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.
