RESUMO
To improve the precision of epithelial ovarian cancer histotyping, Köbel et al. (2016) developed immunohistochemical decision-tree algorithms. These included a six- and four-split algorithm, and separate six-split algorithms for early- and advanced stage disease. In this study, we evaluated the efficacy of these algorithms. A gynecological pathologist determined the hematoxylin and eosin (H&E)-based histotypes of 230 patients. Subsequently, the final histotypes were established by re-evaluating the H&E-stained sections and immunohistochemistry outcomes. For histotype prediction using the algorithms, the immunohistochemical markers Napsin A, p16, p53, progesterone receptor (PR), trefoil factor 3 (TFF3), and Wilms' tumor 1 (WT1) were scored. The algorithmic predictions were compared with the final histotypes to assess their precision, for which the early- and advanced stage algorithms were assessed together as six-split-stages algorithm. The six-split algorithm demonstrated 96.1% precision, whereas the six-split-stages and four-split algorithms showed 93.5% precision. Of the 230 cases, 16 (7%) showed discordant original and final diagnoses; the algorithms concurred with the final diagnosis in 14/16 cases (87.5%). In 12.4%-13.3% of cases, the H&E-based histotype changed based on the algorithmic outcome. The six-split stages algorithm had a lower sensitivity for low-grade serous carcinoma (80% versus 100%), while the four-split stages algorithm showed reduced sensitivity for endometrioid carcinoma (78% versus 92.7-97.6%). Considering the higher sensitivity of the six-split algorithm for endometrioid and low-grade serous carcinoma compared with the four-split and six-split-stages algorithms, respectively, we recommend the adoption of the six-split algorithm for histotyping epithelial ovarian cancer in clinical practice.
RESUMO
High-grade serous ovarian carcinoma (HGSOC) can be categorized into four gene expression-based subtypes, with supposedly distinct prognoses and treatment responses. Murakami et al. translated these gene expression-based subtypes into the histopathological mesenchymal, immunoreactive, solid and proliferative, and papilloglandular subtypes, showing differences in survival outcomes. Miyagawa et al. refined these criteria to improve the interobserver concordance. The current retrospective study evaluated the interobserver variability and the prognostic differences between the histopathologic subtypes using the criteria of both Murakami et al. and Miyagawa et al. in 208 HGSOC cases. The mesenchymal subtype was considered first, followed by the immunoreactive subtype. Non-conforming cases were categorized as solid and proliferative or papilloglandular. The mesenchymal subtype was identified in 122 patients (58.7%) for both criteria. Using the criteria of Murakami et al., 10 cases (4.8%) were immunoreactive, 26 (12.5%) solid and proliferative, and 50 (24%) papilloglandular, with a concordance rate of 62.5% (κ = 0.34, p < .001). Using the Miyagawa et al. criteria, 23 cases (11%) were immunoreactive, 20 (9.6%) solid and proliferative, and 43 (20.7%) papilloglandular. No survival differences were observed between the subtypes. The fair reproducibility of the histopathological subtype classification of HGSOC and the lack of survival differences among these subtypes indicate the need for further refinement of the criteria and exploration of their correlation with overall survival outcomes before clinical application.
RESUMO
Objective: Biobanks play a crucial role in fundamental and translational research by storing valuable biomaterials and data for future analyses. However, the design of their information technology (IT) infrastructures is often customized to specific requirements, thereby lacking the ability to be used for biobanks comprising other (types of) diseases. This results in substantial costs, time, and efforts for each new biobank project. The Dutch multicenter Archipelago of Ovarian Cancer Research (AOCR) biobank has developed an innovative, reusable IT infrastructure capable of adaptation to various biobanks, thereby enabling cost-effective and efficient implementation and management of biobank IT systems. Methods and Results: The AOCR IT infrastructure incorporates preexisting biobank software, mainly managed by Health-RI. The web-based registration tool Ldot is used for secure storage and pseudonymization of patient data. Clinicopathological data are retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank (Palga), both established repositories, reducing administrative workload and ensuring high data quality. Metadata of collected biomaterials are stored in the OpenSpecimen system. For digital pathology research, a hematoxylin and eosin-stained slide from each patient's tumor is digitized and uploaded to Slide Score. Furthermore, adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, genomic data derived from the AOCR samples are stored in cBioPortal. Conclusion: The IT infrastructure of the AOCR biobank represents a new standard for biobanks, offering flexibility to handle diverse diseases and types of biomaterials. This infrastructure bypasses the need for disease-specific, custom-built software, thereby being cost- and time-effective while ensuring data quality and legislative compliance. The adaptability of this infrastructure highlights its potential to serve as a blueprint for the development of IT infrastructures in both new and existing biobanks.
