Survival Dynamics in Advanced Ovarian Cancer: R2 Resection Versus No-Surgery Paths Explored.

BACKGROUND: Cytoreductive surgery is critical for optimal tumor clearance in advanced epithelial ovarian cancer (EOC). Despite best efforts, some patients may experience R2 (>1 cm) resection, while others may not undergo surgery at all. We aimed to compare outcomes between advanced EOC patients undergoing R2 resection and those who had no surgery. METHODS: Retrospective data from 51 patients with R2 resection were compared to 122 patients with no surgery between January 2015 and December 2019 at a UK tertiary referral centre. Progression-free survival (PFS) and overall survival (OS) were the study endpoints. Principal Component Analysis and Term Frequency - Inverse Document Frequency scores were utilized for data discrimination and prediction of R>2 cm from computed tomography pre-operative reports, respectively. RESULTS: No statistical significance was observed, except for age (73 vs 67 years in the no- surgery vs R2 group, P: .001). Principal Components explained 34% of data variances. Reasons for no surgery included age, co-morbidities, patient preference, refractory disease, patient deterioration or disease progression, and absence of measurable intra- abdominal disease). The median PFS and OS were 12 and 14 months for no-surgery, vs 14 and 26 months for R2 (P: .138 and P: .001, respectively). Serous histology and performance status independently predicted PFS in both no-surgery and R2 cohorts. In the no-surgery cohort, serous histology independently predicted OS, while in the R2 cohorts, both serous histology and adjuvant chemotherapy were independent prognostic features for OS. The bi-grams "abdominopelvic ascites" and "solid omental" were amongst those best discriminating between R>2 cm and R1-2 cm. CONCLUSIONS: R2 resection and no-surgery cohorts displayed unfavourable prognosis with a notable degree of uniformity. When cytoreduction results in suboptimal results, the survival benefit may still be higher compared to those who underwent no surgery.

The study examined outcomes in advanced epithelial ovarian cancer (EOC) patients who underwent either R2 (suboptimal) surgical resection or received no surgery at all at a UK tertiary referral center. Sophisticated machine learning methodolgies were used to analyze data patterns and predict the extent of resection (>2 cm) from pre-operative CT reports. Reasons for not undergoing surgery included older age, presence of other medical conditions, patient preference, progressive disease, patient decline, or lack of detectable intra-abdominal disease. Factors like serous histology and performance status iinfluenced the risk of recurrence in both groups, while serous histology and adjuvant chemotherapy predicted the risk of death in the R2 group. Word sequences like "omental disease" and "reduced bulk" helped differentiate between R>2 cm and less extensive resections (R1-2 cm). In summary, both R2 resection and no-surgery groups had poor outcomes, but patients who underwent R2 resection generally had better survival compared to those who received no surgery, even when complete tumor removal was not achieved.

RoBERTa-Assisted Outcome Prediction in Ovarian Cancer Cytoreductive Surgery Using Operative Notes.

INTRODUCTION: Contemporary efforts to predict surgical outcomes focus on the associations between traditional discrete surgical risk factors. We aimed to determine whether natural language processing (NLP) of unstructured operative notes improves the prediction of residual disease in women with advanced epithelial ovarian cancer (EOC) following cytoreductive surgery. METHODS: Electronic Health Records were queried to identify women with advanced EOC including their operative notes. The Term Frequency - Inverse Document Frequency (TF-IDF) score was used to quantify the discrimination capacity of sequences of words (n-grams) regarding the existence of residual disease. We employed the state-of-the-art RoBERTa-based classifier to process unstructured surgical notes. Discrimination was measured using standard performance metrics. An XGBoost model was then trained on the same dataset using both discrete and engineered clinical features along with the probabilities outputted by the RoBERTa classifier. RESULTS: The cohort consisted of 555 cases of EOC cytoreduction performed by eight surgeons between January 2014 and December 2019. Discrete word clouds weighted by n-gram TF-IDF score difference between R0 and non-R0 resection were identified. The words 'adherent' and 'miliary disease' best discriminated between the two groups. The RoBERTa model reached high evaluation metrics (AUROC .86; AUPRC .87, precision, recall, and F1 score of .77 and accuracy of .81). Equally, it outperformed models that used discrete clinical and engineered features and outplayed the performance of other state-of-the-art NLP tools. When the probabilities from the RoBERTa classifier were combined with commonly used predictors in the XGBoost model, a marginal improvement in the overall model's performance was observed (AUROC and AUPRC of .91, with all other metrics the same). CONCLUSION/IMPLICATIONS: We applied a sui generis approach to extract information from the abundant textual surgical data and demonstrated how it can be effectively used for classification prediction, outperforming models relying on conventional structured data. State-of-art NLP applications in biomedical texts can improve modern EOC care.

