The Future of AI in Ovarian Cancer Research: The Large Language Models Perspective.

Conversational large language model (LLM)-based chatbots utilize neural networks to process natural language. By generating highly sophisticated outputs from contextual input text, they revolutionize the access to further learning, leading to the development of new skills and personalized interactions. Although they are not developed to provide healthcare, their potential to address biomedical issues is rather unexplored. Healthcare digitalization and documentation of electronic health records is now developing into a standard practice. Developing tools to facilitate clinical review of unstructured data such as LLMs can derive clinical meaningful insights for ovarian cancer, a heterogeneous but devastating disease. Compared to standard approaches, they can host capacity to condense results and optimize analysis time. To help accelerate research in biomedical language processing and improve the validity of scientific writing, task-specific and domain-specific language models may be required. In turn, we propose a bespoke, proprietary ovarian cancer-specific natural language using solely in-domain text, whereas transfer learning drifts away from the pretrained language models to fine-tune task-specific models for all possible downstream applications. This venture will be fueled by the abundance of unstructured text information in the electronic health records resulting in ovarian cancer research ultimately reaching its linguistic home.

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.

Outcomes of ovarian transposition in cervical cancer; an updated meta-analysis.

BACKGROUND: Cervical cancer is the most common indication for ovarian transposition in reproductive-age women. Ovarian transposition should be performed in premenopausal women undergoing pelvic irradiation to preserve ovarian function, and prevent early menopause. As women become more knowledgeable about their fertility options, it is still unclear who will benefit from the intervention. We updated our previous meta-analysis of ovarian function preservation, symptomatic ovarian cysts, and metastases to the transposed ovaries following ovarian transposition in cervical cancer patients to further guide current clinical practice. METHODS: A systematic search of Medline, Embase, Web of Science, and The Cochrane Library databases, dating from January 1980 to July 2021, was conducted. We computed the summary proportions of women who had ovarian function preservation, non-ovarian cyst formation and metastases to the transposed ovaries following ovarian transposition by random-effects meta-analysis and we explored study heterogeneity by type of radiotherapy. RESULTS: There were 29 publications reporting on 1160 women with cervical cancer who underwent ovarian transposition. In the group that underwent surgery alone, 91% of the women had preserved ovarian function (95% CI 83-100), 89% (95% CI 80-99) of women who did not develop ovarian cysts, and 99% (95% CI 1-5) of women who did not suffer metastases to the transposed ovaries. In the surgery ± brachytherapy (BR) group, the proportion of women with the preserved ovarian function was 93% (95% CI 76-113), 84% (95% CI 69-103) of women who did not develop ovarian cysts, and 99% (95% CI 82-120) of women who did not suffer metastases to the transposed ovaries. In the external beam pelvic radiotherapy (EBRT) ± BR ± surgery group, the proportion of women with the preserved ovarian function was 61% (95% CI 55-69), and 95% (95% CI 85-107) of women who developed ovarian cysts. There were no metastases to the transposed ovaries in that group. CONCLUSIONS: In women with cervical cancer, ovarian transposition offers a significant preservation of the ovarian function. Despite an expected incidence of ovarian cyst formation, it carries almost no risk for metastases to the transposed ovaries.

Survival and Chemosensitivity in Advanced High Grade Serous Epithelial Ovarian Cancer Patients with and without a BRCA Germline Mutation: More Evidence for Shifting the Paradigm towards Complete Surgical Cytoreduction.

