Sadaf Ghaem-Maghami

Understanding and Addressing Challenges With Electronic Health Record Use in Gynecological Oncology: Cross-Sectional Survey of Multidisciplinary Professionals in the United Kingdom and Co-Design of an Integrated Informatics Platform to Support Clinical Decision-Making

Abstract Background Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users. This study aimed to explore multiprofessional experiences of EHR use in gynecological oncology and to develop a co-designed informatics platform to improve decision-making for ovarian cancer care. Objective This study aims to evaluate the perspectives of health care professionals on retrieving routine clinical data from EHRs in the management of ovarian cancer and to design an integrated informatics platform that supports clinical decision-making. Methods We conducted a national cross-sectional survey of 92 UK-based professionals working in gynecological oncology, including oncologists, nurses, radiologists, and other specialists in ovarian cancer. The web-based questionnaire, combining quantitative and free-text responses, assessed their experiences with EHR use, focusing on information retrieval, usability challenges, perceived risks, and benefits. In parallel, a human-centered design approach involving health care professionals, data engineers, and informatics experts codeveloped a digital informatics platform that integrates structured and unstructured data from multiple clinical systems into a unified patient summary view for clinical decision-making. Natural language processing was applied to extract genomic and surgical information from free-text records, with data pipelines validated by clinicians against original clinical system sources. Results Among 92 respondents, 84 out of 91 (92%) routinely accessed multiple EHR systems, with 26 out of 91 (29%) using 5 or more. Notably, 16 out of 92 respondents (17%) reported spending more than 50% of their clinical time searching for patient information. Key challenges included lack of interoperability (35/141 reported challenges, 24.8%), difficulty locating critical data such as genetic results (57/85 respondents, 67%), and poor organization of information. Only 10 out of 92 professionals (11%) strongly agreed that their systems provided well-organized data for clinical use. While ease of access to patient data was a key benefit, 54 out of 90 respondents (60%) reported lacking access to comprehensive patient summaries. To address these issues, our co-designed informatics platform consolidates disparate patients’ data from different EHR systems into a single visual display to support clinical decision-making and audit. Conclusions Current EHR systems are suboptimal for supporting complex gynecological oncology care. Our findings highlight the urgent need for integrated, user-centered clinical decision tools. Fragmentation and lack of interoperability hinder information retrieval and may compromise patient care. Our co-designed ovarian cancer informatics platform is a potential real-world solution to improve data visibility, clinical efficiency, and ultimately the quality of ovarian cancer care.

Homologous recombination deficiency testing in patients with high grade ovarian cancer: factors influencing test success

Testing for tumor BRCA mutations and homologous recombination deficiency (HRD) is recommended for all patients with advanced high-grade epithelial ovarian cancer. Delays in the HRD testing process can significantly affect the treatment offered to patients. HRD testing pathways and sampling processes were analyzed for tests sent from a tertiary gynae-oncology referral center between December 2020 and January 2023. A total of 148 hRD tests were performed in 125 patients. The overall success rate of HRD testing was 69.6%. The success rates of obtaining results were: from diagnostic image-guided biopsy 66.7% ( By optimizing the factors affecting HRD test success, we can obtain faster results and offer patients appropriate treatment at earlier time points to improve patient outcomes.

An Integrated Clinical‐MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer

BackgroundDetermination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.PurposeTo identify clinical features and imaging signatures on T2‐weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study TypeRetrospective.PopulationFour hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence1.5‐T and 3‐T scanners with sagittal T2‐weighted spin echo sequence.AssessmentTumor regions were manually segmented on T2‐weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical TestsA likelihood‐ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.ResultsThree radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively.Data ConclusionThe proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer.Evidence Level4Technical EfficacyStage 2

Human ovarian cancer intrinsic mechanisms regulate lymphocyte activation in response to immune checkpoint blockade

Abstract Immune checkpoint blocking antibodies are currently being tested in ovarian cancer (OC) patients and have shown some responses in early clinical trials. However, it remains unclear how human OC cancer cells regulate lymphocyte activation in response to therapy. In this study, we have established and optimised an in vitro tumour-immune co-culture system (TICS), which is specifically designed to quantify the activation of multiple primary human lymphocyte subsets and human cancer cell killing in response to PD-1/L1 blockade. Human OC cell lines and treatment naïve patient ascites show differential effects on lymphocyte activation and respond differently to PD-1 blocking antibody nivolumab in TICS. Using paired OC cell lines established prior to and after chemotherapy relapse, our data reveal that the resistant cells express low levels of HLA and respond poorly to nivolumab, relative to the treatment naïve cells. In accordance, knockdown of IFNγ receptor expression compromises response to nivolumab in the treatment naïve OC cell line, while enhanced HLA expression induced by a DNA methyltransferase inhibitor promotes lymphocyte activation in TICS. Altogether, our results suggest a ‘cross resistance’ model, where the acquired chemotherapy resistance in cancer cells may confer resistance to immune checkpoint blockade therapy through down-regulation of antigen presentation machinery. As such, agents that can restore HLA expression may be a suitable combination partner for immunotherapy in chemotherapy-relapsed human ovarian cancer patients.

