Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Aydin Visanji, Winnie Li, Jeff Winter et al.

Purpose: Adaptive radiotherapy (ART) enhances treatment precision by adjusting for anatomical changes identified during a course of treatment. While ART is resource-intensive, non-adaptive workflows may also incur significant time and resource burden due to reactive interventions. This study aims to quantify the time-related burden of unplanned activities in non-adaptive CBCT-guided radiation therapy and assess their impact on patients and departmental efficiency. Methods and Materials: A retrospective analysis was conducted on 300 patients treated with CBCT-guided radiation therapy across 20 treatment techniques, encompassing 6,887 treatment fractions. Unplanned activities—including repeated CBCT acquisitions, aborted treatment sessions, additional CT scans, and mid-course replanning—were identified through electronic medical records and imaging logs. Time burden was estimated using electronic medical record timestamps. Results: Repeated CBCTs occurred in 55% of patients, contributing to 201.9 h of additional imaging time. Pelvic disease sites, particularly bladder and gynecologic cancers, exhibited the highest frequency of repeat imaging. Six patients required rescheduling due to unresolved anatomical discrepancies, and 15 underwent treatment plan modifications, with nine requiring repeat planning CTs. The average additional time per affected fraction was 13 min (range: 2–219 min), with some sessions extending beyond 3 h. Conclusions: Non-adaptive CBCT-guided workflows can lead to substantial time toxicity, comparable to or exceeding the resource demands of ART. These findings underscore the need for improved documentation, selective ART implementation, and workflow optimization to enhance patient experience and institutional efficiency.

Rongzhao Zhang, Junqiao Wang, Shuyun Yang et al.

Multimodal clinical reasoning in the field of gastrointestinal (GI) oncology necessitates the integrated interpretation of endoscopic imagery, radiological data, and biochemical markers. Despite the evident potential exhibited by Multimodal Large Language Models (MLLMs), they frequently encounter challenges such as context dilution and hallucination when confronted with intricate, heterogeneous medical histories. In order to address these limitations, a hierarchical Multi-Agent Framework is proposed, which emulates the collaborative workflow of a human Multidisciplinary Team (MDT). The system attained a composite expert evaluation score of 4.60/5.00, thereby demonstrating a substantial improvement over the monolithic baseline. It is noteworthy that the agent-based architecture yielded the most substantial enhancements in reasoning logic and medical accuracy. The findings indicate that mimetic, agent-based collaboration provides a scalable, interpretable, and clinically robust paradigm for automated decision support in oncology.

Amgad Muneer, Muhammad Waqas, Maliazurina B Saad et al.

Cancer research is increasingly driven by the integration of diverse data modalities, spanning from genomics and proteomics to imaging and clinical factors. However, extracting actionable insights from these vast and heterogeneous datasets remains a key challenge. The rise of foundation models (FMs) -- large deep-learning models pretrained on extensive amounts of data serving as a backbone for a wide range of downstream tasks -- offers new avenues for discovering biomarkers, improving diagnosis, and personalizing treatment. This paper presents a comprehensive review of widely adopted integration strategies of multimodal data to assist advance the computational approaches for data-driven discoveries in oncology. We examine emerging trends in machine learning (ML) and deep learning (DL), including methodological frameworks, validation protocols, and open-source resources targeting cancer subtype classification, biomarker discovery, treatment guidance, and outcome prediction. This study also comprehensively covers the shift from traditional ML to FMs for multimodal integration. We present a holistic view of recent FMs advancements and challenges faced during the integration of multi-omics with advanced imaging data. We identify the state-of-the-art FMs, publicly available multi-modal repositories, and advanced tools and methods for data integration. We argue that current state-of-the-art integrative methods provide the essential groundwork for developing the next generation of large-scale, pre-trained models poised to further revolutionize oncology. To the best of our knowledge, this is the first review to systematically map the transition from conventional ML to advanced FM for multimodal data integration in oncology, while also framing these developments as foundational for the forthcoming era of large-scale AI models in cancer research.

