Deep learning segmentation of Primary Sjögren's syndrome affected salivary glands from ultrasonography images

Clinical and laboratory characteristics of salivary gland ultrasonography-positive patients with primary Sjögren's syndrome

OBJECTIVE

This study aimed to investigate the clinical and laboratory characteristics of salivary gland ultrasonography (SGUS)-positive patients with primary Sjögren's syndrome (pSS) compared to SGUS-negative patients and to analyse the diagnostic value of SGUS and labial salivary gland biopsy (LSGB) grading in pSS.

METHODS

A retrospective analysis of patients admitted to the Affiliated Hospital of Yangzhou University between May 2019 and November 2023 was conducted. According to the OMERACT scoring system, patients with pSS were divided into an SGUS-negative group (score <2) and an SGUS-positive group (score ≥2). The patient's age, gender, clinical symptoms, laboratory parameters and diagnostic examinations were compared and analysed, and Spearman correlation analysis was used to analyse the correlation between SGUS, LSGB and influencing factors.

RESULTS

There was no significant difference in dry mouth, dry eyes, tooth loss, fever, joint pain, fatigue, interstitial lung disease or renal tubular acidosis between the two groups, although there were more patients with salivary gland enlargement in the SGUS-positive group (p < 0.05). In terms of high levels of immunoglobulin G (IgG), high levels of rheumatoid factor (RF), anti-nuclear antibody ≥1:320, anti-Sjögren's syndrome A-52KD and anti-Sjögren's syndrome B, the number of cases in the SGUS-positive group was greater than that in the SGUS-negative group (p < 0.05). LSGB samples were graded per the Chisholm-Mason system with significant differences between multiple groups. SGUS score negatively correlated with age and positively correlated with LSGB grade.

CONCLUSION

This study showed that the SGUS score positively correlated with LSGB grade in pSS patients and negatively correlated with patient age. Thus, SGUS and LSGB are consistent in the diagnosis of pSS to reflect the degree of salivary gland involvement, and patients who are SGUS positive have high RF and IgG levels, a variety of autoantibodies positive and a tendency toward salivary gland enlargement.

A Stepwise Approach to Analyzing Musculoskeletal Imaging Data With Artificial Intelligence

The digitization of medical records and expanding electronic health records has created an era of "Big Data" with an abundance of available information ranging from clinical notes to imaging studies. In the field of rheumatology, medical imaging is used to guide both diagnosis and treatment of a wide variety of rheumatic conditions. Although there is an abundance of data to analyze, traditional methods of image analysis are human resource intensive. Fortunately, the growth of artificial intelligence (AI) may be a solution to handle large datasets. In particular, computer vision is a field within AI that analyzes images and extracts information. Computer vision has impressive capabilities and can be applied to rheumatologic conditions, necessitating a need to understand how computer vision works. In this article, we provide an overview of AI in rheumatology and conclude with a five step process to plan and conduct research in the field of computer vision. The five steps include (1) project definition, (2) data handling, (3) model development, (4) performance evaluation, and (5) deployment into clinical care.

Machine and Deep Learning Approaches Applied to Classify Gougerot-Sjögren Syndrome and Jointly Segment Salivary Glands

To diagnose Gougerot-Sjögren syndrome (GSS), ultrasound imaging (US) is a promising tool for helping physicians and experts. Our project focuses on the automatic detection of the presence of GSS using US. Ultrasound imaging suffers from a weak signal-to-noise ratio. Therefore, any classification or segmentation task based on these images becomes a difficult challenge. To address these two tasks, we evaluate different approaches: a classification using a machine learning method along with feature extraction based on a set of measurements following the radiomics guidance and a deep-learning-based classification. We propose, therefore, an innovative method to enhance the training of a deep neural network with a two phases: multiple supervision using joint classification and a segmentation implemented as pretraining. We highlight the fact that our learning methods provide segmentation results similar to those performed by human experts. We obtain proficient segmentation results for salivary glands and promising detection results for Gougerot-Sjögren syndrome; we observe maximal accuracy with the model trained in two phases. Our experimental results corroborate the fact that deep learning and radiomics combined with ultrasound imaging can be a promising tool for the above-mentioned problems.

Automated detection of regions of interest in cartridge case images using deep learning

This paper explores a deep-learning approach to evaluate the position of circular delimiters in cartridge case images. These delimiters define two regions of interest (ROI), corresponding to the breech face and the firing pin impressions, and are placed manually or by an image-processing algorithm. This positioning bears a significant impact on the performance of the image-matching algorithms for firearm identification, and an automated evaluation method would be beneficial to any computerized system. Our contribution consists in optimizing and training U-Net segmentation models from digital images of cartridge cases, intending to locate ROIs automatically. For the experiments, we used high-resolution 2D images from 1195 samples of cartridge cases fired by different 9MM firearms. Our results show that the segmentation models, trained on augmented data sets, exhibit a performance of 95.6% IoU (Intersection over Union) and 99.3% DC (Dice Coefficient) with a loss of 0.014 for the breech face images; and a performance of 95.9% IoU and 99.5% DC with a loss of 0.011 for the firing pin images. We observed that the natural shapes of predicted circles reduce the performance of segmentation models compared with perfect circles on ground truth masks suggesting that our method provide a more accurate segmentation of the real ROI shape. In practice, we believe that these results could be useful for firearms identification. In future work, the predictions may be used to evaluate the quality of delimiters on specimens in a database, or they could determine the region of interest on a cartridge case image.

