Lung cancer screening in the NELSON trial: balancing harms and benefits

__Abstract__ In this thesis, the harms and benefits of lung cancer screening using low-dose computed tomography were investigated. Data of the Dutch-Belgian NELSON trial were used to quantify its harms and benefits and develop strategies to improve the balance between them. If the NELSON trial demonstrates that low-dose CT screening is an effective method to reduce mortality from lun

The evolving role of morphology in endometrial cancer diagnostics: From histopathology and molecular testing towards integrative data analysis by deep learning

Endometrial cancer (EC) diagnostics is evolving into a system in which molecular aspects are increasingly important. The traditional histological subtype-driven classification has shifted to a molecular-based classification that stratifies EC into DNA polymerase epsilon mutated (POLEmut), mismatch repair deficient (MMRd), and p53 abnormal (p53abn), and the remaining EC as no specific molecular profile (NSMP). The molecular EC classification has been implemented in the World Health Organization 2020 classification and the 2021 European treatment guidelines, as it serves as a better basis for patient management. As a result, the integration of the molecular class with histopathological variables has become a critical focus of recent EC research. Pathologists have observed and described several morphological characteristics in association with specific genomic alterations, but these appear insufficient to accurately classify patients according to molecular subgroups. This requires pathologists to rely on molecular ancillary tests in routine workup. In this new era, it has become increasingly challenging to assign clinically relevant weights to histological and molecular features on an individual patient basis. Deep learning (DL) technology opens new options for the integrative analysis of multi-modal image and molecular datasets with clinical outcomes. Proof-of-concept studies in other cancers showed promising accuracy in predicting molecular alterations from H&E-stained tumor slide images. This suggests that some morphological characteristics that are associated with molecular alterations could be identified in EC, too, expanding the current understanding of the molecular-driven EC classification. Here in this review, we report the morphological characteristics of the molecular EC classification currently identified in the literature. Given the new challenges in EC diagnostics, this review discusses, therefore, the potential supportive role that DL could have, by providing an outlook on all relevant studies using DL on histopathology images in various cancer types with a focus on EC. Finally, we touch upon how DL might shape the management of future EC patients. Keywords: computer vision; deep learning; endometrial carcinoma; histopathology image; molecular classification; phenotype; tumour morphology; whole slide image

Tertiary lymphoid structures critical for prognosis in endometrial cancer patients

B-cells play a key role in cancer suppression, particularly when aggregated in tertiary lymphoid structures (TLS). Here, we investigate the role of B-cells and TLS in endometrial cancer (EC). Single cell RNA-sequencing of B-cells shows presence of naïve B-cells, cycling/germinal center B-cells and antibody-secreting cells. Differential gene expression analysis shows association of TLS with L1CAM overexpression. Immunohistochemistry and co-immunofluorescence show L1CAM expression in mature TLS, independent of L1CAM expression in the tumor. Using L1CAM as a marker, 378 of the 411 molecularly classified ECs from the PORTEC-3 biobank are evaluated, TLS are found in 19%. L1CAM expressing TLS are most common in mismatch-repair deficient (29/127, 23%) and polymerase-epsilon mutant EC (24/47, 51%). Multivariable Cox regression analysis shows strong favorable prognostic impact of TLS, independent of clinicopathological and molecular factors. Our data suggests a pivotal role of TLS in outcome of EC patients, and establishes L1CAM as a simple biomarker

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis

Толерантный подход к теме греко-турецких взаимоотношений в новейшей греческой прозе

Целью данной статьи стало рассмотрение нового видения дихотомии «грек-турок» в романах греческих писателей второй половины ХХ века

Automated causal inference in application to randomized controlled clinical trials

Randomized controlled trials (RCTs) are considered the gold standard for testing causal hypotheses in the clinical domain; however, the investigation of prognostic variables of patient outcome in a hypothesized cause–effect route is not feasible using standard statistical methods. Here we propose a new automated causal inference method (AutoCI) built on the invariant causal prediction (ICP) framework for the causal reinterpretation of clinical trial data. Compared with existing methods, we show that the proposed AutoCI allows one to clearly determine the causal variables of two real-world RCTs of patients with endometrial cancer with mature outcome and extensive clinicopathological and molecular data. This is achieved via suppressing the causal probability of non-causal variables by a wide margin. In ablation studies, we further demonstrate that the assignment of causal probabilities by AutoCI remains consistent in the presence of confounders. In conclusion, these results confirm the robustness and feasibility of AutoCI for future applications in real-world clinical analysis

Palliative care needs of advanced cancer patients in the emergency department at the end of life: an observational cohort study

