R2* Impact on Hepatic Fat Quantification With a Commercial Single Voxel Technique at 1.5 and 3.0 T.

Rationale and Objectives: Fat quantification accuracy using a commercial single-voxel high speed T2-corrected multi-echo (HISTO) technique and its robustness to R2* variations at 3.0 T, such as those introduced by iron in liver, has not been fully established. This study evaluated HISTO at 3.0 T and sought to reproduce results at 1.5 T. Methods: Phantoms were prepared with a range of fat content and R2*. Data were acquired at 1.5 T and 3.0 T, using HISTO and a Dixon technique. Fat quantification accuracy was evaluated as a function of R2*. The patient study included 239 consecutive patients. Data were acquired at 1.5 T or 3.0 T, using HISTO and Dixon techniques. The techniques were compared using Bland-Altman plots. Bias significance was evaluated using a one-sample t-test. Results: In phantoms, HISTO was accurate within 10% up to a R2* of 100 s-1 at both field strengths, while Dixon was accurate within 10% where R2* was accurately quantified (up to 350 s-1 at 1.5 T, and 550 s-1 at 3.0 T). In patients, where R2* was <100 s-1, fat quantification from both techniques agreed at 1.5 T (P = .71), but not at 3.0 T (P = .007), with a bias <1%. Conclusion: Results suggest that HISTO is reliable when R2* is <100 s-1, corresponding to patients with at most mild liver iron overload, and that it should be used with caution when R2* is >100 s-1. Dixon should be preferred for hepatic fat quantification due to its robustness to R2* variations.

Investigating genotype-to-phenotype correlation in CHARGE syndrome by deep phenotyping and multiparametric clustering.

CHARGE syndrome, due to CHD7 pathogenic variations, is an autosomal dominant disorder characterized by a large spectrum of severity. Despite the great number of variations reported, no clear genotype-to-phenotype correlation has been reported. Unsupervised machine learning and clustering was undertaken using a retrospective cohort of 42 patients, after deep radiologic and clinical phenotyping, to establish genotype-phenotype correlation for CHD7-related CHARGE syndrome. It resulted in three clusters showing phenotypes of different severities. While no clear genotype-phenotype correlation appeared within the first two clusters, a single patient was outlying the cohort data (cluster 3) with the most atypical phenotype and the most distal frameshift variant in the gene. We added two other patients with similar distal pathogenic variants and observed a tendency toward mild and/or atypical phenotypes. We hypothesized that this finding could potentially be related to escaping nonsense mediated RNA decay, but found no evidence of such decay in vivo for any of the CHD7 pathogenic variation tested. This indicates that this milder phenotype may rather result from the production of a protein retaining all functional domains.

CT-based Bosniak classification of cystic renal lesions: is version 2019 an improvement on version 2005?

OBJECTIVE: To compare the diagnostic performance and inter-reader agreement of the CT-based v2019 versus v2005 Bosniak classification systems for risk stratification of cystic renal lesions (CRL). METHODS: This retrospective study included adult patients with CRL identified on CT scan between 2005 and 2018. The reference standard was histopathology or a minimum 4-year imaging follow-up. The studies were reviewed independently by five readers (three senior, two junior), blinded to pathology results and imaging follow-up, who assigned Bosniak categories based on the 2005 and 2019 versions. Diagnostic performance of v2005 and v2019 Bosniak classifications for distinguishing benign from malignant lesions was calculated by dichotomizing CRL into the potential for ablative therapy (III-IV) or conservative management (I-IIF). Inter-reader agreement was calculated using Light's Kappa. RESULTS: One hundred thirty-nine patients with 149 CRL (33 malignant) were included. v2005 and v2019 Bosniak classifications achieved similar diagnostic performance with a sensitivity of 91% vs 91% and a specificity of 89% vs 88%, respectively. Inter-reader agreement for overall Bosniak category assignment was substantial for v2005 (κ = 0.78) and v2019 (κ = 0.75) between senior readers but decreased for v2019 when the Bosniak classification was dichotomized to conservative management (I-IIF) or ablative therapy (III-IV) (0.80 vs 0.71, respectively). For v2019, wall thickness was the morphological feature with the poorest inter-reader agreement (κ = 0.43 and 0.18 for senior and junior readers, respectively). CONCLUSION: No significant improvement in diagnostic performance and inter-reader agreement was shown between v2005 and v2019. The observed decrease in inter-reader agreement in v2019 when dichotomized according to management strategy may reflect the more stringent morphological criteria. KEY POINTS: • Versions 2005 and 2019 Bosniak classifications achieved similar diagnostic performance, but the specificity of higher risk categories (III and IV) was not increased while one malignant lesion was downgraded to v2019 Bosniak category II (i.e., not subjected to further follow-up). • Inter-reader agreement was similar between v2005 and v2019 but moderately decreased for v2019 when the Bosniak classification was dichotomized according to the potential need for ablative therapies (I-II-IIF vs III-IV).

