Lancet Digital Health最新文献

{"title":"Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge","authors":"Jun Ma PhD ,&nbsp;Yao Zhang PhD ,&nbsp;Song Gu MSc ,&nbsp;Cheng Ge MSc ,&nbsp;Shihao Mae BSc ,&nbsp;Adamo Young MSc ,&nbsp;Cheng Zhu PhD ,&nbsp;Prof Xin Yang PhD ,&nbsp;Prof Kangkang Meng PhD ,&nbsp;Ziyan Huang BSc ,&nbsp;Fan Zhang MSc ,&nbsp;Yuanke Pan MSc ,&nbsp;Shoujin Huang BSc ,&nbsp;Jiacheng Wang PhD ,&nbsp;Mingze Sun PhD ,&nbsp;Prof Rongguo Zhang PhD ,&nbsp;Dengqiang Jia PhD ,&nbsp;Jae Won Choi MD ,&nbsp;Natália Alves MSc ,&nbsp;Bram de Wilde PhD ,&nbsp;Prof Bo Wang PhD","doi":"10.1016/S2589-7500(24)00154-7","DOIUrl":"10.1016/S2589-7500(24)00154-7","url":null,"abstract":"<div><div>Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To address these limitations, we organised the FLARE 2022 challenge to benchmark fast, low-resource, and accurate abdominal organ segmentation algorithms. We first constructed an intercontinental abdomen CT dataset from more than 50 clinical research groups. We then independently validated that deep learning algorithms achieved a median dice similarity coefficient (DSC) of 90·0% (IQR 87·4–91·3%) by use of 50 labelled images and 2000 unlabelled images, which can substantially reduce manual annotation costs. The best-performing algorithms successfully generalised to holdout external validation sets, achieving a median DSC of 89·4% (85·2–91·3%), 90·0% (84·3–93·0%), and 88·5% (80·9–91·9%) on North American, European, and Asian cohorts, respectively. These algorithms show the potential to use unlabelled data to boost performance and alleviate annotation shortages for modern artificial intelligence models.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e815-e826"},"PeriodicalIF":23.8,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of the i2TransHealth e-health intervention on psychological distress among transgender and gender diverse adults from remote areas in Germany: a randomised controlled trial","authors":"Timo O Nieder PhD ,&nbsp;Janis Renner MSc ,&nbsp;Susanne Sehner MSc ,&nbsp;Amra Pepić PhD ,&nbsp;Prof Antonia Zapf PhD ,&nbsp;Martin Lambert MD ,&nbsp;Prof Peer Briken MD ,&nbsp;Arne Dekker PhD","doi":"10.1016/S2589-7500(24)00192-4","DOIUrl":"10.1016/S2589-7500(24)00192-4","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Transgender and gender diverse (TGD) people in remote areas face challenges accessing health-care services, including mental health care and gender-affirming medical treatment, which can be associated with psychological distress. In this study, we aimed to evaluate the effectiveness of a 4-month TGD-informed e-health intervention to improve psychological distress among TGD people from remote areas in northern Germany.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;In a randomised controlled trial done at a single centre in Germany, adults (aged ≥18 years) who met criteria for gender incongruence or gender dysphoria and who lived at least 50 km outside of Hamburg in one of the northern German federal states were recruited and randomly assigned (1:1) to i&lt;sup&gt;2&lt;/sup&gt;TransHealth intervention or a wait list control group. Randomisation was performed with the use of a computer-based code. Due to the nature of the intervention, study participants and clinical staff were aware of treatment allocation, but researchers responsible for data analysis were masked to allocation groups. Study participants in the intervention group (service users) started the i&lt;sup&gt;2&lt;/sup&gt;TransHealth intervention immediately after completing the baseline survey after enrolment. Participants assigned to the control group waited 4 months before they were able to access i&lt;sup&gt;2&lt;/sup&gt;TransHealth services or regular care. The primary outcome was difference in the Brief Symptom Inventory (BSI)-18 summary score between baseline and 4 months, assessed using a linear model analysis. The primary outcome was assessed in the intention-to-treat (ITT) population, which included all randomly assigned participants. The trial was registered with &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;NCT04290286&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Between May 