Laurent Dercle1, Jingchen Ma2, Chuanmiao Xie3, Ai-Ping Chen2, Deling Wang3, Lyndon Luk2, Paul Revel-Mouroz4, Philippe Otal4, Jean-Marie Peron5, Hervé Rousseau4, Lin Lu2, Lawrence H Schwartz2, Fatima-Zohra Mokrane6, Binsheng Zhao2. 1. Columbia University Vagellos College of Physicians and Surgeons, Department of Radiology, New York, New York City, USA; Department of Radiology New York Presbyterian Hospital, USA. Electronic address: ld2752@cumc.columbia.edu. 2. Columbia University Vagellos College of Physicians and Surgeons, Department of Radiology, New York, New York City, USA; Department of Radiology New York Presbyterian Hospital, USA. 3. Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060, China. 4. Radiology Department, Rangueil University Hospital, Toulouse, France. 5. Hepatology Department, Purpan University Hospital, Toulouse, France. 6. Columbia University Vagellos College of Physicians and Surgeons, Department of Radiology, New York, New York City, USA; Department of Radiology New York Presbyterian Hospital, USA; Radiology Department, Rangueil University Hospital, Toulouse, France.
Abstract
PURPOSE: The clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for high quality contrast-enhancement in medical images. We aimed to develop a machine-learning algorithm for Quality Control of Contrast-Enhancement on CT-scan (CECT-QC). METHOD: Multicenter data from four independent cohorts [A, B, C, D] of patients with measurable liver lesions were analyzed retrospectively (patients:time-points; 503:3397): [A] dynamic CTs from primary liver cancer (60:2359); [B] triphasic CTs from primary liver cancer (31:93); [C] triphasic CTs from hepatocellular carcinoma (121:363); [D] portal venous phase CTs of liver metastasis from colorectal cancer (291:582). Patients from cohort A were randomized to training-set (48:1884) and test-set (12:475). A random forest classifier was trained and tested to identify five contrast-enhancement phases. The input was the mean intensity of the abdominal aorta and the portal vein measured on a single abdominal CT scan image at a single time-point. The output to be predicted was: non-contrast [NCP], early-arterial [E-AP], optimal-arterial [O-AP], optimal-portal [O-PVP], and late-portal [L-PVP]. Clinical utility was assessed in cohorts B, C, and D. RESULTS: The CECT-QC algorithm showed performances of 98 %, 90 %, and 84 % for predicting NCP, O-AP, and O-PVP, respectively. O-PVP was reached in half of patients and was associated with a peak in liver malignancy density. Contrast-enhancement quality significantly influenced radiomics features deciphering the phenotype of liver neoplasms. CONCLUSIONS: A single CT-image can be used to differentiate five contrast-enhancement phases for radiomics-based precision medicine in the most common liver neoplasms occurring in patients with or without liver cirrhosis.
PURPOSE: The clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for high quality contrast-enhancement in medical images. We aimed to develop a machine-learning algorithm for Quality Control of Contrast-Enhancement on CT-scan (CECT-QC). METHOD: Multicenter data from four independent cohorts [A, B, C, D] of patients with measurable liver lesions were analyzed retrospectively (patients:time-points; 503:3397): [A] dynamic CTs from primary liver cancer (60:2359); [B] triphasic CTs from primary liver cancer (31:93); [C] triphasic CTs from hepatocellular carcinoma (121:363); [D] portal venous phase CTs of liver metastasis from colorectal cancer (291:582). Patients from cohort A were randomized to training-set (48:1884) and test-set (12:475). A random forest classifier was trained and tested to identify five contrast-enhancement phases. The input was the mean intensity of the abdominal aorta and the portal vein measured on a single abdominal CT scan image at a single time-point. The output to be predicted was: non-contrast [NCP], early-arterial [E-AP], optimal-arterial [O-AP], optimal-portal [O-PVP], and late-portal [L-PVP]. Clinical utility was assessed in cohorts B, C, and D. RESULTS: The CECT-QC algorithm showed performances of 98 %, 90 %, and 84 % for predicting NCP, O-AP, and O-PVP, respectively. O-PVP was reached in half of patients and was associated with a peak in liver malignancy density. Contrast-enhancement quality significantly influenced radiomics features deciphering the phenotype of liver neoplasms. CONCLUSIONS: A single CT-image can be used to differentiate five contrast-enhancement phases for radiomics-based precision medicine in the most common liver neoplasms occurring in patients with or without liver cirrhosis.
Authors: Robert S Benjamin; Haesun Choi; Homer A Macapinlac; Michael A Burgess; Shreyaskumar R Patel; Lei L Chen; Donald A Podoloff; Chuslip Charnsangavej Journal: J Clin Oncol Date: 2007-05-01 Impact factor: 44.544