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Application of Dynamic 18 F-FDG PET/CT for Distinguishing Intrapulmonary Metastases from Synchronous Multiple Primary Lung Cancer.

Weize LvMin YangHongcheng ZhongXiaojin WangShuai YangLei BiJianzhong XianXiaofeng PeiXinghua HeYing WangZhong LinQingdong CaoHongjun JinHong Shan
Published in: Molecular imaging (2022)
It has been a big challenge to distinguish synchronous multiple primary lung cancer (sMPLC) from primary lung cancer with intrapulmonary metastases (IPM). We aimed to assess the clinical application of dynamic 18 F-FDG PET/CT in patients with multiple lung cancer nodules. We enrolled patients with multiple pulmonary nodules who had undergone dynamic 18 F-FDG PET/CT and divided them into sMPLC and IPM groups based on comprehensive features. The SUV max , fitted K i value based on dynamic scanning, and corresponding maximum diameter ( D max ) from the two largest tumors were determined in each patient. We determined the absolute between-tumor difference of SUV max / D max and K i / D max (ΔSUV max / D max ; Δ K i / D max ) and assessed the between-group differences. Further, the diagnostic accuracy was evaluated by ROC analysis and the correlation between ΔSUV max / D max and Δ K i /D max from all groups was determined. There was no significant difference for ΔSUV max / D max between the IPM and sMPLC groups, while the IPM group had a significantly higher Δ K i /D max than the sMPLC group. The AUC of Δ K i / D max for differentiating sMPLC from IPM was 0.80 (cut-off value of K i = 0.0059, sensitivity 79%, specificity 75%, p < 0.001). There was a good correlation (Pearson r = 0.91, 95% CI: 0.79-0.96, p < 0.0001) between ΔSUV max / D max and Δ K i / D max in the IPM group but not in the sMPLC group (Pearson r = 0.45, p > 0.05). Dynamic 18 F-FDG PET/CT could be a useful tool for distinguishing sMPLC from IPM. K i calculation based on Patlak graphic analysis could be more sensitive than SUV max in discriminating IPM from sMPLC in patients with multiple lung cancer nodules.
Keyphrases
  • magnetic resonance imaging
  • magnetic resonance
  • computed tomography
  • machine learning
  • high resolution
  • artificial intelligence
  • data analysis