Development of a prediction model for residual disease in newly diagnosed advanced ovarian cancer

Prognostic Significance of Preoperative NLR, MLR, and PLR Values in Predicting the Outcome of Primary Cytoreductive Surgery in Serous Epithelial Ovarian Cancer

(1) The degree of cytoreduction achieved during primary debulking surgery (PDS) is an important prognostic factor for the survival of patients with epithelial ovarian cancer (EOC). Our aim was to investigate the prognostic value of preoperative laboratory parameters for the outcome of PDS. (2) We analyzed the preoperative laboratory parameters of 150 serous EOC patients who underwent PDS between 2006 and 2013. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values of the variables for predicting the PDS outcome. We used binary logistic regression to examine the independent predictive value of the factors for incomplete cytoreduction. (3) Among the parameters, we established optimal cut-off values for cancer antigen (Ca)-125, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR) to predict the outcome of PDS. The results of binary logistic regression showed that stage (FIGO III-IV), MLR (>0.305), and Ca-125 (>169.15 kU/L) were independent significant predictors of the degree of tumor reduction achieved during PDS. (4) In the future, MLR, especially in combination with other parameters, may be useful in determining prognosis and selecting the best treatment option (PDS or neoadjuvant chemotherapy + interval debulking surgery) for ovarian cancer patients.

A three protein signature fails to externally validate as a biomarker to predict surgical outcome in high-grade epithelial ovarian cancer

Introduction

For patients with advanced epithelial ovarian cancer, complete surgical cytoreduction remains the strongest predictor of outcome. However, identifying patients who are likely to benefit from such surgery remains elusive and to date few surgical outcome prediction tools have been validated. Here we attempted to externally validate a promising three protein signature, which had previously shown strong association with suboptimal surgical debulking (AUC 0.89, accuracy 92.8%), (Riester, M., et al., (2014)).

Methods

238 high-grade epithelial ovarian cancer samples were collected from patients who participated in a large multicentre trial (ICON5). Samples were collected at the time of initial surgery and before randomisation. Surgical outcome data were collated from prospectively collected study records. Immunohistochemical scores were generated by two independent observers for the three proteins in the original signature (POSTN, CXCL14 and pSmad2/3). Predictive values were generated for individual and combination protein signatures.

Results

When assessed individually, none of the proteins showed any evidence of predictive affinity for suboptimal surgical outcome in our cohort (AUC POSTN 0.55, pSmad 2/3 0.53, CXCL 14 0.62). The combined signature again showed poor predictive ability with an AUC 0.58.

Conclusions

Despite showing original promise, when this protein signature is applied to a large external cohort, it is unable to accurately predict surgical outcomes. This could be attributed to overfitting of the original model, or differences in surgical practice between cohorts.

Prediction of Surgical Outcome in Advanced Ovarian Cancer by Imaging and Laparoscopy: A Narrative Review

Maximal-effort upfront or interval debulking surgery is the recommended approach for advanced-stage ovarian cancer. The role of diagnostic imaging is to provide a systematic and structured report on tumour dissemination with emphasis on key sites for resectability. Imaging methods, such as pelvic and abdominal ultrasound, contrast-enhanced computed tomography, whole-body diffusion-weighted magnetic resonance imaging and positron emission tomography, yield high diagnostic performance for diagnosing bulky disease, but they are less accurate for depicting small-volume carcinomatosis, which may lead to unnecessary explorative laparotomies. Diagnostic laparoscopy, on the other hand, may directly visualize intraperitoneal involvement but has limitations in detecting tumours beyond the gastrosplenic ligament, in the lesser sac, mesenteric root or in the retroperitoneum. Laparoscopy has its place in combination with imaging in cases where ima-ging results regarding resectability are unclear. Different imaging models predicting tumour resectability have been developed as an adjunctional objective tool. Incorporating results from tumour quantitative analyses (e.g., radiomics), preoperative biopsies and biomarkers into predictive models may allow for more precise selection of patients eligible for extensive surgery. This review will discuss the ability of imaging and laparoscopy to predict non-resectable disease in patients with advanced ovarian cancer.

Neutrophil interactions with T cells, platelets, endothelial cells, and of course tumor cells

Neutrophils sense microbes and host inflammatory mediators, and traffic to sites of infection where they direct a broad armamentarium of antimicrobial products against pathogens. Neutrophils are also activated by damage-associated molecular patterns (DAMPs), which are products of cellular injury that stimulate the innate immune system through pathways that are similar to those activated by microbes. Neutrophils and platelets become activated by injury, and cluster and cross-signal to each other with the cumulative effect of driving antimicrobial defense and hemostasis. In addition, neutrophil extracellular traps are extracellular chromatin and granular constituents that are generated in response to microbial and damage motifs and are pro-thrombotic and injurious. Although neutrophils can worsen tissue injury, neutrophils may also have a role in facilitating wound repair following injury. A central theme of this review relates to how critical functions of neutrophils that evolved to respond to infection and damage modulate the tumor microenvironment (TME) in ways that can promote or limit tumor progression. Neutrophils are reprogrammed by the TME, and, in turn, can cross-signal to tumor cells and reshape the immune landscape of tumors. Importantly, promising new therapeutic strategies have been developed to target neutrophil recruitment and function to make cancer immunotherapy more effective.

