Biceps Pulley Lesions: Diagnostic Accuracy of Nonarthrographic Shoulder MRI and the Value of Various Diagnostic Signs.

BACKGROUND: There is limited data in the literature regarding the role of nonarthrographic MRI for detecting biceps pulley (BP) lesions. PURPOSE: To assess the accuracy of nonarthrographic MRI for detecting BP lesions, and to evaluate the diagnostic value of various MRI signs (superior glenohumeral ligament discontinuity/nonvisibility, long head of biceps (LHB) displacement sign or subluxation/dislocation, LHB tendinopathy, and supraspinatus and subscapularis tendon lesions) in detecting such lesions. STUDY TYPE: Retrospective. POPULATION: 84 patients (32 in BP-lesion group and 52 in BP-intact group-as confirmed by arthroscopy). FIELD STRENGTH/SEQUENCE: 1.5-T, T1-weighted turbo spin echo (TSE), T2-weighted TSE, and proton density-weighted TSE spectral attenuated inversion recovery (SPAIR) sequences. ASSESSMENT: Three radiologists independently reviewed all MRI data for the presence of BP lesions and various MRI signs. The MRI signs and final MRI diagnoses were tested for accuracy regarding detecting BP lesions using arthroscopy results as the reference standard. Furthermore, the inter-reader agreement (IRA) between radiologists was determined. STATISTICAL TESTS: Student's t-tests, Chi-squared, and Fisher's exact tests, and 4-fold table test were used. The IRA was calculated using Kappa statistics. A P-value <0.05 was considered statistically significant. RESULTS: The sensitivity, specificity, and accuracy of nonarthrographic MRI for detecting BP lesions were 65.6%-78.1%, 90.4%-92.3%, and 81%-86.9%, respectively. The highest accuracy was noticed for the LHB displacement sign (84.5%-86.9%), and the highest sensitivity was registered for the LHB tendinopathy sign (87.5%). Furthermore, the highest specificity was observed for the LHB displacement sign and LHB subluxation/dislocation sign (98.1%-100%). The IRA regarding final MRI diagnosis and MRI signs of BP lesions was good to very good (κ = 0.76-0.98). DATA CONCLUSION: Nonarthrographic shoulder MRI may show good diagnostic accuracy for detecting BP lesions. The LHB displacement sign could serve as the most accurate and specific sign for diagnosis of BP lesions. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

The prognostic value of bone marrow retention index and bone marrow-to-liver ratio of baseline 18F-FDG PET/CT in diffuse large B-cell lymphoma.

OBJECTIVE: To determine prognostic value of bone marrow retention index (RI-bm) and bone marrow-to-liver ratio (BLR) measured on baseline dual-phase 18F-FDG PET/CT in a series of newly diagnosed patients with diffuse large B-cell lymphoma (DLBCL) treated homogeneously with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy. PATIENTS AND METHODS: This prospective study enrolled 135 patients with newly diagnosed DLBCL. All patients underwent dual-phase 18F-FDG PET/CT. The following PET parameters were calculated for both tumor and bone marrow: maximum standardized uptake value (SUVmax) at both time points (SUVmax early and SUVmax delayed), SUVmax increment (SUVinc), RI, and BLR. Patients were treated with R-CHOP regimen and response at end of treatment was assessed. RESULTS: The final analysis included 98 patients with complete remission. At a median follow-up of 22 months, 57 patients showed no relapse, 74 survived, and 24 died. The 2-year relapse-free survival (RFS) values for patients with higher and lower RI-bm were 20% and 65.1%, respectively (p < 0.001), and for patients with higher and lower BLR were 30.2% and 69.6%, respectively (p < 0.001). The 2-year overall survival (OS) values for patients with higher and lower RI-bm were 60% and 76.3%, respectively (p = 0.023), and for patients with higher and lower BLR were 57.3% and 78.6%, respectively (p = 0.035). Univariate analysis revealed that RI-bm and BLR were independent significant prognostic factors for both RFS and OS (hazard ratio [HR] = 4.02, p < 0.001, and HR = 3.23, p < 0.001, respectively) and (HR = 2.83, p = 0.030 and HR = 2.38, p = 0.041, respectively). CONCLUSION: Baseline RI-bm and BLR were strong independent prognostic factors in DLBCL patients. CLINICAL RELEVANCE STATEMENT: Bone marrow retention index (RI-bm) and bone marrow-to-liver ratio (BLR) could represent suitable and noninvasive positron emission tomography/computed tomography (PET/CT) parameters for predicting pretreatment risk in patients with newly diagnosed diffuse large B-cell lymphoma (DLBCL) who were treated with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy. KEY POINTS: • Bone marrow retention index (RI-bm) and bone marrow-to-liver ratio (BLR) are powerful prognostic variables in diffuse large B-cell lymphoma (DLBCL) patients. • High BLR and RI-bm are significantly associated with poor overall survival (OS) and relapse-free survival (RFS). • RI-bm and BLR represent suitable and noninvasive risk indicators in DLBCL patients.

