Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence

Deleted Journal, Journal Year: 2024, Volume and Issue: 1(1)

Published: Jan. 1, 2024

Abstract The advent of radiomics has revolutionized medical image analysis, affording the extraction high dimensional quantitative data for detailed examination normal and abnormal tissues. Artificial intelligence (AI) can be used enhancement a series steps in pipeline, from acquisition preprocessing, to segmentation, feature extraction, selection, model development. aim this review is present most AI methods explaining advantages limitations methods. Some prominent architectures mentioned include Boruta, random forests, gradient boosting, generative adversarial networks, convolutional neural transformers. Employing these models process analysis significantly enhance quality effectiveness while addressing several that reduce predictions. Addressing enable clinical decisions wider adoption. Importantly, will highlight how assist overcoming major bottlenecks implementation, ultimately improving translation potential method.

Language: Английский

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Artificial intelligence methods available for cancer research

Frontiers of Medicine, Journal Year: 2024, Volume and Issue: 18(5), P. 778 - 797

Published: Aug. 8, 2024

Abstract Cancer is a heterogeneous and multifaceted disease with significant global footprint. Despite substantial technological advancements for battling cancer, early diagnosis selection of effective treatment remains challenge. With the convenience large-scale datasets including multiple levels data, new bioinformatic tools are needed to transform this wealth information into clinically useful decision-support tools. In field, artificial intelligence (AI) technologies their highly diverse applications rapidly gaining ground. Machine learning methods, such as Bayesian networks, support vector machines, decision trees, random forests, gradient boosting, K-nearest neighbors, neural network models like deep learning, have proven valuable in predictive, prognostic, diagnostic studies. Researchers recently employed large language tackle dimensions problems. However, leveraging opportunity utilize AI clinical settings will require surpassing obstacles—a major issue lack use available reporting guidelines obstructing reproducibility published review, we discuss methods explore benefits limitations. We summarize healthcare highlight potential role impact on future directions cancer research.

Language: Английский

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Radiogenomics: Hunting Down Liver Metastasis in Colorectal Cancer Patients

Cancers, Journal Year: 2021, Volume and Issue: 13(21), P. 5547 - 5547

Published: Nov. 5, 2021

Radiomics is a developing new discipline that analyzes conventional medical images to extract quantifiable data can be mined for biomarkers show the biology of pathological processes at microscopic levels. These converted into image-based signatures improve diagnostic, prognostic and predictive accuracy in cancer patients. The combination radiomics molecular data, called radiogenomics, has clear implications patients’ management. Though some studies have focused on radiogenomics hepatocellular carcinoma patients, only few examined colorectal metastatic lesions liver. Moreover, need differentiate between liver fundamental accurate diagnosis treatment. In this review, we summarize knowledge gained from hepatic patients their use early diagnosis, response assessment treatment decisions. We also investigate value as possible biomarkers. addition, great potential image mining provide comprehensive view niche formation thoroughly. Finally, challenges current limitations detection premetastatic niche, based are discussed.

Language: Английский

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Artificial intelligence in breast imaging: potentials and challenges

Physics in Medicine and Biology, Journal Year: 2023, Volume and Issue: 68(23), P. 23TR01 - 23TR01

Published: Sept. 18, 2023

Breast cancer, which is the most common type of malignant tumor among humans, a leading cause death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative targeted therapy, endocrine and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced threat breast cancer Furthermore, early imaging screening plays an important role reducing cycle improving prognosis. The recent innovative revolution artificial intelligence (AI) has aided radiologists accurate diagnosis cancer. In this review, we introduce necessity incorporating AI into applications mammography, ultrasonography, magnetic resonance imaging, positron emission tomography/computed tomography based on published articles since 1994. Moreover, challenges discussed.

Language: Английский

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Detecting B-cell lymphoma-6 overexpression status in primary central nervous system lymphoma using multiparametric MRI-based machine learning

Neuroradiology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 24, 2025

In primary central nervous system lymphoma (PCNSL), B-cell lymphoma-6 (BCL-6) is an unfavorable prognostic biomarker. We aim to non-invasively detect BCL-6 overexpression in PCNSL patients using multiparametric MRI and machine learning techniques. 65 (101 lesions) with (PCNSL) diagnosed from January 2013 July 2023, all were randomly divided into a training set validation according ratio of 8 2. ADC map derived DWI (b = 0/1000 s/mm2), fast spin echo T2WI, T2FLAIR, collected at 3.0 T. A total 2234 radiomics features the tumor segmentation area extracted LASSO used select features. Logistic regression (LR), Naive bayes (NB), Support vector (SVM), K-nearest Neighbor, (KNN) Multilayer Perceptron (MLP), for learning, sensitivity, specificity, accuracy F1-score, under curve (AUC) was evaluate detection performance five classifiers, 6 groups combinations different sequences fitted 5 optimal classifier obtained. status could be identified varying degrees 30 models based on radiomics, model improved by combining classifiers. (SVM) combined three sequence group had largest AUC (0.95) satisfactory (0.87) set. Multiparametric promising detecting overexpression.

Language: Английский

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