Absent pulmonary valve syndrome - diagnosis, associations, and outcome in 71 prenatally diagnosed cases.

OBJECTIVE: The objective of this study was to analyze the spectrum of prenatally diagnosed absent pulmonary valve syndrome (APVS) and the outcome from diagnosis onwards. Fetuses with APVS and tetralogy of Fallot (TOF/APVS) and with APVS and intact ventricular septum (APVS/IVS) were included. METHOD: Multicenter retrospective study of the International Prenatal Cardiology Collaboration Group. Clinical and echocardiographic databases of nine referral centers were reviewed from 2012-2016. RESULTS: The cohort included 71 cases, 59 with TOF/APVS and 12 with APVS/IVS. In 18.3% of cases, diagnosis was achieved within first trimester. Association with hydrops fetalis was high within first trimester (69%). No fetus with known outcome survived after first trimester diagnosis. Karyotype anomalies occurred in 45% of cases with known karyotype. Intrauterine fetal demise occurred in 14.3%. Overall survival after initial diagnosis in the total cohort was 28.1% (28.8% TOF/APVS and 25.0% APVS/IVS). Survival to birth was 50% in TOF/APVS and 44.4% in APVS/IVS. Survival of subjects born alive beyond neonatal period was 84.6% in TOF/APVS and 100% in APVS/IVS. CONCLUSION: Diagnosis of APVS is feasible within first trimester. Outcomes remain guarded, especially if first trimester diagnosis is included into the analysis because of associated karyotypic anomalies, the presence of hydrops fetalis, and patent ductus arteriosus. © 2017 John Wiley & Sons, Ltd.

Performance of Publicly Available Large Language Models on Internal Medicine Board-style Questions.

Ongoing research attempts to benchmark large language models (LLM) against physicians' fund of knowledge by assessing LLM performance on medical examinations. No prior study has assessed LLM performance on internal medicine (IM) board examination questions. Limited data exists on how knowledge supplied to the models, derived from medical texts improves LLM performance. The performance of GPT-3.5, GPT-4.0, LaMDA and Llama 2, with and without additional model input augmentation, was assessed on 240 randomly selected IM board-style questions. Questions were sourced from the Medical Knowledge Self-Assessment Program released by the American College of Physicians with each question serving as part of the LLM prompt. When available, LLMs were accessed both through their application programming interface (API) and their corresponding chatbot. Mode inputs were augmented with Harrison's Principles of Internal Medicine using the method of Retrieval Augmented Generation. LLM-generated explanations to 25 correctly answered questions were presented in a blinded fashion alongside the MKSAP explanation to an IM board-certified physician tasked with selecting the human generated response. GPT-4.0, accessed either through Bing Chat or its API, scored 77.5-80.7% outperforming GPT-3.5, human respondents, LaMDA and Llama 2 in that order. GPT-4.0 outperformed human MKSAP users on every tested IM subject with its highest and lowest percentile scores in Infectious Disease (80th) and Rheumatology (99.7th), respectively. There is a 3.2-5.3% decrease in performance of both GPT-3.5 and GPT-4.0 when accessing the LLM through its API instead of its online chatbot. There is 4.5-7.5% increase in performance of both GPT-3.5 and GPT-4.0 accessed through their APIs after additional input augmentation. The blinded reviewer correctly identified the human generated MKSAP response in 72% of the 25-question sample set. GPT-4.0 performed best on IM board-style questions outperforming human respondents. Augmenting with domain-specific information improved performance rendering Retrieval Augmented Generation a possible technique for improving accuracy in medical examination LLM responses.

Continuity Unveiled: Evaluating Cytoreduction Outcomes for Advanced Ovarian Cancer Amidst the COVID-19 Era at an ESGO Designated Centre of Excellence.

