ArticleTomography (Ann Arbor, Mich.)2016
A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.
Article in Tomography (Ann Arbor, Mich.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Promoting the application of pediatric radiomics via an integrated medical engineering approach.Cancer innovation · 2023Review
- 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
- Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives.Frontiers in oncology · 2023Review
- Multiplexed imaging in oncology.Nature biomedical engineering · 2022Review
- Role of CT Images in the Diagnosis of Common Acute Abdominal Diseases in General Surgery.Journal of healthcare engineering · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- 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
- 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
- NCTN Assessment on Current Applications of Radiomics in Oncology.International journal of radiation oncology, biology, physics · 2019Review
- ePAD: An Image Annotation and Analysis Platform for Quantitative Imaging.Tomography (Ann Arbor, Mich.) · 2019Article
- A Comparative Texture Analysis Based on NECT and CECT Images to Differentiate Lung Adenocarcinoma from Squamous Cell Carcinoma.Journal of medical systems · 2019Article
- Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.Cancer · 2018Review
- Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images.Journal of digital imaging · 2018Article
- Quantitative Image Feature Engine (QIFE): an Open-Source, Modular Engine for 3D Quantitative Feature Extraction from Volumetric Medical Images.Journal of digital imaging · 2018Review
- Translational Radiomics: Defining the Strategy Pipeline and Considerations for Application-Part 2: From Clinical Implementation to Enterprise.Journal of the American College of Radiology : JACR · 2018Review
- Digital liver biopsy: Bio-imaging of fatty liver for translational and clinical research.World journal of hepatology · 2018Review
- A review of automatic lung tumour segmentation in the era of 4DCT.Reports of practical oncology and radiotherapy : journal of Greatpoland Cancer Center in Poznan and Polish Society of Radiation OncologyReview
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7 authors.
Funding
Abstract
Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required.
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