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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Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer

Clinical Cancer Research, Journal Year: 2018, Volume and Issue: 24(19), P. 4705 - 4714

Published: June 18, 2018

Abstract Purpose: To develop a radiomics signature based on preoperative MRI to estimate disease-free survival (DFS) in patients with invasive breast cancer and establish nomogram that incorporates the clinicopathological findings. Experimental Design: We identified 294 who underwent MRI. Patients were randomly divided into training (n = 194) validation 100) sets. A (Rad-score) was generated using an elastic net set, cutoff point of divide high- low-risk groups determined receiver-operating characteristic curve analysis. Univariate multivariate Cox proportional hazards model Kaplan–Meier analysis used determine association signature, findings, variables DFS. combining Rad-score findings constructed validate radiomic signatures for individualized DFS estimation. Results: Higher Rad-scores significantly associated worse both sets (P 0.002 0.036, respectively). The estimated [C-index, 0.76; 95% confidence interval (CI); 0.74–0.77] better than (C-index, 0.72; CI, 0.70–0.74) or Rad-score–only nomograms 0.67; 0.65–0.69). Conclusions: is independent biomarker estimation cancer. Combining improved Clin Cancer Res; 24(19); 4705–14. ©2018 AACR.

Language: Английский

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Reproducibility of CT Radiomic Features within the Same Patient: Influence of Radiation Dose and CT Reconstruction Settings

Radiology, Journal Year: 2019, Volume and Issue: 293(3), P. 583 - 591

Published: Oct. 1, 2019

Background Results of recent phantom studies show that variation in CT acquisition parameters and reconstruction techniques may make radiomic features largely nonreproduceable limited use for prognostic clinical studies. Purpose To investigate the effect radiation dose settings on reproducibility features, as well to identify correction factors mitigating these sources variability. Materials Methods This was a secondary analysis prospective study metastatic liver lesions patients who underwent staging with single-energy dual-source contrast material–enhanced between September 2011 April 2012. Technique were altered, resulting 28 data sets per patient included different levels, section thicknesses, kernels, algorithm settings. By using training set (n = 76), reproducible intensity, shape, texture (reproducibility threshold, R2 ≥ 0.95) selected calculated by linear model convert each feature its estimated value reference technique. test 75), hierarchical clustering based 106 measured assessed. Data 78 (mean age, 60 years ± 10; 33 women) 151 included. The percentage deemed any technical 11% (12 106). Of all parameters, reconstructed thickness had largest impact (12.3% [13 106]) if only one parameter changed while other kept constant. results cluster showed improved when dedicated used (ρ 0.39–0.71 vs ρ 0.14–0.47). Conclusion Most are highly affected settings, point being nonreproducible. Selecting along study-specific offers reproducibility. © RSNA, 2019 Online supplemental material is available this article. See also editorial Sosna issue.

Language: Английский

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Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

European Radiology, Journal Year: 2018, Volume and Issue: 29(3), P. 1211 - 1220

Published: Aug. 20, 2018

Language: Английский

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