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Purpose <p>To assess feasibility of lung cancer screening, we analysed lung lesion detectability simulating low-dose and convolutional neural network (CNN) denoised [<sup>18</sup>F]-FDG PET/CT reconstructions.</p> Methods <p>Retrospectively, we analysed lung lesions on full statistics and decimated [<sup>18</sup>F]-FDG PET/CT. Reduced count PET data were emulated according to various percentage levels of total. Full and reduced statistics datasets were denoised using a CNN algorithm trained to recreate full statistics PET. Two readers assessed a detectability score from 3 to 0 for each lesion. The resulting detectability score and quantitative measurements were compared between full statistics and the different decimation levels (100%, 30%, 5%, 2%, 1%) with and without denoising.</p> Results <p>We analysed 141 lung lesions from 49 patients across 588 reconstructions. The dichotomised lung lesion malignancy score was significantly different from 10% decimation without denoising (<i>p</i> &lt; 0.029) and from 5% decimation with denoising (<i>p</i> &lt; 0.001). Compared to full statistics, detectability score distribution differed significantly from 2% decimation without denoising (<i>p</i> &lt; 0.001) and from 5% decimation with denoising (<i>p</i> &lt; 0.001). Detectability scores at same decimation levels with or without denoising differed significantly at 10%, 2%, and 1% decimation (<i>p</i> &lt; 0.019); dichotomised scores did not differ significantly. Denoising significantly increased the proportion of lung lesion scores with a high diagnostic confidence (3 and 0) (<i>p</i> &lt; 0.038).</p> Conclusion <p>Lung lesion detectability was preserved down to 30% of injected activity without denoising and to 10% with denoising. These results support the feasibility of reduced-activity [<sup>18</sup>F]-FDG PET/CT as a potential tool for lung lesion detection. Further studies are warranted to compare this approach with low-dose CT in screening settings.</p>

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Lung lesion detectability on images obtained from decimated and CNN-based denoised [18F]-FDG PET/CT scan: an observer-based study for lung-cancer screening

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月25日

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  • Daphné Faist,
  • Silvano Gnesin,
  • Siria Medici,
  • Alysée Khan,
  • Marie Nicod Lalonde,
  • Niklaus Schaefer,
  • Adrien Depeursinge,
  • Maurizio Conti,
  • Joshua Schaefferkoetter,
  • John O. Prior,
  • Mario Jreige

摘要

Purpose

To assess feasibility of lung cancer screening, we analysed lung lesion detectability simulating low-dose and convolutional neural network (CNN) denoised [18F]-FDG PET/CT reconstructions.

Methods

Retrospectively, we analysed lung lesions on full statistics and decimated [18F]-FDG PET/CT. Reduced count PET data were emulated according to various percentage levels of total. Full and reduced statistics datasets were denoised using a CNN algorithm trained to recreate full statistics PET. Two readers assessed a detectability score from 3 to 0 for each lesion. The resulting detectability score and quantitative measurements were compared between full statistics and the different decimation levels (100%, 30%, 5%, 2%, 1%) with and without denoising.

Results

We analysed 141 lung lesions from 49 patients across 588 reconstructions. The dichotomised lung lesion malignancy score was significantly different from 10% decimation without denoising (p < 0.029) and from 5% decimation with denoising (p < 0.001). Compared to full statistics, detectability score distribution differed significantly from 2% decimation without denoising (p < 0.001) and from 5% decimation with denoising (p < 0.001). Detectability scores at same decimation levels with or without denoising differed significantly at 10%, 2%, and 1% decimation (p < 0.019); dichotomised scores did not differ significantly. Denoising significantly increased the proportion of lung lesion scores with a high diagnostic confidence (3 and 0) (p < 0.038).

Conclusion

Lung lesion detectability was preserved down to 30% of injected activity without denoising and to 10% with denoising. These results support the feasibility of reduced-activity [18F]-FDG PET/CT as a potential tool for lung lesion detection. Further studies are warranted to compare this approach with low-dose CT in screening settings.