Current Medical Imaging Reviews最新文献

Introduction: Mesothelioma is an insidious neoplasm that develops from mesothelial cells. About 80% of mesotheliomas originate in the pleural cavity. Other sites where it has been reported are the peritoneal cavity, tunica vaginalis, and the pericardium.

Case presentation: A 45-year-old female complained of abdominal distention and pain for three months. There was a significant weight loss of approximately 15 kg in the past three months, and there was no family history of any malignancy, tuberculosis, substance abuse, or asbestosis exposure. Physical examination revealed signs of muscle wasting, loss of subcutaneous fat, and hollowing of the eye sockets. There was pitting edema in the bilateral lower limbs; per abdomen examination revealed abdominal distension with umbilicus in the midline. No visible peristalsis or dilated veins were seen all over the abdomen. Hernial sites were normal. Gross ascites were present, and no organomegaly, definitive mass, or lump was palpable. The dull note was heard all over the abdomen, and fluid thrill was noted on percussion. Bowel sounds were normal on auscultation. The ascitic fluid examination revealed the presence of atypical cells. An omentectomy was done and it was sent for histopathological examination.

Conclusion: The specimen of omentectomy was in multiple fragments and measured 17x16x3cm; a few of the fragments were nodular, soft to firm on palpation. The cut section of mass was gray and white with areas of necrosis. Microscopic examination showed sheets of malignant cells. These tumor cells were immunoreactive to EMA, cytokeratin, vimentin, calretinin, WT-1, and D2-40 and immune negative to desmin (highlighting only the entrapped reactive mesothelial cells), inhibin, BerEP4, TTF-1, CD 68, napsin, ER, CEA, CDX2, PR, PAX-8, and SALL4. Ki67 labelling index was 15%. The features were of epithelioid mesothelioma.

Background: Hepatic portal venous gas (HPVG) is very rare; it is associated with multiple gastrointestinal etiologies, with pathophysiology not yet fully understood. It is characteristically fast-progressing and has a high mortality rate. Treatment choice depends on the etiology, including conservative and surgical management.

Case presentation: We report an adult patient (less than 25 years old) of HPVG combined with acute upper gastrointestinal hemorrhage, in which massive gas in the hepatic portal vein system by computed tomography of the abdomen was rapidly dissipated by nasogastric decompression conservative management.

Conclusion: Nasogastric decompression can be an effective treatment approach for HPVG when timely surgical treatment is not required.

Background: The performance of automatic liver segmentation and manual sampling MRI strategies needs be compared to determine interchangeability.

Objective: To compare automatic liver segmentation and manual sampling strategies (manual whole liver segmentation and standardized manual region of interest) for performance in quantifying liver volume and MRI-proton density fat fraction (MRI-PDFF), identifying steatosis grade, and time burden.

Methods: Fifty patients with obesity who underwent liver biopsy and MRI between December 2017 and November 2018 were included. Sampling strategies included automatic and manual whole liver segmentation and 4 and 9 large regions of interest. Intraclass correlation coefficient (ICC), Bland-Altman, linear regression, receiver operating characteristic curve, and Pearson correlation analyses were performed.

Results: Automatic whole liver segmentation liver volume and manual whole liver segmentation liver volume showed excellent agreement (ICC=0.97), high correlation (R2=0.96), and low bias (3.7%, 95% limits of agreement, -4.8%, 12.2%) in liver volume. There was the best agreement (ICC=0.99), highest correlation (R2=1.00), and minimum bias (0.84%, 95% limits of agreement, -0.20%, 1.89%) between automated whole liver segmentation MRI-PDFF and manual whole liver segmentation MRI-PDFF. There was no difference of each paired comparison of receiver operating characteristic curves for detecting steatosis (P=0.07-1.00). The minimum time burden for automatic whole liver segmentation was 0.32 s (0.32-0.33 s).

Conclusion: Automatic measurement has similar effects to manual measurement in quantifying liver volume, MRI-PDFF, and detecting steatosis. Time burden of automatic whole liver segmentation is minimal among all sampling strategies. Manual measurement can be replaced by automatic measurement to improve quantitative efficiency.

