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Influence of CT dose reduction on AI-driven malignancy estimation of incidental pulmonary nodules.
Peters, Alan A; Solomon, Justin B; von Stackelberg, Oyunbileg; Samei, Ehsan; Alsaihati, Njood; Valenzuela, Waldo; Debic, Manuel; Heidt, Christian; Huber, Adrian T; Christe, Andreas; Heverhagen, Johannes T; Kauczor, Hans-Ulrich; Heussel, Claus P; Ebner, Lukas; Wielpütz, Mark O.
Afiliação
  • Peters AA; Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany. alan.peters@insel.ch.
  • Solomon JB; Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland. alan.peters@insel.ch.
  • von Stackelberg O; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany. alan.peters@insel.ch.
  • Samei E; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany. alan.peters@insel.ch.
  • Alsaihati N; Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
  • Valenzuela W; Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
  • Debic M; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.
  • Heidt C; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany.
  • Huber AT; Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
  • Christe A; Carl E. Ravin Advanced Imaging Laboratories, Medical Physics Graduate Program, Clinical Imaging Physics Group, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
  • Heverhagen JT; University Institute for Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse, 3010, Bern, Switzerland.
  • Kauczor HU; Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
  • Heussel CP; Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Im Neuenheimer Feld 156, 69120, Heidelberg, Germany.
  • Ebner L; Department of Diagnostic and Interventional Radiology With Nuclear Medicine, Thoraxklinik at University of Heidelberg, Röntgenstraße 1, 69126, Heidelberg, Germany.
  • Wielpütz MO; Diagnostic and Interventional Radiology, Heidelberg University Hospital, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
Eur Radiol ; 2023 Oct 23.
Article em En | MEDLINE | ID: mdl-37870625
OBJECTIVES: The purpose of this study was to determine the influence of dose reduction on a commercially available lung cancer prediction convolutional neuronal network (LCP-CNN). METHODS: CT scans from a cohort provided by the local lung cancer center (n = 218) with confirmed pulmonary malignancies and their corresponding reduced dose simulations (25% and 5% dose) were subjected to the LCP-CNN. The resulting LCP scores (scale 1-10, increasing malignancy risk) and the proportion of correctly classified nodules were compared. The cohort was divided into a low-, medium-, and high-risk group based on the respective LCP scores; shifts between the groups were studied to evaluate the potential impact on nodule management. Two different malignancy risk score thresholds were analyzed: a higher threshold of ≥ 9 ("rule-in" approach) and a lower threshold of > 4 ("rule-out" approach). RESULTS: In total, 169 patients with 196 nodules could be included (mean age ± SD, 64.5 ± 9.2 year; 49% females). Mean LCP scores for original, 25% and 5% dose levels were 8.5 ± 1.7, 8.4 ± 1.7 (p > 0.05 vs. original dose) and 8.2 ± 1.9 (p < 0.05 vs. original dose), respectively. The proportion of correctly classified nodules with the "rule-in" approach decreased with simulated dose reduction from 58.2 to 56.1% (p = 0.34) and to 52.0% for the respective dose levels (p = 0.01). For the "rule-out" approach the respective values were 95.9%, 96.4%, and 94.4% (p = 0.12). When reducing the original dose to 25%/5%, eight/twenty-two nodules shifted to a lower, five/seven nodules to a higher malignancy risk group. CONCLUSION: CT dose reduction may affect the analyzed LCP-CNN regarding the classification of pulmonary malignancies and potentially alter pulmonary nodule management. CLINICAL RELEVANCE STATEMENT: Utilization of a "rule-out" approach with a lower malignancy risk threshold prevents underestimation of the nodule malignancy risk for the analyzed software, especially in high-risk cohorts. KEY POINTS: • LCP-CNN may be affected by CT image parameters such as noise resulting from low-dose CT acquisitions. • CT dose reduction can alter pulmonary nodule management recommendations by affecting the outcome of the LCP-CNN. • Utilization of a lower malignancy risk threshold prevents underestimation of pulmonary malignancies in high-risk cohorts.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Eur Radiol Assunto da revista: RADIOLOGIA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha