Abstract
Rationale and Objective: Coregistered SPECT and CT imaging (SPECT-CT) has potential for more precise evaluation of regional pulmonary function and may be useful for prediction of postoperative lung function in non-small cell lung cancer (NSCLC) patients. The purpose of the present study was to prospectively assess the capability of coregistered SPECT-CT using krypton-81m (Kr-81m) and technetium-99m-labeled macroaggregated albumin (Tc-99m MAA) for prediction of postoperative lung function of NSCLC patients compared with SPECT and planar imaging. Materials and Methods: Sixty consecutive patients considered candidates for lung resection underwent 16-slice CT, ventilation and perfusion scintigraphy with SPECT examinations, and preoperative and postoperative measurement of FEV1%. In each subject, SPECT and CT data were automatically fused by using commercially available software. Each postoperative FEV1% value was predicted from uptakes of Kr-81m and Tc-99m MAA within total and resected lungs. Then, reproducibility coefficients and the limits of agreement between actual and each predicted postoperative lung function were statistically assessed. Results: Reproducibility coefficients of SPECT-CT (Kr-81m: 5.1%, Tc-99m MAA: 5.2%) were smaller than those of SPECT and planar image using Kr-81m (SPECT: 7.4%, planar image: 12.1%) and using Tc-99m MAA (SPECT: 7.2%, planar image: 11.8%). The limits of agreement for SPECT-CT (Kr-81m: 3.3 ± 10.5%, Tc-99m MAA: 5.4 ± 11.0%) were also smaller than that of SPECT and planar image and small enough for clinical purposes. Conclusions: Coregistered SPECT-CT using Kr-81m and Tc-99m MAA was able to more reproducibly and accurately predict postoperative lung function compared with SPECT and planar imaging.
| Original language | English |
|---|---|
| Pages (from-to) | 830-838 |
| Number of pages | 9 |
| Journal | Academic Radiology |
| Volume | 14 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 07-2007 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging
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