Value proposition of retinal imaging in Alzheimer's disease screening: A review of eight evolving trends.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Current diagnostic modalities of AD generally focus on detecting the presence of amyloid ß and tau protein in the brain (for example, positron emission tomography [PET] and cerebrospinal fluid testing), but these are limited by their high cost, invasiveness, and lack of expertise. Retinal imaging exhibits potential in AD screening and risk stratification, as the retina provides a platform for the optical visualization of the central nervous system in vivo, with vascular and neuronal changes that mirror brain pathology. Given the paradigm shift brought by advances in artificial intelligence and the emergence of disease-modifying therapies, this article aims to summarize and review the current literature to highlight 8 trends in an evolving landscape regarding the role and potential value of retinal imaging in AD screening.

A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children.

The increasing prevalence of myopia worldwide presents a significant public health challenge. A key strategy to combat myopia is with early detection and prediction in children as such examination allows for effective intervention using readily accessible imaging technique. To this end, we introduced DeepMyopia, an artificial intelligence (AI)-enabled decision support system to detect and predict myopia onset and facilitate targeted interventions for children at risk using routine retinal fundus images. Based on deep learning architecture, DeepMyopia had been trained and internally validated on a large cohort of retinal fundus images (n = 1,638,315) and then externally tested on datasets from seven sites in China (n = 22,060). Our results demonstrated robustness of DeepMyopia, with AUCs of 0.908, 0.813, and 0.810 for 1-, 2-, and 3-year myopia onset prediction with the internal test set, and AUCs of 0.796, 0.808, and 0.767 with the external test set. DeepMyopia also effectively stratified children into low- and high-risk groups (p < 0.001) in both test sets. In an emulated randomized controlled trial (eRCT) on the Shanghai outdoor cohort (n = 3303) where DeepMyopia showed effectiveness in myopia prevention compared to NonCyc-based model, with an adjusted relative reduction (ARR) of -17.8%, 95% CI: -29.4%, -6.4%. DeepMyopia-assisted interventions attained quality-adjusted life years (QALYs) of 0.75 (95% CI: 0.53, 1.04) per person and avoided blindness years of 13.54 (95% CI: 9.57, 18.83) per 1 million persons compared to natural lifestyle with no active intervention. Our findings demonstrated DeepMyopia as a reliable and efficient AI-based decision support system for intervention guidance for children.

Deep learning-based image quality assessment for optical coherence tomography macular scans: a multicentre study.

AIMS: To develop and externally test deep learning (DL) models for assessing the image quality of three-dimensional (3D) macular scans from Cirrus and Spectralis optical coherence tomography devices. METHODS: We retrospectively collected two data sets including 2277 Cirrus 3D scans and 1557 Spectralis 3D scans, respectively, for training (70%), fine-tuning (10%) and internal validation (20%) from electronic medical and research records at The Chinese University of Hong Kong Eye Centre and the Hong Kong Eye Hospital. Scans with various eye diseases (eg, diabetic macular oedema, age-related macular degeneration, polypoidal choroidal vasculopathy and pathological myopia), and scans of normal eyes from adults and children were included. Two graders labelled each 3D scan as gradable or ungradable, according to standardised criteria. We used a 3D version of the residual network (ResNet)-18 for Cirrus 3D scans and a multiple-instance learning pipline with ResNet-18 for Spectralis 3D scans. Two deep learning (DL) models were further tested via three unseen Cirrus data sets from Singapore and five unseen Spectralis data sets from India, Australia and Hong Kong, respectively. RESULTS: In the internal validation, the models achieved the area under curves (AUCs) of 0.930 (0.885-0.976) and 0.906 (0.863-0.948) for assessing the Cirrus 3D scans and Spectralis 3D scans, respectively. In the external testing, the models showed robust performance with AUCs ranging from 0.832 (0.730-0.934) to 0.930 (0.906-0.953) and 0.891 (0.836-0.945) to 0.962 (0.918-1.000), respectively. CONCLUSIONS: Our models could be used for filtering out ungradable 3D scans and further incorporated with a disease-detection DL model, allowing a fully automated eye disease detection workflow.

Frequency and clinicopathologic features of renal low-grade oncocytic tumour and eosinophilic vacuolated tumour: reclassification of 605 eosinophilic tumours including patients managed with active surveillance.

