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Fusing multi-modality data is crucial for accurate identification of brain disorder as different modalities can provide complementary perspectives of complex neurodegenerative disease. However, there are at least four common issues associated with the existing fusion methods. First, many existing fusion methods simply concatenate features from each modality without considering the correlations among different modalities. Second, most existing methods often make prediction based on a single classifier, which might not be able to address the heterogeneity of the Alzheimer's disease (AD) progression. Third, many existing methods often employ feature selection (or reduction) and classifier training in two independent steps, without considering the fact that the two pipelined steps are highly related to each other. Forth, there are missing neuroimaging data for some of the participants (e.g., missing PET data), due to the participants' "no-show" or dropout. In this paper, to address the above issues, we propose an early AD diagnosis framework via novel multi-modality latent space inducing ensemble SVM classifier. Specifically, we first project the neuroimaging data from different modalities into a latent space, and then map the learned latent representations into the label space to learn multiple diversified classifiers. Finally, we obtain the more reliable classification results by using an ensemble strategy. More importantly, we present a Complete Multi-modality Latent Space (CMLS) learning model for complete multi-modality data and also an Incomplete Multi-modality Latent Space (IMLS) learning model for incomplete multi-modality data. Extensive experiments using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset have demonstrated that our proposed models outperform other state-of-the-art methods., (Copyright © 2019. Published by Elsevier B.V.)

In this paper, we aim to predict conversion and time-to-conversion of mild cognitive impairment (MCI) patients using multi-modal neuroimaging data and clinical data, via cross-sectional and longitudinal studies. However, such data are often heterogeneous, high-dimensional, noisy, and incomplete. We thus propose a framework that includes sparse feature selection, low-rank affinity pursuit denoising (LRAD), and low-rank matrix completion (LRMC) in this study. Specifically, we first use sparse linear regressions to remove unrelated features. Then, considering the heterogeneity of the MCI data, which can be assumed as a union of multiple subspaces, we propose to use a low rank subspace method (i.e., LRAD) to denoise the data. Finally, we employ LRMC algorithm with three data fitting terms and one inequality constraint for joint conversion and time-to-conversion predictions. Our framework aims to answer a very important but yet rarely explored question in AD study, i.e., when will the MCI convert to AD? This is different from survival analysis, which provides the probabilities of conversion at different time points that are mainly used for global analysis, while our time-to-conversion prediction is for each individual subject. Evaluations using the ADNI dataset indicate that our method outperforms conventional LRMC and other state-of-the-art methods. Our method achieves a maximal pMCI classification accuracy of 84% and time prediction correlation of 0.665., (Copyright © 2018 Elsevier B.V. All rights reserved.)

Alzheimer's disease (AD) is an irreversible neurodegenerative disease and affects a large population in the world. Cognitive scores at multiple time points can be reliably used to evaluate the progression of the disease clinically. In recent studies, machine learning techniques have shown promising results on the prediction of AD clinical scores. However, there are multiple limitations in the current models such as linearity assumption and missing data exclusion. Here, we present a nonlinear supervised sparse regression-based random forest (RF) framework to predict a variety of longitudinal AD clinical scores. Furthermore, we propose a soft-split technique to assign probabilistic paths to a test sample in RF for more accurate predictions. In order to benefit from the longitudinal scores in the study, unlike the previous studies that often removed the subjects with missing scores, we first estimate those missing scores with our proposed soft-split sparse regression-based RF and then utilize those estimated longitudinal scores at all the previous time points to predict the scores at the next time point. The experiment results demonstrate that our proposed method is superior to the traditional RF and outperforms other state-of-art regression models. Our method can also be extended to be a general regression framework to predict other disease scores., Competing Interests: statement The authors have no conflicts of interest to disclose., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Recently, multi-atlas patch-based label fusion has received an increasing interest in the medical image segmentation field. After warping the anatomical labels from the atlas images to the target image by registration, label fusion is the key step to determine the latent label for each target image point. Two popular types of patch-based label fusion approaches are (1) reconstruction-based approaches that compute the target labels as a weighted average of atlas labels, where the weights are derived by reconstructing the target image patch using the atlas image patches; and (2) classification-based approaches that determine the target label as a mapping of the target image patch, where the mapping function is often learned using the atlas image patches and their corresponding labels. Both approaches have their advantages and limitations. In this paper, we propose a novel patch-based label fusion method to combine the above two types of approaches via matrix completion (and hence, we call it transversal). As we will show, our method overcomes the individual limitations of both reconstruction-based and classification-based approaches. Since the labeling confidences may vary across the target image points, we further propose a sequential labeling framework that first labels the highly confident points and then gradually labels more challenging points in an iterative manner, guided by the label information determined in the previous iterations. We demonstrate the performance of our novel label fusion method in segmenting the hippocampus in the ADNI dataset, subcortical and limbic structures in the LONI dataset, and mid-brain structures in the SATA dataset. We achieve more accurate segmentation results than both reconstruction-based and classification-based approaches. Our label fusion method is also ranked 1st in the online SATA Multi-Atlas Segmentation Challenge., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: Video-assisted thoracoscopic surgery (VATS) major lung resection for lung cancer has been an important part of thoracic surgical training program in our institution. In this study, we compared the results of VATS major lung resection performed by surgical trainees with those performed by experienced thoracic surgeons with specialist interest in VATS., Methods: From January 2002 to October 2006, the clinical data of 111 consecutive patients scheduled for VATS major lung resection were prospectively entered into the computerized clinical management system of the local health authority; these include patient demographics, comorbidity, operating time, postoperative complications, and outcome. We retrospectively compared the data of patients who were operated on by trainees with those who were operated on by experienced VATS surgeons., Results: One hundred and eleven patients with clinical stage I and II lung cancer underwent VATS major lung resection. Fifty-one (46%) of the procedures were performed by consultant surgeons and 60 VATS lung resections (54%) were performed by supervised trainees. Patients' demography and risk factors were comparable between the two groups. Trainees spent more time in performing the operation as compared with experienced VATS surgeons (mean operating time 162 minutes, p = 0.01). There was no significant difference in intraoperative or postoperative complications and outcomes between the two groups., Conclusions: Video-assisted thoracic surgery major lung resection for early stage nonsmall-cell lung cancer can be taught to residents who work under the supervision of experienced VATS surgeons. Video-assisted thoracic surgery major lung resection for lung cancer should be an integral part of thoracic surgical training program.

Coronary artery aneurysm requiring surgery is rare. We report a case of a ruptured giant coronary artery aneurysm due to Kawasaki vasculitis which presented with cardiac arrest and was successfully treated by emergency coronary artery bypass grafting. The controversies surrounding the management of this disease are also discussed.

We report the case of a 64-year-old woman who presented with massive hemoptysis. She was found to be bleeding from a pulmonary arteriovenous malformation in the right middle lobe, which had a peculiar blood supply from the right internal mammary artery. Video-assisted thoracic surgery lobectomy was successfully performed for this condition. Limitations of embolization as a treatment modality for this condition are discussed.