Exploring the Potential Role of Upper Abdominal Peritonectomy in Advanced Ovarian Cancer Cytoreductive Surgery Using Explainable Artificial Intelligence.

The Surgical Complexity Score (SCS) has been widely used to describe the surgical effort during advanced stage epithelial ovarian cancer (EOC) cytoreduction. Referring to a variety of multi-visceral resections, it best combines the numbers with the complexity of the sub-procedures. Nevertheless, not all potential surgical procedures are described by this score. Lately, the European Society for Gynaecological Oncology (ESGO) has established standard outcome quality indicators pertinent to achieving complete cytoreduction (CC0). There is a need to define what weight all these surgical sub-procedures comprising CC0 would be given. Prospectively collected data from 560 surgically cytoreduced advanced stage EOC patients were analysed at a UK tertiary referral centre.We adapted the structured ESGO ovarian cancer report template. We employed the eXtreme Gradient Boosting (XGBoost) algorithm to model a long list of surgical sub-procedures. We applied the Shapley Additive explanations (SHAP) framework to provide global (cohort) explainability. We used Cox regression for survival analysis and constructed Kaplan-Meier curves. The XGBoost model predicted CC0 with an acceptable accuracy (area under curve [AUC] = 0.70; 95% confidence interval [CI] = 0.63-0.76). Visual quantification of the feature importance for the prediction of CC0 identified upper abdominal peritonectomy (UAP) as the most important feature, followed by regional lymphadenectomies. The UAP best correlated with bladder peritonectomy and diaphragmatic stripping (Pearson's correlations > 0.5). Clear inflection points were shown by pelvic and para-aortic lymph node dissection and ileocecal resection/right hemicolectomy, which increased the probability for CC0. When UAP was solely added to a composite model comprising of engineered features, it substantially enhanced its predictive value (AUC = 0.80, CI = 0.75-0.84). The UAP was predictive of poorer progression-free survival (HR = 1.76, CI 1.14-2.70, P: 0.01) but not overall survival (HR = 1.06, CI 0.56-1.99, P: 0.86). The SCS did not have significant survival impact. Machine Learning allows for operational feature selection by weighting the relative importance of those surgical sub-procedures that appear to be more predictive of CC0. Our study identifies UAP as the most important procedural predictor of CC0 in surgically cytoreduced advanced-stage EOC women. The classification model presented here can potentially be trained with a larger number of samples to generate a robust digital surgical reference in high output tertiary centres. The upper abdominal quadrants should be thoroughly inspected to ensure that CC0 is achievable.

Prerequisites to improve surgical cytoreduction in FIGO stage III/IV epithelial ovarian cancer and subsequent clinical ramifications.

BACKGROUND: No residual disease (CC 0) following cytoreductive surgery is pivotal for the prognosis of women with advanced stage epithelial ovarian cancer (EOC). Improving CC 0 resection rates without increasing morbidity and no delay in subsequent chemotherapy favors a better outcome in these women. Prerequisites to facilitate this surgical paradigm shift and subsequent ramifications need to be addressed. This quality improvement study assessed 559 women with advanced EOC who had cytoreductive surgery between January 2014 and December 2019 in our tertiary referral centre. Following implementation of the Enhanced Recovery After Surgery (ERAS) pathway and prehabilitation protocols, the surgical management paradigm in advanced EOC patients shifted towards maximal surgical effort cytoreduction in 2016. Surgical outcome parameters before, during, and after this paradigm shift were compared. The primary outcome measure was residual disease (RD). The secondary outcome parameters were postoperative morbidity, operative time (OT), length of stay (LOS) and progression-free-survival (PFS). RESULTS: R0 resection rate in patients with advanced EOC increased from 57.3% to 74.4% after the paradigm shift in surgical management whilst peri-operative morbidity and delays in adjuvant chemotherapy were unchanged. The mean OT increased from 133 + 55 min to 197 + 85 min, and postoperative high dependency/intensive care unit (HDU/ICU) admissions increased from 8.1% to 33.1%. The subsequent mean LOS increased from 7.0 + 2.6 to 8.4 + 4.9 days. The median PFS was 33 months. There was no difference for PFS in the three time frames but a trend towards improvement was observed. CONCLUSIONS: Improved CC 0 surgical cytoreduction rates without compromising morbidity in advanced EOC is achievable owing to the right conditions. Maximal effort cytoreductive surgery should solely be carried out in high output tertiary referral centres due to the associated substantial prerequisites and ramifications.

