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The purpose of this study was to evaluate differences in graft orientation between transtibial (TT) and anteromedial (AM) portal technique using magnetic resonance imaging (MRI) in anterior cruciate ligament (ACL) reconstruction. Fifty-six patients who were undergoing ACL reconstruction underwent MRI of their healthy and reconstructed knee. Thirty patients had ACL reconstruction using the TT (group A), while in the remaining 26 the AM (group B) was used. In the femoral part graft orientation was evaluated in the coronal plane using the femoral graft angle (FGA). The FGA was defined as the angle between the axis of the femoral tunnel and the joint line. In the tibial part graft orientation was evaluated in the sagittal plane using the tibial graft angle (TGA). The TGA was defined as the angle between the axis of the tibial tunnel and a line perpendicular to the long axis of the tibia. The ACL angle of the normal knee in the sagittal view was also calculated. The mean FGA for group A was 72 degrees, while for the group B was 53 degrees and this was statistically significant (P < 0.001). The mean TGA for group A was 64 degrees, while for the group B was 63 degrees (P = 0.256). The mean intact ACL angle for group A was 52 degrees, while for the group B was 51 degrees. The difference between TGA and intact ACL angle was statistically significant (P < 0.001) for both groups. Using the AM portal technique, the ACL graft is placed in a more oblique direction in comparison with the TT technique in the femoral part. However, there are no differences between the two techniques in graft orientation in the tibial part. Normal sagittal obliquity is not restored with both techniques. [ABSTRACT FROM AUTHOR]

The aim of this study was to (1) generate quantitative CT (QCT) densitometric data based on routine abdominal multi-detector (MDCT) examinations and (2) investigate whether these data can be used to differentiate osteoporotic from healthy females. Twenty-five female patients (group A) with a history of radiotherapy were examined both with routine abdominal MDCT and standard QCT to generate a MDCT-to-QCT conversion equation. Twenty-one osteoporotic (group B) and 23 healthy female patients (group C) were also recruited in the study. Patients of groups B and C underwent routine abdominal MDCT examination for various clinical indications. Mean bone mineral density (BMD) in patients of group A was 103.4 mg/ml +/- 32.8 with routine abdominal MDCT and 91.0 mg/ml +/- 28.5 with QCT. Quantitative CT BMD(QCT) values for patients in groups B and C were calculated utilizing the BMD(MDCT) values derived from routine abdominal MDCT data sets and the MDCT to QCT conversion equation: BMD(QCT)=0:78 x BMD(MDCT) + 10:13. The calculated QCT densitometric data adequately differentiated osteoporotic from healthy females (area under ROC curve 0.828, p = 0.05). In conclusion, this study showed that in a group of female patients, QCT data derived from routine abdominal MDCT examinations discriminated osteoporotic from healthy subjects. [ABSTRACT FROM AUTHOR]

Background: Clinical tests used for the detection of meniscal tears in the knee do not present acceptable diagnostic sensitivity and specificity values. Diagnostic accuracy is improved by arthroscopic evaluation or magnetic resonance imaging studies. The objective of this study was to evaluate the diagnostic accuracy of a new dynamic clinical examination test for the detection of meniscal tears.Methods: Two hundred and thirteen symptomatic patients with knee injuries who were examined clinically, had magnetic resonance imaging studies performed, and underwent arthroscopic surgery and 197 asymptomatic volunteers who were examined clinically and had magnetic resonance imaging studies done of their normal knees were included in this study. For clinical examination, the medial and lateral joint-line tenderness test, the McMurray test, the Apley compression and distraction test, the Thessaly test at 5 degrees of knee flexion, and the Thessaly test at 20 degrees of knee flexion were used. For all clinical tests, the sensitivity, specificity, false-positive, false-negative, and diagnostic accuracy rates were calculated and compared with the arthroscopic and magnetic resonance imaging data for the test subjects and the magnetic resonance imaging data for the control population.Results: The Thessaly test at 20 degrees of knee flexion had a high diagnostic accuracy rate of 94% in the detection of tears of the medial meniscus and 96% in the detection of tears of the lateral meniscus, and it had a low rate of false-positive and false-negative recordings. Other traditional clinical examination tests, with the exception of joint-line tenderness, which presented a diagnostic accuracy rate of 89% in the detection of lateral meniscal tears, showed inferior rates.Conclusions: The Thessaly test at 20 degrees of knee flexion can be used effectively as a first-line clinical screening test for meniscal tears, reducing the need for and the cost of modern magnetic resonance imaging methods. [ABSTRACT FROM AUTHOR]

Although percutaneous fixation with iliosacral screws has been shown to be a safe and reproducible method for sacroiliac dislocation and sacral fractures, it is a technically demanding technique, and one of its contraindications is sacral anatomical variations and dysmorphism. The incidence and pattern of S1 and S2 anatomical variations were evaluated in 61 patients (35 women and 26 men) using magnetic resonance imaging of the sacrum in an attempt to explore the possible existence of groups of individuals in whom percutaneous sacroiliac fixation is difficult due to local anatomy.S1 and S2 dimensions in both the transverse and coronal planes were recorded and evaluated. In each individual, S1 and S2 dimensions both in the coronal and transverse planes were proportional, with S2 dimensions being 80% of those of S1 on average. Patients were separated into 4 groups based on the S1 and S2 body size and the asymmetry of dimensions in the transverse and coronal planes. In 48 patients (78.6%), dimensions in both planes were symmetrical despite the varying size of the S1 and S2 body. In 2 patients (3.3%) there was a combination of large transverse plane and small coronal plane dimensions, with large S1 and S2 body size. In 9 patients (14.8%), coronal plane dimensions were disproportionately smaller compared to those of the transverse plane, with a varying size of S1 and S2 body making effective sacroiliac screw insertion a difficult task. Thus, a preoperative imaging study, preferably computed tomography scan, of S1 and S2 body size and coronal plane dimensions and an intraoperative fluoroscopic control of S1 and S2 dimensions on the coronal plane are suggested for safe sacroiliac screw fixation. [ABSTRACT FROM AUTHOR]

A series of conditions can mimic musculoskeletal infections on imaging, complicating their diagnosis and affecting the treatment. Depending on the anatomical location, different conditions can manifest with clinical and imaging findings that mimic infections. Herein we present a wide spectrum of the musculoskeletal disorders of the axial skeleton, long bones, peripheral joints, and soft tissue that may manifest as infectious processes, and we focus on the potential mimics of osteomyelitis, septic arthritis, and infectious spondylodiscitis that are common in clinical practice. We present the typical imaging characteristics of each musculoskeletal infection, followed by mimicking conditions.

Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype in health and disease and to set the basis of comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 ± 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes., (© 2024. The Author(s).)

Objective: Although there is growing evidence that ultrasonography is superior to X-ray for rib fractures' detection, X-ray is still indicated as the most appropriate method. This has partially been attributed to a lack of studies using an appropriate reference modality. We aimed to compare the diagnostic accuracy of ultrasonography and X-ray in the detection of rib fractures, considering CT as the reference standard., Materials and Methods: Within a 2.5-year period, all consecutive patients with clinically suspected rib fracture(s) following blunt chest trauma and available posteroanterior/anteroposterior X-ray and thoracic CT were prospectively studied and planned to undergo thoracic ultrasonography, by a single operator. All imaging examinations were evaluated for cortical rib fracture(s), and their location was recorded. The cartilaginous rib portions were not assessed. CTs and X-rays were evaluated retrospectively. Concomitant thoracic/extra-thoracic injuries were assessed on CT. Comparisons were performed with the Mann-Whitney U test and Fisher's exact test., Results: Fifty-nine patients (32 males, 27 females; mean age, 53.1 ± 16.6 years) were included. CT, ultrasonography, and X-ray (40 posteroanterior/19 anteroposterior views) diagnosed 136/122/42 rib fractures in 56/54/27 patients, respectively. Ultrasonography and X-ray had sensitivity of 100%/40% and specificity of 89.7%/30.9% for rib fractures' detection. Ultrasound accuracy was 94.9% compared to 35.4% for X-rays (P < .001) in detecting individual rib fractures. Most fractures involved the 4th-9th ribs. Upper rib fractures were most commonly overlooked on ultrasonography. Thoracic cage/spine fractures and haemothorax represented the most common concomitant injuries., Conclusion: Ultrasonography appeared to be superior to X-ray for the detection of rib fractures with regard to a reference CT., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Objective: To compare the clinical efficacy of capsule-rupturing versus capsule-preserving ultrasound-guided hydrodilatation in patients with shoulder adhesive capsulitis (AC). To determine potential factors affecting the outcome over a 6-month follow-up., Materials and Methods: Within a 2-year period, 149 consecutive patients with AC were prospectively enrolled and allocated into (i) group-CR, including 39 patients receiving hydrodilatation of the glenohumeral joint (GHJ) with capsular rupture and (ii) group-CP, including 110 patients treated with GHJ hydrodilatation with capsular preservation. Demographics, affected shoulder, and AC grade were recorded. Disabilities of the Arm, Shoulder and Hand (DASH) questionnaire and visual analog scale (VAS) were used for clinical assessment at baseline/1/3/6 months. Comparisons were performed with Mann-Whitney U test and Kolmogorov-Smirnov test. Linear regression was used to identify predictors of outcome. P value < 0.05 defined significance., Results: DASH and VAS scores in both groups improved significantly compared to baseline (P < 0.001) and were significantly lower in the CP compared to CR group at all time-points following intervention (P < 0.001). Capsule rupture was a significant predictor of DASH score at all time-points (P < 0.001). DASH scores correlated to initial DASH score at all time-points (P < 0.001). DASH/VAS scores at 1 month were correlated to the AC grade (P = 0.025/0.02)., Conclusion: GHJ hydrodilatation results in pain elimination and functional improvement till the mid-term in patients with AC, with improved outcome when adopting the capsule-preserving compared to the capsule-rupturing technique. Higher initial DASH score is predictive of impaired functionality in the mid-term., (© 2023. The Author(s).)

Purpose: In recent years, the field of medical imaging has witnessed remarkable advancements, with innovative technologies which revolutionized the visualization and analysis of the human spine. Among the groundbreaking developments in medical imaging, Generative Adversarial Networks (GANs) have emerged as a transformative tool, offering unprecedented possibilities in enhancing spinal imaging techniques and diagnostic outcomes. This review paper aims to provide a comprehensive overview of the use of GANs in spinal imaging, and to emphasize their potential to improve the diagnosis and treatment of spine-related disorders. A specific review focusing on Generative Adversarial Networks (GANs) in the context of medical spine imaging is needed to provide a comprehensive and specialized analysis of the unique challenges, applications, and advancements within this specific domain, which might not be fully addressed in broader reviews covering GANs in general medical imaging. Such a review can offer insights into the tailored solutions and innovations that GANs bring to the field of spinal medical imaging., Methods: An extensive literature search from 2017 until July 2023, was conducted using the most important search engines and identified studies that used GANs in spinal imaging., Results: The implementations include generating fat suppressed T2-weighted (fsT2W) images from T1 and T2-weighted sequences, to reduce scan time. The generated images had a significantly better image quality than true fsT2W images and could improve diagnostic accuracy for certain pathologies. GANs were also utilized in generating virtual thin-slice images of intervertebral spaces, creating digital twins of human vertebrae, and predicting fracture response. Lastly, they could be applied to convert CT to MRI images, with the potential to generate near-MR images from CT without MRI., Conclusions: GANs have promising applications in personalized medicine, image augmentation, and improved diagnostic accuracy. However, limitations such as small databases and misalignment in CT-MRI pairs, must be considered., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction: Secondary fracture prevention is an essential part of hip fracture treatment. Despite this, many patients are discharged without the appropriate anti-osteoporotic medication. The aim of this study is to report the outcomes of the application of an in-hospital, surgeon-led anti-osteoporotic medication algorithm to patients with hip fractures., Materials and Methods: This prospective cohort study followed patients with hip fractures who were treated at a tertiary referral hospital between 2020 and 2022. At discharge, anti-osteoporotic medication according to the Arbeitsgemeinschaft für Osteosynthesefragen (AO) Foundation algorithm was prescribed to all patients. Multivariate Cox regression analysis was used to investigate the risks of non-persistence to medication and of secondary fracture., Results: Two hundred thirteen consecutive patients were prospectively followed. Mean follow-up was 17.2 ± 7.1 months. Persistence to medication at 2 years was 58% (95%CI 51-65%). A secondary osteoporotic fracture occurred in 1/126 (0.8%) persistent patients and 9/87 (11.4%) non-persistent patients. Multivariable Cox regression analysis confirmed that persistence to medication was significantly associated with a lower risk of secondary fracture (cause-specific hazard ratio [csHR] 0.05; 95%CI 0.01-0.45; p = 0.007)., Conclusion: The application of the surgeon-led AO Foundation algorithm enables the in-hospital initiation of anti-osteoporotic treatment, leading to better persistence to medication and decreased incidence of secondary osteoporotic fractures., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: To develop a deep learning methodology that distinguishes early from late stages of avascular necrosis of the hip (AVN) to determine treatment decisions., Methods: Three convolutional neural networks (CNNs) VGG-16, Inception ResnetV2, InceptionV3 were trained with transfer learning (ImageNet) and finetuned with a retrospectively collected cohort of (n = 104) MRI examinations of AVN patients, to differentiate between early (ARCO 1-2) and late (ARCO 3-4) stages. A consensus CNN ensemble decision was recorded as the agreement of at least two CNNs. CNN and ensemble performance was benchmarked on an independent cohort of 49 patients from another country and was compared to the performance of two MSK radiologists. CNN performance was expressed with areas under the curve (AUC), the respective 95% confidence intervals (CIs) and precision, and recall and f1-scores. AUCs were compared with DeLong's test., Results: On internal testing, Inception-ResnetV2 achieved the highest individual performance with an AUC of 99.7% (95%CI 99-100%), followed by InceptionV3 and VGG-16 with AUCs of 99.3% (95%CI 98.4-100%) and 97.3% (95%CI 95.5-99.2%) respectively. The CNN ensemble the same AUCs Inception ResnetV2. On external validation, model performance dropped with VGG-16 achieving the highest individual AUC of 78.9% (95%CI 51.6-79.6%) The best external performance was achieved by the model ensemble with an AUC of 85.5% (95%CI 72.2-93.9%). No significant difference was found between the CNN ensemble and expert MSK radiologists (p = 0.22 and 0.092 respectively)., Conclusion: An externally validated CNN ensemble accurately distinguishes between the early and late stages of AVN and has comparable performance to expert MSK radiologists., Clinical Relevance Statement: This paper introduces the use of deep learning for the differentiation between early and late avascular necrosis of the hip, assisting in a complex clinical decision that can determine the choice between conservative and surgical treatment., Key Points: • A convolutional neural network ensemble achieved excellent performance in distinguishing between early and late avascular necrosis. • The performance of the deep learning method was similar to the performance of expert readers., (© 2023. The Author(s).)