Background: Nowadays, High Intensity Focused Ultrasound (HIFU) is a common surgery option for the treatment of uterine fibroids in China, the immediate response of which is clinically evaluated using Contrast Enhanced (CE) imaging. However, the injection of gadolinium with its potential adverse effect is of concern in CE and therefore, it deserves efforts to find a better imaging method without the need for contrast agent injection for this task.

Objective: To assess the role of diffusion-weighted imaging (DWI) in evaluating the immediate therapeutic response of HIFU treatment for uterine fibroids in comparison with CE.

Methods: 68 patients with 74 uterine fibroids receiving HIFU treatment were enrolled, and immediate treatment response was assessed using post-surgical DWI images. Semi-quantitative ordinal ablation quality grading and quantitative nonperfusion volume (NPV) measurement based on DWI and CE imaging were determined by two experienced radiologists. Agreement of ablation quality grading between DWI and CE was assessed using the weighted kappa coefficient, while intraobserver, interobserver and interprotocol agreements of NPV measurements within and between DWI and CE were evaluated using the intraclass correlation (ICC) and Bland-Altman analysis.

Results: Grading of immediate HIFU treatment response showed a moderate agreement between DWI and CE (weighted kappa = 0.446, p < 0.001). NPV measured in 65 fibroids with DWI of Grade 3~5 showed very high ICCs for the intraobserver and interobserver agreement within DWI and CE (all ICC > 0.980, p < 0.001) and also for the interprotocol agreement between DWI and CE (ICC = 0.976, p < 0.001).

Conclusion: DWI could provide satisfactory ablation quality grading, and reliable NPV quantification results to assess immediate therapeutic responses of HIFU treatment for uterine fibroids in most cases, which suggests that non-contrast enhanced DWI might be potentially used as a more costeffective and convenient method in a large proportion of patients for this task replacing CE imaging.

Background: Nowadays, people attach increasing importance to accurate and timely disease diagnosis and personalized treatment. Because of the uncertainty and latency of the pathogenesis, it is difficult to detect breast tumour early. With higher resolution, magnetic resonance imaging (MRI) has become an important method for early detection of cancer in recent years. At present, DL technology can automatically study imaging features of different depths.

Objective: This work aimed to use DL to study medical image-assisted diagnosis.

Methods: The image data were collected from the patients. ROI (region of interest) containing the complete tumor area in the medical image was generated. The ROI image was extracted, and the extracted feature data were expanded. By constructing a three-dimensional (3D) CNN model, the evaluation indicators of breast tumour diagnosis results have been proposed. In the experiment part, 3D CNN model and other models have been used to diagnose the medical image of breast tumour.

Results: The 3D CNN model exhibited good ROI region extraction effect and breast tumor image diagnosis effect, and the average diagnostic accuracy of breast tumor image diagnosis was 0.736, which has been found to be much higher than other models and could be applied to breast tumor medical image-aided diagnosis.

Conclusion: The 3D CNN model has been trained by combining the two-dimensional CNN training mode, and the evaluation index of diagnostic results has been established. The experimental part verified the medical image diagnosis effect of the 3D CNN model. The model had exhibited a high ROI region extraction effect and breast tumor image diagnosis effect.

Objective: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods.

Methods: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results.

Results: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p < 0.001) and the BI-RADS category with and without AI (κ =0.74, p < 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively.

Conclusion: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients.

Key messages: • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.

Since the authors are not responding to the editor’s request to fulfill the editorial requirement, the article has beenwithdrawn.

Bentham Science apologizes to the readers of the journal for any inconvenience this may have caused.

