Emerging Neuroimaging Biomarkers Across Disease Stage in Parkinson Disease: A Review.
JAMA neurology
Importance:Imaging biomarkers in Parkinson disease (PD) are increasingly important for monitoring progression in clinical trials and also have the potential to improve clinical care and management. This Review addresses a critical need to make clear the temporal relevance for diagnostic and progression imaging biomarkers to be used by clinicians and researchers over the clinical course of PD. Magnetic resonance imaging (diffusion imaging, neuromelanin-sensitive imaging, iron-sensitive imaging, T1-weighted imaging), positron emission tomography/single-photon emission computed tomography dopaminergic, serotonergic, and cholinergic imaging as well as metabolic and cerebral blood flow network neuroimaging biomarkers in the preclinical, prodromal, early, and moderate to late stages are characterized. Observations:If a clinical trial is being carried out in the preclinical and prodromal stages, potentially useful disease-state biomarkers include dopaminergic imaging of the striatum; metabolic imaging; free-water, neuromelanin-sensitive, and iron-sensitive imaging in the substantia nigra; and T1-weighted structural magnetic resonance imaging. Disease-state biomarkers that can distinguish atypical parkinsonisms are metabolic imaging, free-water imaging, and T1-weighted imaging; dopaminergic imaging and other molecular imaging track progression in prodromal patients, whereas other established progression biomarkers need to be evaluated in prodromal cohorts. Progression in early-stage PD can be monitored using dopaminergic imaging in the striatum, metabolic imaging, and free-water and neuromelanin-sensitive imaging in the posterior substantia nigra. Progression in patients with moderate to late-stage PD can be monitored using free-water imaging in the anterior substantia nigra, R2* of substantia nigra, and metabolic imaging. Cortical thickness and gyrification might also be useful markers or predictors of progression. Dopaminergic imaging and free-water imaging detect progression over 1 year, whereas other modalities detect progression over 18 months or longer. The reliability of progression biomarkers varies with disease stage, whereas disease-state biomarkers are relatively consistent in individuals with preclinical, prodromal, early, and moderate to late-stage PD. Conclusions and Relevance:Imaging biomarkers for various stages of PD are readily available to be used as outcome measures in clinical trials and are potentially useful in multimodal combination with routine clinical assessment. This Review provides a critically important template for considering disease stage when implementing diagnostic and progression biomarkers in both clinical trials and clinical care settings.
10.1001/jamaneurol.2021.1312
Imaging the Substantia Nigra in Parkinson Disease and Other Parkinsonian Syndromes.
Bae Yun Jung,Kim Jong-Min,Sohn Chul-Ho,Choi Ji-Hyun,Choi Byung Se,Song Yoo Sung,Nam Yoonho,Cho Se Jin,Jeon Beomseok,Kim Jae Hyoung
Radiology
Parkinson disease is characterized by dopaminergic cell loss in the substantia nigra of the midbrain. There are various imaging markers for Parkinson disease. Recent advances in MRI have enabled elucidation of the underlying pathophysiologic changes in the nigral structure. This has contributed to accurate and early diagnosis and has improved disease progression monitoring. This article aims to review recent developments in nigral imaging for Parkinson disease and other parkinsonian syndromes, including nigrosome imaging, neuromelanin imaging, quantitative iron mapping, and diffusion-tensor imaging. In particular, this article examines nigrosome imaging using 7-T MRI and 3-T susceptibility-weighted imaging. Finally, this article discusses volumetry and its clinical importance related to symptom manifestation. This review will improve understanding of recent advancements in nigral imaging of Parkinson disease. Published under a CC BY 4.0 license.
10.1148/radiol.2021203341
A biological classification of Parkinson's disease: the SynNeurGe research diagnostic criteria.
