Machine intelligence enabled radiomics

Wang, Yue; Herrington, David M.

doi:10.1038/s42256-021-00404-0

Cited by 3 publications

(3 citation statements)

References 8 publications

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“…Intelligent analysis algorithms can be helpful in radiology as an effective aid to physician decision-making in cases of cancer and non-cancer (3)(4)(5). In oncology, structural and functional imaging, pathological tissue sections, and combinations provide valuable insights for screening, diagnosis, treatment, and prognostic assessment.…”

Section: Introductionmentioning

confidence: 99%

“…Radiomics and deep learning have been two rapidly evolving technologies in recent years to achieve this aim, such as the emerging technique of dosiomics, which is an extension of this approach. Their ultimate goal is to create faster and more reliable clinical decision support systems for assisting clinicians, rather than replacing them ( 3 ).…”

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2022

Front. Oncol.

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

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

et al. 2022

Front. Oncol.

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“…These features are collectively termed semantic features [ 8 , 9 ]. Thanks to the advancement in computational mathematics, now, the semantic features can be modeled and quantified, collectively forming the core tenets of radiomics [ 10 , 11 , 12 ]. Radiomics, also known as quantitative imaging [ 13 , 14 , 15 , 16 ], is equipped with the high-throughput extraction of quantitative data, which empowers the conversion of qualitative image features into mineable data [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

et al. 2022

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Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a “one-stop center” synthesis and provide a holistic bird’s eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.

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Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images

Curti,

Merli,

Zengarini

et al. 2024

J Med Syst

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Many automated approaches have been proposed in literature to quantify clinically relevant wound features based on image processing analysis, aiming at removing human subjectivity and accelerate clinical practice. In this work we present a fully automated image processing pipeline leveraging deep learning and a large wound segmentation dataset to perform wound detection and following prediction of the Photographic Wound Assessment Tool (PWAT), automatizing the clinical judgement of the adequate wound healing. Starting from images acquired by smartphone cameras, a series of textural and morphological features are extracted from the wound areas, aiming to mimic the typical clinical considerations for wound assessment. The resulting extracted features can be easily interpreted by the clinician and allow a quantitative estimation of the PWAT scores. The features extracted from the region-of-interests detected by our pre-trained neural network model correctly predict the PWAT scale values with a Spearman's correlation coefficient of 0.85 on a set of unseen images. The obtained results agree with the current state-of-the-art and provide a benchmark for future artificial intelligence applications in this research field.

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Machine intelligence enabled radiomics

Cited by 3 publications

References 8 publications

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

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

Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review

Automated Prediction of Photographic Wound Assessment Tool in Chronic Wound Images

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