Noriyuki Fujima1, Hiroyuki Kameda2, Akiko Tsukahara2, Daisuke Yoshida2, Tomohiro Sakashita3, Akihiro Homma3, Khin Khin Tha4, Kohsuke Kudo2, Hiroki Shirato4. 1. Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan. Electronic address: Noriyuki.Fujima@mb9.seikyou.ne.jp. 2. Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan. 3. Department of Otolaryngology-Head and Neck Surgery, Hokkaido University Graduate School of Medicine, Sapporo, Japan. 4. Department of Radiation Medicine, Hokkaido University Graduate School of Medicine, Sapporo, Japan; The Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education, Sapporo, Japan.
Abstract
OBJECTIVES: To investigate the diagnostic value of tumor blood flow (TBF) obtained with pseudo-continuous arterial spin labeling (pCASL) for the differentiation of squamous cell carcinoma (SCC) and malignant lymphoma (ML) in the nasal or sinonasal cavity. METHODS: Thirty-three patients with SCC and 6 patients with ML in the nasal or sinonasal cavity were retrospectively analyzed. Quantitative TBF values were obtained using whole-tumor region of interest (ROI) from pCASL data. The histogram analysis of TBF values within the tumor ROI was also performed by calculating the coefficient of variation (CV), kurtosis, and skewness. The mean TBF value, histogram CV, kurtosis and skewness of the patients with SCC were compared with those of the ML patients. The diagnostic accuracy to differentiate SCC from ML was also calculated by receiver operating characteristic (ROC) curve analysis. In addition, multiple logistic regression models were also performed to determine their independent predictive value, and diagnostic accuracy with the combined use of these parameters. RESULTS: Between the SCC and ML groups, significant differences were observed in mean TBF, CV, and kurtosis, but not in skewness. In ROC curve analysis, the diagnostic accuracy values for the differentiation of SCC from ML in mean TBF, CV, and kurtosis were all 0.87, respectively. Multiple logistic regression models revealed TBF and CV were respectively independent predictive value. With the combination of these parameters, the diagnostic accuracy was elevated to 0.97. CONCLUSIONS: The TBF value and its histogram analysis obtained with pCASL can help differentiate SCC and ML.
OBJECTIVES: To investigate the diagnostic value of tumor blood flow (TBF) obtained with pseudo-continuous arterial spin labeling (pCASL) for the differentiation of squamous cell carcinoma (SCC) and malignant lymphoma (ML) in the nasal or sinonasal cavity. METHODS: Thirty-three patients with SCC and 6 patients with ML in the nasal or sinonasal cavity were retrospectively analyzed. Quantitative TBF values were obtained using whole-tumor region of interest (ROI) from pCASL data. The histogram analysis of TBF values within the tumor ROI was also performed by calculating the coefficient of variation (CV), kurtosis, and skewness. The mean TBF value, histogram CV, kurtosis and skewness of the patients with SCC were compared with those of the MLpatients. The diagnostic accuracy to differentiate SCC from ML was also calculated by receiver operating characteristic (ROC) curve analysis. In addition, multiple logistic regression models were also performed to determine their independent predictive value, and diagnostic accuracy with the combined use of these parameters. RESULTS: Between the SCC and ML groups, significant differences were observed in mean TBF, CV, and kurtosis, but not in skewness. In ROC curve analysis, the diagnostic accuracy values for the differentiation of SCC from ML in mean TBF, CV, and kurtosis were all 0.87, respectively. Multiple logistic regression models revealed TBF and CV were respectively independent predictive value. With the combination of these parameters, the diagnostic accuracy was elevated to 0.97. CONCLUSIONS: The TBF value and its histogram analysis obtained with pCASL can help differentiate SCC and ML.