Clinical prospective study on disease variability and score generation in patients with Charcot-Marie- Tooth disease type 1A (HMSN1A)

In this clinical prospective study primary and secondary clinical outcome measures in Charcot-Marie-Tooth disease type 1A (CMT1A) were evaluated with regard to their contribution towards discrimination of disease severity. The nine components of the composite Charcot-Marie-Tooth disease Neuropathy Score (Shy et al., 2005) and six additional secondary clinical outcome measures were assessed in a total subset of 479 adult patients with genetically proven CMT1A. Using hierarchical clustering, significant clusters of patients were formed. The impact of each of the CMTNS components and of the secondary clinical parameters were calculated with regard to their power to differentiate these two clusters. Five parameters of the original CMTNS and four secondary clinical outcome measures provide additional significant information in differentiation of the two clusters. From these findings, we derived three new composite measures as score hypotheses and compared their discriminant power with that of the originial CMTNS. As a conclusion, five items from the CMTNS and four secondary clinical outcome measures improve the clinical assessment of patients with CMT1A significantly and are beneficial for upcoming clinical and therapeutic trials.2014-10-1

Prediction of treatment response to transarterial radioembolization of liver metastases: Radiomics analysis of pre-treatment cone-beam CT: A proof of concept study

Purpose To investigate the potential of texture analysis and machine learning to predict treatment response to transarterial radioembolization (TARE) on pre-interventional cone-beam computed tomography (CBCT) images in patients with liver metastases. Materials and Methods In this IRB-approved retrospective single-center study 36 patients with a total of 104 liver metastases (56 % male, mean age 61.1 ± 13 years) underwent CBCT prior to TARE and follow-up imaging 6 months after therapy. Treatment response was evaluated according to RECIST version 1.1 and dichotomized into disease control (partial response/stable disease) versus disease progression (progressive disease). After target lesion segmentation, 104 radiomics features corresponding to seven different feature classes were extracted with the pyRadiomics package. After dimension reduction machine learning classifications were performed on a custom artificial neural network (ANN). Ten-fold cross validation on a previously unseen test data set was performed. Results The average administered cumulative activity from TARE was 1.6 Gbq (± 0.5 Gbq). At a mean follow-up of 5.9 ± 0.8 months disease control was achieved in 82 % of metastases. After dimension reduction, 15 of 104 (15 %) texture analysis features remained for further analysis. On a previously unseen set of liver metastases the Multilayer Perceptron ANN yielded a sensitivity of 94.2 %, specificity of 67.7 % and an area-under-the receiver operating characteristics curve of 0.85. Conclusion Our study indicates that texture analysis-based machine learning may has potential to predict treatment response to TARE using pre-treatment CBCT images of patients with liver metastases with high accuracy

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.

To investigate the applicability and performance of automated machine learning (AutoML) for potential applications in diagnostic neuroradiology. In the medical sector, there is a rapidly growing demand for machine learning methods, but only a limited number of corresponding experts. The comparatively simple handling of AutoML should enable even non-experts to develop adequate machine learning models with manageable effort. We aim to investigate the feasibility as well as the advantages and disadvantages of developing AutoML models compared to developing conventional machine learning models. We discuss the results in relation to a concrete example of a medical prediction application. In this retrospective IRB-approved study, a cohort of 107 patients who underwent gross total meningioma resection and a second cohort of 31 patients who underwent subtotal resection were included. Image segmentation of the contrast enhancing parts of the tumor was performed semi-automatically using the open-source software platform 3D Slicer. A total of 107 radiomic features were extracted by hand-delineated regions of interest from the pre-treatment MRI images of each patient. Within the AutoML approach, 20 different machine learning algorithms were trained and tested simultaneously. For comparison, a neural network and different conventional machine learning algorithms were trained and tested. With respect to the exemplary medical prediction application used in this study to evaluate the performance of Auto ML, namely the pre-treatment prediction of the achievable resection status of meningioma, AutoML achieved remarkable performance nearly equivalent to that of a feed-forward neural network with a single hidden layer. However, in the clinical case study considered here, logistic regression outperformed the AutoML algorithm. Using independent test data, we observed the following classification results (AutoML/neural network/logistic regression): mean area under the curve = 0.849/0.879/0.900, mean accuracy = 0.821/0.839/0.881, mean kappa = 0.465/0.491/0.644, mean sensitivity = 0.578/0.577/0.692 and mean specificity = 0.891/0.914/0.936. The results obtained with AutoML are therefore very promising. However, the AutoML models in our study did not yet show the corresponding performance of the best models obtained with conventional machine learning methods. While AutoML may facilitate and simplify the task of training and testing machine learning algorithms as applied in the field of neuroradiology and medical imaging, a considerable amount of expert knowledge may still be needed to develop models with the highest possible discriminatory power for diagnostic neuroradiology