Bosniak classification version 2019: a prospective comparison of CT and MRI.

OBJECTIVE: To assess the diagnostic accuracy and agreement of CT and MRI in terms of the Bosniak classification version 2019 (BCv2019). MATERIALS AND METHODS: A prospective multi-institutional study enrolled 63 patients with 67 complicated cystic renal masses (CRMs) discovered during ultrasound examination. All patients underwent CT and MRI scans and histopathology. Three radiologists independently assessed CRMs using BCv2019 and assigned Bosniak class to each CRM using CT and MRI. The final analysis included 60 histopathologically confirmed CRMs (41 were malignant and 19 were benign). RESULTS: Discordance between CT and MRI findings was noticed in 50% (30/60) CRMs when data were analyzed in terms of the Bosniak classes. Of these, 16 (53.3%) were malignant. Based on consensus reviewing, there was no difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT (87.8%; 95% CI = 73.8-95.9% versus 75.6%; 95% CI = 59.7-87.6%; p = 0.09, 84.2%; 95% CI = 60.4-96.6% versus 78.9%; 95% CI = 54.4-93.9%; p = 0.5, and 86.7%; 95% CI = 64.0-86.6% versus 76.7%; 95% CI = 75.4-94.1%; p = 0.1, respectively). The number and thickness of septa and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. The inter-reader agreement (IRA) was substantial for determining the Bosniak class in CT and MRI (k = 0.66; 95% CI = 0.54-0.76, k = 0.62; 95% CI = 0.50-0.73, respectively). The inter-modality agreement of the BCv219 between CT and MRI was moderate (κ = 0.58). CONCLUSION: In terms of BCv2019, CT and MRI are comparable in the classification of CRMs with no significant difference in diagnostic accuracy and reliability. KEY POINTS: • There is no significant difference in the sensitivity, specificity, and accuracy of the BCv2019 with MRI and BCv2019 with CT. • The number of septa and their thickness and the presence of enhanced nodules accounted for the majority of variations in Bosniak classes between CT and MRI. • The inter-reader agreement was substantial for determining the Bosniak class in CT and MRI and the inter-modality agreement of the BCv219 between CT and MRI was moderate.

Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer.

Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.

Deep Learning Models for Classification of Dental Diseases Using Orthopantomography X-ray OPG Images.

The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.

Lateral geniculate nucleus volumetry at 3T and 7T: Four different optimized magnetic-resonance-imaging sequences evaluated against a 7T reference acquisition.

PURPOSE: The lateral geniculate nucleus (LGN) is an essential nucleus of the visual pathway, occupying a small volume (60-160 mm3) among the other thalamic nuclei. The reported LGN volumes vary greatly across studies due to technical limitations and due to methodological differences of volume assessment. Yet, structural and anatomical alterations in ophthalmologic and neurodegenerative pathologies can only be revealed by a precise and reliable LGN representation. To improve LGN volume assessment, we first implemented a reference acquisition for LGN volume determination with optimized Contrast to Noise Ratio (CNR) and high spatial resolution. Next, we compared CNR efficiency and rating reliability of 3D Magnetization Prepared Rapid Gradient Echo (MPRAGE) images using white matter nulled (WMn) and grey matter nulled (GMn) sequences and its subtraction (WMn-GMn) relative to the clinical standard Proton Density Turbo Spin Echo (PD 2D TSE) and the reference acquisition. We hypothesized that 3D MPRAGE should provide a higher CNR and volume determination accuracy than the currently used 2D sequences. MATERIALS AND METHODS: In 31 healthy subjects, we obtained at 3 and 7 T the following MR sequences: PD-TSE, MPRAGE with white/grey matter signal nulled (WMn/GMn), and a motion-corrected segmented MPRAGE sequence with a resolution of 0.4 × 0.4 × 0.4 mm3 (reference acquisition). To increase CNR, GMn were subtracted from WMn (WMn-GMn). Four investigators manually segmented the LGN independently. RESULTS: The reference acquisition provided a very sharp depiction of the LGN and an estimated mean LGN volume of 124 ±â€¯3.3 mm3. WMn-GMn had the highest CNR and gave the most reproducible LGN volume estimations between field strengths. Even with the highest CNR efficiency, PD-TSE gave inconsistent LGN volumes with the weakest reference acquisition correlation. The LGN WM rim induced a significant difference between LGN volumes estimated from WMn and GMn. WMn and GMn LGN volume estimations explained most of the reference acquisition volumes' variance. For all sequences, the volume rating reliability were good. On the other hand, the best CNR rating reliability, LGN volume and CNR correlations with the reference acquisition were obtained with GMn at 7 T. CONCLUSION: WMn and GMn MPRAGE allow reliable LGN volume determination at both field strengths. The precise location and identification of the LGN (volume) can help to optimize neuroanatomical and neurophysiological studies, which involve the LGN structure. Our optimized imaging protocol may be used for clinical applications aiming at small nuclei volumetric and CNR quantification.

