Yale tool measures painful calcium deposits in five patients

A deposit in the hand can make eating or bathing painful. On the spine or buttocks, calcinosis cutis can make sitting difficult. At Yale School of Medicine, rheumatologist Monique Hinchcliff and colleagues have now tested a tool designed to measure those calcium deposits more precisely, using scans from five patients with scleroderma and painful symptoms.

The software, built on Xenophon Papademetris' open-source BioImage Suite Web, lets clinicians mark every voxel — a 3D pixel — where calcinosis cutis appears in an image. It then calculates the deposit's volume. That replaces the current practice of making a few rough measurements on X-rays or CT scans and repeating them later, an approach Yale says is both time-consuming and often too imprecise to show whether treatment made a difference.

The patients received treatment for six months, after which the team repeated the measurements. The study showed that changes in the deposits could be quantified, but its scale was too small to establish whether the treatment worked. Calcinosis cutis still has no proven treatment, and no medication has yet been tested in a clinical trial that could provide high-quality evidence.

So what changes in practice? A dependable volume measurement could give researchers a concrete endpoint for future trials instead of relying on anecdotes or broad visual estimates. That may help distinguish treatments that actually reduce deposits from those that merely appear to help in individual cases. The Yale team is also seeking to compare scan changes with patients' own reports of pain and other symptoms, because shrinking a deposit is not enough if people do not feel better.

The next step is more testing. With a new National Institutes of Health grant, the researchers plan to study more patients with painful calcinosis cutis and examine whether imaging changes match their impressions of improvement. They are also working to train AI to identify the deposits automatically, with the hope of embedding the function into radiology systems so a clinician can calculate lesion volume with a button press. For now, the work remains a proof of concept rather than a treatment or a deployed clinical service.