Clinical significance of combined tumour-infiltrating lymphocytes and microsatellite instability status in colorectal cancer: a systematic review and network meta-analysis.

BACKGROUND: Microsatellite instability (MSI) status and tumour-infiltrating lymphocytes (TIL) are established prognostic factors in colorectal cancer. Previous studies evaluating the combination of TIL and MSI status identified distinct colorectal cancer subtypes with unique prognostic associations. However, these studies were often limited by sample size, particularly for MSI-high (MSI-H) tumours, and there is no comprehensive summary of the available evidence. We aimed to review the literature to compare the survival outcomes associated with the subtypes derived from the integrated MSI-TIL classification in patients with colorectal cancer. METHODS: In this systematic review and network meta-analysis, we searched PubMed, Embase, Scopus, and the Cochrane Library without language restrictions, for articles published between Jan 1, 1990, and March 13, 2024. Patient cohorts comparing different combinations of TIL (high or low) and MSI status (MSI or microsatellite stable [MSS]) in patients with surgically resected colorectal cancer were included. Studies were excluded if they focused on neoadjuvant therapy or on other immune markers such as B cells or macrophages. Methodological quality assessment was done with the Newcastle-Ottawa scale; data appraisal and extraction was done independently by two reviewers. Summary estimates were extracted from published reports. The primary outcomes were overall survival, disease-free survival, and cancer-specific survival. A frequentist network meta-analysis was done to compare hazard ratios (HRs) and 95% CI for each outcome. The MSI-TIL subgroups were prognostically ranked based on P-score, bias, magnitude, and precision of associations with each outcome. The protocol is registered with PROSPERO (CRD42023461108). FINDINGS: Of 302 studies initially identified, 21 studies (comprising 14 028 patients) were included in the systematic review and 19 (13 029 patients) in the meta-analysis. Nine studies were identified with a low risk of bias and the remaining ten had a moderate risk of bias. The MSI-TIL-high (MSI-TIL-H) subtype exhibited longer overall survival (HR 0·45, 95% CI 0·34-0·61; I2=77·7%), disease-free survival (0·43, 0·32-0·58; I2=61·6%), and cancer-specific survival (0·53, 0·43-0·66; I2=0%), followed by the MSS-TIL-H subtype for overall survival (HR 0·53, 0·41-0·69; I2=77·7%), disease-free survival (0·52, 0·41-0·64; I2=61·6%), and cancer-specific survival (0·55, 0·47-0·64; I2=0%) than did patients with MSS-TIL-low tumours (MSS-TIL-L). Patients with the MSI-TIL-L subtype had similar overall survival (0·88, 0·66-1·18; I2=77·7%) and disease-free survival (0·93, 0·69-1·26; I2=61·6%), but a modestly longer cancer-specific survival (0·72, 0·57-0·90; I2=0%) than did the MSS-TIL-L subtype. Results from the direct and indirect evidence were strongly congruous. INTERPRETATION: The findings from this network meta-analysis suggest that better survival was only observed among patients with TIL-H colorectal cancer, regardless of MSI or MSS status. The integrated MSI-TIL classification should be further explored as a predictive tool for clinical decision-making in early-stage colorectal cancer. FUNDING: German Research Council (HO 5117/2-2).

Automated curation of large-scale cancer histopathology image datasets using deep learning.

BACKGROUND: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION: Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

Factors associated with immunological non-response after ART initiation: a retrospective observational cohort study.