Radiomic Features Associated with Lymphoma Development in the Parotid Glands of Patients with Primary Sjögren's Syndrome

Non-Hodgkin Lymphoma (NHL) represents a severe complication and the main cause of morbidity in patients with primary Sjögren's syndrome (pSS). This study aimed to assess the role of textural analysis (TA) in revealing lymphoma-associated imaging parameters in the parotid gland (PG) parenchyma of patients with pSS. This retrospective study included a total of 36 patients (54.93 ± 13.34 years old; 91.6% females) diagnosed with pSS according to the American College of Rheumatology and the European League Against Rheumatism criteria (24 subjects with pSS and no lymphomatous proliferation; 12 subjects with pSS and NHL development in the PG, confirmed by the histopathological analysis). All subjects underwent MR scanning between January 2018 and October 2022. The coronal STIR PROPELLER sequence was employed to segment PG and perform TA using the MaZda5 software. A total of 65 PGs underwent segmentation and texture feature extraction (48 PGs were included in the pSS control group, and 17 PGs were included in the pSS NHL group). Following parameter reduction techniques, univariate analysis, multivariate regression, and receiver operating characteristics (ROC) analysis, the following TA parameters proved to be independently associated with NHL development in pSS: CH4S6_Sum_Variance and CV4S6_Inverse_Difference_Moment, with an area under ROC of 0.800 and 0.875, respectively. The radiomic model (resulting by combining the two previously independent TA features), presented 94.12% sensitivity and 85.42% specificity in differentiating between the two studied groups, reaching the highest area under ROC of 0.931 for the chosen cutoff value of 1.556. This study suggests the potential role of radiomics in revealing new imaging biomarkers that might serve as useful predictors for lymphoma development in patients with pSS. Further research on multicentric cohorts is warranted to confirm the obtained results and the added benefit of TA in risk stratification for patients with pSS.

The Role of Radiomics in Salivary Gland Imaging: A Systematic Review and Radiomics Quality Assessment

Background: Radiomics of salivary gland imaging can support clinical decisions in different clinical scenarios, such as tumors, radiation-induced xerostomia and sialadenitis. This review aims to evaluate the methodological quality of radiomics studies on salivary gland imaging. Material and Methods: A systematic search was performed, and the methodological quality was evaluated using the radiomics quality score (RQS). Subgroup analyses according to the first author's professional role (medical or not medical), journal type (radiological journal or other) and the year of publication (2021 or before) were performed. The correlation of RQS with the number of patients was calculated. Results: Twenty-three articles were included (mean RQS 11.34 ± 3.68). Most studies well-documented the imaging protocol (87%), while neither prospective validations nor cost-effectiveness analyses were performed. None of the included studies provided open-source data. A statistically significant difference in RQS according to the year of publication was found (p = 0.009), with papers published in 2021 having slightly higher RQSs than older ones. No differences according to journal type or the first author's professional role were demonstrated. A moderate relationship between the overall RQS and the number of patients was found. Conclusions: Radiomics application in salivary gland imaging is increasing. Although its current clinical applicability can be affected by the somewhat inadequate quality of the papers, a significant improvement in radiomics methodologies has been demonstrated in the last year.

Sjögren syndrome: looking forward to the future

Primary Sjögren's syndrome (pSS) is a heterogeneous disease characterised by a wide spectrum of manifestations that vary according to the different stages of the disease and among different subsets of patients. The aim of this qualitative literature review is to summarise the recent advances that have been reported in pSS, ranging from the early phases to the established disease and its complications. We analysed the diagnostic, prognostic, and management aspects of pSS, with a look into future clinical and research developments. The early phases of pSS, usually antedating diagnosis, allow us to investigate the pathophysiology and risk factors of the overt disease, thus allowing better and timely patient stratification. Salivary gland ultrasound (SGUS) is emerging as a valid complementary, or even alternative, tool for histopathology in the diagnosis of pSS, due to a standardised scoring system with good agreement and performance. Other promising innovations include the application of artificial intelligence to SGUS, ultrasound-guided core needle biopsy, and a wide array of novel diagnostic and prognostic biomarkers. Stratifying pSS patients through the integration of clinical, laboratory, imaging, and histopathological data; differentiating between activity-related and damage-related manifestations; and identifying patients at higher risk of lymphoma development are essential steps for an optimal management and individualised treatment approach. As new treatment options are emerging for both glandular and systemic manifestations, there is a need for a more reliable treatment response evaluation. pSS is a complex and heterogeneous disease, and many distinct aspects should be considered in the different stages of the disease and subsets of patients. In recent years, efforts have been made to improve our understanding of the disease, and certainly in the coming years, some of these novelties will become part of our routine clinical practice, thus improving the management of pSS patients.

Recent Advances of Deep Learning for Computational Histopathology: Principles and Applications

With the remarkable success of digital histopathology, we have witnessed a rapid expansion of the use of computational methods for the analysis of digital pathology and biopsy image patches. However, the unprecedented scale and heterogeneous patterns of histopathological images have presented critical computational bottlenecks requiring new computational histopathology tools. Recently, deep learning technology has been extremely successful in the field of computer vision, which has also boosted considerable interest in digital pathology applications. Deep learning and its extensions have opened several avenues to tackle many challenging histopathological image analysis problems including color normalization, image segmentation, and the diagnosis/prognosis of human cancers. In this paper, we provide a comprehensive up-to-date review of the deep learning methods for digital H&E-stained pathology image analysis. Specifically, we first describe recent literature that uses deep learning for color normalization, which is one essential research direction for H&E-stained histopathological image analysis. Followed by the discussion of color normalization, we review applications of the deep learning method for various H&E-stained image analysis tasks such as nuclei and tissue segmentation. We also summarize several key clinical studies that use deep learning for the diagnosis and prognosis of human cancers from H&E-stained histopathological images. Finally, online resources and open research problems on pathological image analysis are also provided in this review for the convenience of researchers who are interested in this exciting field.