The Use of Artificial Intelligence for Complete Cytoreduction Prediction in Epithelial Ovarian Cancer: A Narrative Review

INTRODUCTION

In patients affected by epithelial ovarian cancer (EOC) complete cytoreduction (CC) has been associated with higher survival outcomes. Artificial intelligence (AI) systems have proved clinical benefice in different areas of healthcare.

OBJECTIVE

To systematically assemble and analyze the available literature on the use of AI in patients affected by EOC to evaluate its applicability to predict CC compared to traditional statistics.

MATERIAL AND METHODS

Data search was carried out through PubMed, Scopus, Ovid MEDLINE, Cochrane Library, EMBASE, international congresses and clinical trials. The main search terms were: Artificial Intelligence AND surgery/cytoreduction AND ovarian cancer. Two authors independently performed the search by October 2022 and evaluated the eligibility criteria. Studies were included when data about Artificial Intelligence and methodological data were detailed.

RESULTS

A total of 1899 cases were analyzed. Survival data were reported in 2 articles: 92% of 5-years overall survival (OS) and 73% of 2-years OS. The median area under the curve (AUC) resulted 0,62. The model accuracy for surgical resection reported in two articles reported was 77,7% and 65,8% respectively while the median AUC was 0,81. On average 8 variables were inserted in the algorithms. The most used parameters were age and Ca125.

DISCUSSION

AI revealed greater accuracy compared against the logistic regression models data. Survival predictive accuracy and AUC were lower for advanced ovarian cancers. One study analyzed the importance of factors predicting CC in recurrent epithelial ovarian cancer and disease free interval, retroperitoneal recurrence, residual disease at primary surgery and stage represented the main influencing factors. Surgical Complexity Scores resulted to be more useful in the algorithms than pre-operating imaging.

CONCLUSION

AI showed better prognostic accuracy if compared to conventional algorithms. However further studies are needed to compare the impact of different AI methods and variables and to provide survival informations.

Development and Validation of 18F-FDG PET/CT-Based Models for Predicting Successful Complete Cytoreduction During Primary Cytoreductive Surgery for Advanced Ovarian cancer

PURPOSE

The aim of this study was to develop an 18F-FDG PET/CT-based model to predict complete cytoreduction during primary cytoreductive surgery (CRS) for ovarian cancer (OC).

PATIENTS AND METHODS

We retrospectively identified patients with stage III-IV OC who underwent primary CRS between June 2013 and February 2020 at 2 tertiary centers. Patients from each hospital were assigned to training and test sets. The abdominal cavity was divided into 3 sections, and data for the PET/CT-derived parameters were collected through image analysis. Various prediction models were constructed by combining clinicopathologic characteristics and PET/CT-derived parameters. The performance of the model with the highest area under the receiver operating characteristic curve (AUC) was externally validated.

RESULTS

The training and test sets included 159 and 166 patients, respectively. The median age of patients in the test set was 55 years; 72.3% of them had stage III tumors, and 65.4% underwent complete cytoreduction. Metabolic tumor volume, total lesion glycolysis, and the number of metastatic lesions above the upper margin of the renal vein (area A) were selected among the PET/CT parameters. The best predictive multivariable model consisted of CA-125 (<750 or ≥750 IU/mL), number of metastatic lesions (<2 or ≥2), and metabolic tumor volume of area A, predicting complete cytoreduction with an AUC of 0.768. The model was validated using a test set. Its predictive performance yielded an AUC of 0.771.

CONCLUSIONS

We successfully developed and validated a preoperative model to predict complete cytoreduction in advanced OC. This model can facilitate patient selection for primary CRS in clinical practice.

Lifestyle and personal factors associated with having macroscopic residual disease after ovarian cancer primary cytoreductive surgery

OBJECTIVE

The presence of macroscopic residual disease after primary cytoreductive surgery (PCS) is an important factor influencing survival for patients with high-grade serous ovarian cancer (HGSC). More research is needed to identify factors associated with having macroscopic residual disease. We analyzed 12 lifestyle and personal exposures known to be related to ovarian cancer risk or inflammation to identify those associated with having residual disease after surgery.

METHODS

This analysis used data on 2054 patients with advanced stage HGSC from the Ovarian Cancer Association Consortium. The exposures were body mass index, breastfeeding, oral contraceptive use, depot-medroxyprogesterone acetate use, endometriosis, first-degree family history of ovarian cancer, incomplete pregnancy, menopausal hormone therapy use, menopausal status, parity, smoking, and tubal ligation. Logistic regression models were fit to assess the association between these exposures and having residual disease following PCS.

RESULTS

Menopausal estrogen-only therapy (ET) use was associated with 33% lower odds of having macroscopic residual disease compared to never use (OR = 0.67, 95%CI 0.46-0.97, p = 0.033). Compared to nulliparous women, parous women who did not breastfeed had 36% lower odds of having residual disease (OR = 0.64, 95%CI 0.43-0.94, p = 0.022), while there was no association among parous women who breastfed (OR = 0.90, 95%CI 0.65-1.25, p = 0.53).