Objectives: To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset., Methods: A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN., Results: Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873-0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752-0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844-0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma., Conclusion: Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors., Critical Relevance Statement: Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors., Key Points: • Differentiation between benign and malignant tumors based on CT is extremely challenging. • Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. • Deep learning can be used to distinguish between benign and malignant renal tumors., (© 2024. The Author(s).)

Objectives: Computed tomography is regarded as the reference-standard imaging modality for the assessment of acute left-sided colonic diverticulitis (ALCD). However, its utility may be impaired by cost issues, limited availability, radiation exposure, and contrast-related adverse effects. Ultrasonography is increasingly advocated as an alternative technique for evaluating ALCD, although there is variation regarding its accuracy in disease diagnosis and staging and in determining alternative diagnoses. The aim of this study was to assess the performance of ultrasonography in diagnosing ALCD, differentiating complicated from non-complicated disease and defining alternative diseases related to left lower quadrant pain., Methods: Within a 2-year period, all consecutive adult patients with clinically suspected ALCD and available abdominal computed tomography were prospectively evaluated and planned to undergo an abdominal ultrasonographic examination, tailored to the assessment of left lower quadrant. Computed tomography (CT) was regarded as the reference standard., Results: A total of 132 patients (60 males, 72 females; mean age: 61.3 ± 11 years) were included. The sensitivity, specificity, and area under curve of ultrasonography for diagnosing ALCD were 88.6, 84.9, and 86.8%, with positive and negative predictive values of 89.7 and 83.3%, respectively. The method had sensitivity, specificity, and area under curve of 77.8, 100, and 88.9%, respectively, for defining complicated disease. The area under the curve for the identification of alternative diseases in patients with left lower quadrant pain was 90.9%., Conclusions: Ultrasonography has high diagnostic accuracy for diagnosing ALCD, differentiating complicated from non-complicated disease and establishing alternative diagnoses related to left lower quadrant pain. A low threshold to get a CT should be maintained as not to miss cases that may mimic ALCD., (© 2023 American Institute of Ultrasound in Medicine.)

Objectives: Post-mortem interval (PMI) estimation has long been relying on sequential post-mortem changes on the body as a function of extrinsic, intrinsic, and environmental factors. Such factors are difficult to account for in complicated death scenes; thus, PMI estimation can be compromised. Herein, we aimed to evaluate the use of post-mortem CT (PMCT) radiomics for the differentiation between early and late PMI., Methods: Consecutive whole-body PMCT examinations performed between 2016 and 2021 were retrospectively included (n = 120), excluding corpses without an accurately reported PMI (n = 23). Radiomics data were extracted from liver and pancreas tissue and randomly split into training and validation sets (70:30%). Following data preprocessing, significant features were selected (Boruta selection) and three XGBoost classifiers were built (liver, pancreas, combined) to differentiate between early (< 12 h) and late (> 12 h) PMI. Classifier performance was assessed with receiver operating characteristics (ROC) curves and areas under the curves (AUC), which were compared by bootstrapping., Results: A total of 97 PMCTs were included, representing individuals (23 females and 74 males) with a mean age of 47.1 ± 23.38 years. The combined model achieved the highest AUC reaching 75% (95%CI 58.4-91.6%) (p = 0.03 compared to liver and p = 0.18 compared to pancreas). The liver-based and pancreas-based XGBoost models achieved AUCs of 53.6% (95%CI 34.8-72.3%) and 64.3% (95%CI 46.7-81.9%) respectively (p > 0.05 for the comparison between liver- and pancreas-based models)., Conclusion: The use of radiomics analysis on PMCT examinations differentiated early from late PMI, unveiling a novel image-based method with important repercussions in forensic casework., Clinical Relevance Statement: This paper introduces the employment of radiomics in forensic diagnosis by presenting an effective automated alternative method of estimating post-mortem interval from targeted tissues, thus paving the way for improvement in speed and quality of forensic investigations., Key Points: • A combined liver-pancreas radiomics model differentiated early from late post-mortem intervals (using a 12-h threshold) with an area under the curve of 75% (95%CI 58.4-91.6%). • XGBoost models based on liver-only or pancreas-only radiomics demonstrated inferior performance to the combined model in predicting the post-mortem interval., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Aim: To investigate the reliability of post-mortem computed tomography (PMCT) in a case series of homicides involving blunt-force, sharp-force, and ballistic trauma., Materials and Methods: The study investigates 16 homicide cases that underwent PMCT before autopsy. Two radiologists assessed the PMCT examinations and the data were compared to the forensic pathology findings. Data were organised in broad categories: foreign bodies, external injuries, soft-tissue and organ injuries, fractures, air in cavities, fluid collections, random pathology, and wound track. Findings were organised by systems: head and neck, thorax, abdomen and pelvis, extremities. Cohen's kappa statistics were used to assess observer agreement., Results: Six gunshot-related homicides (37.5%), seven sharp-force-related homicides (43.75%), two blunt-force-related deaths (12.5%), and one homicide due to mechanical asphyxia (1.25%) were analysed. A total of 64 fractures were reported by the pathologists, 67 by radiologist 1 and 68 by radiologist 2. Agreement was deemed substantial in all cases. Pathologists failed to report gas in cavities while radiologists underreported superficial injuries., Conclusion: An overall observation was that less accurate findings were produced by the blinded radiologist in comparison to the non-blinded one. The extremeness of homicides obscured the interpretation of PMCT leading to the observed discrepancies. The combination of PMCT and autopsies is deemed optimal when investigating homicidal events., (Copyright © 2023 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.)

