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Objective:   Although a positive result of labial salivary gland biopsy (LSGB) is critical for the diagnosis of Sjögren’s syndrome, rheumatologists prefer assessing the non-invasive objective items and hope to learn the predicted probability of positive LSGB before referring patients with suspected Sjögren’s syndrome to receive biopsy. This study aimed to explore the predictive value of combined B-mode ultrasonography (US) and shear-wave elastography (SWE) examination on LSGB results.Methods:   A derivation cohort and later a validation cohort of patients with suspected Sjögren’s syndrome were recruited. All participants received clinical assessments, B-mode US and SWE examination on bilateral parotid and submandibular glands before LSGB. Positive LSGB was defined by a focus score ⩾1 per 4 mm 2  of glandular tissue.Results:   In the derivation cohort of 91 participants, either the total US scores or the total SWE values of four glands significantly distinguished patients with positive LSGB from those with negative results (area under the curve (AUC) = 0.956, 0.825, both p &lt; 0.001). The positive predictive value (PPV) was 100% in patients with total US scores ⩾9 or with total SWE values ⩾33 kPa. The negative predictive value (NPV) was 100% in patients with total US scores &lt;5, but 68% in patients with total SWE values &lt;27 kPa. A matrix risk model was derived based on the combination of total US scores and total SWE values. Patients can be stratified into high, moderate, and low risk of positive LSGB. In the validation cohort of 52 participants, the PPV was 94% in the high-risk subpopulation and the NPV was 93% in the low-risk subpopulation.Conclusion:   A novel matrix risk model based on the combined B-mode US and SWE examination can help rheumatologists to make a shared decision with suspected Sjögren’s syndrome patients on whether the invasive procedure of LSGB should be performed.

Ultrasonography predicts the results of labial salivary gland biopsy in patients with suspected Sjögren’s syndrome: a matrix risk model