MR Examinations Research Articles (Page 1)

To investigate the imaging characteristics of the hepatocyte-specific contrast agent gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) in inflammatory hepatocellular adenoma. The clinical data and magnetic resonance imaging (MRI) manifestations of 13 patients with pathologically confirmed I-HCA were retrospectively analyzed. There were 10 males and 3 females with an average age of 33.1 ± 10.7years. All patients underwent enhanced MR examination with Gd-EOB-DTPA (a hepatocyte-specific contrast agent). Image analysis included the number, location, size, morphology, plain scan signal, enhancement characteristics, and hepatobiliary-specific phase (HBP). The apparent diffusion coefficient (ADC) values of the lesions and surrounding normal liver parenchyma were measured on the ADC map, and the difference was compared by paired sample t-test. In this study, CRP showed a high rate of positive results; there was positive reactivity for CD34 in all patients. Among the 13 cases, 8 cases were single and 5 were multiple, for a total of 26 lesions. The margins of the lesions were all clear, and mostly round or oval; T1WI showed equal or high signal, T2WI showed high signal, DWI showed high signal, the arterial phase was highly enhanced, and the portal phase was not clear. 21 lesions in the hepatobiliary-specific phase had no uptake. The atoll sign was present in only 12% of cases. There was no significant difference between the average ADC value of the lesion and the average ADC value of the adjacent normal liver parenchyma (P = 0.620). The study revealed positive reactivity for C-reactive protein (CRP) and CD34. The Gd-EOB-DTPA-enhanced hepatobiliary phase MRI of I-HCA exhibits certain characteristic features, which serve as an aid in the diagnosis of the disease.

Large tumor size is associated with poor outcomes in patients with hepatocellular carcinoma (HCC). Although some studies have evaluated the treatment response of HCC to transarterial radioembolization (TARE), none of them used radiomics features with machine learning (ML) models in large tumors. To assess the performance of ML models using radiomics to predict the treatment response of TARE in large HCC lesions. This study included 49 patients with a large (>5 cm) HCC who underwent TARE. Treatment response was determined according to modified response evaluation criteria in solid tumors (mRECIST) criteria from the 3-month follow-up MR examinations. Complete or partial response was categorized as the responder group, while stable or progressive disease was classified as the non-responder group. Segmentation was performed on axial T2-weighted (T2W) and contrast-enhanced (CE) T1-weighted images. Classification learning models were used to create prediction models for TARE response. Forty-nine patients (9 female, 40 male; mean age 63.58 ± 8.77) were included. None of the clinical, laboratory, and radiologic characteristics except the neutrophil counts showed statistical significance. Radiomics models obtained from CE-T1 and T2W images demonstrated an accuracy rate of 79.6%, while the area under the curve (AUC) rates were 0.92 and 0.77, respectively. The clinical model showed an accuracy rate of 77.6% and an AUC of 0.65. No statistically significant difference was found among all the models (P = 0.092). ML-based models constructed with radiomics features obtained from MR images before the TARE procedure might predict response in large HCC lesions.

The value of a deep learning image reconstruction algorithm for assessing vertebral compression fractures using dual-energy computed tomography.

To investigate whether heating contributions produced by radiofrequency (RF) and gradient fields superpose sufficiently at the worst-case locations to justify their simultaneous consideration in magnetic resonance imaging (MRI) implant safety labeling. Six implant models were positioned in an ASTM phantom and realistically implanted in two anatomical human models, and exposed to gradient and RF fields at 64 MHz and 128 MHz. The simulations with the anatomical body models considered different axial exposure landmarks inside the RF and gradient body coils. The exposures were scaled to represent two sets of scenarios: either limited by the implant's MR conditional labeling to a fixed peak temperature rise, or representing an EPI or TrueFISP examination with clinically relevant parameters, where the implant label is not limiting. The temperature enhancement due to the combined RF and gradient sources, evaluated with respect to the maximum values obtained separately, depends on the implant, pulse sequence, and exposure landmark. A maximum relative enhancement of about 65% was found in the ASTM phantom, and maximum absolute enhancements above 0.3 K were found in anatomical models with realistic pulse sequences. There are clinically relevant MR examination scenarios where the maximum heating contributions produced by RF and gradient fields combine, enhancing the local peak temperature increase beyond that obtained from either assessment alone. The results prove to be useful for defining safety margins on the maximum allowable temperature increase, avoiding the requirement of a combined gradient coil and RF test.