Ultrasound-guided hydrodistention has been established as an effective minimally invasive treatment option for glenohumeral joint adhesive capsulitis (AC). Nonetheless, the long-term outcomes of the procedure have not yet been established. A total of 202 patients with AC were prospectively recruited and followed up for a total of 2 years. Pain and functionality were assessed with the use of the visual analogue scale (VAS) and the disabilities of the arm, shoulder, and hand (DASH) score, respectively, at the beginning and the end of the follow-up period. The relapse of AC over the 2-year period and the effect of diabetes were also evaluated in the treatment cohort. The Mann-Whitney U test was used to compare mean scores at the two time points, and Cox survival analysis and χ 2 test were used to assess the effect of diabetes on AC relapse. VAS and DASH scores were significantly lower at 2 years compared with the beginning of the follow-up period ( p < 0.001). Diabetes was diagnosed in 38/202 patients (18.8%) and was found to be significantly associated with recurrence of the disease ( p < 0.001). In conclusion, in this observational study, we have demonstrated that ultrasound-guided hydrodistention is linked to excellent long-term outcomes for the treatment of AC, which are significantly worse in patients with diabetes.

Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms., (© 2023. Springer Nature Limited.)

The increasing evidence of oncocytic renal tumors positive in 99m Tc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of 99m Tc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of 99m Tc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7-100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7-100%) and visual evaluation of 99m Tc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5-99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and 99m Tc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that 99m Tc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with 99m Tc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of 99m Tc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of 99m Tc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.

Multiple myeloma (MM) is one of the most common hematological malignancies affecting the bone marrow. Radiomics analysis has been employed in the literature in an attempt to evaluate the bone marrow of MM patients. This manuscript aimed to systematically review radiomics research on MM while employing a radiomics quality score (RQS) to accurately assess research quality in the field. A systematic search was performed on Web of Science, PubMed, and Scopus. The selected manuscripts were evaluated (data extraction and RQS scoring) by three independent readers (R1, R2, and R3) with experience in radiomics analysis. A total of 23 studies with 2682 patients were included, and the median RQS was 10 for R1 (IQR 5.5-12) and R3 (IQR 8.3-12) and 11 (IQR 7.5-12.5) for R2. RQS was not significantly correlated with any of the assessed bibliometric data (impact factor, quartile, year of publication, and imaging modality) ( p > 0.05). Our results demonstrated the low quality of published radiomics research in MM, similarly to other fields of radiomics research, highlighting the need to tighten publication standards.

Objective: The effect of diabetes on adhesive capsulitis (AC) and its impact on the outcomes of ultrasound (US)-guided hydrodistension of the glenohumeral joint are still unclear. We aimed to identify predictors of US-guided hydrodistension outcomes, while assessing the performance of the method in diabetic compared to non-diabetic patients., Materials and Methods: A total of 135 patients with AC who underwent US-guided hydrodistension were prospectively included. Demographics and factors linked to chronic inflammation and diabetes were recorded and patients were followed-up for 6 months. Functionality and pain were evaluated with the Disabilities of the Arm, Shoulder and Hand (DASH) and the Visual Analogue Scale (VAS) score. Statistical analysis was performed with Mann-Whitney U test, linear, and binary logistic regression., Results: Diabetes was identified in 25/135 patients (18.5%). Diabetic patients had worse DASH and VAS score at presentation (P < 0.0001) and presented with a higher grade of AC (P < 0.0001) and lower range of motion (P < 0.01) compared to non-diabetics. Higher DASH (P = 0.025) and VAS scores (P = 0.039) at presentation were linked to worse functionality at 6 months. Presence and duration of diabetes, and the number of hydrodistension repeats, correlated with worse VAS and DASH scores at 6 months. The number of procedure repeats was the only independent predictor of complete pain resolution at 6 months (OR 0.418, P = 003)., Conclusion: Diabetes is linked to more severe AC at presentation and worse outcomes in patients undergoing US-guided hydrodistension. In resistant cases, repeating the intervention is independently linked to worse outcomes for at least 6 months post-intervention., (© 2022. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

Considering the current widespread use of imaging as an integral part of managing hip pain, variable hip geometries and anatomical variants are increasingly being detected. These variants are commonly found in the acetabulum and proximal femur, as well as the surrounding capsule-labral tissues. The morphology of specific anatomical spaces confined by the proximal femur and the bony pelvis may also vary significantly among individuals. Familiarity with the spectrum of imaging appearances of the hip is necessary to identify variant hip morphologies with or without potential clinical relevance and reduce an unnecessary work-up and overdiagnosis. We describe anatomical variations and variable morphologies of the bony structures comprising the hip joint and the soft tissues, around the hip. The potential clinical significance of these findings is further analyzed in conjunction with the patient's profile., Competing Interests: None declared., (Thieme. All rights reserved.)

