BACKGROUND:Evidence indicates that artificial intelligence (AI) can improve mammography screening by increasing cancer detection and reducing screen reading workload, but its effect on interval cancers (primary breast cancers diagnosed between two screening rounds or within 2 years after the last scheduled screening that were not detected at screening) is unknown. We aimed to compare the interval cancer rate in AI-supported mammography screening with standard double reading without AI. METHODS:In this Swedish randomised, controlled, non-inferiority, single-blinded, population-based screening accuracy trial, participants were allocated in a 1:1 ratio to either AI-supported mammography screening (the intervention group) or standard double reading without AI (the control group). AI was used to triage examinations to single or double reading by radiologists and for detection support. This is a protocol-defined analysis of the primary outcome, interval cancer rate, with a 20% non-inferiority margin. Secondary outcomes reported in this analysis are interval cancer characteristics, sensitivity, specificity, and sensitivity by age, breast density, and cancer type (in-situ and invasive). Other secondary outcomes from the trial that have been previously reported are referenced in the Methods section of this Article. The trial is registered with ClinicalTrials.gov (NCT04838756) and is complete. FINDINGS:Between April 12, 2021, and Dec 7, 2022, 105 934 women were randomly assigned to the intervention or control group, of whom 19 were excluded from the analysis. Median age was 53·8 years (IQR 46·5-63·3) in the intervention group and 53·7 years (46·5-63·2) in the control group. Interval cancer rates were 1·55 (95% CI 1·23-1·92) and 1·76 (1·42-2·15) per 1000 participants in the intervention and control group respectively, a non-inferior proportion ratio of 0·88 (95% CI 0·65-1·18; p=0·41). Descriptively, the intervention group had fewer interval cancers that were invasive (75 vs 89), T2+ (38 vs 48), or non-luminal A (43 vs 59) than the control group. Sensitivity was higher in the intervention group (80·5% [95% CI 76·4-84·2]) than the control group (73·8% [68·9-78·3]; p=0·031), an effect consistent across age and breast density, and for invasive cancer but not for in-situ cancer. Specificity was 98·5% (95% CI 98·4-98·6) for both groups (p=0·88). INTERPRETATION:AI-supported mammography screening showed consistently favourable outcomes compared with standard double reading, with a non-inferior interval cancer rate, fewer interval cancers with unfavourable characteristics, higher sensitivity, and the same specificity, while also reducing screen reading workload. These findings imply that AI-supported mammography screening can efficiently improve screening performance compared with standard double reading and may be considered for implementation in clinical practice. FUNDING:Swedish Cancer Society, Confederation of Regional Cancer Centres, Swedish governmental funding for clinical research.
BACKGROUND:Vessel invasion (VI) in transurethral resection of bladder tumor (TURBT) is associated with lymph node metastases and reduced survival. Separation of blood (BVI) and lymph (LVI) vessel invasion with immunohistochemistry (IHC) in cystectomy (RC) has indicated different prognosis and could guide treatment already at the time of TURBT. The prognostic impact of BVI and LVI at TURBT has not been elucidated. OBJECTIVE:To examine BVI and LVI separately in TURBT using IHC and investigate their value for predicting nodal metastases, extravesical disease, distant metastases and survival after RC. METHODS:We reviewed TURBT specimens from a retrospective, population-based series of 291 patients later treated with RC regarding VI on routine- stained sections (hematoxylin-eosin-saffron; VI-HES). One tumor block per case was stained using D2-40/CD31 antibodies for separate analysis of BVI and LVI. RESULTS:The frequency of LVI and BVI was 31% and 20%, and VI-HES 31%. BVI independently predicted extravesical disease at RC and distant metastases within 12 months. LVI and VI-HES predicted lymph node metastases. BVI showed reduced recurrence-free survival (RFS; hazard ratio (HR) 1.7, 95% confidence interval (CI) 1.0-2.7, P = 0.035) and disease-specific survival (DSS; HR 1.8, CI 1.1-2.9, P = 0.018) in multivariable analysis, whereas LVI was not significantly related to survival. VI-HES showed reduced DSS and marginal significance for reduced RFS. CONCLUSIONS:At TURBT, IHC improves detection of vessel invasion and differentiates BVI from LVI, enhancing risk stratification compared with VI-HES. Presence of BVI in TURBT specimens predicts distant metastases and reduced survival and should be incorporated in clinical decision-making.