AIMS: Low-grade oncocytic tumour (LOT) and eosinophilic vacuolated tumour (EVT) are recently described emerging entities, which demonstrate distinct features but are not yet recognised as separate neoplasms in the fifth WHO classification. Published series to date have been largely multi-institutional and based on surgically resected tumours. This study aims to determine the frequency, clinicopathologic features and outcome of LOT and EVT in a single institutional series of oncocytic/eosinophilic renal neoplasms, including patients managed with active surveillance and non-surgical intervention. METHODS AND RESULTS: Cases were identified from a consecutive institutional series of in-house renal tumours diagnosed on biopsy and/or nephrectomy (2003-2023). Tumours with a diagnosis or differential diagnosis of oncocytoma, chromophobe renal cell carcinoma or oncocytic neoplasm not otherwise specified (including LOT, EVT and tumours with overlapping hybrid features) were retrospectively reviewed and classified/reclassified.In total, 605 oncocytic/eosinophilic renal neoplasms were reviewed, among which 33 LOT (5.5%) and 5 EVT (0.8%) were identified. LOT were CK7+, CD117- and GATA3+ (94%). EVT were CD117+, CK7 focal+ (80%) and cathepsin K+ (80%). At the median follow-up of 34 months (range 2-253) and 56 months (range 8-90) for LOT and EVT, respectively, there was no evidence of recurrence following ablation/surgical resection, metastasis or death from disease for all patients, including the 22 managed with active surveillance (20 LOT and 2 EVT). CONCLUSIONS: LOT and EVT comprised a minority of oncocytic renal neoplasms in this series. We report a large institutional series including patients managed non-surgically, with no adverse outcome, adding to the existing literature indicating a benign outcome.

Integrated image-based deep learning and language models for primary diabetes care.

Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.

Randomized 20-year infancy-onset dietary intervention, life-long cardiovascular risk factors and retinal microvasculature.

BACKGROUND AND AIMS: Retinal microvasculature characteristics predict cardiovascular morbidity and mortality. This study investigated associations of lifelong cardiovascular risk factors and effects of dietary intervention on retinal microvasculature in young adulthood. METHODS: The cohort is derived from the longitudinal Special Turku Coronary Risk Factor Intervention Project study. The Special Turku Coronary Risk Factor Intervention Project is a 20-year infancy-onset randomized controlled dietary intervention study with frequent study visits and follow-up extending to age 26 years. The dietary intervention aimed at a heart-healthy diet. Fundus photographs were taken at the 26-year follow-up, and microvascular measures [arteriolar and venular diameters, tortuosity (simple and curvature) and fractal dimensions] were derived (n = 486). Cumulative exposure as the area under the curve for cardiovascular risk factors and dietary components was determined for the longest available time period (e.g. from age 7 months to 26 years). RESULTS: The dietary intervention had a favourable effect on retinal microvasculature resulting in less tortuous arterioles and venules and increased arteriolar fractal dimension in the intervention group when compared with the control group. The intervention effects were found even when controlled for the cumulative cardiovascular risk factors. Reduced lifelong cumulative intake of saturated fats, main target of the intervention, was also associated with less tortuous venules. Several lifelong cumulative risk factors were independently associated with the retinal microvascular measures, e.g. cumulative systolic blood pressure with narrower arterioles. CONCLUSIONS: Infancy-onset 20-year dietary intervention had favourable effects on the retinal microvasculature in young adulthood. Several lifelong cumulative cardiovascular risk factors were independently associated with retinal microvascular structure.

Fit-for-Purpose Ki-67 Immunohistochemistry Assays for Breast Cancer.

