Development and validation of an image biomarker to identify metabolic dysfunction associated steatohepatitis: MR-MASH score.

BACKGROUND AND AIMS: Diagnosis of metabolic dysfunction-associated steatohepatitis (MASH) requires histology. In this study, a magnetic resonance imaging (MRI) score was developed and validated to identify MASH in patients with metabolic dysfunction-associated steatotic liver disease (MASLD). Secondarily, a screening strategy for MASH diagnosis was investigated. METHODS: This prospective multicentre study included 317 patients with biopsy-proven MASLD and contemporaneous MRI. The discovery cohort (Spain, Portugal) included 194 patients. NAFLD activity score (NAS) and fibrosis were assessed with the NASH-CRN histologic system. MASH was defined by the presence of steatosis, lobular inflammation, and ballooning, with NAS ≥4 with or without fibrosis. An MRI-based composite biomarker of Proton Density Fat Fraction and waist circumference (MR-MASH score) was developed. Findings were afterwards validated in an independent cohort (United States, Spain) with different MRI protocols. RESULTS: In the derivation cohort, 51% (n = 99) had MASH. The MR-MASH score identified MASH with an AUC = .88 (95% CI .83-.93) and strongly correlated with NAS (r = .69). The MRI score lower cut-off corresponded to 88% sensitivity with 86% NPV, while the upper cut-off corresponded to 92% specificity with 87% PPV. MR-MASH was validated with an AUC = .86 (95% CI .77-.92), 91% sensitivity (lower cut-off) and 87% specificity (upper cut-off). A two-step screening strategy with sequential MR-MASH examination performed in patients with indeterminate-high FIB-4 or transient elastography showed an 83-84% PPV to identify MASH. The AUC of MR-MASH was significantly higher than that of the FAST score (p < .001). CONCLUSIONS: The MR-MASH score has clinical utility in the identification and management of patients with MASH at risk of progression.

Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR.

Accumulation of excess iron in the body, or systemic iron overload, results from a variety of causes. The concentration of iron in the liver is linearly related to the total body iron stores and, for this reason, quantification of liver iron concentration (LIC) is widely regarded as the best surrogate to assess total body iron. Historically assessed using biopsy, there is a clear need for noninvasive quantitative imaging biomarkers of LIC. MRI is highly sensitive to the presence of tissue iron and has been increasingly adopted as a noninvasive alternative to biopsy for detection, severity grading, and treatment monitoring in patients with known or suspected iron overload. Multiple MRI strategies have been developed in the past 2 decades, based on both gradient-echo and spin-echo imaging, including signal intensity ratio and relaxometry strategies. However, there is a general lack of consensus regarding the appropriate use of these methods. The overall goal of this article is to summarize the current state of the art in the clinical use of MRI to quantify liver iron content and to assess the overall level of evidence of these various methods. Based on this summary, expert consensus panel recommendations on best practices for MRI-based quantification of liver iron are provided.

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC.

BACKGROUND: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. METHODS: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. RESULTS/CONCLUSIONS: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.

Precise whole liver automatic segmentation and quantification of PDFF and R2* on MR images.

