Reproducibility of radiomics quality score: an intra- and inter-rater reliability study

OBJECTIVES

To investigate the intra- and inter-rater reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items' score in a large multireader study.

METHODS

Nine raters with different backgrounds were randomly assigned to three groups based on their proficiency with RQS utilization: Groups 1 and 2 represented the inter-rater reliability groups with or without prior training in RQS, respectively; group 3 represented the intra-rater reliability group. Thirty-three original research papers on radiomics were evaluated by raters of groups 1 and 2. Of the 33 papers, 17 were evaluated twice with an interval of 1 month by raters of group 3. Intraclass coefficient (ICC) for continuous variables, and Fleiss' and Cohen's kappa (k) statistics for categorical variables were used.

RESULTS

The inter-rater reliability was poor to moderate for total RQS (ICC 0.30-055, p < 0.001) and very low to good for item's reproducibility (k - 0.12 to 0.75) within groups 1 and 2 for both inexperienced and experienced raters. The intra-rater reliability for total RQS was moderate for the less experienced rater (ICC 0.522, p = 0.009), whereas experienced raters showed excellent intra-rater reliability (ICC 0.91-0.99, p < 0.001) between the first and second read. Intra-rater reliability on RQS items' score reproducibility was higher and most of the items had moderate to good intra-rater reliability (k - 0.40 to 1).

CONCLUSIONS

Reproducibility of the total RQS and the score of individual RQS items is low. There is a need for a robust and reproducible assessment method to assess the quality of radiomics research.

CLINICAL RELEVANCE STATEMENT

There is a need for reproducible scoring systems to improve quality of radiomics research and consecutively close the translational gap between research and clinical implementation.

KEY POINTS

• Radiomics quality score has been widely used for the evaluation of radiomics studies. • Although the intra-rater reliability was moderate to excellent, intra- and inter-rater reliability of total score and point-by-point scores were low with radiomics quality score. • A robust, easy-to-use scoring system is needed for the evaluation of radiomics research.

How young radiologists use contrast media and manage adverse reactions: an international survey

OBJECTIVES

To collect real-world data about the knowledge and self-perception of young radiologists concerning the use of contrast media (CM) and the management of adverse drug reactions (ADR).

METHODS

A survey (29 questions) was distributed to residents and board-certified radiologists younger than 40 years to investigate the current international situation in young radiology community regarding CM and ADRs. Descriptive statistics analysis was performed.

RESULTS

Out of 454 respondents from 48 countries (mean age: 31.7 ± 4 years, range 25-39), 271 (59.7%) were radiology residents and 183 (40.3%) were board-certified radiologists. The majority (349, 76.5%) felt they were adequately informed regarding the use of CM. However, only 141 (31.1%) received specific training on the use of CM and 82 (18.1%) about management ADR during their residency. Although 266 (58.6%) knew safety protocols for handling ADR, 69.6% (316) lacked confidence in their ability to manage CM-induced ADRs and 95.8% (435) expressed a desire to enhance their understanding of CM use and handling of CM-induced ADRs. Nearly 300 respondents (297; 65.4%) were aware of the benefits of contrast-enhanced ultrasound, but 249 (54.8%) of participants did not perform it. The preferred CM injection strategy in CT parenchymal examination and CT angiography examination was based on patient's lean body weight in 318 (70.0%) and 160 (35.2%), a predeterminate fixed amount in 79 (17.4%) and 116 (25.6%), iodine delivery rate in 26 (5.7%) and 122 (26.9%), and scan time in 31 (6.8%) and 56 (12.3%), respectively.

CONCLUSION

Training in CM use and management ADR should be implemented in the training of radiology residents.

CRITICAL RELEVANCE STATEMENT

We highlight the need for improvement in the education of young radiologists regarding contrast media; more attention from residency programs and scientific societies should be focused on training about contrast media use and the management of adverse drug reactions.

