Data pipelines in radiation oncology: lessons from software engineering
Bibliographic record
Abstract
Processes automation in clinical environments is highly desirable as it reduces the workload and eliminates error-prone tasks. Extract/transform/load (ETL) operations for instance are often used to move data from an IT system to another. At our institution, ETL operations are used to federate prostate cancer patient data in a research database used to evaluate survival and toxicities. In these data pipelines, laboratory results and dosimetric indices are pulled from hospital IT systems and treatment planning systems (TPS) respectively. Maintaining these pipelines is not trivial however as several external factors can break them: new firewall rules, software updates, change in the vocabulary used, etc. It is therefore important to design the pipelines so that they can cope elegantly with frequent changes. Lessons learned from software engineering guided us towards the use of factory design pattern and data structures such as patient tree representations to handle the inherent complexity of ETL tasks in an ever changing IT ecosystem. Memory usage by the pipeline is also a concern, which was handled by the virtual proxy design pattern. This methodology will be presented through a case study where dosimetric indices are generated from DICOM-RT files derived from brachytherapy treatment planning. The pipeline first queries a research-dedicated PACS server, retrieves the files required, performs consistency checks, and calculates dosimetric indices. Results are written back into the DICOM files, using appropriate tags defined by the standard for this purpose. Any exception raised during the process is contextualized and sent to the developer for potential action. Implementation details and software engineering guidelines will be presented as well avenues for improvement, including the use of tools such as Apache Airflow to schedule and monitor ETL processes in a clinical setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.032 |
| 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 | 0.007 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".