Current Medical Imaging Reviews最新文献

Background: Primary cardiac tumors are rare. Most primary cardiac tumors are benign, with approximately 10.83% being malignant. We present a rare case of Primary Cardiac Angiosarcoma (PCA) with multiple metastases diagnosed using multimodality imaging, to enhance the understanding of PCA among clinicians and radiologists.

Case description: A 29-year-old woman presented to our hospital with a 2-day history of chest tightness, chest pain, palpitations, and dyspnea after physical activity. Ultrasonography and Computed Tomography (CT) of the heart revealed a mass in the right atrium. Cardiac magnetic resonance imaging suggested either a large cardiac lymphoma or angiosarcoma. The histopathological diagnosis confirmed a cardiac angiosarcoma. Positron Emission Tomography-Computed Tomography (PET/CT) revealed intense 18F-fluorodeoxyglucose (18F-FDG) uptake in the right side of the heart, with a maximum standardized uptake value of 10.9. Three months later, the patient was re-examined using abdominal CT, echocardiography, and PET/CT. PET/CT revealed increased 18F-FDG uptake which had become more extensive, with multifocal metastatic nodules in both the lungs and mediastinum. The patient was lost to follow-up after being discharged on May 1, 2022.

Conclusion: The combined evaluation using multimodality imaging plays a vital role in determining the precise size and localization of the PCA, detecting distant metastases, and assessing patient prognosis.

Objective: This study aimed to investigate the utility of ultrasonic attenuation imaging (ATI) in assessing the relationship between hepatic fat content and lipid metabolism in patients diagnosed with type 2 diabetes mellitus (T2DM) and non-alcoholic fatty liver disease (NAFLD).

Methods: 239 patients diagnosed with T2DM were included, with liver fat quantified using proton density fat fraction (PDFF). We analyzed the variance in ATI across various grades of fatty liver and its correlation with clinical parameters. Additionally, a receiver operating characteristic curve (ROC) was employed to evaluate the diagnostic accuracy of ATI for different degrees of fatty liver, determining optimal diagnostic thresholds while calculating sensitivity and specificity. Furthermore, we assessed the reliability of ATI and SWE in measuring liver acoustic attenuation and elastic stiffness using the intraclass correlation coefficient (ICC).

Results: We observed significant variations in ATI across different grades of fatty liver (p<0.001). ATI exhibited positive correlations with SWE, BMI, GLU (OH), steatosis grade, ALT, TG, and UA, while demonstrating a negative correlation with HDL-c. Notably, the correlation coefficient with steatosis grade was 0.76, indicating a strong association. The equation for the stepwise multiple linear regression model used is as follows: ATI=0.338+0.014×TG+0.052×BMI+0.001×ALT+0.113×SWE. AUROCs indicated the best cutoffs for ATI in different degrees of steatosis to be as follows: ≥ S1 = 0.665 dB·cm-1·MHz-1 (AUC = 0.857); ≥ S2 = 0.705 dB·cm-1·MHz-1 (AUC = 0.921); ≥ S3 = 0.745 dB·cm-1·MHz-1 (AUC = 0.935). The ICC values for ATI and SWE in liver-mimicking measurements exceeded 0.75 (p<0.001), signifying excellent repeatability.

Conclusion: The ATI could quantitatively assess the severity of fatty liver, enabling effective identification of patients suitable for liver biopsy referral.

Background: Muscle infiltration of bladder cancer has become the most important index to evaluate its prognosis. Machine learning is expected to accurately identify its muscle infiltration status on images.

Objective: This study aimed to establish and validate a machine learning prediction model based on multi-phase contrast-enhanced CT (MCECT) for preoperatively evaluating the muscle-invasive status of bladder cancer.

Methods: A retrospective study was conducted on bladder cancer patients who underwent surgical resection and pathological confirmation by MCECT scans. They were randomly divided into training and testing groups at a ratio of 8:2; we used an independent external testing set for verification. The radiomics features of lesions were extracted from MCECT images and radiomics signatures were established by dual sample T-test and least absolute shrinkage selection operator (LASSO) regression. Afterward, four machine learning classifier models were established. The receiver operating characteristic (ROC) curve, calibration, and decision curve analysis were employed to evaluate the efficiency of the model and analyze diagnostic performance using accuracy, precision, sensitivity, specificity, and F1-score.

Results: The best predictive model was found to have logic regression as the classifier. The AUC value was 0.89 (5-fold cross-validation range 0.83-0.96) in the training group, 0.80 in the test group, and 0.87 in the external testing group. In the testing and external testing group, the diagnostic accuracy, precision, sensitivity, specificity, and F1-score were 0.759, 0.826, 0.863, 0.729, 0.785, and 0.794, 0.755, 0.953, 0.720, and 0.809, respectively.

Conclusion: The machine learning model showed good accuracy in predicting the muscle infiltration status of bladder cancer before surgery.

