In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging

La osteoporosis es una enfermedad ósea que se manifiesta con una menor densidad ósea y el deterioro de la arquitectura del hueso esponjoso. Ambos factores aumentan la fragilidad ósea y el riesgo de sufrir fracturas óseas, especialmente en mujeres, donde existe una alta prevalencia. El diagnóstico actual de la osteoporosis se basa en la cuantificación de la densidad mineral ósea (DMO) mediante la técnica de absorciometría dual de rayos X (DXA). Sin embargo, la DMO no puede considerarse de manera aislada para la evaluación del riesgo de fractura o los efectos terapéuticos. Existen otros factores, tales como la disposición microestructural de las trabéculas y sus características que es necesario tener en cuenta para determinar la calidad del hueso y evaluar de manera más directa el riesgo de fractura. Los avances técnicos de las modalidades de imagen médica, como la tomografía computarizada multidetector (MDCT), la tomografía computarizada periférica cuantitativa (HR-pQCT) y la resonancia magnética (RM) han permitido la adquisición in vivo con resoluciones espaciales elevadas. La estructura del hueso trabecular puede observarse con un buen detalle empleando estas técnicas. En particular, el uso de los equipos de RM de 3 Teslas (T) ha permitido la adquisición con resoluciones espaciales muy altas. Además, el buen contraste entre hueso y médula que proporcionan las imágenes de RM, así como la utilización de radiaciones no ionizantes sitúan a la RM como una técnica muy adecuada para la caracterización in vivo de hueso trabecular en la enfermedad de la osteoporosis. En la presente tesis se proponen nuevos desarrollos metodológicos para la caracterización morfométrica y mecánica del hueso trabecular en tres dimensiones (3D) y se aplican a adquisiciones de RM de 3T con alta resolución espacial. El análisis morfométrico está compuesto por diferentes algoritmos diseñados para cuantificar la morfología, la complejidad, la topología y los parámetros de anisotropía del tejido trabecular. En cuanto a la caracterización mecánica, se desarrollaron nuevos métodos que permiten la simulación automatizada de la estructura del hueso trabecular en condiciones de compresión y el cálculo del módulo de elasticidad. La metodología desarrollada se ha aplicado a una población de sujetos sanos con el fin de obtener los valores de normalidad del hueso esponjoso. Los algoritmos se han aplicado también a una población de pacientes con osteoporosis con el fin de cuantificar las variaciones de los parámetros en la enfermedad y evaluar las diferencias con los resultados obtenidos en un grupo de sujetos sanos con edad similar.Los desarrollos metodológicos propuestos y las aplicaciones clínicas proporcionan resultados satisfactorios, presentando los parámetros una alta sensibilidad a variaciones de la estructura trabecular principalmente influenciadas por el sexo y el estado de enfermedad. Por otra parte, los métodos presentan elevada reproducibilidad y precisión en la cuantificación de los valores morfométricos y mecánicos. Estos resultados refuerzan el uso de los parámetros presentados como posibles biomarcadores de imagen en la enfermedad de la osteoporosis.Alberich Bayarri, Á. (2010). In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8981Palanci

Optimisation of ultrasound liver perfusion through a digital reference object and analysis tool

[EN] Background Conventional ultrasound (US) provides important qualitative information, although there is a need to evaluate the influence of the input parameters on the output signal and standardise the acquisition for an adequate quantitative perfusion assessment. The present study analyses how the variation in the input parameters influences the measurement of the perfusion parameters. Methods A software tool with simulator of the conventional US signal was created, and the influence of the different input variables on the derived biomarkers was analysed by varying the image acquisition configuration. The input parameters considered were the dynamic range, gain, and frequency of the transducer. Their influence on mean transit time (MTT), the area under the curve (AUC), maximum intensity (MI), and time to peak (TTP) parameters as outputs of the quantitative perfusion analysis was evaluated. A group of 13 patients with hepatocarcinoma was analysed with both a commercial tool and an in-house developed software. Results The optimal calculated inputs which minimise errors while preserving images¿ readability consisted of gain of 15¿dB, dynamic range of 60¿dB, and frequency of 1.5¿MHz. The comparison between the in-house developed software and the commercial software provided different values for MTT and AUC, while MI and TTP were highly similar. Conclusion Input parameter selection introduces variability and errors in US perfusion parameter estimation. Our results may add relevant insight into the current knowledge of conventional US perfusion and its use in lesions characterisation, playing in favour of optimised standardised parameter configuration to minimise variability.Alberich-Bayarri, Á.; Tomás-Cucarella, J.; Torregrosa-Lloret, A.; Saiz Rodríguez, FJ.; Martí-Bonmatí, L. (2019). 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ESR Statement on the Validation of Imaging Biomarkers