CONCLUSIONS

The association between ET and having no macroscopic residual disease is plausible given a strong underlying biologic hypothesis between this exposure and diagnosis with HGSC. If this or the parity finding is replicated, these factors could be included in risk stratification models to determine whether HGSC patients should receive PCS or neoadjuvant chemotherapy.

The BUMPy road of peritoneal metastases in ovarian cancer

Ovarian cancer is the most common cause of death due to gynecologic malignancies, with more than 70% of patients presenting with advanced stage disease at the time of diagnosis. The extent and distribution of tumor guide primary treatment selection and clinical management. While primary cytoreductive surgery with complete tumor resection improves survival, patients with extensive peritoneal disease may benefit from neoadjuvant chemotherapy first to reduce tumor burden followed by interval cytoreductive surgery. Imaging plays an essential role in triaging patients including selecting patients who may benefit from neoadjuvant chemotherapy before cytoreductive surgery. Interestingly, there are no universally established criteria to predict resectability and local practices depend on local guidelines and surgeon preferences. Nevertheless, certain anatomical tumor locations are known to be difficult to resect and are associated with suboptimal cytoreduction or require special surgical considerations. This review discusses the recent advances in the initial management of patients with ovarian cancer, a practical approach to the assessment and communication of peritoneal metastases locations on CT and MRI. It also explores recent advances in genomics profiling and radiomics that may influence the initial management of these patients.

Preoperative Predictors of Optimal Tumor Resectability in Patients With Epithelial Ovarian Cancer

Background

Several studies have shown that whether complete tumor resection can be achieved during debulking surgery depends on various patient-related factors. However, none of these studies was conducted among patients with epithelial ovarian cancer (EOC) in sub-Saharan Africa. In this study, we aimed to determine the preoperative predictors of optimal tumor resectability (OTR) during primary debulking surgery (PDS) in patients with EOC.

Methodology

In this study, we reviewed all patients with histologically diagnosed EOC who underwent PDS between January 2011 and December 2020. We included 83 patients with complete clinical records for subsequent data analysis. Descriptive statistics were computed for patients’ data, and binary logistic regression analysis was used to assess the strength of associations between patients’ preoperative characteristics and OTR.

Results

The overall rate of OTR was 53.0%, while the rate in advanced EOC patients was 36.1%. In the univariate analyses, pleural effusion, ascites, tumor bilaterality, size of the largest tumor, retroperitoneal lymph nodes, omental caking, peritoneal thickening, significant extrapelvic tumor, serum cancer antigen-125 (CA-125) levels, and hemoglobin levels were recorded as the predictors of OTR. However, after adjusting for covariates in the final multivariate models, we found that the absence of moderate-to-large pleural effusion (odds ratio (OR) = 5.60; 95% confidence interval (CI) = 1.32, 23.71) and having serum CA-125 levels of ≤370 U/mL (OR = 6.80; 95% CI = 1.19, 38.79) were the overall independent predictors of OTR while not having any preexisting comorbidity (OR = 18.21; 95% CI = 2.40, 38.10), and the absence of pleural effusions (OR = 13.75; 95% CI = 1.80, 24.85) or enlarged retroperitoneal lymph nodes (OR = 11.95; 95% CI = 1.35, 16.07) were predictors of OTR in advanced EOC patients.

Conclusions

We demonstrated that the radiological absence of pleural effusions and enlarged retroperitoneal lymph nodes and having no preexisting medical morbidity and serum CA-125 levels of ≤370 U/mL were the independent predictors of OTR during PDS. The preliminary data generated from this study can be used to develop variables for a prediction model in a future validation study.

An Orthotopic Murine Model of Peritoneal Carcinomatosis of Ovarian Origin for Intraoperative PDT

Advanced ovarian cancer is the most serious among gynecological malignancies and is associated with 35% five-year overall survival. Surgery is the first therapeutic indication, and the absence of remaining macroscopic lesions is the most important prognostic factor. However, tumor dissemination over the whole abdominal cavity largely contributes to the difficulty of complete surgical resection. Consequently, any therapeutic approach that may complete surgical resection should improve patient survival. Considering that some sites are not suitable for surgery because of their close location to vital organs, intraoperative photodynamic therapy (ioPDT) appears to be a complementary therapeutic approach to surgery to obtain the lowest residual disease.Relevant in vivo cancer models that closely resemble human ovarian cancer are essential for preclinical research of alternative antitumor therapeutic strategies. Thus, we propose a comprehensive protocol to set up an orthotopic ovarian xenograft in mice leading to peritoneal carcinomatosis that could be harnessed for antitumor therapeutic application and evaluation.

Ascites volume at the time of primary debulking and overall survival of patients with advanced epithelial ovarian cancer

OBJECTIVES

To investigate the impact of malignant ascites volume on the outcomes of patients with advanced epithelial ovarian carcinoma who undergo primary debulking surgery.

METHODS

Patients diagnosed with stage III-IV epithelial ovarian carcinoma and bulky intra-abdominal (TIIIC) disease between 2010 and 2015, who underwent primary debulking surgery followed by multi-agent chemotherapy and known status of residual disease, were drawn from the National Cancer Database. Based on available information, the presence and volume of malignant ascites was categorized as absent, low (<980 mL), and high (>980 mL) volume. Median overall survival was determined from Kaplan-Meier curves and compared with the log rank test. A multivariate Cox model was constructed to control for confounders.