Immune Checkpoint Inhibitors Targeting the PD-1/PD-L1 Pathway in Advanced, Recurrent Endometrial Cancer: A Scoping Review with SWOT Analysis.

Results of recent clinical trials using the immune check point inhibitors (ICI) pembrolizumab or dostarlimab with/without lenvatinib has led to their approval for specific molecular subgroups of advanced recurrent endometrial cancer (EC). Herein, we summarise the clinical data leading to this first tissue-agnostic approval. As this novel therapy is not yet available in the United Kingdom standard care setting, we explore the strengths, weaknesses, opportunities, and threats (SWOT) of ICI treatment in EC. Major databases were searched focusing on clinical trials using programmed cell death protein 1 (PD-1) and its ligand (PD-L1) ICI which ultimately contributed to anti-PD-1 approval in EC. We performed a data quality assessment, reviewing survival and safety analysis. We included 15 studies involving 1609 EC patients: 458 with mismatch repair deficiency (MMRd)/microsatellite instability-high (MSI-H) status and 1084 with mismatch repair proficiency/microsatellite stable (MMRp/MSS) status. Pembrolizumab/dostarlimab have been approved for MMRd ECs, with the addition of lenvatinib for MMRp cases in the recurrent setting. Future efforts will focus on the pathological assessment of biomarkers to determine molecular phenotypes that correlate with response or resistance to ICI in order to identify patients most likely to benefit from this treatment.

Development of a Novel Intra-Operative Score to Record Diseases' Anatomic Fingerprints (ANAFI Score) for the Prediction of Complete Cytoreduction in Advanced-Stage Ovarian Cancer by Using Machine Learning and Explainable Artificial Intelligence.

BACKGROUND: The Peritoneal Carcinomatosis Index (PCI) and the Intra-operative Mapping for Ovarian Cancer (IMO), to a lesser extent, have been universally validated in advanced-stage epithelial ovarian cancer (EOC) to describe the extent of peritoneal dissemination and are proven to be powerful predictors of the surgical outcome with an added sensitivity of assessment at laparotomy of around 70%. This leaves room for improvement because the two-dimensional anatomic scoring model fails to reflect the patient's real anatomy, as seen by a surgeon. We hypothesized that tumor dissemination in specific anatomic locations can be more predictive of complete cytoreduction (CC0) and survival than PCI and IMO tools in EOC patients. (2) Methods: We analyzed prospectively data collected from 508 patients with FIGO-stage IIIB-IVB EOC who underwent cytoreductive surgery between January 2014 and December 2019 at a UK tertiary center. We adapted the structured ESGO ovarian cancer report to provide detailed information on the patterns of tumor dissemination (cancer anatomic fingerprints). We employed the extreme gradient boost (XGBoost) to model only the variables referring to the EOC disseminated patterns, to create an intra-operative score and judge the predictive power of the score alone for complete cytoreduction (CC0). Receiver operating characteristic (ROC) curves were then used for performance comparison between the new score and the existing PCI and IMO tools. We applied the Shapley additive explanations (SHAP) framework to support the feature selection of the narrated cancer fingerprints and provide global and local explainability. Survival analysis was performed using Kaplan-Meier curves and Cox regression. (3) Results: An intra-operative disease score was developed based on specific weights assigned to the cancer anatomic fingerprints. The scores range from 0 to 24. The XGBoost predicted CC0 resection (area under curve (AUC) = 0.88 CI = 0.854-0.913) with high accuracy. Organ-specific dissemination on the small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum were the most crucial features globally. When added to the composite model, the novel score slightly enhanced its predictive value (AUC = 0.91, CI = 0.849-0.963). We identified a "turning point", ≤5, that increased the probability of CC0. Using conventional logistic regression, the new score was superior to the PCI and IMO scores for the prediction of CC0 (AUC = 0.81 vs. 0.73 and 0.67, respectively). In multivariate Cox analysis, a 1-point increase in the new intra-operative score was associated with poorer progression-free (HR: 1.06; 95% CI: 1.03-1.09, p < 0.005) and overall survival (HR: 1.04; 95% CI: 1.01-1.07), by 4% and 6%, respectively. (4) Conclusions: The presence of cancer disseminated in specific anatomical sites, including small bowel mesentery, large bowel serosa, and diaphragmatic peritoneum, can be more predictive of CC0 and survival than the entire PCI and IMO scores. Early intra-operative assessment of these areas only may reveal whether CC0 is achievable. In contrast to the PCI and IMO scores, the novel score remains predictive of adverse survival outcomes.

Explaining the Elusive Nature of a Well-Defined Threshold for Blood Transfusion in Advanced Epithelial Ovarian Cancer Cytoreductive Surgery.

There is no well-defined threshold for intra-operative blood transfusion (BT) in advanced epithelial ovarian cancer (EOC) surgery. To address this, we devised a Machine Learning (ML)-driven prediction algorithm aimed at prompting and elucidating a communication alert for BT based on anticipated peri-operative events independent of existing BT policies. We analyzed data from 403 EOC patients who underwent cytoreductive surgery between 2014 and 2019. The estimated blood volume (EBV), calculated using the formula EBV = weight × 80, served for setting a 10% EBV threshold for individual intervention. Based on known estimated blood loss (EBL), we identified two distinct groups. The Receiver operating characteristic (ROC) curves revealed satisfactory results for predicting events above the established threshold (AUC 0.823, 95% CI 0.76-0.88). Operative time (OT) was the most significant factor influencing predictions. Intra-operative blood loss exceeding 10% EBV was associated with OT > 250 min, primary surgery, serous histology, performance status 0, R2 resection and surgical complexity score > 4. Certain sub-procedures including large bowel resection, stoma formation, ileocecal resection/right hemicolectomy, mesenteric resection, bladder and upper abdominal peritonectomy demonstrated clear associations with an elevated interventional risk. Our findings emphasize the importance of obtaining a rough estimate of OT in advance for precise prediction of blood requirements.

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score.

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods: Machine learning and deep learning methods using artificial neural networks (ANN) were used together with conventional logistic regression to predict continuous and binary LOS outcomes for HGSOC patients. The models were evaluated in a post-hoc internal validation set and a Graphical User Interface (GUI) was developed to demonstrate the clinical feasibility of sophisticated LOS predictions. (3) Results: For binary LOS predictions at differential time points, the accuracy ranged between 70-98%. Feature selection identified surgical complexity, pre-surgery albumin, blood loss, operative time, bowel resection with stoma formation, and severe postoperative complications (CD3-5) as independent LOS predictors. For the GUI numerical LOS score, the ANN model was a good estimator for the standard deviation of the LOS distribution by ± two days. (4) Conclusions: We demonstrated the development and application of both quantitative and qualitative AI models to predict LOS in advanced-stage EOC patients following their cytoreduction. Accurate identification of potentially modifiable factors delaying hospital discharge can further inform services performing root cause analysis of LOS.

Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer.

(1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598−0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69−0.85; p < 0.05 vs. AUC 0.739; 95% [CI] 0.655−0.823; p < 0.95). We identified "turning points" that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with <12 years of experience, age <53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score >4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications.

Collection of cancer Patient Reported Outcome Measures (PROMS) to link with primary and secondary electronic care records to understand and improve long term cancer outcomes: A protocol paper.

