Sara Lewis1,2, Steven Peti3,4, Stefanie J Hectors4, Michael King3, Ally Rosen3, Amita Kamath3, Juan Putra5, Swan Thung5, Bachir Taouli3,4. 1. Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA. sara.lewis@mountsinai.org. 2. Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY, USA. sara.lewis@mountsinai.org. 3. Department of Radiology, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY, 10029, USA. 4. Translational and Molecular Imaging Institute (TMII), Icahn School of Medicine at Mount Sinai, New York, NY, USA. 5. Department of Pathology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Abstract
OBJECTIVE: To evaluate the ability of volumetric quantitative apparent diffusion coefficient (ADC) histogram parameters and LI-RADS categorization to distinguish hepatocellular carcinoma (HCC) from other primary liver cancers [intrahepatic cholangiocarcinoma (ICC) and combined HCC-ICC]. METHODS: Sixty-three consecutive patients (44 M/19F; mean age 62 years) with primary liver cancers and pre-treatment MRI including diffusion-weighted imaging (DWI) were included in this IRB-approved single-center retrospective study. Tumor type was categorized pathologically. Qualitative tumor features and LI-RADS categorization were assessed by 2 independent observers. Lesion volume of interest measurements (VOIs) were placed on ADC maps to extract first-order radiomics (histogram) features. ADC histogram metrics and qualitative findings were compared. Binary logistic regression and AUROC were used to assess performance for distinction of HCC from ICC and combined tumors. RESULTS: Sixty-five lesions (HCC, n = 36; ICC, n = 17; and combined tumor, n = 12) were assessed. Only enhancement pattern (p < 0.015) and capsule were useful for tumor diagnosis (p < 0.014). ADC 5th/10th/95th percentiles were significant for discrimination between each tumor types (all p values < 0.05). Accuracy of LI-RADS for HCC diagnosis was 76.9% (p < 0.0001) and 69.2% (p = 0.001) for both observers. The combination of male gender, LI-RADS, and ADC 5th percentile yielded an AUROC/sensitivity/specificity/accuracy of 0.90/79.3%/88.9%/81.5% and 0.89/86.2%/77.8%/80.0% (all p values < 0.027) for the diagnosis of HCC compared to ICC and combined tumors for both observers, respectively. CONCLUSION: The combination of quantitative ADC histogram parameters and LI-RADS categorization yielded the best prediction accuracy for distinction of HCC compared to ICC and combined HCC-ICC.
OBJECTIVE: To evaluate the ability of volumetric quantitative apparent diffusion coefficient (ADC) histogram parameters and LI-RADS categorization to distinguish hepatocellular carcinoma (HCC) from other primary liver cancers [intrahepatic cholangiocarcinoma (ICC) and combined HCC-ICC]. METHODS: Sixty-three consecutive patients (44 M/19F; mean age 62 years) with primary liver cancers and pre-treatment MRI including diffusion-weighted imaging (DWI) were included in this IRB-approved single-center retrospective study. Tumor type was categorized pathologically. Qualitative tumor features and LI-RADS categorization were assessed by 2 independent observers. Lesion volume of interest measurements (VOIs) were placed on ADC maps to extract first-order radiomics (histogram) features. ADC histogram metrics and qualitative findings were compared. Binary logistic regression and AUROC were used to assess performance for distinction of HCC from ICC and combined tumors. RESULTS: Sixty-five lesions (HCC, n = 36; ICC, n = 17; and combined tumor, n = 12) were assessed. Only enhancement pattern (p < 0.015) and capsule were useful for tumor diagnosis (p < 0.014). ADC 5th/10th/95th percentiles were significant for discrimination between each tumor types (all p values < 0.05). Accuracy of LI-RADS for HCC diagnosis was 76.9% (p < 0.0001) and 69.2% (p = 0.001) for both observers. The combination of male gender, LI-RADS, and ADC 5th percentile yielded an AUROC/sensitivity/specificity/accuracy of 0.90/79.3%/88.9%/81.5% and 0.89/86.2%/77.8%/80.0% (all p values < 0.027) for the diagnosis of HCC compared to ICC and combined tumors for both observers, respectively. CONCLUSION: The combination of quantitative ADC histogram parameters and LI-RADS categorization yielded the best prediction accuracy for distinction of HCC compared to ICC and combined HCC-ICC.
Authors: Artit Jirapatnakul; Anthony P Reeves; Sara Lewis; Xiangmeng Chen; Teng Ma; Rowena Yip; Xing Chin; Shuang Liu; Ponni V Perumalswami; David F Yankelevitz; Michael Crane; Andrea D Branch; Claudia I Henschke Journal: Eur J Radiol Date: 2019-10-25 Impact factor: 3.528
Authors: Gehad Ahmad Saleh; Ahmed Abdel Khalek Abdel Razek; Lamiaa Galal El-Serougy; Walaa Shabana; Rihame Abd El-Wahab Journal: Pol J Radiol Date: 2022-01-17