Santiago Moreno, Pablo Meseguer, Rocío del Amor et al.

Accurate annotation of cutaneous neoplasm biopsies represents a major challenge due to their wide morphological variability, overlapping histological patterns, and the subtle distinctions between benign and malignant lesions. Vision-language foundation models (VLMs), pre-trained on paired image-text corpora, learn joint representations that bridge visual features and diagnostic terminology, enabling zero-shot localization and classification of tissue regions without pixel-level labels. However, most existing VLM applications in histopathology remain limited to slide-level tasks or rely on coarse interactive prompts, and they struggle to produce fine-grained segmentations across gigapixel whole-slide images (WSIs). In this work, we introduce a zero-shot visual-language segmentation pipeline for whole-slide images (ZEUS), a fully automated, zero-shot segmentation framework that leverages class-specific textual prompt ensembles and frozen VLM encoders to generate high-resolution tumor masks in WSIs. By partitioning each WSI into overlapping patches, extracting visual embeddings, and computing cosine similarities against text prompts, we generate a final segmentation mask. We demonstrate competitive performance on two in-house datasets, primary spindle cell neoplasms and cutaneous metastases, highlighting the influence of prompt design, domain shifts, and institutional variability in VLMs for histopathology. ZEUS markedly reduces annotation burden while offering scalable, explainable tumor delineation for downstream diagnostic workflows.

Jielong Lu, Zhihao Wu, Jiajun Yu et al.

Integrating multi-omics datasets through data-driven analysis offers a comprehensive understanding of the complex biological processes underlying various diseases, particularly cancer. Graph Neural Networks (GNNs) have recently demonstrated remarkable ability to exploit relational structures in biological data, enabling advances in multi-omics integration for cancer subtype classification. Existing approaches often neglect the intricate coupling between heterogeneous omics, limiting their capacity to resolve subtle cancer subtype heterogeneity critical for precision oncology. To address these limitations, we propose a framework named Graph Transformer for Multi-omics Cancer Subtype Classification (GTMancer). This framework builds upon the GNN optimization problem and extends its application to complex multi-omics data. Specifically, our method leverages contrastive learning to embed multi-omics data into a unified semantic space. We unroll the multiplex graph optimization problem in that unified space and introduce dual sets of attention coefficients to capture structural graph priors both within and among multi-omics data. This approach enables global omics information to guide the refining of the representations of individual omics. Empirical experiments on seven real-world cancer datasets demonstrate that GTMancer outperforms existing state-of-the-art algorithms.

Laura Connolly, Tamas Ungi, Adnan Munawar et al.

Purpose: Delineating tumor boundaries during breast-conserving surgery is challenging as tumors are often highly mobile, non-palpable, and have irregularly shaped borders. To address these challenges, we introduce a cooperative robotic guidance system that applies haptic feedback for tumor localization. In this pilot study, we aim to assess if and how this system can be successfully integrated into breast cancer care. Methods: A small haptic robot is retrofitted with an electrocautery blade to operate as a cooperatively controlled surgical tool. Ultrasound and electromagnetic navigation are used to identify the tumor boundaries and position. A forbidden region virtual fixture is imposed when the surgical tool collides with the tumor boundary. We conducted a study where users were asked to resect tumors from breast simulants both with and without the haptic guidance. We then assess the results of these simulated resections both qualitatively and quantitatively. Results: Virtual fixture guidance is shown to improve resection margins. On average, users find the task to be less mentally demanding, frustrating, and effort intensive when haptic feedback is available. We also discovered some unanticipated impacts on surgical workflow that will guide design adjustments and training protocol moving forward. Conclusion: Our results suggest that virtual fixtures can help localize tumor boundaries in simulated breast-conserving surgery. Future work will include an extensive user study to further validate these results and fine-tune our guidance system.