INTRODUCTION: More people are living with and beyond a cancer diagnosis. There is limited understanding of the long-term effects of cancer and cancer treatment on quality of life and personal and household finances when compared to people without cancer. In a separate protocol we have proposed to link de-identified data from electronic primary care and hospital records for a large population of cancer survivors and matched controls. In this current protocol, we propose the linkage of Patient Reported Outcomes Measures data to the above data for a subset of this population. The aim of this study is to investigate the full impact of living with and beyond a cancer diagnosis compared to age and gender matched controls. A secondary aim is to test the feasibility of the collection of Patient Reported Outcomes Measures (PROMS) data and the linkage procedures of the PROMs data to electronic health records data. MATERIALS AND METHODS: This is a cross-sectional study, aiming to recruit participants treated at the Leeds Teaching Hospitals National Health Service Trust. Eligible patients will be cancer survivors at around 5 years post-diagnosis (breast, colorectal and ovarian cancer) and non-cancer patient matched controls attending dermatology out-patient clinics. They will be identified by running a query on the Leeds Teaching Hospitals Trust patient records system. Approximately 6000 patients (2000 cases and 4000 controls) will be invited to participate via post. Participants will be invited to complete PROMs assessing factors such as quality of life and finances, which can be completed on paper or online (surveys includes established instruments, and bespoke instruments (demographics, financial costs). This PROMs data will then be linked to routinely collected de-identified data from patient's electronic primary care and hospital records. DISCUSSION: This innovative work aims to create a truly 'comprehensive patient record' to provide a broad picture of what happens to cancer patients across their cancer pathway, and the long-term impact of cancer treatment. Comparisons can be made between the cases and controls, to identify the aspects of life that has had the greatest impact following a cancer diagnosis. The feasibility of linking PROMs data to electronic health records can also be assessed. This work can inform future support offered to people living with and beyond a cancer diagnosis, clinical practice, and future research methodologies.

Cervical cancer and COVID-an assessment of the initial effect of the pandemic and subsequent projection of impact for women in England: A cohort study.

OBJECTIVE: To review the effect of the COVID-19 pandemic on the diagnosis of cervical cancer and model the impact on workload over the next 3 years. DESIGN: A retrospective, control, cohort study. SETTING: Six cancer centres in the North of England representing a combined population of 11.5 million. METHODS: Data were collected retrospectively for all diagnoses of cervical cancer during May-October 2019 (Pre-COVID cohort) and May-October 2020 (COVID cohort). Data were used to generate tools to forecast case numbers for the next 3 years. MAIN OUTCOME MEASURES: Histology, stage, presentation, onset of symptoms, investigation and type of treatment. Patients with recurrent disease were excluded. RESULTS: 406 patients were registered across the study periods; 233 in 2019 and 173 in 2020, representing a 25.7% (n = 60) reduction in absolute numbers of diagnoses. This was accounted for by a reduction in the number of low stage cases (104 in 2019 to 77 in 2020). Adding these data to the additional cases associated with a temporary cessation in screening during the pandemic allowed development of forecasts, suggesting that over the next 3 years there would be 586, 228 and 105 extra cases of local, regional and distant disease, respectively, throughout England. Projection tools suggest that increasing surgical capacity by two or three cases per month per centre would eradicate this excess by 12 months and 7 months, respectively. CONCLUSIONS: There is likely to be a significant increase in cervical cancer cases presenting over the next 3 years. Increased surgical capacity could mitigate this with little increase in morbidity or mortality. TWEETABLE ABSTRACT: Covid will result in 919 extra cases of cervical cancer in England alone. Effects can be mitigated by increasing surgical capacity.

Ovarian cancer and KiSS-1 gene expression: A consideration of the use of Kisspeptin plus Kisspeptin aptamers in diagnostics and therapy.

Gynaecological cancers continue to present a significant health burden upon the health of the global female population. This deficit is most prominent with ovarian cancer which possesses the lowest survival rate compared to all other cancers occurring within this anatomical region, with an annual UK-mortality of 7,300. The poor tolerability and selectively of the treatment options that are currently available is likely to have contributed to this high mortality rate thus, demonstrating the need for the development of enhanced therapeutic approaches. Aptamer technology would involve the engineering of specifically sequenced oligonucleotide chains, which bind to macromolecular targets with a high degree of affinity and selectively. Recent in-vitro studies conducted upon the clinical utility of this technique have supported its superiority in targeting individual therapeutic drug targets compared to various other targeting moieties currently within therapeutic use such as, monoclonal antibodies. For this reason, the employment of this technique is likely to be favourable in reducing the incidence of non-specific, chemotherapy-associated adverse effects. Kisspeptin is a naturally expressed polypeptide with an established role in the development of the reproductive system and other proposed roles in influencing the ability of ovarian cancer growths to exhibit the metastasis hallmark. This distinctive feature would indicate the potential for the manipulation of this pathway through the application of aptamer structures in developing a novel prophylactic strategy and improve the long-term outcome for ovarian cancer patients.

Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning.