New therapies are being developed for breast cancer, and in this process, some "old" biomarkers are reutilized and given a new purpose. It is not always recognized that by changing a biomarker's intended use, a new biomarker assay is created. The Ki-67 biomarker is typically assessed by immunohistochemistry (IHC) to provide a proliferative index in breast cancer. Canadian laboratories assessed the analytical performance and diagnostic accuracy of their Ki-67 IHC laboratory-developed tests (LDTs) of relevance for the LDTs' clinical utility. Canadian clinical IHC laboratories enrolled in the Canadian Biomarker Quality Assurance Pilot Run for Ki-67 in breast cancer by invitation. The Dako Ki-67 IHC pharmDx assay was employed as a study reference assay. The Dako central laboratory was the reference laboratory. Participants received unstained slides of breast cancer tissue microarrays with 32 cases and performed their in-house Ki-67 assays. The results were assessed using QuPath, an open-source software application for bioimage analysis. Positive percent agreement (PPA, sensitivity) and negative percent agreement (NPA, specificity) were calculated against the Dako Ki-67 IHC pharmDx assay for 5%, 10%, 20%, and 30% cutoffs. Overall, PPA and NPA varied depending on the selected cutoff; participants were more successful with 5% and 10%, than with 20% and 30% cutoffs. Only 4 of 16 laboratories had robust IHC protocols with acceptable PPA for all cutoffs. The lowest PPA for the 5% cutoff was 85%, for 10% was 63%, for 20% was 14%, and for 30% was 13%. The lowest NPA for the 5% cutoff was 50%, for 10% was 33%, for 20% was 50%, and for 30% was 57%. Despite many years of international efforts to standardize IHC testing for Ki-67 in breast cancer, our results indicate that Canadian clinical LDTs have a wide analytical sensitivity range and poor agreement for 20% and 30% cutoffs. The poor agreement was not due to the readout but rather due to IHC protocol conditions. International Ki-67 in Breast Cancer Working Group (IKWG) recommendations related to Ki-67 IHC standardization cannot take full effect without reliable fit-for-purpose reference materials that are required for the initial assay calibration, assay performance monitoring, and proficiency testing.

Repeatability, interocular correlation and agreement of optic nerve head vessel density in healthy eyes: a swept-source optical coherence tomographic angiography study.

AIM: To assess the repeatability, interocular correlation, and agreement of quantitative swept-source optical coherence tomography angiography (OCTA) optic nerve head (ONH) parameters in healthy subjects. METHODS: Thirty-three healthy subjects were enrolled. The ONH of both eyes were imaged four times by a swept-source-OCTA using a 3 mm ×3 mm scanning protocol. Images of the radial peripapillary capillary were analyzed by a customized Matlab program, and the vessel density, fractal dimension, and vessel diameter index were measured. The repeatability of the four scans was determined by the intraclass correlation coefficient (ICC). The most well-centered optic disc from the four repeated scans was then selected for the interocular correlation and agreement analysis using the Pearson correlation coefficient, ICC and Bland-Altman plots. RESULTS: All swept-source-OCTA ONH parameters exhibited certain repeatability, with ICC>0.760 and coefficient of variation (CoV)≤7.301%. The obvious interocular correlation was observed for papillary vessel density (ICC=0.857), vessel diameter index (ICC=0.857) and fractal dimension (ICC=0.906), while circumpapillary vessel density exhibited moderate interocular correlation (ICC=0.687). Bland-Altman plots revealed an agreement range of -5.26% to 6.21% for circumpapillary vessel density. CONCLUSION: OCTA ONH parameters demonstrate good repeatability in healthy subjects. The interocular correlations of papillary vessel density, fractal dimension and vessel diameter index are high, but the correlation for circumpapillary vessel density is moderate.

Inter-Individual Topographic Variation of Choroidal Thickness in Healthy Eyes on Swept-Source Optical Coherence Tomography.

Purpose: To investigate the topographic characters of inter-individual variations of the macular choroidal thickness (CT). Methods: This was a retrospective study. Macular CT data for 900 0.2 × 0.2-mm grids from 410 healthy eyes were collected from swept-source optical coherence tomography. Following the analysis of factors associated with mean CT, the ß-coefficients of the included associated factors in each grid were summarized for choroidal thickness changes analysis. Additionally, the coefficient of variance (CoV), coefficient of determination (CoD), and coefficient of variance unexplained (CoVU) for CT were calculated in each individual grid to investigate the inter-individual choroidal variations pattern. Results: Sex (ß = -17.26, female vs. male), age (ß = -1.61, per 1 year), and axial length (ß = -18.62, per 1 mm) were associated with mean macular CT. Females had a thinner choroid in all 900 grids (0.5-26.9 µm). As age increased, the CT noticeably decreased (8.74-19.87 µm per 10 years) in the temporal regions. With axial length elongation, the thinning (7.94-24.91 µm per 1 mm) was more evident in subfoveal and nasal regions. Both the CoV (34.69%-58.00%) and CoVU (23.05%-40.78%) were lower in the temporal regions, whereas the CoD (18.41%-39.66%) was higher in the temporal regions. Conclusions: Choroidal thinning is more predominant in the subfoveal and nasal regions with axial length elongation, but in the temporal region with aging. The inter-individual variation of CT is higher and less determined by sex, age, or axial length in the nasal regions. Translational Relevance: Topographic variation should be considered when interpreting choroidal thickness.

