title: Quantitative functional MRI biomarkers improved early detection of colorectal liver metastases.
abstract: OBJECTIVE
  To implement and evaluate the performance of a computerized statistical tool designed for robust and quantitative analysis of hemodynamic response imaging (HRI) -derived maps for the early identification of colorectal liver metastases (CRLM).
  
  
  METHODS
  CRLM-bearing mice were scanned during the early stage of tumor growth and subsequently during the advanced-stage. Three experienced radiologists marked various suspected-foci on the early stage anatomical images and classified each as either highly certain or as suspected tumors. The statistical model construction was based on HRI maps (functional-MRI combined with hypercapnia and hyperoxia) using a supervised learning paradigm which was further trained either with the advanced-stage sets (late training; LT) or with the early stage sets (early training; ET). For each group of foci, the classifier results were compared with the ground-truth.
  
  
  RESULTS
  The ET-based classification significantly improved the manual classification of the highly certain foci (P < 0.05) and was superior compared with the LT-based classification (P < 0.05). Additionally, the ET-based classification, offered high sensitivity (57-63%), accompanied with high positive predictive value (>94%) and high specificity (>98%) for suspected-foci.
  
  
  CONCLUSIONS
  The ET-based classifier can strengthen the radiologist's classification of highly certain foci. Additionally, it can aid in classifying suspected-foci, thus enabling earlier intervention which can often be lifesaving.
authors.affiliation: The Goldyne Savad Institute for Gene Therapy, Hadassah Hebrew University Medical Center, Jerusalem, Israel.
authors.forename: Yifat
authors.initials: Y
authors.lastname: Edrei
authors.forename: Moti
authors.initials: M
authors.lastname: Freiman
authors.forename: Miri
authors.initials: M
authors.lastname: Sklair-Levy
authors.forename: Galia
authors.initials: G
authors.lastname: Tsarfaty
authors.forename: Eitan
authors.initials: E
authors.lastname: Gross
authors.forename: Leo
authors.initials: L
authors.lastname: Joskowicz
authors.forename: Rinat
authors.initials: R
authors.lastname: Abramovitch
mesh_terms: D000230:Adenocarcinoma / Q000175:diagnosis* / Q000556:secondary*; D000818:Animals; D045744:Cell Line, Tumor; D015179:Colorectal Neoplasms / Q000175:diagnosis*; D055088:Early Detection of Cancer / Q000379:methods*; D019073:HT29 Cells; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000556:secondary*; D018810:Magnetic Resonance Angiography / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D051379:Mice; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: SVM; cancer; hemodynamic response imaging; machine learning