Wenqi Shi1, Sichi Kuang1, Sue Cao1, Bing Hu1, Sidong Xie1, Simin Chen1, Yinan Chen2, Dashan Gao3, Yunqiang Chen3, Yajing Zhu2, Hanxi Zhang1, Hui Liu2, Meng Ye2, Claude B Sirlin4, Jin Wang5. 1. Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, China. 2. 12 Sigma Technologies, NO. 420 Fenglin Road, Xuhui District, Shanghai, China. 3. 12 Sigma Technologies, 11975 El Camino Real, Suite 102, San Diego, CA, 92130, USA. 4. Liver Imaging Group, Department of Radiology, University of California at San Diego, Medical Center Drive, La Jolla, CA, 92037, USA. 5. Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Road, Tianhe District, Guangzhou, China. wangjin3@mail.sysu.edu.cn.
Abstract
PURPOSE: To evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in differentiating hepatocellular carcinoma (HCC) from other focal liver lesions (FLLs) compared with a four-phase protocol. METHODS: Three hundred and forty-two patients (mean age 49.1 ± 10.5 years, range 19-86 years, 65.8% male) scanned with a four-phase CT protocol (precontrast, arterial, portal-venous and delayed phases) were retrospectively enrolled. A total of 449 FLLs were categorized into HCC and non-HCC groups based on the best available reference standard. Three convolutional dense networks (CDNs) with the input of four-phase CT images (model A), three-phase images without portal-venous phase (model B) and three-phase images without precontrast phase (model C) were trained on 80% of lesions and evaluated in the other 20% by receiver operating characteristics (ROC) and confusion matrix analysis. The DeLong test was performed to compare the areas under the ROC curves (AUCs) of A with B, B with C, and A with C. RESULTS: The diagnostic accuracy in differentiating HCC from other FLLs on test sets was 83.3% for model A, 81.1% for model B and 85.6% for model C, and the AUCs were 0.925, 0.862 and 0.920, respectively. The AUCs of models A and C did not differ significantly (p = 0.765), but the AUCs of models A and B (p = 0.038) and of models B and C (p = 0.028) did. CONCLUSIONS: When combined with a CDN, a three-phase CT protocol without precontrast showed similar diagnostic accuracy as a four-phase protocol in differentiating HCC from other FLLs, suggesting that the multiphase CT protocol for HCC diagnosis might be optimized by removing the precontrast phase to reduce radiation dose.
PURPOSE: To evaluate whether a three-phase dynamic contrast-enhanced CT protocol, when combined with a deep learning model, has similar accuracy in differentiating hepatocellular carcinoma (HCC) from other focal liver lesions (FLLs) compared with a four-phase protocol. METHODS: Three hundred and forty-two patients (mean age 49.1 ± 10.5 years, range 19-86 years, 65.8% male) scanned with a four-phase CT protocol (precontrast, arterial, portal-venous and delayed phases) were retrospectively enrolled. A total of 449 FLLs were categorized into HCC and non-HCC groups based on the best available reference standard. Three convolutional dense networks (CDNs) with the input of four-phase CT images (model A), three-phase images without portal-venous phase (model B) and three-phase images without precontrast phase (model C) were trained on 80% of lesions and evaluated in the other 20% by receiver operating characteristics (ROC) and confusion matrix analysis. The DeLong test was performed to compare the areas under the ROC curves (AUCs) of A with B, B with C, and A with C. RESULTS: The diagnostic accuracy in differentiating HCC from other FLLs on test sets was 83.3% for model A, 81.1% for model B and 85.6% for model C, and the AUCs were 0.925, 0.862 and 0.920, respectively. The AUCs of models A and C did not differ significantly (p = 0.765), but the AUCs of models A and B (p = 0.038) and of models B and C (p = 0.028) did. CONCLUSIONS: When combined with a CDN, a three-phase CT protocol without precontrast showed similar diagnostic accuracy as a four-phase protocol in differentiating HCC from other FLLs, suggesting that the multiphase CT protocol for HCC diagnosis might be optimized by removing the precontrast phase to reduce radiation dose.
Authors: Cynthia H McCollough; Andrew N Primak; Natalie Braun; James Kofler; Lifeng Yu; Jodie Christner Journal: Radiol Clin North Am Date: 2009-01 Impact factor: 2.303
Authors: Nicolas Coudray; Paolo Santiago Ocampo; Theodore Sakellaropoulos; Navneet Narula; Matija Snuderl; David Fenyö; Andre L Moreira; Narges Razavian; Aristotelis Tsirigos Journal: Nat Med Date: 2018-09-17 Impact factor: 53.440
Authors: Holger Wenz; Máté E Maros; Mathias Meyer; Joshua Gawlitza; Alex Förster; Holger Haubenreisser; Stefan Kurth; Stefan O Schoenberg; Christoph Groden; Thomas Henzler Journal: Eur J Radiol Open Date: 2016-07-26