A radiomics approach to the diagnosis of femoroacetabular impingement

IntroductionFemoroacetabular Impingement (FAI) is a hip pathology characterized by impingement of the femoral head-neck junction against the acetabular rim, due to abnormalities in bone morphology. FAI is normally diagnosed by manual evaluation of morphologic features on magnetic resonance imaging (MRI). In this study, we assess, for the first time, the feasibility of using radiomics to detect FAI by automatically extracting quantitative features from images.Material and methods17 patients diagnosed with monolateral FAI underwent pre-surgical MR imaging, including a 3D Dixon sequence of the pelvis. An expert radiologist drew regions of interest on the water-only Dixon images outlining femur and acetabulum in both impingement (IJ) and healthy joints (HJ). 182 radiomic features were extracted for each hip. The dataset numerosity was increased by 60 times with an ad-hoc data augmentation tool. Features were subdivided by type and region in 24 subsets. For each, a univariate ANOVA F-value analysis was applied to find the 5 features most correlated with IJ based on p-value, for a total of 48 subsets. For each subset, a K-nearest neighbor model was trained to differentiate between IJ and HJ using the values of the radiomic features in the subset as input. The training was repeated 100 times, randomly subdividing the data with 75%/25% training/testing.ResultsThe texture-based gray level features yielded the highest prediction max accuracy (0.972) with the smallest subset of features. This suggests that the gray image values are more homogeneously distributed in the HJ in comparison to IJ, which could be due to stress-related inflammation resulting from impingement.ConclusionsWe showed that radiomics can automatically distinguish IJ from HJ using water-only Dixon MRI. To our knowledge, this is the first application of radiomics for FAI diagnosis. We reported an accuracy greater than 97%, which is higher than the 90% accuracy for detecting FAI reported for standard diagnostic tests (90%). Our proposed radiomic analysis could be combined with methods for automated joint segmentation to rapidly identify patients with FAI, avoiding time-consuming radiological measurements of bone morphology

Automatic segmentation of optical coherence tomography pullbacks of coronary arteries treated with bioresorbable vascular scaffolds: Application to hemodynamics modeling

Automatic algorithms for stent struts segmentation in optical coherence tomography (OCT) images of coronary arteries have been developed over the years, particularly with application on metallic stents. The aim of this study is three-fold: (1) to develop and to validate a segmentation algorithm for the detection of both lumen contours and polymeric bioresorbable scaffold struts from 8-bit OCT images, (2) to develop a method for automatic OCT pullback quality assessment, and (3) to demonstrate the applicability of the segmentation algorithm for the creation of patient-specific stented coronary artery for local hemodynamics analysis

A framework for computational fluid dynamic analyses of patient-specific stented coronary arteries from optical coherence tomography images

The clinical challenge of percutaneous coronary interventions (PCI) is highly dependent on the recognition of the coronary anatomy of each individual. The classic imaging modality used for PCI is angiography, but advanced imaging techniques that are routinely performed during PCI, like optical coherence tomography (OCT), may provide detailed knowledge of the pre-intervention vessel anatomy as well as the post-procedural assessment of the specific stent-to-vessel interactions. Computational fluid dynamics (CFD) is an emerging investigational tool in the setting of optimization of PCI results. In this study, an OCT-based reconstruction method was developed for the execution of CFD simulations of patient-specific coronary artery models which include the actual geometry of the implanted stent. The method was applied to a rigid phantom resembling a stented segment of the left anterior descending coronary artery. The segmentation algorithm was validated against manual segmentation. A strong correlation was found between automatic and manual segmentation of lumen in terms of area values. Similarity indices resulted >96% for the lumen segmentation and >77% for the stent strut segmentation. The 3D reconstruction achieved for the stented phantom was also assessed with the geometry provided by X-ray computed micro tomography scan, used as ground truth, and showed the incidence of distortion from catheter-based imaging techniques. The 3D reconstruction was successfully used to perform CFD analyses, demonstrating a great potential for patient-specific investigations. In conclusion, OCT may represent a reliable source for patient-specific CFD analyses which may be optimized using dedicated automatic segmentation algorithms

Coronary stenting: from optical coherence tomography to fluid dynamic simulations

The presence of stents within coronary arteries alters the hemodynamic condition. Computational fluid dynamics (CFD) simulations offer the possibility to study local hemodynamics of a stented artery to identify the stimuli of instent restenosis, i.e. the local reduction of lumen size after stent deployment. The results of CFD simulations are more accurate when the analyses are performed with a model reproducing real in vivo conditions. For this purpose, optical coherence tomography (OCT) is a promising tool to reconstruct 3D geometries of stented coronary arteries, due to its higher resolution compared to the other imaging techniques. In the present work a reconstruction method of stented coronary bifurcation geometrical models starting from OCT images was developed. An OCT exam performed in a stented coronary bifurcation silicone sample was considered. The vessel and the stent were reconstructed separately, and then they were merged together. Vessel reconstruction was performed with a semi-automatic process: the main branch was reconstructed by fitting the lumen boundary with ellipses and subsequently by creating a mesh of the vessel; the side branch was created like an ideal cylinder. Stent struts were identified with an automatic algorithm; then, the stent was reconstructed in a manual way. After the creation of the 3D geometry of the bifurcation, a transient fluid dynamic simulation was carried out. CFD results showed that the highest risk of restenosis is located in the region near the bifurcation