KEY POINTS

• This survey investigated training of young radiologists about use of contrast media and management adverse reactions. • Most young radiologists claimed they did not receive dedicated training. • An extreme heterogeneity of responses was observed about contrast media indications/contraindications and injection strategy.

Performance of thoracic ultrasonography compared with chest radiography for the detection of rib fractures using computed tomography as a reference standard

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.

Large language models in radiology: fundamentals, applications, ethical considerations, risks, and future directions

With the advent of large language models (LLMs), the artificial intelligence revolution in medicine and radiology is now more tangible than ever. Every day, an increasingly large number of articles are published that utilize LLMs in radiology. To adopt and safely implement this new technology in the field, radiologists should be familiar with its key concepts, understand at least the technical basics, and be aware of the potential risks and ethical considerations that come with it. In this review article, the authors provide an overview of the LLMs that might be relevant to the radiology community and include a brief discussion of their short history, technical basics, ChatGPT, prompt engineering, potential applications in medicine and radiology, advantages, disadvantages and risks, ethical and regulatory considerations, and future directions.

Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT)

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.

Perspectives for the Use of Umbilical Cord Blood in Transplantation and Beyond: Initiatives for an Advanced and Sustainable Public Banking Program in Greece

The umbilical cord blood (UCB) donated in public UCB banks is a source of hematopoietic stem cells (HSC) alternative to bone marrow for allogeneic HSC transplantation (HSCT). However, the high rejection rate of the donated units due to the strict acceptance criteria and the wide application of the haploidentical HSCT have resulted in significant limitation of the use of UCB and difficulties in the economic sustainability of the public UCB banks. There is an ongoing effort within the UCB community to optimize the use of UCB in the field of HSCT and a parallel interest in exploring the use of UCB for applications beyond HSCT i.e., in the fields of cell therapy, regenerative medicine and specialized transfusion medicine. In this report, we describe the mode of operation of the three public UCB banks in Greece as an example of an orchestrated effort to develop a viable UCB banking system by (a) prioritizing the enrichment of the national inventory by high-quality UCB units from populations with rare human leukocyte antigens (HLA), and (b) deploying novel sustainable applications of UCB beyond HSCT, through national and international collaborations. The Greek paradigm of the public UCB network may become an example for countries, particularly with high HLA heterogeneity, with public UCB banks facing sustainability difficulties and adds value to the international efforts aiming to sustainably expand the public UCB banking system.

Generative adversarial networks for spine imaging: A critical review of current applications

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.

Deep learning enables the differentiation between early and late stages of hip avascular necrosis

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.

Ultrasound-guided hydrodilatation for adhesive capsulitis: capsule-preserving versus capsule-rupturing technique

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.

Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset

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.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII

PURPOSE

To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies.

METHODS

We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated.

RESULT

In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community.

CONCLUSION

In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers.

CRITICAL RELEVANCE STATEMENT

A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning.

KEY POINTS

• A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score ( https://metricsscore.github.io/metrics/METRICS.html ) and a repository created to collect feedback from the radiomics community ( https://github.com/metricsscore/metrics ).

Performance of Ultrasonography in the Diagnosis of Acute Colonic Diverticulitis

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.

Post-mortem CT radiomics for the prediction of time since death

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.

Post-mortem CT in the investigation of homicides

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.

Long-Term Outcomes of Ultrasound-Guided Hydrodistension for Adhesive Capsulitis: A Prospective Observational Study

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 χ2 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.

Radiomics for the Detection of Active Sacroiliitis Using MR Imaging

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.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

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.

Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors

The increasing evidence of oncocytic renal tumors positive in ^99mTc 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 ^99mTc 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 ^99mTc 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 ^99mTc 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 ^99mTc 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 ^99mTc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with ^99mTc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of ^99mTc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of ^99mTc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.

Radiomics Analysis for Multiple Myeloma: A Systematic Review with Radiomics Quality Scoring

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.

Ultrasound-guided hydrodistension for adhesive capsulitis: a longitudinal study on the effect of diabetes on treatment outcomes

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.