RESUMO
BACKGROUND: Venous thromboembolism (VTE) is a frequent complication in patients with ovarian cancer after major surgery. Based on limited data, international guidelines recommend extended thromboprophylaxis for up to 28 days. OBJECTIVES: To assess the incidence of VTE and bleeding within 30 days following major surgery in patients with ovarian cancer and to evaluate the association between VTE and thromboprophylaxis duration. METHODS: This was a single-center, retrospective, "before-after" cohort study in patients with ovarian cancer undergoing major surgery. Before July 2019, the local protocol mandated a standard course of thromboprophylaxis during hospital stay only. From July 2019 onward, patients received extended thromboprophylaxis for 28 days. The cumulative incidences of VTE and major bleeding within 30 days after surgery were estimated using the Kaplan-Meier method, with 95% confidence intervals (CIs). Cox regression analysis was performed to evaluate the association between thromboprophylaxis duration and VTE incidence. RESULTS: Between January 2018 and December 2020, 250 women were included, of which 118 (47.2%) received extended and 132 (52.8%) standard thromboprophylaxis. During follow-up, 12 patients developed VTE (cumulative incidence, 4.8%; 95% CI, 2.1-7.4) and 2 major bleeding (cumulative incidence 0.8%; 95% CI, 0.0-1.9). Compared with standard thromboprophylaxis, VTE incidence was numerically lower with extended duration of thromboprophylaxis (5/118 [4.2%] vs 7/132 [5.3%]) but not significantly different (hazard ratio, 0.80; 95% CI, 0.25-2.52). The risk of major bleeding was similar in both groups (1/118 [0.8%] vs 1/132 [0.8%]; hazard ratio, 1.12; 95% CI, 0.07-17.89). CONCLUSIONS: The cumulative VTE incidence in patients with ovarian cancer following major surgery was considerable. Extended thromboprophylaxis was safe and associated with a numerically lower risk of VTE but not significantly different.
Assuntos
Neoplasias Ovarianas , Tromboembolia Venosa , Humanos , Feminino , Anticoagulantes/efeitos adversos , Tromboembolia Venosa/diagnóstico , Tromboembolia Venosa/epidemiologia , Tromboembolia Venosa/etiologia , Estudos de Coortes , Estudos Retrospectivos , Hemorragia/induzido quimicamente , Hemorragia/epidemiologia , Hemorragia/complicações , Neoplasias Ovarianas/cirurgia , Neoplasias Ovarianas/complicações , Neoplasias Ovarianas/tratamento farmacológico , HospitaisRESUMO
OBJECTIVES: Ovarian cancer has the worst overall survival rate of all gynecologic malignancies. For the majority of patients, the 5-year overall survival rate of less than 50% has hardly improved over the last decades. To improve the outcome of patients with all subtypes of ovarian cancer, large-scale fundamental and translational research is needed. To accommodate these types of ovarian cancer research, we have established a Dutch nationwide, interdisciplinary infrastructure and biobank: the Archipelago of Ovarian Cancer Research (AOCR). The AOCR will facilitate fundamental and translational ovarian cancer research and enhance interdisciplinary, national, and international collaboration. DESIGN: The AOCR biobank is a prospective ovarian cancer biobank in which biomaterials are collected, processed, and stored in a uniform matter for future (genetic) scientific research. All 19 Dutch hospitals in which ovarian cancer surgery is performed participate and collaborate in the AOCR biobank. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients of 16 years and older with suspected or diagnosed ovarian, fallopian tube, or primary peritoneal cancer are recruited for participation. Patients who agree to participate give written informed consent for collection, storage, and issue of their biomaterials for future studies. After inclusion, different blood samples are taken at various predefined time points both before and during treatment. In case of a diagnostic paracentesis or biopsy, the residual biomaterials of these procedures are stored in the biobank. During surgery, primary tumor tissue and, if applicable, tissue from metastatic sites are collected and stored. From each patient, a representative histological hematoxylin and eosin stained slide is digitalized for research purposes, including reassessment by a panel of gynecologic pathologists. Clinical and pathological data are obtained on a per-study basis from Dutch registries. Research proposals for the issue of biomaterials and data are evaluated by both the Archipelago Scientific Committee and the Steering Committee. Researchers using the biomaterials from the AOCR biobank are encouraged to enrich the biobank with data and materials resulting from their analyses and experiments. LIMITATIONS: The implementation and first 4 years of collection are financed by an infrastructural grant from the Dutch Cancer Society. Therefore, the main limitation is that the costs for sustaining the biobank after the funding period will have to be covered. This coverage will come from incorporation of budget for biobanking in future grant applications and from fees from external researchers and commercial parties using the biomaterials stored in the AOCR biobank. Moreover, we will apply for grants aimed at sustaining and improving research infrastructures and biobanks. CONCLUSIONS: With the establishment of the Dutch nationwide, interdisciplinary Archipelago of Ovarian Cancer Research infrastructure and biobank, fundamental and translational research on ovarian cancer can be greatly improved. The ultimate aim of this infrastructure is that it will lead to improved diagnostics, treatment, and survival of patients with ovarian cancer.