12, 2020, and May 2, 2022, 177 TGD people were assessed for eligibility, of whom 174 were included in the ITT population (n=90 in the intervention group, n=84 in the control group). Six participants did not provide data for the primary outcome at 4 months, and thus 168 people were included in the analysis population (88 participants in the intervention group and 80 participants in the control group). At 4 months, in the intervention group, the adjusted mean change in BSI-18 from baseline was –0·65 (95% CI –2·25 to 0·96; p=0·43) compared with 2·34 (0·65 to 4·02; p=0·0069) in the control group. Linear model analysis identified a significant difference at 4 months between the groups with regard to change in BSI-18 summary scores from baseline (between-group difference –2·98 [95% CI –5·31 to –0·65]; p=0·012). Adverse events were rare: there were two suicide attempts and one participant was admitted to hospital in the intervention group, and in the control group, there was one case of self-harm and one case of self-harm followed by hospital admission.&lt;/div&gt;&lt;/d","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e883-e893"},"PeriodicalIF":23.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to Lancet Digit Health 2024; published online Sept 17. https://doi.org/10.1016/S2589-7500(24)00143-2","authors":"","doi":"10.1016/S2589-7500(24)00220-6","DOIUrl":"10.1016/S2589-7500(24)00220-6","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Page e777"},"PeriodicalIF":23.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combating medical misinformation and rebuilding trust in the USA","authors":"Clara E Tandar ,&nbsp;John C Lin ,&nbsp;Fatima Cody Stanford","doi":"10.1016/S2589-7500(24)00197-3","DOIUrl":"10.1016/S2589-7500(24)00197-3","url":null,"abstract":"","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e773-e774"},"PeriodicalIF":23.8,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142394366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotional competence self-help mobile phone app versus cognitive behavioural self-help app versus self-monitoring app to promote mental wellbeing in healthy young adults (ECoWeB PROMOTE): an international, multicentre, parallel, open-label, randomised controlled trial","authors":"Prof Edward R Watkins PhD ,&nbsp;Fiona C Warren PhD ,&nbsp;Alexandra Newbold PhD ,&nbsp;Prof Claire Hulme PhD ,&nbsp;Timothy Cranston BSc ,&nbsp;Benjamin Aas PhD ,&nbsp;Holly Bear PhD ,&nbsp;Prof Cristina Botella PhD ,&nbsp;Felix Burkhardt PhD ,&nbsp;Prof Thomas Ehring PhD ,&nbsp;Prof Mina Fazel PhD ,&nbsp;Prof Johnny R J Fontaine PhD ,&nbsp;Mads Frost PhD ,&nbsp;Prof Azucena Garcia-Palacios PhD ,&nbsp;Ellen Greimel PhD ,&nbsp;Christiane Hößle PhD ,&nbsp;Arpine Hovasapian PhD ,&nbsp;Veerle E I Huyghe BSc ,&nbsp;Prof Kostas Karpouzis PhD ,&nbsp;Johanna Löchner PhD ,&nbsp;Prof Rod S Taylor PhD","doi":"10.1016/S2589-7500(24)00149-3","DOIUrl":"10.1016/S2589-7500(24)00149-3","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Based on evidence that mental health is more than an absence of mental disorders, there have been calls to find ways to promote flourishing at a population level, especially in young people, which requires effective and scalable interventions. Despite their potential for scalability, few mental wellbeing apps have been rigorously tested in high-powered trials, derived from models of healthy emotional functioning, or tailored to individual profiles. We aimed to test a personalised emotional competence self-help app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to promote mental wellbeing in healthy young people.