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CT colonography: preliminary assessment of a double-read paradigm that uses computer-aided detection as the first reader.
No full textPURPOSE:To compare diagnostic performance and time efficiency of double-reading first-reader computer-aided detection (CAD) (DR FR CAD) followed by radiologist interpretation with that of an unassisted read using segmentally unblinded colonoscopy as reference standard.
MATERIALS AND METHODS:
The local ethical committee approved this study. Written consent to use examinations was obtained from patients. Three experienced radiologists searched for polyps 6 mm or larger in 155 computed tomographic (CT) colonographic studies (57 containing 10 masses and 79 polyps ≥ 6 mm). Reading was randomized to either unassisted read or DR FR CAD. Data sets were reread 6 weeks later by using the opposite paradigm. DR FR CAD consists of evaluation of CAD prompts, followed by fast two-dimensional review for mass detection. CAD sensitivity was calculated. Readers' diagnoses and reviewing times with and without CAD were compared by using McNemar and Student t tests, respectively. Association between missed polyps and lesion characteristics was explored with multiple regression analysis.
RESULTS:
With mean rate of 19 (standard deviation, 14; median, 15; range, 4-127) false-positive results per patient, CAD sensitivity was 90% for lesions 6 mm or larger. Readers' sensitivity and specificity for lesions 6 mm or larger were 74% (95% confidence interval [CI]: 65%, 84%) and 93% (95% CI: 89%, 97%), respectively, for the unassisted read and 77% (95% CI: 67%, 85%) and 90% (95% CI: 85%, 95%), respectively, for DR FR CAD (P = .343 and P = .189, respectively). Overall unassisted and DR FR CAD reviewing times were similar (243 vs 239 seconds; P = .623); DR FR CAD was faster when the number of CAD marks per patient was 20 or fewer (187 vs 220 seconds, P <01). Odds ratio of missing a polyp with CAD decreased as polyp size increased (0.6) and for polyps visible on both prone and supine scans (0.12); it increased for flat lesions (9.1).
CONCLUSION:
DR FR CAD paradigm had similar performance compared with unassisted interpretation but better time efficiency when 20 or fewer CAD prompts per patient were generated
EuroPACS/IFCARS/MIR Joint Session on Image Processing Workflow and Management in the Clinical Practice
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Panel Discussion on the roles: the radiologist, the surgeon, the computer scientist, the economist
No full textAdversarial radiomics: the rising of potential risks in medical imaging from adversarial learning
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Integration of imaging biomarkers into systems biomedicine: a renaissance for medical imaging
Full text linkSystems biomedicine consists in the integration of biosciences, medicine and computer sciences. Systems biomedicine is supposed to allow a holistic approach to the human subject and its disease states. This paper outlines the basic concepts and open issues in this field and provides an outlook for the integration of medical imaging procedures in the growing area of systems biomedicine. The terms “Systems biomedicine”, “Systems medicine” were used for bibliographic search in Pubmed and Web of sciences. Most relevant papers were selected for inclusion in this paper; a synthesis of the papers is presented. An integration of methods is required to best exploit the potential of the multi-‘omics biobanks, in which imaging biomarker data represent an added value. To obtain such integration, imaging biomarker data from different “systems” should be in a manageable format. The recent evolution of AI and the hardware improvements by parallel and fast computing are bringing us towards a new age of molecular and morphologic imaging. Although there will always be a qualitative aspect to imaging, AI and quantitative metrics will supplement and complement the current “human” methods of interpretation of imaging data in a holistic approach to individual patient management
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