Imaging of Anatomical Variants Around the Hip

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.

The role of MRI and Ultrasonography in Diagnosis and Treatment of Glenohumeral Joint Adhesive Capsulitis

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.

Ultrasound-Guided Hydrodistension for Adhesive Capsulitis: Is There Any Adjunct Effect of Immediate Post-Procedural Manipulation Over Instructed Physical Therapy?

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.

Systematic review with radiomics quality score of cholangiocarcinoma: an EuSoMII Radiomics Auditing Group Initiative

OBJECTIVES

To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies.

METHODS

A systematic search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients.

RESULTS

A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1-13) for reader 1, 8 (22.2%, IQR 3-12) for reader 2, and 10 (27.8%; IQR 5-14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62-0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4, p = 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5, p < 0.001 for reader 1).

CONCLUSIONS

Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data.

Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip

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.

Research in Musculoskeletal Radiology: Setting Goals and Strategic Directions

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.

Differentiation between subchondral insufficiency fractures and advanced osteoarthritis of the knee using transfer learning and an ensemble of convolutional neural networks

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.

Performance of Lung Ultrasound for Monitoring Interstitial Lung Disease

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.

Predicting long-term outcomes of ultrasound-guided percutaneous irrigation of calcific tendinopathy with the use of machine learning

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.

High-Resolution Imaging for the Analysis and Reconstruction of 3D Microenvironments for Regenerative Medicine: An Application-Focused Review

The rapid evolution of regenerative medicine and its associated scientific fields, such as tissue engineering, has provided great promise for multiple applications where replacement and regeneration of damaged or lost tissue is required. In order to evaluate and optimise the tissue engineering techniques, visualisation of the material of interest is crucial. This includes monitoring of the cellular behaviour, extracellular matrix composition, scaffold structure, and other crucial elements of biomaterials. Non-invasive visualisation of artificial tissues is important at all stages of development and clinical translation. A variety of preclinical and clinical imaging methods-including confocal multiphoton microscopy, optical coherence tomography, magnetic resonance imaging (MRI), and computed tomography (CT)-have been used for the evaluation of artificial tissues. This review attempts to present the imaging methods available to assess the composition and quality of 3D microenvironments, as well as their integration with human tissues once implanted in the human body. The review provides tissue-specific application examples to demonstrate the applicability of such methods on cardiovascular, musculoskeletal, and neural tissue engineering.

Surgical and Radiological Anatomy of the Medial Patellofemoral Ligament: A Magnetic Resonance Imaging and Cadaveric Study

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.

Development of a patient-reported outcomes and experience measure for Orthognathic surgery: validation of the Northwick Park Orthognathic Questionnaire (NOQ)

Orthognathic surgery aims to correct dentoskeletal and facial discrepancies. The expected benefits are functional, cosmetic, and psychological. In a previous publication, this group assessed the determinants of patient satisfaction to formulate the Northwick Park Orthognathic Questionnaire (NOQ). The aim of the present study was to validate this questionnaire. A total of 118 postoperative patients prospectively completed the NOQ, 30 of whom completed the questionnaire a second time. The mean completion rate was 87.6 ± 10%. Response reproducibility was high: 92% of patients gave identical responses (range 81-100%). The intra-class correlation coefficient (ICC) was 0.96 (0.96 ± 0.072). Average test-retest scores for each domain were as follows (range in parenthesis): reasons for treatment 93% (60-100%), preoperative experience 96% (81-100%), preparation for surgery 95% (81-100%), inpatient experience 89% (55-100%), post-discharge experience 83% (55-100%), benefits of treatment 92% (71-100%), overall patient education 91% (62-100%). Internal validity using Cronbach's alpha was 0.72 (standard deviation 0.23, range 0.5-1). The results confirm the consistency of responses and the reliability of the information collected with the NOQ. The NOQ is a novel questionnaire and a valid metric to quantify a patient's perception of their experience. Its adoption may aid in making targeted improvements to patient care.