BACKGROUND: Among people living with HIV (PLHIV) on antiretroviral therapy (ART), the mortality of immunological non-responders (INRs) is higher than that of immunological responders (IRs). However, factors associated with immunological non-response following ART are not well documented. METHODS: We obtained data for HIV patients from the National Free Antiretroviral Treatment Program database in China. Patients were grouped into IRs (CD4 cell count ≥ 350 cells/µl after 24 months' treatment), immunological incomplete responders (ICRs) (200-350 cells/µl) and INRs (< 200 cells/µl). Multivariable logistic regression was used to assess factors associated with immunological non-response. RESULTS: A total of 3900 PLHIV were included, among whom 2309 (59.2%) were IRs, 1206 (30.9%) ICRs and 385 (9.9%) INRs. In multivariable analysis, immunological non-response was associated with being male (2.07, 1.39-3.09), older age [40-49 years (vs. 18-29 years): 2.05, 1.29-3.25; 50-59 years: 4.04, 2.33-7.00; ≥ 60 years: 5.51, 2.84-10.67], HBV co-infection (1.63, 1.14-2.34), HCV co-infection (2.01, 1.01-4.02), lower CD4 + T cell count [50-200 cells/µl (vs. 200-350 cells/µl): 40.20, 16.83-96.01; < 50 cells/µl: 215.67, 85.62-543.26] and lower CD4/CD8 ratio (2.93, 1.98-4.34) at baseline. Compared with patients treated with non-nucleoside reverse transcriptase inhibitors (NNRTIs) based regimens, those receiving protease inhibitors (PIs) based regimens were less likely to be INRs (0.47, 0.26-0.82). CONCLUSIONS: We found a sizable immunological non-response rate among HIV-infected patients. Being male, older age, coinfection with HBV and HCV, lower CD4 + T cell count and lower CD4/CD8 ratio are risk factors of immunological non-response, whereas PIs-based regimens is a protective factor.

CpG-biomarkers in tumor tissue and prediction models for the survival of colorectal cancer: A systematic review and external validation study.

The research aimed to identify previously published CpG-methylation-based prognostic biomarkers and prediction models for colorectal cancer (CRC) prognosis and validate them in a large external cohort. A systematic search was conducted, analyzing 298 unique CpGs and 12 CpG-based prognostic models from 28 studies. After adjustment for clinical variables, 48 CpGs and five prognostic models were confirmed to be associated with survival. However, the discrimination ability of the models was insufficient, with area under the receiver operating characteristic curves ranging from 0.53 to 0.62. Calibration accuracy was mostly poor, and no significant added prognostic value beyond traditional clinical variables was observed. All prognostic models were rated at high risk of bias. While a fraction of CpGs showed potential clinical utility and generalizability, the CpG-based prognostic models performed poorly and lacked clinical relevance.

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study.

BACKGROUND: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine. METHODS: In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables. FINDINGS: We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors. INTERPRETATION: Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work. FUNDING: The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.

Encrypted federated learning for secure decentralized collaboration in cancer image analysis.

Artificial intelligence (AI) has a multitude of applications in cancer research and oncology. However, the training of AI systems is impeded by the limited availability of large datasets due to data protection requirements and other regulatory obstacles. Federated and swarm learning represent possible solutions to this problem by collaboratively training AI models while avoiding data transfer. However, in these decentralized methods, weight updates are still transferred to the aggregation server for merging the models. This leaves the possibility for a breach of data privacy, for example by model inversion or membership inference attacks by untrusted servers. Somewhat-homomorphically-encrypted federated learning (SHEFL) is a solution to this problem because only encrypted weights are transferred, and model updates are performed in the encrypted space. Here, we demonstrate the first successful implementation of SHEFL in a range of clinically relevant tasks in cancer image analysis on multicentric datasets in radiology and histopathology. We show that SHEFL enables the training of AI models which outperform locally trained models and perform on par with models which are centrally trained. In the future, SHEFL can enable multiple institutions to co-train AI models without forsaking data governance and without ever transmitting any decryptable data to untrusted servers.

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies.

BACKGROUND: DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. METHODS: We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively. RESULTS: Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. CONCLUSIONS: There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study.

Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.

Voluntary Medical Male Circumcision and Incident HIV Infection Among Men Who Have Sex With Men in China (The CoM Study): Protocol for a Randomized Controlled Trial.

