Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

 Predicting failure to recover is absolutely useless! I'd have you all fired.

Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

Jinfen Kong^* Danfen Zhang

• Department of Radiology, Yuhuan Second People's Hospital, Yuhuan, Taizhou, Zhejiang, China

Background: Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients.

Methods: We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients.

Results: Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84–0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74–0.86) for predicting reperfusion in AIS.

Conclusion: Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients.

Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.

ESOC 2022: ‘Brain-age’ biomarkers predict stroke recovery more accurately than chronological age

But you're not predicting recovery, you're predicting failure to recover. Since there are NO 100% recovery protocols you're putting the cart before the horse. Solve 100% recovery protocols first and then you can do recovery prediction models. DO YOU NOT UNDERSTAND?

ESOC 2022: ‘Brain-age’ biomarkers predict stroke recovery more accurately than chronological age

Stroke experts have identified how radiomics—an emerging, image-quantifying technology—can be used to extract biomarkers from clinical brain magnetic resonance imaging (MRI) scans in stroke patients and estimate their relative ‘brain age’. The technique demonstrates that using relative brain age, rather than chronological age, can enhance stroke surveillance and improve predictions around post-stroke recovery, according to a study presented at the European Stroke Organisation Conference (ESOC 2022; 4–6 May, Lyon, France).

The study analysed 4,163 ischaemic stroke patients across the USA and Europe. It showed that stroke patients with ‘older-appearing’ brains, characterised by a higher predicted brain age than chronological age, were more likely to suffer from hypertension, diabetes mellitus, or have a history of smoking or prior stroke. Patients with older-appearing brains were also less likely to achieve a favourable post-stroke outcome in comparison with their younger-looking counterparts.

Led by Martin Bretzner (Harvard Medical School, Boston, USA), the research team considered that, while chronological age measures the amount of time a person has lived, it is less likely to precisely capture how ‘well’ a patient has aged. By estimating the age of a patient’s brain, this novel biomarker-based approach provides insight into the resilience of a brain to time and cardiovascular risk factors, and how well patients recover from stroke.

The technique—radiomics—leverages advanced mathematical analysis to explore neuroimaging data available to clinicians, allowing experts to predict patients’ relative brain age compared to other stroke survivors and analyse their overall brain health.

“Age is one of the most influential determinants of post-stroke outcomes, but little is known about the impact of neuroimaging-derived biological brain age,” said Bretzner. “Our results show that quantifying relative brain age in stroke patients can be beneficial in assessing a patient’s brain health globally, and useful in predicting how well the patient will recover from a stroke. It would also be very easy to communicate on this biomarker with clinicians and patients, as everyone instinctively understands the negative implications of an accelerated brain-ageing process.”

In addition, the study found that relative brain age impacted stroke outcomes independently from chronological age and stroke severity. Having previously suffered from a stroke was the most influential clinical factor that impacted relative brain age, followed by diabetes.

“These findings stress the importance of minimising cardiovascular risk factors, and also highlight how cardiovascular health and brain health are tightly intertwined,” added Bretzner. “Identifying potentially modifiable risk factors that impact brain health by using radiomics and relative brain age as a biomarker could lead to the development of stroke prevention interventions, and aid recovery. We hope that this research will serve as a support to identify fragile stroke patients that require more intensive prevention techniques, treatments and surveillance in the future.”

 

Prediction of the progression from mild cognitive impairment to Alzheimer’s disease using a radiomics-integrated model

 With no protocols to prevent this disease progression, knowing this does no good at all.

Prediction of the progression from mild cognitive impairment to Alzheimer’s disease using a radiomics-integrated model

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Zhen-Yu Shu#, De-Wang Mao#, Yu-yun Xu, ...

First Published July 15, 2021 Research Article 

https://doi.org/10.1177/17562864211029551

Article information

Abstract

Objective:

This study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD).