Cystic fibrosis-related liver disease: Clinical presentations, diagnostic and monitoring approaches in the era of CFTR modulator therapies.

Cystic fibrosis (CF) is the most common autosomal recessive disease in the Caucasian population. Cystic fibrosis-related liver disease (CFLD) is defined as the pathogenesis related to the underlying CFTR defect in biliary epithelial cells. CFLD needs to be distinguished from other liver manifestations that may not have any pathological significance. The clinical/histological presentation and severity of CFLD vary. The main histological presentation of CFLD is focal biliary fibrosis, which is usually asymptomatic. Portal hypertension develops in a minority of cases (about 10%) and may require specific management including liver transplantation for end-stage liver disease. Portal hypertension is usually the result of the progression of focal biliary fibrosis to multilobular cirrhosis during childhood. Nevertheless, non-cirrhotic portal hypertension as a result of porto-sinusoidal vascular disease is now identified increasingly more frequently, mainly in young adults. To evaluate the effect of new CFTR modulator therapies on the liver, the spectrum of hepatobiliary involvement must first be precisely classified. This paper discusses the phenotypic features of CFLD, its underlying physiopathology and relevant diagnostic and follow-up approaches, with a special focus on imaging.

Malignancy risk stratification of cystic renal lesions based on a contrast-enhanced CT-based machine learning model and a clinical decision algorithm.

OBJECTIVE: To distinguish benign from malignant cystic renal lesions (CRL) using a contrast-enhanced CT-based radiomics model and a clinical decision algorithm. METHODS: This dual-center retrospective study included patients over 18 years old with CRL between 2005 and 2018. The reference standard was histopathology or 4-year imaging follow-up. Training and testing datasets were acquired from two institutions. Quantitative 3D radiomics analyses were performed on nephrographic phase CT images. Ten-fold cross-validated LASSO regression was applied to the training dataset to identify the most discriminative features. A logistic regression model was trained to classify malignancy and tested on the independent dataset. Reported metrics included areas under the receiver operating characteristic curves (AUC) and balanced accuracy. Decision curve analysis for stratifying patients for surgery was performed in the testing dataset. A decision algorithm was built by combining consensus radiological readings of Bosniak categories and radiomics-based risks. RESULTS: A total of 149 CRL (139 patients; 65 years [56-72]) were included in the training dataset-35 Bosniak(B)-IIF (8.6% malignancy), 23 B-III (43.5%), and 23 B-IV (87.0%)-and 50 CRL (46 patients; 61 years [51-68]) in the testing dataset-12 B-IIF (8.3%), 10 B-III (60.0%), and 9 B-IV (100%). The machine learning model achieved high diagnostic performance in predicting malignancy in the testing dataset (AUC = 0.96; balanced accuracy = 94%). There was a net benefit across threshold probabilities in using the clinical decision algorithm over management guidelines based on Bosniak categories. CONCLUSION: CT-based radiomics modeling accurately distinguished benign from malignant CRL, outperforming the Bosniak classification. The decision algorithm best stratified lesions for surgery and active surveillance. KEY POINTS: • The radiomics model achieved excellent diagnostic performance in identifying malignant cystic renal lesions in an independent testing dataset (AUC = 0.96). • The machine learning-enhanced decision algorithm outperformed the management guidelines based on the Bosniak classification for stratifying patients to surgical ablation or active surveillance.

Hepatic manifestations of cystic fibrosis.

PURPOSE OF REVIEW: Liver disease in cystic fibrosis (CF) usually develops before puberty, is often asymptomatic and slowly progressive. Multilobular cirrhosis develops in approximately 5-10% of patients by the age of 18, and is a significant contributor to the morbidity and mortality. No therapy, including ursodeoxycholic acid and cystic fibrosis transmembrane conductance regulator correctors or potentiators, has proven effective to prevent or halt the progression of liver disease towards cirrhosis and portal hypertension. This review provides the current knowledge in the epidemiology of CF liver disease and development of noninvasive tools to assess liver disease severity and progression overtime in order to optimize clinical management and therapeutic options. RECENT FINDINGS: Liver disease not only develops during childhood but also later in the lifetime of patients with CF; the incidence of cirrhosis with portal hypertension increases progressively reaching 10% by age 30. Several noninvasive tools to measure liver stiffness as an indirect measure of fibrosis are being investigated, and show promising results for the assessment of early stages of liver fibrosis and disease progression. SUMMARY: Identifying noninvasive biomarkers is fundamental to improving early diagnosis, monitoring disease evolution and measuring treatment effects. A prerequisite is the use of consistent definitions for CF- liver disease (LD) in clinical trials.