Coronary stenting: from optical coherence tomography to fluid dynamic simulations

The presence of stents within coronary arteries alters the hemodynamic condition. Computational fluid dynamics (CFD) simulations offer the possibility to study local hemodynamics of a stented artery to identify the stimuli of instent restenosis, i.e. the local reduction of lumen size after stent deployment. The results of CFD simulations are more accurate when the analyses are performed with a model reproducing real in vivo conditions. For this purpose, optical coherence tomography (OCT) is a promising tool to reconstruct 3D geometries of stented coronary arteries, due to its higher resolution compared to the other imaging techniques. In the present work a reconstruction method of stented coronary bifurcation geometrical models starting from OCT images was developed. An OCT exam performed in a stented coronary bifurcation silicone sample was considered. The vessel and the stent were reconstructed separately, and then they were merged together. Vessel reconstruction was performed with a semi-automatic process: the main branch was reconstructed by fitting the lumen boundary with ellipses and subsequently by creating a mesh of the vessel; the side branch was created like an ideal cylinder. Stent struts were identified with an automatic algorithm; then, the stent was reconstructed in a manual way. After the creation of the 3D geometry of the bifurcation, a transient fluid dynamic simulation was carried out. CFD results showed that the highest risk of restenosis is located in the region near the bifurcation

Stability assessment of first order statistics features computed on ADC maps in soft-tissue sarcoma

Radiomics extracts a large number of features from medical images to perform a quantitative characterization. Aim of this study was to assess radiomic features stability and relevance. Apparent diffusion coefficient (ADC) maps were computed from diffusion-weighted magnetic resonance images (DW-MRI) of 18 patients diagnosed with soft-tissue sarcomas (STSs). Thirty-seven intensity-based features were computed on the regions of interest (ROIs). First, ROIs of the images were subjected to translations and rotations in specific ranges. The 37 features computed on the original and transformed ROIs were compared in terms of percentage of variations. The intra-class correlation coefficient (ICC) was computed. To be accepted, a feature should satisfy the following conditions: the ICC after a minimum entity transformation is > 0.6 and the ICC after a maximum entity translation is < 0.4. In total, 31 features out of 37 were accepted by the algorithm. This stability analysis can be used as a first step in the features selection process

Table1_A radiomics approach to the diagnosis of femoroacetabular impingement.xlsx

IntroductionFemoroacetabular Impingement (FAI) is a hip pathology characterized by impingement of the femoral head-neck junction against the acetabular rim, due to abnormalities in bone morphology. FAI is normally diagnosed by manual evaluation of morphologic features on magnetic resonance imaging (MRI). In this study, we assess, for the first time, the feasibility of using radiomics to detect FAI by automatically extracting quantitative features from images.Material and methods17 patients diagnosed with monolateral FAI underwent pre-surgical MR imaging, including a 3D Dixon sequence of the pelvis. An expert radiologist drew regions of interest on the water-only Dixon images outlining femur and acetabulum in both impingement (IJ) and healthy joints (HJ). 182 radiomic features were extracted for each hip. The dataset numerosity was increased by 60 times with an ad-hoc data augmentation tool. Features were subdivided by type and region in 24 subsets. For each, a univariate ANOVA F-value analysis was applied to find the 5 features most correlated with IJ based on p-value, for a total of 48 subsets. For each subset, a K-nearest neighbor model was trained to differentiate between IJ and HJ using the values of the radiomic features in the subset as input. The training was repeated 100 times, randomly subdividing the data with 75%/25% training/testing.ResultsThe texture-based gray level features yielded the highest prediction max accuracy (0.972) with the smallest subset of features. This suggests that the gray image values are more homogeneously distributed in the HJ in comparison to IJ, which could be due to stress-related inflammation resulting from impingement.ConclusionsWe showed that radiomics can automatically distinguish IJ from HJ using water-only Dixon MRI. To our knowledge, this is the first application of radiomics for FAI diagnosis. We reported an accuracy greater than 97%, which is higher than the 90% accuracy for detecting FAI reported for standard diagnostic tests (90%). Our proposed radiomic analysis could be combined with methods for automated joint segmentation to rapidly identify patients with FAI, avoiding time-consuming radiological measurements of bone morphology.</p