OBJECTIVE: To automate the segmentation of whole liver parenchyma on multi-echo chemical shift encoded (MECSE) MR examinations using convolutional neural networks (CNNs) to seamlessly quantify precise organ-related imaging biomarkers such as the fat fraction and iron load. METHODS: A retrospective multicenter collection of 183 MECSE liver MR examinations was conducted. An encoder-decoder CNN was trained (107 studies) following a 5-fold cross-validation strategy to improve the model performance and ensure lack of overfitting. Proton density fat fraction (PDFF) and R2* were quantified on both manual and CNN segmentation masks. Different metrics were used to evaluate the CNN performance over both unseen internal (46 studies) and external (29 studies) validation datasets to analyze reproducibility. RESULTS: The internal test showed excellent results for the automatic segmentation with a dice coefficient (DC) of 0.93 ± 0.03 and high correlation between the quantification done with the predicted mask and the manual segmentation (rPDFF = 1 and rR2* = 1; p values < 0.001). The external validation was also excellent with a different vendor but the same magnetic field strength, proving the generalization of the model to other manufacturers with DC of 0.94 ± 0.02. Results were lower for the 1.5-T MR same vendor scanner with DC of 0.87 ± 0.06. Both external validations showed high correlation in the quantification (rPDFF = 1 and rR2* = 1; p values < 0.001). In both internal and external validation datasets, the relative error for the PDFF and R2* quantification was below 4% and 1% respectively. CONCLUSION: Liver parenchyma can be accurately segmented with CNN in a vendor-neutral virtual approach, allowing to obtain reproducible automatic whole organ virtual biopsies. KEY POINTS: • Whole liver parenchyma can be automatically segmented using convolutional neural networks. • Deep learning allows the creation of automatic pipelines for the precise quantification of liver-related imaging biomarkers such as PDFF and R2*. • MR "virtual biopsy" can become a fast and automatic procedure for the assessment of chronic diffuse liver diseases in clinical practice.

MR imaging assessment and quantification of liver iron.

Iron overload is a common clinical problem resulting from hereditary hemochromatosis or secondary hemosiderosis (mainly associated with transfusion therapy), being also associated with chronic liver diseases and metabolic disorders. Excess of iron accumulates in organs like the liver, pancreas and heart. Without treatment, patients with iron overload disorders will develop liver cirrhosis, diabetes and cardiomyopathy. Iron quantification is therefore crucial not only for diagnosis of iron overload but also to monitor iron-reducing therapies. Liver iron concentration is considered the surrogate marker of total body iron stores. Because liver biopsy is invasive and prone to high variability and sampling bias, MR imaging has emerged as a non-invasive method and gained wide acceptance, now being considered the standard of care for assessing iron overload. Nevertheless, there are different MR techniques for iron quantification and there is still no consensus about the best technique or postprocessing tool for hepatic iron quantification, with the choice of imaging technique depending mainly on the local expertise as well on the available equipment and software. Because different methods should not be used interchangeably, it is important to choose one method and use the same one when following up patients over time.

Pneumatosis cystoides intestinalis - an incidental finding with unpredictable evolution / Pneumatose cistoide intestinal - um achado incidental com evolução imprevisível

Abstract Pneumatosis cystoides intestinalis is an uncommon disease with unknown etiology characterized by the presence of multiple gas-filled cysts within the submucosa or subserosa of the intestinal wall. Pneumoperitoneum and/or intestinal perforation are complications that may be associated with pneumatosis cystoides intestinalis. The patients are often prone to misdiagnosis or mistreatment.We are presenting a case of pneumatosis cystoides intestinalis in a 42 year-old woman affected by peritoneal free air and numerous, diffuse, bubble-like intramural gas collections into the jejunum and ileum, showed in CT-enterography images. The woman had a carcinoid tumor located in jejunum two years ago, treated with enterectomy. Recent complaints of nonspecific symptoms of abdominal discomfort and diarrhea motivated the realization of CT scan, serum chromogranin and urine 5-hidroxindolacetic acid for hypothesis of tumor carcinoid recurrence withdraw. The only change found was the presence of pneumatosis cystoides intestinalis in CT-enterography images without intestinal necrosis, bleeding or evident obstruction. For that reason no surgical procedure was realized and the patient stayed on surveillance. Actually, the patient complaints are sporadic abdominal discomfort, without pneumatosis cystoides intestinalis clinical evidence. Conclusion: The treatment plan of patient with PCI depends on underlying cause and clinical condition severity. When conservative treatment is adopted the clinical evolution of pneumatosis cystoides intestinalis is unpredictable and can even disappear in an indeterminate number of patients.