Consolato M. Sergi

AbstractPediatric cancer remains the leading cause of disease-related death among children aged 1–14 years. A few risk factors have been conclusively identified, including exposure to pesticides, high-dose radiation, and specific genetic syndromes, but the etiology underlying most events remains unknown. The tumor microenvironment (TME) includes stromal cells, vasculature, fibroblasts, adipocytes, and different subsets of immunological cells. TME plays a crucial role in carcinogenesis, cancer formation, progression, dissemination, and resistance to therapy. Moreover, autophagy seems to be a vital regulator of the TME and controls tumor immunity. Autophagy is an evolutionarily conserved intracellular process. It enables the degradation and recycling of long-lived large molecules or damaged organelles using the lysosomal-mediated pathway. The multifaceted role of autophagy in the complicated neoplastic TME may depend on a specific context. Autophagy may function as a tumor-suppressive mechanism during early tumorigenesis by eliminating unhealthy intracellular components and proteins, regulating antigen presentation to and by immune cells, and supporting anti-cancer immune response. On the other hand, dysregulation of autophagy may contribute to tumor progression by promoting genome damage and instability. This perspective provides an assortment of regulatory substances that influence the features of the TME and the metastasis process. Mesenchymal cells in bone and soft-tissue sarcomas and their signaling pathways play a more critical role than epithelial cells in childhood and youth. The investigation of the TME in pediatric malignancies remains uncharted primarily, and this unique collection may help to include novel advances in this setting.

J. Alvarez, Mohamad H Horani, Jacqueline Huynh

Abstract Disclosure: J. Alvarez: None. M.H. Horani: None. Background: Pancreatic adenocarcinoma is often diagnosed at an advanced stage due to late onset of clinical features including poor appetite, weight loss, weakness, epigastric pain, and rarely impaired glucose tolerance. In patients who present with impaired glucose tolerance or diabetic ketoacidosis without prior history of diabetes, pancreatic adenocarcinoma should be considered. Clinical Case: Patient is a 45-year woman with depression, HTN, irritable bowel syndrome who was found unresponsive by family and presented to the ED for acute encephalopathy. On exam, she was obtunded, tachypneic, with fruity-smelling breath and dry mucous membranes. Initial labs showed glucose of 950 mg/dL (70-99 mg/dL), lipase 539 U/L (8-78 U/L), lactic acid 4 mmol/L 0.5-2 mmol/L), ketone-BHB 170.75 mg/dL (0-2.81 mg/dL), A1c 16.5%. She had no prior history or family history of diabetes. She was admitted to hospital for DKA management.CXR to evaluate dyspnea showed bilateral opacities so CT chest/abdomen/pelvis without contrast was ordered. This showed bilateral multilobar pneumonia, trace pleural effusions, 6.7 x 3.8 cm lobulated fluid collection along pancreatic tail, 12.1 x 6.6 x 4.4 cm RLQ cystic lesion and 4.4 cm lesion vs loculation along the right pelvic sidewall.Initially, GI recommended repeat imaging of the pancreas in 4-6 weeks. OBGYN was consulted for pelvic masses and recommended IR-guided biopsy of right pelvic mass which showed rare mildly atypical mucinous epithelium present, cystic mucinous neoplasm cannot be excluded.CEA and CA125 tumor markers returned elevated. These biopsies showed adenocarcinoma (solid component) and rare atypical mucinous epithelium consistent with cystic mucinous neoplasm (cystic fluid). Patient was deemed medically stable for discharge with recommended follow-up to appropriate specialists. Following discharge, she was officially diagnosed with Stage III (cT4, cN0, cM0) pancreatic adenocarcinoma and started on appropriate chemotherapeutic regimen. Discussion: The current understanding is long-standing type 2 diabetes mellitus as a risk factor for pancreatic cancer based on previous studies.The inverse relationship of pancreatic cancer causing new onset diabetes is more rare, but if cancer is suspected in these patients it could potentially help in the diagnosis of early stage adenocarcinoma. Other case reports have suggested implementation of the enriching new-onset diabetes for pancreatic cancer (ENDPAC) score in addition to CT and EUS as screening for pancreatic cancer may help with earlier diagnosis. References: Menakuru, S. R., Priscu, A., Dhillon, V. S., & Salih, A. (2022). Diabetic Ketoacidosis as the Initial Presenting Symptom of Pancreatic Adenocarcinoma: A Discussion about Screening Utilizing ENDPAC Scoring Coupled with CT Scans and Endoscopic Ultrasound. Case Reports in Oncology, 15(3), 942-949. https://doi.org/10.1159/00052698 Presentation: 6/1/2024

