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.

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.

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.

Granulosa cell ovarian cancer with synchronous multiple bone metastases: case report of extreme rarity.

BACKGROUND: Adult granulosa cell tumor (AGCT) of the ovary generally has a good prognosis. Recurrences tend to be late and confined to the abdominopelvis. Bone metastases are extremely rare. We report an extremely rare case of AGCT with synchronous multiple bone metastases and discuss diagnostic procedures and management. CASE DESCRIPTION: A 35-year-old woman presented with abdominal bloating. On the day of surgery, acting on the complaint of right shoulder pain, an X-ray revealed a permeative lesion involving the neck of humerus, suggestive of a metastatic pathologic fracture. The patient underwent a full staging debulking surgery. Further imaging demonstrated multiple bone metastases. Histology confirmed an AGCT of the ovary. Diagnosis was established by a core bone biopsy from the left femur showing cells consistent with those seen with granulosa cell tumor. The patient received adjuvant chemotherapy with concurrent zoledronic acid as targeted therapy for her bone metastases. Endocrine systemic maintenance treatment was given. The patient rapidly deteriorated and died from her disease at 20 months from the initial diagnosis. CONCLUSION: Unpredictable biological behavior and clinical manifestations raise a high degree of suspicion for accurate AGCT diagnosis. Management of bone metastases often warrants input from the multidisciplinary team, and treatment may involve chemotherapy, palliative radiotherapy, or orthopaedic interventions.

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.

Recurrent Hemoptysis After Bronchial Artery Embolization: Prediction Using a Nomogram and Artificial Neural Network Model.

OBJECTIVE. The purpose of this study was to develop an effective nomogram and artificial neural network (ANN) model for predicting recurrent hemoptysis after bronchial artery embolization (BAE). MATERIALS AND METHODS. The institutional ethics review boards of the two participating hospitals approved this study. Patients with hemoptysis who were treated with BAE were allocated to either the training cohort (Hospital A) or the validation cohort (Hospital B). The predictors of recurrent hemoptysis were identified by univariable and multivariable analyses in the training cohort. A nomogram and ANN model were then developed, and the accuracy was validated by the Harrell C statistic and ROC curves in both the training and validation cohorts. RESULTS. A total of 242 patients (training cohort, 141; validation cohort, 101) were enrolled in this study. The univariable and multivariable analyses revealed that age of 60 years old or older (hazard ratio [HR], 3.921; 95% CI, 1.267-12.127; p = 0.018), lung cancer (HR, 18.057; 95% CI, 4.124-79.068; p < 0.001), bronchial-pulmonary shunts (HR, 11.981; 95% CI, 2.593-55.356; p = 0.001), and nonbronchial systemic artery involvement (HR, 4.194; 95% CI, 1.596-11.024; p = 0.004) were predictors of recurrent hemoptysis. The developed nomogram and ANN model had high accuracy, with a Harrell C statistic of 0.849 (95% CI, 0.778-0.919) internally (for the training cohort) and 0.799 (95% CI, 0.701-0.897) externally (for the validation cohort). The optimal cutoff value of the recurrent hemoptysis risk was 0.16. CONCLUSION. The nomogram and ANN model could effectively predict the risk for recurrent hemoptysis after BAE. Further interventions should be considered for patients with a high suspicion of risk (> 0.16) according to the nomogram.

Prognostic nomogram for the combination therapy of percutaneous catheter drainage and antibiotics in pyogenic liver abscess patients.

PURPOSE: To identify the predictors for recovery of pyogenic liver abscess (PLA) patients treated with percutaneous catheter drainage (PCD) and antibiotics, and then develop an effective nomogram to predict the recovery time. MATERIALS AND METHODS: The retrospective study included consecutive PLA patients treated with PCD and antibiotics. We defined the overall recovery time (ORT) as the time from the PCD procedure to the time of clinical success or failure. Based on the ORT, its predictors were identified with univariate and multivariate analyses. Then, a nomogram was developed to predict the ORT, and was internally validated by using Harrell's c statistic. RESULTS: A total of 116 patients and 142 PCD procedures with a median ORT of 15.0±10.6 days were included. Gas-formation (GF; HR: 0.486 [95% CI 0.312-0.757]; P = 0.001), diabetes mellitus (DM; HR: 0.455 [95% CI 0.303-0.682]; P<0.001), and preinterventional septic shock (PSS; HR: 0.276 [95% CI 0.158-0.483]; P < 0.001) were identified as predictors for the ORT of combination therapy after univariate and multivariate analyses, which indicated a significantly longer ORT than those patients without. The prognostic analyses demonstrated that the more predictors (GF, DM, and PSS) a patient exhibited, the longer ORT for the combination therapy. A nomogram was developed to predict the ORT and revealed high accuracy, with Harrell's c statistic of 0.73. CONCLUSION: GF, DM, and PSS were predictors for the recovery of PLA patients treated with PCD and antibiotics. The nomogram was effective in predicting the ORT of combination therapy.

