The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Theranostics, Journal Year: 2019, Volume and Issue: 9(5), P. 1303 - 1322

Published: Jan. 1, 2019

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring temporal spatial characteristics of tumor.Progress computational methods, especially artificial intelligence medical image process analysis, has converted these images into quantitative minable data associated with clinical events oncology management.This concept was first described as radiomics 2012.Since then, computer scientists, radiologists, oncologists have gravitated towards this new tool exploited advanced methodologies to mine information behind images.On basis a great quantity radiographic novel technologies, researchers developed validated radiomic models that may improve accuracy diagnoses therapy response assessments.Here, we review recent methodological developments radiomics, including acquisition, segmentation, feature extraction, modelling, well rapidly developing deep learning technology.Moreover, outline main applications diagnosis, treatment planning evaluations field aim personalized medicine.Finally, discuss challenges scope applicability methods.

Language: Английский

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Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: March 6, 2020

Abstract Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate treatment options and therefore avoiding unnecessary surgery complications. Here, we report deep learning radiomics (DLR) conventional ultrasound shear wave elastography predicting ALN status preoperatively cancer. Clinical parameter combined DLR yields the best diagnostic performance between disease-free axilla any metastasis areas under receiver operating characteristic curve (AUC) 0.902 (95% confidence interval [CI]: 0.843, 0.961) test cohort. This clinical can also discriminate low heavy metastatic burden disease AUC 0.905 CI: 0.814, 0.996) Our study offers a noninvasive imaging biomarker to predict extent

Language: Английский

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Radiomics in breast cancer classification and prediction

Seminars in Cancer Biology, Journal Year: 2020, Volume and Issue: 72, P. 238 - 250

Published: May 1, 2020

Language: Английский

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Radiomics: from qualitative to quantitative imaging

British Journal of Radiology, Journal Year: 2020, Volume and Issue: 93(1108)

Published: Feb. 26, 2020

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and turn it into valuable predictive outcomes. As result of advances both computational hardware machine learning algorithms, computers are making great strides obtaining quantitative information from correlating with Radiomics, its two forms “handcrafted deep,” emerging field that translates images data yield biological enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, monitoring. Handcrafted radiomics multistage process which features based on shape, pixel intensities, texture extracted radiographs. Within this review, we describe the steps: starting data, how extracted, correlate clinical outcomes, resulting models used make predictions, such as survival, detection classification diagnostics. The application deep learning, second arm radiomics, place workflow discussed, along advantages disadvantages. To better illustrate technologies being used, provide real-world applications oncology, showcasing research well covering limitations future direction.

Language: Английский

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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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Rapid review: radiomics and breast cancer

Breast Cancer Research and Treatment, Journal Year: 2018, Volume and Issue: 169(2), P. 217 - 229

Published: Feb. 2, 2018

Language: Английский

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Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement

European Radiology, Journal Year: 2019, Volume and Issue: 30(1), P. 523 - 536

Published: July 26, 2019

Language: Английский

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Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics

European Journal of Nuclear Medicine and Molecular Imaging, Journal Year: 2019, Volume and Issue: 46(13), P. 2656 - 2672

Published: June 18, 2019

The aim of this systematic review was to analyse literature on artificial intelligence (AI) and radiomics, including all medical imaging modalities, for oncological non-oncological applications, in order assess how far the image mining research stands from routine application. To do this, we applied a trial phases classification inspired drug development process. Among articles considered inclusion PubMed were multimodality AI radiomics investigations, with validation analysis aimed at relevant clinical objectives. Quality assessment selected papers performed according QUADAS-2 criteria. We developed criteria studies. Overall 34,626 retrieved, 300 applying inclusion/exclusion criteria, 171 high-quality (QUADAS-2 ≥ 7) identified analysed. In 27/171 (16%), 141/171 (82%), 3/171 (2%) studies an AI-based algorithm, model, combined radiomics/AI approach, respectively, described. A total 26/27(96%) 1/27 (4%) classified as phase II III, respectively. Consequently, 13/141 (9%), 10/141 (7%), 111/141 (79%), 7/141 (5%) 0, I, II, All three categorised trials. results are promising but still not mature enough tools be implemented setting widely used. transfer learning well-known process, some specific adaptations discipline could represent most effective way algorithms become standard care tools.

Language: Английский

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Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region

Frontiers in Oncology, Journal Year: 2020, Volume and Issue: 10

Published: Jan. 31, 2020

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis predicting ALN using ultrasound, and investigate the value of both intratumoral peritumoral regions prediction. Methods: We retrospectively enrolled 479 cancer patients 2,395 ultrasound images. Based on intratumoral, peritumoral, combined intra- regions, three CNNs built DenseNet, models random forest, respectively. By combining molecular subtype, another built. All training cohort (343 1,715 images) evaluated testing (136 680 ROC analysis. Another prospective 16 was further test models. Results: AUCs image-only training/testing cohorts 0.957/0.912 for region, 0.944/0.775 0.937/0.748 which numerically higher than their corresponding 0.940/0.886, 0.920/0.724, 0.913/0.693. overall performance image-molecular terms slightly increased 0.962/0.933, 0.951/0.813, 0.931/0.794, region significantly better those either or (p < 0.05). In study, CNN model achieved highest AUC 0.95 among all Conclusions: showed For models, performance.

Language: Английский

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Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast‐enhanced MRI

Journal of Magnetic Resonance Imaging, Journal Year: 2018, Volume and Issue: 49(1), P. 131 - 140

Published: Sept. 1, 2018

Sentinel lymph node (SLN) status is an important prognostic factor for patients with breast cancer, which currently determined in clinical practice by invasive SLN biopsy.To noninvasively predict metastasis cancer using dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) intra- and peritumoral radiomics features combined or without clinicopathologic characteristics of the primary tumor.Retrospective.A total 163 (55 positive 108 negative SLN).1.5T, T1 -weighted DCE-MRI.A 590 radiomic were extracted each patient from both intratumoral regions interest. To avoid overfitting, dataset was randomly separated into a training set (∼67%) validation (∼33%). The prediction models built logistic regression on most significant characteristics. performance further evaluated independent set.Mann-Whitney U-test, Spearman correlation, least absolute shrinkage selection operator (LASSO) regression, receiver operating characteristic (ROC) analysis performed.Combining characteristics, six automatically selected to establish model metastasis. In set, area under ROC curve (AUC) 0.869 (NPV = 0.886). Using alone same procedure, 4 AUC 0.806 0.824).This first attempt demonstrate feasibility DCE-MRI cancer. Clinicopathologic improved performance. This study provides noninvasive methods evaluate guiding treatment patients, can potentially benefit those SLN, eliminating unnecessary removal associated complications, step towards precision medicine.1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:131-140.

Language: Английский

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