Adhesive capsulitis is a common disorder of the glenohumeral joint. Delayed diagnosis is the result of overlapping signs and symptoms with other disorders affecting the shoulder. Typically, the disease shows gradual progression of pain and loss of the range of motion. The hallmark of the physical examination is limitation of both passive and active motion without any associated degenerative changes on plain radiographs. Conservative and/or surgical treatments have shown conflicting results. Poor outcome may be related to co-morbid factors mainly including prolonged immobilization, rotator cuff pathology and diabetes mellitus among others. This review will present the current literature data on the natural history and pathophysiology of the disease, and will highlight the role of imaging in the prompt and accurate diagnosis as well as in the imaged-guided treatment with emphasis on ultrasonography., Competing Interests: The authors declare no conflict of interest., (© 2023 The Mediterranean Journal of Rheumatology (MJR).)

Objectives: To compare the additive value of immediate post-procedural manipulation versus physiotherapy, following ultrasound (US)-guided hydrodistention of the glenohumeral joint (GHJ) in patients with adhesive capsulitis (AC) and define predictors of outcome., Methods: Within a 19-month period, 161 consecutive patients with AC were prospectively enrolled and allocated to two groups according to treatment, based on patients' individual preferences: 1) group-I, US-guided hydrodistension plus immediate post-procedural manipulations and 2) group-II, US-guided hydrodistension plus supervised physiotherapy program. The Disabilities of the Arm, Shoulder, and Hand (DASH) questionnaire and a visual analog scale (VAS) were used for clinical assessment at baseline (immediately after treatment), 1, 3, and 6 months. Comparisons were performed with Mann-Whitney U test and Kolmogorov-Smirnov test. Linear regression was used to identify predictors of outcome. P value <.05 defined significance., Results: GHJ hydrodistension with manipulation or physiotherapy was linked to clinical improvement at all follow-up time-points. DASH scores of group-I remained constantly lower than DASH scores of group-II at all time-points (P < .001). VAS scores were lower in group-I than group-II at 1 and 3 months (P < .001 and P = .0019, respectively). Both groups had improved to a similar degree with respect to pain at 6 months (P = .29). The performance of post-interventional manipulations was predictive of improved shoulder functionality (as assessed with DASH scores) at all time-points, while low-grade disease and milder symptoms at presentation were associated with improved short-term pain., Conclusions: Immediate post-procedural manipulations appeared to be superior to physiotherapy following GHJ hydrodistension for AC in terms of shoulder functionality during a 6-month follow-up period. Post-interventional manipulations, the stage of AC and lower DASH and VAS scores at presentations were predictive of improved outcome., (© 2022 American Institute of Ultrasound in Medicine.)

The radiologists were traditionally working in the background. What upgraded them as physicians during the second half of the past century was their clinical training and function precipitated by the evolution of Interventional Radiology and Medical Imaging, especially with ultrasonography. These allowed them to participate in patient's diagnosis and treatment by direct contact as well asvia multidisciplinary medical consultations. The wide application of teleradiology and PACS pushed radiologists back again which is no longer acceptable, especially in view of the amazing applications of artificial intelligence (AI) in Radiology. It is our belief that clinical radiologists have to be able to control the penetration of AI in Radiology, securing their work for the benefit of both clinicians and patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper: [Prof. A. Karantanas is chairing the Subspecialties and Allied Sciences Committee in European Society of Radiology]., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: Anterior knee instability is usually encountered in the context of trauma, with the clinical examination and imaging focusing on anterior cruciate ligament (ACL) disruption. Limited data exist on magnetic resonance imaging (MRI) of acute anterior knee instability in the absence of recent trauma., Purpose: To provide the first comprehensive account of MRI findings in acute anterior knee laxity in the absence of acute trauma and to evaluate predictors of ACL integrity and pain., Material and Methods: A total of 84 consecutive patients with non-traumatic knee instability were prospectively studied. Instability was assessed with Lachman's, pivot shift, and Lelli's tests. MRI findings were recorded, and ACL integrity was surgically confirmed in all 24 cases of MRI suggesting tear and in 21/60 cases of MRI suggesting no tear. Binary logistic regression models were used to identify predictors of ACL tears and pain, and Mann-Whitney U test served for comparisons between continuous variables. The study was approved by the institutional review board., Results: Osteoarthritis and notch bony outgrowth (NBO) were present in 44% and 42.9% of all knees, respectively. NBO did not correlate with osteoarthritis ( P  = 0.606). NBO (odds ratio [OR] = 4.157; P  = 0.016) and ACL grafts (OR = 9.277; P  = 0.01) predisposed to non-traumatic ACL tears (torn in 28.6% of total cases). Presence of osteoarthritis was predictive of pain (OR = 17.671; P  < 0.001)., Conclusion: We present a comprehensive analysis of MRI findings in clinically significant non-traumatic anterior instability, showing that NBO and ACL grafts predispose in non-traumatic ACL tears, whereas osteoarthritis is the only predictor of pain.

Differential diagnosis between avascular necrosis (AVN) and transient osteoporosis of the hip (TOH) can be complicated even for experienced MSK radiologists. Our study attempted to use MR images in order to develop a deep learning methodology with the use of transfer learning and a convolutional neural network (CNN) ensemble, for the accurate differentiation between the two diseases. An augmented dataset of 210 hips with TOH and 210 hips with AVN was used to finetune three ImageNet-trained CNNs (VGG-16, InceptionResNetV2, and InceptionV3). An ensemble decision was reached in a hard-voting manner by selecting the outcome voted by at least two of the CNNs. Inception-ResNet-V2 achieved the highest AUC (97.62%) similar to the model ensemble, followed by InceptionV3 (AUC of 96.82%) and VGG-16 (AUC 96.03%). Precision for the diagnosis of AVN and recall for the detection of TOH were higher in the model ensemble compared to Inception-ResNet-V2. Ensemble performance was significantly higher than that of an MSK radiologist and a fellow (P < 0.001). Deep learning was highly successful in distinguishing TOH from AVN, with a potential to aid treatment decisions and lead to the avoidance of unnecessary surgery.

