Quantitative functional MRI biomarkers improved early detection of colorectal liver metastases

Yifat Edrei, Moti Freiman, Miri Sklair-Levy, Galia Tsarfaty, Eitan Gross, Leo Joskowicz, Rinat Abramovitch

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose 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). Materials and 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. Conclusion 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. J. Magn. Reson. Imaging 2014;39:1246-1253. © 2013 Wiley Periodicals, Inc.

Original languageEnglish
Pages (from-to)1246-1253
Number of pages8
JournalJournal of Magnetic Resonance Imaging
Volume39
Issue number5
DOIs
StatePublished - May 2014
Externally publishedYes

Keywords

  • cancer
  • hemodynamic response imaging
  • machine learning
  • SVM

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