Methods:

357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T₁WI MR images were automatically segmented into white matter, gray matter and cerebrospinal fluid (CSF), and radiomics features were extracted from each tissue. Based on the data from the training group, a radiomics signature was built using logistic regression after dimensionality reduction. The radiomics signatures, in combination with the apolipoprotein E4 (APOE4) and baseline neuropsychological scales, were used to build an integrated model using machine learning. The receiver operating characteristics (ROC) curve and data of the test group were used to evaluate the diagnostic accuracy and reliability of the model, respectively. In addition, the clinical prognostic efficacy of the model was evaluated based on the time of progression from MCI to AD.

Results:

Stepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798.

Conclusion:

The integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.

Keywords

Alzheimer’s disease, machine learning, magnetic resonance imaging, mild cognitive impairment, radiomics

Introduction

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with high morbidity.¹ To date, there is no single treatment that can stop or reverse the progression of AD. Mild cognitive impairment (MCI), a transitional state between normal ageing and dementia, has been identified as a high risk factor for AD. Epidemiological studies have indicated that approximately 10–12% of patients with MCI progress to AD each year.² Since many elderly individuals have MCI, but do not meet the diagnostic criteria for AD, early intervention for individuals at this stage may effectively delay progression of the disease.³

MCI does not affect daily activities, and individuals with MCI have normal cognitive function.⁴ However, MCI exhibits heterogeneous features in cognitive function and clinical progression, and the clinical outcomes remain uncertain. Some MCI patients remain stable, or even revert to normal functions, whereas others progress towards AD.⁵ Therefore, there is an urgent need to define biomarkers that can identify and predict high-risk individuals with MCI who will progress to AD, as these individuals will require subsequent intervention. Currently, biochemical changes in the cerebrospinal fluid (CSF) and neuroimaging measures of brain anatomy and function have been identified as reliable biomarkers of AD.^6–8 Such features include increased CSF tau, hypometabolism in the posterior cingulate, and hippocampal atrophy.^9,10 Using a machine-learning model with the combined use of these biomarkers, the automatic diagnosis and prognosis of AD patients has been proven to be reliable and highly accurate.¹¹ However, the applicability of these biomarkers may be limited due to the high prevalence of AD, high cost of these techniques, and their relative difficulty of use.

Radiomics is a new approach based on the deep cross-fusing of medicine and computer science. It reflects the heterogeneity of disease through image features and has the characteristics of being low cost and non-invasive.^12,13 In the early years, this new method was widely used in oncology,¹⁴ and radiomics has now been used in the diagnosis and categorical assessment of MCI and AD.¹⁵ In the past few years, neuroimaging studies have shown that white matter (WM) degeneration and demyelination in the microscopic and macroscopic structure of WM are important physiological features in the identification of risk and progression of AD.¹⁶ These microstructural changes can be identified in three-dimensional whole-brain WM radiomics analyses.¹⁷ In addition, grey matter (GM) atrophy and pathological changes in the CSF can identify very early changes associated with pathological ageing and AD.¹⁸ As a result, we hypothesize that whole-brain GM and CSF radiomics analysis may explain the heterogeneity of brain tissue in patients with MCI. We further believe that a whole-brain-based radiological approach may be more powerful than single region analysis in accurately identifying patients who may progress to AD.

Since AD is a complex neurodegenerative disorder, it is clear that a single marker cannot accurately diagnose AD and monitor disease progression.¹⁹ However, radiomics, in combination with clinical data and gene data, can be used to establish a disease prediction model to improve the prediction accuracy.^20,21 In addition, it is known that the E4 allele of the apolipoprotein (APOE4) is the major known genetic risk factor for late-onset AD.²² The addition of APOE4 and neuropsychological scale data analysis can improve the diagnosis of MCI.^23,24 Therefore, a combination of different markers may provide a more comprehensive approach for the early diagnosis and monitoring of AD.

This study aimed to identify possible novel whole-brain biomarkers with radiomics using conventional magnetic resonance imaging (MRI) techniques, and to develop an integrated model using radiomics in combination with genetic traits and neuropsychological scales. This approach was used to identified individuals with MCI at high risk of progressing to AD. This radiomics-integrated model may be helpful to develop individualized and accurate medical plans in clinical practice.