Weifang Kong

Purpose: To report a rare case of Gardner syndrome (GS) initially presenting with bilateral breast masses, emphasizing the diagnostic challenges of atypical manifestations and the importance of multidisciplinary collaboration. Methods: A retrospective analysis was conducted using clinical data, multimodal imaging (ultrasound, mammography, magnetic resonance imaging, and computed tomography), histopathological evaluation via core needle biopsy, and multidisciplinary team (MDT) consultation. Colonoscopy was performed to confirm systemic involvement. Results: A 27-year-old woman presented with bilateral breast masses that were initially suspected to be malignant. Imaging revealed irregular spiculated lesions mimicking invasive carcinoma. Histopathological examination confirmed the presence of fibromatosis in both breasts and the right interpectoral region. Systemic evaluation revealed craniofacial osteomas, subcutaneous/muscular soft tissue tumors, left adrenal adenoma, and multiple colorectal polyps (superficial serrated adenomas). MDT review confirmed GS based on intestinal polyposis, osteomas, soft-tissue tumors, and histopathological criteria. Conclusion: This case underscores the critical role of multimodal imaging, histopathology, and MDT collaboration in clinical diagnosing GS with rare breast involvement. A systematic evaluation of extracolonic manifestations (e.g., adrenal adenomas) and the recognition of fibromatosis mimicking malignancy are essential for early diagnosis. Genetic evaluation and interdisciplinary management optimize the outcomes of complex hereditary syndromes.

Objectives To develop and validate a deep learning model based on three-dimensional features (DL_3D) for distinguishing lung adenocarcinoma (LUAD) from tuberculoma (TBM). Materials and methods A total of 1160 patients were collected from three hospitals. A vision transformer network-based DL_3D model was trained, and its performance in differentiating LUAD from TBM was evaluated using validation and external test sets. The performance of the DL_3D model was compared with that of two-dimensional features (DL_2D), radiomics, and six radiologists. Diagnostic performance was assessed using the area under the receiver operating characteristic curves (AUCs) analysis. Results The study included 840 patients in the training set (mean age, 54.8 years [range, 19–86 years]; 514 men), 210 patients in the validation set (mean age, 54.3 years [range, 18–86 years]; 128 men), and 110 patients in the external test set (mean age, 54.7 years [range, 22–88 years]; 51 men). In both the validation and external test sets, DL_3D exhibited excellent diagnostic performance (AUCs, 0.895 and 0.913, respectively). In the test set, the DL_3D model showed better performance (AUC, 0.913; 95% CI: 0.854, 0.973) than the DL_2D (AUC, 0.804, 95% CI: 0.722, 0.886; p < 0.001), radiomics (AUC, 0.676, 95% CI: 0.574, 0.777; p < 0.001), and six radiologists (AUCs, 0.692 to 0.810; p value range < 0.001–0.035). Conclusion The DL_3D model outperforms expert radiologists in distinguishing LUAD from TBM. Key Points Question Can a deep learning model perform in differentiating LUAD from TBM on non-enhanced CT images? Findings The DL_3D model demonstrated higher diagnostic performance than the DL_2D model, radiomics model, and six radiologists in differentiating LUAD and TBM. Clinical relevance The DL_3D model could accurately differentiate between LUAD and TBM, which can help clinicians make personalized treatment plans. Graphical Abstract

To analyze the correlation between the main perfusion parameters of the left ventricle and various physiological and coronary artery disease (CAD) risk factors or comorbidities using dynamic stress computed tomography myocardial perfusion imaging (CT-MPI) in patients without obstructive coronary stenosis. This retrospective analysis included 119 patients without obstructive coronary artery stenosis in computed tomography angiography (CTA), and without perfusion defects in CT-MPI. Patients were categorized into groups based on the presence or absence of physiological and CAD risk factors or comorbidities. The global myocardial blood flow (MBF), myocardial blood volume (MBV), and perfused capillary blood volume (PCBV) of the left ventricle were compared between groups, and correlations with continuous variables were analyzed. Multivariate linear regression was used to identify independent factors. Perfusion parameters were higher (MBF, 149.41 ± 26.38 vs. 159.20 ± 21.31 ml/100 ml/min, MBV, 17.09 ± 2.37 vs.18.84 ± 1.89, and PCBV, 9.82 ± 2.21 vs. 11.47 ± 1.79 ml/100 ml [all P < 0.05]) in female patients than in male patients. Hypertension and overweight/obesity resulted in lower perfusion parameters (hypertension vs. normotension: MBF, 148.09 ± 21.15 vs. 161.47 ± 25.13 ml/100 ml/min, PCBV, 10.25 ± 2.23 vs. 11.22 ± 1.96 ml/100 ml; overweight/obesity vs. none: MBF, 148.82 ± 20.98 vs. 159.51 ± 25.44 ml/100 ml/min, PCBV, 10.20 ± 1.93 vs. 11.15 ± 2.22 ml/100 ml [all P < 0.05]). Body surface area (BSA), body mass index, stress heart rate (HR), incremental HR, coronary total plaque volume, and stress systolic blood pressure were significantly correlated with perfusion parameters (all P < 0.05). Stress HR, BSA, and hypertension were independent predictors of MBF, stress HR and sex were independent predictors of MBV, and stress HR and BSA were independent predictors of PCBV. Dynamic stress CT-MPI myocardial perfusion is affected by stress HR, sex, and BSA, and can identify early perfusion distribution in hypertension and obesity/overweight.

