Radiomics based detection and characterization of suspicious lesions on full field digital mammograms

Sapate, Suhas G.; Mahajan, Abhishek; Talbar, Sanjay N.; Sable, Nilesh; Desai, Subhash; Thakur, Meenakshi

doi:10.1016/j.cmpb.2018.05.017

Cited by 31 publications

(15 citation statements)

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“…Radiomics holds the potential to revolutionize the conventional tumor characterization and replace classic approaches based on macroscopic variables and can be used to distinguish between malignant and benign lesions . Breast cancer lesions, automatically detected using connected component labeling and adaptive fuzzy region growing algorithm, were classified using radiomic features as benign mass or malignant tumor on digital mammography, dynamic contrast‐enhanced (DCE) MRI, and ultrasound . A radiomic model based on mean apparent diffusion coefficient (ADC), had better accuracy than radiologist assessment for characterization of prostate lesions as clinically significant cancer (Gleason grade group ≥ 2) during prospective MRI interpretation .…”

Section: Overview Of Research and Clinical Applications Of Cancer Radmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

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Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

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Section: Overview Of Research and Clinical Applications Of Cancer Radmentioning

confidence: 99%

Machine and deep learning methods for radiomics

et al. 2020

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“…It is a sharpangled, whisker-like, slender or thin-short, flaming or irregularly shaped shadow extending from the breast mass to the surrounding glandular tissue with occasional calcification. Recently, it has been reported that mammography analysis based on radiomics can improve the diagnosis of breast cancer and help to quantify the tumor [163].…”

Section: Discussionmentioning

confidence: 99%

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et al. 2020

Classic Imaging Signs

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Contents 4.1 Multinodule Accumulation Sign 4.2 Lobulation Sign 4.3 Spinous Protuberant Sign 4.4 Vacuole Sign 4.5 Coarse Spicules Sign 4.6 Pleural Indentation Sign 4.7 Beaded Septum Sign 4.8 Honeycomb Sign 4.9 Withered Tree Sign 4.10 CT Angiogram Sign 4.11 Air Bronchiologram Sign 4.12 Air Bronchogram 4.13 Positive Bronchus Sign 4.14 Mucous Bronchogram 4.15 Gloved Finger Sign 4.16 Feeding Vessel Sign 4.17 Vascular Convergence Sign 4.18 Silhouette Sign 4.19 Luftsichel Sign

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“…In the relatively recent radiomics approach, quantitative analysis of radiological images (mainly CT [37][38][39], magnetic resonance imaging (MRI) [40][41][42], and positron emission tomography (PET) [43] images, but also ultrasounds [44], mammograms [45], and radiography) by extraction of a large number of image features (up to a few hundred or thousands) can be combined with ML classifiers to produce prognostic and predictive models [39].…”

Section: Imagingmentioning

confidence: 99%

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

et al. 2021

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: Artificial intelligence (AI) is a branch of computer science dedicated to giving machines or computers the ability to perform human-like cognitive functions, such as learning, problem-solving, and decision making. Since it is showing superior performance than well-trained human beings in many areas, such as image classification, object detection, speech recognition, and decision-making, AI is expected to change profoundly every area of science, including healthcare and the clinical application of physics to healthcare, referred to as medical physics. As a result, the Italian Association of Medical Physics (AIFM) has created the “AI for Medical Physics” (AI4MP) group with the aims of coordinating the efforts, facilitating the communication, and sharing of the knowledge on AI of the medical physicists (MPs) in Italy. The purpose of this review is to summarize the main applications of AI in medical physics, describe the skills of the MPs in research and clinical applications of AI, and define the major challenges of AI in healthcare.

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Radiomics based detection and characterization of suspicious lesions on full field digital mammograms

Cited by 31 publications

References 50 publications

Machine and deep learning methods for radiomics

Machine and deep learning methods for radiomics

Chest

Artificial Intelligence and the Medical Physicist: Welcome to the Machine

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