The Bentham editorial policy on article withdrawal can be found at https://benthamscience.com/journal/33/editorialpolicy

Bentham science disclaimer: It is a condition of publication that manuscripts submitted to this journal have not been published and will not be simultaneously submitted or published elsewhere. Furthermore, any data, illustration, structure, or table that has been published elsewhere must be reported, and copyright permission for reproduction must be obtained. Plagiarism is strictly forbidden, and by submitting the article for publication, the authors agree that the publishershave the legal right to take appropriate action against the authors if plagiarism or fabricated information is discovered. By submitting a manuscript, the authors agree that the copyright of their article is transferred to the publishers if and when the article is accepted for publication.

Aim: Hepatic perivascular epithelioid cell tumors (PEComa) often mimic hepatocellular carcinoma (HCC) in patients without cirrhosis. This study aimed to develop a nomogram using imaging characteristics on Gd-EOB-DTPA-enhanced MRI and to distinguish PEComa from HCC in a noncirrhotic liver.

Methods: Forty patients with non-cirrhotic Gd-EOB-DTPA-enhanced magnetic resonance imaging(MRI) were included in our study. A multivariate logistic regression model was used to select significant variables to distinguish PEComa from HCC. A nomogram was developed based on the regression model. The performance of the nomogram was assessed with respect to the ROC curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical usefulness of the nomogram.

Results: Two significant predictors were identified: the appearance of an early draining vein and the T1D value of tumors. The ROC curve showed that the area under the curve (AUC) of the model to predict the risk of PEComa was 0.91 (95% CI: 0.80~1) and showed that the model had high specificity (92.3%) and sensitivity (88.9%). The nomogram incorporating these two predictors showed favorable calibration, which was validated using 1000 resampling procedures, and the corrected C-index of this model was 0.90. Furthermore, DCA analysis showed that the model had clinical practicability.

Conclusion: In conclusion, the nomogram model showed favorable predictive accuracy for distinguishing PEComa from HCC in non-cirrhotic patients and may aid in clinical decision-making.

Background: The human skeletal system relies heavily on the integrity of bones, which provide structural support and safeguard vital organs. Accurate detection is paramount for effective diagnosis. Conventional methods for identifying fractures manually are not only time-consuming but also susceptible to errors.

Methods: The proposed methodology hinges on a patch antenna operating at 2.4 GHz and a bone phantom housing a simulated fracture, where the antenna is scanned. The collected signals are then processed with Delay-and-Sum (DAS), and Delay-Multiply-and-Sum (DMAS) reconstruction algorithms. The resulting images offer visual insights into the location of fractures.

Results: Through experimentation, the efficacy of the images varies considerably in terms of their capacity for noise and artifact suppression. While DAS exhibits reasonable effectiveness, it suppresses noise and artifacts comprehensively. In contrast, DMAS offers clearer and more precise images of bone fractures.

Conclusion: In summary, the research introduces a cost-effective and non-invasive strategy for detecting bone fractures. By involving a patch antenna at 2.4 GHz, along with image reconstruction algorithms like DMAS and DAS, one can effectively visualize the location of bone fractures. The experimental results highlight the superiority of DMAS over DAS in terms of contrast resolution, making it a highly promising avenue for fracture detection.

Background: Secondary cardiac tumors are a rare disease that is hard to detect when the tumor is small and asymptomatic. This case report focuses on a massive pulmonary metastasis filling almost the entire left atrium. Multimodal enhancement imaging, cardiac contrast-enhanced ultrasound (CEUS), enhanced electron computed tomography, and positron emission tomography imaging were applied to detect the malignant origin of this case. The aim of this project was to provide an important basis for clinical treatment and decision-making with multimodal imaging.

Case presentation: The patient was hospitalized with suspected to be a lumbar spine fracture. According to the multimodal imaging, pathologically confirmed to suffer a cardiac metastasis from small cell lung cancer. EP-regimen (Etoposide 0.1gd 1-5+Nedaplatin 30mgd 1-4) was selected for the systemic chemotherapy of the patient. During three years of follow-up, the left intra-atrial occupancy was significantly reduced.

Conclusion: Multimodality imaging can cover up the deficiencies of single imaging examinations and further clarify and enrich the understanding of the relationship between the location and the surrounding structure of the mass, thus providing a good reference for clinical treatment and decisionmaking.