ArticleJournal of medical imaging (Bellingham, Wash.)2015
Core samples for radiomics features that are insensitive to tumor segmentation: method and pilot study using CT images of hepatocellular carcinoma.
Article in Journal of medical imaging (Bellingham, Wash.), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Performance of alternative manual and automated deep learning segmentation techniques for the prediction of benign and malignant lung nodules.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Radiomics quality score in renal masses: a systematic assessment on current literature.The British journal of radiology · 2022Review
- Precision of MRI radiomics features in the liver and hepatocellular carcinoma.European radiology · 2022Article
- Study on the changes of CT texture parameters before and after HCC treatment in the efficacy evaluation and survival predication of patients with HCC.Frontiers in oncology · 2022Article
- Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review.Quantitative imaging in medicine and surgery · 2021Review
- Quantitative image features from radiomic biopsy differentiate oncocytoma from chromophobe renal cell carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2021Article
- Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2021Article
- Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.European journal of cancer (Oxford, England : 1990) · 2021Article
- Identification of robust and reproducible CT-texture metrics using a customized 3D-printed texture phantom.Journal of applied clinical medical physics · 2021Article
- The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.Frontiers in oncology · 2021Review
- In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition.Diagnostics (Basel, Switzerland) · 2020Article
- [Research progress of radiomics with artificial intelligence in precise diagnosis and treatment of hepatocellular carcinoma].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2020Article
- The application of texture quantification in hepatocellular carcinoma using CT and MRI: a review of perspectives and challenges.Cancer imaging : the official publication of the International Cancer Imaging Society · 2020Review
- Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence.Investigative radiology · 2020Review
- Machine and deep learning methods for radiomics.Medical physics · 2020Review
- Multisite Technical and Clinical Performance Evaluation of Quantitative Imaging Biomarkers from 3D FDG PET Segmentations of Head and Neck Cancer Images.Tomography (Ann Arbor, Mich.) · 2020Article
- Deep learning-based segmentation of breast masses in dedicated breast CT imaging: Radiomic feature stability between radiologists and artificial intelligence.Computers in biology and medicine · 2020Article
- Predicting the tumor response to chemoradiotherapy for rectal cancer: Model development and external validation using MRI radiomics.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2020Article
- Association of bone mineral density with bone texture attributes extracted using routine magnetic resonance imaging.Clinics (Sao Paulo, Brazil) · 2020Article
- Radiomics approach to distinguish between well differentiated liposarcomas and lipomas on MRI.The British journal of surgery · 2019Article
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Authors and funding
7 authors.
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Abstract
The purpose of this study is to investigate the utility of obtaining "core samples" of regions in CT volume scans for extraction of radiomic features. We asked four readers to outline tumors in three representative slices from each phase of multiphasic liver CT images taken from 29 patients (1128 segmentations) with hepatocellular carcinoma. Core samples were obtained by automatically tracing the maximal circle inscribed in the outlines. Image features describing the intensity, texture, shape, and margin were used to describe the segmented lesion. We calculated the intraclass correlation between the features extracted from the readers' segmentations and their core samples to characterize robustness to segmentation between readers, and between human-based segmentation and core sampling. We conclude that despite the high interreader variability in manually delineating the tumor (average overlap of 43% across all readers), certain features such as intensity and texture features are robust to segmentation. More importantly, this same subset of features can be obtained from the core samples, providing as much information as detailed segmentation while being simpler and faster to obtain.
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