Literature DB >> 23151831

Efficacy of computer-aided detection as a second reader for 6-9-mm lesions at CT colonography: multicenter prospective trial.

Daniele Regge1, Patrizia Della Monica, Giovanni Galatola, Cristiana Laudi, Antonella Zambon, Loredana Correale, Roberto Asnaghi, Brunella Barbaro, Claudia Borghi, Delia Campanella, Maria Carla Cassinis, Riccardo Ferrari, Andrea Ferraris, Cesare Hassan, Rita Golfieri, Franco Iafrate, Gabriella Iussich, Andrea Laghi, Roberto Massara, Emanuele Neri, Lapo Sali, Silvia Venturini, Giovanni Gandini.   

Abstract

PURPOSE: To assess the effect of computer-aided detection (CAD) as a second reader on the sensitivity and specificity of computed tomographic (CT) colonography in detecting 6-9-mm colorectal cancer (CRC) lesions.
MATERIALS AND METHODS: Individuals with clinical indications for colonoscopy--either for symptoms or as part of participating in a surveillance program or CRC screening--were prospectively enrolled at one of 10 academic centers between July 2007 and May 2009. Institutional review board approval was obtained at each clinical site, and all participants provided written informed consent. All participants underwent CT colonography and colonoscopy on the same day. Experienced readers interpreted the CT colonography images unassisted and then reviewed all colorectal lesion-like structures pinpointed by the CAD algorithm. Segmental unblinding of CT colonoscopy findings at colonoscopy was utilized. The sensitivity and specificity of unassisted and CAD-assisted reading in identifying individuals with 6-9-mm lesions were calculated and compared by means of pairwise analysis.
RESULTS: A total of 618 participants (mean age, 57.9 years; 54.5% male) were included in the final analysis. Of these participants, 464 (75.1%) had no lesions 6 mm or larger, and 52 (8.4%) had 6-9-mm lesions. The sensitivity of CT colonography with unassisted reading and that with CAD-assisted reading in identifying individuals with 6-9-mm lesions was 65.4% (95% confidence interval [CI]: 50.9%, 78.0%) and 76.9% (95% CI: 63.2%, 87.5%; P = .016), respectively. No significant change in specificity was observed: The specificity of CT colonography with unassisted and that with CAD-assisted reading was 91.8% (95% CI: 88.9%, 94.1%) and 90.9% (95% CI: 88.0%, 93.4%; P = .063), respectively. Evaluation of CAD candidates required an additional 1.6 minutes (25th-75th percentile: 1.0 minute to 3.4 minutes).
CONCLUSION: The addition of CAD to reading performed by experienced readers resulted in a significant benefit in the detection of 6-9-mm polyps at CT colonography in this cohort. SUPPLEMENTAL MATERIAL: http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12120376/-/DC1. RSNA, 2012

Entities:  

Mesh:

Year:  2012        PMID: 23151831     DOI: 10.1148/radiol.12120376

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  14 in total

1.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

2.  Computer-based self-training for CT colonography with and without CAD.

Authors:  Lapo Sali; Silvia Delsanto; Daniela Sacchetto; Loredana Correale; Massimo Falchini; Andrea Ferraris; Giovanni Gandini; Giulia Grazzini; Franco Iafrate; Gabriella Iussich; Lia Morra; Andrea Laghi; Mario Mascalchi; Daniele Regge
Journal:  Eur Radiol       Date:  2018-05-23       Impact factor: 5.315

Review 3.  Imaging-based screening: maximizing benefits and minimizing harms.

Authors:  Jessica C Germino; Joann G Elmore; Ruth C Carlos; Christoph I Lee
Journal:  Clin Imaging       Date:  2015-06-12       Impact factor: 1.605

Review 4.  CT colonography with computer-aided detection: recognizing the causes of false-positive reader results.

Authors:  Igor Trilisky; Kristen Wroblewski; Michael W Vannier; John M Horne; Abraham H Dachman
Journal:  Radiographics       Date:  2014 Nov-Dec       Impact factor: 5.333

Review 5.  Computed tomography colonography for the practicing radiologist: A review of current recommendations on methodology and clinical indications.

Authors:  Paola Scalise; Annalisa Mantarro; Francesca Pancrazi; Emanuele Neri
Journal:  World J Radiol       Date:  2016-05-28

6.  Multiparametric magnetic resonance imaging of the prostate with computer-aided detection: experienced observer performance study.

Authors:  Valentina Giannini; Simone Mazzetti; Enrico Armando; Silvia Carabalona; Filippo Russo; Alessandro Giacobbe; Giovanni Muto; Daniele Regge
Journal:  Eur Radiol       Date:  2017-04-06       Impact factor: 5.315

7.  CT colonography: effect of computer-aided detection of colonic polyps as a second and concurrent reader for general radiologists with moderate experience in CT colonography.

Authors:  Thomas Mang; Luca Bogoni; Vikram X Anand; Dass Chandra; Andrew J Curtin; Anna S Lev-Toaff; Gerardo Hermosillo; Ralph Noah; Vikas Raykar; Marcos Salganicoff; Robert Shaw; Susan Summerton; Rafel F R Tappouni; Helmut Ringel; Michael Weber; Matthias Wolf; Nancy A Obuchowski
Journal:  Eur Radiol       Date:  2014-05-10       Impact factor: 5.315

Review 8.  CT colonography for population screening of colorectal cancer: hints from European trials.

Authors:  Lapo Sali; Daniele Regge
Journal:  Br J Radiol       Date:  2016-09-14       Impact factor: 3.039

9.  The effect of computer-aided detection markers on visual search and reader performance during concurrent reading of CT colonography.

Authors:  Emma Helbren; Thomas R Fanshawe; Peter Phillips; Susan Mallett; Darren Boone; Alastair Gale; Douglas G Altman; Stuart A Taylor; David Manning; Steve Halligan
Journal:  Eur Radiol       Date:  2015-01-12       Impact factor: 5.315

10.  Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers.

Authors:  Valentina Giannini; Simone Mazzetti; Giovanni Cappello; Valeria Maria Doronzio; Lorenzo Vassallo; Filippo Russo; Alessandro Giacobbe; Giovanni Muto; Daniele Regge
Journal:  Diagnostics (Basel)       Date:  2021-05-28
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.