The new algorithm is capable of providing pathologists with automated assessments of scanned slide images
Roche has introduced its CE-IVD approved automated digital pathology algorithm, the uPath PD-L1 (SP263) image analysis for non-small cell lung cancer (NSCLC).
The Swiss multinational healthcare company said that the new algorithm is capable of providing pathologists with automated assessments of scanned slide images and help in diagnosis and targeted treatment options for patients.
In addition, the algorithm has been validated on the VENTANA PD-L1 (SP263) assay and is integrated within the Roche uPath enterprise software, a digital platform for case management, collaboration and reporting.
Roche Diagnostics CEO Thomas Schinecker said: “Improving diagnostic consistency and certainty is crucial in providing faster, higher-quality and more accurate diagnoses to cancer patients.
“Our uPath PD-L1 (SP263) image analysis for non-small cell lung cancer is the first next-generation CE-IVD PD-L1 algorithm to the clinical market. It expands on our growing digital pathology suite for VENTANA assays that aid physicians in providing the most accurate treatment decisions for patients with the most common type of lung cancer.”
The uPath PD-L1 (SP263) image analysis for NSCLC algorithm is used with VENTANA PD-L1 (SP263) Assay
The uPath PD-L1 (SP263) image analysis for NSCLC algorithm has been designed to help pathologists in the detection and semi-quantitative measurement of PD-L1 protein in formalin-fixed, paraffin-embedded NSCLC tissue.
The algorithm is indicated for use with the CE-mark approved VENTANA PD-L1 (SP263) Assay, to identify patients for treatment with therapies with the more than 50% PD-L1 TC positivity cut-off in accordance with the approved therapeutic product labelling.
The uPath PD-L1 (SP263) image analysis is intended for in vitro diagnostic use for display, detection, counting, review and classification of tissues and cells of clinical interest based on particular morphology, color, intensity, size, pattern and shape.
The whole-slide automated analysis feature of the algorithm is said to leverage artificial intelligence to offer actionable assessment of the scanned slide images that is objective and reproducible.
The solution is designed to enable clinicians to secure results for arterial blood gas values from patients with respiratory or metabolic abnormalities through a less invasive venous puncture and the support of a digital algorithm.