Background and Objectives: Approximately 10−15% of high-grade serous ovarian cancer (HGSOC) cases are related to BRCA germline mutations. Better survival rates and increased chemosensitivity are reported in patients with a BRCA 1/2 germline mutation. However, the FIGO stage and histopathological entity may have been confounding factors. This study aimed to compare chemotherapy response and survival between patients with and without a BRCA 1/2 germline mutation in advanced HGSOC receiving neoadjuvant chemotherapy (NACT). Materials and Methods: A cohort of BRCA-tested advanced HGSOC patients undergoing cytoreductive surgery following NACT was analyzed for chemotherapy response and survival. Neoadjuvant chemotherapy served as a vehicle to assess chemotherapy response on biochemical (CA125), histopathological (CRS), biological (dissemination), and surgical (residual disease) levels. Univariate and multivariate analyses for chemotherapy response and survival were utilized. Results: Thirty-nine out of 168 patients had a BRCA ½ germline mutation. No differences in histopathological chemotherapy response between the patients with and without a BRCA ½ germline mutation were observed. Survival in the groups of patients was comparable Irrespective of the BRCA status, CRS 2 and 3 (HR 7.496, 95% CI 2.523−22.27, p < 0.001 & HR 4.069, 95% CI 1.388−11.93, p = 0.011), and complete surgical cytoreduction (p = 0.017) were independent parameters for a favored overall survival. Conclusions: HGSOC patients with or without BRCA ½ germline mutations, who had cytoreductive surgery, showed comparable chemotherapy responses and subsequent survival. Irrespective of BRCA status, advanced-stage HGSOC patients have a superior prognosis with complete surgical cytoreduction and good histopathological response to chemotherapy.

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.

Obstetric Outcomes in Women With Early Bulky Cervical Cancer Downstaged by Neoadjuvant Chemotherapy to Allow for Fertility-Sparing Surgery: A Meta-analysis and Metaregression.

OBJECTIVE: It is difficult to critically outline the optimal treatment for women with early-stage cervical cancer (eCC) wishing fertility preservation. Neoadjuvant chemotherapy (NAC) to downstage "bulky" eCC could potentially lead to fertility-sparing surgery (FSS) in a wider patient population. The rationale is to provide oncological safety balanced with maximal fertility effort. We aimed to obtain the most accurate fertility outcomes for eCC women treated with NAC followed by FSS and identify potential factors favoring fertility. METHODS: A systematic search of MEDLINE, EMBASE, Web of Science, and Cochrane Database was performed. Studies that reported obstetric outcomes of eCC women treated with NAC followed by FSS were located. For the meta-analysis, we calculated the proportions of women who had the outcomes per total number of women who were considered for FSS. For the meta-regression, we extracted the relative risk of the outcome variables to enable comparison of the results across the studies. RESULTS: Seven studies enrolling 86 patients were included in the meta-analysis. Pooling of results from seven studies rendered summary proportions of 0.49 (95% confidence interval [CI], 0.32-0.66) and 0.42 (95% CI, 0.32-0.53) for the outcomes of pregnancies and live births, respectively. The outcome of first- and second-trimester losses by pooling seven studies rendered a summary proportion of 0.16 (95% CI, 0.09-0.27). For the outcome of premature deliveries, pooling of results from five studies rendered a summary proportion of 0.06 (95% CI, 0.02-0.16). This reached 0.29 (95% CI, 0.15-0.48) in women who achieved live births. In multivariate meta-regression, the more radical surgical approach resulted in a less favorable pregnancy rate compared with the less radical surgical approach (P = 0.015). CONCLUSIONS: This strategy achieves live births in four of 10 eCC women who desire fertility, whereas their risk of miscarriage is low. Three of 10 live births will be premature.

The Impact of Saphenous Vein Sparing During Inguinal Lymphadenectomy on Postoperative Morbidity in Women With Vulval Cancer: An Updated Per-Groin Meta-analysis of Short-term Outcomes.