RESULTS

2493 patients were identified; 31.9% (n=795) had no ascites, 40.2% (n=1001) had low, and 28% (n=697) had high volume malignant ascites. Rate of complete gross resection was higher for patients with no ascites (65.9%) compared with those with low (35.6%) and high (23%) volume ascites (p<0.001). After controlling for stage, histology, grade, age, and comorbidities, compared with those with no ascites, patients with low (odds ratio (OR) 3.49, 95% confidence intervals (CI) 2.89 to 4.26) and high (OR 6.40, 95% CI 5.07 to 8.06) volume ascites were more likely to have gross residual disease. For patients who achieved complete gross resection after controlling for confounders compared with patients with no ascites, those with low (hazard ratio (HR) 1.37, 95% CI 1.09 to 1.72) and high volume ascites (HR 1.94, 95% CI 1.47 to 2.55) had worse overall survival. Similarly, patients with low volume ascites had better survival compared with those with high volume ascites (HR 0.71 95% CI 0.54 to 0.93).

CONCLUSIONS

The presence and volume of malignant ascites at the time of primary debulking surgery was associated with the likelihood of achieving a complete gross resection and worse overall survival.

The Influence of "Omental Cake" Presence on the Completeness of Cytoreduction in Advanced-stage Ovarian Cancer

BACKGROUND/AIM

The presence of "omental cake" has been considered since long as a negative prognostic factor in patients diagnosed with advanced-stage ovarian cancer. The aim of this paper was to study the impact of "omental cake" presence on the perioperative outcomes in advanced-stage ovarian cancer.

PATIENTS AND METHODS

Between 2015 and 2020, 85 patients were submitted to surgery for advanced-stage ovarian cancer.

RESULTS

Among cases diagnosed with "omental cake" the rate of complete cytoreduction was significantly diminished when compared to cases in which this entity was not revealed. In the meantime, the presence of omental cake among cases in which complete debulking was achievable was significantly associated with a higher number of digestive resections when compared to the other cases submitted to surgery and in which tumoral transformation of the omentum was encountered.

CONCLUSION

The presence of "omental cake" is associated with a higher rate of incomplete resections. However, in cases in which complete debulking was achieved, the presence of omental cake was associated with a significantly higher number of digestive tract resections.

Prediction of optimal debulking surgery in ovarian cancer

The mainstay management of advanced ovarian cancer is maximal cytoreductive surgery followed by chemotherapy. Neoadjuvant chemotherapy (NACT) and interval debulking surgery (IDS) are alternative treatments for patients with comorbidity, poor performance status, and predicted for suboptimal debulking surgery. It is the invariable principle in any situation that no residual disease after the completion of surgery is useful for patients with ovarian cancer. Therefore, the prediction of optimal debulking before the treatment of ovarian cancer is of utmost importance. Many studies have reported on the use of serum biomarkers, such as cancer antigen 125 (CA125) or human epididymis 4 (HE4), and imaging studies, such as computed tomography (CT), diffusion-weighted magnetic resonance imaging (DW-MRI), and positron emission tomography (PET)/CT, to identify adequate surgical candidates for primary debulking surgery (PDS). Laparoscopy has also been studied as a reliable tool for the prediction of optimal debulking. Here, we summarize a review of the related literature.

A Prediction Model for Optimal Primary Debulking Surgery Based on Preoperative Computed Tomography Scans and Clinical Factors in Patients With Advanced Ovarian Cancer: A Multicenter Retrospective Cohort Study

OBJECTIVE

This study assessed the predictive value of preoperative computed tomography (CT) scans and clinical factors for optimal debulking surgery (ODS) in patients with advanced ovarian cancer (AOC).

METHODS

Patients with AOC in International Federation of Gynecology and Obstetrics (FIGO) stage III-IV who underwent primary debulking surgery (PDS) between 2016 and 2019 from nine tertiary Chinese hospitals were included. Large-volume ascites, diffuse peritoneal thickening, omental cake, retroperitoneal lymph node enlargement (RLNE) below and above the inferior mesenteric artery (IMA), and suspected pelvic bowel, abdominal bowel, liver surface, liver parenchyma and portal, spleen, diaphragm and pleural lesions were evaluated on CT. Preoperative factors included age, platelet count, and albumin and CA125 levels.

RESULTS

Overall, 296 patients were included, and 250 (84.5%) underwent ODS. The prediction model included age >60 years (P=0.016; prediction index value, PIV=1), a CA125 level >800 U/ml (P=0.033, PIV=1), abdominal bowel metastasis (P=0.034, PIV=1), spleen metastasis (P<0.001, PIV=2), diaphragmatic metastasis (P=0.014, PIV=2), and an RLNE above the IMA (P<0.001, PIV=2). This model had superior discrimination (AUC=0.788>0.750), and the Hosmer-Lemeshow test indicated its stable calibration (P=0.600>0.050). With the aim of maximizing the accuracy of prediction and minimizing the rate of inappropriate explorations, a total PIV ≥5 achieved the highest accuracy of 85.47% and identified patients who underwent suboptimal PDS with a specificity of 100%.

