Multi-modality CADx: ROC study of the effect on radiologists' accuracy in characterizing breast masses on mammograms and 3D ultrasound images.
Rationale and Objectives: To investigate the effect of a computer-aided diagnosis (CADx) system on radiologists' performance in discriminating malignant and benign masses on mammograms and three-dimensional (3D) ultrasound (US) images.Materials and Methods: Our dataset contained mammograms and 3D US...
| Publicado en: | Academic Radiology Vol. 16; no. 7; pp. 810 - 819 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jul2009
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105352766&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105352766 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Jul2009 vid: 16 iid: 7 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 105352766 NLM19375953 2010310026 10.1016/j.acra.2009.01.011 NLM19375953 PMC2722036 105352766 ppf: 810 ppct: 9 formats: tig: atl: Multi-modality CADx: ROC study of the effect on radiologists' accuracy in characterizing breast masses on mammograms and 3D ultrasound images. aug: au: Sahiner B Chan HP Hadjiiski LM Roubidoux MA Paramagul C Bailey JE Nees AV Blane CE Adler DD Patterson SK Klein KA Pinsky RW Helvie MA Sahiner, Berkman Chan, Heang-Ping Hadjiiski, Lubomir M Roubidoux, Marilyn A Paramagul, Chintana Bailey, Janet E Nees, Alexis V affil: Department of Radiology, The University of Michigan, MIB C480A, 1500 East Medical Center Drive, Ann Arbor, MI 48109-5842, USA sug: subj: Breast Neoplasms Radiography Breast Neoplasms Ultrasonography Diagnostic Imaging Methods Image Interpretation, Computer Assisted Methods Mammography Methods Adult Aged Aged, 80 and Over Female Middle Age Observer Bias Reproducibility of Results ROC Curve Sensitivity and Specificity Subtraction Technique Human Adult: 19-44 years Aged: 65+ years Aged, 80 & over Middle Aged: 45-64 years Female ab: Rationale and Objectives: To investigate the effect of a computer-aided diagnosis (CADx) system on radiologists' performance in discriminating malignant and benign masses on mammograms and three-dimensional (3D) ultrasound (US) images.Materials and Methods: Our dataset contained mammograms and 3D US volumes from 67 women (median age, 51; range: 27-86) with 67 biopsy-proven breast masses (32 benign and 35 malignant). A CADx system was designed to automatically delineate the mass boundaries on mammograms and the US volumes, extract features, and merge the extracted features into a multi-modality malignancy score. Ten experienced readers (subspecialty academic breast imaging radiologists) first viewed the mammograms alone, and provided likelihood of malignancy (LM) ratings and Breast Imaging and Reporting System assessments. Subsequently, the reader viewed the US images with the mammograms, and provided LM and action category ratings. Finally, the CADx score was shown and the reader had the opportunity to revise the ratings. The LM ratings were analyzed using receiver-operating characteristic (ROC) methodology, and the action category ratings were used to determine the sensitivity and specificity of cancer diagnosis.Results: Without CADx, readers' average area under the ROC curve, A(z), was 0.93 (range, 0.86-0.96) for combined assessment of the mass on both the US volume and mammograms. With CADx, their average A(z) increased to 0.95 (range, 0.91-0.98), which was borderline significant (P = .05). The average sensitivity of the readers increased from 98% to 99% with CADx, while the average specificity increased from 27% to 29%. The change in sensitivity with CADx did not achieve statistical significance for the individual radiologists, and the change in specificity was statistically significant for one of the radiologists.Conclusions: A well-trained CADx system that combines features extracted from mammograms and US images may have the potential to improve radiologists' performance in distinguishing malignant from benign breast masses and making decisions about biopsies. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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