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BackgroundThe Global Gynecological Oncology Surgical Outcomes Collaborative (GO SOAR) has developed a network of gynecological oncology surgeons, surgical departments, and other interested parties that have the long-term ability to collaborate on outcome studies. Presented is the protocol for the GO SOAR2 study.Primary ObjectivesTo compare survival following interval and delayed cytoreductive surgery, between delayed cytoreductive surgery and no surgery (chemotherapy alone); and international variations in access to cytoreductive surgery for women with stage III–IV epithelial ovarian cancer.Study HypothesesThere is no difference in survival following interval and delayed cytoreductive surgery; there is poorer survival with no surgery compared with delayed cytoreductive surgery; and there are international disparities in prevalent practice and access to cytoreductive surgery in women with stage III–IV epithelial ovarian cancer.Trial DesignInternational, multicenter, mixed-methods cohort study. Participating centers, will review medical charts/electronic records of patients who had been consecutively diagnosed with stage III–IV ovarian cancer between January 1, 2006 and December 31, 2021. Qualitative interviews will be conducted to identify factors determining international variations in prevalent practice and access to cytoreductive surgery.Major Inclusion/Exclusion CriteriaInclusion criteria include women with stage III–IV epithelial ovarian cancer, undergoing interval (after 3–4 cycles of chemotherapy) or delayed (≥5 cycles of chemotherapy) cytoreductive surgeries or no cytoreductive surgery (≥5 cycles of chemotherapy alone).Primary EndpointsOverall survival (defined from date of diagnosis to date of death); progression-free survival (defined from date of diagnosis to date of first recurrence); facilitator/barriers to prevalent practice and access to cytoreductive surgery.Sample SizeIn order to determine whether there is a difference in survival following interval and delayed cytoreductive surgery and no surgery, data will be abstracted from 1000 patients.Estimated Dates for Completing Accrual and Presenting ResultsIt is estimated that recruitment will be completed by 2023, and results published by 2024.Trial RegistrationNCT05523804

The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is mainly attributed to late diagnosis. We assessed the predictive performance of our previously reported urine biomarker panel for earlier detection of PDAC (LYVE1, REG1B and TFF1) in prediagnostic samples, alone and in combination with plasma CA19-9. This nested case-control study included 99 PDAC cases with urine samples prospectively collected up to 5 years prior to PDAC diagnosis and 198 matched controls. The samples were obtained from the Shanghai Women's Health Study (SWHS), the Shanghai Men's Health Studies (SMHS) and the Southern Community Cohort Study (SCCS). The urine biomarkers were measured by ELISA. Plasma CA19-9 was quantified by Luminex. Multiple logistic regression and Wilcoxon rank-sum and Mann-Whitney test were used for analysis. The internal validation approach was applied and the validated AUC estimators are reported on. The algorithm of urinary protein panel, urine creatinine and age named PancRISK, displayed similar AUC as CA19-9 up to 1 year before PDAC diagnosis (AUC = 0.79); however, the combination enhanced the AUCs to 0.89, and showed good discriminative ability (AUC = 0.77) up to 2 years. The combination showed sensitivity (SN) of 72% at 90% specificity (SP), and SP of 59% at 90% SN up to 1 year and 60% SN with 80% SP and 53% SP with 80% SN up to 2 years before PDAC diagnosis. Adding the clinical information on BMI value resulted in the overall improvement in performance of the PancRISK score. When combined with CA19-9, the urinary panel reached a workable model for detecting PDAC cases up to 2 years prior to diagnosis.

Abstract Serial MRI is an essential assessment tool in prostate cancer (PCa) patients enrolled on active surveillance (AS). However, it has only moderate sensitivity for predicting histopathological tumour progression at follow-up, which is in part due to the subjective nature of its clinical reporting and variation among centres and readers. In this study, we used a long short-term memory (LSTM) recurrent neural network (RNN) to develop a time series radiomics (TSR) predictive model that analysed longitudinal changes in tumour-derived radiomic features across 297 scans from 76 AS patients, 28 with histopathological PCa progression and 48 with stable disease. Using leave-one-out cross-validation (LOOCV), we found that an LSTM-based model combining TSR and serial PSA density (AUC 0.86 [95% CI: 0.78–0.94]) significantly outperformed a model combining conventional delta-radiomics and delta-PSA density (0.75 [0.64–0.87]; p = 0.048) and achieved comparable performance to expert-performed serial MRI analysis using the Prostate Cancer Radiologic Estimation of Change in Sequential Evaluation (PRECISE) scoring system (0.84 [0.76–0.93]; p = 0.710). The proposed TSR framework, therefore, offers a feasible quantitative tool for standardising serial MRI assessment in PCa AS. It also presents a novel methodological approach to serial image analysis that can be used to support clinical decision-making in multiple scenarios, from continuous disease monitoring to treatment response evaluation. Key Points •LSTM RNN can be used to predict the outcome of PCa AS using time series changes in tumour-derived radiomic features and PSA density. •Using all available TSR features and serial PSA density yields a significantly better predictive performance compared to using just two time points within the delta-radiomics framework. •The concept of TSR can be applied to other clinical scenarios involving serial imaging, setting out a new field in AI-driven radiology research.

