Overview of radiomics in breast cancer diagnosis and prognostication

The Breast, Journal Year: 2019, Volume and Issue: 49, P. 74 - 80

Published: Nov. 6, 2019

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

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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Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools

The Oncologist, Journal Year: 2019, Volume and Issue: 25(2), P. e231 - e242

Published: Oct. 14, 2019

Abstract The detection of lymph node metastasis affects the management patients with primary breast cancer significantly in terms staging, treatment, and prognosis. main goal for radiologist is to determine detect presence metastatic disease nonpalpable axillary nodes a positive predictive value that high enough initially select upfront dissection. Features are suggestive adenopathy may be seen different imaging modalities, but ultrasound method choice evaluating performing image-guided interventions. This review aims provide comprehensive overview available modalities assessment diagnosed cancer.

Language: Английский

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Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative

European Radiology, Journal Year: 2022, Volume and Issue: 33(3), P. 1884 - 1894

Published: Oct. 25, 2022

The main aim of the present systematic review was a comprehensive overview Radiomics Quality Score (RQS)-based reviews to highlight common issues and challenges radiomics research application evaluate relationship between RQS features.

Language: Английский

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PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2019, Volume and Issue: 46(7), P. 1468 - 1477

Published: March 26, 2019

Language: Английский

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Radiomics in hepatocellular carcinoma: a quantitative review

Hepatology International, Journal Year: 2019, Volume and Issue: 13(5), P. 546 - 559

Published: Aug. 31, 2019

Language: Английский

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Prostate MRI radiomics: A systematic review and radiomic quality score assessment

European Journal of Radiology, Journal Year: 2020, Volume and Issue: 129, P. 109095 - 109095

Published: May 30, 2020

Language: Английский

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Breast cancer, screening and diagnostic tools: All you need to know

Critical Reviews in Oncology/Hematology, Journal Year: 2020, Volume and Issue: 157, P. 103174 - 103174

Published: Nov. 11, 2020

Language: Английский

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A New Challenge for Radiologists: Radiomics in Breast Cancer

BioMed Research International, Journal Year: 2018, Volume and Issue: 2018, P. 1 - 10

Published: Oct. 8, 2018

Over the last decade, field of medical imaging experienced an exponential growth, leading to development radiomics, with which innumerable quantitative features are obtained from digital images, providing a comprehensive characterization tumor. This review aims assess role this emerging diagnostic tool in breast cancer, focusing on ability radiomics predict malignancy, response neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk recurrence.

Language: Английский

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Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma

Scientific Reports, Journal Year: 2018, Volume and Issue: 8(1)

Published: Sept. 4, 2018

Abstract Triple-negative breast cancer (TNBC) is sometimes mistaken for fibroadenoma due to its tendency show benign morphology on ultrasound (US) albeit aggressive nature. This study aims develop a radiomics score based US texture analysis differential diagnosis between TNBC and fibroadenoma, evaluate diagnostic performance compared with pathologic results. We retrospectively included 715 pathology-proven fibroadenomas 186 TNBCs which were examined by three different machines. developed the using penalized logistic regression least absolute shrinkage selection operator (LASSO) from 730 extracted features consisting of 14 intensity-based features, 132 textural 584 wavelet-based features. The constructed showed significant difference all machines ( p < 0.001). Although dependency type machine, we more elaborate subgroup in examinations performed iU22. subsequent also good performance, even BI-RADS category 3 or 4a lesions (AUC 0.782) presumed as probably low suspicious malignancy radiologists. It was expected assist radiologist’s reduce number invasive biopsies, although standardization should be overcome before clinical application.

Language: Английский

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