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Harmonizing by reducing inter-run variability: performance evaluation of a quality assurance program for antinuclear antibody detection by indirect immunofluorescence
摘要： Background: The introduction of automated anti-nuclear antibody (ANA) indirect immunofluorescence (IIF) analysis may allow for more harmonized ANA IIF reporting, provided that a thorough quality assurance program controls this process. The aim of this study was to evaluate various quality indicators used for ANA IIF analysis with the final goal of optimizing the iQC program. Methods: In an experimental setup, we introduced artificial errors, mimicking plausible problems during routine practice on a QUANTA-Lyser-NOVA View? system (Inova Diagnostics, San Diego, CA, USA). Predetermined quality indicators were evaluated against predefined acceptance criteria. In addition, we retrospectively investigated the applicability of the selected quality indicators in the daily routine practice during three pre-defined periods. Results: Both the experimental as the retrospective study revealed that pre-analytical, analytical and post-analytical errors were not highlighted by company internal quality control (iQC) materials. The use of patient derived iQC samples, median fluorescence intensity results per run and the percentage of positive ANA IIF results as additional quality indicators ensured a more adequate ANA IIF quality assurance. Furthermore, negative and moderate positive sample iQC materials merit clinical validation, as titer changes of >1 correspond to clinically important shifts. Traditional Westgard rules, including a clinically defined stop limit, revealed to be useful in monitoring of the supplemental quality indicators. Conclusions: A thorough ANA IIF quality assurance for daily routine practice necessitates the addition of supplemental quality indicators in combination with well-defined acceptance criteria.
关键词： automation,indirect immunofluorescence,antinuclear antibodies,quality control
更新于2025-09-23 15:23:52
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A computer-aided diagnosis system for HEp-2 fluorescence intensity classification
摘要： Background and objective: The indirect immunofluorescence (IIF) on HEp-2 cells is the recommended technique for the detection of antinuclear antibodies. However, it is burdened by some limitations, as it is time consuming and subjective, and it requires trained personnel. In other fields the adoption of deep neural networks has provided an effective high-level abstraction of the raw data, resulting in the ability to automatically generate optimized high-level features. Methods: To alleviate IIF limitations, this paper presents a computer-aided diagnosis (CAD) system classifying HEp-2 fluorescence intensity: it represents each image using an Invariant Scattering Convolutional Network (Scatnet), which is locally translation invariant and stable to deformations, a characteristic useful in case of HEp-2 samples. To cope with the inter-observer discrepancies found in the dataset, we also introduce a method for gold standard computation that assigns a label and a reliability score to each HEp-2 sample on the basis of annotations provided by expert physicians. Features by Scatnet and gold standard information are then used to train a Support Vector Machine. Results: The proposed CAD is tested on a new dataset of 1771 images annotated by three independent medical centers. The performances achieved by our CAD in recognizing positive, weak positive and negative samples are also compared against those obtained by other two approaches presented so far in the literature. The same system trained on this new dataset is then tested on two public datasets, namely MIVIA and I3Asel. Conclusions: The results confirm the effectiveness of our proposal, also revealing that it achieves the same performance as medical experts.
关键词： HEp-2 samples,Deep learning,Invariant Scattering Convolutional Networks,Computer-aided diagnosis,Indirect immunofluorescence
更新于2025-09-23 15:21:21