Over the past few decades, more and more patients come on follow-up studies such as active surveillance and screening, which results in a vast amount of time-series data in the health department. Each Patient typically has a small but different number of visits to the doctor and the time interval between the visits is heterogeneous. Nowadays, many machine learning tasks in relation to time series data are carried out using deep recurrent neural networks (RNN). However, deep neural networks consume enormous computational power as all weights in the network need to be trained through back-propagation. Conversely, echo state network (ESN), another form of RNN, demonstrates low training cost and the potential of it is still largely untapped. Therefore, in this article we will develop a new methodology that can classify aforementioned time-series data using the echo state network. We will also discuss how to address the heterogeneity in the time interval arising from the data of this type and how our model can also potentially fit other time-series data.

PDF file - 206K, Table S1 Numbers of test positive cases and controls using CA19-9 37 U/mL and CA125 30 U/mL cut-offs Figure S1 Scatter plots showing distribution of CA19-9, CA125, CEACAM1 and REG3A levels against time to diagnosis for discovery set. Zero represents the point of clinical diagnosis. Figure S2 Examples of CA19-9 and CA125 levels in individual cases with serial/longitudinal samples. Figure S3 Box and whisker plots showing serum levels of CA19-9 and CA125 for case control validation samples grouped into different time to diagnosis groups. Whisker limits represent the 5th and 95th percentiles, the box limits represent interquartile range, the horizontal line the median and the cross the mean. Case and control groups were compared using the Mann-Whitney test; significant P values (

Purpose: In the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), women in the multimodal (MMS) arm had a serum CA125 test (first-line), with those at increased risk, having repeat CA125/ultrasound (second-line test). CA125 was interpreted using the “Risk of Ovarian Cancer Algorithm” (ROCA). We report on performance of other serial algorithms and a single CA125 threshold as a first-line screen in the UKCTOCS dataset.Experimental Design: 50,083 post-menopausal women who attended 346,806 MMS screens were randomly split into training and validation sets, following stratification into cases (ovarian/tubal/peritoneal cancers) and controls. The two longitudinal algorithms, a new serial algorithm, method of mean trends (MMT) and the parametric empirical Bayes (PEB) were trained in the training set and tested in the blinded validation set and the performance characteristics, including that of a single CA125 threshold, were compared.Results: The area under receiver operator curve (AUC) was significantly higher (P = 0.01) for MMT (0.921) compared with CA125 single threshold (0.884). At a specificity of 89.5%, sensitivities for MMT [86.5%; 95% confidence interval (CI), 78.4–91.9] and PEB (88.5%; 95% CI, 80.6–93.4) were similar to that reported for ROCA (sensitivity 87.1%; specificity 87.6%; AUC 0.915) and significantly higher than the single CA125 threshold (73.1%; 95% CI, 63.6–80.8).Conclusions: These findings from the largest available serial CA125 dataset in the general population provide definitive evidence that longitudinal algorithms are significantly superior to simple cutoff values for ovarian cancer screening. Use of these newer algorithms requires incorporation into a multimodal strategy. The results highlight the importance of incorporating serial change in biomarker levels in cancer screening/early detection strategies. Clin Cancer Res; 24(19); 4726–33. ©2018 AACR.

MMT / PEB values in training and validation of the algorithms based on the primary analysis

Purpose: Biomarkers for the early detection of pancreatic cancer are urgently needed. The primary objective of this study was to evaluate whether increased levels of serum CA19-9, CA125, CEACAM1, and REG3A are present before clinical presentation of pancreatic cancer and to assess the performance of combined markers for early detection and prognosis.Experimental Design: This nested case–control study within the UKCTOCS included 118 single and 143 serial serum samples from 154 postmenopausal women who were subsequently diagnosed with pancreatic cancer and 304 matched noncancer controls. Samples were split randomly into independent training and test sets. CA19-9, CA125, CEACAM1, and REG3A were measured using ELISA and/or CLIA. Performance of markers to detect cancers at different times before diagnosis and for prognosis was evaluated.Results: At 95% specificity, CA19-9 (>37 U/mL) had a sensitivity of 68% up to 1 year, and 53% up to 2 years before diagnosis. Combining CA19-9 and CA125 improved sensitivity as CA125 was elevated (>30 U/mL) in approximately 20% of CA19-9–negative cases. CEACAM1 and REG3A were late markers adding little in combined models. Average lead times of 20 to 23 months were estimated for test-positive cases. Prediagnostic levels of CA19-9 and CA125 were associated with poor overall survival (HR, 2.69 and 3.15, respectively).Conclusions: CA19-9 and CA125 have encouraging sensitivity for detecting preclinical pancreatic cancer, and both markers can be used as prognostic tools. This work challenges the prevailing view that CA19-9 is upregulated late in the course of pancreatic cancer development. Clin Cancer Res; 21(3); 622–31. ©2014 AACR.