Resumo A pneumatose cistoide intestinal é uma doença incomum, de etiologia desconhecida, caracterizada pela presença de múltiplos cistos preenchidos com gás na submucosa ou subserosa da parede intestinal. O pneumoperitoneu e/ou a perfuração intestinal são complicações que podem estar associadas à pneumatose cistoide intestinal. Os pacientes geralmente estão sujeitos a erros de diagnóstico ou de tratamento.Apresentamos um caso de pneumatose cistoide intestinal em paciente do sexo feminino, 42 anos de idade, com ar livre peritoneal e numerosas coleções gasosas intramurais, difusas e semelhantes a bolhas no jejuno e íleo, visualizados em imagens de enterografia por tomografia computadorizada (TC). Há dois anos, a paciente teve um tumor carcinoide localizado no jejuno que foi tratado com enterectomia. As queixas recentes de sintomas inespecíficos, desconforto abdominal e diarreia motivaram a realização da TC e exame de cromogranina sérica e ácido 5-hidroxindolacético na urina para excluir a hipótese de recorrência do tumor carcinoide. A única alteração encontrada foi a presença de pneumatose cistoide intestinal em imagens de enterografia por TC sem necrose intestinal, sangramento ou obstrução evidente. Por esse motivo, nenhum procedimento cirúrgico foi realizado, e a paciente permaneceu em observação. Atualmente, a queixa da paciente é de desconforto abdominal esporádico, sem evidência clínica de pneumatose cistoide intestinal. Conclusão: O plano de tratamento de pacientes com PCI depende da causa subjacente e da gravidade da condição clínica. Quando o tratamento conservador é adotado, a evolução clínica da pneumatose cistoide intestinal é imprevisível e pode até desaparecer em alguns pacientes.

Liver MRI: From basic protocol to advanced techniques.

Liver MR is a well-established modality with multiparametric capabilities. However, to take advantage of its full capacity, it is mandatory to master the technique and optimize imaging protocols, apply advanced imaging concepts and understand the use of different contrast media. Physiologic artefacts although inherent to upper abdominal studies can be minimized using triggering techniques and new strategies for motion control. For standardization, the liver MR protocol should include motion-resistant T2-w sequences, in-op phase GRE T1 and T2-w fast spin echo sequences with fat suppression. Diffusion-weighted imaging (DWI) is mandatory, especially for detection of sub-centimetre metastases. Contrast-enhanced MR is the cornerstone of liver MR, especially for lesion characterization. Although extracellular agents are the most extensively used contrast agents, hepatobiliary contrast media can provide an extra-layer of functional diagnostic information adding to the diagnostic value of liver MR. The use of high field strength (3T) increases SNR but is more challenging especially concerning artefact control. Quantitative MR belongs to the new and evolving field of radiomics where the use of emerging biomarkers such as perfusion or DWI can derive new information regarding disease detection, prognostication and evaluation of tumour response. This information can overcome some of the limitations of current tests, especially when using vascular disruptive agents for oncologic treatment assessment. MR is, today, a robust, mature, multiparametric imaging modality where clinical applications have greatly expanded from morphology to advanced imaging. This new concept should be acknowledged by all those involved in producing high quality, high-end liver MR studies.

Accurate simultaneous quantification of liver steatosis and iron overload in diffuse liver diseases with MRI.