INTRODUCTION: Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within learning datasets. We designed a study to support the feature selection for the 2-year prognostic period and compared the performance of several Machine Learning prediction algorithms for accurate 2-year prognosis estimation in advanced-stage high grade serous ovarian cancer (HGSOC) patients. METHODS: The prognosis estimation was formulated as a binary classification problem. Dataset was split into training and test cohorts with repeated random sampling until there was no significant difference (p = 0.20) between the two cohorts. A ten-fold cross-validation was applied. Various state-of-the-art supervised classifiers were used. For feature selection, in addition to the exhaustive search for the best combination of features, we used the-chi square test of independence and the MRMR method. RESULTS: Two hundred nine patients were identified. The model's mean prediction accuracy reached 73%. We demonstrated that Support-Vector-Machine and Ensemble Subspace Discriminant algorithms outperformed Logistic Regression in accuracy indices. The probability of achieving a cancer-free state was maximised with a combination of primary cytoreduction, good performance status and maximal surgical effort (AUC 0.63). Standard chemotherapy, performance status, tumour load and residual disease were consistently predictive of the mid-term overall survival (AUC 0.63-0.66). The model recall and precision were greater than 80%. CONCLUSION: Machine Learning appears to be promising for accurate prognosis estimation. Appropriate feature selection is required when building an HGSOC model for 2-year prognosis prediction. We provide evidence as to what combination of prognosticators leads to the largest impact on the HGSOC 2-year prognosis.

Breast Cancer Patients at Increased Risk of Developing Type II Endometrial Cancer: Relative and Absolute Risk Estimation and Implications for Counseling.

Introduction Breast cancer (BC) is a recognized risk factor for endometrial cancer (EC). Emerging literature indicates that it confers a higher risk of type II EC (T2EC) than type I EC (T1EC). Although some surgeons offer a prophylactic hysterectomy to BC patients referred for risk-reducing bilateral salpingo-oophorectomy, insufficient evidence prevents this from being the standard practice. We aimed to quantify their absolute risk and relative risk (RR) of developing both EC subtypes and identify a higher-risk group that could be considered for prophylactic hysterectomy. Methodology This retrospective service evaluation compared patients diagnosed with BC between 2008 and 2014, who subsequently developed EC within 10 years to those who did not. Absolute risk and RR were calculated using the numbers of regional BC and EC cases within this group, alongside 2009 UK female population and EC incidence statistics. Binary logistic regression generated adjusted odds ratios (ORs) for patient- and disease-specific variables. Results A total of 45 BC patients developed EC, 24 had T1EC and 21 had T2EC. Their RR of developing EC was greater than that of the general population (RR: 12.44, p < 0.0001). Notably, this was higher for T2EC (RR: 33.96, p < 0.001) than T1EC (RR: 8.63, p < 0.0001). Nonetheless, the absolute risk remained low. Tamoxifen exposure was significantly more prevalent among T2EC patients (adjusted OR: 79.61, p = 0.003). Increased age at BC diagnosis was associated with T1EC (adjusted OR: 1.10, p = 0.043) and T2EC (adjusted OR: 1.13, p = 0.03). Neither smoking status nor family history of BC was significantly associated with any outcome. Conclusion Women with BC were more likely to develop T2EC than T1EC, and although the absolute risk was low, the cumulative risk was substantial enough to warrant vigilance. Tamoxifen exposure was significantly predictive of EC, particularly T2EC, and might facilitate risk estimation. Older women at BC diagnosis who receive tamoxifen treatment should be screened and closely monitored for EC. However, given the limitations of normal screening methods for the detection of T2EC, counseling for a prophylactic hysterectomy should also be considered. Clarification of the menopausal status will help make more meaningful recommendations.

Machine Learning-Based Risk Prediction of Critical Care Unit Admission for Advanced Stage High Grade Serous Ovarian Cancer Patients Undergoing Cytoreductive Surgery: The Leeds-Natal Score.