Background: There is uncertainty about overdiagnosis in mammography screening. Methods: We aimed to estimate the effect of screening on breast cancer incidence and overdiagnosis in the NHS Breast Screening Programme in England. The study included 57,493 cases and 105,653 controls, with cases defined as women diagnosed at ages 47–89 with primary breast cancer, invasive or ductal carcinoma in situ, in 2010 or 2011. Where possible, two controls were selected per case, matched on date of birth and screening area. Conditional logistic regression was used to estimate the effect of screening on breast cancer risk, with adjustment for potential self-selection bias. Results were combined with national incidence data to estimate absolute rates of overdiagnosis. Overdiagnosis was calculated as the cumulative excess of cancers diagnosed in the age group 50–77 in a woman attending three-yearly screening between ages 50 and 70 compared with a woman attending no screens. Results: The estimated number of cases overdiagnosed in women attending all screens in the programme was 679.3 per 100,000 without adjustment for self-selection bias and 261.2 per 100,000 with adjustment. These corresponded to an estimated 9.5% of screen-detected cancers overdiagnosed without adjustment and 3.7% with adjustment for self-selection. Conclusions: The NHS Breast Screening Programme in England confers at worst modest levels of overdiagnosis.

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers15051625/s1, Table S1: Search strategy for literature search., Background: Risk-reducing salpingo-oophorectomy (RRSO) is the gold standard method of ovarian cancer risk reduction, but the data are conflicting regarding the impact on breast cancer (BC) outcomes. This study aimed to quantify BC risk/mortality in BRCA1/BRCA2 carriers after RRSO. Methods: We conducted a systematic review (CRD42018077613) of BRCA1/BRCA2 carriers undergoing RRSO, with the outcomes including primary BC (PBC), contralateral BC (CBC) and BC-specific mortality (BCSM) using a fixed-effects meta-analysis, with subgroup analyses stratified by mutation and menopause status. Results: RRSO was not associated with a significant reduction in the PBC risk (RR = 0.84, 95%CI: 0.59–1.21) or CBC risk (RR = 0.95, 95%CI: 0.65–1.39) in BRCA1 and BRCA2 carriers combined but was associated with reduced BC-specific mortality in BC-affected BRCA1 and BRCA2 carriers combined (RR = 0.26, 95%CI: 0.18–0.39). Subgroup analyses showed that RRSO was not associated with a reduction in the PBC risk (RR = 0.89, 95%CI: 0.68–1.17) or CBC risk (RR = 0.85, 95%CI: 0.59–1.24) in BRCA1 carriers nor a reduction in the CBC risk in BRCA2 carriers (RR = 0.35, 95%CI: 0.07–1.74) but was associated with a reduction in the PBC risk in BRCA2 carriers (RR = 0.63, 95%CI: 0.41–0.97) and BCSM in BC-affected BRCA1 carriers (RR = 0.46, 95%CI: 0.30–0.70). The mean NNT = 20.6 RRSOs to prevent one PBC death in BRCA2 carriers, while 5.6 and 14.2 RRSOs may prevent one BC death in BC-affected BRCA1 and BRCA2 carriers combined and BRCA1 carriers, respectively. Conclusions: RRSO was not associated with PBC or CBC risk reduction in BRCA1 and BRCA2 carriers combined but was associated with improved BC survival in BC-affected BRCA1 and BRCA2 carriers combined and BRCA1 carriers and a reduced PBC risk in BRCA2 carriers., Rosetrees Trust CF1\100001, Barts Charity ECMG1C3R

Objectives The National Health Service Bowel Cancer Screening Programme (NHS BCSP) in England has replaced guaiac faecal occult blood testing by faecal immunochemical testing (FIT). There is interest in fully exploiting FIT measures to improve bowel cancer (CRC) screening strategies. In this paper, we estimate the relationship of the quantitative haemoglobin concentration provided by FIT in faecal samples with underlying pathology. From this we estimate thresholds required for given levels of sensitivity to CRC and high-risk adenomas (HRA). Methods Data were collected from a pilot study of FIT in England in 2014, in which 27,238 participants completed a FIT. Those with a faecal haemoglobin concentration (f-Hb) of at least 20 µg/g were referred for further investigation, usually colonoscopy. Truncated regression models were used to explore the relationship between bowel pathology and FIT results. Regression results were applied to estimate sensitivity to different abnormalities for a number of thresholds. Results Participants with CRC and HRA had significantly higher f-Hb, and this remained unchanged after adjusting for age and sex. While a threshold of 20 μg/g was estimated to capture 82.2% of CRC and 64.0% of HRA, this would refer 7.8% of participants for colonoscopy. The current programme threshold used in England of 120 μg/g was estimated to identify 47.8% of CRC and 25.0% of HRA. Conclusions Under the current diagnostic policy of dichotomising FIT results, a very low threshold would be required to achieve high sensitivity to CRC and HRA, which would place further strain on colonoscopy resources. The NHS BCSP in England might benefit from a diagnostic policy that makes greater use of the quantitative nature of FIT.