BACKGROUND: Systematic reviews and meta-analyses based on observational studies have shown voluntary medical male circumcision (VMMC) may reduce HIV risk among men who have sex with men (MSM). There is a lack of randomized controlled trial (RCT) data assessing the efficacy of VMMC. OBJECTIVE: The primary objective of this study was to assess the efficacy of VMMC for preventing HIV acquisition among MSM who primarily engage in insertive anal sex. METHODS: A multicenter RCT will be conducted among MSM in 8 cities in China. Eligible participants are men aged 18-49 years who self-report ≥2 male sex partners in the past 6 months, predominantly practice insertive anal sex, and are willing to undergo circumcision. Interested men who satisfy inclusion criteria will be tested for HIV 1 month before enrollment and at enrollment, and only those who are HIV negative will be enrolled. At baseline, all enrolled participants will be asked to report sociodemographic characteristics and sexual behaviors; provide a blood sample for HIV, syphilis, and herpes simplex virus type 2 testing; and provide a penile swab for human papillomavirus testing. Participants will be randomly assigned to the intervention or control group. Those in the intervention group will receive VMMC and undergo a web-based weekly follow-up assessment of postsurgery healing for 6 consecutive weeks. All participants will be tested for HIV at 3-, 6-, 9-, and 12-month follow-ups. All participants will also be asked to report sexual behaviors and undergo repeat herpes simplex virus type 2 and human papillomavirus testing at 6- and 12-month follow-ups. The primary end point is HIV seroconversion. Secondary end points are the safety and satisfaction with VMMC and the changes in sexual behaviors after VMMC. The grouped censored data will be analyzed by intention-to-treat approach. RESULTS: Recruitment for the RCT began in August 2020 and continued through July 2022. Data collection is expected to be completed by July 2023, and full data analysis is going to be completed by September 2023. CONCLUSIONS: This study will be the first RCT to assess the efficacy of VMMC in preventing HIV infection among MSM. Results from this trial will provide preliminary evidence for the potential efficacy of VMMC to reduce incident HIV infection among MSM. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2000039436; https://www.chictr.org.cn/showproj.html?proj=63369. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/47160.

Spatiotemporal Distribution of HIV Self-testing Kits Purchased on the Web and Implications for HIV Prevention in China: Population-Based Study.

BACKGROUND: HIV self-testing (HIVST) holds great promise for expanding HIV testing. Nonetheless, large-scale data on HIVST behavior are scant. Millions of HIVST kits are sold through e-commerce platforms each year. OBJECTIVE: This study aims to analyze the spatiotemporal distribution of the HIVST kit-purchasing population (HIVSTKPP) in China. METHODS: Deidentified transaction data were retrieved from a leading e-commerce platform in China. A joinpoint regression model was used to examine annual trends of the HIVSTKPP rates by calculating average annual percentage change. Bayesian spatiotemporal analysis was performed to locate hot spots with HIVSTKPP rates. Spatial autocorrelation analysis and space-time cluster analysis were conducted to identify clusters of HIVSTKPP. High-high clusters of HIVSTKPP can be identified by spatial autocorrelation analysis, and high-high clusters indicate that a region and its surrounding region jointly had a higher-than-average HIVSTKPP rate. Spatial regression analysis was used to elucidate the association between the number of HIV testing facilities, urbanization ratio (the proportion of urban population in the total population), and gross domestic product per capita and the HIVSTKPP. RESULTS: Between January 1, 2016, and December 31, 2019, a total of 2.18 million anonymous persons in China placed 4.15 million orders and purchased 4.51 million HIVST kits on the web. In each of these 4 years, the observed monthly size of the HIVSTKPP peaked in December, the month of World AIDS Day. HIVSTKPP rates per 100,000 population significantly increased from 20.62 in 2016 to 64.82 in 2019 (average annual percentage change=48.2%; P<.001). Hot spots were mainly located in municipalities, provincial capitals, and large cities, whereas high-high clusters and high-demand clusters were predominantly detected in cities along the southeast coast. We found positive correlations between a region's number of HIV testing facilities, urbanization ratio, and gross domestic product per capita and the HIVSTKPP. CONCLUSIONS: Our study identified key areas with larger demand for HIVST kits for public health policy makers to reallocate resources and optimize the HIV care continuum. Further research combining spatiotemporal patterns of HIVST with HIV surveillance data is urgently needed to identify potential gaps in current HIV-monitoring practices.

Awareness and uptake of non-occupational post-exposure prophylaxis (nPEP) among gay, bisexual and other men who have sex with men in China: a cross-sectional study.

Non-occupational post-exposure prophylaxis (nPEP) is recommended to reduce the likelihood of HIV infection after potential exposure. However, little is known about this practice among Chinese gay, bisexual and other men who have sex with men (GBM). GBM were enrolled from both centers for disease prevention and control (CDCs) and community health centers in six cities in China. Multivariable logistics regression was used to assess factors associated with awareness of and willingness to use nPEP. A total of 516 eligible GBM were included, 67.2% of whom were aware of nPEP, 76.0% were willing to use nPEP, and 2.3% had ever used nPEP. GBM who had college or higher education, had disclosed sexual orientation, had increased number of male sex partners in the last 6 months, and had ever tested for HIV were more likely to be aware of nPEP. Willingness to use nPEP was significantly associated with college or higher education, STI history, gay app use, and awareness of nPEP prior to study. Uptake of nPEP is still low among MSM in China. Efforts are needed to improve awareness of and access to nPEP among GBM, especially those at higher risk of HIV infection.

