Perfusion MR imaging detection of carcinoma arising from preexisting salivary gland pleomorphic adenoma by computer-assisted analysis of time-signal intensity maps.

Abstract

Tumor perfusion can be evaluated by analyzing the time-signal intensity curve (TIC) after dynamic contrast-enhanced (DCE) MR imaging. Accordingly, TIC profiles are characteristic of some benign and malignant salivary gland tumors. A carcinoma ex pleomorphic adenoma (CXPA) arises from a long-standing pleomorphic adenoma (PA) and has a distinctive prognostic risk depending on the tumor growth potential such as invasion beyond the preexisting capsule. Differentiating CXPA from PA can be very challenging. In this study, we have attempted to discriminate CXPA from PA based on a two-dimensional TIC mapping algorithm. TIC mapping analysis was performed on 8 patients with CXPA and 20 patients with PA after dynamic contrast-enhanced (DCE) MR imaging using a 1.5-T MR system. The TIC profiles obtained were automatically categorized into 5 types based on the enhancement ratio, maximum time, and washout ratio (Type 1 TIC with flat profile, Type 2 TIC with slow uptake, Type 3 TIC with rapid uptake and a low washout ratio, Type 4 TIC with rapid uptake and a high washout ratio, and Type 5 TIC not otherwise specific). The percentage tumor areas with each of the 5 TIC types were compared between CXPAs and PAs. Stepwise differentiation and cluster analysis using multiple TIC cut-off thresholds distinguished CXPAs from PAs with 75% sensitivity, 95% specificity, 86% accuracy, and 86% positive and 90% negative predictive values, when tumors with ≤1.1% Type 1 and ≥15% Type 4, or those with ≤1.1% Type 1, ≥78.1% Type 2, ≥16.1% Type 3, and 1.1% Type 1, ≥78.1% Type 2, and ≥16.1% Type 3 areas were diagnosed as CXPAs. The overall TIC profiles predicted some aggressive CXPA growth patterns. These results suggest that stepwise differentiation based on TIC mapping is helpful in differentiating CXPAs from PAs.