ArticleBMC medical imaging2024
Quantitative evaluation of pancreatic neuroendocrine tumors utilizing dual-source CT perfusion imaging.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveWe aimed to quantitatively analyze the perfusion characteristics of pancreatic neuroendocrine tumors (pNETs) utilizing dual-source CT imaging.
methodsDual-source CT perfusion scans were obtained from patients with pNETs confirmed by surgical or biopsy pathology. Perfusion parameters, including blood flow (BF), blood volume (BV), capillary permeability surface (PS), mean transit time (MTT), contrast transit time to the start (TTS), and contrast transit time to the peak (TTP), were statistically analyzed and compared with nearby healthy tissue. Time density curves (TDCs) were plotted to further understand the dynamic enhancement characteristics of the tumors. Additionally, receiver operating characteristic curves (ROCs) were generated to assess their diagnostic value.
resultsTwenty patients with pNETs, containing 26 lesions, were enrolled in the study, including 6 males with 8 lesions and 14 females with 18 lesions. The average values of BF, BV, PS, MTT, TTP and TTS for the 26 lesions (336.61 ± 216.72 mL/100mL/min, 41.96 ± 16.99 mL/100mL, 32.90 ± 11.91 mL/100 mL/min, 9.44 ± 4.40 s, 19.14 ± 5.6 s, 2.57 ± 1.6 s) were different from those of the adjacent normal pancreatic tissue (44.32 ± 55.35 mL/100mL/min, 28.64 ± 7.95 mL/100mL, 26.69 ± 14.88 mL/100 mL/min, 12.89 ± 3.69 s, 20.33 ± 5.18 s, 2.69 ± 1.71 s). However, there were no statistical differences in PS and TTS between the lesions and the adjacent normal pancreatic tissue (P > 0.05). The areas under the ROC curve for BF, BV, and PS were all greater than 0.5, whereas the areas under the ROC curve for MTT, TTP, and TTS were all less than 0.5.
conclusionCT perfusion parameters such as BF, BV, MTT, and TTP can distinguish pNETs from healthy tissue. The area under the ROC curve for BF, BV, and PS demonstrates substantial differentiating power for diagnosing pNET lesions.
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