Incidence and mortality of non-AIDS-defining cancers among people living with HIV: A systematic review and meta-analysis.

Background: Non-AIDS-defining cancers (NADCs) are now becoming a rising cause of morbidity among people living with HIV (PLHIV) in the highly active antiretroviral therapy (HAART) era. We conducted a systematic review and meta-analysis to estimate the summary risk of incidence and mortality of a wide range of NADCs among PLHIV compared with the general population. Methods: This systematic review and meta-analysis was registered in the PROSPERO (registration number CRD42020222020). We searched PubMed, EMBASE, Cochrane library, and Web of Science for relevant studies published before Jan 24, 2022. Cohort or registry linkage studies comparing the incidence or mortality of individual NADCs in PLHIV with that in the general population were included. Studies simply reporting outcomes of cancer precursor lesions or combined NADCs were excluded. We calculated pooled standardised incidence (SIRs) and standardised mortality ratios (SMRs) and their 95% confidence intervals (CIs) using random-effects models, and used robust variance estimation to account for non-independence in study-level effect sizes. Findings: We identified 92 publications arising from 46 independent studies including 7 articles out of 7 studies from developing countries. Among the 40 types of NADCs investigated, all of the 20 infection-related NADCs, cancers related with human papillomavirus infection in particular, and half of the 20 non-infection-related NADCs occurred in excess in PLHIV compared with the general population. This risk pattern was consistent in most WHO regions and in both high-income and low-and middle-income countries. The increased SIRs for various NADCs were more evident among PLHIV with advanced immunodeficiency, and was explored by HIV transmission route, and use of HAART. PLHIV had increased mortality for anal cancer (SMR 124·07, 95% CI 27·31-563·72), Hodgkin lymphoma (41·03, 2·91-577·88), liver cancer (8·36, 3·86-18·11), lung cancer (3·95, 1·52-10·26), and skin melanoma (3·95, 1·28-12·2). Interpretation: PLHIV had increased incidence and mortality for a wide spectrum of NADCs. Primary prevention and effective treatment for NADCs in this population is urgently needed. Funding: Natural Science Foundation of China Excellent Young Scientists Fund, Natural Science Foundation of China International/Regional Research Collaboration Project, National Science and Technology Major Project of China, Sanming Project of Medicine in Shenzhen, High Level Project of Medicine in Longhua, Shenzhen, Shenzhen Science and Technology Innovation Commission Basic Research Program, Special Support Plan for High-Level Talents of Guangdong Province, the Guangzhou Basic Research Program on People's Livelihood Science and Technology, the National Natural Science Foundation of China.

Outness and its correlates among gay and bisexual men attending university in China: a cross-sectional study.

BACKGROUND: Disclosure of sexual orientation to others (outness) might be associated with sexual and mental health among gay and bisexual men (GBM) attending university. We aimed to characterise outness and investigate factors correlated with outness among GBM attending university in China. METHODS: Between September 2018 and March 2019, GBM attending university were recruited in six cities in China. Information on sociodemographic characteristics, outness and sexual behaviours were collected using a self-administered questionnaire. Each participant was tested for HIV/STIs. Correlates of outness were assessed using multivariable logistic regression. RESULTS: A total of 400 GBM attending university were recruited, of whom 251 (62.8%) had disclosed their sexual orientation. Men who served as student leaders (adjusted odds ratio [AOR]=2.28, 95% CI: 1.46-3.54) and donated blood (AOR 1.85, 95% CI: 1.05-3.24) were more likely to disclose their sexual orientation, whereas men who had sex with a female (AOR 0.19, 95% CI: 0.05-0.74) and had group sex (AOR 0.52, 95% CI: 0.30-0.89) were less likely to disclose their sexual orientation. Mental health status, HIV/STI infections were not associated with outness. CONCLUSIONS: GBM attending university who disclosed their sexual orientation were more likely to be involved with student work and less likely to engage in high-risk sexual behaviours. More attention and education could focus on non-disclosing GBM men attending university through peer education or other ways.