Purpose: Subchondral insufficiency fractures (SIF) and advanced osteoarthritis (OA) of the knee are usually seen in conjunction with bone marrow lesions (BMLs) and their differentiation may pose a significant diagnostic challenge. We aimed to develop a convolutional neural network (CNN) ensemble which could successfully differentiate between these two entities., Materials and Methods: A total of 212 knees with SIF and 102 knees with advanced OA with BMLs were retrospectively included. Coronal fat suppressed PD-w images were augmented, resized and normalized, reaching a total of 1174 images. Data was used to fine-tune three ImageNet-pretrained CNNs (VGG-16, InceptionV3 and Inception-ResNet-V2). Agreement of at least two networks was recorded as the decision of the network ensemble. Ensemble performance was compared to that of two MSK radiologists on the validation set. Receiver operating characteristics (ROC) curves and the respective areas under the curve (AUC) were used to evaluate human and machine performance., Results: InceptionV3 achieved the highest AUC (93.68%) and VGG-16 the lowest AUC (82.18%) among individual CNNs. CNN ensemble achieved the highest overall performance with an AUC of 95.97%. The first of the two MSK radiologists achieved a performance similar to the ensemble, reaching an AUC of 91.95%. The second radiologist achieved lower AUC of 82.76% which was lower than both the other specialist and the ensemble (P < 0.001)., Conclusion: A CNN ensemble was highly accurate in differentiating between SIF and OA, achieving a higher or equal performance to MSK radiologists., Competing Interests: Declarations of Competing Interest None, (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The future of musculoskeletal (MSK) radiology is being built on research developments in the field. Over the past decade, MSK imaging research has been dominated by advancements in molecular imaging biomarkers, artificial intelligence, radiomics, and novel high-resolution equipment. Adequate preparation of trainees and specialists will ensure that current and future leaders will be prepared to embrace and critically appraise technological developments, will be up to date on clinical developments, such as the use of artificial tissues, will define research directions, and will actively participate and lead multidisciplinary research. This review presents an overview of the current MSK research landscape and proposes tangible future goals and strategic directions that will fortify the future of MSK radiology., Competing Interests: None declared., (Thieme. All rights reserved.)

Objectives: In this study, we sought to assess the validity of lung ultrasound (LUS) during the follow-up of patients with a wide spectrum of interstitial lung diseases (ILDs)., Methods: Twenty-four patients (13 males, 11 females; mean age ± SD, 65.4 ± 14.3 years; age range, 40-84 years) with a diagnosis of ILDs who were admitted to the Interstitial Lung Disease Unit were prospectively enrolled. Patients were examined with a 56-lung intercostal space LUS protocol in lateral decubitus position, at baseline, 6-months, and 1-year. The LUS score was defined as the sum of B-lines counted in each intercostal space. All patients underwent complete pulmonary function tests at baseline and follow-up time-points. High-resolution computed tomography (HRCT) was performed at baseline and during follow-up, according to personalized patients' needs. All HRCT studies were graded according to the Warrick scoring system (WS)., Results: Pooled data analysis showed a significant correlation between WS and LUS scores (P < .001). For separate time-point analysis, a significant correlation between LUS scores and WS was found at baseline (P < .001) and 1 year (P = .005). LUS scores negatively correlated with alveolar volume (VA) (P < .046) and diffusing capacity for carbon monoxide (DLCO) (P < .001) at 6 months and with transfer coefficient of the lung for carbon monoxide (KCO) (P < .031) and DLCO (P = .002) at 12-months. A multivariate regression model showed DLCO to be an independent predictor of LUS score at 1 year (P = .026)., Conclusions: Our results highlight the validity and potential applicability of LUS for disease monitoring in a wide spectrum of ILDs., (© 2021 American Institute of Ultrasound in Medicine.)

Microvascular wall abnormalities demonstrated by nailfold capillaroscopy in systemic sclerosis (SSc) may result in microhemorrhagic deposition of erythrocyte-derived iron. Such abnormalities precede fibrosis, which is orchestrated by myofibroblasts. Iron induces endothelial-to-mesenchymal transition in vitro, which is reversed by reactive oxygen species (ROS) scavengers. The conversion of quiescent fibroblasts into profibrotic myofibroblasts has also been associated with ROS-mediated activation of TGF-β1. Given that iron overload predisposes to ROS formation, we hypothesized that the uptake of erythrocyte-derived iron by resident cells promotes fibrosis. Firstly, we show that iron induces oxidative stress in skin-derived and synovial fibroblasts in vitro, as well as in blood mononuclear cells ex vivo. The biological relevance of increased oxidative stress was confirmed by showing the concomitant induction of DNA damage in these cell types. Similar results were obtained in vivo, following intravenous iron administration. Secondly, using magnetic resonance imaging we show an increased iron deposition in the fingers of a patient with early SSc and nailfold microhemorrhages. While a systematic magnetic resonance study to examine tissue iron levels in SSc, including internal organs, is underway, herein we propose that iron may be a pathogenetic link between microvasculopathy and fibrosis and an additional mechanism responsible for increased oxidative stress in SSc.

The aim of this study is to investigate the possibility of predicting histological grade in patients with endometrial cancer on the basis of intravoxel incoherent motion (IVIM)-related histogram analysis parameters. This prospective study included 52 women with endometrial cancer (EC) who underwent MR imaging as initial staging in our hospital, allocated into low-grade (G1 and G2) and high-grade (G3) tumors according to the pathology reports. Regions of interest (ROIs) were drawn on the diffusion weighted images and apparent diffusion coefficient (ADC), true diffusivity (D), and perfusion fraction (f) using diffusion models were computed. Mean, median, skewness, kurtosis, and interquartile range (IQR) were calculated from the whole-tumor histogram. The IQR of the diffusion coefficient (D) was significantly lower in the low-grade tumors from that of the high-grade group with an adjusted p -value of less than 5% (0.048). The ROC curve analysis results of the statistically significant IQR of the D yielded an accuracy, sensitivity, and specificity of 74.5%, 70.1%, and 76.5% respectively, for discriminating low from high-grade tumors, with an optimal cutoff of 0.206 (×10 -3 mm 2 /s) and an AUC of 75.4% (95% CI: 62.1 to 88.8). The IVIM modeling coupled with histogram analysis techniques is promising for preoperative differentiation between low- and high-grade EC tumors.

Objective: To evaluate the performance of two machine learning models in predicting the long-term complete pain resolution in patients undergoing ultrasound-guided percutaneous irrigation of calcific tendinopathy (US-PICT)., Materials and Methods: Within a 3-year period, 100 consecutive patients who underwent US-PICT for rotator cuff disease were prospectively enrolled. The location, maximum diameter, and type of each calcification were recorded. The degree of calcium retrieval was graded as complete or incomplete. Shoulder pain was assessed with the visual analogue scale (VAS) at baseline, 1-week, 1-month, and 1-year post-treatment. Measurements related to procedural details, patient, and calcification characteristics were used to construct a machine learning model for the prediction of pain at 1-year post-US-PICT. Two distinct models were built, one including VAS data at 1 week and another additionally including pain data at 1-month post-treatment. Variable importance analysis was performed for the 1-week model. Model performance was evaluated by using receiver operating characteristics (ROC) curves and the respective areas under the curve (AUC)., Results: The model exhibited an AUC of 69.2% for the prediction of complete pain resolution at 1 year. The addition of VAS scores at 1 month did not significantly alter the performance of the algorithm. Age and baseline VAS scores were the most important variables for classification performance., Conclusion: The presented machine learning model exhibited an AUC of almost 70% in predicting complete pain resolution at 1 year. Pain data at 1 month do not appear to improve the performance of the algorithm., (© 2021. ISS.)