OBJECTIVE: Inguinofemoral lymphadenectomy (IFL) is included in the standard surgical management of early-stage vulval cancer (VC) but is often accompanied by surgical complications. Efforts have been made to limit the postoperative morbidity by adopting more conservative IFL techniques without compromising the surgical outcomes. Saphenous vein (SV) preservation during IFL for VC appears to reduce the incidence of postoperative complications including lymphedema. To ascertain the efficacy of SV preservation, we aimed to revisit the impact of SV preservation on short-term per groin complications by updating on a previous meta-analysis to further guide current clinical practice. METHODS: A systematic literature review was conducted to identify studies that reported postoperative complications following IFL with SV preservation and controls (SV ligation during IFL) in VC patients. We included articles in English language and avoided date restrictions. Direct-comparison meta-analysis was performed between the use of SV preservation and SV ligation for the short-term outcomes of lymphedema, cellulitis, and wound dehiscence/breakdown. Fixed- and random-effects models were fitted to calculate the odds ratios (ORs) and 95% confidence intervals (CIs). RESULTS: Five studies were included in the final analysis. Direct-comparison per-groin meta-analysis between SV preservation and SV ligation significantly decreased the odds for developing lymphedema (OR, 0.363; 95% CI, 0.228-0.578; P < 0.001), cellulitis (OR, 0.481; 95% CI, 0.28-0.825; P = 0.008), and wound dehiscence/breakdown (OR, 0.296; 95% CI, 0.191-0.458; P < 0.001). When SV sparing was clearly the sole intervention, lymphedema was the only complication in which the positive effect of SV sparing is exerted (OR, 0.28; 95% CI, 0.149-0.526; P < 0.001). CONCLUSIONS: This per groin meta-analysis updates on the current evidence suggesting the SV sparing improves postoperative outcomes following IFL in VC patients. Where sentinel biopsy is not indicated, this risk-reducing strategy should be considered in selected VC patients undergoing IFL until a multicenter randomized controlled trial becomes available.

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.

Routine Intraoperative Frozen Section Examination to Minimize Bimodal Treatment in Early-Stage Cervical Cancer.

OBJECTIVE: In early-stage cervical cancer, single modality therapy is the main objective, to minimize patient morbidity while offering equivalent cure rates. Intraoperative frozen section examination (FSE) of lymph nodes (LNs) can facilitate this aim, ensuring that radical surgery is avoided in patients requiring adjuvant therapy for metastatic LN involvement. We aimed to evaluate the accuracy of routine intraoperative FSE of pelvic LNs during the surgical staging of early-stage cervical cancers and identify a group at low risk for nodal metastases. METHODS: A retrospective cohort study of 94 women aged 23 to 80 years who underwent primary surgery and planned intraoperative FSE of the pelvic LNs at the gynecological cancer center in Oxford was performed. The diagnostic value of FSE and the prediction of metastatic nodal disease were assessed by use of preoperative and intraoperative variables. RESULTS: A total of 1825 LNs were submitted for FSE. Of 94 women (13.8%), 13 had positive LNs at FSE. Two false-negative cases were reported with micrometastases but no false-positive cases. Frozen section examination as a diagnostic test reached a sensitivity of 86.7% and a specificity of 100%. A regression model including grade I to II and tumor size of less than 20 mm identified a low-risk group for LN involvement. CONCLUSIONS: In light of diverse practice patterns, FSE should be routinely offered to women with early-stage cervical cancer in a 1-step protocol. We equally devised a model to predict those patients at least risk of nodal disease, who may be spared of FSE.

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.

Pre-Treatment of platinum resistant ovarian cancer cells with an MMP-9/MMP-2 inhibitor prior to cisplatin enhances cytotoxicity as determined by high content screening.

Platinum resistance is a major cause of treatment failure in ovarian cancer. We previously identified matrix metalloproteinase 9 (MMP-9) as a potential therapeutic target of chemoresistant disease. A2780cis (cisplatin-resistant) and A2780 (cisplatin-sensitive) ovarian carcinoma cell lines were used. The cytotoxic effect of MMP-9/MMP-2 inhibitor, (2R)-2-[(4-Biphenylsulfonyl) amino]-3 phenylpropionic acid (C21H19NO4S) alone or in combination with cisplatin was determined using high content screening. Protein expression was examined using immunohistochemistry and ELISA. Co-incubation of cisplatin and an MMP-9/MMP-2 inhibitor, (2R)-2-[(4-Biphenylsulfonyl) amino]-3 phenylpropionic acid (C21H19NO4S) resulted in significantly greater cytotoxicity as compared to either treatment alone in a cisplatin resistant MMP-9 overexpressing cell line; A2780cis. In addition, pre-incubating with MMP-9i prior to cisplatin further enhances the cytotoxic effect. No significant difference was observed in MMP-9 protein in tissue but a trend towards increased MMP-9 was observed in recurrent serum. We propose that MMP-9/MMP-2i may be utilized in the treatment of recurrent/chemoresistant ovarian cancers that overexpress MMP-9 mRNA but its role in vivo remains to be evaluated.

