Early MCI-to-AD Conversion Prediction Using Future Value Forecasting of Multimodal Features.

In Alzheimer's disease (AD) progression, it is imperative to identify the subjects with mild cognitive impairment before clinical symptoms of AD appear. This work proposes a technique for decision support in identifying subjects who will show transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) in the future. We used robust predictors from multivariate MRI-derived biomarkers and neuropsychological measures and tracked their longitudinal trajectories to predict signs of AD in the MCI population. Assuming piecewise linear progression of the disease, we designed a novel weighted gradient offset-based technique to forecast the future marker value using readings from at least two previous follow-up visits. Later, the complete predictor trajectories are used as features for a standard support vector machine classifier to identify MCI-to-AD progressors amongst the MCI patients enrolled in the Alzheimer's disease neuroimaging initiative (ADNI) cohort. We explored the performance of both unimodal and multimodal models in a 5-fold cross-validation setup. The proposed technique resulted in a high classification AUC of 91.2% and 95.7% for 6-month- and 1-year-ahead AD prediction, respectively, using multimodal markers. In the end, we discuss the efficacy of MRI markers as compared to NM for MCI-to-AD conversion prediction.

Predicting Progression From Mild Cognitive Impairment to Alzheimer's Disease Using Autoregressive Modelling of Longitudinal and Multimodal Biomarkers.

Mild cognitive impairment is a preclinical stage of Alzheimer's disease (AD). For effective treatment of AD, it is important to identify mild cognitive impairment (MCI) patients who are at a high risk of developing AD over the course of time. In this study, autoregressive modelling of multiple heterogeneous predictors of Alzheimer's disease is performed to capture their evolution over time. The models are trained using three different arrangements of longitudinal data. These models are then used to estimate future biomarker readings of individual test subjects. Finally, standard support vector machine classifier is employed for detecting MCI patients at risk of developing AD over the coming years. The proposed models are thoroughly evaluated for their predictive capability using both cognitive scores and MRI-derived measures. In a stratified five-fold cross validation setup, our proposed methodology delivered highest AUC of 88.93% (Accuracy = 84.29%) and 88.13% (Accuracy = 83.26%) for 1 year and 2 year ahead AD conversion prediction, respectively, on the most widely used Alzheimer's disease neuroimaging initiative data. The notable conclusions of this study are: 1) Clinical changes in MRI-derived measures can be better forecasted than cognitive scores, 2) Multiple predictor models deliver better conversion prediction than single biomarker models, 3) Cognitive score boosted by MRI-derived measures delivers better short-term ahead conversion prediction, and 4) Neuropsychological scores alone can deliver good accuracy for long-term conversion prediction.

A Nonparametric Approach for Mild Cognitive Impairment to AD Conversion Prediction: Results on Longitudinal Data.

The goal of this study is to introduce a nonparametric technique for predicting conversion from Mild Cognitive impairment (MCI)-to-Alzheimer's disease (AD). Progression of a slowly progressing disease such as AD benefits from the use of longitudinal data; however, research till now is limited due to the insufficient patient data and short follow-up time. A small dataset size invalidates the estimation of underlying disease progression model; hence, a supervised nonparametric method is proposed. While depicting a real-world setting, longitudinal data of three years are employed for training, whereas only the baseline visit's data is used for validation. The train set is preprocessed for extraction of two dense clusters representing the subjects who remain stable at MCI or progress to AD after three years of the baseline visit. Similarity between these clusters and the test point is calculated in Euclidean space. Multiple features from two modalities of biomarkers, i.e., neuropsychological measures (NM) and structural magnetic resonance imaging (MRI) morphometry are also analyzed. Due to the limited MCI dataset size (NM: 145, MRI: 52, NM+MRI: 29), leave-one-out cross validation setup is employed for performance evaluation. The algorithm performance is noted for both unimodal case and bimodal cases. Superior performance (accuracy: 89.66%, sensitivity: 87.50%, specificity: 92.31%, precision: 93.33%) is delivered by multivariate predictors. Three notable conclusions of this study are: 1) Longitudinal data are more powerful than the temporal data, 2) MRI is a better predictor of MCI-to-AD conversion than NM, and 3) multivariate predictors outperform single predictor models.

Potential lung nodules identification for characterization by variable multistep threshold and shape indices from CT images.

Computed tomography (CT) is an important imaging modality. Physicians, surgeons, and oncologists prefer CT scan for diagnosis of lung cancer. However, some nodules are missed in CT scan. Computer aided diagnosis methods are useful for radiologists for detection of these nodules and early diagnosis of lung cancer. Early detection of malignant nodule is helpful for treatment. Computer aided diagnosis of lung cancer involves lung segmentation, potential nodules identification, features extraction from the potential nodules, and classification of the nodules. In this paper, we are presenting an automatic method for detection and segmentation of lung nodules from CT scan for subsequent features extraction and classification. Contribution of the work is the detection and segmentation of small sized nodules, low and high contrast nodules, nodules attached with vasculature, nodules attached to pleura membrane, and nodules in close vicinity of the diaphragm and lung wall in one-go. The particular techniques of the method are multistep threshold for the nodule detection and shape index threshold for false positive reduction. We used 60 CT scans of "Lung Image Database Consortium-Image Database Resource Initiative" taken by GE medical systems LightSpeed16 scanner as dataset and correctly detected 92% nodules. The results are reproducible.