ImportanceWhile much of the attention on the COVID-19 pandemic was directed at the daily counts of cases and those with serious disease overwhelming health services, increasingly, reports have appeared of people who experience debilitating symptoms after the initial infection. This is popularly known as long COVID.ObjectiveTo estimate by country and territory of the number of patients affected by long COVID in 2020 and 2021, the severity of their symptoms and expected pattern of recoveryDesignWe jointly analyzed ten ongoing cohort studies in ten countries for the occurrence of three major symptom clusters of long COVID among representative COVID cases. The defining symptoms of the three clusters (fatigue, cognitive problems, and shortness of breath) are explicitly mentioned in the WHO clinical case definition. For incidence of long COVID, we adopted the minimum duration after infection of three months from the WHO case definition. We pooled data from the contributing studies, two large medical record databases in the United States, and findings from 44 published studies using a Bayesian meta-regression tool. We separately estimated occurrence and pattern of recovery in patients with milder acute infections and those hospitalized. We estimated the incidence and prevalence of long COVID globally and by country in 2020 and 2021 as well as the severity-weighted prevalence using disability weights from the Global Burden of Disease study.ResultsAnalyses are based on detailed information for 1906 community infections and 10526 hospitalized patients from the ten collaborating cohorts, three of which included children. We added published data on 37262 community infections and 9540 hospitalized patients as well as ICD-coded medical record data concerning 1.3 million infections. Globally, in 2020 and 2021, 144.7 million (95% uncertainty interval [UI] 54.8–312.9) people suffered from any of the three symptom clusters of long COVID. This corresponds to 3.69% (1.38–7.96) of all infections. The fatigue, respiratory, and cognitive clusters occurred in 51.0% (16.9–92.4), 60.4% (18.9–89.1), and 35.4% (9.4–75.1) of long COVID cases, respectively. Those with milder acute COVID-19 cases had a quicker estimated recovery (median duration 3.99 months [IQR 3.84–4.20]) than those admitted for the acute infection (median duration 8.84 months [IQR 8.10–9.78]). At twelve months, 15.1% (10.3–21.1) continued to experience long COVID symptoms.Conclusions and relevanceThe occurrence of debilitating ongoing symptoms of COVID-19 is common. Knowing how many people are affected, and for how long, is important to plan for rehabilitative services and support to return to social activities, places of learning, and the workplace when symptoms start to wane.Key PointsQuestionWhat are the extent and nature of the most common long COVID symptoms by country in 2020 and 2021?FindingsGlobally, 144.7 million people experienced one or more of three symptom clusters (fatigue; cognitive problems; and ongoing respiratory problems) of long COVID three months after infection, in 2020 and 2021. Most cases arose from milder infections. At 12 months after infection, 15.1% of these cases had not yet recovered.MeaningThe substantial number of people with long COVID are in need of rehabilitative care and support to transition back into the workplace or education when symptoms start to wane.

Introduction This multicentre, retrospective study aimed to establish correlation between estimated tumour volume doubling times (TVDT) from a series of interval breast cancers with their clinicopathological features. The potential impact of delayed diagnosis on prognosis was also explored. Materials and methods Interval cancers, where screening mammograms demonstrated changes that were retrospectively classified as either uncertain or suspicious, were reviewed from five screening units within the UK NHS Breast Screening Programme (NHSBSP). Data collected included the time interval between screening mammogram and cancer diagnosis, the size of the initial mammographic abnormality and of the subsequent cancer, demographics, mammographic density and tumour biology. We estimated volume doubling times and the estimated change in size and node status, which would have followed if these cancers had been detected at the previous screen. Results 306 interval cancers meeting the inclusion criteria were identified. Average time from screening to diagnosis was 644 days (SD 276 days). 19% were diagnosed in the first twelve months, 42% in the subsequent twelve months and 39% thereafter. Overall average estimated TVDT was 167 days (95% CI 151–186). Significant differences were noted with age (p = 0.01), grade (p, Highlights • Unfavourable tumour grade and ER negativity were associated with shorter duration tumour volume doubling time. • Delayed diagnosis of the interval cancers in this series was estimated to reduce ten year survival from 86% to 82%. • Breast cancers in younger women were estimated to display faster growth, i.e., shorter doubling time.

