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.

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.

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.

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 ).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.