Lingchao Mao, Hairong Wang, Leland S. Hu et al.

Cancer remains one of the most challenging diseases to treat in the medical field. Machine learning has enabled in-depth analysis of rich multi-omics profiles and medical imaging for cancer diagnosis and prognosis. Despite these advancements, machine learning models face challenges stemming from limited labeled sample sizes, the intricate interplay of high-dimensionality data types, the inherent heterogeneity observed among patients and within tumors, and concerns about interpretability and consistency with existing biomedical knowledge. One approach to surmount these challenges is to integrate biomedical knowledge into data-driven models, which has proven potential to improve the accuracy, robustness, and interpretability of model results. Here, we review the state-of-the-art machine learning studies that adopted the fusion of biomedical knowledge and data, termed knowledge-informed machine learning, for cancer diagnosis and prognosis. Emphasizing the properties inherent in four primary data types including clinical, imaging, molecular, and treatment data, we highlight modeling considerations relevant to these contexts. We provide an overview of diverse forms of knowledge representation and current strategies of knowledge integration into machine learning pipelines with concrete examples. We conclude the review article by discussing future directions to advance cancer research through knowledge-informed machine learning.

Arastoo Vossough, Nastaran Khalili, Ariana M. Familiar et al.

Brain tumors are the most common solid tumors and the leading cause of cancer-related death among children. Tumor segmentation is essential in surgical and treatment planning, and response assessment and monitoring. However, manual segmentation is time-consuming and has high inter-operator variability, underscoring the need for more efficient methods. We compared two deep learning-based 3D segmentation models, DeepMedic and nnU-Net, after training with pediatric-specific multi-institutional brain tumor data using based on multi-parametric MRI scans.Multi-parametric preoperative MRI scans of 339 pediatric patients (n=293 internal and n=46 external cohorts) with a variety of tumor subtypes, were preprocessed and manually segmented into four tumor subregions, i.e., enhancing tumor (ET), non-enhancing tumor (NET), cystic components (CC), and peritumoral edema (ED). After training, performance of the two models on internal and external test sets was evaluated using Dice scores, sensitivity, and Hausdorff distance with reference to ground truth manual segmentations. Dice score for nnU-Net internal test sets was (mean +/- SD (median)) 0.9+/-0.07 (0.94) for WT, 0.77+/-0.29 for ET, 0.66+/-0.32 for NET, 0.71+/-0.33 for CC, and 0.71+/-0.40 for ED, respectively. For DeepMedic the Dice scores were 0.82+/-0.16 for WT, 0.66+/-0.32 for ET, 0.48+/-0.27, for NET, 0.48+/-0.36 for CC, and 0.19+/-0.33 for ED, respectively. Dice scores were significantly higher for nnU-Net (p<=0.01). External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0.87+/-0.13 (0.91) and 0.83+/-0.18 (0.89), respectively. Pediatric-specific data trained nnU-Net model is superior to DeepMedic for whole tumor and subregion segmentation of pediatric brain tumors.

Fekede Asefa Kumsa, Jay H. Fowke, Soheil Hashtarkhani et al.