We report a rare case of a low-lying anomalous muscle belly, the articularis genu muscle, as a cause of snapping and pain. The initial MR examination was reported as normal. Dynamic ultrasound evaluation allowed identification of the low-lying muscle as the etiology of the snapping and pain. Ultrasound-guided anesthetic injection confirmed the diagnosis with relief of symptoms and allowed subsequent further treatment with botulinum toxin. To our knowledge, the articularis genu muscle has not been previously described as a cause of knee snapping.

Objective: To evaluate the diagnostic performance of fluorine 18 fluorodeoxyglucose positron emission tomography/magnetic resonance imaging (18F-FDG PET/MR) in the preoperative staging of hypopharyngeal cancer (HPC), compare it with conventional enhanced computed tomography (CT) and MR, and further explore the prognostic value of its metabolic and diffusion metrics for HPC. Methods: This retrospective study included 33 patients with pathologically confirmed HPC. All patients underwent preoperative 18F-FDG PET/MR, CT, and MR examination. The staging performance of the three modalities was evaluated using pathological staging as a reference. Additionally, metabolic indicators and diffusion-related parameters from PET/MR were collected to investigate their impact on larynx preservation and survival. Results: PET/MR demonstrated accuracies of 90.9% and 71.4% in the preoperative T and N staging, respectively, significantly higher than those of CT (54.5%, p = 0.001; 42.9%, p = 0.021) and MR (66.7%, p = 0.016; 42.9%, p = 0.021). On the whole, significant differences emerged in the maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), minimum apparent diffusion coefficient (ADCmin), and mean ADC (ADCmean) and combined ratios across different T stages, while SUVmax, mean SUV (SUVmean), total lesion glycolysis (TLG), and MTV varied significantly across different N stages. The ADCmin and ADCmean showed good predictive capability for larynx preservation, with AUCs of 0.857 and 0.920 (p < 0.05), respectively. In Cox multivariate analysis of overall survival, high-level ADCmean (p = 0.004) and low-level TLG/ADCmean (p = 0.022) were significantly associated with better survival. Conclusion: In HPC, 18F-FDG PET/MR imaging significantly surpasses CT and MR in preoperative diagnostic staging. Its diffusion-related parameters have substantial prognostic value, with high ADC values associated with larynx preservation. ADCmean and TLG/ADCmean are potential prognostic indicators for HPC.

Objective. Prone (face-down) postures, commonly used in breast examination, as well as in wrist and elbow imaging, change the the induced current path in the body and therefore the incident electric fields to the implants during MR examination, potentially leading to significant variations from risk predictions made in supine postures. The goal of this work is to investigates the impact of prone breast examination postures, compared to supine MR examination postures, on the RF-induced heating of medical implants.Approach:To assess these effects, the Virtual Population anatomical model Ella was modified with breast shapes typical of a prone examination, and posed with one or both arms raised above the head. The head was positioned either adjacent to the bore wall, typical for breast MR examinations, or centrally within the bore, similar to supine positioning. The RF-induced electric fields and worst-case power depositions were compared with those from the supine Ella model for representative generic pacemaker, deep brain stimulator (DBS), and cochlear implants, at breast imaging landmarks.Main results.The results indicate that prone positions typical of breast examination can lead to differences in incident electric field and corresponding RF-heating of, at least, up to 5 dB at 1.5 T, and up to 2.5 dB at 3.0 T, for cochlear implants, 1-2 dB for DBS, and 1 dB for pacemakers, compared to the corresponding supine imaging landmarks.Significance.These findings underscore the importance of considering patient posture in RF safety assessments of implantable medical devices in the head and neck regions. To avoid underestimation of local RF exposure near these implants, a comprehensive posture-aware safety protocol should be designed and considered during the RF safety evaluation and labeling.

