Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Frontiers in Oncology, Journal Year: 2022, Volume and Issue: 12

Published: Feb. 17, 2022

The high-throughput extraction of quantitative imaging features from medical images for the purpose radiomic analysis, i.e., radiomics in a broad sense, is rapidly developing and emerging research field that has been attracting increasing interest, particularly multimodality multi-omics studies. In this context, analysis multidimensional data plays an essential role assessing spatio-temporal characteristics different tissues organs their microenvironment. Herein, recent developments method, including manually defined features, acquisition preprocessing, lesion segmentation, feature extraction, selection dimension reduction, statistical model construction, are reviewed. addition, deep learning-based techniques automatic segmentation being analyzed to address limitations such as rigorous workflow, manual/semi-automatic annotation, inadequate criteria, multicenter validation. Furthermore, summary current state-of-the-art applications technology disease diagnosis, treatment response, prognosis prediction perspective radiology images, histopathology three-dimensional dose distribution data, oncology, presented. potential value diagnostic therapeutic strategies also further analyzed, first time, advances challenges associated with dosiomics radiotherapy summarized, highlighting latest progress radiomics. Finally, robust framework presented recommendations future development discussed, but not limited factors affect stability (medical big multitype expert knowledge medical), data-driven processes (reproducibility interpretability studies, alternatives various institutions, prospective researches clinical trials), thoughts on directions (the capability achieve open platform analysis).

Language: Английский

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Introduction to radiomics for a clinical audience

Clinical Radiology, Journal Year: 2023, Volume and Issue: 78(2), P. 83 - 98

Published: Jan. 11, 2023

Radiomics is a rapidly developing field of research focused on the extraction quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding disease processes and provide clinical decision support. To date, most radiomics has been oncological applications; however, it increasingly being used in raft other diseases. This review gives an overview for audience, including pipeline common pitfalls associated with each stage. Key studies oncology are presented focus both those use analysis alone integrate its multimodal data streams. Importantly, applications outside also presented. Finally, we conclude by offering vision future, how might impact practice as radiologists.

Language: Английский

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Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems

Diagnostics, Journal Year: 2023, Volume and Issue: 13(10), P. 1691 - 1691

Published: May 10, 2023

We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson's disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features 0 (baseline) applied hybrid machine learning systems (HMLSs).

Language: Английский

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Fusion-based tensor radiomics using reproducible features: Application to survival prediction in head and neck cancer

Computer Methods and Programs in Biomedicine, Journal Year: 2023, Volume and Issue: 240, P. 107714 - 107714

Published: July 8, 2023

Language: Английский

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Non-contrast Cine Cardiac Magnetic Resonance image radiomics features and machine learning algorithms for myocardial infarction detection

Computers in Biology and Medicine, Journal Year: 2021, Volume and Issue: 141, P. 105145 - 105145

Published: Dec. 16, 2021

Robust differentiation between infarcted and normal tissue is important for clinical diagnosis precision medicine. The aim of this work to investigate the radiomic features develop a machine learning algorithm myocardial infarction (MI) viable tissues/normal cases in left ventricular myocardium on non-contrast Cine Cardiac Magnetic Resonance (Cine-CMR) images.Seventy-two patients (52 with MI 20 healthy control patients) were enrolled study. MR imaging was performed 1.5 T MRI using following parameters: TR = 43.35 ms, TE 1.22 flip angle 65°, temporal resolution 30-40 ms. N4 bias field correction applied correct inhomogeneity images. All images segmented verified simultaneously by two cardiac experts consensus. Subsequently, extraction within whole (3D volume) end-diastolic volume phase. Re-sampling 1 × mm3 voxels intensities VOI discretized 64 bins. Radiomic normalized obtain Z-scores, followed Student's t-test statistical analysis comparison. A p-value < 0.05 used as threshold statistically significant differences false discovery rate (FDR) report q-value (FDR adjusted p-value). extracted ranked MSVM-RFE algorithm, then Spearman correlation eliminate highly correlated (R2 > 0.80). Ten different algorithms classification metrics evaluation various parameters models' evaluation.In univariate analysis, highest area under curve (AUC) receiver operating characteristic (ROC) value achieved Maximum 2D diameter slice (M2DS) shape feature (AUC 0.88, 1.02E-7), while average AUCs 0.62 ± 0.08. In multivariate Logistic Regression 0.93 0.03, Accuracy 0.86 0.05, Recall 0.87 0.1, Precision 0.03 F1 Score 0.90 0.04) SVM 0.92 0.85 0.04, 0.01, 0.88 0.04 0.02) yielded optimal performance best radiomics analysis.This study demonstrated that Cine-CMR enables accurately detect MI, which could potentially be an alternative diagnostic method Late Gadolinium Enhancement (LGE-CMR).

Language: Английский

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Deep versus Handcrafted Tensor Radiomics Features: Prediction of Survival in Head and Neck Cancer Using Machine Learning and Fusion Techniques

Diagnostics, Journal Year: 2023, Volume and Issue: 13(10), P. 1696 - 1696

Published: May 11, 2023

Although handcrafted radiomics features (RF) are commonly extracted via software, employing deep (DF) from learning (DL) algorithms merits significant investigation. Moreover, a "tensor'' paradigm where various flavours of given feature generated and explored can provide added value. We aimed to employ conventional tensor DFs, compare their outcome prediction performance RFs.

Language: Английский

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Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements

Ageing Research Reviews, Journal Year: 2024, Volume and Issue: 99, P. 102410 - 102410

Published: July 6, 2024

Language: Английский

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PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease

Journal of Neuroscience Methods, Journal Year: 2025, Volume and Issue: unknown, P. 110363 - 110363

Published: Jan. 1, 2025

Language: Английский

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Combining quantitative susceptibility mapping to radiomics in diagnosing Parkinson’s disease and assessing cognitive impairment

European Radiology, Journal Year: 2022, Volume and Issue: 32(10), P. 6992 - 7003

Published: April 23, 2022

Language: Английский

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Applications of machine learning and deep learning in SPECT and PET imaging: General overview, challenges and future prospects

Pharmacological Research, Journal Year: 2023, Volume and Issue: 197, P. 106984 - 106984

Published: Nov. 1, 2023

The integration of positron emission tomography (PET) and single-photon computed (SPECT) imaging techniques with machine learning (ML) algorithms, including deep (DL) models, is a promising approach. This enhances the precision efficiency current diagnostic treatment strategies while offering invaluable insights into disease mechanisms. In this comprehensive review, we delve transformative impact ML DL in domain. Firstly, brief analysis provided how these algorithms have evolved which are most widely applied Their different potential applications nuclear then discussed, such as optimization image adquisition or reconstruction, biomarkers identification, multimodal fusion development diagnostic, prognostic, progression evaluation systems. because they able to analyse complex patterns relationships within data, well extracting quantitative objective measures. Furthermore, discuss challenges implementation, data standardization limited sample sizes, explore clinical opportunities future horizons, augmentation explainable AI. Together, factors propelling continuous advancement more robust, transparent, reliable

Language: Английский

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