Prostate cancer is one of the leading causes of cancer-related mortality among men in the U.S. We examined the role of neighborhood obesogenic attributes on prostate cancer risk and mortality in the Southern Community Cohort Study (SCCS). From 34,166 SCCS male participants, 28,356 were included in the analysis. We assessed relationship between neighborhood socioeconomic status (nSES) and neighborhood obesogenic environment indices including restaurant environment index, retail food environment index, parks, recreational facilities, and businesses and prostate cancer risk and mortality by controlling for individual-level factors using a multivariable Cox proportional hazards model. We further stratified prostate cancer risk analysis by race and body mass index (BMI). Median follow-up time was 133 months, and mean age was 51.62 years. There were 1,524 (5.37%) prostate cancer diagnoses and 98 (6.43%) prostate cancer deaths during follow-up. Compared to participants residing in wealthiest quintile, those residing in the poorest quintile had a higher risk of prostate cancer, particularly among non-obese men with a BMI less than 30. The restaurant environment index was associated with a higher prostate cancer risk in overweight (BMI equal or greater 25) White men. Obese Black individuals without any neighborhood recreational facilities had a 42% higher risk compared to those with any access. Compared to residents in wealthiest quintile and most walkable area, those residing within the poorest quintile or the least walkable area had a higher risk of prostate cancer death.

Eli M. Soyfer, Angela G. Fleischman

Myeloproliferative Neoplasm (MPN) is a group of chronic blood cancers that arise from a hematopoietic stem cell (HSC) clone with somatic mutations causing constitutive activation of myeloid cytokine receptor signaling. In addition to elevated blood cell counts, MPN typically presents with increased inflammatory signaling and inflammation symptoms. Therefore, while being a clonally derived neoplasm, MPN has much in common with chronic non-cancerous inflammatory conditions, such as rheumatoid arthritis, lupus, and many more. MPN and chronic inflammatory disease (CID) share similar chronicity, symptoms, dependency on the immune system, environmental triggers, and treatments. Overall, we will highlight the similarities between an MPN and CID. We highlight that while MPN is classified as a cancer, its behavior is more aligned to that of a chronic inflammatory disease. We propose that MPN should inhabit a fluid/spectrum between auto-inflammatory disease and cancer.

Natascha Roehlen, M. Muller, Z. Nehme et al.

BACKGROUND AND AIMS Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Despite new treatment approvals, treatment response and prognosis of patients with advanced HCC remain poor. Claudin-1 (CLDN1) is a membrane protein expressed not only at tight junctions but also non-junctionally such as the basolateral membrane of the human hepatocyte. While CLDN1 within tight junctions is well characterized, the role of non-junctional CLDN1 and its role as a therapeutic target in HCC remains unexplored. METHODS Using humanized monoclonal antibodies (mAbs) targeting specifically the extracellular loop of human non-junctional CLDN1 and a large series of patient-derived cell-based and animal model systems we aimed to investigate the role of CLDN1 as a therapeutic target for HCC. RESULTS Targeting non-junctional CLDN1 markedly suppressed tumor growth and invasion in cell line-based models of HCC and patient-derived 3D ex vivo models. Moreover, the robust effect on tumor growth was confirmed in vivo in a large series of cell line-derived xenograft (CDX) and patient-derived xenograft (PDX) mouse models. Mechanistic studies including single-cell RNA sequencing of multicellular patient HCC tumorspheres suggested that CLDN1 regulates tumor stemness, metabolism, oncogenic signaling and perturbs the tumor immune microenvironment. CONCLUSIONS Our results provide the rationale for targeting CLDN1 in HCC and pave the way to novel therapeutic interventions with CLDN1 mAbs aimed at improving the limited efficacy of current therapies. IMPACT AND IMPLICATIONS Hepatocellular carcinoma (HCC) is a cancer with high mortality and unsatisfactory treatment options. Here we identified the cell surface protein Claudin-1 as a target for treatment of advanced HCC. Monoclonal antibodies targeting Claudin-1 inhibit tumor growth in patient-derived ex vivo and in vivo models by modulating signaling, cell stemness and the tumor immune microenvironment. Given the differentiated mechanism of action, the identification of Claudin-1 as a novel therapeutic target for HCC provides an opportunity to break the plateau of limited treatment response. These results of this preclinical study pave the way for the clinical development of Claudin-1 specific antibodies for treatment of HCC in patients. It is therefore of key impact for physicians, scientists and drug developers in the field of liver cancer and GI oncology.

