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Radiology Radiology

Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics.

In Biomarkers in medicine

In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.

Wang Yao, Wang Yan, Guo Chunjie, Xie Xuping, Liang Sen, Zhang Ruochi, Pang Wei, Huang Lan


cancer genotypes, deep learning, imaging phenotype, prediction and associations analysis, radiogenomics, radiomics

General General

Current status and future perspective on artificial intelligence for lower endoscopy.

In Digestive endoscopy : official journal of the Japan Gastroenterological Endoscopy Society

The global incidence and mortality rate of colorectal cancer remains high. Colonoscopy is regarded as the gold standard examination for detecting and eradicating neoplastic lesion. However, there are some uncertainties in colonoscopy practice that are related to limitations in human performance. First, approximately one-fourth of colorectal neoplasms are missed on a single colonoscopy. Second, it is still difficult for non-experts to perform adequately regarding optical biopsy. Third, recording of some quality indicators (e.g. cecal intubation, bowel preparation, and withdrawal speed) which are related to adenoma detection rate, is sometimes incomplete. With recent improvements in machine learning techniques and advances in computer performance, artificial intelligence-assisted computer-aided diagnosis is being increasingly utilized by endoscopists. In particular, the emergence of deep-learning, data-driven machine learning techniques have made the development of computer-aided systems easier than that of conventional machine learning techniques, the former currently being considered the standard artificial intelligence engine of computer-aided diagnosis by colonoscopy. To date, computer-aided detection systems seem to have improved the rate of detection of neoplasms. Additionally, computer-aided characterization systems may have the potential to improve diagnostic accuracy in real-time clinical practice. Furthermore, some artificial intelligence-assisted systems that aim to improve the quality of colonoscopy have been reported. The implementation of computer-aided system clinical practice may provide additional benefits such as helping in educational poorly performing endoscopists and supporting real-time clinical decision making. In this review, we have focused on computer-aided diagnosis during colonoscopy reported by gastroenterologists and discussed its status, limitations, and future prospects.

Misawa Masashi, Kudo Shin-Ei, Mori Yuichi, Maeda Yasuharu, Ogawa Yushi, Ichimasa Katsuro, Kudo Toyoki, Wakamura Kunihiko, Hayashi Takemasa, Miyachi Hideyuki, Baba Toshiyuki, Ishida Fumio, Itoh Hayato, Oda Masahiro, Mori Kensaku


Radiology Radiology

Automatic segmentation, classification and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI.

In Medical physics ; h5-index 59.0

PURPOSE : Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using MRI is crucial for therapeutic decision-making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI.

METHODS : A total of 202 MRI scans from 29 patients with chiasmatic OPG scanned longitudinally were retrospectively collected and included in this study. Data included T2 and post-contrast T1 weighted images. The entire tumor volume and its components were manually annotated by a senior neuro-radiologist, and inter- and intra-rater variability of the entire tumor volume was assessed in a subset of scans. Automatic tumor segmentation was performed using deep-learning method with U-Net+ResNet architecture. A 5-fold cross-validation scheme was used to evaluate the automatic results relative to manual segmentation. Voxel based classification of the tumor into enhanced, non-enhanced and cystic components was performed using fuzzy c-means clustering.

RESULTS : The results of the automatic tumor segmentation were: mean dice score=0.736±0.025, precision=0.918±0.014, and recall=0.635±0.039 for the validation data, and dice score =0.761±0.011, precision=0.794±0.028, and recall=0.742±0.012 for the test data. The accuracy of the voxel based classification of tumor components was 0.94, with precision=0.89, 0.97, 0.85 and recall= 1.00, 0.79, 0.94 for the non-enhanced, enhanced and cystic components, respectively.

CONCLUSION : This study presents methods for automatic segmentation of chiasmatic OPG tumors and classification into the different components of the tumor, based on conventional MRI. Automatic quantitative longitudinal assessment of these tumors may improve radiological monitoring, facilitate early detection of disease progression and optimize therapy management.

Artzi Moran, Gershov Sapir, Ben-Sira Liat, Roth Jonathan, Kozyrev Danil, Shofty Ben, Gazit Tomer, Halag-Milo Tali, Constantini Shlomi, Ben Bashat Dafna


Deep learning, Fuzzy c-means clustering, Optic pathway gliomas, Segmentation

General General

Applications of Genome-Wide Screening and Systems Biology Approaches in Drug Repositioning.

In Cancers

Modern drug discovery through de novo drug discovery entails high financial costs, low success rates, and lengthy trial periods. Drug repositioning presents a suitable approach for overcoming these issues by re-evaluating biological targets and modes of action of approved drugs. Coupling high-throughput technologies with genome-wide essentiality screens, network analysis, genome-scale metabolic modeling, and machine learning techniques enables the proposal of new drug-target signatures and uncovers unanticipated modes of action for available drugs. Here, we discuss the current issues associated with drug repositioning in light of curated high-throughput multi-omic databases, genome-wide screening technologies, and their application in systems biology/medicine approaches.

Mohammadi Elyas, Benfeitas Rui, Turkez Hasan, Boren Jan, Nielsen Jens, Uhlen Mathias, Mardinoglu Adil


drug repositioning, genomic screens, machine learning, systems medicine, systems pharmacology

Radiology Radiology

Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis.

In Experimental and therapeutic medicine

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.

Trivizakis Eleftherios, Tsiknakis Nikos, Vassalou Evangelia E, Papadakis Georgios Z, Spandidos Demetrios A, Sarigiannis Dimosthenis, Tsatsakis Aristidis, Papanikolaou Nikolaos, Karantanas Apostolos H, Marias Kostas


COVID-19, artificial intelligence, deep learning analysis, multi-institutional data

General General

Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data

ArXiv Preprint

Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. The high-resolution longitudinal activity data collected by smartphone applications nowadays make it possible to conduct remote and convenient health assessment. However, out-of-lab tests often suffer from poor quality controls as well as irregularly collected observations, leading to noisy test results. To address these issues, we propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild. The proposed method first synchronizes discrete activity tests into multimodal features at unified time points. Next, it distills and enriches local and global representations from noisy data across modalities and temporal observations by two attention modules. With the proposed mechanisms, our model is capable of handling noisy observations and at the same time extracting refined temporal features for improved prediction performance. Quantitative and qualitative results on a large public dataset demonstrate the effectiveness of the proposed approach.

Weijian Li, Wei Zhu, E. Ray Dorsey, Jiebo Luo