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Imaging Tool Quantifies Calcinosis Cutis Volume to Monitor Treatment

By HospiMedica International staff writers
Posted on 21 Aug 2026

Calcinosis cutis involves calcium deposits in the skin or soft tissues that can develop anywhere in the body, causing pain, impaired mobility, and disability. More...

Lesions along the spine or buttocks can make sitting difficult, while deposits in the hands may interfere with everyday activities. Treatment decisions are complicated by the lack of reliable methods to measure lesion burden and track changes over time. To improve this assessment, researchers have developed a proof-of-concept imaging approach that quantifies calcinosis cutis volume in individual patients.

Researchers at Yale School of Medicine developed the Scleroderma calcinosis cutis score (SC2S) and implemented it using the open-source BioImage Suite Web platform. Reported in a study published in Rheumatology on June 11, 2026, the approach is designed to provide a reproducible, patient-level measure that allows clinicians and investigators to determine whether calcinosis lesions grow or shrink over the course of treatment.

The method allows clinicians to identify all voxels, or three-dimensional pixels, on clinical images that correspond to calcinosis cutis. The software then calculates total lesion volume from these highlighted regions, providing a quantitative measure of disease burden. This approach is intended to replace the crude manual estimates currently derived from serial X-rays or computed tomography scans, which can be time-consuming and imprecise.

Investigators applied the tool in five patients with scleroderma and painful, symptomatic calcinosis cutis. After six months of treatment, volumetric measurements were repeated to assess changes in lesion burden. The study showed that calcinosis volume could be measured accurately at the individual-patient level over time, supporting the feasibility of using the approach for quantitative assessment in prospective clinical trials.

Next steps include automating segmentation with artificial intelligence and integrating the tool into routine radiology systems to generate lesion volumes at the point of care. The group has secured new National Institutes of Health support to test the method in a larger cohort and to evaluate whether imaging changes align with patient‑reported improvement, including symptom relief. The overarching objective is to enable data‑driven trials that can identify effective therapies for this challenging condition.

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Yale School of Medicine


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