Erdi Kara, T. L. Jackson, Chartese Jones et al.

As an adoptive cellular therapy, Chimeric Antigen Receptor T-cell (CAR T-cell) therapy has shown remarkable success in hematological malignancies, but only limited efficacy against solid tumors. Compared with blood cancers, solid tumors present a unique set of challenges that ultimately neutralize the function of CAR T-cells. One such barrier is antigen heterogeneity - variability in the expression of the antigen on tumor cells. Success of CAR T-cell therapy in solid tumors is unlikely unless almost all the tumor cells express the specific antigen that CAR T-cells target. A critical question for solving the heterogeneity problem is whether CAR T therapy induces bystander effects, such as antigen spreading. Antigen spreading occurs when CAR T-cells activate other endogenous antitumor CD8 T cells against antigens that were not originally targeted. In this work, we develop a mathematical model of CAR T-cell therapy for solid tumors that takes into consideration both antigen heterogeneity and bystander effects. Our model is based on in vivo treatment data that includes a mixture of target antigen-positive and target antigen-negative tumor cells. We use our model to simulate large cohorts of virtual patients to gain a better understanding of the relationship between bystander killing. We also investigate several strategies for enhancing the bystander effect and thus increasing the overall efficacy of CAR T-cell therapy for solid tumor.

Madalina Tuluc, Jennifer Johnson, Adam J Luginbuhl et al.

Karen Brändle, PhD, Jean-Luc Bulliard, PhD

E. N. Voropaeva, T. I. Pospelova, A. M. Nesterets et al.

The purpose of the study was to present up-to-date data on the regulation of expression, function in normal tissues and multidirectional activity in the oncogenesis of miR-143/145 microRNAs cluster, as well as to evaluate the possibilities and limitations of the therapeutic use of microRNAs of this cluster in malignant neoplasms. Material and methods. The search for available domestic and foreign literary sources published in PubMed and RSCI databases over the past 10 years has been carried out. 427 articles were found, of which 41 were included in this review. Results. The conservative cluster miR-143/145 is one of the most intensively studied in tumors. Based on the results of the analysis of differential miRNA expression, in vitro experiments in cancer cell lines and in vivo in mouse tumor models, a decrease in miR-143 and miR-145 levels was shown in malignant neoplasms of epithelial origin. Until recently, these miRNAs were considered classical oncosuppressors. The data presented in the review demonstrate that the results of a number of studies taking into account the cellular aspects of microRNA expression contradict this concept. miR-143 microRNA, for example, is known to participate in the metabolic restructuring of the tumor and the activation of neoangiogenesis. It has been shown that the oncosuppressive or pro-oncogenic activity of miR-143 and miR-145 depend on the tissue and cellular context and can be explained by the presence of several regulated targets that have opposite effects on oncogenesis. Taken together, the data obtained suggest the need to exercise caution when choosing the microRNAs of the described cluster for exogenous therapeutic delivery. Conclusion. Further detailed decoding of the mechanisms of miR-143 and miR-145 functioning in various types of tissues and cells, as well as identification of new MRNA targets are necessary for a better understanding of the involvement of these molecules in oncogenesis.

Long Wu, Huan Wu, Fei Huang et al.