Somatic Tumor Testing in Prostate Cancer: Experience of a Tertiary Care Center Including Pathologist-Driven Reflex Testing of Localized Tumors at Diagnosis.

Somatic tumor testing in prostate cancer (PCa) can guide treatment options by identifying clinically actionable variants in DNA damage repair genes, including acquired variants not detected using germline testing alone. Guidelines currently recommend performing somatic tumor testing in metastatic PCa, whereas there is no consensus on the role of testing in regional disease, and the optimal testing strategy is only evolving. This study evaluates the frequency, distribution, and pathologic correlates of somatic DNA damage repair mutations in metastatic and localized PCa following the implementation of pathologist-driven reflex testing at diagnosis. A cohort of 516 PCa samples were sequenced using a custom next-generation sequencing panel including homologous recombination repair and mismatch repair genes. Variants were classified based on the Association for Molecular Pathology/American Society of Clinical Oncology/College of American Pathologists guidelines. In total, 183 (35.5%) patients had at least one variant, which is as follows: 72 of 516 (13.9%) patients had at least 1 tier I or tier II variant, whereas 111 of 516 (21.5%) patients had a tier III variant. Tier I/II variant(s) were identified in 27% (12/44) of metastatic biopsy samples and 13% (61/472) of primary samples. Overall, 12% (62/516) of patients had at least 1 tier I/II variant in a homologous recombination repair gene, whereas 2.9% (10/516) had at least 1 tier I/II variant in a mismatch repair gene. The presence of a tier I/II variant was not significantly associated with the grade group (GG) or presence of intraductal/cribriform carcinoma in the primary tumor. Among the 309 reflex-tested hormone-naive primary tumors, tier I/II variants were identified in 10% (31/309) of cases, which is as follows: 9.2% (9/98) GG2; 9% (9/100) GG3; 9.1% (4/44) GG4; and 13.4% (9/67) GG5 cases. Our findings confirm the use of somatic tumor testing in detecting variants of clinical significance in PCa and provide insights that can inform the design of testing strategies. Pathologist-initiated reflex testing streamlines the availability of the results for clinical decision-making; however, pathologic parameters such as GG and the presence of intraductal/cribriform carcinoma may not be reliable to guide patient selection.

DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images.

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

Association between deep learning measured retinal vessel calibre and incident myocardial infarction in a retrospective cohort from the UK Biobank.

BACKGROUND: Cardiovascular disease is a leading cause of global death. Prospective population-based studies have found that changes in retinal microvasculature are associated with the development of coronary artery disease. Recently, artificial intelligence deep learning (DL) algorithms have been developed for the fully automated assessment of retinal vessel calibres. METHODS: In this study, we validate the association between retinal vessel calibres measured by a DL system (Singapore I Vessel Assessment) and incident myocardial infarction (MI) and assess its incremental performance in discriminating patients with and without MI when added to risk prediction models, using a large UK Biobank cohort. RESULTS: Retinal arteriolar narrowing was significantly associated with incident MI in both the age, gender and fellow calibre-adjusted (HR=1.67 (95% CI: 1.19 to 2.36)) and multivariable models (HR=1.64 (95% CI: 1.16 to 2.32)) adjusted for age, gender and other cardiovascular risk factors such as blood pressure, diabetes mellitus (DM) and cholesterol status. The area under the receiver operating characteristic curve increased from 0.738 to 0.745 (p=0.018) in the age-gender-adjusted model and from 0.782 to 0.787 (p=0.010) in the multivariable model. The continuous net reclassification improvements (NRIs) were significant in the age and gender-adjusted (NRI=21.56 (95% CI: 3.33 to 33.42)) and the multivariable models (NRI=18.35 (95% CI: 6.27 to 32.61)). In the subgroup analysis, similar associations between retinal arteriolar narrowing and incident MI were observed, particularly for men (HR=1.62 (95% CI: 1.07 to 2.46)), non-smokers (HR=1.65 (95% CI: 1.13 to 2.42)), patients without DM (HR=1.73 (95% CI: 1.19 to 2.51)) and hypertensive patients (HR=1.95 (95% CI: 1.30 to 2.93)) in the multivariable models. CONCLUSION: Our results support DL-based retinal vessel measurements as markers of incident MI in a predominantly Caucasian population.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.

Review of emerging trends and projection of future developments in large language models research in ophthalmology.