BackgroundAccording to PI-RADS v2.1, peripheral PI-RADS 3 lesions are upgraded to PI-RADS 4 if dynamic contrast-enhanced MRI is positive (3+1 lesions), however those lesions are radiologically challenging. We aimed to define criteria by expert consensus and test applicability by other radiologists for sPC prediction of PI-RADS 3+1 lesions and determine their value in integrated regression models.MethodsFrom consecutive 3 Tesla MR examinations performed between 08/2016 to 12/2018 we identified 85 MRI examinations from 83 patients with a total of 94 PI-RADS 3+1 lesions in the official clinical report. Lesions were retrospectively assessed by expert consensus with construction of a newly devised feature catalogue which was utilized subsequently by two additional radiologists specialized in prostate MRI for independent lesion assessment. With reference to extended fused targeted and systematic TRUS/MRI-biopsy histopathological correlation, relevant catalogue features were identified by univariate analysis and put into context to typically available clinical features and automated AI image assessment utilizing lasso-penalized logistic regression models, also focusing on the contribution of DCE imaging (feature-based, bi- and multiparametric AI-enhanced and solely bi- and multiparametric AI-driven).ResultsThe feature catalog enabled image-based lesional risk stratification for all readers. Expert consensus provided 3 significant features in univariate analysis (adj. p-value <0.05; most relevant feature T2w configuration: “irregular/microlobulated/spiculated”, OR 9.0 (95%CI 2.3-44.3); adj. p-value: 0.016). These remained after lasso penalized regression based feature reduction, while the only selected clinical feature was prostate volume (OR<1), enabling nomogram construction. While DCE-derived consensus features did not enhance model performance (bootstrapped AUC), there was a trend for increased performance by including multiparametric AI, but not biparametric AI into models, both for combined and AI-only models.ConclusionsPI-RADS 3+1 lesions can be risk-stratified using lexicon terms and a key feature nomogram. AI potentially benefits more from DCE imaging than experienced prostate radiologists.Clinical trial numberNot applicable.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40644-025-00916-7.

The purpose of this study is to compare three commonly used classification systems for MRI grading of acute muscle injury concerning their inter-reader reliability. Ethical committee approval was obtained. Inclusion criteria comprised patients with acute muscle injury, age ≥ 18years, and signed informed consent. MR examinations were evaluated by four independent musculoskeletal radiologists. Muscle injuries were graded according to the British Athletics Muscle Injury Classification (BAMIC), the Munich Consensus Injury Classification (MCIC), and the Chan et al. Injury Classification (CIC). Inter-reader reliability was quantified with Fleiss' Kappa (κ) and associated 95% confidence interval (CI). One hundred eleven acute muscle injuries in 110 patients (84% males) were assessed. Injured muscle groups included 85 thigh injuries (44 hamstrings, 41 non-hamstrings), 19 lower leg injuries, and 7 injuries in other locations. κ values (CI) were 0.506 (0.499, 0.514) for BAMIC, 0.566 (0.549, 0.584) for MCIC, and 0.306 (0.302, 0.311) for CIC. The highest reproducibility was seen for non-hamstring injuries in the thigh using MCIC 0.749 (0.720, 0.777), the lowest for lower leg injuries using CIC 0.199 (0.185, 0.213). Injury severity showed greater reproducibility (κ = 0.594-0.696) than the location of the injury within the muscle (κ = 0.349-0.576). The MCIC and BAMIC demonstrate moderate inter-reader reliability, whereas the CIC demonstrates fair inter-reader reliability. The challenge with the classifications is the reproducibility of localizing the injury anatomically within the muscle, rather than classifying injury severity. Non-hamstring thigh injuries were most reproducible with MCIC, while lower leg injuries were least reproducible with CIC.

The aims of this study are to optimize Magnetic Resonance Imaging (MRI) as a predictive modality for therapy response in advanced-stage cervical cancer and to identify predictors of this response in relation to survivin expression. This case-control study was conducted from January 2023 to May 2024, with total 35 subjects. The target population comprised patients with stages IIB to IIIC2 (FIGO 2018) cervical cancer. MR examination was performed three times: pre therapy, in the mid cycle of external radiation (20-30Gy), and 2 months after complete therapy. The study analyzed relations between age, tumor size, nodal metastasis, ADC and T2 parameters on MR, and survivin levels, with final therapeutic response. The predictive model for final therapy response was developed using four variables: patient age, tumor size, nodal metastasis, and the T2 tumor-to-muscle ratio on MRI #2. The scoring system showed the minimum total score was 0 and the maximum total score was 6. The cut-off score on this predictive model is score 3 to differentiate between the prediction of good or poor response with the sensitivity of 92,86% and a specificity of 85,71%. This study found that T2 tumor-to-muscle ratio (T2 t/m ratio) on MR in the mid-cycle external radiation is a potential predictive factor of final therapy response on advanced-stage cervical cancer. A predictive model for assessing the final response could effectively incorporate clinical and MR parameters, including patient age, tumor size, nodal metastasis findings on MR, and Ratio T2 t/m on MR in the mid-cycle external radiation.