Achieving complete surgical cytoreduction in advanced stage high grade serous ovarian cancer (HGSOC) patients warrants an availability of Critical Care Unit (CCU) beds. Machine Learning (ML) could be helpful in monitoring CCU admissions to improve standards of care. We aimed to improve the accuracy of predicting CCU admission in HGSOC patients by ML algorithms and developed an ML-based predictive score. A cohort of 291 advanced stage HGSOC patients with fully curated data was selected. Several linear and non-linear distances, and quadratic discriminant ML methods, were employed to derive prediction information for CCU admission. When all the variables were included in the model, the prediction accuracies were higher for linear discriminant (0.90) and quadratic discriminant (0.93) methods compared with conventional logistic regression (0.84). Feature selection identified pre-treatment albumin, surgical complexity score, estimated blood loss, operative time, and bowel resection with stoma as the most significant prediction features. The real-time prediction accuracy of the Graphical User Interface CCU calculator reached 95%. Limited, potentially modifiable, mostly intra-operative factors contributing to CCU admission were identified and suggest areas for targeted interventions. The accurate quantification of CCU admission patterns is critical information when counseling patients about peri-operative risks related to their cytoreductive surgery.

Predicting complete cytoreduction for advanced ovarian cancer patients using nearest-neighbor models.

BACKGROUND: The foundation of modern ovarian cancer care is cytoreductive surgery to remove all macroscopic disease (R0). Identification of R0 resection patients may help individualise treatment. Machine learning and AI have been shown to be effective systems for classification and prediction. For a disease as heterogenous as ovarian cancer, they could potentially outperform conventional predictive algorithms for routine clinical use. We investigated the performance of an AI system, the k-nearest neighbor (k-NN) classifier, to predict R0, comparing it with logistic regression. Patients diagnosed with advanced stage, high grade serous ovarian, tubal and primary peritoneal cancer, undergoing surgical cytoreduction from 2015 to 2019, was selected from the ovarian database. Performance variables included age, BMI, Charlson Comorbidity Index, timing of surgery, surgical complexity and disease score. The k-NN algorithm classified R0 vs non-R0 patients using 3-20 nearest neighbors. Prediction accuracy was estimated as percentage of observations in the training set correctly classified. RESULTS: 154 patients were identified, with mean age of 64.4 + 10.5 yrs., BMI of 27.2 + 5.8 and mean SCS of 3 + 1 (1-8). Complete and optimal cytoreduction was achieved in 62 and 88% patients. The mean predictive accuracy was 66%. R0 resection prediction of true negatives was as high as 90% using k = 20 neighbors. CONCLUSIONS: The k-NN algorithm is a promising and versatile tool for R0 resection prediction. It slightly outperforms logistic regression and is expected to improve accuracy with data expansion.

The value of MRI in management of endometrial hyperplasia with atypia.

BACKGROUND: The value of the magnetic resonance imaging (MRI) in the assessment of women with endometrial hyperplasia and its role in diagnosis of myometrial invasion or coexistence of cancer is not known. This study aimed to evaluate the accuracy and usefulness of MRI in the management of patients diagnosed on endometrial biopsy with complex endometrial hyperplasia with atypia (CEHA). METHODS: A retrospective study of 86 cases diagnosed with endometrial hyperplasia with atypia on the initial endometrial biopsy in a tertiary university teaching hospital between 2010 and 2015 was carried out. The MRI accuracy in predicting malignant changes and influence the clinical management was compared among women who had either pelvic MRI, transvaginal ultrasound (TVUS), or no additional imagistic studies. RESULTS: MRI was performed in 24 (28%) and TVUS in 11 (13%)cases, while 51 (59%) women had no additional imagistic studies. In the group of women with no imaging studies, 26/51 (51%) were surgically treated and 8/26 (31%) were diagnosed with endometrial cancer (EEC) stage 1a. In the group of women who had TVUS, 5/11 (45%) were surgically treated and none was diagnosed with EEC. In the group of women who underwent an MRI examination, 20/24 (83%) were surgically treated. Among these, 11/20 (55%) were diagnosed with EEC, 7 had EEC stage 1a, and 4 had EEC stage 1b. Although MRI was able to identify malignant changes with a good sensitivity (91.7%), it had a low specificity in characterisation of malignant transformation (8%). MRI correctly identified 31% of the stage 1a and 33% of the stage 1b endometrial cancer. CONCLUSION: In this study, we found a potential diagnostic value of MRI for identifying malignant transformation in patients with CEHA. However, pelvic MRI has a rather weak predictive value of myometrial invasion in women with CEHA and concurrent EEC. The diagnostic and therapeutic benefits of MRI assessment in patients with CEHA need further validation.