CONCLUSIONS

We developed a prediction model based on two preoperative clinical factors and four radiological criteria to predict unsatisfactory debulking surgery in patients with AOC. The accuracy of this prediction model needs to be validated and adjusted in further multicenter prospective studies.

Clinical Utility of Preoperative Assessment in Ovarian Cancer Cytoreduction

Ovarian cancer is the deadliest gynecologic cancer, in part due to late presentation. Many women have vague early symptoms and present with disseminated disease. Cytoreductive surgery can be extensive, involving multiple organ systems. Novel therapies and recent clinical trials have provided evidence that, compared to primary cytoreduction, neoadjuvant chemotherapy has equivalent survival outcomes with less morbidity. There is increasing need for validated tools and mechanisms for clinicians to determine the optimal management of ovarian cancer patients.

Imaging before cytoreductive surgery in advanced ovarian cancer patients

Appropriate and accurate pre-operative imaging in epithelial ovarian cancer patients may allow selection of patients that may mostly benefit either from primary cytoreductive surgery or from neoadjuvant chemotherapy. If the patient is considered suitable for upfront surgery, pre-operative imaging may help in planning the surgical approach, to forecast the operating time, and to estimate the need for other consulting specialists. Currently, computed tomography (CT) imaging is the standard of care for pre-operative evaluation of ovarian cancer patients; however, advanced magnetic resonance imaging (MRI) is emerging as a technique that may overcome the limitations of CT imaging, especially for small peritoneal deposits in difficult-to-resect sites. Positron emission tomography (PET)-CT imaging in the pre-operative setting is currently limited, whereas the use of the new hybrid technique PET-MRI is still under evaluation. Since criteria that may preclude optimal cytoreductive surgery may vary, depending on the aggressiveness of the surgical procedure and surgeon skill, multidisciplinary consensus conferences are the ideal platform to evaluate extent of the disease and surgical strategy.

Pre-operative prediction of residual disease after interval cytoreduction for epithelial ovarian cancer using HE4

BACKGROUND

Presence of residual disease after cytoreductive surgery is an important negative prognostic factor for patients with advanced stage epithelial ovarian cancer. Surgery is of limited benefit when the diameter of residual disease is >1 cm. Residual disease is difficult to predict before surgery. The multivariate model Cancer Ovarii Non-invasive Assessment of Treatment Strategy (CONATS) index, based on serum biomarker HE4, age, and World Health Organization performance status, predicted no visible residual disease in patients undergoing primary cytoreductive surgery with an area under the curve (AUC) of 0.85. The AUC of predicting residual disease >1 cm was not reported, although this can be of importance for pre-operative decision making, especially in fragile patients. We tested this model for predicting residual disease >1 cm in patients undergoing interval cytoreduction.

METHODS

We retrospectively included patients with advanced epithelial ovarian cancer who underwent interval cytoreduction between January 2010 and December 2017 in two tertiary centers in the Netherlands. HE4 was measured with electrochemiluminescence in pre-operative samples. The CONATS index was used to predict residual disease. AUCs were calculated to predict residual disease >1 cm.

RESULTS

A total of 273 patients were included. Mean (SD) age was 64 (11) years. Median number of cycles of neoadjuvant chemotherapy was 3 (range 3-6) and the most common regimen used consisted of carboplatin and paclitaxel. Before interval cytoreduction, 19 patients (7%) showed complete response to chemotherapy, 251 patients (92%) showed partial response, and 3 patients (1%) showed stable disease at imaging. Following surgery, 232 patients (85%) had residual disease ≤1 cm and 41 patients (15%) had residual disease >1 cm. The AUC was 0.80 for predicting residual disease >1 cm. In patients ≥70 years of age the AUC was 0.82.

CONCLUSION

The CONATS index predicts surgical outcome after interval cytoreduction and is useful in counseling patients about the chance of whether an optimal interval cytoreduction can be achieved. This could be especially helpful in counseling elderly patients in whom surgery has a high risk of complications.

An update on preoperative assessment of the resectability of advanced ovarian cancer

The best prognosis for advanced ovarian cancer is provided by no residual disease after primary cytoreductive surgery. It is thus important to be able to predict resectability that will result in complete cytoreduction, while avoiding unnecessary surgery that may leave residual disease. No single procedure appears to be sufficiently accurate and reliable to predict resectability. The process should include a preoperative workup based on clinical examination, biomarkers, especially tumor markers, and imaging, for which computed tomography, as well as sonography, magnetic resonance imaging and positron-emission tomography, can be used. This workup should provide sufficient information to determine whether complete cytoreduction is possible or if not, to propose neoadjuvant chemotherapy which is preferable in this case. For the remaining patients, laparoscopy is broadly recommended as an ultimate triage step. However, its modalities are still debated, and several scores have been proposed for standardization and improving accuracy. The risk of false negatives requires a final assessment of resectability as the first stage of cytoreductive surgery by laparotomy. Composite models, consisting of several criteria of workup and, sometimes, laparoscopy have been proposed to improve the accuracy of the predictive process. Regardless of the modality, the process appears to be accurate and reliable for predicting residual disease but less so for predicting complete cytoreduction and thus avoiding unnecessary surgery and an inappropriate treatment strategy. Overall, the proposed procedures are heterogeneous, sometimes unvalidated, or do not consider advances in surgery. Future techniques and/or models are still needed to improve the prediction of complete resectability.