Swarm learning for decentralized artificial intelligence in cancer histopathology.

Artificial intelligence (AI) can predict the presence of molecular alterations directly from routine histopathology slides. However, training robust AI systems requires large datasets for which data collection faces practical, ethical and legal obstacles. These obstacles could be overcome with swarm learning (SL), in which partners jointly train AI models while avoiding data transfer and monopolistic data governance. Here, we demonstrate the successful use of SL in large, multicentric datasets of gigapixel histopathology images from over 5,000 patients. We show that AI models trained using SL can predict BRAF mutational status and microsatellite instability directly from hematoxylin and eosin (H&E)-stained pathology slides of colorectal cancer. We trained AI models on three patient cohorts from Northern Ireland, Germany and the United States, and validated the prediction performance in two independent datasets from the United Kingdom. Our data show that SL-trained AI models outperform most locally trained models, and perform on par with models that are trained on the merged datasets. In addition, we show that SL-based AI models are data efficient. In the future, SL can be used to train distributed AI models for any histopathology image analysis task, eliminating the need for data transfer.

Incidence Trends of Five Common Sexually Transmitted Infections Excluding HIV From 1990 to 2019 at the Global, Regional, and National Levels: Results From the Global Burden of Disease Study 2019.

Objective: Sexually transmitted infections (STIs) are common worldwide and pose a challenge to public health. We conducted this study to assess the annual incidence of five common STIs, including syphilis, chlamydia, gonorrhea, trichomoniasis, and genital herpes at the global, regional, and national levels. Materials and Methods: We obtained detailed data on STIs excluding HIV from 1990 to 2019 from the Global Burden of Disease (GBD) 2019 database. Estimated annual percentage change (EAPC) was calculated to quantify trends in age-standardized incidence rates (ASR) of STIs, stratified by gender, sociodemographic index (SDI) region, and pathogenic microorganism. Results: Globally, incident cases of STIs increased by 58.15% from 486.77 million in 1990 to 769.85 million in 2019, but the annual change in ASR was only -0.04% (95% CI -0.09 to 0.01) per year. EAPC was 0.16 (0.06 to 0.26) for syphilis, 0.09 (0.05 to 0.13) for genital herpes, 0.06 (0.03 to 0.09) for trichomoniasis, -0.21 (-0.36 to -0.06) for chlamydia, and -0.14 (-0.19 to -0.08) for gonorrhea. High SDI regions reported significant increases in ASR of syphilis and chlamydia. Conclusions: The burden of disease from STIs remains large, though control of STIs has contributed to the decreasing incidence in most regions, especially in the low-SDI regions. Globally, over the past 20 years, the ASR has remained stable for trichomoniasis and genital herpes decreased for chlamydia and gonorrhea, and increased for syphilis.

Acceptability of male circumcision for HIV prevention among men who have sex with men in China: a short report.

Recent evidence shows that circumcision is associated with lower HIV prevalence among MSM. We assessed the acceptability of circumcision for preventing HIV and that of Shang Ring circumcision (SRC) among men who have sex with men (MSM) in China. 538 adult MSM were recruited from six cities in China between January and March 2019. Participants were surveyed by an online, self-administered questionnaire. The acceptability of circumcision was assessed before and after the potential protective effect of circumcision against HIV was informed, and subsequently men's willingness to undergo SRC was assessed. The level of circumcision was 16.4%. Of 450 uncircumcised MSM, their willingness to be circumcised in the following six months increased significantly from 32.2% to 55.6% after the information session. Three quarters of men who were willing to undergo circumcision accepted SRC. MSM who perceived that circumcision could maintain genital hygiene were more likely to accept circumcision after the information session, whereas those who regarded circumcision as an embarrassing surgery were disinclined to be circumcised. The low circumcision rate, along with its high acceptability in Chinese MSM, suggests a great potential benefit of circumcision intervention if proved effective. SRC might be a popular circumcision procedure in this population.

Uptake and adverse reactions of COVID-19 vaccination among people living with HIV in China: a case-control study.