A bilayer photoreceptor-retinal tissue model with gradient cell density design: A study of microvalve-based bioprinting.

ARPE-19 and Y79 cells were precisely and effectively delivered to form an in vitro retinal tissue model via 3D cell bioprinting technology. The samples were characterized by cell viability assay, haematoxylin and eosin and immunofluorescent staining, scanning electrical microscopy and confocal microscopy, and so forth. The bioprinted ARPE-19 cells formed a high-quality cell monolayer in 14 days. Manually seeded ARPE-19 cells were poorly controlled during and after cell seeding, and they aggregated to form uneven cell layer. The Y79 cells were subsequently bioprinted on the ARPE-19 cell monolayer to form 2 distinctive patterns. The microvalve-based bioprinting is efficient and accurate to build the in vitro tissue models with the potential to provide similar pathological responses and mechanism to human diseases, to mimic the phenotypic endpoints that are comparable with clinical studies, and to provide a realistic prediction of clinical efficacy.

Tissue engineering of retina and Bruch's membrane: a review of cells, materials and processes.

The biological, structural and functional configuration of Bruch's membrane (BM) is significantly relevant to age-related macular degeneration (AMD) and other chorioretinal diseases, and AMD is one of the leading causes of blindness in the elderly worldwide. The configuration may worsen along with the ageing of retinal pigment epithelium and BM that finally leads to AMD. Thus, the scaffold-based tissue-engineered retina provides an innovative alternative for retinal tissue repair. The cell and material requirements for retinal repair are discussed including cell sheet engineering, decellularised membrane and tissue-engineered membranes. Further, the challenges and potential in realising a whole tissue model construct for retinal regeneration are highlighted herein. This review article provides a framework for future development of tissue-engineered retina as a preclinical model and possible treatments for AMD.

Hybrid three-dimensional (3D) bioprinting of retina equivalent for ocular research.

In this article, a hybrid retina construct was created via three-dimensional (3D) bioprinting technology. The construct was composed of a PCL ultrathin membrane, ARPE-19 cell monolayer and Y79 cell-laden alginate/pluronic bioink. 3D bioprinting technology was applied herein to deliver the ARPE-19 cells and Y79 cell-laden bioink to ensure homogeneous ARPE-19 cell seeding; subsequently, two distinctive Y79 cell-seeding patterns were bioprinted on top of the ARPE-19 cell monolayer. The bioprinted ARPE-19 cells were evaluated by prestoblue assay, F-actin, and hematoxylin/eosin (HE) staining, and then the cells were observed under laser scanning and invert microscopy for 14 days. The Y79 cells in alginate/pluronic bioink after bioprinting had been closely monitored for 7 days. Live/dead assay and scanning electrical microscopy (SEM) were employed to investigate Y79 cell viability and morphology. Both the ARPE-19 and Y79 cells were in excellent condition, and the successfully bioprinted retina model could be utilized in drug delivery, disease mechanism and treatment method discoveries.

Dimethyl 2-(4-bromophenyl)-10,11-dimethoxy-2,3,7,8-tetrahydrospiro[azepino[2,1-a]isoquinoline-3,9'-fluorene]-4,5-dicarboxylate.

The title compound, C38H32BrNO6, is a new photochromic tetrahydroazepinoisoquinoline (THAI). The longest spiro bond [1.589 (4) A] can be broken very easily by UV light, leading to ring opening. This explains the photochromic behaviour.

A new photochromic tetrahydroindolizine.

The title compound, dimethyl 10b'-(4-fluorostyryl)-8',9'-dimethoxy-4-nitro-5',6'-dihydrospiro[9H-fluorene-9,1'(10b'H)-pyrrolo[2,1-a]isoquinoline]-2',3'-dicarboxylate, C38H31FN2O8, is a new photochromic tetrahydroindolizine. One of the C-C bonds at the spiro C atom is very long [1.630 (2) A], thus explaining the photochromic behaviour.