Spontaneous gas in a retroperitoneal mass: check the testis!

Testicular germ cell tumor is the most common cancer in 20-to 35-years-old men. There are known risk factors such as undescended testicle(s) and history of testicular cancer. Most lesions are germ cell tumors with two main subtypes: seminomas and non-seminomatous germ cell tumors.

On-the-fly point annotation for fast medical video labeling.

PURPOSE: In medical research, deep learning models rely on high-quality annotated data, a process often laborious and time-consuming. This is particularly true for detection tasks where bounding box annotations are required. The need to adjust two corners makes the process inherently frame-by-frame. Given the scarcity of experts' time, efficient annotation methods suitable for clinicians are needed. METHODS: We propose an on-the-fly method for live video annotation to enhance the annotation efficiency. In this approach, a continuous single-point annotation is maintained by keeping the cursor on the object in a live video, mitigating the need for tedious pausing and repetitive navigation inherent in traditional annotation methods. This novel annotation paradigm inherits the point annotation's ability to generate pseudo-labels using a point-to-box teacher model. We empirically evaluate this approach by developing a dataset and comparing on-the-fly annotation time against traditional annotation method. RESULTS: Using our method, annotation speed was 3.2 × faster than the traditional annotation technique. We achieved a mean improvement of 6.51 ± 0.98 AP@50 over conventional method at equivalent annotation budgets on the developed dataset. CONCLUSION: Without bells and whistles, our approach offers a significant speed-up in annotation tasks. It can be easily implemented on any annotation platform to accelerate the integration of deep learning in video-based medical research.

Canadian radiology: 2024 update.

Radiology in Canada is advancing through innovations in clinical practices and research methodologies. Recent developments focus on refining evidence-based practice guidelines, exploring innovative imaging techniques and enhancing diagnostic processes through artificial intelligence. Within the global radiology community, Canadian institutions play an important role by engaging in international collaborations, such as with the American College of Radiology to refine implementation of the Ovarian-Adnexal Reporting and Data System for ultrasound and magnetic resonance imaging. Additionally, researchers have participated in multidisciplinary collaborations to evaluate the performance of artificial intelligence-driven diagnostic tools for chronic liver disease and pediatric brain tumors. Beyond clinical radiology, efforts extend to addressing gender disparities in the field, improving educational practices, and enhancing the environmental sustainability of radiology departments. These advancements highlight Canada's role in the global radiology community, showcasing a commitment to improving patient outcomes and advancing the field through research and innovation. This update underscores the importance of continued collaboration and innovation to address emerging challenges and further enhance the quality and efficacy of radiology practices worldwide.

Improving pain control during transarterial chemoembolization for hepatocellular carcinoma performed under local anesthesia with multimodal analgesia.

PURPOSE: The purpose of this study was to assess the performance of a reinforced analgesic protocol (RAP) on pain control in patients undergoing conventional trans-arterial chemoembolization (cTACE) for hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Eighty-one consecutive patients (57 men, 24 women) with a mean age of 69 ± 10 (standard deviation) years (age range: 49-92 years) underwent 103 cTACEs. Standard antalgic protocol (50 mg hydroxyzine, 10 mg oxycodone, 8 mg ondansetron, and lidocaine for local anesthesia) was prospectively compared to a RAP (standard + 40 mg 2-h infusion nefopam and 50 mg tramadol). The individual pain risk was stratified based on age, the presence of cirrhosis and alcoholic liver disease, and patients were assigned to a low-risk group (standard protocol) or high-risk group (RAP). The primary endpoint was severe periprocedural abdominal pain (SAP), defined as a visual analog scale score ≥30/100. A predefined intermediate analysis was performed to monitor the benefit-risk of the RAP. Based on the intermediate analysis, all patients were treated with the RAP. RESULTS: The intermediate analysis performed after 52 cTACE showed that 2/17 (12%) high-risk patients (i.e., those receiving the RAP) experienced SAP compared to 15/35 (43%) low-risk patients (odds ratio [OR] = 0.18; 95% confidence interval [CI]: 0.02-0.98; P = 0.03). Analysis of all procedures showed that 12/67 (18%) patients in cTACE receiving the RAP experienced SAP compared to 15/36 (42%) patients who did not receive it (OR = 3.27; 95% CI: 1.32-8.14; P = 0.01). There were no statistical differences in adverse events, particularly for nausea, between groups. CONCLUSION: Reinforcing the analgesic protocol by combining non-opioid and opioid molecules reduces perioperative pain in patients undergoing cTACE for HCC.

Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease.

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.

Comparison of Transient Elastography, ShearWave Elastography, Magnetic Resonance Elastography and FibroTest as routine diagnostic markers for assessing liver fibrosis in children with Cystic Fibrosis.

BACKGROUND AND OBJECTIVE: Reliable markers are needed for early diagnosis and follow-up of liver disease in Cystic Fibrosis (CF). The objective was to evaluate the diagnostic performance of Transient Elastography (TE), Real-Time ShearWave Ultrasound Elastography (SWE), Magnetic Resonance Elastography (MRE) and the FibroTest as markers of Cystic Fibrosis Liver Disease (CFLD). METHODS: A monocentric prospective cross-modality comparison study was proposed to all children (6 to 18 years of age) attending the CF center. Based on liver ultrasound findings, participants were classified into 3 groups: multinodular liver or portal hypertension (Nodular US/PH, advanced CFLD), heterogeneous increased echogenicity (Heterogeneous US, CFLD) or neither (Normal/Homogeneous US, no CFLD). The 4 tests were performed on the same day. The primary outcome was the FibroTest value and liver stiffness measurements (LSM). RESULTS: 55 participants (mean age 12.6 ± 3.3 years; 25 girls) were included between 2015 and 2018: 23 in group Nodular US/PH, 8 in group Heterogeneous US and 24 in group Normal/Homogeneous US (including 4 with steatosis). LSM on TE, SWE and MRE were higher in participants with CFLD (groups Nodular US/PH and Heterogeneous US) compared to others (group Normal/Homogeneous US) (p<0.01), while FibroTest values did not differ (p = 0.09). The optimal cut-off values for predicting CFLD on TE, SWE and MRE were 8.7 (AUC=0.83, Se=0.71, Sp=0.96), 7.8 (AUC=0.85, Se=0.73, Sp=0.96) and 4.15 kPa (AUC=0.68, Se=0.73, Sp=0.64), respectively. LSM predicted the occurrence of major liver-related events at 3 years. TE and SWE were highly correlated (Spearman's ρ=0.9) and concordant in identifying advanced CFLD (Cohen's κ=0.84) while MRE was moderately correlated and concordant with TE (ρ=0.41; κ=36) and SWE (ρ=0.5; κ=0.50). CONCLUSION: This study demonstrated excellent diagnostic performance of TE, SWE and MRE for the diagnosis of CFLD.

CT features associated with underlying malignancy in patients with diagnosed mesenteric panniculitis.

PURPOSE: The purpose of this study was to identify abdominal computed tomography (CT) features associated with underlying malignancy in patients with mesenteric panniculitis (MP). MATERIALS AND METHODS: This single-institution retrospective longitudinal cohort study included patients with MP and a minimum 1-year abdominopelvic CT follow-up or 2-year clinical follow-up after initial abdominopelvic CT examination. Two radiologists, blinded to patients' medical records, conjointly reviewed CT-based features of MP. Electronic medical records were reviewed for newly diagnosed malignancies with the following specific details: type (lymphoproliferative disease or solid malignancy), location (possible mesenteric drainage or distant), stage, time to diagnosis. An expert panel of three radiologists and one hemato-oncologist, who were blinded to the initial CT-based MP features, assessed the probability of association between MP and malignancy based on the malignancy characteristics. RESULTS: From 2006 to 2016, 444 patients with MP were included. There were 272 men and 172 women, with a median age of 64 years (age range: 25-89); the median overall follow-up was 36 months (IQR: 22, 60; range: 12-170). A total of 34 (8%) patients had a diagnosis of a new malignancy; 5 (1%) were considered possibly related to the MP, all being low-grade B-cell non-Hodgkin lymphomas. CT features associated with the presence of an underlying malignancy were the presence of an MP soft-tissue nodule with a short axis >10 mm (P < 0.0001) or lymphadenopathy in another abdominopelvic region (P < 0.0001). Associating these two features resulted in high diagnostic performance (sensitivity 100%; [95% CI: 57-100]; specificity 99% [95% CI: 98-100]). All related malignancies were identified. CONCLUSION: Further workup to rule out an underlying malignancy is only necessary in the presence of an MP soft-tissue nodule >10 mm or associated abdominopelvic lymphadenopathy.