BACKGROUND/AIM: The COVID-19 pandemic brought unprecedented global changes, necessitating adjustments to address public health challenges. The impact on advanced epithelial ovarian cancer (EOC) surgery, marked by increased perioperative risks, and changes in management plans was explored in this study based on promptly published British Gynaecologic Cancer Society (BGCS) and European Society of Gynaecologic Oncology (ESGO) guidelines. PATIENTS AND METHODS: Retrospective data from 332 patients with advanced EOC who underwent cytoreductive surgery at a UK tertiary center were analyzed, and the outcomes were compared between pre-COVID-19 (2018-2019) (n=189) and COVID-19 era (2020-2021) (n=143) cohorts, covering the same timeframe (March to December). Primary outcomes included residual disease (RD) and progression-free survival (PFS), while secondary outcomes were the ESGO quality indicators (QIs) for advanced EOC surgery. Kaplan-Meier curves were produced to illustrate PFS. RESULTS: Complete cytoreduction rates remained comparable at 74.07% and 72.03% for pre-COVID-19 and COVID-19 groups, respectively. Differences were observed in ECOG performance status (p=0.015), Intensive Care Unit (ICU) admissions (p=0.039) with less interval debulking surgeries (p=0.03), lower surgical complexity scores (p=0.02), and longer operative times in the COVID-19 group (p=0.01) compared to the pre-COVID-19 group. The median PFS rates were 37 months and 34 months in the pre-COVID-19 and COVID-19 groups, respectively (p=0.08). The surgical QIs 1-3 remained uncompromised during the COVID-19 era. CONCLUSION: Management modifications prompted by the COVID-19 pandemic did not adversely impact cytoreduction rates or PFS.

Comparison of the Results of Prenatal and Postnatal Echocardiography and Postnatal Cardiac MRI in Children with a Congenital Heart Defect.

OBJECTIVE: In fetuses with suspicion of congenital heart disease (CHD), assessment by segmental fetal echocardiography is of great importance. This study sought to examine the concordance of expert fetal echocardiography and postnatal MRI of the heart at a high-volume paediatric heart centre. METHODS: The data of two hundred forty-two fetuses have been gathered under the condition of full pre- and postnatal and the presence of a pre- and postnatal diagnosis of CHD. The haemodynamically leading diagnosis was determined for each test person and was then sorted into diagnostic groups. The diagnoses and diagnostic groups were used for the comparison of diagnostic accuracy in fetal echocardiography. RESULTS: All comparisons between the diagnostic methods for detection of congenital heart disease showed an "almost perfect" (Cohen's Kappa > 0.9) strength of agreement for the diagnostic groups. The diagnosis made by prenatal echocardiography showed a sensitivity of 90-100%, a specificity and a negative predictive value of 97-100%, and a positive predictive value of 85-100%. The diagnostic congruence resulted in an "almost perfect" strength of agreement for all evaluated diagnoses (transposition of great arteries, double outlet right ventricle, hypoplastic left heart, tetralogy of Fallot, atrioventricular septal defect). An agreement of Cohen's Kappa > 0.9 was achieved for all groups, with exception of the diagnosis of double outlet right ventricle (0.8) in prenatal echocardiography compared to postnatal echocardiography. This study came to the result of a sensitivity of 88-100%, a specificity and negative predictive value of 97-100%, and a positive predictive value of 84-100%. The performance of cardiac magnetic resonance imaging (MRI) as an additional measure to echocardiography had an added value in the description of the malposition of the great arteries when diagnosed with double outlet right ventricle and in the detailed description of the anatomy of the lung circulation. CONCLUSIONS: Prenatal echocardiography could be shown to be a reliable method for detection of congenital heart disease when regarding the slightly lower accuracy of diagnosis for double outlet right ventricle and right heart anomalies. Furthermore, the impact of examiner experience and the consideration of follow-up examinations for further improvement of diagnosis accuracy may not be underestimated. The main advantage of an additional MRI is the possibility to obtain a detailed anatomic description of the blood vessels of the lung and the outflow tract. The conduction of further studies that include false-negative and false-positive cases, and studies that are not set within the high-risk-group, as well as studies in a less specialized setting, would allow the completion and investigation of possible differences and discrepancies when comparing the results that have been obtained in this study.

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.

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.

The Evolution and Developing Importance of Fetal Magnetic Resonance Imaging in the Diagnosis of Congenital Cardiac Anomalies: A Systematic Review.

Magnetic Resonance Imaging (MRI) is a reliable method, with a complementary role to Ultrasound (US) Echocardiography, that can be used to fully comprehend and precisely diagnose congenital cardiac malformations. Besides the anatomical study of the fetal cardiovascular system, it allows us to study the function of the fetal heart, remaining, at the same time, a safe adjunct to the classic fetal echocardiography. MRI also allows for the investigation of cardiac and placental diseases by providing information about hematocrit, oxygen saturation, and blood flow in fetal vessels. It is crucial for fetal medicine specialists and pediatric cardiologists to closely follow the advances of fetal cardiac MRI in order to provide the best possible care. In this review, we summarize the advance in techniques and their practical utility to date.