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This international, multicentre, parallel, open-label, randomised controlled trial within a cohort multiple randomised trial (including a parallel trial of depression prevention) was done at four university trial sites in four countries (the UK, Germany, Spain, and Belgium). Participants were recruited from schools and universities and via social media from the four respective countries. Eligible participants were aged 16–22 years with well adjusted emotional competence profiles and no current or past diagnosis of major depression. Participants were randomised (1:1:1) to usual practice plus either the emotional competence app, the CBT app or the self-monitoring app, by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. The primary outcome was mental wellbeing (indexed by the Warwick–Edinburgh Mental Well Being Scale [WEMWBS]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. Outcome assessors were masked to group allocation. The study is registered with &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;NCT04148508&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, and is closed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Between Oct 15, 2020, and Aug 3, 2021, 2532 participants were enrolled, and 847 were randomly assigned to the emotional competence app, 841 to the CBT app, and 844 to the self-monitoring app. Mean age was 19·2 years (SD 1·8). Of 2532 participants self-reporting gender, 1896 (74·9%) were female, 613 (24·2%) were male, 16 (0·6%) were neither, and seven (0·3%) were both. 425 participants in the emotional competence app group, 443 in the CT app group, and 447 in the self-monitoring app group completed the follow-up assessment at 3 months. There was no difference in mental wellbeing between the groups at 3 months (global p=0·47). The emotional competence app did not differ from the CBT app (mean difference in WEMWBS –0·21 [95% CI –1·08 to 0·66]) or the self-monitoring app (0·32 [–0·54 to 1·19]) and the CBT app did not differ from the self-monitoring app (0·53 [–0·33 to 1·39]). 14 of 1315 participants were admitted to or treated in hospital (or both) for mental health-related r","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e904-e913"},"PeriodicalIF":23.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emotional competence self-help app versus cognitive behavioural self-help app versus self-monitoring app to prevent depression in young adults with elevated risk (ECoWeB PREVENT): an international, multicentre, parallel, open-label, randomised controlled trial","authors":"Prof Edward R Watkins PhD ,&nbsp;Fiona C Warren PhD ,&nbsp;Alexandra Newbold PhD ,&nbsp;Prof Claire Hulme PhD ,&nbsp;Timothy Cranston BSc ,&nbsp;Benjamin Aas PhD ,&nbsp;Holly Bear PhD ,&nbsp;Prof Cristina Botella PhD ,&nbsp;Felix Burkhardt PhD ,&nbsp;Prof Thomas Ehring PhD ,&nbsp;Prof Mina Fazel PhD ,&nbsp;Prof Johnny R J Fontaine PhD ,&nbsp;Mads Frost PhD ,&nbsp;Prof Azucena Garcia-Palacios PhD ,&nbsp;Ellen Greimel PhD ,&nbsp;Christiane Hößle PhD ,&nbsp;Arpine Hovasapian PhD ,&nbsp;Veerle E I Huyghe BSc ,&nbsp;Prof Kostas Karpouzis PhD ,&nbsp;Johanna Löchner PhD ,&nbsp;Prof Rod S Taylor PhD","doi":"10.1016/S2589-7500(24)00148-1","DOIUrl":"10.1016/S2589-7500(24)00148-1","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Effective, scalable interventions are needed to prevent poor mental health in young people. Although mental health apps can provide scalable prevention, few have been rigorously tested in high-powered trials built on models of healthy emotional functioning or tailored to individual profiles. We aimed to test a personalised emotional competence app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to prevent an increase in depression symptoms in young people.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;This multicentre, parallel, open-label, randomised controlled trial, within a cohort multiple randomised trial (including a parallel trial of wellbeing promotion) was done at four university trial sites in the UK, Germany, Spain, and Belgium. Participants were recruited from schools, universities, and social media from the four respective countries. Eligible participants were aged 16–22 years with increased vulnerability indexed by baseline emotional competence profile, without current or past diagnosis of major depression. Participants were randomly assigned (1:1:1) to usual practice plus either the personalised emotional competence self-help app, the generic CBT self-help app, or the self-monitoring app by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. Outcome assessors were masked to group allocation. The primary outcome was depression symptoms (according to Patient Health Questionnaire-9 [PHQ-9]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. The study is registered with &lt;span&gt;&lt;span&gt;ClinicalTrials.gov&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, &lt;span&gt;&lt;span&gt;NCT04148508&lt;/span&gt;&lt;svg&gt;&lt;path&gt;&lt;/path&gt;&lt;/svg&gt;&lt;/span&gt;, and is closed.