Background Ovarian cancer has a poor survival rate due to late diagnosis and improved methods are needed for its early detection. Our primary objective was to identify and incorporate additional biomarkers into longitudinal models to improve on the performance of CA125 as a first-line screening test for ovarian cancer. Methods This case–control study nested within UKCTOCS used 490 serial serum samples from 49 women later diagnosed with ovarian cancer and 31 control women who were cancer-free. Proteomics-based biomarker discovery was carried out using pooled samples and selected candidates, including those from the literature, assayed in all serial samples. Multimarker longitudinal models were derived and tested against CA125 for early detection of ovarian cancer. Results The best performing models, incorporating CA125, HE4, CHI3L1, PEBP4 and/or AGR2, provided 85.7% sensitivity at 95.4% specificity up to 1 year before diagnosis, significantly improving on CA125 alone. For Type II cases (mostly high-grade serous), models achieved 95.5% sensitivity at 95.4% specificity. Predictive values were elevated earlier than CA125, showing the potential of models to improve lead time. Conclusions We have identified candidate biomarkers and tested longitudinal multimarker models that significantly improve on CA125 for early detection of ovarian cancer. These models now warrant independent validation.

BackgroundThe long-term sequelae of coronavirus disease 2019 (COVID-19) in children remain poorly characterised. This study aimed to assess long-term outcomes in children previously hospitalised with COVID-19 and associated risk factors.MethodsThis is a prospective cohort study of children (≤18 years old) admitted to hospital with confirmed COVID-19. Children admitted between 2 April 2020 and 26 August 2020 were included. Telephone interviews used the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 Health and Wellbeing Follow-up Survey for Children. Persistent symptoms (>5 months) were further categorised by system(s) involved.Results518 out of 853 (61%) eligible children were available for the follow-up assessment and included in the study. Median (interquartile range (IQR)) age was 10.4 (3–15.2) years and 270 (52.1%) were girls. Median (IQR) follow-up since hospital discharge was 256 (223–271) days. At the time of the follow-up interview 126 (24.3%) participants reported persistent symptoms, among which fatigue (53, 10.7%), sleep disturbance (36, 6.9%) and sensory problems (29, 5.6%) were the most common. Multiple symptoms were experienced by 44 (8.4%) participants. Risk factors for persistent symptoms were: older age “6–11 years” (OR 2.74, 95% CI 1.37–5.75) and “12–18 years” (OR 2.68, 95% CI 1.41–5.4), and a history of allergic diseases (OR 1.67, 95% CI 1.04–2.67).ConclusionsA quarter of children experienced persistent symptoms months after hospitalisation with acute COVID-19 infection, with almost one in 10 experiencing multisystem involvement. Older age and allergic diseases were associated with higher risk of persistent symptoms at follow-up.

