Harnessing the potential of data in clinical PACS with an open-source DICOM server
Bibliographic record
Abstract
Picture Archiving and Communication Systems (PACS) used in clinical workflows are rich with data in the form of images as well as associated annotations, segmentations and finding reports by specialists in radiology, nuclear medicine or radiation oncology. From a research perspective, annotated images constitute the raw material for radiomic, machine learning and similar AI-based initiatives. It is not straightforward however to harness the potential of the tremendous amount of data collected in PACS on a daily basis. Research infrastructures must be designed to interfere minimally with clinical workflows and to provide mechanisms to prevent unauthorized access to images. Furthermore, these infrastructure must provide user-friendly interfaces for data operations since raw images and annotations are rarely suitable for direct ingestion by downstream analysis pipelines. At our institution, an imaging research infrastructure has been deployed that addresses these performance, confidentiality and conviviality issues. The solution is based on Orthanc, an open-source and lightweight DICOM server acting as a controlled gateway to clinical PACS. Functionalities are exposed to users through a REST API, who can perform several operations (query, upload, receive, transfer) in the programming language of their choice. The REST API is particularly useful to easily perform queries, browse metadata and parse objects such as DICOM Structured Reports (SR). In this paper, the architecture of the solution is presented in terms of hardware, software and network topology. Three use cases are also detailed, each addressing different needs: a simple retrieve operation to physically access images, a data pipeline solution for automatic calculation of image-derived metrics, and an automated parser used to find occurence of specific terms in radiology finding reports. Our experience shows that relying on a DICOM-compliant software enforces good practices and fosters standard awareness, with benefits in terms of interoperability and long-term usability of data. En
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".