ArticlePLOS digital health2026
The deployment of ProKnow for cloud-based clinical research in radiotherapy.
Article in PLOS digital health, 2026. 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
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ProKnow is an archive and restore tool for radiation oncology and imaging data, peer review, distributed contouring, study of metrics and discovery of trends; it represents a common ground for plan analysis and comparison due to an integrated industry standard Dose-Volume Histogram (DVH) engine, which can be used for all patient datasets. The aim of this work is to present a deep and easy-to-use implementation of the Elekta ProKnow DS cloud-based Picture Archiving and Communications in Radiotherapy (RT-PACS) system within our department. Two de-identification workflows of the DICOM data are presented, the first one is accomplished via the ProKnow Dicom Agent (PDA) and the second one involves a trusted third-party service. We can access ProKnow not only through the user interface, but also through the Application Programming Interface (API) with scripts written in the Python language to extract information from the uploaded data, calculate and store metrics as well as upload clinical data. A drawback of the currently implemented de-identification process is that in a subsequent clinical data upload, matching the original and de-identified IDs is not possible. To mitigate this drawback, we collaborate with the MeDICLMU(Data Integration Center [DIC]) for development and implementation of an automated de-identification process via a trusted third party service. With this architecture, it will be possible to merge clinical data in local DIC databases with de-identified data in ProKnow at any point in time, facilitating the longitudinal outcomes analysis. We used ProKnow for a retrospective dosimetric feasibility study of an isotoxic dose-escalated radiotherapy proof-of-concept for glioblastoma. Furthermore, to ensure protocol-compliant irradiation planning for the preparation of a prospective dose-escalation trial, we conducted a dummy run with 10 collaborating institutes in Germany. RT-structures were automatically downloaded (via the API) and the Dice Score and Hausdorff Distance were calculated and set as metric in ProKnow.
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.