Medical imaging capable of generating imaging biomarkers, specifically radiology and nuclear medicine image acquisition and analysis processes, differs from frequently used comparators like blood or urine biomarkers. This difference arises from the sample acquisition methodology. While different analysis methodologies and equipment provide slightly different results in any analytical domain, unlike blood or urine analysis where the samples are obtained by simple extraction or excretion, in radiology the acquisition of the sample is heterogeneous by design, since complex equipment from different vendors is used. Therefore, with this additional degree of freedom in medical imaging, there is still risk of persistent heterogeneity of image quality through time, due to different technological implementations across vendors and protocols used in different centres. Quantitative imaging biomarkers have yet to demonstrate an impact on clinical practice due to this lack of comprehensive standardisation in terms of technical aspects of image acquisition, analysis algorithms, processes and clinical validation. The aim is establishing a standard methodology based on metrology for the validation of image acquisition and analysis methods used in the extraction of biomarkers and radiomics data. The appropriate implementation of the guidelines herein proposed by radiology departments, research institutes and industry will allow for a significant reduction in inter-vendor & inter-centre variability in imaging biomarkers and determine the measurement error obtained, enabling them to be used in imaging-based criteria for diagnosis, prognosis or treatment response, ultimately improving clinical workflows and patient care. The validation of developed analytical methods must be based on a technical performance validation and clinical validation

A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

[EN] There is growing interest in applying quantitative diffusion techniques to magnetic resonance imaging for cancer diagnosis and treatment. These measurements are used as a surrogate marker of tumor cellularity and aggressiveness, although there may be factors that introduce some bias to these approaches. Thus, we explored a novel methodology based on confidence habitats and voxel uncertainty to improve the power of the apparent diffusion coefficient to discriminate between benign and malignant neuroblastic tumor profiles in children. We were able to show this offered an improved sensitivity and negative predictive value relative to standard voxel-based methodologies. Background/Aim: In recent years, the apparent diffusion coefficient (ADC) has been used in many oncology applications as a surrogate marker of tumor cellularity and aggressiveness, although several factors may introduce bias when calculating this coefficient. The goal of this study was to develop a novel methodology (Fit-Cluster-Fit) based on confidence habitats that could be applied to quantitative diffusion-weighted magnetic resonance images (DWIs) to enhance the power of ADC values to discriminate between benign and malignant neuroblastic tumor profiles in children. Methods: Histogram analysis and clustering-based algorithms were applied to DWIs from 33 patients to perform tumor voxel discrimination into two classes. Voxel uncertainties were quantified and incorporated to obtain a more reproducible and meaningful estimate of ADC values within a tumor habitat. Computational experiments were performed by smearing the ADC values in order to obtain confidence maps that help identify and remove noise from low-quality voxels within high-signal clustered regions. The proposed Fit-Cluster-Fit methodology was compared with two other methods: conventional voxel-based and a cluster-based strategy. Results: The cluster-based and Fit-Cluster-Fit models successfully differentiated benign and malignant neuroblastic tumor profiles when using values from the lower ADC habitat. In particular, the best sensitivity (91%) and specificity (89%) of all the combinations and methods explored was achieved by removing uncertainties at a 70% confidence threshold, improving standard voxel-based sensitivity and negative predictive values by 4% and 10%, respectively. Conclusions: The Fit-Cluster-Fit method improves the performance of imaging biomarkers in classifying pediatric solid tumor cancers and it can probably be adapted to dynamic signal evaluation for any tumor.This study was funded by PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, empowered by imaging biomarkers), a Horizon 2020 vertical bar RIA project (Topic SC1-DTH-07-2018), grant agreement no: 826494.Cerdà Alberich, L.; Sangüesa Nebot, C.; Alberich-Bayarri, A.; Carot Sierra, JM.; Martinez De Las Heras, B.; Veiga Canuto, D.; Cañete, A.... (2020). A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors. Cancers. 12(12):1-14. https://doi.org/10.3390/cancers12123858114121

Tissue iron quantification in chronic liver diseases using MRI shows a relationship between iron accumulation in liver, spleen, and bone marrow