Knee Capsule Anatomy: An MR Imaging and Cadaveric Study

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.

MRI findings of acute anterior instability of the knee in the absence of recent trauma

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.

Radiomics and machine learning to predict aggressive type 2 endoleaks after endovascular aneurysm repair: a proof of concept

BACKGROUND

Persistent type 2 endoleaks (T2EL) require lifelong surveillance to avoid potentially life-threatening complications.

PURPOSE

To evaluate the performance of radiomic features (RF) derived from computed tomography angiography (CTA), for differentiating aggressive from benign T2ELs after endovascular aneurysm repair (EVAR).

MATERIAL AND METHODS

A prospective study was performed on patients who underwent EVAR from January 2018 to January 2020. Analysis was performed in patients who were diagnosed with T2EL based on the CTA of the first postoperative month and were followed at six months and one year. Patients were divided into two groups according to the change of aneurysm sac dimensions. Segmentation of T2ELs was performed and RF were extracted. Feature selection for subsequent machine-learning analysis was evaluated by means of artificial intelligence. Two support vector machines (SVM) classifiers were developed to predict the aneurysm sac dimension changes at one year, utilizing RF from T2EL at one- and six-month CTA scans, respectively.

RESULTS

Among the 944 initial RF of T2EL, 58 and 51 robust RF from the one- and six-month CTA scans, respectively, were used for the machine-learning model development. The SVM classifier trained on one-month signatures was able to predict sac expansion at one year with an area under curve (AUC) of 89.3%, presenting 78.6% specificity and 100% sensitivity. Similarly, the SVM classifier developed with six-month radiomics data showed an AUC of 95.5%, specificity of 90.9%, and sensitivity of 100%.

CONCLUSION

Machine-learning algorithms utilizing CTA-derived RF may predict aggressive T2ELs leading to aneurysm sac expansion after EVAR.

Ultrasound-guided treatment of Morton's neuroma

Morton’s neuroma is a painful lesion of the interdigital nerve, usually at the third intermetatarsal space, associated with fibrotic changes in the nerve, microvascular degeneration, and deregulation of sympathetic innervation. Patients usually present with burning or sharp metatarsalgia at the dorsal or plantar aspect of the foot. The management of Morton’s neuroma starts with conservative measures, usually with limited efficacy, including orthotics and anti-inflammatory medication. When conservative treatment fails, a series of minimally invasive ultrasound-guided procedures can be employed as second-line treatments prior to surgery. Such procedures include infiltration of the area with a corticosteroid and local anesthetic, chemical neurolysis with alcohol or radiofrequency thermal neurolysis. Ultrasound aids in the accurate diagnosis of Morton’s neuroma and guides the aforementioned treatment, so that significant and potentially long-lasting pain reduction can be achieved. In cases of initial treatment failure, the procedure can be repeated, usually leading to the complete remission of symptoms. Current data shows that minimally invasive treatments can significantly reduce the need for subsequent surgery in patients with persistent Morton’s neuroma unresponsive to conservative measures. The purpose of this review is to present current data on the application of ultrasound for the diagnosis and treatment of Morton’s neuroma, with emphasis on the outcomes of ultrasound-guided treatments.

US-guided high-volume injection for Achilles tendinopathy

Achilles tendinopathy is a common overuse condition affecting the adult population. The incidence is on the rise because of greater participation of people in recreational or competitive sporting activities. Chronic Achilles tendinopathy occurs most commonly in the tendon’s mid-portion, and it is challenging to manage, leading to significant patient morbidity. Despite conservative management many patients still require surgical intervention. The mechanism underlying pain is not entirely understood; however, high-resolution color Doppler ultrasound has shown that neovascularisation could be involved. Minimally-invasive treatments for chronic Achilles tendinopathy may prevent the need for surgery when conservative methods have failed. Ultrasound provides an option to guide therapeutic interventions accurately, so that treatment is delivered to the desired site of pathology. High-volume image-guided injection is a relatively new technique where a high volume of liquid is injected between the anterior aspect of the Achilles tendon and the Kager’s fat pad, used to strip away the neovascularity and disrupt the nerve ingrowth seen in chronic cases of Achilles tendinopathy. High-volume image-guided injection has shown promising results in terms of reducing pain and improving function in patients where conservative measures have failed. This review aims to describe the fundamental technical factors, and investigate the efficacy of high-volume image-guided injection with reference to the available literature.