To discuss the diagnosis of fetal bilateral anophthalmia, which is a congenital two-sided absence of the eyeballs and is the most severe form of structural malformation of the eye. An ultrasound examination of a male fetus at 25 weeks of gestation revealed the absence of the eyeball and lenses on both sides. No other associated developmental anomalies were noted. Early ultrasound examinations at 11+ 3 weeks and 15 weeks of gestation did not reveal any orbital pathology. To clarify the diagnosis of isolated congenital bilateral anophthalmia, magnetic resonance imaging of the fetal face and head was performed. MR examination was carried out using a General Electric Optima 450i + GEM device, with 48-channel head coil, with the patient freely breathing in T2 SSFSE mode, in axial, coronal and sagittal projections, slice thickness 4 mm. During the MRI, the preliminary diagnosis was confirmed; the eyeballs and lenses were absent on both sides. No genetic studies were conducted due to the family's financial limitations. The pregnancy was terminated. A pathological examination confirmed the diagnosis of isolated bilateral anophthalmia. Prenatal diagnosis of bilateral anophthalmia has an important social significance. Prenatal diagnosis of isolated congenital bilateral anophthalmia using ultrasound diagnostics is possible from the end of the first to the beginning of the second trimester of pregnancy. Magnetic resonance imaging is of great importance in clarifying diagnosis of congenital anophthalmia.

To investigate the predictive value of Gd-EOB-DTPA enhanced MRI functional liver imaging score and spontaneous portal shunt on the occurrence of first hepatitis decompensation in patients with chronic hepatitis B (CHB). Clinical and MRI data of 443 patients with CHB who received Gd-EOB-DTPA enhanced MRI scanning from October 2019 to October 2022 were retrospectively collected. All patients had a complete clinical laboratory examination 1 week before and after MR examination. According to the FIB-4 score and Child-Pugh(CP) grading criteria, the patients were divided into 4 groups: CLD group, CPA group, CPB group, and CPC group. The correlation between clinical laboratory indicators such as aspartate aminotransferase (AST), alanine aminotransferase (ALT), and imaging parameters such as functional liver imaging score (FLIS), spontaneous portosystemic shunt (SPSS), splenic craniocaudal diameter (SCCD), portal vein width and splenic vein width of patients in different groups was compared. Intragroup correlation coefficient (ICC) was used to evaluate the consistency of FLIS, SPSS and SCCD results among different observers. The ROC curve was used to compare the diagnostic efficiency of each imaging parameter for different groups of patients. The laboratory and imaging parameters that differed across groups were analyzed using Cox regression to evaluate the predictive efficacy of each parameter for the occurrence of first liver decompensation in patients with high-risk CHB in the CLD group and the CPA and CPB groups. FLIS and its 3 parameters (EnQS, ExQS, and PVQS) were moderately strongly correlated with clinical groups (r=-0.370 to -0.543, P<0.001). The consistency of FLIS and its 3 quantitative parameters among different observers was high (r=0.965, P<0.001). SCCD and AUC of 0.844 (95% CI: 0.792-0.896) were the optimal parameters for distinguishing LC between the CLD group and the CPA group. The best FLIS criteria for predicting LC in CLD/CPA groups and for predicting LC in CPA/B groups were ≥4. The AUC for the differentiation of LC from CPB/C in the CLD/CPA groups was 0.801 (95% CI: 0.759-0.843), while the AUC for CPB and CPC was 0.858 (95% CI: 0.813-0.903). FLIS is not an independent predictor of first hepatitis decompensation in patients with CHB. Univariate analysis showed that SPSS, SCCD≥14.36cm, age, and AST were independent risk factors for first hepatitis decompensation. The dichotomy of SPSS combined with SCCD can provide a better predictor of the first hepatitis decompensation event. FLIS has the best diagnostic efficacy in classifying liver function in patients with CHB. SPSS and SCCD have high predictive value in predicting the first hepatitis decompensation event in the CLD group and the CPA and CPB groups.