Models to predict outcomes after primary debulking surgery: Independent validation of models to predict suboptimal cytoreduction and gross residual disease

OBJECTIVE

Treatment planning requires accurate estimation of surgical complexity (SC) and residual disease (RD) at primary debulking surgery (PDS) for advanced ovarian cancer (OC). We sought to independently validate two published computed tomography (CT) prediction models.

METHODS

We included stage IIIC/IV OC patients who underwent PDS from 2003 to 2011. Two prediction models which included imaging and clinical variables to predict RD > 1 and any gross RD, respectively, were applied to our cohort. Two radiologists scored CTs. Discrimination was estimated using the c-index and calibration were assessed by comparing the observed and predicted estimates.

RESULTS

The validation cohort consisted of 276 patients; median age of the cohort was 64 years old and majority had serous histology. The validation and model development cohorts were similar in terms of baseline characteristics, however the RD rates differed between cohorts (9.4% vs 25.4% had RD >1 cm; 50.7% vs. 66.6% had gross RD). Model 1, the model to predict RD >1 cm, did not validate well. The c-index of 0.653 for the validation cohort was lower than reported in the development cohort (0.758) and the model over-predicted the proportion with RD >1 cm. The second model to predict gross RD had excellent discrimination with a c-index of 0.762.

CONCLUSIONS

We are able to validate a CT model to predict presence of gross RD in an independent center; the separate model to predict RD >1 cm did not validate. Application of the model to predict gross RD can help with clinical decision making in advanced ovarian cancer.

Diagnosis and Treatment of Ovarian Cancer

Epithelial ovarian cancer classically presents with vague persistent gastrointestinal, urologic, or nonacute abdominal/pelvic symptoms (bloating, early satiety, discomfort). Ultimately, a pelvic examination or imaging identifies an adnexal mass typically with accompanied advanced peritoneal dissemination. Management involves aggressive cytoreductive surgery in combination with platinum and taxane chemotherapy. Over the last 20 years, optimal resection and mode and timing of chemotherapy have evolved. The authors review the initial diagnosis and management and present the available data and recommendations to guide the decision tree of when to use neoadjuvant, intraperitoneal, HIPEC, dose-dense, and maintenance chemotherapy in the front-line treatment of epithelial ovarian cancer.

[Diagnostic value of imaging (ultrasonography, doppler, CT, MR, PET-CT) for the diagnosis of a suspicious ovarian mass and staging of ovarian, tubal or primary peritoneal cancer: Article drafted from the French Guidelines in oncology entitled "Initial management of patients with epithelial ovarian cancer" developed by FRANCOGYN, CNGOF, SFOG, GINECO-ARCAGY under the aegis of CNGOF and endorsed by INCa]

Transvaginal ultrasound is the first-line examination allowing characterizing 80 to 90% of adnexal masses (LP1). If performed by an expert, a subjective analysis is optimal. If performed by a non-expert, combining the use of Simple Rules with subjective analysis can achieve the diagnostic performance of an expert (LP1). Whichever the chosen model (subjective analysis by an expert or combination of the Simple Rules with a subjective analysis by a non-expert), a second-line examination will have to be proposed in the complex or indeterminate cases (about 20% of the masses) (grade A). The best-performing second-line test for characterization is pelvic MRI (LP1). If read by an expert, a pathological hypothesis can or should be suggested (grade D). In case of non-expert reading, the use of the ADNEXMR score allows a reliable assessment of the positive predictive value of malignancy to guide the patient towards the best management (gradeC). For preoperative assessment and evaluation of resectability of ovarian, fallopian tube or primary peritoneal cancer, it is recommended to perform a chest abdomen and pelvis CT with contrast agent injection (LP2, grade B). In the event of a contraindication to the injection of iodinated contrast agent (severe renal insufficiency, GFR <30mL/min), an abdomen and pelvis MRI completed with a non-injected chest CT may be proposed (LP3, grade C). By analogy, the same examinations are recommended to evaluate the disease after neo-adjuvant chemotherapy (LP3, Recommendation grade C). Further studies will be required to determine whether PET-CT provides better lymph node assessment before retroperitoneal and pelvic lymphadenectomy. PET-CT may be used to eliminate lymph node involvement in the absence of suspicious lymph nodes on morphological examination (LP3, grade C). The report should specify the localizations leading to a risk of incomplete cytoreductive surgery and lesions outside the field explored during surgery.

CT prediction of surgical outcome in patients with advanced epithelial ovarian carcinoma undergoing neoadjuvant chemotherapy

OBJECTIVE

A scoring system has been proposed to predict gross residual disease at primary debulking surgery (PDS) for advanced epithelial ovarian cancer. This scoring system has not been assessed in patients undergoing neoadjuvant chemotherapy (NACT). The aim of this study is to assess the reproducibility and prognostic significance of the scoring system when applied to women undergoing NACT followed by interval debulking surgery (IDS).

METHODS

A retrospective cohort study was conducted of patients with advanced ovarian cancer who underwent NACT and IDS between 2005 and 2014. Change in tumor burden using computed tomography (CT) at diagnosis (T0) and after initiation of NACT but before IDS (T1) was independently assessed by two radiologists blinded to outcomes using two read criteria: a scoring system utilizing clinical and radiologic criteria and RECIST 1.1. Relationship between CT assessments to surgical outcome, progression free survival (PFS) and overall survival (OS) were evaluated. Reader agreement was measured using Fleiss's kappa (ĸ).