Objective: This study aimed to develop and assess the performance of a YOLOv8 algorithm-aided detection model for identifying rib fractures on multiplane reconstruction (MPR) images, addressing the limitations of current AI models and the labor-intensive nature of manual diagnosis.

Methods: Ethical approval was obtained, and a dataset comprising 624 MPR images, confirmed by CT, was collected from three regions of Tongji Hospital between May 2020 and May 2023. The images were categorized into training, validation, and external test sets. A musculoskeletal radiologist labeled the images, and a YOLOV8n model was trained and validated using these datasets. The performance metrics, including sensitivity, specificity, accuracy, precision, recall, and F1 score, were calculated.

Results: The refined YOLO model demonstrated high diagnostic accuracy, with sensitivity, specificity, and accuracy rates of 96%, 97%, and 97%, respectively. The AI model significantly outperformed the radiologist in terms of diagnostic speed, with an average interpretation time of 2.02 seconds for 144 images compared to 288 seconds required by the radiologist.

Conclusion: The YOLOv8 algorithm shows promise in expediting the diagnosis of rib fractures on MPR images with high accuracy, potentially improving clinical efficiency and reducing the workload for radiologists. Future work will focus on enhancing the model with more feature learning capabilities and integrating it into the PACS system for human-computer interaction.

Aim: This research aims to develop and evaluate a novel health classification and severity detection system based on Vision Transformers (ViTs) for fetal ultrasound imagery. This contributes to improved precision in fetal health status detection and abnormalities with more accurate results than other traditional models.

Background: Amidst the other imperatives of resource-deficient developing nations, mitigating neonatal mortality rates is a challenge that demands precisionbased solutions in the era of artificial intelligence. Though the advent of machine learning models has added an optimal dimension to deal with emerging complexity in fetal ultrasound imagery, there is a call to address the huge gap in the demanded precision for prediction than the existing interpretation.

Objective: This research strives to formulate and access a novel health classification and severity detection system based on the implementation of the Vision Transformers frameworks. This pioneering investigation represents an unparalleled exploration into the efficacy of ViTs for discerning intricate patterns within fetal ultrasonographic imagery, facilitating precise categorization of fetal well-being and prognosticating the magnitude of potential anomalies.

Methodology: A private and confidential dataset of 500 fetal ultrasound images has been collected from diverse hospitals. Each image has been annotated by radiologists according to two main labels: the health status of the fetus, which includes healthy, mild, moderate, or severe, and the severity of abnormalities as a continuous measure. At different levels, the dataset underwent pre-processing via distinct techniques. Then, the composite loss function Cross-Entropy has been deployed to train the optimized VIT model using the Adam algorithm.

Results: The classification accuracy of the proposed model is 90% for detecting the severity with an F1-score of 0.87 and MAE of 0.30. The research ascertained that the model ViT evinced a superlative efficacy for the capturing of fine-grained spatial relations in ultrasound images to produce revolutionary predictions.

Conclusion: These results emphasize that ViTs have the potential to revolutionize fetal health monitoring and will contribute significantly to reducing neonatal mortality by supplying clinicians with accurate and reliable predictions for early interventions. This work stands as a yardstick for further diagnostic applications using AI in fetal health care.

Background: There remains a lack of methods to accurately assess the efficacy of neoadjuvant chemoradiotherapy for locally advanced rectal cancer.

Objective: This study aimed to investigate the value of shear-wave elastography in evaluating the treatment response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer.

Materials and methods: This prospective observational study enrolled 275 patients with locally advanced rectal cancer who received neoadjuvant chemoradiotherapy during September 2021-March 2023. All patients underwent endorectal ultrasound and shear-wave elastography examination before total mesorectal excision. Clinical baseline data, endorectal ultrasound, and shear-wave elastography examination data were collected from all patients. The independent predictors of complete response were analyzed and screened, followed by the establishment of a logistic regression model. The diagnostic efficacy of the model was compared with that of radiologists.

Results: The results of binary multivariate logistic regression suggested that the mean elastography value of the tumor lesion acted as an independent predictor for the diagnosis of complete response [OR: 0.894 (0.816, 0.981)]. The optimal cutoff value was 14.6 kPa. The area under the receiver operating characteristic curve of the model for predicting complete response in the training and test cohorts was 0.850 and 0.824, respectively. The diagnostic accuracy of the model was significantly higher than that of radiologists (P < 0.001).

Conclusion: Shear-wave elastography can be used as a feasible method to evaluate the complete response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy.

Introduction: The fundamental problem with the transmission and storage of medical images is their inherent redundancy and large size necessitating higher bandwidth and a significant amount of storage space.

Objectives: The main objective is to enhance the compression efficiency through accurate segmentation followed by non-uniform compression through a cascade of encoders.

Background: Due to a sharp growth in digital imaging data, it is highly desirable to reduce the size of medical images by a significant amount, without losing clinically important diagnostic information. The majority of the compression techniques reported in the literature use either manual or traditional segmentation techniques to extract the informative parts of the images. The methods based upon non-uniform compression require accurate extraction of the informative part of the image to achieve higher compression rate.

