ArticleCancer imaging : the official publication of the International Cancer Imaging Society2014
Clinical evaluation of semi-automatic landmark-based lesion tracking software for CT-scans.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- coPERCIST: AI-assisted PET-CT response assessment.European journal of nuclear medicine and molecular imaging · 2026Article
- AI lesion tracking in PET/CT imaging: a proposal for a Siamese-based CNN pipeline applied to PSMA PET/CT scans.European journal of nuclear medicine and molecular imaging · 2025Article
- FLAIRfusion Processing with Contrast Inversion : Improving Detection and Reading Time of New Cerebral MS Lesions.Clinical neuroradiology · 2018Article
- Semi-automatic cone beam CT segmentation of in vivo pre-clinical subcutaneous tumours provides an efficient non-invasive alternative for tumour volume measurements.The British journal of radiology · 2015Article
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Authors and funding
8 authors.
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Abstract
backgroundTo evaluate a semi-automatic landmark-based lesion tracking software enabling navigation between RECIST lesions in baseline and follow-up CT-scans.
methodsThe software automatically detects 44 stable anatomical landmarks in each thoraco/abdominal/pelvic CT-scan, sets up a patient specific coordinate-system and cross-links the coordinate-systems of consecutive CT-scans. Accuracy of the software was evaluated on 96 RECIST lesions (target- and non-target lesions) in baseline and follow-up CT-scans of 32 oncologic patients (64 CT-scans). Patients had to present at least one thoracic, one abdominal and one pelvic RECIST lesion. Three radiologists determined the deviation between lesions' centre and the software's navigation result in consensus.
resultsThe initial mean runtime of the system to synchronize baseline and follow-up examinations was 19.4 ± 1.2 seconds, with subsequent navigation to corresponding RECIST lesions facilitating in real-time. Mean vector length of the deviations between lesions' centre and the semi-automatic navigation result was 10.2 ± 5.1 mm without a substantial systematic error in any direction. Mean deviation in the cranio-caudal dimension was 5.4 ± 4.0 mm, in the lateral dimension 5.2 ± 3.9 mm and in the ventro-dorsal dimension 5.3 ± 4.0 mm.
conclusionThe investigated software accurately and reliably navigates between lesions in consecutive CT-scans in real-time, potentially accelerating and facilitating cancer staging.
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