A Robust and Versatile Automated Glycoanalytical Technology for Serum Antibodies and Acute Phase Proteins: Ovarian Cancer Case Study.

The direct association of the genome, transcriptome, metabolome, lipidome and proteome with the serum glycome has revealed systems of interconnected cellular pathways. The exact roles of individual glycoproteomes in the context of disease have yet to be elucidated. In a move toward personalized medicine, it is now becoming critical to understand disease pathogenesis, and the traits, stages, phenotypes and molecular features that accompany it, as the disruption of a whole system. To this end, we have developed an innovative technology on an automated platform, "GlycoSeqCap," which combines N-glycosylation data from six glycoproteins using a single source of human serum. Specifically, we multiplexed and optimized a successive serial capture and glycoanalysis of six purified glycoproteins, immunoglobulin G (IgG), immunoglobulin M (IgM), immunoglobulin A (IgA), transferrin (Trf), haptoglobin (Hpt) and alpha-1-antitrypsin (A1AT), from 50 µl of human serum. We provide the most comprehensive and in-depth glycan analysis of individual glycoproteins in a single source of human serum to date. To demonstrate the technological application in the context of a disease model, we performed a pilot study in an ovarian cancer cohort (n = 34) using discrimination and classification analyses to identify aberrant glycosylation. In our sample cohort, we exhibit improved selectivity and specificity over the currently used biomarker for ovarian cancer, CA125, for early stage ovarian cancer. This technology will establish a new state-of-the-art strategy for the characterization of individual serum glycoproteomes as a diagnostic and monitoring tool which represents a major step toward understanding the changes that take place during disease.

Integrated eicosanoid lipidomics and gene expression reveal decreased prostaglandin catabolism and increased 5-lipoxygenase expression in aggressive subtypes of endometrial cancer.

Eicosanoids comprise a diverse group of bioactive lipids which orchestrate inflammation, immunity, and tissue homeostasis, and whose dysregulation has been implicated in carcinogenesis. Among the various eicosanoid metabolic pathways, studies of their role in endometrial cancer (EC) have very much been confined to the COX-2 pathway. This study aimed to determine changes in epithelial eicosanoid metabolic gene expression in endometrial carcinogenesis; to integrate these with eicosanoid profiles in matched clinical specimens; and, finally, to investigate the prognostic value of candidate eicosanoid metabolic enzymes. Eicosanoids and related mediators were profiled using liquid chromatography-tandem mass spectrometry in fresh frozen normal, hyperplastic, and cancerous (types I and II) endometrial specimens (n = 192). Sample-matched epithelia were isolated by laser capture microdissection and whole genome expression analysis was performed using microarrays. Integration of eicosanoid and gene expression data showed that the accepted paradigm of increased COX-2-mediated prostaglandin production does not apply in EC carcinogenesis. Instead, there was evidence for decreased PGE2 /PGF2α inactivation via 15-hydroxyprostaglandin dehydrogenase (HPGD) in type II ECs. Increased expression of 5-lipoxygenase (ALOX5) mRNA was also identified in type II ECs, together with proportional increases in its product, 5-hydroxyeicosatetraenoic acid (5-HETE). Decreased HPGD and elevated ALOX5 mRNA expression were associated with adverse outcome, which was confirmed by immunohistochemical tissue microarray analysis of an independent series of EC specimens (n = 419). While neither COX-1 nor COX-2 protein expression had prognostic value, low HPGD combined with high ALOX5 expression was associated with the worst overall and progression-free survival. These findings highlight HPGD and ALOX5 as potential therapeutic targets in aggressive EC subtypes. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

An Unusual Presentation of Endometrial Cancer with Bilateral Adrenal Metastases at the Time of Presentation and an Updated Descriptive Literature Review.