BACKGROUND: Large language models (LLMs) are fast emerging as potent tools in healthcare, including ophthalmology. This systematic review offers a twofold contribution: it summarises current trends in ophthalmology-related LLM research and projects future directions for this burgeoning field. METHODS: We systematically searched across various databases (PubMed, Europe PMC, Scopus and Web of Science) for articles related to LLM use in ophthalmology, published between 1 January 2022 and 31 July 2023. Selected articles were summarised, and categorised by type (editorial, commentary, original research, etc) and their research focus (eg, evaluating ChatGPT's performance in ophthalmology examinations or clinical tasks). FINDINGS: We identified 32 articles meeting our criteria, published between January and July 2023, with a peak in June (n=12). Most were original research evaluating LLMs' proficiency in clinically related tasks (n=9). Studies demonstrated that ChatGPT-4.0 outperformed its predecessor, ChatGPT-3.5, in ophthalmology exams. Furthermore, ChatGPT excelled in constructing discharge notes (n=2), evaluating diagnoses (n=2) and answering general medical queries (n=6). However, it struggled with generating scientific articles or abstracts (n=3) and answering specific subdomain questions, especially those regarding specific treatment options (n=2). ChatGPT's performance relative to other LLMs (Google's Bard, Microsoft's Bing) varied by study design. Ethical concerns such as data hallucination (n=27), authorship (n=5) and data privacy (n=2) were frequently cited. INTERPRETATION: While LLMs hold transformative potential for healthcare and ophthalmology, concerns over accountability, accuracy and data security remain. Future research should focus on application programming interface integration, comparative assessments of popular LLMs, their ability to interpret image-based data and the establishment of standardised evaluation frameworks.

Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis.

BACKGROUND: Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention. PURPOSE: To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances. DATA SOURCES: We searched seven electronic libraries up to 12 February 2023. STUDY SELECTION: We included studies using AI to detect DME from FP or OCT images. DATA EXTRACTION: We extracted study characteristics and performance parameters. DATA SYNTHESIS: Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation. LIMITATIONS: Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation. CONCLUSIONS: This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans.

Effects of firsthand tobacco smoking on retinal vessel caliber: a systematic review and meta-analysis.

PURPOSE: To review the effects of firsthand tobacco smoking on central retinal arteriolar equivalent (CRAE) and central retinal venular equivalent (CRVE) of firsthand tobacco smokers. METHODS: We performed a search on EMBASE and PubMed for studies up to 15th July 2022. Two independent reviewers selected studies with baseline data of CRAE and CRVE of current smokers, nonsmokers, and former smokers. Initial search identified 893 studies, of which 10 were included in the meta-analysis. Two independent reviewers extracted data from the included studies. The quality of studies was assessed by the Newcastle-Ottawa Scale. RESULTS: In this meta-analysis, 7431 nonsmokers, 2448 current smokers and 5786 former smokers, as well as 7404 nonsmokers, 2430 current smokers and 5763 former smokers were included in CRAE and CRVE analysis respectively. Nonsmokers had narrower CRVE (Weighted mean difference [WMD], -12.15; 95% CI, -17.33 - -6.96) and CRAE (WMD, -4.77; 95% CI, -7.96 - -1.57) than current smokers, and narrower CRVE (WMD, -3.08; 95% CI, -6.06 - -0.11) than former smokers. Current smokers had wider CRVE (WMD, 10.42; 95% CI, 7.80 - 13.04) and CRAE (WMD, 7.05; 95% CI, 6.65 - 7.46) than former smokers. Subgroup analysis and sensitivity analysis were performed. CONCLUSION: Firsthand tobacco smoking resulted in wider CRAE and CRVE in current and former smokers, particularly in CRVE, and such changes may not be reversible after smoking cessation. Therefore, retinal vessel caliber may reflect the effects of firsthand tobacco smoking and be used to estimate the risk of cardiovascular diseases.

AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.

Cardiovascular disease (CVD) remains the leading cause of death worldwide. Assessing of CVD risk plays an essential role in identifying individuals at higher risk and enables the implementation of targeted intervention strategies, leading to improved CVD prevalence reduction and patient survival rates. The ocular vasculature, particularly the retinal vasculature, has emerged as a potential means for CVD risk stratification due to its anatomical similarities and physiological characteristics shared with other vital organs, such as the brain and heart. The integration of artificial intelligence (AI) into ocular imaging has the potential to overcome limitations associated with traditional semi-automated image analysis, including inefficiency and manual measurement errors. Furthermore, AI techniques may uncover novel and subtle features that contribute to the identification of ocular biomarkers associated with CVD. This review provides a comprehensive overview of advancements made in AI-based ocular image analysis for predicting CVD, including the prediction of CVD risk factors, the replacement of traditional CVD biomarkers (e.g., CT-scan measured coronary artery calcium score), and the prediction of symptomatic CVD events. The review covers a range of ocular imaging modalities, including colour fundus photography, optical coherence tomography, and optical coherence tomography angiography, and other types of images like external eye images. Additionally, the review addresses the current limitations of AI research in this field and discusses the challenges associated with translating AI algorithms into clinical practice.