Purpose Patients with advanced cancer commonly visit the emergency department (ED) during the last 3 months of life. Identification of these patients and their palliative care needs help initiating appropriate care according to patients' wishes. Our objective was to provide insight into ED visits of advanced cancer patients at the end of life. Methods Adult palliative patients with solid tumours who died = 1 per day. ED visits were initiated by patients and family in 34.0% and 51.9% occurred during out-of-office hours. Dyspnoea (21.0%) or pain (18.6%) were most reported symptoms. Before the ED visit, limitations on life-sustaining treatments were discussed in 33.8%, during or after the ED visit in 70.7%. Median stay at the ED was 3:29 h (range 00:12-18:01 h), and 319 (76.0%) were hospitalized. Median survival was 18 days (IQ range 7-41). One hundred four (24.8%) died within 7 days after the ED visit, of which 71.2% in-hospital. Factors associated with approaching death were lung cancer, neurologic deterioration, dyspnoea, hypercalcemia, and jaundice. Conclusion ED visits of advanced cancer patients often lead to hospitalization and in-hospital deaths. Timely recognition of patients with limited life expectancies and urgent palliative care needs, and awareness among ED staff of the potential of ED-initiated palliative care may improve the end-of-life trajectory of these patients.Development and application of statistical models for medical scientific researc

Performance of a HER2 testing algorithm specific for p53-abnormal endometrial cancer

Aims Human epidermal growth factor receptor 2 (HER2) amplification in endometrial cancer (EC) is almost completely confined to the p53-abnormal (p53abn) molecular subtype and independent of histological subtype. HER2 testing should therefore be molecular subtype-directed. However, the most optimal approach for HER2 testing in EC has not been fully established. Therefore, we developed an EC-specific HER2 immunohistochemistry (IHC) scoring method and evaluated its reproducibility and performance to establish an optimal diagnostic HER2 testing algorithm for p53abn EC. Methods and results HER2 IHC slides of 78 p53abn EC were scored by six gynaecopathologists according to predefined EC-specific IHC scoring criteria. Interobserver agreement was calculated using Fleiss' kappa and the first-order agreement coefficient (AC1). The consensus IHC score was compared with HER2 dual in-situ hybridisation (DISH) results. Sensitivity and specificity were calculated. A substantial interobserver agreement was found using three- or two-tiered scoring [kappa = 0.675, 95% confidence interval (CI) = 0.633-0.717; AC1 = 0.723, 95% CI = 0.643-0.804 and kappa = 0.771, 95% CI = 0.714-0.828; AC1 = 0.774, 95% CI = 0.684-0.865, respectively]. Sensitivity and specificity for the identification of HER2-positive EC was 100 and 97%, respectively, using a HER2 testing algorithm that recommends DISH in all cases with moderate membranous staining in >10% of the tumour (IHC+). Performing DISH on all IHC-2+ and -3+ cases yields a sensitivity and specificity of 100%. Conclusions Our EC-specific HER2 IHC scoring method is reproducible. A screening strategy based on IHC scoring on all cases with subsequent DISH testing on IHC-2+/-3+ cases has perfect test accuracy for identifying HER2-positive EC.Biological, physical and clinical aspects of cancer treatment with ionising radiatio

The self-perceived palliative care barriers and educational needs of clinicians working in hospital primary care teams and referral patterns: lessons learned from a single-center survey and cohort study

Background: Within the generalist-plus-specialist palliative care model, palliative care is mainly provided by nurses and physicians of hospital primary care teams. Palliative care consultation teams (PCCTs) support these clinicians in adequately caring for patients with advanced illnesses. Our team started in 2012. The aim of this study was to assess the self-perceived barriers, educational needs and awareness of available palliative care support options among our hospital primary care teams. In addition, palliative care referral patterns were evaluated.Methods: Single-center mixed methods study. Outcomes of two surveys of primary care team clinicians (2012 and 2016) on barriers to palliative care, educational needs and awareness of palliative care support options were compared ( chi-square, Mann-Whitney U tests, qualitative analysis). Palliative care referral characteristics were evaluated [2012- 2017], including referral timing (survival since referral) (descriptive statistics, Kaplan-Meier methodology). Predictions of survival at referral were analyzed (weighted Kappa).Results: In 2012 and 2016, the most frequently reported barrier was the late initiation of the palliative care approach. Clinicians reported a need for education on physical symptom management and basic palliative care principles. Awareness of support options increased from 2012 to 2016, including improved familiarity with the PCCT (56% vs. 85%, P= 3 months after referral) (P=0.016). Median survival after referral was 0.9 (range, 0-83.3) months. Referring physicians overestimated survival in 44% of patients (kappa 0.36, 95% CI: 0.30-0.42).Conclusions: Primary care team clinicians persistently reported needing support with basic palliative care skills. PCCTs should continuously focus on educating primary care teams and promoting the use of guidelines. Because physicians tend to overestimate survival and usually referred patients late for specialist palliative care, consultation teams should support primary care teams to identify, treat and refer patients with palliative care needs in a timely manner.Biological, physical and clinical aspects of cancer treatment with ionising radiatio