Comparative Evaluation of Chest Ultrasonography and Computed Tomography as Predictors of Malignant Pleural Effusion: A Prospective Study.

Malignant pleural effusion (MPE) is a manifestation of advanced cancer that requires a prompt and accurate diagnosis. Ultrasonography (US) and computed tomography (CT) are valuable imaging techniques for evaluating pleural effusions; however, their relative predictive ability for a malignant origin remains debatable. This prospective study aimed to compare chest US with CT findings as predictors of malignancy in patients with undiagnosed exudative pleural effusion. Fifty-four adults with undiagnosed exudative pleural effusions underwent comprehensive clinical evaluation including chest US, CT, and histopathologic biopsy. Blinded radiologists evaluated the US and CT images for features suggestive of malignancy, based on predefined criteria. Diagnostic performance measures were calculated using histopathology as a reference standard. Of the 54 patients, 33 (61.1%) had MPEs confirmed on biopsy. No significant differences between US and CT were found in detecting parietal pleural abnormalities, lung lesions, chest wall invasion, or liver metastasis. US outperformed CT in identifying diaphragmatic pleural thickening ≥10 mm (33.3% vs. 6.1%, p < 0.001) and nodularity (45.5% vs. 3%, p < 0.001), whereas CT was superior for mediastinal thickening (48.5% vs. 15.2%, p = 0.002). For diagnosing MPE, diaphragmatic nodularity detected by US had 45.5% sensitivity and 100% specificity, whereas CT mediastinal thickening had 48.5% sensitivity and 90.5% specificity. Both US and CT demonstrate reasonable diagnostic performance for detecting MPE, with particular imaging findings favoring a malignant origin. US may be advantageous for evaluating diaphragmatic pleural involvement, whereas CT is more sensitive to mediastinal abnormalities.

Predicting the Consistency of Pituitary Macroadenomas: The Utility of Diffusion-Weighted Imaging and Apparent Diffusion Coefficient Measurements for Surgical Planning.

Understanding the consistency of pituitary macroadenomas is crucial for neurosurgeons planning surgery. This retrospective study aimed to evaluate the utility of diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) as non-invasive imaging modalities for predicting the consistency of pituitary macroadenomas. This could contribute to appropriate surgical planning and therefore reduce the likelihood of incomplete resections. The study included 45 patients with pathologically confirmed pituitary macroadenomas. Conventional MRI sequences, DWIs, ADC maps, and pre- and post-contrast MRIs were performed. Two neuroradiologists assessed all of the images. Neurosurgeons assessed the consistency of the tumor macroscopically, and histopathologists examined it microscopically. The MRI findings were compared with postoperative data. According to the operative data, macroadenomas were divided into the two following categories based on their consistency: aspirable (n = 27) and non-aspirable tumors (n = 18). A statistically significant difference in DWI findings was found when comparing macroadenomas of different consistencies (p < 0.001). Most aspirable macroadenomas (66.7%) were hyperintense according to DWI and hypointense on ADC maps, whereas most non-aspirable macroadenomas (83.3%) were hypointense for DWI and hyperintense on ADC maps. At a cut-off value of 0.63 × 10-3 mm2/s, the ADC showed a sensitivity of 85.7% and a specificity of 75% for the detection of non-aspirable macroadenomas (AUC, 0.946). The study concluded that DWI should be routinely performed in conjunction with ADC measurements in the preoperative evaluation of pituitary macroadenomas. This approach may aid in surgical planning, ensure that appropriate techniques are utilized, and reduce the risk of incomplete resection.