PURPOSE: To evaluate the diagnostic performances of 3 Tesla multi-echo chemical shift-encoded gradient echo magnetic resonance (MECSE-MR) imaging to simultaneously quantify liver steatosis and iron overload in a wide spectrum of diffuse liver diseases having biopsy as reference standard. METHODS: MECSE-MR-acquired images were used to calculate fat fraction and iron content in a single breath-hold in 109 adult patients. Proton density fat fraction (PDFF) was prospectively estimated using complex-based data reconstruction with multipeak fat modeling. Water R2* was used to estimate iron content. Biopsy was obtained in all cases, grading liver steatosis, siderosis, inflammation, and fibrosis. Differences in PDFF and R2* values across histopathological grades were analyzed, and ROC curves analyses evaluated the MR diagnostic performance. RESULTS: Calculated fat fraction measurements showed significant differences (p < 0.001) among steatosis grades, being unaffected by the presence of inflammation or fibrosis (p ≥ 0.05). A strong correlation was found between fat fraction and steatosis grade (R S = 0.718, p < 0.001). Iron deposits did not affect fat fraction quantitation (p ≥ 0.05), except in cases with severe iron overload (grade 4). A strong positive correlation was also observed between R2* measurements and iron grades (R S = 0.704, p < 0.001). Calculated R2* values were not different across grades of steatosis, inflammation, and fibrosis (p ≥ 0.05). CONCLUSION: A MECSE-MR sequence simultaneously quantifies liver steatosis and siderosis, regardless coexisting liver inflammation or fibrosis, with high accuracy in a wide spectrum of diffuse liver disorders. This sequence can be acquired within a single breath-hold and can be implemented in the routine MR evaluation of the liver.

Evaluation of fibrosis and inflammation in diffuse liver diseases using intravoxel incoherent motion diffusion-weighted MR imaging.

PURPOSE: The purpose of the study was to evaluate the role of intravoxel incoherent motion (IVIM) diffusion model for the assessment of liver fibrosis and inflammation in diffuse liver disorders, also considering the presence of liver steatosis and iron deposits. METHODS: Seventy-four patients were included, with liver biopsy and a 3 Tesla abdominal magnetic resonance imaging examination, with an IVIM diffusion-weighted sequence (single-shot spin-echo echo-planar sequence, with gradient reversal fat suppression; 6 b-values: 0, 50, 200, 400, 600, and 800 s/mm2). Histological evaluation comprised the Ishak modified scale, for grading inflammation and fibrosis, plus steatosis and iron loading classification. The liver apparent diffusion coefficient (ADC) and IVIM parameters (D, D*, f) were calculated from the IVIM images. The relationship between IVIM parameters and histopathological scores were evaluated by ANOVA and Spearman correlation tests. A test-retest experiment assessed reproducibility and repeatability in 10 healthy volunteers and 10 randomly selected patient studies. RESULTS: ADC and f values were lower with higher fibrosis stages (p = 0.009, p = 0.006, respectively) and also with higher necro-inflammatory activity grades (p = 0.02, p = 0.017, respectively). Considered together, only fibrosis presented a significant effect on ADC and f measurements (p < 0.05), whereas inflammation had no significant effect (p > 0.05). A mild correlation was found between ADC and f with fibrosis (R S = -0.32 and R S = -0.38; p < 0.05) and inflammation (R S = -0.31 and R S = -0.32, p < 0.05; respectively). The AUROC for ADC and f measurements with the different dichotomizations between fibrosis or inflammation grades were only fair (0.670 to 0.749, p < 0.05). Neither D nor D* values were significantly different between liver fibrosis or inflammation grades. D measurements were significantly different across histologic grades of steatosis (p < 0.001) and iron overload (p < 0.001), whereas f measurements showed significant differences across histologic steatosis grades (p = 0.005). There was an excellent agreement between the different readers for ADC, f, and D. CONCLUSIONS: Although fibrosis presented a significant effect on ADC and f, IVIM measurements are not accurate enough to stage liver fibrosis or necro-inflammatory activity in diffuse liver diseases. D values were influenced by steatosis and iron overload.

Liver teratoma.

True liver teratomas are exceptional tumors, with only 25 cases reported in the radiology literature. Most cases reported were either intraperitoneal or retroperitoneal teratomas that had invaded the liver. The authors present a 27-year-old woman with a liver complex mass found incidentally at computed tomography (CT). The different imaging studies demonstrated that the lesion was composed of macroscopic fat, with a central soft tissue component and a calcification. The patient underwent surgery and the final diagnosis was a benign liver teratoma.