Background: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01-4·94) deaths and 105 million (95·0-116) DALYs for both sexes combined, representing 44·4% (41·3-48·4) of all cancer deaths and 42·0% (39·1-45·6) of all DALYs. There were 2·88 million (2·60-3·18) risk-attributable cancer deaths in males (50·6% [47·8-54·1] of all male cancer deaths) and 1·58 million (1·36-1·84) risk-attributable cancer deaths in females (36·3% [32·5-41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6-28·4) and DALYs by 16·8% (8·8-25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9-42·8] and 33·3% [25·8-42·0]). Interpretation: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. We are grateful to the surveillance systems, including cancer registries, that generated and shared observed cancer burden data. S M Aljunid acknowledges the Department of Health Policy and Management, College of Public Health, Kuwait University for the approval and support to participate in this research project. H Ariffin acknowledges support from the Ministry of Higher Education, Malaysia (grant FRGS/1/2021/SKK0/UM/01/1). F Barra acknowledges support from Lega Italiana per la Lotta contro i Tumori - LILT - Bando 5 x 1000 anno 2019. L Belo and M Carvalho acknowledge the support from FCT in the scope of the project UIDP/04378/2020 and UIDB/04378/2020 of UCIBIO and the project LA/P/0140/2020 of i4HB. A J Cohen was supported by the Health Effects Institute, Boston, MA, USA. J Conde acknowledges financial support from the European Research Council - ERC Starting Grant 848325. V M Costa acknowledges her grant (SFRH/BHD/110001/2015), received by Portuguese national funds through Fundação para a Ciência e Tecnologia (FCT), IP, under the Norma Transitória DL57/2016/CP1334/CT0006. T C Ekundayo was supported by the African-German Network of Excellence in Science (AGNES), the Federal Ministry of Education and Research (BMBF) and the Alexander von Humboldt Foundation (AvH). N Ghith acknowledges support from a grant from Novo Nordisk Foundation (NNF16OC0021856). J C Glasbey is support by a Doctoral Research Fellowship from the National Institute of Health Research (NIHR300175). V K Gupta and V B Gupta acknowledge funding support from National Health and Medical Research Council (NHMRC), Australia. C Herteliu, A Pana, and M Ausloos acknowledge partial support by a grant of the Romanian National Authority for Scientific Research and Innovation, CNDS-UEFISCDI, project number PN-III-P4-ID-PCCF-2016-0084. C Herteliu is also partially supported by a grant of the Romanian Ministry of Research Innovation and Digitalization, MCID, project number ID-585-CTR-42-PFE-2021. S Hussain was supported from Operational Programme Research, Development and Education–Project, Postdoc2MUNI (number CZ.02.2. 69/0.0/0.0/18_053/0016952). M Jakovljevic acknowledges partial support through the grant OI 175 014 of the Ministry of Education Science and Technological Development of the Republic of Serbia. J H Kauppila acknowledges research grants from Sigrid Jusélius Foundation and the Finnish Cancer Foundation. M N Khatib acknowledges support from Datta Meghe Institute of Medical Sciences (deemed-to-be-university). Y J Kim was supported by the Research Management Centre, Xiamen University Malaysia [XMUMRF/2020-C6/ITCM/0004]. S L Koulmane Laxminarayana acknowledges institutional assistance by Manipal Academy of Higher Education, Manipal. K Krishan is supported by the UGC Centre of Advanced Study (Phase II), awarded to the Department of Anthropology, Panjab University, Chandigarh, India. I Landires is a member of the Sistema Nacional de Investigación (SNI), which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). M-C Li was supported by the Ministry of Science and Technology, Taiwan (MOST 110-2314-B-003-001). G Liu acknowledges support from the CREATE Hope scientific fellowship from Lung Foundation Australia. J Liu acknowledges support from the National Natural Science Foundation (72122001). J A Loureiro was supported by Scientific Employment Stimulus (FCT; CEECINST/00049/2018). E Mathews is supported by a Clinical and Public Health Early Career Fellowship (grant number IA/CPHE/17/1/503345) from the DBT India Alliance/Wellcome Trust Department of Biotechnology, India Alliance (2018–2023). T J Meretoja was supported by an unrestricted grant from Cancer Foundation Finland sr. S Mohammed acknowledges a fellowship grant from Alexander von Humboldt Foundation, outside the submitted work. M Molokhia is supported by the National Institute for Health Research Biomedical Research Center at Guy’s and St Thomas’ National Health Service Foundation Trust and King’s College London. L Monasta received support from the Italian Ministry of Health at the Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste - Italy (RC 34/2017). U Mons is supported by the Marga and Walter Boll Foundation, Kerpen, Germany. M A Moosavi acknowledges the financial support of National Institute of Genetics Engineering and Biotechnology (NIGEB). J Musa acknowledges support from the NIH/FICK43TW011416 for research-protected time for cervical cancer research and career development at University of Jos. V Nuñez-Samudio is a member of the Sistema Nacional de Investigación (SNI), which is supported by Panama’s Secretaría Nacional de Ciencia, Tecnología e Innovación (SENACYT). O O Odukoya acknowledges support by the Fogarty International Center of the National Institutes of Health under the award number K43TW010704 for research-protected time. The content is solely the responsibility of all the authors and does not necessarily represent the official views of the National Institutes of Health. A S Oguntade acknowledges funding by a doctoral scholarship from the Nuffield Department of Population Health, University of Oxford (Oxford Population Health). J R Padubidri acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal for their constant support in research collaborations. R G Pestell acknowledges support from NIH grant W81XWH1810605 Breast Cancer Research, Breakthrough Grant R21 CA235139-01. Z Z Piracha acknowledges the International Center of Medical Sciences Research (ICMSR), Islamabad (44000), Pakistan. R A Radhakrishnan acknowledges support from Wellcome Trust/DBT India Alliance - IA/CPHI/18/1/503927. U Saeed acknowledges the International Center of Medical Sciences Research (ICMSR), Islamabad, Pakistan. A M Samy acknowledges the support from Ain Shams University and the Egyptian Fulbright Mission Program. F Sha was supported by the Shenzhen Science and Technology Program (grant number KQTD20190929172835662). H R Shahsavari acknowledges the Institute for Advanced Studies in Basic Sciences (IASBS) Research Council. A Shetty acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal for all the academic support. D A S Silva acknowledges financing in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES)—Finance Code 001 and D A S Silva is supported in part by CNPq-Brazil (309589/2021-5). L M L R Silva was supported by project CENTRO-04-3559-FSE-000162, Fundo Social Europeu (FSE). Am Singh is supported by the International Graduate Research Scholarship, University of Tasmania. R Suliankatchi Abdulkader acknowledges support from ICMR—National Institute of Epidemiology. B Unnikrishnan acknowledges Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal. H Xiao acknowledges support from the Public Health Sciences Division of the Fred Hutchinson Cancer Research Center. X Xu is supported by the University of New South Wales (Australia) Scientia Program. C Yu was supported by the National Natural Science Foundation of China (grant number 82173626) and Wuhan Medical Research Program of Joint Fund of Hubei Health Committee (grant number WJ2019H304). Sí

Earlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough. The primary objective of the work presented here was to use a unique dataset, that is both large and prospectively collected, to quantify a set of 96 cancer-associated proteins and construct multi-marker models with the capacity to accurately predict PDAC years before diagnosis. The data is part of a nested case control study within UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 219 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 248 matched non-cancer controls. We developed a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets. With a pool of 10 base-learners and a Bayesian averaging meta-learner, we can predict PDAC status with an AUC of 0.91 (95% CI 0.75 - 1.0), sensitivity of 92% (95% CI 0.54 - 1.0) at 90% specificity, up to 1 year to diagnosis, and at an AUC of 0.85 (95% CI 0.74 - 0.93) up to 2 years to diagnosis (sensitivity of 61%, 95 % CI 0.17 - 0.83, at 90% specificity). These models also use clinical covariates such as hormone replacement therapy use (at randomization), oral contraceptive pill use (ever) and diabetes and outperform biomarker combinations cited in the literature.

Introduction/Background* The Global Gynaecological Oncology Surgical Outcomes Collaborative aims to develop a network of gynaecological oncology surgeons, surgical departments and other interested parties that will have the long-term ability to collaborate on outcome studies. Presented is the protocol for the first collaborative study. Our primary objective is to evaluate international variation in thirty day post-operative morbidity and mortality following gynaecological oncology surgery between very high/high and medium/low human development index country settings. Methodology Design International, multi-centre, prospective, observational, cohort-study. Two study-arms: very high/high and medium/low human development index (HDI) country settings. ClinicalTrials.gov registry: NCT04579861. Inclusion criteria Women aged ≥18 years undergoing elective/emergency, curative/palliative surgery for primary/recurrent tubo-ovarian/peritoneal, endometrial, cervical, vulval, vaginal, gestational trophoblastic malignancies. Surgical modality may be open, minimal access (laparoscopic/robotic), or vaginal. Exclusion criteria Non-gynaecological disease as the primary malignancy, diagnostic procedures, or any procedure not requiring a skin incision under general/regional anaesthesia. Recruitment Patient data will be collected over a consecutive thirty day period through Gynaecological Oncology multidisciplinary teams and clinics across different HDI country groups. All data is collected on a customised, secure, password protected, central REDCap database. Primary outcome Thirty day post-operative morbidity and mortality defined as per Clavien Dindo classification system. Secondary outcomes Intra-operative morbidity/mortality; rate of tumour clearance; international prospective surgical outcomes database; comparison of current practice against selected tumour specific audit standards derived from the European Society of Gynaecological Oncology guidelines. Sample size 1100 (550/arm) inflated by 20% to account for missing data and loss to follow up, at 90% power, α=0.05, will be able to determine a 10% point difference in thirty day post-operative morbidity and mortality following Gynaecological Oncology surgery between high/high and medium/low HDI country settings Result(s)* GO SOAR1 is open to recruitment internationally. Conclusion* The GO SOAR Collaborative aims to improve surgical outcomes through collaborative research. It will provide risk adjusted patient level outcome data collected via a centralised database to advise HDI country group specific policy formation.

IntroductionThe SARS-CoV-2 global pandemic has caused a crisis disrupting health systems worldwide. While efforts are being made to determine the extent of the disruption, the impact on gynecological oncology trainees/training has not been explored. We conducted an international survey of the impact of SARS-CoV-2 on clinical practice, medical education, and mental well-being of surgical gynecological oncology trainees.MethodsIn our cross-sectional study, a customized web-based survey was circulated to surgical gynecological oncology trainees from national/international organizations from May to November 2020. Validated questionnaires assessed mental well-being. The Wilcoxon rank-sum test and Fisher’s exact test were used to analyse differences in means and proportions. Multiple linear regression was used to evaluate the effect of variables on psychological/mental well-being outcomes. Outcomes included clinical practice, medical education, anxiety and depression, distress, and mental well-being.ResultsA total of 127 trainees from 34 countries responded. Of these, 52% (66/127) were from countries with national training programs (UK/USA/Netherlands/Canada/Australia) and 48% (61/127) from countries with no national training programs. Altogether, 28% (35/125) had suspected/confirmed COVID-19, 28% (35/125) experienced a fall in household income, 20% (18/90) were self-isolated from households, 45% (57/126) had to re-use personal protective equipment, and 22% (28/126) purchased their own. In total, 32.3% (41/127) of trainees (16.6% (11/66) from countries with a national training program vs 49.1% (30/61) from countries with no national training program, p=0.02) perceived they would require additional time to complete their training fellowship. The additional training time anticipated did not differ between trainees from countries with or without national training programs (p=0.11) or trainees at the beginning or end of their fellowship (p=0.12). Surgical exposure was reduced for 50% of trainees. Departmental teaching continued throughout the pandemic for 69% (87/126) of trainees, although at reduced frequency for 16.1% (14/87), and virtually for 88.5% (77/87). Trainees reporting adequate pastoral support (defined as allocation of a dedicated mentor/access to occupational health support services) had better mental well-being with lower levels of anxiety/depression (p=0.02) and distress (pConclusionSARS-CoV-2 has negatively impacted the surgical training, household income, and psychological/mental well-being of surgical gynecological oncology trainees. The overall clinical impact was worse for trainees in countries with no national training program than for those in countries with a national training program, although national training program trainees reported greater distress. COVID-19 sickness increased anxiety/depression. The recovery phase must focus on improving mental well-being and addressing lost training opportunities.

