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Lesion Detection Assessment in the Liver: Standard vs Low Radiation Dose Using Varied Post-Processing Techniques

ABSTRACT: To compare 2 different image creation/processing techniques during a standard CT scan in order to "see" problems in the liver and learn which method provides better image quality. The techniques use new artificial intelligence software to decrease image noise, which helps the radiologist to evaluate.

DISEASE(S): Colorectal Carcinoma,Neoplasm Metastasis,Carcinoma,Liver Metastases,Diseases Of Liver,Colorectal Neoplasms,Colon Carcinoma,Liver Diseases

PROVIDER: 2244256 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Functional Optimization of Designer Cardiac Organoids Enabled by Machine Learning Techniques

Project description:Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principle for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs (Circle 200, Circle 600, Circle 1000, Rectangle 1:1, Rectangle 1:4, Star 1:1, and Star 1:4). We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We successfully clustered and refined our cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium rising time in distinguishing organoids based on geometric patterns and clustering results. This innovative integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

2024-05-17 | GSE267438 | GEO

Comparison of Flat Colorectal Lesion Detection by Artificial Intelligence-assisted Colonoscopy Versus Endoscopists

Project description:The development of artificial intelligence (AI) systems in the field of colorectal endoscopy is currently booming, colorectal cancer being, by its frequency and severity, a real public health problem. In terms of image analysis, AI is indeed able to perform many tasks simultaneously (lesion detection, classification, and segmentation) and to combine them. Lesion detection is thus the starting point of the whole chain to choose at the end the most appropriate treatment for the patient. Large-scale studies have demonstrated the superiority of artificial intelligence-assisted detection over the usual detection by gastroenterologists, mainly for the detection of sub-centimeter polyps. However, the investigators have shown that a recent computer-aided detection system (CADe) such as the ENDO-AID software in combination with the EVIS X1 video column (Olympus, Tokyo, Japan) may present difficulties in the detection of flat lesions such as sessile serrated lesions (SSLs) and non-granular laterally spreading tumors (LST-NGs). This represents a major challenge because in addition to their shape being difficult to identify for the human eye in practice and where AI assistance would be of great value, these rare lesions are associated with advanced histology. In addition, the investigators recently described the case of a worrisome false negative of AI-assisted colonoscopy, which failed to detect a flat adenocarcinoma in the transverse colon. Therefore, it is important to measure the false negative rate of AI detection based on the macroscopic shape of the lesion. Comparing this rate to the human endoscopist’s false negatives would improve the performance of AI for this specific lesion subtype in the future.

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Multidimensional raw files used for MZA benchmarking

Project description:These data files are supplementary material for the publication "MZA: a data conversion tool to facilitate software development and artificial intelligence research in multidimensional mass spectrometry". Journal of Proteome Research https://doi.org/10.1021/acs.jproteome.2c00313. MZA is available at https://github.com/PNNL-m-q/mza.

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Vaginal microbiota sampling techniques

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eDNA filtering techniques affect biodiversity

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Artificial intelligence-based histopathology image analysis identifies a novel subset of endometrial cancers with distinct genomic features and unfavourable outcome

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Identification of relationships between Molecular and Imaging Phenotypes in Non-small cell lung cancer using radiogenomics Map

Project description:Purpose: To create a radiogenomic map linking computed tomographic (CT) image features and gene expression profiles generated by RNA sequencing for patients with non-small cell lung cancer (NSCLC). Methods: A cohort of 113 patients with NSCLC diagnosed between April 2008 and September 2014 who had preoperative CT data and tumor tissue available was studied. For each tumor, a thoracic radiologist recorded 87 semantic image features, selected to reflect radiologic characteristics of nodule shape, margin, texture, tumor environment, and overall lung characteristics. Next, total RNA was extracted from the tissue and analyzed with RNA sequencing technology. Ten highly coexpressed gene clusters, termed metagenes, were identified, validated in publicly available gene-expression cohorts, and correlated with prognosis. Next, a radiogenomics map was built that linked semantic image features to metagenes by using the t statistic and the Spearman correlation metric with multiple testing correction. Results: RNA sequencing analysis resulted in 10 metagenes that capture a variety of molecular pathways, including the epidermal growth factor (EGF) pathway. A radiogenomic map was created with 32 statistically significant correlations between semantic image features and metagenes. Conclusions: Radiogenomic analysis of NSCLC showed multiple associations between semantic image features and metagenes that represented canonical molecular pathways

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2022-04-27 | PXD023904 | Pride

multiomics depicting the immunometabolism landscape of steatotic livers undergoing bariatric surgery and diet intervention

Project description:Metabolic dysfunction-associated steatotic liver disease (MASLD) is a highly prevalent chronic liver disease worldwide that encompasses a spectrum of steatosis, inflammation, and fibrosis. Evidence suggests that weight loss approaches such as dietary restriction (DR) and sleeve gastrectomy (SG) can lead to remission of hepatic steatosis and inflammation. However, it remains unclear about the effects of weight loss on the hepatic immune mechanisms in MASLD. Therefore, this study aims to elucidate the intricate immunometabolic landscape of steatotic livers following DI and BS by employing the sleeve gastrectomy (a typical BS procedure) and comparable food intake after sham surgery in a rat model of MASLD. Single-cell (sc) and single-nuclei (sn) transcriptome analysis together with spatial metabolomics and immunohistochemistry were utilized to depict immunometabolic landscape, while circulating markers were assessed in serum. Furthermore, artificial intelligence (AI)-based image analysis was introduced to characterize the distribution of hepatocytes, myeloid cells and lymphocytes.

2024-09-12 | GSE254841 | GEO

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