OBJECTIVES: The coronavirus disease-2019 (COVID-19) pandemic continues to ravage the world. People living with HIV (PLHIV) are one of the most vulnerable groups. This study aims to identify the factors associated with the uptake and adverse reactions of COVID-19 vaccination. METHODS: We recruited PLHIV in China by convenience sampling between 7 and 23 February 2021. Participants were asked to complete an online questionnaire. Chi-squared test and multivariable logistic regression were used to assess factors associated with vaccine uptake. RESULTS: A total of 527 vaccinated and 1091 unvaccinated PLHIV were recruited. Individuals who had a higher education, engaged in occupations with a higher risk of COVID-19 infection, received influenza or pneumonia vaccine in the past 3 years (5.40, 3.36-8.77), believed in the effectiveness of vaccines (3.01, 2.20-4.12), and received media information regarding COVID-19 vaccine (2.23, 1.61-3.11), were more likely to be vaccinated. Concerning about adverse reactions (0.31, 0.22-0.44), negative impact on the progression of HIV/AIDS (0.36, 0.26-0.50) or antiretroviral therapy (ART) (0.61, 0.44-0.85), disclosure of HIV infection status (0.69, 0.49-0.96), comorbidities (0.33, 0.22-0.47), being unmarried (0.43, 0.28-0.66) and older age were negatively associated with vaccination. Of the 527 vaccinated PLHIV, 155 (29.4%) PLHIV reported adverse reactions, with pain at the injection site being the most common (18.2%). CONCLUSIONS: PLHIV, who are concerned about adverse reactions, negative impact on ART outcome and disclosure of HIV infection status, were less likely to adopt COVID-19 vaccination. To increase vaccination coverage among PLHIV, health-care professionals should emphasize the benefits and necessity of vaccination and provide consultancy regarding adverse reactions.

Development and external validation of a prognostic model for survival of people living with HIV/AIDS initiating antiretroviral therapy.

Background: Most existing prognostic models for people living with HIV/AIDS (PLWHA) were derived from cohorts in high-income settings established a decade ago and may not be applicable for contemporary patients, especially for patients in developing settings. The aim of this study was to develop and externally validate a prognostic model for survival in PLWHA initiating ART based on a large population-based cohort in China. Methods: We obtained data for patients from the Chinese National Free Antiretroviral Treatment Program database. The derivation cohort consisted of PLWHA treated between February 2004 and December 2019 in a tertiary center in Guangzhou, South China, and validation cohort of patients treated between February 2004 to December 2018 in another tertiary hospital in Shenyang, Northeast China. We included ART-naive patients aged above 16 who initiated a combination ART regimen containing at least three drugs and had at least one follow-up record. We assessed 20 candidate predictors including patient characteristics, disease characteristics, and laboratory tests for an endpoint of death from all causes. The prognostic model was developed from a multivariable cox regression model with predictors selected using the least absolute shrinkage and selection operator (Lasso). To assess the model's predictive ability, we quantified the discriminative power using the concordance (C) statistic and calibration accuracy by comparing predicted survival probabilities with observed survival probabilities estimated with the Kaplan-Meier method. Findings: The derivation cohort included 16481 patients with a median follow-up of 3·41 years, among whom 735 died. The external validation cohort comprised 5751 participants with a median follow-up of 2·71 years, of whom 185 died. The final model included 10 predictors: age, body mass index, route of HIV acquisition, coinfection with tuberculosis, coinfection with hepatitis C virus, haemoglobin, CD4 cell count, platelet count, aspartate transaminase, and plasma glucose. The C-statistic was 0·84 (95% confidence interval 0·82-0·85) in internal validation after adjustment of optimism and 0·84 (0·82-0·87) in external validation, which remained consistently above 0·75 in all landmark time points within five years of follow up when using time-updated laboratory measurements. The calibration accuracy was satisfactory in both derivation and validation cohorts. Interpretation: We have developed and externally validated a model to predict long-term survival in PLWHA on ART. This model could be applied to individualized patient counseling and management during treatment, and future innovative trial design. Funding: Natural Science Foundation of China Excellent Young Scientists Fund, Natural Science Foundation of China International/Regional Research Collaboration Project, Natural Science Foundation of China Young Scientist Fund, the National Science and Technology Major Project of China,National Special Research Program of China for Important Infectious Diseases, 13th Five-Year Key Special Project of Ministry of Science and Technology, and the Joint-innovation Program in Healthcare for Special Scientific Research Projects of Guangzhou.