What sets
Radial apart.
Radial turns a DICOM archive into a queryable dataset: structure names standardized, cases sorted into cohorts, plans scored against protocol limits, and metrics watched as new cases accrue.
Structure mapping that learns your institution.
Every ROI gets a predicted TG-263 name with a confidence and the reason it matched, including geometry: a contour sitting a uniform 5 mm outside the cord reads as a PRV expansion, whatever its name. High confidence auto-confirms; the rest queue for one-keystroke review.
Every decision teaches Radial: confirm a variant once and Radial offers to backfill every existing structure that matches.
Every case sorted into
its cohort with evidence.
Radial groups incoming cases by treatment site, dose, and fractionation, then proposes an assignment per group, every case carrying a “why this assignment” drawer.
You still confirm and nothing files itself silently. But one decision covers a group, and mis-assigned legacy cases surface for moving.
Your metrics, first-class.
Define any derived metric as free-form algebra over plan fields or dose endpoints bound to real structures. A live preview shows how many cases actually compute and how many come back null before you save; broken expressions are refused.
Saved metrics become columns, chart fields, and monitored signals, computed live for every cohort.
Every metric, watched every night.
Pin any metric and Radial charts it as cases accrue with a one-click toggle from raw to case-mix-adjusted, so a run of hard patients doesn’t read as a process change. Scope a chart to one machine, TPS or technique.
Alerts are controlled for false discovery, and every point drills down to its case.
| Covariate | Coeff | p | Fitted range |
|---|---|---|---|
| ptv_volume | 0.031 | 0.003 | 41–612 cc |
| fractions | −0.42 | 0.021 | 15–35 |
Every constraint on one axis.
Each constraint is rescaled as a fraction of its own limit, so every measure lands on one axis. Everything to the right is beyond the limit. The sheet names the widest offenders at the top.
Also worth knowing.
Queries that show their exclusions.
Every clause reports which cases it excludes, down to “this clause alone excludes 12 cases.” You can bypass one and re-count without touching the saved query.
Fix a contour without leaving the cohort.
Draw, brush, livewire, nudge, and fill, plus slice interpolation, in the viewer. Each save writes a new version beside the original; the import is never overwritten.
Identifiers masked, at render time.
One switch swaps names, MRNs, and dates for synthetic data (or redacts them). Exports mask patient identifiers by default.
Ask in plain language.
Ask for a population DVH, a protocol evaluation, a benchmark. It runs the same tools as the rest of Radial and renders real charts and tables, not a text summary.
And the fundamentals are all here.
The baseline capabilities you'd expect from any platform in this space, listed here rather than demonstrated, because they work exactly as you'd expect.
- DICOM query/retrieve from PACS and TPS
- Staged import queue, duplicate conflicts reconciled
- Drag-and-drop upload
- Three-plane DICOM image viewer
- Structure and isodose overlays
- DVH engine based on dicompyler-core
- Population DVH with percentile bands
- Protocol libraries and in-house scorecards
- Mayo-syntax clinical goals with cohort pass rates
- Cohort metric tables across structures
- Per-cohort reports and CSV export
- Self-hosted in your own environment
- Admin, editor, and viewer roles
- Multi-institution separation
Table stakes, done properly.
Start with the story →Your script ran.
Can you trust the dataset?
Physicists have been scripting data out of the TPS and OIS for years; we’ve written our share, and good scripts get real answers. The hard part starts after the script runs. The platform case, in five lines:
Curation is the hard part
Cord vs SpinalCord vs SC_PRV03; which of six plans was treated. Radial standardizes deterministically, with review queues: errors surfaced, not absorbed.
Ingest once
Every new question sends a script back through the whole archive, file by file. Radial ingests into a database built for cohort-wide queries; after that, answers take seconds.
Any TPS
Scripts target one vendor’s API and often need rework when it changes. Radial is DICOM-native: collaborators, archives, and partner sites included.
Reproducible
In-house scripts tend to leave with their author. Radial is the same validated engine for everyone, benchmarked against dicompyler-core; the methods section becomes one sentence.
Alive after the paper
A script produces one CSV, once. Radial keeps scoring, monitoring, and assigning as new cases accrue.
We don’t replace your science. We replace the wiring.
See it live,
on a real cohort.
Every panel on this page is drawn from real screens in the product. Book a booth demo at ASTRO 2026 and we’ll run the ones you care about.