Canadian Multicentric Pan-TRK (CANTRK) Immunohistochemistry Harmonization Study.

Tumor-agnostic testing for NTRK1-3 gene rearrangements is required to identify patients who may benefit from TRK inhibitor therapies. The overarching objective of this study was to establish a high-quality pan-TRK immunohistochemistry (IHC) screening assay among 18 large regional pathology laboratories across Canada using pan-TRK monoclonal antibody clone EPR17341 in a ring study design. TRK-fusion positive and negative tumor samples were collected from participating sites, with fusion status confirmed by panel next-generation sequencing assays. Each laboratory received: (1) unstained sections from 30 cases of TRK-fusion-positive or -negative tumors, (2) 2 types of reference standards: TRK calibrator slides and IHC critical assay performance controls (iCAPCs), (3) EPR17341 antibody, and (4) suggestions for developing IHC protocols. Participants were asked to optimize the IHC protocol for their instruments and detection systems by using iCAPCs, to stain the 30 study cases, and to report the percentage scores for membranous, cytoplasmic, and nuclear staining. TRK calibrators were used to assess the analytical sensitivity of IHC protocols developed by using the 2 reference standards. Fifteen of 18 laboratories achieved diagnostic sensitivity of 100% against next-generation sequencing. The diagnostic specificity ranged from 40% to 90%. The results did not differ significantly between positive scores based on the presence of any type of staining vs the presence of overall staining in ≥1% of cells. The median limit of detection measured by TRK calibrators was 76,000 molecules/cell (range 38,000 to >200,000 molecules/cell). Three different patterns of staining were observed in 19 TRK-positive cases, cytoplasmic-only in 7 samples, nuclear and cytoplasmic in 9 samples, and cytoplasmic and membranous in 3 samples. The Canadian multicentric pan-TRK study illustrates a successful strategy to accelerate the multicenter harmonization and implementation of pan-TRK immunohistochemical screening that achieves high diagnostic sensitivity by using laboratory-developed tests where laboratories used centrally developed reference materials. The measurement of analytical sensitivity by using TRK calibrators provided additional insights into IHC protocol performance.

PD-L1 expression in fine-needle aspiration cell blocks of head and neck squamous-cell carcinoma and its cytohistological concordance.

BACKGROUND: PD-L1 immunoexpression in head and neck squamous-cell carcinoma (HNSCC) determines immunotherapy eligibility. Patients are often diagnosed using fine-needle aspiration (FNA) of metastatic lymph nodes, however, the cytohistologic correlation of the combined positive score (CPS) is largely unknown. METHODS: This study retrospectively identified 96 paired histologic (HS) and cytologic specimens (CyS), between 2016 and 2020, diagnosed with HNSCC. Cases with <100 tumor cells (n = 54) or missing block(s) (n = 8) were excluded. All 34 case pairs were scored with CPS using the PD-L1 22C3 pharmDx assay at clinically relevant cut-offs of <1%, 1%-19%, and ≥20% independently by three observers blinded to the case pairs (CyS with corresponding HS). RESULTS: The CPS (<1/1-19/≥20) for CyS and HS were as follows: 10(29.4%)/10(29.4%)/14(41.2%) and 2(5.9%)/13(38.2%)/19(55.9%), respectively. There was fair overall cytohistologic agreement (OA) of 76.5% (k = 0.261) at the CPS cut-off of 1. The OA did not differ significantly between site-matched (n = 13) and -unmatched (n = 21) case pairs (p = .4653). CyS has a specificity and positive predictive value (PPV) of 100% but a negative predictive value (NPV) of only 20% as compared to its paired HS. CONCLUSIONS: Our study demonstrates fair CPS cytohistologic correlation in HNSCC specimens using the PD-L1 IHC 22C3 pharmDx assay with high PPV but low NPV. This suggest that determining PD-L1 status in FNA specimens can play an important role in the clinical management of HNSCC patients.