Radiomics in Medical Imaging—Detection, Extraction and Segmentation

Tian, Jie; Dong, Di; Liu, Zhenyu; Zang, Yali; Wei, Jingwei; Song, Jiangdian; Mu, Wei; Wang, Shuo; Zhou, Mu

doi:10.1007/978-3-319-68843-5_11

Cited by 5 publications

(2 citation statements)

References 163 publications

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“…Large quantities of high-level (or synthetic) features from radiomics studies significantly impede clinical interpretation. In this regard, the establishment of a knowledge database has been suggested to define high-level tumor imaging characteristic for enriching interpretation in medical centers (Tian et al 2018). Another concern that arises from radiomics is the reproducibility of results that depend on a number of factors, including technical complexity, study design, imaging parameters, and standardization for validation study.…”

Section: Discussionmentioning

confidence: 99%

Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker

Zargari

Heidari

et al. 2018

Phys. Med. Biol.

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This study aimed to investigate the feasibility of integrating image features computed from both spatial and frequency domain to better describe the tumor heterogeneity for precise prediction of tumor response to postsurgical chemotherapy in patients with advanced-stage ovarian cancer. A computer-aided scheme was applied to first compute 133 features from five categories namely, shape and density, fast Fourier transform, discrete cosine transform (DCT), wavelet, and gray level difference method. An optimal feature cluster was then determined by the scheme using the particle swarm optimization algorithm aiming to achieve an enhanced discrimination power that was unattainable with the single features. The scheme was tested using a balanced dataset (responders and non-responders defined using 6 month PFS) retrospectively collected from 120 ovarian cancer patients. By evaluating the performance of the individual features among the five categories, the DCT features achieved the highest predicting accuracy than the features in other groups. By comparison, a quantitative image marker generated from the optimal feature cluster yielded the area under ROC curve (AUC) of 0.86, while the top performing single feature only had an AUC of 0.74. Furthermore, it was observed that the features computed from the frequency domain were as important as those computed from the spatial domain. In conclusion, this study demonstrates the potential of our proposed new quantitative image marker fused with the features computed from both spatial and frequency domain for a reliable prediction of tumor response to postsurgical chemotherapy.

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Section: Discussionmentioning

confidence: 99%

Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker

Zargari

Heidari

et al. 2018

Phys. Med. Biol.

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show abstract

“…This process converted medical imaging data into a highly structured, multi-dimensional dataset that is crucial for the quantification and characterization of liver fibrosis. 25 For uniformity and to ensure the reliability of the extraction process across different scans, all images underwent resampling to a uniform resolution of 1×1 mm via nearest-neighbor interpolation. From each identified ROI, we extracted a detailed suite of 849 features.…”

Section: Radiomics Feature Extractionmentioning

confidence: 99%

Application of Interpretable Machine Learning Models Based on Ultrasonic Radiomics for Predicting the Risk of Fibrosis Progression in Diabetic Patients with Nonalcoholic Fatty Liver Disease

Meng,

Wu,

Zhang

et al. 2023

DMSO

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Patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes mellitus (T2DM) face a significant risk of hepatic fibrosis. Liver stiffness measurement (LSM) is commonly used to exclude advanced fibrosis, but its effectiveness in predicting fibrosis progression, especially in initially fibrosis-free patients, remains under-investigated. Although radiomics and machine learning (ML) models show promise in interpreting intricate data and predicting clinical outcomes, their application in assessing the fibrosis progression risk has not been fully explored. This study aimed to address this gap by developing and validating ML-based models to identify patients at risk of fibrosis progression using clinical data and multimodal radiomics features, thereby enhancing NAFLD and T2DM management. Methods: The study involved a retrospective analysis of 618 diabetic patients with NAFLD. These patients were divided into training and external validation cohorts. Based on LSM values, patients were classified into "Low-risk" and "Fibrosis-risk" groups. Radiomics features from multimodal ultrasound imaging were extracted, standardized, and utilized to develop various ML models. The models were internally validated based on these radiomics or clinical data, and the optimal model's feature importance was analyzed using the Shapley Additive Explanations (SHAP) approach, followed by external validation. Results: Of the 618 patients, 18.1% demonstrated an LSM≥6.5kPa, indicating a higher risk of hepatic fibrosis. The study identified 25 significant fibrosis-related radiomics features, with the support vector machine (SVM) model demonstrating superior performance in both internal and external validations. The SHAP analysis identified five key determinants of fibrosis risk, which included three radiomics features from shear wave elastography (SWE) and two from grayscale imaging. Conclusion:This study demonstrates the utility of an SVM model based on radiomics features derived from SWE and grayscale imaging for predicting fibrosis progression in diabetic patients with NAFLD, thereby enabling timely and effective therapeutic interventions.

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Using Commercial and Open-Source Tools for Artificial Intelligence: A Case Demonstration on a Complete Radiomics Pipeline

Stamoulou,

Spanakis,

Nikiforaki

et al. 2023

Imaging Informatics for Healthcare Professionals

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Radiomics in Medical Imaging—Detection, Extraction and Segmentation

Cited by 5 publications

References 163 publications

Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker

Prediction of chemotherapy response in ovarian cancer patients using a new clustered quantitative image marker

Application of Interpretable Machine Learning Models Based on Ultrasonic Radiomics for Predicting the Risk of Fibrosis Progression in Diabetic Patients with Nonalcoholic Fatty Liver Disease

Using Commercial and Open-Source Tools for Artificial Intelligence: A Case Demonstration on a Complete Radiomics Pipeline

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