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Using a single abdominal computed tomography image to differentiate five contrast-enhancement phases: A machine-learning algorithm for radiomics-based precision medicine.
Dercle, Laurent; Ma, Jingchen; Xie, Chuanmiao; Chen, Ai-Ping; Wang, Deling; Luk, Lyndon; Revel-Mouroz, Paul; Otal, Philippe; Peron, Jean-Marie; Rousseau, Hervé; Lu, Lin; Schwartz, Lawrence H; Mokrane, Fatima-Zohra; Zhao, Binsheng.
Afiliación
  • Dercle L; 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.
  • Ma J; 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.
  • Xie C; 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.
  • Chen AP; 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.
  • Wang D; 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.
  • Luk L; 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.
  • Revel-Mouroz P; Radiology Department, Rangueil University Hospital, Toulouse, France.
  • Otal P; Radiology Department, Rangueil University Hospital, Toulouse, France.
  • Peron JM; Hepatology Department, Purpan University Hospital, Toulouse, France.
  • Rousseau H; Radiology Department, Rangueil University Hospital, Toulouse, France.
  • Lu L; 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.
  • Schwartz LH; 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.
  • Mokrane FZ; 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.
  • Zhao B; 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.
Eur J Radiol ; 125: 108850, 2020 Apr.
Article en En | MEDLINE | ID: mdl-32070870
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Interpretación de Imagen Asistida por Computador / Intensificación de Imagen Radiográfica / Tomografía Computarizada por Rayos X / Carcinoma Hepatocelular / Medios de Contraste / Neoplasias Hepáticas Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Eur J Radiol Año: 2020 Tipo del documento: Article Pais de publicación: Irlanda
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Interpretación de Imagen Asistida por Computador / Intensificación de Imagen Radiográfica / Tomografía Computarizada por Rayos X / Carcinoma Hepatocelular / Medios de Contraste / Neoplasias Hepáticas Tipo de estudio: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: Eur J Radiol Año: 2020 Tipo del documento: Article Pais de publicación: Irlanda