Head and neck paragangliomas (HNPGs) are rare neuroendocrine tumors whose hypervascular nature allows differentiation from many other head and neck neoplasms. We aimed to investigate the sensitivity of arterial spin-labeling (ASL) MR sequences for the detection of HNPGs. All head and neck MR examinations performed at a single tertiary institution between 2015 and 2023 were searched. Studies using ASL sequences that indicated either clinical suspicion for or ultimate imaging diagnosis of HNPG were identified. These studies were independently reviewed by 2 neuroradiologists blinded to the original radiology reports to determine, in a stepwise fashion, the following: 1) whether there was asymmetrically elevated blood flow on ASL imaging, 2) whether ASL findings correlated with lesions identifiable on conventional anatomic images, and 3) whether lesions likely reflected paragangliomas on the basis of correlations with clinical, laboratory, pathology, and other radiology data (Disagreement between raters was resolved by consensus.). The Cohen κ coefficient and the sensitivity of ASL in identifying HNPGs were calculated. Eighty-four patients were included in the analysis (mean age, 54 [SD, 14] years and 47 women). Thirty patients had lesions confirmed or found likely to be HNPG, and 54 patients had lesions found unlikely to be HNPG or had no identifiable lesion. Among 46 of 84 patients with ASL blood flow asymmetry, 43 (93%) had lesions correlated with a lesion identifiable on anatomic imaging. Asymmetrically elevated ASL blood flow that correlated with a lesion demonstrated a sensitivity of 100% for reader A and 97% for reader B for identifying HNPG. The Cohen κ coefficient was 0.90 (SD, 0.11) between the 2 readers (P < .001). Among 18 cases with pathology- or dotatate PET-proved HNPG, the sensitivity was 100% for reader A and 94% for reader B. Asymmetrically elevated blood flow on ASL imaging demonstrates high sensitivity for the detection of HNPG, with almost perfect interrater agreement.

Despite its high negative predictive value (NPV) for clinically significant prostate cancer (csPCa), MRI suffers from a substantial number of false positives, especially for intermediate-risk cases. In this work, we determine whether a deep learning model trained with PI-RADS-guided representation learning can disambiguate the PI-RADS 3 classification, detect csPCa from bi-parametric prostate MR images, and avoid unnecessary benign biopsies. This study included 28,263 MR examinations and radiology reports from 21,938 men imaged for known or suspected prostate cancer between 2015 and 2023 at our institution (21 imaging locations with 34 readers), with 6352 subsequent biopsies. We trained a deep learning model, a representation learner (RL), to learn how radiologists interpret conventionally acquired T2-weighted and diffusion-weighted MR images, using exams in which the radiologists are confident in their risk assessments (PI-RADS 1 and 2 for the absence of csPCa vs. PI-RADS 4 and 5 for the presence of csPCa, n=21,465). We then trained biopsy-decision models to detect csPCa (Gleason score ≥7) using these learned image representations, and compared them to the performance of radiologists, and of models trained on other clinical variables (age, prostate volume, PSA, and PSA density) for treatment-naïve test cohorts consisting of only PI-RADS 3 (n=253, csPCa=103) and all PI-RADS (n=531, csPCa=300) cases. On the 2 test cohorts (PI-RADS-3-only, all-PI-RADS), RL-based biopsy-decision models consistently yielded higher AUCs in detecting csPCa (AUC=0.73 [0.66, 0.79], 0.88 [0.85, 0.91]) compared with radiologists (equivocal, AUC=0.79 [0.75, 0.83]) and the clinical model (AUCs=0.69 [0.62, 0.75], 0.78 [0.74, 0.82]). In the PIRADS-3-only cohort, all of whom would be biopsied using our institution's standard of care, the RL decision model avoided 41% (62/150) of benign biopsies compared with the clinical model (26%, P<0.001), and improved biopsy yield by 10% compared with the PI-RADS ≥3 decision strategy (0.50 vs. 0.40). Furthermore, on the all-PI-RADS cohort, RL decision model avoided 27% of additional benign biopsies (138/231) compared to radiologists (33%, P<0.001) with comparable sensitivity (93% vs. 92%), higher NPV (0.87 vs. 0.77), and biopsy yield (0.75 vs. 0.64). The combination of clinical and RL decision models further avoided benign biopsies (46% in PI-RADS-3-only and 62% in all-PI-RADS) while improving NPV (0.82, 0.88) and biopsy yields (0.52, 0.76) across the 2 test cohorts. Our PI-RADS-guided deep learning RL model learns summary representations from bi-parametric prostate MR images that can provide additional information to disambiguate intermediate-risk PI-RADS 3 assessments. The resulting RL-based biopsy decision models also outperformed radiologists in avoiding benign biopsies while maintaining comparable sensitivity to csPCa for the all-PI-RADS cohort. Such AI models can easily be integrated into clinical practice to supplement radiologists' reads in general and improve biopsy yield for any equivocal decisions.

