OBJECTIVE: To define the computed tomography (CT) perfusion characteristics of head and neck squamous cell carcinoma. METHODS: Fourteen consecutive patients with untreated squamous cell cancers of head and neck underwent CT of the head and neck along with CT perfusion imaging through the primary site. For the perfusion studies, CT density changes in blood and tissues were kinetically analyzed using the commercially available CT Perfusion 2 software (General Electric Medical Systems. Milwaukee, WI) on a GE Advantage Windows workstation. This yielded parameter maps of fractional tissue blood volume (mL/100 g), blood flow (mL x 100 g(-1) x min(-1)), mean transit time (s), and microvascular permeability surface area product (mL x 100 g(-1) x min(-1)). One head and neck radiologist analyzed perfusion data. Regions of interest (ROI) were placed over the primary tumor site, tongue base, and adjacent muscle groups. The average values of tissue blood volume (BV), blood flow (BF), mean transit time (MTT), and capillary permeability surface area product (CP) were then calculated for the tumor and compared with the average values for the tongue base and adjacent musculature. To determine a statistically significant difference between the tumor and muscle parameters, the Wilcoxon sign test, a nonparametric test for paired data, was employed. RESULTS: The average values of CP, BF, and BV were higher in primary tumor (41.9, 132.9, 6.2, respectively) than in tongue base or adjacent muscular structures. The MTT was reduced in primary tumors (4.0) compared with adjacent normal structures. The above differences were statistically significant (P<0.05). CONCLUSIONS: We obtained baseline perfusion data for head and neck squamous cell cancers and compared it with adjacent normal structures. Our initial results suggest that CT perfusion parameters (CP, BF, BV, and MTT) can be used to help differentiate head and neck squamous cell carcinoma (SCCA) from adjacent normal tissue.
OBJECTIVE: To define the computed tomography (CT) perfusion characteristics of head and neck squamous cell carcinoma. METHODS: Fourteen consecutive patients with untreated squamous cell cancers of head and neck underwent CT of the head and neck along with CT perfusion imaging through the primary site. For the perfusion studies, CT density changes in blood and tissues were kinetically analyzed using the commercially available CT Perfusion 2 software (General Electric Medical Systems. Milwaukee, WI) on a GE Advantage Windows workstation. This yielded parameter maps of fractional tissue blood volume (mL/100 g), blood flow (mL x 100 g(-1) x min(-1)), mean transit time (s), and microvascular permeability surface area product (mL x 100 g(-1) x min(-1)). One head and neck radiologist analyzed perfusion data. Regions of interest (ROI) were placed over the primary tumor site, tongue base, and adjacent muscle groups. The average values of tissue blood volume (BV), blood flow (BF), mean transit time (MTT), and capillary permeability surface area product (CP) were then calculated for the tumor and compared with the average values for the tongue base and adjacent musculature. To determine a statistically significant difference between the tumor and muscle parameters, the Wilcoxon sign test, a nonparametric test for paired data, was employed. RESULTS: The average values of CP, BF, and BV were higher in primary tumor (41.9, 132.9, 6.2, respectively) than in tongue base or adjacent muscular structures. The MTT was reduced in primary tumors (4.0) compared with adjacent normal structures. The above differences were statistically significant (P<0.05). CONCLUSIONS: We obtained baseline perfusion data for head and neck squamous cell cancers and compared it with adjacent normal structures. Our initial results suggest that CT perfusion parameters (CP, BF, BV, and MTT) can be used to help differentiate head and neck squamous cell carcinoma (SCCA) from adjacent normal tissue.
Authors: M T Truong; N Saito; A Ozonoff; J Wang; R Lee; M M Qureshi; S Jalisi; O Sakai Journal: AJNR Am J Neuroradiol Date: 2011-07-14 Impact factor: 3.825
Authors: Chaan S Ng; Adam G Chandler; James C Yao; Delise H Herron; Ella F Anderson; Chusilp Charnsangavej; Brian P Hobbs Journal: J Comput Assist Tomogr Date: 2014 Jul-Aug Impact factor: 1.826
Authors: D Gandhi; D B Chepeha; T Miller; R C Carlos; C R Bradford; R Karamchandani; F Worden; A Eisbruch; T N Teknos; G T Wolf; S K Mukherji Journal: AJNR Am J Neuroradiol Date: 2006-01 Impact factor: 3.825