Artificial intelligence-based automated matching of pulmonary nodules on follow-up chest CT
The AI algorithm achieved a correct nodule matching rate of 87.2% and up to 97.8% when considering nodules detected in both baseline and follow-up scans. Matching accuracy depended on nodule number and localization. This algorithm has the potential to support response evaluation criteria in solid tumor-based evaluations in clinical practice.
Authors
- Nicola Fink (ORCID: https://orcid.org/0000-0002-3089-9606)
- Anna Theresa Stüber (ORCID: https://orcid.org/0000-0001-5236-4373)
- Sophia S. Goller (ORCID: https://orcid.org/0000-0002-0964-8386)
- Boj Hoppe (ORCID: https://orcid.org/0000-0001-6248-5128)
- Theresia Aschauer
- Felix Escher
- Jens Ricke (ORCID: https://orcid.org/0000-0001-8762-8594)
- Bastian O. Sabel (ORCID: https://orcid.org/0000-0001-6027-7309)
- Jan Rudolph (ORCID: https://orcid.org/0000-0002-4849-8034)
- Johannes Rueckel
- Jonathan I. Sperl (ORCID: https://orcid.org/0000-0003-4528-1507)
Institutions
- LMU Klinikum (DE)
- Asklepios Fachkliniken München-Gauting (DE)
- German Center for Lung Research (DE)
- Siemens Healthineers (Germany) (DE)
- Data:Lab Munich (Germany) (DE)
- Ludwig-Maximilians-Universität München (DE)
Publication Details
- Journal
- European Radiology Experimental
- Published
- 2025-05-02
- DOI
- https://doi.org/10.1186/s41747-025-00579-w
- Citations
- 4
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 6.07
Funders
- Siemens Healthineers