Purpose To compare the diagnostic efficacy of different relative myocardial blood flow (MBF) ratios in computed tomography perfusion (CTP) for myocardial ischemia in patients with obstructive coronary artery disease (CAD). Methods Between October 2020 and March 2024, patients with suspected or known obstructive CAD who underwent CTP + coronary computed tomography angiography and invasive coronary angiography/fractional flow reserve were retrospectively selected. Patients and vessels were categorized into ischemia and non-ischemia groups. The diagnostic efficacies of the three relative MBF ratios were compared in patients with obstructive CAD. Results This study included 48 patients (144 vessels). Notably, 34 of the 48 patients (70.83%) and 49 of the 144 vessels (34.03%) were considered to have myocardial ischemia. The area under the curve of Ratio-hi (0.944, 95% confidence interval: 0.893–0.976) was higher than those of Ratio-av, Ratio-Q3, and MBF-lowest; However, no statistical differences were found (P>0.005). The cutoff value for detecting Ratio-hi was 0.667, and the sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were 91.8%, 83.2%, 75%, 95.24%, and 86.81%, respectively. Conclusion Relative MBF ratios, especially Ratio-hi, demonstrated excellent performance and exhibited greater robustness in diagnosing myocardial ischemia in patients with obstructive CAD.

The aim of this study was to explore the related factors linked to the development and infectivity of tuberculosis. This was achieved by comparing the clinical characteristics of patients with pulmonary tuberculosis (TB) who tested positive in smear Mycobacterium tuberculosis tests with this who tested negative in smear mycobacterium tests but positive in sputum Gene Xpert tests. We gathered clinical data of 1612 recently hospitalized patients diagnosed with pulmonary tuberculosis who tested positive either in sputum Gene-Xpert test or sputum smear Mycobacterium tuberculosis tests. The data was collected from January 1, 2018 to August 5, 2023, at Sichuan Provincial People's Hospital. We conducted separately analyzes and comparisons of the clinical characteristics between the two groups of patients, aiming to discussed the related factors influencing the development and infectivity of tuberculosis. In comparison to the GeneXpert positive group, the sputum smear positive group exhibited a higher proportion of elderly patients (aged 75–89) and individuals classified as underweight (BMI < 18.5 kg/m²). Furthermore, this group was more prone to experiencing symptoms such as weight loss, coughing and sputum production, hemoptysis, shortness of breath, and difficulty breathing. Moreover, they are also more likely to develop extrapulmonary tuberculosis, such as tuberculous meningitis, tuberculous pleurisy, and tuberculous peritonitis. These clinical features, when present, not only increase the likelihood of a positive result in sputum smear tests but also suggest a high infectivity of pulmonary tuberculosis. Elderly individuals (aged 75 to 89) who are underweight (BMI < 18.5 kg/m²), display symptom of cough, expectoration, hemoptysis and dyspnea-particularly cough and expectoration-and those with extra pulmonary tuberculosis serve as indicators of highly infectious pulmonary tuberculosis patients. These patients may present with more severe condition, carrying a higher bacteria, and being more prone to bacterial elimination. Identification of these patients is crucial, and prompt actions such as timely and rapid isolation measures, cutting off transmission routes, and early empirical treatment of tuberculosis are essential to control the development of the disease.