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;Between Oct 15, 2020, and Aug 3, 2021, 1262 participants were enrolled, including 417 to the emotional competence app, 423 to the CBT app, and 422 to the self-monitoring app. Mean age was 18·8 years (SD 2·0). Of 1262 participants self-reporting gender, 984 (78·0%) were female, 253 (20·0%) were male, 15 (1·2%) were neither, and ten (0·8%) were both. 178 participants in the emotional competence app group, 191 in the CBT app group, and 199 in the self-monitoring app group completed the follow-up assessment at 3 months. At 3 months, depression symptoms were lower with the CBT app than the self-monitoring app (mean difference in PHQ-9 –1·18 [95% CI –2·01 to –0·34]; p=0·006), but depression symptoms did not differ between the emotional competence app and the CBT app (0·63 [–0·22 to 1·49]; p=0·15) or the self-monitoring app and emotional competence app (–0·54 [–1·39 to 0·31]; p=0·21). 31 of the 541 participants who completed any of the follow-up assessments received treatment in hospital or were admitted to hospital for mental health-related reasons considered unrelated to interventions (eight in the emotio","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 12","pages":"Pages e894-e903"},"PeriodicalIF":23.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142378381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demographic reporting in biosignal datasets: a comprehensive analysis of the PhysioNet open access database","authors":"Sarah Jiang ,&nbsp;Perisa Ashar ,&nbsp;Md Mobashir Hasan Shandhi ,&nbsp;Jessilyn Dunn","doi":"10.1016/S2589-7500(24)00170-5","DOIUrl":"10.1016/S2589-7500(24)00170-5","url":null,"abstract":"<div><div>The PhysioNet open access database (PND) is one of the world's largest and most comprehensive repositories of biosignal data and is widely used by researchers to develop, train, and validate algorithms. To contextualise the results of such algorithms, understanding the underlying demographic distribution of the data is crucial—specifically, the race, ethnicity, sex or gender, and age of study participants. We sought to understand the underlying reporting patterns and characteristics of the demographic data of the datasets available on PND. Of the 181 unique datasets present in the PND as of July 6, 2023, 175 involved human participants, with less than 7% of studies reporting on all four of the key demographic variables. Furthermore, we found a higher rate of reporting sex or gender and age than race and ethnicity. In the studies that did include participant sex or gender, the samples were mostly male. Additionally, we found that most studies were done in North America, particularly in the USA. These imbalances and poor reporting of representation raise concerns regarding potential embedded biases in the algorithms that rely on these datasets. They also underscore the need for universal and comprehensive reporting practices to ensure equitable development and deployment of artificial intelligence and machine learning tools in medicine.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e871-e878"},"PeriodicalIF":23.8,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mobile phone interventions to improve health outcomes among patients with chronic diseases: an umbrella review and evidence synthesis from 34 meta-analyses","authors":"Shufang Sun PhD ,&nbsp;Otto Simonsson PhD ,&nbsp;Prof Stephen McGarvey PhD ,&nbsp;John Torous MD ,&nbsp;Simon B Goldberg PhD","doi":"10.1016/S2589-7500(24)00119-5","DOIUrl":"10.1016/S2589-7500(24)00119-5","url":null,"abstract":"<div><div>This umbrella review of 34 meta-analyses, representing 235 randomised controlled trials done across 52 countries and 48 957 participants and ten chronic conditions, aimed to evaluate evidence on the efficacy of mobile phone interventions for populations with chronic diseases. We evaluated the strengths of evidence via the