Radiomic data mining and machine learning on preoperative pituitary adenoma MRI

Pituitary adenomas are among the most frequent intracranial tumors, accounting for the majority of sellar/suprasellar masses in adults. MRI is the preferred imaging modality for detecting pituitary adenomas. Radiomics represents the conversion of digital medical images into mineable high-dimensional data. This process is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology and that these relationships can be revealed via quantitative image analyses. The aim of this thesis is to apply machine learning algorithms on parameters obtained by texture analysis on MRI images in order to distinguish functional from non-functional pituitary macroadenomas, to predict their ki-67 proliferation index class, and to predict pituitary macroadenoma surgical consistency prior to an endoscopic endonasal procedure

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management

Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes

differential diagnosis of benign and malignant vertebral compression fractures using conventional and advanced mri techniques

Atraumatic vertebral compression fractures (VCFs) are commonly encountered in clinical practice and often represent a diagnostic challenge. MRI plays a major role in the differential diagnosis of benign and malignant VCFs, due to its high contrast resolution and the possibility to obtain quantitative and functional data with the employment of advanced sequences. Computer-aided diagnosis systems are also applied on MRI images for this purpose, showing promising results. In this setting, aim of this pictorial review is to elucidate the role of MRI in the differential diagnosis of VCFs with a specific focus on advanced and post-processing imaging techniques

Intracranial extension of orbital inflammatory pseudotumor: a case report and literature review

Background: Orbital inflammatory pseudotumor is a rare inflammatory condition of unknown cause that may extend intracranially, usually as a dural-based infiltrate. Here we report the first case of orbital pseudotumor presenting with intra-axial Magnetic Resonance Imaging (MRI) changes. Case presentation: A 57-year-old white female, with a 3-month history of headache and right palpebral edema, presented with marked right temporal lobe edema with ominous MRI appearance, and ipsilateral alterations of orbital and periorbital structures. Following steroid therapy, both intracranial and orbital involvement dramatically improved. Conclusion: Orbital inflammatory pseudotumor with chronic inflammation may infrequently present with intracranial involvement, mimicking more aggressive diseases, even showing intra-axial enhancement after i.v. contrast administration in brain MRI. Awareness of this possibility may help neurologists to choose the appropriate therapeutic approach

Oncologic Imaging and Radiomics: A Walkthrough Review of Methodological Challenges

Imaging plays a crucial role in the management of oncologic patients, from the initial diagnosis to staging and treatment response monitoring. Recently, it has been suggested that its importance could be further increased by accessing a new layer of previously hidden quantitative data at the pixel level. Using a multi-step process, radiomics extracts potential biomarkers from medical images that could power decision support tools. Despite the growing interest and rising number of research articles being published, radiomics is still far from fulfilling its promise of guiding oncologic imaging toward personalized medicine. This is, at least partly, due to the heterogeneous methodological quality in radiomic research, caused by the complexity of the analysis pipelines. In this review, we aim to disentangle this complexity with a stepwise approach. Specifically, we focus on challenges to face during image preprocessing and segmentation, how to handle imbalanced classes and avoid information leaks, as well as strategies for the proper validation of findings

State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma

The most common liver malignancy is hepatocellular carcinoma (HCC), which is also associated with high mortality. Often HCC develops in a chronic liver disease setting, and early diagnosis as well as accurate screening of high-risk patients is crucial for appropriate and effective management of these patients. While imaging characteristics of HCC are well-defined in the diagnostic phase, challenging cases still occur, and current prognostic and predictive models are limited in their accuracy. Radiomics and machine learning (ML) offer new tools to address these issues and may lead to scientific breakthroughs with the potential to impact clinical practice and improve patient outcomes. In this review, we will present an overview of these technologies in the setting of HCC imaging across different modalities and a range of applications. These include lesion segmentation, diagnosis, prognostic modeling and prediction of treatment response. Finally, limitations preventing clinical application of radiomics and ML at the present time are discussed, together with necessary future developments to bring the field forward and outside of a purely academic endeavor

Meningioma MRI radiomics and machine learning: systematic review, quality score assessment, and meta-analysis

Purpose To systematically review and evaluate the methodological quality of studies using radiomics for diagnostic and predictive purposes in patients with intracranial meningioma. To perform a meta-analysis of machine learning studies for the prediction of intracranial meningioma grading from pre-operative brain MRI. Methods Articles published from the year 2000 on radiomics and machine learning applications in brain imaging of meningioma patients were included. Their methodological quality was assessed by three readers with the radiomics quality score, using the intra-class correlation coefficient (ICC) to evaluate inter-reader reproducibility. A meta-analysis of machine learning studies for the preoperative evaluation of meningioma grading was performed and their risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool. Results In all, 23 studies were included in the systematic review, 8 of which were suitable for the meta-analysis. Total (possible range, −8 to 36) and percentage radiomics quality scores were respectively 6.96 ± 4.86 and 19 ± 13% with a moderate to good inter-reader reproducibility (ICC = 0.75, 95% confidence intervals, 95%CI = 0.54–0.88). The meta-analysis showed an overall AUC of 0.88 (95%CI = 0.84–0.93) with a standard error of 0.02. Conclusions Machine learning and radiomics have been proposed for multiple applications in the imaging of meningiomas, with promising results for preoperative lesion grading. However, future studies with adequate standardization and higher methodological quality are required prior to their introduction in clinical practice

Diagnostic contribution of Magnetic Resonance Imaging in an atypical presentation of Motor Neuron Disease

Motor neuron disease (MND) is a neurodegenerative disease determining progressive and relentless motor deterioration involving both upper and lower motor neurons (UMN and LMN); several variants at onset are described. Here we describe a case of MND presenting as pure spastic monoparesis in which magnetic resonance imaging (MRI) gave a substantial contribution in confirming the diagnosis and assessing the severity of UMN involvement. An isolated pyramidal syndrome, with complete absence of LMN signs, is a rare phenotype in the context of MND (less than 4% of total cases), especially if restricted to only one limb. Several other elements made this case an unusual presentation of MND: the late age of onset (8th decade), the subacute evolution of symptoms (raising the suspicion of an ischemic or inflammatory, rather than degenerative, etiology), the patient’s past medical history (achalasia, erythema nodosum), the increase of inflammatory indices. Conventional MRI showed no focal lesions that could explain the clinical features; therefore, we used advanced MR sequences. Diffusion tensor imaging (DTI) evaluation evidenced bilateral impairment of corticospinal tract (CST) diffusion metrics, with clear right-left asymmetry, pointing to a neurodegenerative etiology, which clinically appeared less likely at that time. Magnetic resonance spectroscopy (MRS) showed a significant reduction of NAA/Cho + Cr ratio in the motor cortex (MC), further supporting the hypothesis of UMN degeneration. In conclusion, in this particular case of MND, whose nosographic framing has not been fully defined, advanced MRI techniques with DTI and MRS proved to be of great usefulness in confirming a diffuse UMN involvement, possibly at a more advanced stage than its clinical expression