Aim: To investigate iron loading within the liver, pancreas, spleen, and bone marrow using magnetic resonance imaging (MRI) transverse relaxation rate (R2*), in patients with diffuse liver diseases; to evaluate the relationships between iron accumulation in these tissue compartments; and to assess the association between tissue iron overload and the pattern of hepatic cellular iron distribution (hepatocytes versus Kupffer cells). Material and methods: Fifty-six patients with diffuse liver diseases had MRI-derived R2* values, using a multi-echo chemical-shift encoded MRI sequence, of the liver, pancreas, spleen, and vertebral bone marrow. All patients had liver biopsy samples scored for hepatic iron grading (0–4) and iron cellular distribution (within hepatocytes only or within both hepatocytes and Kupffer cells). Results: Liver R2* increased with histological iron grade (RS=0.58, p<0.001) and correlated with spleen (RS=0.71, p<0.001) and bone marrow R2* (RS=0.66, p<0.001), but not with pancreatic R2* (RS=0.22, p=0.096). Splenic and bone marrow R2* values were also correlated (RS=0.72, p<0.001). Patients with iron inside Kupffer cells had the highest R2* in liver, spleen and bone marrow. Conclusions: Patients with chronic diffuse liver diseases have concomitant hepatic, splenic, and bone marrow iron loading. The highest hepatic iron scores and iron inside Kupffer cells were associated with the highest splenic and bone marrow deposits, suggesting systemic iron accumulation in the mononuclear phagocytic system.The present study was supported by the Teaching and Research Department of Centro Hospitalar do Porto with a research grant number DEFI:309/12(213-DEFI/251-CES). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. L.M.B. and A.A.B. are co-founders of QUIBIM SME

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.This work was partially funded by a research grant from the Teaching and Research Department of Centro Hospitalar do Porto (DEFI:309/12(213-DEFI/251-CES)) and from a Spanish Ministry of Health and Carlos III Health Institute funding grant (PI12/01262). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer

[EN] Background: Estimation of the depth of myometrial invasion (MI) in endometrial cancer is pivotal in the preoperatively staging. Magnetic resonance (MR) reports suffer from human subjectivity. Multiparametric MR imaging radiomics and parameters may improve the diagnostic accuracy. Purpose: To discriminate between patients with MI ¿ 50% using a machine learning-based model combining texture features and descriptors from preoperatively MR images. Study Type: Retrospective. Population: One hundred forty-three women with endometrial cancer were included. The series was split into training (n = 107, 46 with MI ¿ 50%) and test (n = 36, 16 with MI ¿ 50%) cohorts. Field Strength/Sequences: Fast spin echo T2-weighted (T2W), diffusion-weighted (DW), and T1-weighted gradient echo dynamic contrast-enhanced (DCE) sequences were obtained at 1.5 or 3 T magnets. Assessment: Tumors were manually segmented slice-by-slice. Texture metrics were calculated from T2W and ADC map images. Also, the apparent diffusion coefficient (ADC), wash-in slope, wash-out slope, initial area under the curve at 60 sec and at 90 sec, initial slope, time to peak and peak amplitude maps from DCE sequences were obtained as parameters. MR diagnostic models using single-sequence features and a combination of features and parameters from the three sequences were built to estimate MI using Adaboost methods. The pathological depth of MI was used as gold standard. Statistical Test: Area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, positive predictive value, negative predictive value, precision and recall were computed to assess the Adaboost models performance. Results: The diagnostic model based on the features and parameters combination showed the best performance to depict patient with MI ¿ 50% in the test cohort (accuracy = 86.1% and AUROC = 87.1%). The rest of diagnostic models showed a worse accuracy (accuracy = 41.67%¿63.89% and AUROC = 41.43%¿63.13%). Data Conclusion: The model combining the texture features from T2W and ADC map images with the semi-quantitative parameters from DW and DCE series allow the preoperative estimation of myometrial invasion. Evidence Level: 4 Technical Efficacy: Stage 3This study received funding from the Global Investigator Initiated Research Committee (GIIRC) research program by Bracco S.p.A (2015/0724). The funders had no role in study design, data collection and analysis and preparation of the manuscript.Rodriguez Ortega, A.; Alegre, A.; Lago, V.; Carot Sierra, JM.; Ten-Esteve, A.; Montoliu, G.; Domingo, S.... (2021). Machine Learning-Based Integration of Prognostic Magnetic Resonance Imaging Biomarkers for Myometrial Invasion Stratification in Endometrial Cancer. Journal of Magnetic Resonance Imaging. 54(3):987-995. https://doi.org/10.1002/jmri.27625S98799554