Advanced Ovarian Cancer: Weighing the Risks and Benefits of Surgery

The Role of Systematic Lymphadenectomy in Low-Grade Serous Ovarian Cancer: A Systematic Review and Meta-Analysis

OBJECTIVE

To evaluate the role of systematic lymphadenectomy in low-grade serous ovarian cancer (LGSOC) and determine its impact on clinical outcomes in overall survival (OS) and disease-free survival (DFS) terms.

METHODS

A comprehensive, systematic computer literature search on PubMed was performed using the following Medical Subject Headings (MeSH) terms: "low grade serous ovarian cancer" AND/OR "lymphadenectomy" AND/OR "staging" AND/OR "ovarian cancer" AND/OR "cytoreduction". Separate searches were performed with MeSH terms on MEDLINE and EMBASE to extract all the relevant literature available. We included only patients with histologically confirmed LGSOC.

RESULTS

Three studies were considered in the quantitative analysis. Systematic lymphadenectomy in LGSOC failed to provide a significant OS or PFS benefit in LGSOC when compared to no lymphadenectomy in the entire (all the stages) population (for OS: HR = 1.15, 95% CI [0.42, 3.18] I2 = 84% and for PFS: HR = 1.46, 95% CI [0.63, 3.41], I2 = 71%), nor did it in the subtype analysis regarding FIGO stages. For FIGO early-stage I-II LGSOC, the DFS data were pooled (HR = 1.48, 95% CI [0.58, 3.78], I2 = 75%). In patients with advanced-stage (FIGO II-IV), we also failed to prove survival benefit for lymphadenectomy in OS (HR = 1.74, 95% CI [0.87, 3.48], I2 = 11%) or DFS (HR = 1.48, 95% CI [0.58, 3.78], I2 = 75%) compared to no lymphadenectomy.

CONCLUSION

More extensive prospective research is mandatory to understand the real impact of lymphadenectomy on survival in LGSOC. The existing literature does not provide strong evidence.

Mechanical properties of CTCs in patients with diagnosed ovarian cancer

The incidence and mortality of gynecologic cancers have been constantly increasing in China over the last 2 decades, which become a major health concern for women. Survival rates of gynecologic cancers are generally not satisfactory and decrease along with the advancing stage, this is mainly due to the lack of a clear prognostic evaluation during the treatment, which brings difficulties to the treatment. Therefore, more accurate prognostic evaluation methods are urgently needed. To solve this problem, this article explores the changes in the biomechanical properties of cells. Changes in the biomechanical properties of circulating tumor cells (CTCs) were explored by nano detection technology. The reference criteria for clinical evaluation of ovarian cancer (Age, FIGO stage, Histologic type, CA-125, Ascites, Single/Double, Residual lesion, and Chemotherapy) were compared and analyzed. The results showed that the average cell height of CTCs was 4.12 ± 0.83 μm before chemotherapy and 4.87 ± 0.71 μm after chemotherapy, with an average increase of 18.203 %. The apparent Young's modulus (E) was 3.884 ± 0.045 kPa before chemotherapy and 4.514 ± 0.025 kPa after chemotherapy, which increased by 0.63 kPa. The ROC analysis of FIGO stage of ovarian cancer patients showed that Young's modulus of cells could better reflect the accuracy of the evaluation of FIGO stage of patients, with the accuracy reaching 76.7 %, which was higher than the detection accuracy of CA-125 (72.6 %). In conclusion, the mechanical properties of CTCs can indicate the FIGO stage and diagnosis of patients and predict the prognostic risk of patients.

Nomogram to predict postoperative complications after cytoreductive surgery for advanced epithelial ovarian cancer: A multicenter retrospective cohort study

Objective

To establish nomograms to predict the risk of postoperative complications following cytoreductive surgery in patients with advanced epithelial ovarian cancer (AEOC).

Methods

A multicenter retrospective cohort study that included patients with FIGO stage IIIC-IV epithelial ovarian cancer who underwent cytoreductive surgery was designed. By using univariate and multivariate analyses, patient preoperative characteristics were used to predict the risk of postoperative complications. Multivariate modeling was used to develop Nomograms.

Results

Overall, 585 AEOC patients were included for analysis (training cohort = 426, extrapolation cohort = 159). According to the findings, the training cohort observed an incidence of postoperative overall and severe complications of 28.87% and 6.10%, respectively. Modified frailty index (mFI) (OR 1.96 and 2.18), FIGO stage (OR 2.31 and 3.22), and Surgical Complexity Score (SCS) (OR 1.16 and 1.23) were the clinical factors that were most substantially associated to the incidence of overall and severe complications, respectively. The resulting nomograms demonstrated great internal discrimination, good consistency, and stable calibration, with C-index of 0.74 and 0.78 for overall and severe complications prediction, respectively. A satisfactory external discrimination was also indicated by the extrapolation cohort, with the C-index for predicting overall and severe complications being 0.92 and 0.91, respectively.

Conclusions

The risk of considerable postoperative morbidity exists after cytoreductive surgery for AEOC. These two nomograms with good discrimination and calibration might be useful to guide clinical decision-making and help doctors assess the probability of postoperative complications for AEOC patients.

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.

A Clinical Diagnostic Value Analysis of Serum CA125, CA199, and HE4 in Women with Early Ovarian Cancer: Systematic Review and Meta-Analysis

Objective

To evaluate the value of combined detection of serum CA125, CA199, and HE4 in the diagnosis of ovarian cancer.