In endometrial cancer (EC), adrenal metastases are rare indicating advanced disease. We report an unusual presentation of EC with solitary adrenal metastases at the time of diagnosis and provide with an updated literature review. A 68-year-old woman was referred with postmenopausal bleeding of several weeks' duration. Imaging revealed a heterogenous uterine mass and bilateral malignant adnexal masses. Hysteroscopy, endometrial biopsies, and radiological guided biopsies of the adrenal masses confirmed poorly differentiated EC. A PET-CT reported both adrenal metastases being hypermetabolic and suspicious for malignancy. The patient received six neoadjuvant chemotherapy cycles with Carboplatin and Paclitaxel. A repeated CT scan confirmed size reduction for both primary tumour and metastases. The adrenal metastases were no longer PET-avid on repeat PET-CT scan. The patient received a course of hormonal treatment and as per adrenal MDT, she underwent total laparoscopic hysterectomy and bilateral salpingo-oophorectomy followed by bilateral retroperitoneal laparoscopic adrenalectomy two months later. The patient remains asymptomatic on maintenance hydrocortisone 18 months post diagnosis. This is the first report of solitary synchronous adrenal metastases in a patient with EC. Central MDT review is key in providing individualised treatment recommendations of such rare entity.

Characterization of Primary Cilia in Normal Fallopian Tube Epithelium and Serous Tubal Intraepithelial Carcinoma.

OBJECTIVES: The aim of this study was to investigate the distribution of primary cilia on secretory cells in normal fallopian tube (FT) and serous tubal intraepithelial carcinoma (STIC). METHODS: Fallopian tube tissue samples were obtained from 4 females undergoing prophylactic hysterectomies and 6 patients diagnosed with STIC. A mogp-TAg transgenic mouse STIC sample was also compared with a wild-type mouse FT sample. Serous tubal intraepithelial carcinoma was identified by hematoxylin and eosin staining and confirmed by positive Ki-67 and p53 immunohistochemical staining of tissue sections. We assessed the relative distribution of primary cilia on secretory cells and motile cilia on multiple ciliated cells by immunofluorescence and immunohistochemical staining. Ciliary function was assessed by immunofluorescence staining of specific ciliary marker proteins and responsiveness to Sonic Hedgehog signaling. RESULTS: Primary cilia are widespread on secretory cells in the ampulla, isthmus, and in particular, the fimbriae of human FT where they may appear to mediate ciliary-mediated Sonic Hedgehog signaling. A statistically significant reduction in the number of primary cilia on secretory cells was observed in human STIC samples compared with normal controls (P < 0.0002, Student t test), supported by similar findings in a mouse STIC sample. Immunohistochemical staining for dynein axonemal heavy chain 5 discriminated multiple motile cilia from primary cilia in human FT. CONCLUSIONS: Primary cilia are widespread on secretory cells in the ampulla, isthmus, and in particular, the fimbriae of the human FT but are significantly reduced in both human and mouse STIC samples. Immunohistochemical staining for ciliary proteins may have clinical utility for early detection of STIC.

5-Flouorouracil Is an Attractive Medical Treatment in Women With Vaginal Intraepithelial Neoplasia: A Meta-Analysis.

OBJECTIVE: In the absence of standard guidelines, the management of vaginal intraepithelial neoplasia (VaIN) remains a field of debate. The aim of this systematic review and meta-analysis was to ascertain the 5-flouorouracil (5-FU) effectiveness in this context. MATERIALS AND METHODS: A literature search was conducted throughout the PubMed, EMBASE, SCOPUS, ClinicalTrials.gov, and Cochrane Databases for relevant studies. We computed the summary proportions of women treated for VaIN with 5-FU for the outcomes of complete response and recurrence by random-effects meta-analysis. We also performed a subgroup analysis by computing the summary proportions for complete response among women with high-grade VaIN, persistent disease, and recurrence respectively. RESULTS: Fourteen observational studies reporting on 358 women included in the study. The study quality was moderate. The summary proportions of women who had complete response after the first 5-FU course were 82.18% (95% CI = 69.80%-88.82%). The summary proportions of women who recurred were 16.42% (95% CI = 7.39%-28.14%). The summary proportions of women with complete response in the high-grade VaIN, persistent disease, and recurrence subgroups were 77.53% (95% CI = 59.90%-91.15%), 53.92% (95% CI = 34.62%-72.61%), and 72.32% (95% CI = 48.12%-91.05%), respectively. CONCLUSIONS: This is the first meta-analysis to date to provide a convincing overview of 5-FU efficacy on the VaIN treatment. Albeit a medium risk of bias warrants some caution with interpretation of the results, 5-FU can be an attractive alternative to surgery, especially among young women with multifocal and recurrent disease.