Objectives: To utilise machine learning, unsupervised clustering and multivariate modelling in order to predict severe early joint space narrowing (JSN) from anatomical hip parameters while identifying factors related to joint space width (JSW) in dysplastic and non-dysplastic hips., Methods: A total of 507 hip CT examinations of patients 20-55 years old were retrospectively examined, and JSW, center-edge (CE) angle, alpha angle, anterior acetabular sector angle (AASA), and neck-shaft angle (NSA) were recorded. Dysplasia and severe JSN were defined with CE angle < 25 o and JSW< 2 mm, respectively. A random forest classifier was developed to predict severe JSN based on anatomical and demographical data. Multivariate linear regression and two-step unsupervised clustering were performed to identify factors linked to JSW., Results: In dysplastic hips, lateral or anterior undercoverage alone was not correlated to JSN. AASA (p < 0.005) and CE angle (p < 0.032) were the only factors significantly correlated with JSN in dysplastic hips. In non-dysplastic hips, JSW was inversely correlated to CE angle, AASA, and age and positively correlated to NSA (p < 0.001). A random forest classifier predicted severe JSN (AUC 69.9%, 95%CI 47.9-91.8%). TwoStep cluster modelling identified two distinct patient clusters one with low and one with normal JSW and different anatomical characteristics., Conclusion: Machine learning predicted severe JSN and identified population characteristics related to normal and abnormal joint space width. Dysplasia in one plane was found to be insufficient to cause JSN, highlighting the need for hip anatomy assessment on multiple planes., Key Points: • Neither anterior nor lateral acetabular dysplasia was sufficient to independently reduce joint space width in a multivariate linear regression model of dysplastic hips. • A random forest classifier was developed based on measurements and demographic parameters from 507 hip joints, achieving an area under the curve of 69.9% in the external validation set, in predicting severe joint space narrowing based on anatomical hip parameters and age. • Unsupervised TwoStep cluster analysis revealed two distinct population groups, one with low and one with normal joint space width, characterised by differences in hip morphology., (© 2021. European Society of Radiology.)

The purpose of this study was to compare the measurement of several anatomical features of the medial patellofemoral ligament (MPFL) between magnetic resonance imaging (MRI) and by direct fashion during dissection. We hypothesized that the measurements between these two techniques would agree. MRI of 30 fresh-frozen cadaveric knees was followed by dissection. MPFL patella and femoral attachment were evaluated; their shape, length, and width were measured; and measurements were compared. MRI was deemed unreliable for the determination of several of the aforementioned anatomical features. Important findings include: (a) observations on MPFL attachment at medial patella side and attachment to quadriceps were identical between dissection and MRI; (b) average width at patella insertion was significantly different between the two methods ( p = 0.002); and (c) an attachment to the quadriceps tendon was present in 20/30 specimens and d. detailed measurements of a thin, non-linear, and three-dimensional structure, such as the MPFL, cannot be performed on MRI, due to technical difficulties. This anatomical radiological study highlights the shape, anatomical measurements (length and width), and attachment of the MPFL using a relatively large cadaveric sample and suggests that MRI is not reliable for detailed imaging of its three-dimensional anatomy.

This research focuses on the anatomical insertion of the synovial capsule around the knee. The attachments of the capsule were measured in 50 knee MR imaging studies with large intraarticular effusion. Corresponding measurements were performed in 20 fresh frozen cadaveric specimens, for validation. Femoral and tibial capsular reflections were defined as the distances between the attachment sites of the capsule and the femoral or tibial joint line and they were recorded in three coronal planes (anterior/middle/posterior). On MR imaging, the lateral/medial femoral capsular reflection mean values were 6.5/4.57 cm, 2.74/1.74 cm and 1.52/1.99 cm in the anterior, middle and posterior plane, respectively. MR imaging-based measurements did not differ significantly compared to corresponding cadaveric measurements. The mean values of the lateral/medial tibial capsular reflection on MR imaging were 0.09/0.11 cm, 0.34/0.26 cm and 0.62/0.34 cm in the anterior, middle and posterior plane, respectively. On cadaveric dissection, the maximum mean value was 1.45 cm, measured on the lateral side of the anterior plane. Apart from the lateral aspect of the posterior plane, MR imaging measurements were significantly lower, compared to the corresponding cadaveric measurements. The greatest femoral and tibial capsular reflections were found on the anterior and lateral side of the anterior plane. MR imaging appears to underestimate the distal extent of the knee capsule. Anatomical details of the knee capsule should be considered for safe insertion of external fixator pins.

Gastro-entero-pancreatic neuroendocrine tumors (GEP-NETs) represent a heterogeneous group of rare neoplasms with increasing incidence over the last decades. Localization of GEP-NETs and their metastases is a vital component for the implementation of accurate and patient-tailored treatment strategies. Addressing this challenge requires the employment of multidisciplinary imaging approaches, with hybrid positron emission tomography/computed tomography (PET/CT) imaging techniques standing at the forefront of this effort. GEP-NETs exhibit several pathophysiologic characteristics, which can serve as highly specific molecular targets that can be effectively visualized and quantified by means of PET-radiopharmaceuticals, facilitating diagnosis, accurate staging and efficient monitoring of treatment response. Furthermore, the capability for whole-body, in-vivo, non-invasive characterization of the molecular heterogeneity of the disease, provides strong prognostic information, while enabling the selection of patients suitable for precision-based theranostic approaches. The dual tracer ( 18 F-FDG & 68 Ga-DOTA-peptides) PET/CT imaging approach is the current optimal diagnostic imaging strategy, since it enables tumor localization, accurate staging, non-invasive whole-body total tumor burden characterization of disease heterogeneity, while providing strong prognostic information and guidance towards treatment strategy. Moreover, 64 Cu-DOTATATE has been recently approved by FDA for SSTRs positive NETs, promising substantial diagnostic and logistical benefits. Furthermore, 18 F-DOPA offers diagnostic capabilities for serotonin-secreting GEP-NETs which are not characterized by cell-surface over-expression of somatostatin receptors (SSTRs) and cannot be seen on morphological imaging. In addition, PET/CT with agents targeting the expression of glucagon-like peptide-1 receptor (GLP-R1) should be considered in cases of clinical suspicion for insulinomas that cannot be detected by morphological imaging or STTRs PET/CT imaging., (Copyright © 2021. Published by Elsevier B.V.)