Machine learning identifies factors related to early joint space narrowing in dysplastic and non-dysplastic hips

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.

Advanced clinical imaging for the evaluation of stem cell based therapies

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.

Musculoskeletal trauma imaging in the era of novel molecular methods and artificial intelligence

Over the past decade rapid advancements in molecular imaging (MI) and artificial intelligence (AI) have revolutionized traditional musculoskeletal radiology. Molecular imaging refers to the ability of various methods to in vivo characterize and quantify biological processes, at a molecular level. The extracted information provides the tools to understand the pathophysiology of diseases and thus to early detect, to accurately evaluate the extend and to apply and evaluate targeted treatments. At present, molecular imaging mainly involves CT, MRI, radionuclide, US, and optical imaging and has been reported in many clinical and preclinical studies. Although originally MI techniques targeted at central nervous system disorders, later on their value on musculoskeletal disorders was also studied in depth. Meaningful exploitation of the large volume of imaging data generated by molecular and conventional imaging techniques, requires state-of-the-art computational methods that enable rapid handling of large volumes of information. AI allows end-to-end training of computer algorithms to perform tasks encountered in everyday clinical practice including diagnosis, disease severity classification and image optimization. Notably, the development of deep learning algorithms has offered novel methods that enable intelligent processing of large imaging datasets in an attempt to automate decision-making in a wide variety of settings related to musculoskeletal trauma. Current applications of AI include the diagnosis of bone and soft tissue injuries, monitoring of the healing process and prediction of injuries in the professional sports setting. This review presents the current applications of novel MI techniques and methods and the emerging role of AI regarding the diagnosis and evaluation of musculoskeletal trauma.

Osteogenic differentiation of bone marrow mesenchymal stem cells on chitosan/gelatin scaffolds: gene expression profile and mechanical analysis

In the present study we explore the extracellular matrix (ECM) produced by human bone marrow mesenchymal stem/stromal cells (BM-MSCs) induced to undergo osteogenic differentiation within porous chitosan/gelatin (CS:Gel) scaffolds by investigating their multiple gene expression profile and mechanical behavior. Initially, the efficiency of the BM-MSCs osteogenic differentiation within the constructs was confirmed by the significant rise in the expression of the osteogenesis associated genes DLX5, RUNX2, ALP and OSC. In line with these findings, OSC and Col1A1 protein expression was also detected in BM-MSCs on the CS:Gel scaffolds at day 14 of osteogenic differentiation. We then profiled, for the first time, the expression of 84 cell adhesion and ECM molecules using PCR arrays. The arrays, which were conducted at day 14 of osteogenic differentiation, demonstrated that 49 genes including collagens, integrins, laminins, ECM proteases, catenins, thrombospondins, ECM protease inhibitors and cell-cell adhesion molecules were differentially expressed in BM-MSCs seeded on scaffolds compared to tissue culture polystyrene control. Moreover, we performed dynamic mechanical analysis of the cell-loaded scaffolds on days 0, 7 and 14 to investigate the correlation between the biological results and the mechanical behavior of the constructs. Our data demonstrate a significant increase in the stiffness of the constructs with storage modulus values of 2 MPa on day 7, compared to 0.5 MPa on day 0, following a drop of the stiffness at 0.8 MPa on day 14, that may be attributed to the significant increase of specific ECM protease gene expression such as MMP1, MMP9, MMP11 and MMP16 at this time period.