Validating Brain Tumor Reporting and Data System (BT-RADS) as a Diagnostic Tool for Glioma Follow-Up after Surgery.

Gliomas are a type of brain tumor that requires accurate monitoring for progression following surgery. The Brain Tumor Reporting and Data System (BT-RADS) has emerged as a potential tool for improving diagnostic accuracy and reducing the need for repeated operations. This prospective multicenter study aimed to evaluate the diagnostic accuracy and reliability of BT-RADS in predicting tumor progression (TP) in postoperative glioma patients and evaluate its acceptance in clinical practice. The study enrolled patients with a history of partial or complete resection of high-grade glioma. All patients underwent two consecutive follow-up brain MRI examinations. Five neuroradiologists independently evaluated the MRI examinations using the BT-RADS. The diagnostic accuracy of the BT-RADS for predicting TP was calculated using histopathology after reoperation and clinical and imaging follow-up as reference standards. Reliability based on inter-reader agreement (IRA) was assessed using kappa statistics. Reader acceptance was evaluated using a short survey. The final analysis included 73 patients (male, 67.1%; female, 32.9%; mean age, 43.2 ± 12.9 years; age range, 31-67 years); 47.9% showed TP, and 52.1% showed no TP. According to readers, TP was observed in 25-41.7% of BT-3a, 61.5-88.9% of BT-3b, 75-90.9% of BT-3c, and 91.7-100% of BT-RADS-4. Considering >BT-RADS-3a as a cutoff value for TP, the sensitivity, specificity, and accuracy of the BT-RADS were 68.6-85.7%, 84.2-92.1%, and 78.1-86.3%, respectively, according to the reader. The overall IRA was good (κ = 0.75) for the final BT-RADS classification and very good for detecting new lesions (κ = 0.89). The readers completely agreed with the statement "the application of the BT-RADS should be encouraged" (score = 25). The BT-RADS has good diagnostic accuracy and reliability for predicting TP in postoperative glioma patients. However, BT-RADS 3 needs further improvements to increase its diagnostic accuracy.

Diagnostic Accuracy of Integrating Ultrasound and Shear Wave Elastography in Assessing Carpal Tunnel Syndrome Severity: a Prospective Observational Study.

Purpose: Carpal tunnel syndrome (CTS) is a common condition characterized by compression of the median nerve (MN) within the carpal tunnel. Accurate diagnosis and assessment of CTS severity are crucial for appropriate management decisions. This study aimed to investigate the combined diagnostic utility of B-mode ultrasound (US) and shear wave elastography (SWE) for assessing the severity of CTS in comparison to electrodiagnostic tests (EDT). Materials and Methods: This prospective observational study was conducted over 9-month periods at a tertiary care hospital. A total of 48 patients (36 females, 12 males; mean age 44 ± 10.9 years; age range 28-57 years) with clinically suspected CTS were enrolled. All patients underwent EDT, US, and SWE. Based on the EDT results, CTS cases were categorized into four groups: mild, moderate, severe, and negative. The cross-sectional area (CSA) and elasticity (E) of the MN were measured at the tunnel inlet (CSAu and Eu) and pronator quadratus region (CSAo and Eo). The differences (CSAu-CSAo and Eu-Eo) were calculated. The primary outcomes were the diagnostic performance of CSAu, CSAu-CSAo, Eu, and Eu-Eo in differentiating moderate/severe from mild/negative CTS compared to EDT findings. Secondary outcomes included a correlation of US/SWE parameters with EDT grades and between each other. ANOVA, correlation, regression, and receiver operating characteristic (ROC) curve analyses were performed. Results: CSAu and CSAu-CSAo increased progressively with worsening CTS severity. E measurements were significantly higher in moderate-to-severe CTS compared to mild or negative cases. The combined metric of CSAu-CSAo at a 5 mm threshold exhibited enhanced performance, with a higher sensitivity (83.3%), specificity (100%), and area under the curve (AUC) (0.98), surpassing the results of CSAu when used independently. Similarly, the SWE measurements indicated that Eu-Eo at a 56.1kPa cutoff achieved an AUC of 0.95, with a sensitivity of 93.3% and specificity of 94.4%, outperforming the metrics for Eu when used alone, which had an AUC of 0.93, with identical sensitivity and specificity values (93.3% and 94.4%, respectively). Conclusion: The integration of ultrasound, shear wave elastography, and electrodiagnostic tests provides a comprehensive approach to evaluate anatomical and neurological changes and guide management decisions for CTS.

