Wang et al., 2020 - Google Patents
External validation of a mammographic texture marker for breast cancer risk in a case–control studyWang et al., 2020
View PDF- Document ID
- 9820977242418325339
- Author
- Wang C
- Brentnall A
- Mainprize J
- Yaffe M
- Cuzick J
- Harvey J
- Publication year
- Publication venue
- Journal of Medical Imaging
External Links
Snippet
Purpose: The pattern of dense tissue on a mammogram appears to provide additional information than overall density for risk assessment, but there has been little consistency in measures of texture identified. The purpose of this study is thus to validate a mammographic …
- 206010006187 Breast cancer 0 title abstract description 34
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
- G06F19/328—Health insurance management, e.g. payments or protection against fraud
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/53—Immunoassay; Biospecific binding assay
- G01N33/574—Immunoassay; Biospecific binding assay for cancer
- G01N33/57407—Specifically defined cancers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by transmitting the radiation through the material
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Yang et al. | Development and validation of deep learning algorithms for scoliosis screening using back images | |
Conant et al. | Breast cancer screening using tomosynthesis in combination with digital mammography compared to digital mammography alone: a cohort study within the PROSPR consortium | |
Chang et al. | Multi-institutional assessment and crowdsourcing evaluation of deep learning for automated classification of breast density | |
Brentnall et al. | A case-control study to add volumetric or clinical mammographic density into the Tyrer-Cuzick breast cancer risk model | |
Ha et al. | Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography | |
Ueda et al. | Artificial intelligence-supported lung cancer detection by multi-institutional readers with multi-vendor chest radiographs: a retrospective clinical validation study | |
Bocchino et al. | Performance of a new quantitative computed tomography index for interstitial lung disease assessment in systemic sclerosis | |
Sovio et al. | Comparison of fully and semi-automated area-based methods for measuring mammographic density and predicting breast cancer risk | |
Jeffreys et al. | Initial experiences of using an automated volumetric measure of breast density: the standard mammogram form | |
Hong et al. | Bone radiomics score derived from DXA hip images enhances hip fracture prediction in older women | |
Warner et al. | Automated percent mammographic density, mammographic texture variation, and risk of breast cancer: a nested case-control study | |
Folle et al. | Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density | |
CN116324874A (en) | Artificial intelligence prediction for prognosis of prostate cancer | |
Weiss et al. | Deep learning to estimate lung disease mortality from chest radiographs | |
Yuan et al. | Prognostic Impact of the Findings on Thin-Section Computed Tomography in stage I lung adenocarcinoma with visceral pleural invasion | |
Cui et al. | Predicting shockwave lithotripsy outcome for urolithiasis using clinical and stone computed tomography texture analysis variables | |
Albuquerque et al. | Osteoporosis screening using machine learning and electromagnetic waves | |
Sansone et al. | Radiomic features of breast parenchyma: assessing differences between FOR PROCESSING and FOR PRESENTATION digital mammography | |
Saillard et al. | Blind validation of MSIntuit, an AI-based pre-screening tool for MSI detection from histology slides of colorectal cancer | |
Tari et al. | Breast density evaluation according to BI-RADS 5th edition on digital breast tomosynthesis: AI automated assessment versus human visual assessment | |
Lim et al. | Validation for measurements of skeletal muscle areas using low-dose chest computed tomography | |
Tsai et al. | Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk | |
Fuhrman et al. | Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning | |
Wang et al. | External validation of a mammographic texture marker for breast cancer risk in a case–control study | |
Van Den Oever et al. | Qualitative evaluation of common quantitative metrics for clinical acceptance of automatic segmentation: a case study on heart contouring from CT images by deep learning algorithms |