RESULTS

76 patients were analyzed. Optimal surgical outcome was achieved in 69 (91%) of patients. Median progression free survival was 13.2 months and overall survival was 32.6 months, respectively. Predictive score change from T0 to T1 of >1 (denoting an improvement in disease burden) was associated with optimal cytoreduction (p = 0.02 and 0.01 for readers 1 and 2, respectively). Neither predictive score nor RECIST 1.1 assessment was predictive of OS or PFS. Reader agreement was substantial for predictive score (κ = 0.77) and moderate for RECIST (κ = 0.51) assessments.

CONCLUSIONS

A change in score before and after neoadjuvant chemotherapy minimizes reader variability and predicts surgical outcome.

Impact of residual disease on overall survival in women with Federation of Gynecology and Obstetrics stage IIIB-IIIC vs stage IV epithelial ovarian cancer after primary surgery

INTRODUCTION

The objective of this study was to determine the impact of intra-abdominal residual disease size, type (carcinomatosis, tumor mass or both), and location (upper/lower abdominal/both) on overall survival in women with Federation of Gynecology and Obstetrics (FIGO) stage IIIB-IIIC vs stage IV epithelial ovarian cancer who underwent primary debulking surgery.

MATERIAL AND METHODS

Altogether 2092 women diagnosed with advanced epithelial ovarian cancer undergoing primary debulking surgery in Denmark during 2005-2016 were identified in the Danish Gynecological Cancer Database. The impact of residual disease size, type, and location were evaluated using univariate and multivariate analyses.

RESULTS

Complete cytoreduction (residual disease = 0) was achieved in 47.3% and 38.4% of women with stage IIIB-IIIC and IV epithelial ovarian cancer, respectively. A benefit in overall survival was observed in women with residual disease = 0 compared with women with residual disease, and among women with residual disease ≤1 cm compared with residual disease >2 cm in both stages IIIB-IIIC and stage IV in multivariate analyses. Multivariate analyses showed an inferior overall survival for women with both residual carcinomatosis and residual tumor mass compared with those with residual tumor mass or residual carcinomatosis only for stage IIIB-IIIC and IV, and an inferior overall survival for women with residual disease located in both the upper and lower abdomen compared with residual disease in the upper abdomen only in stages IIIB-IIIC.

CONCLUSIONS

Our results confirm the positive prognostic impact of both complete cytoreduction and residual disease ≤1 cm in stages IIIB-IIIC as well as stage IV epithelial ovarian cancer. Women with stage IV do benefit from cytoreductive surgery and should be considered for primary debulking surgery, if residual disease = 0 can initially be expected.

Predictive significance of preoperative CT findings for suboptimal cytoreduction in advanced ovarian cancer: a meta-analysis

Background

Computed tomography (CT) has been extensively used in predicting suboptimal cytoreduction (SCR) in advanced ovarian cancer (OC). However, disagreements remain in literatures on the predictive value of CT findings for SCR. This meta-analysis was designed to determine the ability of eight preoperative CT findings to predict SCR in advanced OC.

Materials and methods

A comprehensive literature search was conducted for eligible studies to identify the association between the eight preoperative CT findings and SCR in advanced OC. The predictive performances of preoperative CT findings were expressed in terms of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) with pooled proportion.

Results

A total of 10 studies and 1,614 patients were included in this meta-analysis. Large volume ascites had the highest sensitivity (64%, CI 56–71%), with a PLR of 1.3 (CI 1.1–1.5) and an NLR of 0.73 (0.59–0.90), while lymph node involvement had the highest specificity (89%, CI 79–94%). The highest DOR of 3 (CI 2–4) was achieved in peritoneal involvement and large bowel mesentery involvement. The other CT findings had poorer predictive performance.

Conclusion

Preoperative CT findings have a poor discriminative capacity to predict SCR in advanced OC. Preoperative CT predictors should be used with caution amid clinical decision-making.

Model for Prediction of Optimal Debulking of Epithelial Ovarian Cancer

Background: Primary cytoreduction surgery followed by chemotherapy is the cornerstone treatment for epithelial ovarian cancer (EOC). In patients with a low probability of optimal primary surgical debulking, neoadjuvant chemotherapy (NACT) followed by interval debulking increases the chance of optimal surgery. The aim of this study was to develop a model to identify preoperative predictors for suboptimal cytoreduction. Methods: Medical records of patients with EOC who underwent primary cytoreductive surgery in a referral tertiary gyneco-oncology center were reviewed from 2007 to 2017. Data were collected on a range of characteristics including demographic features, comorbidities, serum tumor markers, hematologic markers, preoperative imaging, surgical procedures, and pathologic reports. Univariate and multivariate analyses were performed to clarify the ability of preoperative factors to predict suboptimal primary surgery. Results: The majority of patients (71.3%) who underwent primary cytoreductive surgery were optimally debulked. Based on the Youden index, the best cut-off point for the serum CA125 level to distinguish suboptimal debulking was 420U/ml with 0.730 (95%CI:0.559 to 0.862) sensitivity and 0.783 (0.684 to 0.862) specificity. Multiple logistic regression results showed that serum CA125 level >420 U/ ml (p value <0.001), the presence of liver metastasis on preoperative imaging (p value: 0.041) and ascites (p value: 0.032) or massive ascites (p value:0.010) significantly increased the risk of suboptimal debulking (logit p = 2.36 CA125 level +1.85 Liverinvolvement +1.68 presence of Ascites+ 2.28 Massive Ascites). Conclusion:The present study suggests that a serum CA125 level >420 U/ml, the presence of ascites or massive ascites and liver metastasis are strong predictors of suboptimal primary surgery in cases of EOC. Based on the constructed model, with any of these 4 factors, the probability of suboptimal debulking in EOC is more than 80%.