The Lancet. Neurology
With the hope that disease-modifying treatments could target the molecular basis of Parkinson's disease, even before the onset of symptoms, we propose a biologically based classification. Our classification acknowledges the complexity and heterogeneity of the disease by use of a three-component system (SynNeurGe): presence or absence of pathological α-synuclein (S) in tissues or CSF; evidence of underlying neurodegeneration (N) defined by neuroimaging procedures; and documentation of pathogenic gene variants (G) that cause or strongly predispose to Parkinson's disease. These three components are linked to a clinical component (C), defined either by a single high-specificity clinical feature or by multiple lower-specificity clinical features. The use of a biological classification will enable advances in both basic and clinical research, and move the field closer to the precision medicine required to develop disease-modifying therapies. We emphasise the initial application of these criteria exclusively for research. We acknowledge its ethical implications, its limitations, and the need for prospective validation in future studies.
10.1016/S1474-4422(23)00404-0
Ischemic stroke associated with amyloid-related imaging abnormalities in a patient treated with lecanemab.
Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION:Anti-amyloid antibody therapies such as lecanemab are increasingly being used to treat Alzheimer's disease (AD). These therapies are associated with a high rate of amyloid-related imaging abnormalities (ARIA). METHODS:We review the case history of a patient who developed ARIA associated with lecanemab treatment. RESULTS:In addition to microhemorrhages and cerebral edema that are recognized features of ARIA, the patient developed several ischemic strokes. The patient also experienced frequent electrographic seizures without overt clinical seizures. The patient demonstrated clinical and radiographic improvement after steroid treatment. DISCUSSION:Our case suggests that ischemic strokes may be a feature of ARIA and highlights the importance of having a high clinical suspicion for seizures in ARIA. As anti-amyloid therapies are likely going to be increasingly used to treat AD, it is important to appreciate the spectrum of clinical and radiographic findings that can result as side effects from this class of therapies. HIGHLIGHTS:We report a patient who developed severe amyloid-related imaging abnormalities (ARIA) after treatment with lecanemab. Our report suggests that ischemic strokes may be a novel imaging feature of ARIA. Our report highlights the need for high clinical suspicion for seizures in ARIA.
10.1002/alz.14223
Multimodality Imaging of Dementia: Clinical Importance and Role of Integrated Anatomic and Molecular Imaging.
Patel Kunal P,Wymer David T,Bhatia Vinay K,Duara Ranjan,Rajadhyaksha Chetan D
Radiographics : a review publication of the Radiological Society of North America, Inc
Neurodegenerative diseases are a devastating group of disorders that can be difficult to accurately diagnose. Although these disorders are difficult to manage owing to relatively limited treatment options, an early and correct diagnosis can help with managing symptoms and coping with the later stages of these disease processes. Both anatomic structural imaging and physiologic molecular imaging have evolved to a state in which these neurodegenerative processes can be identified relatively early with high accuracy. To determine the underlying disease, the radiologist should understand the different distributions and pathophysiologic processes involved. High-spatial-resolution MRI allows detection of subtle morphologic changes, as well as potential complications and alternate diagnoses, while molecular imaging allows visualization of altered function or abnormal increased or decreased concentration of disease-specific markers. These methodologies are complementary. Appropriate workup and interpretation of diagnostic studies require an integrated, multimodality, multidisciplinary approach. This article reviews the protocols and findings at MRI and nuclear medicine imaging, including with the use of flurodeoxyglucose, amyloid tracers, and dopaminergic transporter imaging (ioflupane). The pathophysiology of some of the major neurodegenerative processes and their clinical presentations are also reviewed; this information is critical to understand how these imaging modalities work, and it aids in the integration of clinical data to help synthesize a final diagnosis. Radiologists and nuclear medicine physicians aiming to include the evaluation of neurodegenerative diseases in their practice should be aware of and familiar with the multiple imaging modalities available and how using these modalities is essential in the multidisciplinary management of patients with neurodegenerative diseases.RSNA, 2020.
10.1148/rg.2020190070
Alzheimer Disease Anti-Amyloid Immunotherapies: Imaging Recommendations and Practice Considerations for Monitoring of Amyloid-Related Imaging Abnormalities.
AJNR. American journal of neuroradiology
With full FDA approval and Centers for Medicare & Medicaid Services coverage of lecanemab and donanemab, a growing number of practices are offering anti-amyloid immunotherapy to appropriate patients with cognitive impairment or mild dementia due to amyloid-positive Alzheimer disease. The goal of this article is to provide updated practical considerations for radiologists, including implementation of MR imaging protocols, workflows, and reporting and communication practices relevant to anti-amyloid immunotherapy and monitoring for amyloid-related imaging abnormalities (ARIA). On the basis of consensus discussion within an expanded American Society of Neuroradiology (ASNR) Alzheimer, ARIA, and Dementia Study Group, our purpose is the following: 1) summarize the FDA guidelines for the evaluation of radiographic ARIA; 2) review the 3 key MRI sequences for ARIA monitoring and standardized imaging protocols on the basis of ASNR-industry collaborations; 3) provide imaging recommendations for 3 key patient scenarios; 4) highlight the role of the radiologist in the care team for this population; 5) discuss implementation of MRI protocols to detect ARIA in diverse practice settings; and 6) present the results of the 2023 ASNR international neuroradiologist practice survey on dementia and ARIA imaging.
10.3174/ajnr.A8469
Radiomics in breast cancer: Current advances and future directions.
Cell reports. Medicine
Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.
10.1016/j.xcrm.2024.101719
Application of magnetic resonance imaging radiomics in endometrial cancer: a systematic review and meta-analysis.
La Radiologia medica
PURPOSE:We aimed to systematically assess the methodological quality and clinical potential application of published magnetic resonance imaging (MRI)-based radiomics studies about endometrial cancer (EC). METHODS:Studies of EC radiomics analyses published between 1 January 2000 and 19 March 2023 were extracted, and their methodological quality was evaluated using the radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). Pairwise correlation analyses and separate meta-analyses of studies exploring differential diagnoses and risk prediction were also performed. RESULTS:Forty-five studies involving 3 aims were included. The mean RQS was 13.77 (range: 9-22.5); publication bias was observed in the areas of 'index test' and 'flow and timing'. A high RQS was significantly associated with therapy selection-aimed studies, low QUADAS-2 risk, recent publication year, and high-performance metrics. Raw data from 6 differential diagnosis and 34 risk prediction models were subjected to meta-analysis, revealing diagnostic odds ratios of 23.81 (95% confidence interval [CI] 8.48-66.83) and 18.23 (95% CI 13.68-24.29), respectively. CONCLUSION:The methodological quality of radiomics studies involving patients with EC is unsatisfactory. However, MRI-based radiomics analyses showed promising utility in terms of differential diagnosis and risk prediction.
10.1007/s11547-024-01765-3
Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives.
Critical reviews in oncology/hematology
Radiomics, analysing quantitative features from medical imaging, has rapidly become an emerging field in translational oncology. Radiomics has been investigated in several neoplastic malignancies as it might allow for a non-invasive tumour characterization and for the identification of predictive and prognostic biomarkers. Over the last few years, evidence has been accumulating regarding potential clinical applications of machine learning in many crucial moments of cancer patients' history. However, the incorporation of radiomics in clinical decision-making process is still limited by low data reproducibility and study variability. Moreover, the need for prospective validations and standardizations is emerging. In this narrative review, we summarize current evidence regarding radiomic applications in high-incidence cancers (breast and lung) for screening, diagnosis, staging, treatment choice, response, and clinical outcome evaluation. We also discuss pro and cons of the radiomic approach, suggesting possible solutions to critical issues which might invalidate radiomics studies and propose future perspectives.
10.1016/j.critrevonc.2024.104479
Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging.
Circulation. Cardiovascular imaging
Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.
10.1161/CIRCIMAGING.123.015490