Advanced strain elastography is a reliable approach for prostate cancer detection in patients with elevated PSA levels.

This study aimed to examine the validity and reproducibility of strain elastography (SE) for detecting prostate cancer (PCa) in patients with elevated prostate-specific antigen (PSA) levels. The study included 107 patients with elevated PSA levels. All eligible patients underwent transrectal ultrasound (TRUS) with real-time elastography (RTE) to detect suspicious lesions. Two readers independently evaluated the lesions and assigned a strain ratio and elastography score to each lesion. Histopathology was used as a reference standard to estimate the validity of RTE in predicting malignant lesions. An intraclass correlation (ICC) was performed to detect reliability of the strain ratios and elastography scores. TRUS-guided biopsy detected malignancies in 64 (59.8%) patients. TRUS with RTE revealed 122 lesions. The strain ratio index (SRI) cut-off values to diagnose malignancy were 4.05 and 4.35, with sensitivity, specificity, and accuracy of 94.7%, 91.3%, and 93.4%, respectively. An elastography score > 3 was the best cut-off value for detecting malignancy. According to readers, the sensitivity, specificity, and accuracy were 91.3-94.7%, 89.5-93.4%, and 91.3-90.9%, respectively. Excellent inter-reader agreement was recorded for SRI and elastography scores, with ICC of 0.937 and 0.800, respectively. SE proves to be an efficient tool for detecting PCa with high accuracy in patients with elevated PSA levels.

MRI-Based Classification for Tibial Spine Fracture: Detection Efficacy, Classification Accuracy, and Reliability.

RATIONALE AND OBJECTIVES: Recently, a new MRI-based classification for evaluating tibial spine fractures (TSFs) was developed to aid in treating these injuries. Our objective was to assess the detection efficacy, classification accuracy, and reliability of this classification in detecting and grading TSFs, as well as its impact on treatment strategy, compared to the Meyers and McKeever (MM) classification. MATERIALS AND METHODS: A retrospective study included 68 patients with arthroscopically confirmed TSFs. All patients had plain radiography and conventional MRI of the affected knee before arthroscopy. Three experienced radiologists independently reviewed all plain radiographs and MRI data and graded each patient according to MM and MRI-based classifications. The detection efficacy, classification accuracy, and inter-rater agreement of both classifications were evaluated and compared, using arthroscopic findings as the gold standard. RESULTS: The final analysis included 68 affected knees. Compared to the MM classification, the MRI-based classification produced 22.0% upgrade of TSFs and 11.8% downgrade of TSFs. According to the reviewers, the fracture classification accuracy of the MRI-based classification (91.2-95.6%) was significantly higher than that of the MM classification (73.5-76.5%, p = 0.002-0.01). The fracture detection rate of MRI-based classification (94.1-98.5%) was non-significantly higher than that of the MM classification (83.8-89.7%, p = 0.07-0.4). The soft tissue injury detection accuracy for MRI-based classification was 91.2-94.1%. The inter-rater reliability for grading TSFs was substantial for both the MM classification (κ = 0.69) and MRI-based classification (κ = 0.79). CONCLUSION: MRI-based classification demonstrates greater accuracy and reliability compared to MM classification for detecting and grading TSFs and associated soft tissue injuries.

Insights Into MRI Neuroimaging Patterns of COVID-19 in Children: A Retrospective Comprehensive Analysis.

