A review of feature selection methods in medical applications

Computers in Biology and Medicine, Journal Year: 2019, Volume and Issue: 112, P. 103375 - 103375

Published: July 31, 2019

Language: Английский

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Tutorial: multivariate classification for vibrational spectroscopy in biological samples

Nature Protocols, Journal Year: 2020, Volume and Issue: 15(7), P. 2143 - 2162

Published: June 17, 2020

Language: Английский

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A review in radiomics: Making personalized medicine a reality via routine imaging

Medicinal Research Reviews, Journal Year: 2021, Volume and Issue: 42(1), P. 426 - 440

Published: July 26, 2021

Radiomics is the quantitative analysis of standard-of-care medical imaging; information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. performed by extracting hand-crafted radiomics features or via deep learning algorithms. has evolved tremendously in last decade, becoming a bridge between imaging and precision medicine. exploits sophisticated image tools coupled with statistical elaboration extract wealth hidden inside images, such as computed tomography (CT), magnetic resonance (MR), Positron emission (PET) scans, routinely everyday practice. Many efforts have been devoted recent years standardization validation approaches, demonstrate their usefulness robustness beyond any reasonable doubts. However, booming publications commercial applications approaches warrant caution proper understanding all factors involved avoid "scientific pollution" overly enthusiastic claims researchers clinicians alike. For these reasons present review aims guidebook sorts, describing process radiomics, its pitfalls, challenges, opportunities, along ability improve decision-making, from oncology respiratory medicine pharmacological genotyping studies.

Language: Английский

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Radiomics and radiogenomics in gliomas: a contemporary update

British Journal of Cancer, Journal Year: 2021, Volume and Issue: 125(5), P. 641 - 657

Published: May 6, 2021

Abstract The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour or secondary gliomas (high-grade transformation low-grade lesions), as well dilemmas with identification radiation necrosis, progression, pseudoprogression on MRI. Radiomics radiogenomics promise to offer precise diagnosis, predict prognosis, assess response modern chemotherapy/immunotherapy therapy. This is achieved a triumvirate morphological, textural, functional signatures, derived from high-throughput extraction quantitative voxel-level MR image metrics. However, lack standardisation acquisition parameters inconsistent methodology between working groups have made validations unreliable, hence multi-centre studies involving heterogenous study populations warranted. We elucidate novel radiomic radiogenomic workflow concepts state-of-the-art descriptors in sub-visual processing, relevant literature applications such machine learning techniques glioma management.

Language: Английский

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Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling

Energy, Journal Year: 2021, Volume and Issue: 237, P. 121543 - 121543

Published: July 26, 2021

Language: Английский

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Machine Learning-Based Radiomics Signatures for EGFR and KRAS Mutations Prediction in Non-Small-Cell Lung Cancer

International Journal of Molecular Sciences, Journal Year: 2021, Volume and Issue: 22(17), P. 9254 - 9254

Published: Aug. 26, 2021

Early identification of epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations is crucial for selecting a therapeutic strategy patients with non-small-cell lung cancer (NSCLC). We proposed machine learning-based model feature selection prediction EGFR KRAS in NSCLC by including the least number most semantic radiomics features. included cohort 161 from 211 The Cancer Imaging Archive (TCIA) analyzed low-dose computed tomography (LDCT) images detecting mutations. A total 851 features, which were classified into 9 categories, obtained through manual segmentation extraction LDCT. evaluated our models using validation set consisting 18 derived same TCIA dataset. results showed that genetic algorithm plus XGBoost classifier exhibited favorable performance, an accuracy 0.836 0.86 mutations, respectively. demonstrated noninvasive signatures could robustly predict NSCLC.

Language: Английский

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Machine learning for microbiologists

Nature Reviews Microbiology, Journal Year: 2023, Volume and Issue: 22(4), P. 191 - 205

Published: Nov. 15, 2023

Language: Английский

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Lung cancer detection from CT scans using modified DenseNet with feature selection methods and ML classifiers

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 224, P. 119961 - 119961

Published: March 25, 2023

Language: Английский

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CerCan·Net: Cervical cancer classification model via multi-layer feature ensembles of lightweight CNNs and transfer learning

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 229, P. 120624 - 120624

Published: June 2, 2023

Language: Английский

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A similarity-based method for prediction of drug side effects with heterogeneous information

Mathematical Biosciences, Journal Year: 2018, Volume and Issue: 306, P. 136 - 144

Published: Oct. 5, 2018

Language: Английский

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