. In group Mor, 28 patients with bilateral OR lesions showed bilateral OR thinning with a heterogeneous signal, and 14 patients with unilateral OR lesions showed ipsilateral OR thinning with a heterogeneous signal. In the remaining nine patients without OR lesions and in group Con, the bilateral OR had a normal appearance. In the patients, a negative correlation was found between DD and OR thickness and a positive correlation was found between MPV and OR thickness.Conclusions: We confirmed iron deposition in the OR in the RRMS patients, and the OR thickness was lower in the patients than in the controls.Key Points: • Enhanced T 2* -weighted magnetic resonance angiography (ESWAN) provides new insights into multiple sclerosis (MS). • Focal destruction of the optic radiation (OR) is detectable by ESWAN. • Iron deposition in OR can be measured on ESWAN phase image in MS patients. • OR thickness was lower in the patients than in the controls. • Iron deposition and thickness changes of the OR are associated with disease duration. [ABSTRACT FROM AUTHOR]

• Analyzing the area around tumors in dynamic contrast-enhanced MRI helps distinguish benign from malignant breast lesions. • Integrating deep transfer learning features contributes to improved diagnostic accuracy of the model. • Radiomics and deep transfer learning models show significant potential in supporting clinicians' decision-making processes. To conduct the fusion of radiomics and deep transfer learning features from the intratumoral and peritumoral areas in breast DCE-MRI images to differentiate between benign and malignant breast tumors, and to compare the diagnostic accuracy of this fusion model against the assessments made by experienced radiologists. This multi-center study conducted a retrospective analysis of DCE-MRI images from 330 women diagnosed with breast cancer, with 138 cases categorized as benign and 192 as malignant. The training and internal testing sets comprised 270 patients from center 1, while the external testing cohort consisted of 60 patients from center 2. A fusion feature set consisting of radiomics features and deep transfer learning features was constructed from both intratumoral (ITR) and peritumoral (PTR) areas. The Least absolute shrinkage and selection operator (LASSO) based support vector machine was chosen as the classifier by comparing its performance with five other machine learning models. The diagnostic performance and clinical usefulness of fusion model were verified and assessed through the area under the receiver operating characteristics (ROC) and decision curve analysis. Additionally, the performance of the fusion model was compared with the diagnostic assessments of two experienced radiologists to evaluate its relative accuracy. The study strictly adhered to CLEAR and METRICS guidelines for standardization to ensure rigorous and reproducible methods. The findings show that the fusion model, utilizing radiomics and deep transfer learning features from the ITR and PTR, exhibited exceptional performance in classifying breast tumors, achieving AUCs of 0.950 in the internal testing set and 0.921 in the external testing set. This performance significantly surpasses that of models relying on singular regional radiomics or deep transfer learning features alone. Moreover, the fusion model demonstrated superior diagnostic accuracy compared to the evaluations conducted by two experienced radiologists, thereby highlighting its potential to support and enhance clinical decision-making in the differentiation of benign and malignant breast tumors. The fusion model, combining multi-regional radiomics with deep transfer learning features, not only accurately differentiates between benign and malignant breast tumors but also outperforms the diagnostic assessments made by experienced radiologists. This underscores the model's potential as a valuable tool for improving the accuracy and reliability of breast tumor diagnosis. [ABSTRACT FROM AUTHOR]

Background: The preoperative prediction of the pathological nuclear grade of clear cell renal cell carcinoma (CCRCC) is crucial for clinical decision making. However, radiomics features from one or two computed tomography (CT) phases are required to predict the CCRCC grade, which reduces the predictive performance and generalizability of this method. We aimed to develop and externally validate a multiparameter CT radiomics-based model for predicting the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grade of CCRCC., Methods: A total of 500 CCRCC patients at The First, Second, and Yongchuan Hospitals of Chongqing Medical University between January 2016 and May 2022 were retrospectively enrolled in this study. The patients were divided into the training set (n=268), internal testing set (n=115), and two external testing sets (testing set 1, n=62; testing set 2, n=55). Radiomics features were extracted from multi-phase CT images, and radiomics signatures (RSs) were created by least absolute shrinkage and selection operator (LASSO) regression. In addition, a clinical model was developed. A combined model was also established that integrated the RSs with the clinical factors, and was visualized via a nomogram. The performance of the established model was assessed using area under the curve (AUC) values, a calibration curve analysis, and a decision curve analysis (DCA)., Results: Among the four RSs and the clinical model, the RS-Triphasic had the best predictive performance with AUCs of 0.88 [95% confidence interval (CI): 0.85-0.91] and 0.84 (95% CI: 0.74-0.95) in the training and testing sets, respectively, and 0.82 (95% CI: 0.72-0.93) and 0.82 (95% CI: 0.71-0.93) in external testing sets 1 and 2. Integrating the RS-Triphasic, RS-corticomedullary phase (CMP), RS-nephrographic phase (NP), RS-non-contrast phase (NCP) with the clinical risk factors, a combined model was established with AUCs of 0.92 (95% CI: 0.89-0.94), 0.86 (95% CI: 0.76-0.95), 0.84 (95% CI: 0.73-0.95), and 0.82 (95% CI: 0.70-0.94) for the training, internal testing, and external testing sets 1 and 2, respectively. The DCA indicated that the nomogram had a greater overall net benefit than the clinical and radiomics models., Conclusions: The multiparameter CT RS fusion-based model had high accuracy in differentiating between high- and low-grade CCRCC preoperatively. Thus, it has great potential as a useful tool for personalized treatment planning and clinical decision making for CCRCC patients., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-35/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)