ObjectivePituitary adenomas (PAs), craniopharyngiomas (CRs), Rathke’s cleft cysts (RCCs), and tuberculum sellar meningiomas (TSMs) are common sellar region lesions with similar imaging characteristics, making differential diagnosis challenging. This study aims to develop and evaluate machine learning models using MRI-based radiomics features to differentiate these lesions.MethodsTwo hundred and fifty-eight pathologically diagnosed sellar region lesions, including 54 TSMs, 81 CRs, 61 RCCs and 63 PAs, were retrospectively studied. All patients underwent conventional MR examinations. Feature extraction and data normalization and balance were performed. Extreme gradient boosting (XGBoost), support vector machine (SVM), and logistic regression (LR) models were trained with the radiomics features. Five-fold cross-validation was used to evaluate model performance.ResultsThe XGBoost model showed better performance than the SVM and LR models built from contrast-enhanced T1-weighted MRI features (balanced accuracy 0.83, 0.77, 0.75; AUC 0.956, 0.938, 0.929, respectively). Additionally, these models demonstrated significant differences in sensitivity (P = 0.032) and specificity (P = 0.045). The performance of the XGBoost model was superior to that of the SVM and LR models in differentiating sellar region lesions by using contrast-enhanced T1-weighted MRI features.ConclusionThe proposed model has the potential to improve the diagnostic accuracy in differentiating sellar region lesions.

Our purpose was to evaluate the performance of MR imaging/cholangiography for ductal cholangiocarcinoma (CCA) diagnosis and to search for specific MR features of ductal CCA among primary sclerosing cholangitis (PSC) patients. We retrospectively analyzed 31 patients from a single center, each with a diagnosis of PSC, and suspicion of ductal CCA. Ductal CCA had been suspected during multidisciplinary team meetings when high-grade biliary stenosis was associated with focal thickening of the biliary wall. Two radiologists blinded to clinical information and imaging history independently reviewed patients' MR examinations using a standardized model created for this study. Fisher's exact test and Student's t-test were used to analyze the population's characteristics. Fisher's exact test and the chi-square test were used to compare associations of categorical variables (each standard model's item) with the final diagnosis. Interobserver agreement was assessed by Cohen's κ coefficient. Our population had a mean age of 42.7 ± 13.6 years and included 68% males. The final diagnosis was ductal CCA for 14 patients, and inflammatory stenosis for 17 patients. For diagnosing CCA, MR imaging/cholangiography exhibited a sensitivity of 43-50% and specificity of 70-76%, with low positive predictive (58-60%) and negative predictive (62-63%) values. Interobserver agreement ranged from κ = 0.04-0.75. Univariate analysis revealed no significant association between individual MR imaging/cholangiography features and CCA diagnosis. MR imaging/cholangiography showed suboptimal performance for ductal CCA diagnosis among PSC patients and we did not find any specific feature to distinguish ductal CCA from inflammatory stenosis. Question Diagnosing ductal cholangiocarcinoma in patients with primary sclerosing cholangitis remains challenging without known predictive MR imaging features. Findings MR imaging/cholangiography exhibited low sensitivity, specificity, and interobserver reliability for ductal cholangiocarcinoma diagnosis in primary sclerosing cholangitis and lacks reliability for distinguishing between benign and malignant strictures. Clinical relevance Diagnosing ductal cholangiocarcinoma in patients with primary sclerosing cholangitis remains challenging and our retrospective study demonstrates that MR imaging lacks reliability in distinguishing between benign and malignant high-grade strictures and did not find any specific MR feature of ductal CCA.

Development and validation of a diagnostic nomogram model for osteoporosis in the elderly using 3D multi-echo Dixon sequence combined with magnetization transfer imaging.