Methods

Relevant articles retrieved from PubMed, Elsevier Science, Springer, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases were screened strictly according to inclusion and exclusion criteria. Included literature published from January 2005 to December 2021. (2) Serum HE4, CA125, CA199, and their combination for ovarian cancer diagnostic tests were studied, and healthy subjects or patients with the benign disease were taken as a control group. (3) Pathological tissue diagnosis as the gold standard. (4) Complete original data can be obtained. (5) The sample size was ≥20. (6) Language is limited to Chinese and English. Data features and QUADAS table were extracted from the included literature, and QUADAS evaluation tool detail table was used for the included study. Conduct quality evaluation. Statistical analysis was carried out using meta-disc software version 1.4. Appropriate effect model was selected to merge the effect size, and the forest maps of merge sensitivity, merge specificity, and merge likelihood ratio were obtained.

Results

The results of meta-analysis showed that there was a statistical difference in diagnostic specificity analysis of CA125 (OR = 1.91, 95% CI (1.58, 2.32), P < 0.00001, I² = 67%, Z = 6.58); diagnostic sensitivity analysis of CA125 (OR = 2.50, 95% CI (1.73, 3.62), P < 0.00001, I² = 0%, Z = 4.90); diagnostic specificity analysis of CA199 (OR = 1.98, 95% CI (1.60, 2.44), P < 0.00001, I² = 89%, Z = 6.35); diagnostic sensitivity analysis of CA199 (OR = 1.92, 95% CI (1.46, 2.52), P < 0.00001, I² = 73%, Z = 4.70); diagnostic specificity analysis of HE4 (OR = 2.08, 95% CI (1.65, 2.63), P < 0.00001, I² = 73%, Z = 6.19); diagnostic sensitivity analysis of HE4 (OR = 2.37, 95% CI (1.87, 3.00), P < 0.00001, I² = 83%, Z = 7.19).

Conclusion

In the clinical assisted diagnosis of ovarian cancer, combined detection of CA125, CA199, and HE4 has the stronger discriminant ability and higher accuracy than single detection of CA125, which can improve the diagnostic efficiency.

Relationship Between Neoadjuvant Chemotherapy and Log Odds of Positive Lymph Nodes and Their Prognostic Role in Advanced Ovarian Cancer Patients With Optimal Cytoreductive Surgery

Purpose

To analyze the relationship between neoadjuvant chemotherapy (NACT) and log odds of positive lymph nodes (LODDS), as well as their prognostic role in advanced ovarian cancer (AOC) patients with optimal cytoreductive surgery.

Patients and Methods

From the SEER database during 2010-2016, we identified 1,012 AOC patients with optimal cytoreductive surgery. Univariable and multivariable logistic regression was performed to identify the relationship between NACT and LODDS. Overall survival (OS) and cancer-specific survival (CSS) were assessed using the Kaplan-Meier method and log-rank test. Univariable and multivariable Cox regression was conducted to determine the independent prognostic factors for OS and CSS.

Results

Factors associated with significantly higher NACT odds included older (≥60 years old), married, tumor size ≥ 15 cm, FIGO IV, and LODDS ≤ 0.1. Multivariable Cox regression model confirmed older (≥60 years old), unmarried, separated, divorced, widowed, mucinous histology type, tumor size ≥ 15 cm, and LODDS > 0.1 were correlated with increased risks of OS and CSS. NACT was not an independent prognostic factor for OS and CSS. In the subgroup analyses, LODDS was an independent prognostic factor for OS and CSS in patients with < 75 years old, married, unmarried, FIGO III, and tumor size < 15 cm.

Conclusion

NACT did not show any survival benefit in AOC patients with optimal cytoreductive surgery, but it may be beneficial in reducing LODDS. Meanwhile, clinicians can use grade of LODDS to predict the prognosis of AOC patients with optimal cytoreductive surgery.

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.

Low preoperative skeletal muscle density is predictive for negative postoperative outcomes in older women with ovarian cancer

OBJECTIVE

To determine the predictive value of lumbar skeletal muscle mass and density for postoperative outcomes in older women with advanced stage ovarian cancer.

METHODS

A multicenter, retrospective cohort study was performed in women ≥ 70 years old receiving surgery for primary, advanced stage ovarian cancer. Skeletal muscle mass and density were assessed in axial CT slices on level L3. Low skeletal muscle mass was defined as skeletal muscle index < 38.50 cm²/m². Low skeletal muscle density was defined as one standard deviation below the mean (muscle attenuation < 22.55 Hounsfield Units). The primary outcome was any postoperative complication ≤ 30 days after surgery. Secondary outcomes included severe complications, infections, delirium, prolonged hospital stay, discharge destination, discontinuation of adjuvant chemotherapy and mortality.

RESULTS

In analysis of 213 patients, preoperative low skeletal muscle density was associated with postoperative complications ≤ 30 days after surgery (Odds Ratio (OR) 2.83; 95% Confidence Interval (CI) 1.41-5.67), severe complications (OR 3.01; 95%CI 1.09-8.33), infectious complications (OR 2.79; 95%CI 1.30-5.99) and discharge to a care facility (OR 3.04; 95%CI 1.16-7.93). Preoperative low skeletal muscle mass was only associated with infectious complications (OR 2.32; 95%CI 1.09-4.92). In a multivariable model, low skeletal muscle density was of added predictive value for postoperative complications (OR 2.57; 95%CI 1.21-5.45) to the strongest existing predictor functional impairment (KATZ-ADL ≥ 2).

CONCLUSION

Low skeletal muscle density, as a proxy of muscle quality, is associated with poor postoperative outcomes in older patients with advanced stage ovarian cancer. These findings can contribute to postoperative risk assessment and clinical decision making.