| Title: |
Improving assessment of lesions in longitudinal CT scans: a bi-institutional reader study on an AI-assisted registration and volumetric segmentation workflow. |
| Authors: |
Hering, A.D.; Westphal, M.; Gerken, A.; Almansour, H.; Maurer, M.; Geisler, B.; Kohlbrandt, T.; Eigentler, T.; Amaral, T.; Lessmann, N.; Gatidis, S.; Hahn, H.; Nikolaou, K.; Othman, A.; Moltz, J.; Peisen, F. |
| Source: |
International Journal of Computer Assisted Radiology and Surgery, 19, 9, pp. 1689-1697 |
| Publication Year: |
2024 |
| Collection: |
Radboud University: DSpace |
| Subject Terms: |
Medical Imaging - Radboud University Medical Center |
| Description: |
Contains fulltext : 310257.pdf (Publisher’s version ) (Open Access) ; PURPOSE: AI-assisted techniques for lesion registration and segmentation have the potential to make CT-based tumor follow-up assessment faster and less reader-dependent. However, empirical evidence on the advantages of AI-assisted volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans is lacking. The aim of this study was to assess the efficiency, quality, and inter-reader variability of an AI-assisted workflow for volumetric segmentation of lymph node and soft tissue metastases in follow-up CT scans. Three hypotheses were tested: (H1) Assessment time for follow-up lesion segmentation is reduced using an AI-assisted workflow. (H2) The quality of the AI-assisted segmentation is non-inferior to the quality of fully manual segmentation. (H3) The inter-reader variability of the resulting segmentations is reduced with AI assistance. MATERIALS AND METHODS: The study retrospectively analyzed 126 lymph nodes and 135 soft tissue metastases from 55 patients with stage IV melanoma. Three radiologists from two institutions performed both AI-assisted and manual segmentation, and the results were statistically analyzed and compared to a manual segmentation reference standard. RESULTS: AI-assisted segmentation reduced user interaction time significantly by 33% (222 s vs. 336 s), achieved similar Dice scores (0.80-0.84 vs. 0.81-0.82) and decreased inter-reader variability (median Dice 0.85-1.0 vs. 0.80-0.82; ICC 0.84 vs. 0.80), compared to manual segmentation. CONCLUSION: The findings of this study support the use of AI-assisted registration and volumetric segmentation for lymph node and soft tissue metastases in follow-up CT scans. The AI-assisted workflow achieved significant time savings, similar segmentation quality, and reduced inter-reader variability compared to manual segmentation. ; 01 september 2024 |
| Document Type: |
article in journal/newspaper |
| Language: |
unknown |
| Relation: |
https://repository.ubn.ru.nl//bitstream/handle/2066/310257/310257.pdf; https://hdl.handle.net/2066/310257 |
| DOI: |
10.1007/s11548-024-03181-4 |
| Availability: |
https://hdl.handle.net/2066/310257; https://repository.ubn.ru.nl//bitstream/handle/2066/310257/310257.pdf; https://doi.org/10.1007/s11548-024-03181-4 |
| Accession Number: |
edsbas.BE684C5F |
| Database: |
BASE |