The aim of this study is to present a case of a patient with spondylodiscitis. Spondylodiscitis is a infection of the spine involving the vertebral body and/or intervertebral disc, often caused by Staphylococcus aureus and Enterobacteriaceae. It most commonly affects the lumbar spine, begins in the vertebral body endplates and can lead to destruction of bone structures and involvement of the intervertebral disc and surrounding tissues. A 53-year-old man developed lumbar spine pain after an infection of unknown origin accompanied by fever and weakness. After two weeks, when the pain worsened and radiated to the left buttock, an MR examination showed features of L3/L4 spondylodiscitis. During hospitalization, a follow-up MR examination revealed destruction of the L3 and L4 vertebral bodies, inflammatory changes within the L3/L4 intervertebral disc, and widening of the intervertebral space. Inflammatory granules in the anterior part of the spinal canal with slight pressure on the meningeal sac were identified. A CT scan confirmed vertebral destruction. Empirical antibiotic therapy (clindamycin, ceftriaxone, vancomycin, rifampicin) was followed by spinal stabilization. After a year, CT scan showed improvement - reduced destruction of the L3/L4 vertebral bodies and no palpable infiltrative lesions. The stabilizing material was removed. Spondylodiscitis is a infection of the spine, often diagnosed late because of nonspecific symptoms such as back pain and fever. The MRI is the gold standard for diagnosis. Diagnosis is based on clinical, laboratory and imaging findings. It requires the cooperation of surgeons, radiologists and microbiologists. Early detection improves prognosis and quality of life.

The aim of this study is to present a case of a patient with stress fractures. Stress fractures, otherwise known as fatigue fractures, result from abnormal and repetitive strain of healthy bone. This leads to micro damages and subsequent fractures. They are most commonly encountered after a sudden and excessive increase in physical activity and most frequently located in the metatarsal, heel, tibia, fibula and femur. This case presents a twenty-nine year old patient, diagnosed with obesity, not involved in sport ever before. Since the end of the spring, he started to exercise intensively at the gym, particularly lifting weights. Since the end of summer, he developed increasingly frequent feet pain, so he reported to an orthopedist. An ultrasound examination of the ankle joints was performed and it did not show any soft tissue damage. Subsequent MR imaging of both ankle joints showed a fatigue fracture: the talus bone of both feet and in the left cuboid bone. Excessive physical effort, especially in patients who have not previously participated in sport, can cause fatigue fractures. The diagnostic procedure should include patient's history, X-ray and ultrasound. Fractures at an early stage may not be visible on X- rays, however an MR examination can reveal bone lesions or a fracture line. In the treatment of stress fractures, modification of activity, use of orthosis, direct cooling, short term use of pain drugs and rehabilitation are usually recommended.

BackgroundTo investigate the feasibility of detecting local recurrent nasopharyngeal carcinoma (rNPC) using unenhanced magnetic resonance images (MRI) and optimize a layered management strategy for follow-up with a deep learning model.MethodsDeep learning models based on 3D DenseNet or ResNet frames using unique sequence (T1WI, T2WI, or T1WIC) or a combination of T1WI and T2WI sequences (T1_T2) were developed to detect local rNPC. A deep-learning-assisted recurrent NPC detecting simultaneous tactic (DARNDEST) utilized DenseNet was optimized by superimposing the T1WIC model over the T1_T2 model in a specific population. Diagnostic efficacy (accuracy, sensitivity, specificity) and examination cost of a single MR scan were compared among the conventional method, T1_T2 model, and DARNDEST using McNemar’s Z test.ResultsNo significant differences in overall accuracy, sensitivity, and specificity were found between the T1WIC model and T1WI, T2WI, or T1_T2 models in both test sets (all P > 0.0167). The DARNDEST had higher accuracy and sensitivity but lower specificity than the T1_T2 model in both the internal (accuracy, 85.91% vs. 84.99%; sensitivity, 90.36% vs. 84.26%; specificity, 82.20% vs. 85.59%) and external (accuracy, 86.14% vs. 84.16%; sensitivity, 90.32% vs. 84.95%; specificity, 82.57% vs. 83.49%) test sets. The cost of a single MR examination using DARNDEST was $330,724 (internal) and $328,971 (external) with a hypothetical cohort of 1,000 patients, relative to $313,250 of the T1_T2 model and $340,865 of the conventional method.ConclusionsDetecting local rNPC using unenhanced MRI with deep learning is feasible and DARNDEST-driven follow-up management is efficient and economic.