Background: Noninvasively detecting epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma patients before targeted therapy remains a challenge. This study aimed to develop a 3-dimensional (3D) convolutional neural network (CNN)-based deep learning model to predict EGFR mutation status using computed tomography (CT) images. Methods: We retrospectively collected 660 patients from 2 large medical centers. The patients were divided into training (n=528) and external test (n=132) sets according to hospital source. The CNN model was trained in a supervised end-to-end manner, and its performance was evaluated using an external test set. To compare the performance of the CNN model, we constructed 1 clinical and 3 radiomics models. Furthermore, we constructed a comprehensive model combining the highest-performing radiomics and CNN models. The receiver operating characteristic (ROC) curves were used as primary measures of performance for each model. Delong test was used to compare performance differences between different models. Results: Compared with the clinical [training set, area under the curve (AUC) =69.6%, 95% confidence interval (CI), 0.661–0.732; test set, AUC =68.4%, 95% CI, 0.609–0.752] and the highest-performing radiomics models (training set, AUC =84.3%, 95% CI, 0.812–0.873; test set, AUC =72.4%, 95% CI, 0.653–0.794) models, the CNN model (training set, AUC =94.3%, 95% CI, 0.920–0.961; test set, AUC =94.7%, 95% CI, 0.894–0.978) had significantly better predictive performance for predicting EGFR mutation status. In addition, compared with the comprehensive model (training set, AUC =95.7%, 95% CI, 0.942–0.971; test set, AUC =87.4%, 95% CI, 0.820–0.924), the CNN model had better stability. Conclusions: The CNN model has excellent performance in non-invasively predicting EGFR mutation status in patients with lung adenocarcinoma and is expected to become an auxiliary tool for clinicians.

To explore the diagnostic efficacy of tomosynthesis spot compression (TSC) compared with conventional spot compression (CSC) for ambiguous findings on full-field digital mammography (FFDM). In this retrospective study, 122 patients (including 108 patients with dense breasts) with ambiguous FFDM findings were imaged with both CSC and TSC. Two radiologists independently reviewed the images and evaluated lesions using the Breast Imaging Reporting and Data System. Pathology or at least a 1-year follow-up imaging was used as the reference standard. Diagnostic efficacies of CSC and TSC were compared, including area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The mean glandular dose was recorded and compared for TSC and CSC. Of the 122 patients, 63 had benign lesions and 59 had malignant lesions. For Reader 1, the following diagnostic efficacies of TSC were significantly higher than those of CSC: AUC (0.988 vs. 0.906, P = 0.001), accuracy (93.4% vs. 77.8%, P = 0.001), specificity (87.3% vs. 63.5%, P = 0.002), PPV (88.1% vs. 70.5%, P = 0.010), and NPV (100% vs. 90.9%, P = 0.029). For Reader 2, TSC showed higher AUC (0.949 vs. 0.909, P = 0.011) and accuracy (83.6% vs. 71.3%, P = 0.022) than CSC. The mean glandular dose of TSC was higher than that of CSC (1.85 ± 0.53 vs. 1.47 ± 0.58 mGy, P < 0.001) but remained within the safety limit. TSC provides better diagnostic efficacy with a slightly higher but tolerable radiation dose than CSC. Therefore, TSC may be a candidate modality for patients with ambiguous findings on FFDM.

Background: This study aimed to investigate the diagnostic value of machine-learning (ML) models with multiple classifiers based on non-enhanced CT Radiomics features for differentiating anterior mediastinal cysts (AMCs) from thymomas, and high-risk from low risk thymomas.Methods: In total, 201 patients with AMCs and thymomas from three centers were included and divided into two groups: AMCs vs. thymomas, and high-risk vs low-risk thymomas. A radiomics model (RM) was built with 73 radiomics features that were extracted from the three-dimensional images of each patient. A combined model (CM) was built with clinical features and subjective CT finding features combined with radiomics features. For the RM and CM in each group, five selection methods were adopted to select suitable features for the classifier, and seven ML classifiers were employed to build discriminative models. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic performance of each combination.Results: Several classifiers combined with suitable selection methods demonstrated good diagnostic performance with areas under the curves (AUCs) of 0.876 and 0.922 for the RM and CM in group 1 and 0.747 and 0.783 for the RM and CM in group 2, respectively. The combination of support vector machine (SVM) as the feature-selection method and Gradient Boosting Decision Tree (GBDT) as the classification algorithm represented the best comprehensive discriminative ability in both group. Comparatively, assessments by radiologists achieved a middle AUCs of 0.656 and 0.626 in the two groups, which were lower than the AUCs of the RM and CM. Most CMs exhibited higher AUC value compared to RMs in both groups, among them only a few CMs demonstrated better performance with significant difference in group 1.Conclusion: Our ML models demonstrated good performance for differentiation of AMCs from thymomas and low-risk from high-risk thymomas. ML based on non-enhanced CT radiomics may serve as a novel preoperative tool.