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinical setting. Lesion detection is central to neuroradiology and precedes all further processes which include but are not limited to lesion characterization, quantification, longitudinal disease assessment, prognosis, and prediction of treatment response. A number of machine learning algorithms focusing on lesion detection have been developed or are currently under development which may either support or extend the imaging process. Examples include machine learning applications in stroke, aneurysms, multiple sclerosis, neuro-oncology, neurodegeneration, and epilepsy

Chronische Sportverletzungen des Kniegelenks

Chronic sports injuries of the knee joint are common and mainly caused by repetitive (micro) trauma and exertion. Chronic insertion tendinopathies and avulsion fractures and symptoms related to entrapment, friction and impingement can be pathophysiologically distinguished in athletes. In this review, we depict the characteristic magnetic resonance imaging (MRI) findings of the most commonly occurring pathologies

Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible

OBJECTIVES The aim of this study was to test whether texture analysis and machine learning enable the detection of myocardial infarction (MI) on non-contrast-enhanced low radiation dose cardiac computed tomography (CCT) images. MATERIALS AND METHODS In this institutional review board-approved retrospective study, we included non-contrast-enhanced electrocardiography-gated low radiation dose CCT image data (effective dose, 0.5 mSv) acquired for the purpose of calcium scoring of 27 patients with acute MI (9 female patients; mean age, 60 ± 12 years), 30 patients with chronic MI (8 female patients; mean age, 68 ± 13 years), and in 30 subjects (9 female patients; mean age, 44 ± 6 years) without cardiac abnormality, hereafter termed controls. Texture analysis of the left ventricle was performed using free-hand regions of interest, and texture features were classified twice (Model I: controls versus acute MI versus chronic MI; Model II: controls versus acute and chronic MI). For both classifications, 6 commonly used machine learning classifiers were used: decision tree C4.5 (J48), k-nearest neighbors, locally weighted learning, RandomForest, sequential minimal optimization, and an artificial neural network employing deep learning. In addition, 2 blinded, independent readers visually assessed noncontrast CCT images for the presence or absence of MI. RESULTS In Model I, best classification results were obtained using the k-nearest neighbors classifier (sensitivity, 69%; specificity, 85%; false-positive rate, 0.15). In Model II, the best classification results were found with the locally weighted learning classification (sensitivity, 86%; specificity, 81%; false-positive rate, 0.19) with an area under the curve from receiver operating characteristics analysis of 0.78. In comparison, both readers were not able to identify MI in any of the noncontrast, low radiation dose CCT images. CONCLUSIONS This study indicates the ability of texture analysis and machine learning in detecting MI on noncontrast low radiation dose CCT images being not visible for the radiologists' eye

Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy-Preliminary results

PURPOSE To test in a first proof-of-concept study whether texture analysis (TA) allows for the detection of myocardial tissue alterations in hypertrophic cardiomyopathy (HCM) on non-contrast T1-weighted cardiac magnetic resonance (CMR) images using machine learning based approaches. METHODS This retrospective, IRB-approved study included 32 patients with known HCM. Thirty patients with normal CMR served as controls. Regions-of-interest for TA encompassing the left ventricle were drawn on short-axis non-contrast T1-weighted images using a freely available software package. Step-wise dimension reduction and texture feature selection was performed for selecting features enabling the detection of myocardial tissue alterations in HCM patients on non-contrast T1-weighted CMR images. RESULTS Comparing HCM patients and controls, four texture features were identified showing significant differences between groups (Grey-level Non-uniformity [GLevNonU]: 74 ± 17 vs. 38 ± 9, p < .001; Energy of wavelet coefficients in low-frequency sub-bands [WavEnLL]: 58 ± 5 vs. 48 ± 10, p < .001; Fraction: 0.70 ± 0.07 vs. 0.78 ± 0.05, p < .001; Sum Average: 16.6 ± 0.4 vs. 17.0 ± 0.5, p = .007). A model containing the single parameter GLevNonU proved to be the best for differentiating between HCM patients and controls with a sensitivity/specificity of 91%/93%. A cut-off of GLevNonU ≥46 allowed for distinguishing HCM patients from controls with a sensitivity/specificity of 94%/90%. Even in patients without late gadolinium enhancement (LGE), the defined cut-off led to a differentiation of LGE- patients from healthy controls with 100% sensitivity and 90% specificity. CONCLUSIONS TA on non-contrast T1-weighted images allows for the detection of myocardial tissue alterations in the setting of HCM with excellent accuracy, delivering potential novel parameters for a non-contrast assessment of myocardial texture alterations

3D image fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement: Intuitive delineation of myocardial hypoperfusion and scar

BACKGROUND Since patients with myocardial hypoperfusion due to coronary artery disease (CAD) with preserved viability are known to benefit from revascularization, accurate differentiation of hypoperfusion from scar is desirable. PURPOSE To develop a framework for 3D fusion of whole-heart dynamic cardiac MR perfusion and late gadolinium enhancement (LGE) to delineate stress-induced myocardial hypoperfusion and scar. STUDY TYPE Prospective feasibility study. SUBJECTS Sixteen patients (61 ± 14 years, two females) with known/suspected CAD. FIELD STRENGTH/SEQUENCE 1.5T (nine patients); 3.0T (seven patients); whole-heart dynamic 3D cardiac MR perfusion (3D-PERF, under adenosine stress); 3D LGE inversion recovery sequences (3D-SCAR). ASSESSMENT A software framework was developed for 3D fusion of 3D-PERF and 3D-SCAR. Computation steps included: 1) segmentation of the left ventricle in 3D-PERF and 3D-SCAR; 2) semiautomatic thresholding of perfusion/scar data; 3) automatic calculation of ischemic/scar burden (ie, pathologic relative to total myocardium); 4) projection of perfusion/scar values onto artificial template of the left ventricle; 5) semiautomatic coregistration to an exemplary heart contour easing 3D orientation; and 6) 3D rendering of the combined datasets using automatically defined color tables. All tasks were performed by two independent, blinded readers (J.S. and R.M.). STATISTICAL TESTS Intraclass correlation coefficients (ICC) for determining interreader agreement. RESULTS Image acquisition, postprocessing, and 3D fusion were feasible in all cases. In all, 10/16 patients showed stress-induced hypoperfusion in 3D-PERF; 8/16 patients showed LGE in 3D-SCAR. For 3D-PERF, semiautomatic thresholding was possible in all patients. For 3D-SCAR, automatic thresholding was feasible where applicable. Average ischemic burden was 11 ± 7% (J.S.) and 12 ± 7% (R.M.). Average scar burden was 8 ± 5% (J.S.) and 7 ± 4% (R.M.). Interreader agreement was excellent (ICC for 3D-PERF = 0.993, for 3D-SCAR = 0.99). DATA CONCLUSION 3D fusion of 3D-PERF and 3D-SCAR facilitates intuitive delineation of stress-induced myocardial hypoperfusion and scar. LEVEL OF EVIDENCE 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018