A Systematic Review of Systematic Reviews on the Use of Aromatase Inhibitors for the Treatment of Endometriosis: The Evidence to Date.

Endometriosis is a chronic gynecologic condition that affects around 6-10% of reproductive age women. This clinical entity is characterized with pelvic pain, dysmenorrhea, dyspareunia, and infertility which are the most often presenting symptoms. Aromatase P450 is the key enzyme for ovarian estrogen biosynthesis and there is evidence that endometriotic lesions express aromatase and are able to synthesize their own estrogens. Aromatase inhibitors (AIs) are potent drugs that suppress the estrogen synthesis via suppression of aromatase. We performed a systematic review of systematic reviews and narrative reviews on the use of aromatase inhibitors in the medical management of endometriosis. We searched: PubMed (1950-2022), Google Scholar (2004-2022), Cochrane Library (2010-2022) and Researchgate (2010-2022). The search included the following medical subject headings (MeSH) or keywords: "Aromatase Inhibitors" AND "Endometriosis" AND "Systematic reviews" OR "Systematic review" AND "Reviews" OR "Reviews" AND "Endometriosis". The electronic database search yielded initially 12,106 studies from the different databases. Further assessment of the studies resulted in exclusion of (n = 12,015) studies due to duplicates and irrelevance; Finally, 24 studies were selected for inclusion, 5 were Systematic reviews and 19 were Narrative reviews. The 5 systematic reviews were assessed by AMSTAR-2 criteria and were found to have low quality. Narrative reviews were assessed with SANRA criteria and were found to have high-quality aromatase inhibitors are potent drugs that can manage the endometriosis-related symptoms in cases where initial medical management has failed to show positive results. However, their use is limited by the adverse effects that are linked with menopausal symptoms. aromatase inhibitors can be administered as an alternative treatment in patients. Future studies with randomized design are required to reach safer conclusions and further investigation. These studies should define the therapeutic dose, new add-back therapy modalities. Future directions should examine the most-appropriate way of administration and the duration of therapy.

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.

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.

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.

Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer.

Complete surgical cytoreduction (R0 resection) is the single most important prognosticator in epithelial ovarian cancer (EOC). Explainable Artificial Intelligence (XAI) could clarify the influence of static and real-time features in the R0 resection prediction. We aimed to develop an AI-based predictive model for the R0 resection outcome, apply a methodology to explain the prediction, and evaluate the interpretability by analysing feature interactions. The retrospective cohort finally assessed 571 consecutive advanced-stage EOC patients who underwent cytoreductive surgery. An eXtreme Gradient Boosting (XGBoost) algorithm was employed to develop the predictive model including mostly patient- and surgery-specific variables. The Shapley Additive explanations (SHAP) framework was used to provide global and local explainability for the predictive model. The XGBoost accurately predicted R0 resection (area under curve [AUC] = 0.866; 95% confidence interval [CI] = 0.8−0.93). We identified "turning points" that increased the probability of complete cytoreduction including Intraoperative Mapping of Ovarian Cancer Score and Peritoneal Carcinomatosis Index < 4 and <5, respectively, followed by Surgical Complexity Score > 4, patient's age < 60 years, and largest tumour bulk < 5 cm in a surgical environment of optimized infrastructural support. We demonstrated high model accuracy for the R0 resection prediction in EOC patients and provided novel global and local feature explainability that can be used for quality control and internal audit.

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.

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.