Artificial Intelligence methods can be very effective in classification tasks that involve the processing of ordered sequences of data. Here we explore two different approaches to tackle the problem of ovarian cancer detection from a sequence of longitudinal measurements of several biomarkers. The first approach relies on a Bayesian hierarchical model whose fundamental assumption is that measurements taken from case subjects exhibit a changepoint in one or several biomarkers. The second approach is a purely discriminative machine learning algorithm based on the use of recurrent neural networks, a kind of artificial neural network specially suited to the processing of inputs of different lengths.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease., Graphical abstract, Demichev, Tober-Lau et al., present a time-resolved molecular map of the COVID-19, measuring plasma proteomes of patients with COVID-19 along with an extensive panel of clinical diagnostic parameters at 687-time points. They describe the specificity and dynamics, as well as the predictive and prognostic power of the molecular signatures in COVID-19.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Plasma proteomics instead identified 14 proteins that showed concentration trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors in an exploratory (AUROC 0.81) as well as in the independent validation cohort (AUROC of 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.Trial registrationGerman Clinical Trials Register DRKS00021688

Nearly half of patients with prostate cancer (PCa) harbour low- or intermediate-risk disease considered suitable for active surveillance (AS). However, up to 44% of patients discontinue AS within the first five years, highlighting the unmet clinical need for robust baseline risk-stratification tools that enable timely and accurate prediction of tumour progression. In this proof-of-concept study, we sought to investigate the added value of MRI-derived radiomic features to standard-of-care clinical parameters for improving baseline prediction of PCa progression in AS patients. Tumour T2-weighted imaging (T2WI) and apparent diffusion coefficient radiomic features were extracted, with rigorous calibration and pre-processing methods applied to select the most robust features for predictive modelling. Following leave-one-out cross-validation, the addition of T2WI-derived radiomic features to clinical variables alone improved the area under the ROC curve for predicting progression from 0.61 (95% confidence interval [CI] 0.481–0.743) to 0.75 (95% CI 0.64–0.86). These exploratory findings demonstrate the potential benefit of MRI-derived radiomics to add incremental benefit to clinical data only models in the baseline prediction of PCa progression on AS, paving the way for future multicentre studies validating the proposed model and evaluating its impact on clinical outcomes.

Funder: National Institute of Health Research Cambridge Biomedical Research Centre, Funder: Engineering and Physical Sciences Research Council Imaging Centre in Cambridge and Manchester, Funder: Cambridge Experimental Cancer Medicine Centre, Funder: Gates Cambridge Trust; doi: http://dx.doi.org/10.13039/501100005370, OBJECTIVES: To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS: The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS: The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS: PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS: • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.

Aim Breast density is a risk factor for breast cancer. As density changes across a woman's life span, we studied for how long a single density measurement taken in (post-)menopausal women remains informative. Methods We used data from Singaporean women who underwent a single mammography screen at age 50–64 years. For each case with breast cancer diagnosed at screening or in the subsequent 10 years, whether screen detected or diagnosed following symptoms, two age-matched controls were selected. We studied the excess risk of breast cancer, calculated as an odds ratio (OR) with conditional logistic regression and adjusted for body mass index, associated with 26–50% and with 51–100% density compared with ≤25% density by time since screening. Results In total, 490 women had breast cancer, of which 361 were diagnosed because of symptoms after screening. Women with 51–100% breast density had an excess risk of breast cancer that did not seem to attenuate with time. In 1–3 years after screening, the OR was 2.22 (95% confidence interval [CI]: 1.07–4.61); in 4–6 years after screening, the OR was 4.09 (95% CI: 2.21–7.58), and in 7–10 years after screening, the OR was 5.35 (95% CI: 2.57–11.15). Excess risk with a stable OR of about 2 was also observed for women with 26–50% breast density. These patterns were robust when the analyses were limited to post-menopausal women, non-users of hormonal replacement therapy and after stratification by age at density measurement. Conclusion A single breast density measurement identifies women with an excess risk of breast cancer during at least the subsequent 10 years., Highlights • Dense mammographic areas confer an excess breast cancer risk for at least 10 years. • This risk does not wane with time. • The risk is not limited to women who took hormonal replacement therapy.