Lumacaftor-ivacaftor effects on cystic fibrosis-related liver involvement in adolescents with homozygous F508 del-CFTR.

BACKGROUND: The effects of lumacaftor-ivacaftor on cystic fibrosis transmembrane conductance regulator (CFTR)-associated liver disease remain unclear. The objective of the study was to describe the effect of this treatment on features of liver involvement in a cystic fibrosis (CF) adolescent population homozygous for F508del. METHODS: Clinical characteristics, liver blood tests, abdominal ultrasonography (US), and pancreas and liver proton density fat fraction (PDFF) by magnetic resonance imaging, were obtained at treatment initiation and at 12 months for all patients. Biomarkers of CFTR activity (sweat chloride test, nasal potential difference, and intestinal current measurement) were assessed at initiation and at 6 months therapy. RESULTS: Of the 37 patients who started ivacaftor/lumacaftor treatment, 28 were eligible for analysis. In this group, before treatment initiation, 4 patients were diagnosed with multinodular liver and portal hypertension, 19 with other forms of CF liver involvement, and 5 with no signs of liver involvement. During treatment, no hepatic adverse reactions were documented, and no patient developed liver failure. Serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST) and gammaglutamyl transferase (GGT) decreased significantly following initiation of lumacaftor-ivacaftor, and remained so after 12 months treatment. This was not correlated with changes in clinical status, liver and pancreas US and PDFF, fecal elastase, or lumacaftor-ivacaftor serum levels. The most "responsive" patients demonstrated a significant increase in biomarkers of CFTR activity. CONCLUSIONS: These results may suggest a potential beneficial effect of CFTR modulators on CF liver disease and warrant further investigation in larger, prospective studies.

Artificial intelligence-based radiomics models in endometrial cancer: A systematic review.

BACKGROUND: Radiological preoperative assessment of endometrial cancer (EC) is in some cases not precise enough and its performances improvement could lead to a clinical benefit. Radiomics is a recent field of application of artificial intelligence (AI) in radiology. AIMS: To investigate the contribution of radiomics on the radiological preoperative assessment of patients with EC; and to establish a simple and reproducible AI Quality Score applicable to Machine Learning and Deep Learning studies. METHODS: We conducted a systematic review of current literature including original articles that studied EC through imaging-based AI techniques. Then, we developed a novel Simplified and Reproducible AI Quality score (SRQS) based on 10 items which ranged to 0 to 20 points in total which focused on clinical relevance, data collection, model design and statistical analysis. SRQS cut-off was defined at 10/20. RESULTS: We included 17 articles which studied different radiological parameters such as deep myometrial invasion, lympho-vascular space invasion, lymph nodes involvement, etc. One article was prospective, and the others were retrospective. The predominant technique was magnetic resonance imaging. Two studies developed Deep Learning models, while the others machine learning ones. We evaluated each article with SRQS by 2 independent readers. Finally, we kept only 7 high-quality articles with clinical impact. SRQS was highly reproducible (Kappa = 0.95 IC 95% [0.907-0.988]). CONCLUSION: There is currently insufficient evidence on the benefit of radiomics in EC. Nevertheless, this field is promising for future clinical practice. Quality should be a priority when developing these new technologies.

Multimodality Imaging and Artificial Intelligence for Tumor Characterization: Current Status and Future Perspective.

Research in medical imaging has yet to do to achieve precision oncology. Over the past 30 years, only the simplest imaging biomarkers (RECIST, SUV,…) have become widespread clinical tools. This may be due to our inability to accurately characterize tumors and monitor intratumoral changes in imaging. Artificial intelligence, through machine learning and deep learning, opens a new path in medical research because it can bring together a large amount of heterogeneous data into the same analysis to reach a single outcome. Supervised or unsupervised learning may lead to new paradigms by identifying unrevealed structural patterns across data. Deep learning will provide human-free, undefined upstream, reproducible, and automated quantitative imaging biomarkers. Since tumor phenotype is driven by its genotype and thus indirectly defines tumoral progression, tumor characterization using machine learning and deep learning algorithms will allow us to monitor molecular expression noninvasively, anticipate therapeutic failure, and lead therapeutic management. To follow this path, quality standards have to be set: standardization of imaging acquisition as it has been done in the field of biology, transparency of the model development as it should be reproducible by different institutions, validation, and testing through a high-quality process using large and complex open databases and better interpretability of these algorithms.