Wrist osteoarthritis (OA) is a common painful condition that affects the patient's quality of life by limiting the range of wrist motion and grip strength. Wrist OA often results from scapholunate advanced collapse (SLAC) or scaphoid nonunion advanced collapse (SNAC). Early diagnosis of SLAC and SNAC is crucial because it affects treatment planning. Thus, radiologists should be able to interpret the early imaging findings. This pictorial review discusses the pathophysiology and the clinical symptoms of SLAC and SNAC and presents the imaging findings with emphasis on the proper imaging algorithm. Finally, it focuses on the treatment according to the degenerative status of each of these patterns.

Differentiation between transient osteoporosis (TOH) and avascular necrosis (AVN) of the hip is a longstanding challenge in musculoskeletal radiology. The purpose of this study was to utilize MRI-based radiomics and machine learning (ML) for accurate differentiation between the two entities. A total of 109 hips with TOH and 104 hips with AVN were retrospectively included. Femoral heads and necks with segmented radiomics features were extracted. Three ML classifiers (XGboost, CatBoost and SVM) using 38 relevant radiomics features were trained on 70% and validated on 30% of the dataset. ML performance was compared to two musculoskeletal radiologists, a general radiologist and two radiology residents. XGboost achieved the best performance with an area under the curve (AUC) of 93.7% (95% CI from 87.7 to 99.8%) among ML models. MSK radiologists achieved an AUC of 90.6% (95% CI from 86.7% to 94.5%) and 88.3% (95% CI from 84% to 92.7%), respectively, similar to residents. The general radiologist achieved an AUC of 84.5% (95% CI from 80% to 89%), significantly lower than of XGboost ( p = 0.017). In conclusion, radiomics-based ML achieved a performance similar to MSK radiologists and significantly higher compared to general radiologists in differentiating between TOH and AVN.

Introduction : As stem cell treatments reach closer to the clinic, the need for appropriate noninvasive imaging for accurate disease diagnosis, treatment planning, follow-up, and early detection of complications, is constantly rising. Clinical radiology affords an extensive arsenal of advanced imaging techniques, to provide anatomical and functional information on the whole spectrum of stem cell treatments from diagnosis to follow-up. Areas covered : This manuscript aims at providing a critical review of major published studies on the utilization of advanced imaging for stem cell treatments. Uses of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and positron emission tomography (PET) are reviewed and interrogated for their applicability to stem cell imaging. Expert opinion : A wide spectrum of imaging methods have been utilized for the evaluation of stem cell therapies. The majority of published techniques are not clinically applicable, using methods exclusively applicable to animals or technology irrelevant to current clinical practice. Harmonization of preclinical methods with clinical reality is necessary for the timely translation of stem cell therapies to the clinic. Methods such as diffusion weighted MRI, hybrid imaging, and contrast-enhanced ultrasound hold great promise and should be routinely incorporated in the evaluation of patients receiving stem cell treatments.

Colorectal cancer (CRC) constitutes the third most commonly diagnosed cancer in males and the second in females. Precise histopathological classification of CRC tissue pathology is the cornerstone not only for diagnosis but also for patients' management decision making. An automated system able to accurately classify different CRC tissue regions may increase diagnostic precision and alleviate clinical workload. However, tissue classification is a challenging task due to the variability in morphological and textural characteristics present in histopathology images. In this study, an artificial neural network was trained to classify between eight classes of CRC tissue image patches derived from a public dataset with 5000 CRC histopathology image tiles. A total of 532 multi-level pathomics features examined at different scales were extracted by visual descriptors such as local binary patterns, wavelet transforms and Gabor filters. An exhaustive evaluation involving a variety of wavelet families and parameters was performed in order to shed light on the impact of scale on pathomics based CRC tissue differentiation. Our model achieved a performance accuracy of 95.3% with tenfold cross validation demonstrating superior performance compared to 87.4% reported in recent studies. Furthermore, we experimentally showed that the first and the second levels of the wavelet approximations can be used without compromising classification performance., (© 2021. The Author(s).)

Objectives: Imaging assessment for the clinical management of femoroacetabular impingement (FAI) is controversial because of a paucity of evidence-based guidance and notable variability among practitioners. Hence, expert consensus is needed because standardised imaging assessment is critical for clinical practice and research. We aimed to establish expert-based statements on FAI imaging by using formal methods of consensus building., Methods: The Delphi method was used to formally derive consensus among 30 panel members from 13 countries. Forty-four questions were agreed upon, and relevant seminal literature was circulated and classified in major topics to produce answering statements. The level of evidence was noted for all statements, and panel members were asked to score their level of agreement (0-10). This is the second part of a three-part consensus series and focuses on 'General issues' and 'Parameters and reporting'., Results: Forty-seven statements were generated and group consensus was reached for 45. Twenty-five statements pertaining to 'General issues' (9 addressing diagnosis, differential diagnosis, and postoperative imaging) and 'Parameters and reporting' (16 addressing femoral/acetabular parameters) were produced., Conclusions: The available evidence was reviewed critically, recommended criteria for diagnostic imaging highlighted, and the roles/values of different imaging parameters assessed. Radiographic evaluation (AP pelvis and a Dunn 45° view) is the cornerstone of hip-imaging assessment and the minimum imaging study that should be performed when evaluating adult patients for FAI. In most cases, cross-sectional imaging is warranted because MRI is the 'gold standard' imaging modality for the comprehensive evaluation, differential diagnosis assessment, and FAI surgical planning., Key Points: • Diagnostic imaging for FAI is not standardised due to scarce evidence-based guidance on which imaging modalities and diagnostic criteria/parameters should be used. • Radiographic evaluation is the cornerstone of hip assessment and the minimum study that should be performed when assessing suspected FAI. Cross-sectional imaging is justified in most cases because MRI is the 'gold standard' modality for comprehensive FAI evaluation. • For acetabular morphology, coverage (Wiberg's angle and acetabular index) and version (crossover, posterior wall, and ischial spine signs) should be assessed routinely. On the femoral side, the head-neck junction morphology (α° and offset), neck morphology (NSA), and torsion should be assessed.