Methods: This research proposes unsupervised machine learning modified fuzzy c-means (FCM) clustering-based segmentation for accurate extraction of informative parts of MR images. The spatial constraints of the images are extracted using an automated region-growing algorithm and incorporated into the objective function of FCM clustering (RG-FCM) to enhance the performance of the segmentation process even in the presence of noise. Further, informative and background parts are subjected to two separate series of encoders, with higher bit rates for the informative part of the image.

Results: Empirical analysis was done on the Magnetic Resonance Imaging (MRI)dataset, and experimental results indicate that the proposed technique outperforms similar existing techniques in terms of segmentation and compression metrics.

Conclusion: This integration of different segmentation techniques exhibits improvement in Jaccard and dice indexes, and cascade of different encoders endorse the superior performance of the proposed compression technique. The proposed technique can help in achieving higher compression of medical images without compromising clinically significant information.

Introduction: Primary Squamous Cell Carcinoma of the Liver (PSCCL) is an exceptionally rare clinical entity characterized by diagnostic challenges, aggressive behavior, and poor prognosis. Globally, few studies have investigated PSCCL.

Case presentation: We report the case of a 76-year-old male patient with PSCCL, detailing his clinical presentation and imaging findings, to offer insights into the preoperative diagnosis of this disease. The patient presented with upper abdominal pain that had lasted for over two weeks without any specific triggers. Laboratory tests revealed abnormal liver function. Ultrasound examination showed a large, solid, hypoechoic mass in the right anterior lobe of the liver with heterogeneous internal echoes. Color Doppler imaging detected limited blood flow signals. Contrast-enhanced Computed Tomography (CT) of the whole abdomen revealed a low-density mass with indistinct margins in the right lobe of the liver, showing uneven and progressive peripheral enhancement. Comprehensive whole-body CT, gastroscopy, and liver biopsy were performed, excluding metastatic disease in other organs. Based on the pathological findings from a percutaneous ultrasound-guided liver biopsy, the patient was diagnosed with PSCCL.

Conclusion: PSCCL is a rare malignancy that presents significant diagnostic difficulties, often evading easy identification through clinical and imaging examinations. This case report aims to contribute to improving the preoperative diagnosis of PSCCL.

Purpose: MRI could be considered as a non-destructive disease diagnosis procedure, this procedure does not allow directly molecular types of cancer. Herein, we aimed to evaluate the correlation of breast MRI background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) with the molecular subtypes and immunohistochemical markers of breast cancer.

Methods: This was a single-cross-sectional retrospective study.Fifty-six patients diagnosed with unilateral breast cancer who underwent breast MRI scans before needle biopsy or surgery were selected. The relationship between qualitative and quantitative BPE/FGT ratios and the expression of breast cancer molecular subtypes and immunohistochemical markers were evaluated in patients with breast cancer.

Results: Quantitative BPE (BPE%) of luminal A and luminal B was significantly lower than that of triple-negative breast cancer. There was no significant difference in the qualitative BPE/FGT between the different breast cancer subtypes. The quantitative BPE (BPE%) of estrogen receptor (ER)- negative tumors was higher than that of the ER-positive tumors, and the expression of FGT%, BPE%, and other immunohistochemical markers (human epidermal growth factor receptor-2(HER-2), progesterone receptor (PR), and Ki-67) were not significantly different. The proportion of high BPE distribution in HER-2 positive tumors was higher than that in the HER-2 negative group; however, there was no significant difference in the expression of qualitative BPE/FGT and other immunohistochemical markers (ER, PR, and Ki-67).

Conclusion: There were significant differences in the levels of BPE among the different molecular subtypes. Therefore, BPE may be a potential imaging biomarker for the diagnosis of the molecular subtypes of breast cancer.

Background: An effective preoperative diagnosis between minute pulmonary meningothelial-like nodules (MPMNs) and adenocarcinoma in situ (AIS) can provide clinicians with appropriate treatment strategies.

Objective: This study aimed to differentiate MPMNs from AIS via computed tomography (CT) radiomics approaches.

Methods: Clinical and imaging data from fifty-one patients diagnosed with MPMNs and 55 patients diagnosed with AIS were collected from Jiangsu Province Hospital and Nanjing First Hospital from January 2016 to December 2022. All patients underwent chest CT scans before surgery. All CT images were segmented with ITK-SNAP software, and the radiomics features were further extracted with the Python PyRadiomics package. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select the optimal radiomics features for the construction of the model. The ROC curve was used to evaluate the diagnostic efficacy of the model.

Results: After feature reduction and selection, 16 radiomics features were selected to construct the radiomics model. In the test set, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the k-nearest neighbor model were 87.5%, 88.9%, 96.9%, 77.8%, and 88.5%, respectively, and the AUC of the ROC curve was 0.969 (95% CI: 0.72-1.00).

Conclusion: The CT radiomics model has exhibited high diagnostic value in the differential diagnosis between MPMNs and AIS.