Fusar-Poli and Radua methodology. Compared with usual care, mobile apps had convincing effects on glycated haemoglobin reduction among adults with type 2 diabetes (d=0·44). Highly suggestive effects were found for both text messages and apps on various outcomes, including medication adherence (among patients with HIV in sub-Saharan Africa and people with cardiovascular disease), glucose management in type 2 diabetes, and blood pressure reduction in hypertension. Many effects (42%) were non-significant. Various gaps were identified, such as a scarcity of reporting on moderators and publication bias by meta-analyses, little research in low-income and lower-middle-income countries, and little reporting on adverse events.</div></div>","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 11","pages":"Pages e857-e870"},"PeriodicalIF":23.8,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning assessment of senescence-associated nuclear morphologies in mammary tissue from healthy female donors to predict future risk of breast cancer: a retrospective cohort study","authors":"Indra Heckenbach PhD ,&nbsp;Mark Powell MD ,&nbsp;Sophia Fuller MA ,&nbsp;Jill Henry MBA ,&nbsp;Sam Rysdyk JD ,&nbsp;Jenny Cui MS ,&nbsp;Amanuel Abraha Teklu MSc ,&nbsp;Prof Eric Verdin MD ,&nbsp;Prof Christopher Benz MD ,&nbsp;Morten Scheibye-Knudsen MD DMSc","doi":"10.1016/S2589-7500(24)00150-X","DOIUrl":"10.1016/S2589-7500(24)00150-X","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;Cellular senescence has been associated with cancer as either a barrier mechanism restricting autonomous cell proliferation or a tumour-promoting microenvironmental mechanism that secretes proinflammatory paracrine factors. With most work done in non-human models and the heterogeneous nature of senescence, the precise role of senescent cells in the development of cancer in humans is not well understood. Furthermore, more than 1 million non-malignant breast biopsies are taken every year that could be a major resource for risk stratification. We aimed to explore the clinical relevance for breast cancer development of markers of senescence in mammary tissue from healthy female donors.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;In this retrospective cohort study, we applied single-cell deep learning senescence predictors, based on nuclear morphology, to histological images of haematoxylin and eosin-stained breast biopsy samples from healthy female donors at the Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center (Indianapolis, IN, USA). All KTB participants (aged ≥18 years) who underwent core biopsies for research purposes between 2009 and 2019 were eligible for the study. Senescence was predicted in the epithelial (terminal duct lobular units [TDLUs] and non-TDLU epithelium), stromal, and adipose tissue compartments using validated models, previously trained on cells induced to senescence by ionising radiation (IR), replicative exhaustion (or replicative senescence; RS), or antimycin A, atazanavir–ritonavir, and doxorubicin (AAD) exposures. To benchmark our senescence-based cancer prediction results, we generated 5-year Gail scores—the current clinical gold standard for breast cancer risk prediction—for participants aged 35 years and older on the basis of characteristics at the time of tissue donation. The primary outcome was estimated odds of breast cancer via logistic modelling for each tissue compartment based on predicted senescence scores in cases (participants who had been diagnosed with breast cancer as of data cutoff, July 31, 2022) and controls (those who had not been diagnosed with breast cancer).