BackgroundColorectal cancer (CRC) is a globally significant health concern, necessitating effective preventive strategies through identifying modifiable risk factors. Constipation, characterized by infrequent bowel movements or difficulty passing stools, has been proposed as a potential CRC risk factor. However, establishing causal links between constipation and CRC remains challenging due to observational study limitations.MethodsMendelian randomization (MR) utilizes genetic variants as instrumental variables, capitalizing on genetically determined variation to assess causal relationships. In this dual-sample bidirectional MR study, we extracted genetic data from independent cohorts with CRC (Include colon cancer and rectal cancer) and constipation cases. Genome-wide association studies (GWAS) identified constipation and CRC-associated genetic variants used as instruments to infer causality. The bidirectional MR analysis evaluated constipation’s impact on CRC risk and the possibility of reverse causation.ResultsEmploying bidirectional MR, we explored the causal relationship between constipation and CRC using publicly available GWAS data. Analysis of constipation’s effect on CRC identified 26 significant SNPs, all with strong instrumental validity. IVW-random effect analysis suggested a potential causal link [OR = 1.002(1.000, 1.004); P = 0.023], although alternative MR approaches were inconclusive. Investigating CRC’s impact on constipation, 28 significant SNPs were identified, yet IVW analyses found no causal effect [OR = 0.137(0.007, 2.824); P = 0.198]. Other MR methods also yielded no significant causal association. We analyzed constipation separately from colon and rectal cancer using the same methodology in both directions, and no causal relationship was obtained.ConclusionOur bidirectional MR study suggests a potential constipation-CRC link, with mixed MR approach outcomes. Limited evidence supports constipation causing CRC. Reliable instruments, minimal heterogeneity, and robust analyses bolster these findings, enriching understanding. Future research should explore additional factors to enhance comprehension and clinical implications.

Filip Herbst, Paul W. Dickman, Louise Moberg et al.

Abstract Background An investigation of trends of incidence and net survival (NS) for endometrial cancer in Sweden. Methods Morphologically verified endometrial carcinoma diagnosed 1960 to 2014 were collected from the nation-wide Swedish Cancer Registry. Endometrial cancer patients were assessed with regards to time trends for incidence and 54,825 cases remained for survival analyses. Cases diagnosed 1995 to 2014 were categorized according to detailed morphology and from 2005 to 2014 FIGO stage was also categorized. Results There was a trend of increasing incidence of endometrial carcinoma for women above 55 years of age. NS was improved at 5- and 10-year follow-up. The 5-year net survival in 2010–2014 was 86%. The most prominent improvement in NS was found in the elderly women above 75 years of age. Conclusions This study observed increased incidence of endometrial cancer in Sweden from 1960 to 2014. The progress in diagnostics and treatment, seem to have improved the net survival, especially in elderly women.

Mark B. Ulanja, Bryce D. Beutler, Kwabena Oppong Asafo‐Agyei et al.

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and the coronavirus 19 (COVID‐19) pandemic have had a lasting impact on the care of cancer patients. The impact on patients with gastrointestinal (GI) malignancies remains incompletely understood. We aimed to assess the impact of COVID‐19 on mortality, length of stay (LOS), and cost of care among patients with GI malignancies, and identify differences in outcomes based on primary tumor site. Methods We analyzed discharge encounters collected from the National Inpatient Sample (NIS) between March 2020 and December 2020 using propensity score matching (PSM) and COVID‐19 as the treatment effect. Results Of the 87,684 patient discharges with GI malignancies, 1892 were positive for COVID‐19 (C+) and eligible for matching in the PSM model. Following PSM analysis, C+ with GI tumors demonstrated increased incidence of mortality compared to their COVID‐19‐negative (C‐) counterparts (21.3% vs. 11.9%, p < 0.001). C+ patients with colorectal cancer (CRC) had significantly higher mortality compared to those who were C‐ (40% vs. 24%; p = 0.035). In addition, C+ patients with GI tumors had a longer mean LOS (9.4 days vs. 6.9 days; p < 0.001) and increased cost of care ($26,048.29 vs. $21,625.2; p = 0.001) compared to C‐ patients. C+ patients also had higher odds of mortality secondary to myocardial infarction relative to C‐ patients (OR = 3.54, p = 0.001). Conclusions C+ patients with GI tumors face approximately double the odds of mortality, increased LOS, and increased cost of care compared to their C‐ counterparts. Outcome disparities were most pronounced among patients with CRC.