A New Challenge for Radiologists: Radiomics in Breast Cancer

Crivelli, Paola; Ledda, Roberta Eufrasia; Parascandolo, Nicola; Fara, Alberto; Soro, Daniela; Conti, Maurizio

doi:10.1155/2018/6120703

Cited by 70 publications

(67 citation statements)

References 48 publications

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“…This feature extraction activity is typically realized by means of pattern recognition algorithms and provides, as a result, a set of numbers, each one representing a quantitative description of a specific either geometrical or physical property of the image portion under consideration. In oncological applications, examples of features are tumor size, shape, intensity, and texture, collectively providing a comprehensive tumor characterization, called the radiomics signature of the tumor [ 13 ]. From an epistemological perspective, radiomics is based on the hypothesis that the extracted features reflect mechanisms occurring at genetic and molecular levels [ [13] , [14] , [15] , [16] , [17] ].…”

Section: Introductionmentioning

confidence: 99%

Overview of radiomics in breast cancer diagnosis and prognostication

et al. 2020

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Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication.

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Section: Introductionmentioning

confidence: 99%

Overview of radiomics in breast cancer diagnosis and prognostication

et al. 2020

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“…The feature extraction process, especially image filtering and texture matrix calculation, was markedly affected by the different methods used. These flaws may incur serious bias, leading to unrepeatable studies and invalid conclusions (14)(15)(16)(35)(36)(37). Recently, concerns and possible solutions have been proposed to this challenge (13,23,38).…”

Section: Discussionmentioning

confidence: 99%

“…Vibration of effects, which are the result of the preference and subjective experience of the investigators in this process, as well as adjustment of parameters of the machine learning methods, make the feature-screening process unreliable and difficult to reproduce (7,13). Moreover, because most studies do not use the same criteria to standardize feature extraction, an understanding of imaging features that can potentially influence tasks remains limited; redundant features are inevitably used (14)(15)(16)(17). The redundancy of features and the inconsistent criteria used for feature extraction inevitably hamper the reproducibility and weaken the interpretation of research results (18,19).…”

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confidence: 99%

Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies

Wang

Liu

Xie

et al. 2020

Radiology: Imaging Cancer

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To summarize the data of previously reported medical imaging features on human malignancies to provide a scientific basis for more credible imaging feature selection for future studies. Materials and Methods: A search was performed in PubMed from database inception through March 23, 2018, for studies clearly stating the decoding of medical imaging features for malignancy-related objectives and/or hypotheses. The Newcastle-Ottawa scale was used for quality assessment of the included studies. Unsupervised hierarchical clustering was performed on the manually extracted features from each included study to identify the application rules of medical imaging features across human malignancies. CT images of 1000 retrospective patients with non-small cell lung cancer were used to reveal a pattern for the value distribution of complex texture features. Results: A total of 5026 imaging features of malignancies affecting 20 parts of the human body from 930 original articles were collated and assessed in this study. A meta-feature construct was proposed to facilitate the investigation of details of any high-dimensional complex imaging features of malignancy. A correlation atlas was constructed to clarify the general rules of applying medical imaging features to the analysis of human malignancy. Assessment of this data revealed a pattern of value distributions of the most commonly reported texture features across human malignancies. Furthermore, the significant expression of the gene mutational signature 1B across human cancer was highly consistent with the presence of the run length imaging feature across different human malignancy types. Conclusion: The results of this study may facilitate more credible imaging feature selection in all oncology tasks across a wide spectrum of human malignancies and help to reduce bias and redundancies in future medical imaging studies. Supplemental material is available for this article.

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“…24,25 The evolution of nerve texture analysis is represented by the advent of radiomics, an advanced quantitative image analysis that extracts a large amount of data from medical images, with the final outcome of providing information that is not visible to the human eye. [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] The possibility of studying the phenotype of peripheral nerves on images acquired with standard proto-cols and analyzing these images with widely available radiomics software packages could open new possibilities to study peripheral nerve pathology far beyond simple CSA evaluation. 7,9,10,18 MRI of peripheral nerves is also challenging mostly due to the thin nature of the nerves, the difficulties in selecting appropriate nerve boundaries, the difficulties in image interpretation, and the complex anatomy.…”

Section: Nerve Echotexture Evaluationmentioning

confidence: 99%

Peripheral Nerves: Not Only Cross-sectional Area in the Era of Radiomics

Tagliafico

González

Rossi

et al. 2020

Semin Musculoskelet Radiol

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The peripheral nervous system is increasingly being investigated using medical imaging as a complement or in association with electrodiagnostics tests. The application of imaging techniques, such as ultrasound (US) and magnetic resonance imaging (MRI), allows detailed visualization of the peripheral nervous system. According to the European Society of Musculoskeletal Radiology, the use of US for nerve evaluation is strongly encouraged. In addition, the role of US is further enhanced by the wide application of US-guided techniques to diagnose or to treat peripheral nerve disorders.Standard evaluation of peripheral nerves on US usually relies on cross-sectional area evaluation with different cutoff values in the osteofibrous tunnels and outside them. In several anatomical areas, side-to-side comparison is highly recommended because it helps distinguish subtle variations by using the unaffected limb as an internal control.US is widely used to perform US-guided interventional procedures on peripheral nerves. The recent development of radiomics and machine and deep learning applied to peripheral nerves may reveal new insights beyond the capabilities of the human eye. Radiomics may have a role in expanding the diagnostic capabilities of US and MRI in the study of peripheral nerve pathology, especially when the cross-sectional area is not markedly increased.

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A New Challenge for Radiologists: Radiomics in Breast Cancer

Cited by 70 publications

References 48 publications

Overview of radiomics in breast cancer diagnosis and prognostication

Overview of radiomics in breast cancer diagnosis and prognostication

Decoding and Systematization of Medical Imaging Features of Multiple Human Malignancies

Peripheral Nerves: Not Only Cross-sectional Area in the Era of Radiomics

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