RATIONALE AND OBJECTIVES: Neurological complications associated with coronavirus disease (COVID-19) have been reported in children; however, data on neuroimaging findings remain limited. This study aimed to comprehensively examine neuroimaging patterns of COVID-19 in children and their relationship with clinical outcomes. MATERIALS AND METHODS: This retrospective cross-sectional study involved reviewing the medical records and MRI scans of 95 children who developed new neurological symptoms within 2-4 weeks of clinical and laboratory confirmation of COVID-19. Patients were categorized into four groups based on guidelines approved by the Centers for Disease Control and Prevention (CDC). Initial brain/spinal MRI was performed. Images were reviewed by three blinded radiologists, and the findings were analyzed and categorized based on the observed patterns in the brain and spinal cord. Follow-up MRI was performed and analyzed to track lesion progression. RESULTS: Encephalopathy was the most common neurological symptom (50.5%). The most common initial MRI involvement patterns were non-confluent multifocal hyperintense white matter (WM) lesions (36.8%) and ischemia (18.9%). Most patients who underwent follow-up MRI (n = 56) showed complete resolution (69.9%); however, some patients developed encephalomalacia and myelomalacia (23.2% and 7.1%, respectively). Non-confluent hyperintense WM lesions were associated with good outcomes (45.9%, P = 0.014), whereas ischemia and hemorrhage were associated with poor outcomes (44.1%, P < 0.001). CONCLUSION: This study revealed diverse neuroimaging patterns in pediatric COVID-19 patients. Non-confluent WM lesions were associated with good outcomes, whereas ischemia and hemorrhage were associated with poorer prognoses. Understanding these patterns is crucial for their early detection, accurate diagnosis, and appropriate management.

Precision Mapping of Intrahepatic Biliary Anatomy and Its Anatomical Variants Having a Normal Liver Using 2D and 3D MRCP.

Despite significant advances in hepatobiliary surgery, biliary injury and leakage remain typical postoperative complications. Thus, a precise depiction of the intrahepatic biliary anatomy and anatomical variant is crucial in preoperative evaluation. This study aimed to evaluate the precision of 2D and 3D magnetic resonance cholangiopancreatography (MRCP) in exact mapping of intrahepatic biliary anatomy and its variants anatomically in subjects with normal liver using intraoperative cholangiography (IOC) as a reference standard. Thirty-five subjects with normal liver activity were imaged via IOC and 3D MRCP. The findings were compared and statistically analyzed. Type I was observed in 23 subjects using IOC and 22 using MRCP. Type II was evident in 4 subjects via IOC and 6 via MRCP. Type III was observed equally by both modalities (4 subjects). Both modalities observed type IV in 3 subjects. The unclassified type was observed in a single subject via IOC and was missed in 3D MRCP. Accurate detection by MRCP of intrahepatic biliary anatomy and its anatomical variants was made in 33 subjects out of 35, with an accuracy of 94.3% and a sensitivity of 100%. In the remaining two subjects, MRCP results provided a false-positive pattern of trifurcation. MRCP competently maps the standard biliary anatomy.

The Impact of Adding Digital Breast Tomosynthesis to BI-RADS Categorization of Mammographically Equivocal Breast Lesions.

Digital mammography (DM) is the cornerstone of breast cancer detection. Digital breast tomosynthesis (DBT) is an advanced imaging technique used for diagnosing and screening breast lesions, particularly in dense breasts. This study aimed to evaluate the impact of combining DBT with DM on the BI-RADS categorization of equivocal breast lesions. We prospectively evaluated 148 females with equivocal BI-RADS breast lesions (BI-RADS 0, 3, and 4) with DM. All patients underwent DBT. Two experienced radiologists analyzed the lesions. They then assigned a BI-RADS category for each lesion according to the BI-RADS 2013 lexicon, using DM, DBT, and integrated DM and DBT. We compared the results based on major radiological characteristics, BI-RADS classification, and diagnostic accuracy, using the histopathological examination of the lesions as a reference standard. The total number of lesions was 178 on DBT and 159 on DM. Nineteen lesions were discovered using DBT and were missed by DM. The final diagnoses of 178 lesions were malignant (41.6%) and benign (58.4%). Compared to DM, DBT produced 34.8% downgrading and 32% upgrading of breast lesions. Compared with DM, DBT decreased the number of BI-RADS 4 and 3. All the upgraded BI-RADS 4 lesions were confirmed to be malignant. The combination of DM and DBT improves the diagnostic accuracy of BI-RADS for evaluating and characterizing mammographic equivocal breast lesions and allows for proper BI-RADS categorization.

Magnetic Resonance Imaging Features of Rhino-Orbito-Cerebral Mucormycosis in Post-COVID-19 Patients: Radio-Pathological Correlation.

