ArticleProceedings of SPIE--the International Society for Optical Engineering2025
Evaluating the robustness of features generated by a foundation model from CT with different reconstruction parameters.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Foundation models are increasingly recognized for their ability to extract robust imaging features by learning generalized representations from large datasets of unannotated images. Foundation models show promise in generating task-agnostic features that outperform conventional methods in diagnostic and prognostic tasks. However, the clinical application of imaging biomarkers is often hampered by their sensitivity to variations in image acquisition and reconstruction parameters. Hence, we evaluated the robustness of features extracted using the pretrained foundation model from low-dose computed tomography (LDCT) scans under varying image conditions. We analyzed 59 LDCT scans containing at least one nodule from individuals who underwent lung cancer screening. Each scan consisted of 6 variations of image conditions, including combinations of three reconstruction kernels (smooth, medium, sharp) and two slice thicknesses (1.0 mm, 2.0 mm). 56 nodules had nodule outcome labels (cancer vs. non-cancer) available, of which 7 had cancer outcomes. Features were extracted using the foundation model across all image conditions. The feature-level robustness was evaluated by computing the concordance correlation coefficient (CCC) between each feature value extracted from the reference condition (Medium/1.0) and the other 5 conditions. We also examined the impact of varying image conditions on the features' predictive ability in nodule outcome classification. Pyradiomics, a well-established radiomic feature extractor, served as a baseline for comparison. The foundation model consistently yielded high mean CCC values ranging from 0.937 (Smooth/2.0) to 0.984 (Sharp/1.0), indicating robustness against variations in image conditions. Moreover, the foundation model's features demonstrated high performance in nodule outcome classification compared to Pyradiomics features. However, inconsistencies in performance across different image conditions underscore the need for further exploration of harmonization techniques and modeling strategies to enhance the generalizability and clinical utility of imaging biomarkers.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.