Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease

Machine learning prediction prior to onset of mild cognitive impairment using T1-weighted magnetic resonance imaging radiomic of the hippocampus

BACKGROUND

Early identification of individuals who progress from normal cognition (NC) to mild cognitive impairment (MCI) may help prevent cognitive decline. We aimed to build predictive models using radiomic features of the bilateral hippocampus in combination with scores from neuropsychological assessments.

METHODS

We utilized the Alzheimer's Disease Neuroimaging Initiative (ADNI) database to study 175 NC individuals, identifying 50 who progressed to MCI within seven years. Employing the Least Absolute Shrinkage and Selection Operator (LASSO) on T1-weighted images, we extracted and refined hippocampal features. Classification models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and light gradient boosters (LightGBM), were built based on significant neuropsychological scores. Model validation was conducted using 5-fold cross-validation, and hyperparameters were optimized with Scikit-learn, using an 80:20 data split for training and testing.

RESULTS

We found that the LightGBM model achieved an area under the receiver operating characteristic (ROC) curve (AUC) value of 0.89 and an accuracy of 0.79 in the training set, and an AUC value of 0.80 and an accuracy of 0.74 in the test set.

CONCLUSION

The study identified that T1-weighted magnetic resonance imaging radiomic of the hippocampus would be used to predict the progression to MCI at the normal cognitive stage, which might provide a new insight into clinical research.

Radiomics across modalities: a comprehensive review of neurodegenerative diseases

Radiomics allows extraction from medical images of quantitative features that are able to reveal tissue patterns that are generally invisible to human observers. Despite the challenges in visually interpreting radiomic features and the computational resources required to generate them, they hold significant value in downstream automated processing. For instance, in statistical or machine learning frameworks, radiomic features enhance sensitivity and specificity, making them indispensable for tasks such as diagnosis, prognosis, prediction, monitoring, image-guided interventions, and evaluating therapeutic responses. This review explores the application of radiomics in neurodegenerative diseases, with a focus on Alzheimer's disease, Parkinson's disease, Huntington's disease, and multiple sclerosis. While radiomics literature often focuses on magnetic resonance imaging (MRI) and computed tomography (CT), this review also covers its broader application in nuclear medicine, with use cases of positron emission tomography (PET) and single-photon emission computed tomography (SPECT) radiomics. Additionally, we review integrated radiomics, where features from multiple imaging modalities are fused to improve model performance. This review also highlights the growing integration of radiomics with artificial intelligence and the need for feature standardisation and reproducibility to facilitate its translation into clinical practice.

Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population

Brain atrophy and cortical thinning are typically observed in people with Alzheimer's disease (AD) and, to a lesser extent, in those with mild cognitive impairment. In asymptomatic middle-aged apolipoprotein ε4 (ΑPOE4) carriers, who are at higher risk of future AD, study reports are discordant with limited evidence of brain structural differences between carriers and non-carriers of the ε4 allele. Alternative imaging markers with higher sensitivity at the presymptomatic stage, ideally quantified using typically acquired structural MRI scans, would thus be of great benefit for the detection of early disease, disease monitoring and subject stratification. In the present cross-sectional study, we investigated textural properties of T1-weighted 3T MRI scans in relation to APOE4 genotype, age and sex. We pooled together data from the PREVENT-Dementia and ALFA studies focused on midlife healthy populations with dementia risk factors (analysable cohort: 1585 participants; mean age 56.2 ± 7.4 years). Voxel-based and texture (examined features: contrast, entropy, energy, homogeneity) based morphometry was used to identify areas of volumetric and textural differences between APOE4 carriers and non-carriers. Textural maps were generated and were subsequently harmonised using voxel-wise COMBAT. For all analyses, APOE4, sex, age and years of education were used as model predictors. Interactions between APOE4 and age were further examined. There were no group differences in regional brain volume or texture based on APOE4 carriership or when age × APOE4 interactions were examined. Older people tended to have a less homogeneous textural profile in grey and white matter and a more homogeneous profile in the ventricles. A more heterogeneous textural profile was observed for females in areas such as the ventricles, frontal and parietal lobes and for males in the brainstem, cerebellum, precuneus and cingulate. Overall, we have shown the absence of volumetric and textural differences between APOE4 carriers and non-carriers at midlife and have established associations of textural features with ageing and sex.

Radiomics evaluation for the early detection of Alzheimer's dementia using T1-weighted MRI

INTRODUCTION

Alzheimer's disease (AD), the most common cause of dementia, presents a global health crisis with its prevalence expected to triple worldwide by 2050, emphasizing the urgent need for early diagnosis to delay progression and improve patient quality of life. Our project aims to detect AD in its early phase by identifying subtle neuroanatomical changes with Radiomics features, offering a more accurate diagnosis.

METHODS

The AssemblyNet segmentation model was used to analyze brain changes by employing anonymized T1 MRI scans from 416 patients. For each segmented label we extracted Radiomic features. After preprocessing of Radiomic features we trained four models, Gradient Booster, Random Forest, Support Vector Classifier, and XGBoost, in a 70%/20%/10% train, validation and test split. All models were hyperparameter tuned with GridSearch, Cross validation and evaluated with accuracy on the test data.

RESULTS

208 T1-weighted MRI scans were segmented, with 132 segmentation labels per patient, 1130 Radiomic features per segmentation, totalling in over 31 million features. For all four models we achieved accuracies between 0.71 and 0.86, and the machine learning model with highest accuracy were XGBoost, achieving an accuracy at 0.86 on the segmentation of the left inferior lateral ventricle.

CONCLUSION

Our study's use of segmentation on T1-weighted MRI scans resulted promising accuracies for early AD diagnosis with the machine learning model XGBoost, peaking at 0.86 accuracy. Future research should aim to expand datasets and refine methodologies for broader applicability.

IMPLICATION FOR PRACTICE

Implementing Radiomics for early AD detection using T1-weighted MRI scans could substantially improve diagnostic accuracy, enabling earlier interventions that may delay disease progression and improve outcomes, thereby requiring radiographers to adopt more advanced imaging techniques and analysis tools, as well as additional training to effectively interpret complex Radiomic data.

Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?

The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.