There has been a notable increase in rhino-orbito-cerebral mucormycosis (ROCM) post-coronavirus disease 2019 (COVID-19), which is an invasive fungal infection with a fatal outcome. Magnetic resonance imaging (MRI) is a valuable tool for early diagnosis of ROCM and assists in the proper management of these cases. This study aimed to describe the characteristic MRI findings of ROCM in post-COVID-19 patients to help in the early diagnosis and management of these patients. This retrospective descriptive study was conducted at a single hospital and included 52 patients with COVID-19 and a histopathologically proven ROCM infection who were referred for an MRI of the paranasal sinuses (PNS) due to sino-orbital manifestations. Two radiologists reviewed all the MR images in consensus. The diagnosis was confirmed by histopathological examination. The maxillary sinus was the most commonly affected PNS (96.2%). In most patients (57.7%), multiple sinuses were involved with the black turbinate sign on postcontrast images. Extrasinus was evident in 43 patients with orbital involvement. The pterygopalatine fossa was involved in four patients. Three patients had cavernous sinus extension, two had pachymeningeal enhancement, and one had epidural collection. The alveolar margin was affected in two patients, and five patients had an extension to the cheek. The awareness of radiologists by the characteristic MRI features of ROCM in post-COVID-19 patients helps in early detection, early proper management, and prevention of morbid complications.

Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients.

During the early stages of the pandemic, computed tomography (CT) of the chest, along with serological and clinical data, was frequently utilized in diagnosing COVID-19, particularly in regions facing challenges such as shortages of PCR kits. In these circumstances, CT scans played a crucial role in diagnosing COVID-19 and guiding patient management. The COVID-19 Reporting and Data System (CO-RADS) was established as a standardized reporting system for cases of COVID-19 pneumonia. Its implementation necessitates a high level of agreement among observers to prevent any potential confusion. This study aimed to assess the inter-observer agreement between physicians from different specialties with variable levels of experience in their CO-RADS scoring of CT chests for confirmed COVID-19 patients, and to assess the feasibility of applying this reporting system to those having little experience with it. All chest CT images of patients with positive RT-PCR tests for COVID-19 were retrospectively reviewed by seven observers. The observers were divided into three groups according to their type of specialty (three radiologists, three house officers, and one pulmonologist). The observers assessed each image and categorized the patients into five CO-RADS groups. A total of 630 participants were included in this study. The inter-observer agreement was almost perfect among the radiologists, substantial among a pulmonologist and the house officers, and moderate-to-substantial among the radiologists, the pulmonologist, and the house officers. There was substantial to almost perfect inter-observer agreement when reporting using the CO-RADS among observers with different experience levels. Although the inter-observer variability among the radiologists was high, it decreased compared to the pulmonologist and house officers. Radiologists, house officers, and pulmonologists applying the CO-RADS can accurately and promptly identify typical CT imaging features of lung involvement in COVID-19.

Computerized Analysis of Mammogram Images for Early Detection of Breast Cancer.

Breast cancer is widespread worldwide and can be cured if diagnosed early. Using digital mammogram images and image processing with artificial intelligence can play an essential role in breast cancer diagnosis. As many computerized algorithms for breast cancer diagnosis have significant limitations, such as noise handling and varying or low contrast in the images, it can be difficult to segment the abnormal region. These challenges could be overcome by proposing a new pre-processing model, exploring its impact on the post-processing module, and testing it on an extensive database. In this research work, the three-step method is proposed and validated on large databases of mammography images. The first step corresponded to the database classification, followed by the second step, which removed the pectoral muscle from the mammogram image. The third stage utilized new image-enhancement techniques and a new segmentation module to detect abnormal regions in a well-enhanced image to diagnose breast cancer. The pre-and post-processing modules are based on novel image processing techniques. The proposed method was tested using data collected from different hospitals in the Qassim Health Cluster, Qassim Province, Saudi Arabia. This database contained the five categories in the Breast Imaging and Reporting and Data System and consisted of 2892 images; the proposed method is analyzed using the publicly available Mammographic Image Analysis Society database, which contained 322 images. The proposed method gives good contrast enhancement with peak-signal to noise ratio improvement of 3 dB. The proposed method provides an accuracy of approximately 92% on 2892 images of Qassim Health Cluster, Qassim Province, Saudi Arabia. The proposed method gives approximately 97% on the Mammographic Image Analysis Society database. The novelty of the proposed work is that it could work on all Breast Imaging and Reporting and Data System categories. The performance of the proposed method demonstrated its ability to improve the diagnostic performance of the computerized breast cancer detection method.

Robust Gaussian and Nonlinear Hybrid Invariant Clustered Features Aided Approach for Speeded Brain Tumor Diagnosis.

Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient's life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.