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;4382 female donors (median age at donation 45 years [IQR 34–57]) were eligible for the study. As of data cutoff (median follow-up of 10 years [7–11]), 86 (2·0%) had developed breast cancer a mean of 4·8 years (SD 2·84) after date of donation and 4296 (98·0%) had not received a breast cancer diagnosis. Among the 86 cases, we found significant differences in adipose-specific IR and AAD senescence prediction scores compared with controls. Risk analysis showed that individuals in the upper half (above the median) of scores for the adipose tissue IR model had higher odds of developing breast cancer (odds ratio [OR] 1·71 [95% CI 1·10–2·68]; p=0·019), whereas the adipose AAD model revealed a reduced odds of developing breast cancer (OR 0·57 [0·36–0·88]; p=0·013). For the othe","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e681-e690"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of the COVID-19 pandemic on health-care use among patients with cancer in England, UK: a comprehensive phase-by-phase time-series analysis across attendance types for 38 cancers","authors":"Yen Yi Tan MRes ,&nbsp;Wai Hoong Chang MSc ,&nbsp;Michail Katsoulis PhD ,&nbsp;Prof Spiros Denaxas PhD ,&nbsp;Prof Kayla C King PhD ,&nbsp;Murray P Cox DSc ,&nbsp;Charles Davie MD ,&nbsp;Prof Francois Balloux PhD ,&nbsp;Alvina G Lai PhD","doi":"10.1016/S2589-7500(24)00152-3","DOIUrl":"10.1016/S2589-7500(24)00152-3","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;div&gt;The COVID-19 pandemic resulted in the widespread disruption of cancer health provision services across the entirety of the cancer care pathway in the UK, from screening to treatment. The potential long-term health implications, including increased mortality for individuals who missed diagnoses or appointments, are concerning. However, the precise impact of lockdown policies on national cancer health service provision across diagnostic groups is understudied. We aimed to systematically evaluate changes in patterns of attendance for groups of individuals diagnosed with cancer, including the changes in attendance volume and consultation rates, stratified by both time-based exposures and by patient-based exposures and to better understand the impact of such changes on cancer-specific mortality.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;In this retrospective, cross-sectional, phase-by-phase time-series analysis, by using primary care records linked to hospitals and the death registry from Jan 1, 1998, to June 17, 2021, we conducted descriptive analyses to quantify attendance changes for groups stratified by patient-based exposures (Index of Multiple Deprivation, ethnicity, age, comorbidity count, practice region, diagnosis time, and cancer subtype) across different phases of the COVID-19 pandemic in England, UK. In this study, we defined the phases of the COVID-19 pandemic as: pre-pandemic period (Jan 1, 2018, to March 22, 2020), lockdown 1 (March 23 to June 21, 2020), minimal restrictions (June 22 to Sept 20, 2020), lockdown 2 (Sept 21, 2020, to Jan 3, 2021), lockdown 3 (Jan 4 to March 21, 2021), and lockdown restrictions lifted (March 22 to March 31, 2021). In the analyses we examined changes in both attendance volume and consultation rate. We further compared changes in attendance trends to cancer-specific mortality trends. Finally, we conducted an interrupted time-series analysis with the lockdown on March 23, 2020, as the intervention point using an autoregressive integrated moving average model.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Findings&lt;/h3&gt;&lt;div&gt;From 561 611 eligible individuals, 7 964 685 attendances were recorded. During the first lockdown, the median attendance volume decreased (–35·30% [IQR –36·10 to –34·25]) compared with the preceding pre-pandemic period, followed by a median change of 4·38% (2·66 to 5·15) during minimal restrictions. More drastic reductions in attendance volume were seen in the second (–48·71% [–49·54 to –48·26]) and third (–71·62% [–72·23 to –70·97]) lockdowns. These reductions were followed by a 4·48% (3·45 to 7·10) increase in attendance when lockdown restrictions were lifted. The median consultation rate change during the first lockdown was 31·32% (25·10 to 33·60), followed by a median change of –0·25% (–1·38 to 1·68) during minimal restrictions. The median consultation rate decreased in the second (–33·89% [–34·64 to –33·18]) and third (–4·98% [–5·71 to –4·00]) lockdowns, followed by a 416·16% increase (40","PeriodicalId":48534,"journal":{"name":"Lancet Digital Health","volume":"6 10","pages":"Pages e691-e704"},"PeriodicalIF":23.8,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142320341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}