BACKGROUND:The Testicular Cancer Consortium (TECAC) was established in 2012 and is comprised of researchers from over 25 centers in Europe and North America. TECAC's overarching goal is to investigate the genetic susceptibility of testicular germ cell tumors (TGCT) to better understand their biology, impact prevention strategies, and inform treatment decisions. OBJECTIVES:To provide an overview of TECAC genetic and phenotypic holdings. MATERIALS AND METHODS:TECAC has composed by-laws describing the consortium structure and governance, codified the processes for manuscript development and data transfer, and developed guidance for the transfer of biological samples and access to data. RESULTS:TECAC has assembled a vast amount of genetic information on males with TGCT-including SNP-array data on over 13,500 cases, whole-exome sequencing data on over 4500 cases, and low-pass whole-genome sequence data on over 2700 cases. Genetic information on males without TGCT (controls) is derived from studies designed to assess risk factors for TGCT and from publicly available resources. When available, corresponding phenotypic information is collected and harmonized. Fifteen publications have resulted from genetic and phenotypic information curated by TECAC. DISCUSSION:The sharing of genetic and phenotypic data by TECAC centers to inform large studies of TGCT susceptibility has led to novel insights into the genetic architecture of this cancer, including the roles of genes involved in male germ cell development, sex determination, chromosomal segregation, and RNA transcription. These findings would not have been achievable by individual centers or smaller collaborative efforts. CONCLUSION:We invite investigators from any discipline who have access to collections of germline DNA, somatic cell DNA, or genomic information on males with TGCT to consider joining TECAC to further strengthen our efforts to reduce the global burden of TGCT.
Introduction Cancer registries remain the gold standard for global cancer monitoring, yet complementing them with electronic health records and claims can significantly enhance the understanding of the cancer burden by providing a more complete picture of the patient journey. The main aim of this project is to serve as a proof of concept for using real-world data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to monitor cancer epidemiology over time and characterise patients’ clinical history and outcomes.Methods and analysis This study will be conducted as an observational cohort study using a multinational network of large real-world data sources mapped to the OMOP CDM. Electronic health records (EHR) from primary and secondary care, health insurance claims and cancer registry data will be included. To date, 20 databases from 16 countries, mainly from Europe but also North America and Asia, have committed to participate in the project.We will investigate the temporal trends in incidence, prevalence and survival of 36 cancers across haematopoietic and solid tumours from 2000 (or the start of accurate data if later) to the last year with complete data. Data from all individuals registered in each of the participating data sources will be eligible for inclusion in the study. For primary care EHR and claims, individuals will be required to have at least 1 year of prior observation to ensure the identification of incident cases and adequate capture of patient characteristics. We will estimate crude and age-standardised incidence and 5-year partial prevalence. Additionally, we will estimate crude and age-standardised overall survival at 1, 5 and 10 years for the total study period and by diagnosis year groups defined according to data availability. All study objectives will be investigated at the database level, with results stratified by age and sex. For incidence and survival analyses, additional stratifications will be performed by clinical conditions and smoking status (where available). We will use the National Cancer Institute (NCI) Joinpoint Regression Programme to model overall trends in cancer incidence and the NCI JPSurv software to estimate trends in survival. Finally, we will characterise individuals diagnosed with an incident cancer based on demographics, clinical conditions and medication use at different time windows.Findings will be presented separately for each database and further summarised through descriptive aggregation by country and data source type.Ethics and dissemination Each data partner will obtain study approval from their local institutional review boards prior to study execution. Distributed queries will be employed, whereby standardised analytical code is shared and run at each site locally. Deidentified, aggregated results will be returned from all participating sites. A minimum cell count of five will be used when reporting results, depending on each collaborator’s data governance requirements.All study code will be publicly available, and findings will be submitted to open science journals to promote transparency and reproducibility.