Transcriptional analysis of multiple ovarian cancer cohorts reveals prognostic and immunomodulatory consequences of ERV expression

Background Endogenous retroviruses (ERVs) play a role in a variety of biological processes, including embryogenesis and cancer. DNA methyltransferase inhibitors (DNMTi)-induced ERV expression triggers interferon responses in ovarian cancer cells via the viral sensing machinery. Baseline expression of ERVs also occurs in cancer cells, though this process is poorly understood and previously unexplored in epithelial ovarian cancer (EOC). Here, the prognostic and immunomodulatory consequences of baseline ERV expression was assessed in EOC. Methods ERV expression was assessed using EOC transcriptional data from The Cancer Genome Atlas (TCGA) and from an independent cohort (Hammersmith Hospital, HH), as well as from untreated or DNMTi-treated EOC cell lines. Least absolute shrinkage and selection operator (LASSO) logistic regression defined an ERV expression score to predict patient prognosis. Immunohistochemistry (IHC) was conducted on the HH cohort. Combination of DNMTi treatment with γδ T cells was tested in vitro, using EOC cell lines and patient-derived tumor cells. Results ERV expression was found to define clinically relevant subsets of EOC patients. An ERV prognostic score was successfully generated in TCGA and validated in the independent cohort. In EOC patients from this cohort, a high ERV score was associated with better survival (log-rank p=0.0009) and correlated with infiltration of CD8+PD1+T cells (r=0.46, p=0.0001). In the TCGA dataset, a higher ERV score was found in BRCA1/2 mutant tumors, compared to wild type (p=0.015), while a lower ERV score was found in CCNE1 amplified tumors, compared to wild type (p=0.019). In vitro, baseline ERV expression dictates the level of ERV induction in response to DNMTi. Manipulation of an ERV expression threshold by DNMTi resulted in improved EOC cell killing by cytotoxic immune cells. Conclusions These findings uncover the potential for baseline ERV expression to robustly inform EOC patient prognosis, influence tumor immune infiltration and affect antitumor immunity.

Diagnostic performance of quantitative measures from [18F]FDG PET/CT, [18F]FEC PET/CT, and DW-MRI in the detection of lymph node metastases in endometrial and cervical cancer: data from the MAPPING study

Abstract Purpose To evaluate the diagnostic performance of quantitative measures derived from [ 18 F]FDG PET/CT, [ 18 F]FEC PET/CT, and DW-MRI in the detection of lymph node metastases in endometrial and cervical cancer with comparison to standard visual PET analysis with histology as the reference standard. Methods Subanalysis of quantitative data from the prospective multicentre MAPPING study. Nodal and tumour SUV max from [ 18 F]FDG PET/CT and [ 18 F]FEC PET/CT and ADC mean from DW-MRI were documented. Nodal-to-tumour ratios (NTR) and SUV max -to-ADC mean ratio (STAR) were calculated. Optimal cut-offs of quantitative measures were compared to visual assessment on a regional basis using histopathology as the reference standard. Results Scans from 112 patients (36 cervical and 76 endometrial cancers; 340 nodal regions) were eligible for quantitative image analysis. Lower ADC mean on DW-MRI was observed in metastatic nodes for cervical cancer but not for endometrial cancer. Quantitative measures were significantly higher in malignant than benign nodal regions on [ 18 F]FDG PET/CT and [ 18 F]FEC PET/CT in endometrial cancer. SUV max cut-offs showed similar performance to visual assessment in the diagnosis of metastatic lymph nodes in endometrial cancer whilst ADC mean cut-offs showed significantly lower specificity than visual assessment. Interobserver agreement was excellent for SUV max measurements on both [ 18 F]FDG PET/CT and [ 18 F]FEC PET/CT, but poor for ADC mean on DW-MRI. Conclusion Quantitative measures from [ 18 F]FDG PET/CT, [ 18 F]FEC PET/CT, or DW-MRI did not outperform visual assessment in the detection of nodal metastases in endometrial cancer. Therefore, the implementation of these quantitative measures as standalone diagnostic tools in routine clinical practice is not recommended.