Predictive modeling for determination of microscopic residual disease at primary cytoreduction: An NRG Oncology/Gynecologic Oncology Group 182 Study

OBJECTIVE

Microscopic residual disease following complete cytoreduction (R0) is associated with a significant survival benefit for patients with advanced epithelial ovarian cancer (EOC). Our objective was to develop a prediction model for R0 to support surgeons in their clinical care decisions.

METHODS

Demographic, pathologic, surgical, and CA125 data were collected from GOG 182 records. Patients enrolled prior to September 1, 2003 were used for the training model while those enrolled after constituted the validation data set. Univariate analysis was performed to identify significant predictors of R0 and these variables were subsequently analyzed using multivariable regression. The regression model was reduced using backward selection and predictive accuracy was quantified using area under the receiver operating characteristic area under the curve (AUC) in both the training and the validation data sets.

RESULTS

Of the 3882 patients enrolled in GOG 182, 1480 had complete clinical data available for the analysis. The training data set consisted of 1007 patients (234 with R0) while the validation set was comprised of 473 patients (122 with R0). The reduced multivariable regression model demonstrated several variables predictive of R0 at cytoreduction: Disease Score (DS) (p<0.001), stage (p=0.009), CA125 (p<0.001), ascites (p<0.001), and stage-age interaction (p=0.01). Applying the prediction model to the validation data resulted in an AUC of 0.73 (0.67 to 0.78, 95% CI). Inclusion of DS enhanced the model performance to an AUC of 0.83 (0.79 to 0.88, 95% CI).

CONCLUSIONS

We developed and validated a prediction model for R0 that offers improved performance over previously reported models for prediction of residual disease. The performance of the prediction model suggests additional factors (i.e. imaging, molecular profiling, etc.) should be explored in the future for a more clinically actionable tool.

Impact of ascites volume on clinical outcomes in ovarian cancer: A cohort study

OBJECTIVES

To investigate the impact of ascites volume on ovarian cancer outcomes.

METHODS

Clinicopathologic features of a cohort of patients with ovarian cancer were obtained from a curated database at a single institution. Progression free survival (PFS) and overall survival (OS) were recorded. Ascites volume at primary surgery was dichotomized at 2000mL and comparisons for high and low volume ascites were made. Additionally, to elucidate interactions between ascites and ovarian tumor progression, we evaluated the effect of intraperitoneal administrations of murine cell-free ascites versus saline in a syngeneic mouse model of epithelial ovarian cancer.

RESULTS

Out of 685 patients identified, 58% had ascites present at the time of initial surgery. Considering the volume of ascites continuously, each liter of ascites was associated with shorter PFS (HR=1.12, 95% CI: 1.07-1.17) and OS (HR=1.12, 95%CI: 1.07-1.17). Patients with ascites greater than the median of 2000mL had significantly shorter PFS (14.5months vs. 22.7months; p<0.001) and OS (27.7months vs. 42.9months; p<0.001). After adjusting for stage, presence of ascites was inversely associated with ability to achieve optimal cytoreductive surgery. Consistent with these correlative results in patients, intraperitoneal administrations of murine cell-free ascites accelerated ovarian cancer progression in mice.

CONCLUSIONS

The volume of ascites at initial diagnosis of ovarian cancer correlated with worse PFS and OS. The effect of large volume on prognosis is likely to be in part related to reduced likelihood for complete resection of tumor (R0). If these findings are confirmed in independent studies, consideration should be made to add the presence of large volume ascites at diagnosis to the staging criteria for ovarian cancer.

Classification of Ovarian Cancer Surgery Facilitates Treatment Decisions in a Gynecological Multidisciplinary Team

Objective

Proper planning of intervention and care of ovarian cancer surgery is of outmost importance and involves a wide range of personnel at the departments involved. The aim of this study is to evaluate the introduction of an ovarian surgery classification (COVA) system for facilitating multidisciplinary team (MDT) decisions.

Materials and Methods

Four hundred eighteen women diagnosed with ovarian cancers (n = 351) or borderline tumors (n = 66) were selected for primary debulking surgery from January 2008 to July 2013. At an MDT meeting, women were allocated into 3 groups named “pre-COVA” 1 to 3 classifying the expected extent of the primary surgery and need for postoperative care. On the basis of the operative procedures performed, women were allocated into 1 of the 3 corresponding COVA 1 to 3 groups. The outcome measure was the predictive value of the pre-COVA score compared with the actual COVA performed.

Results

The MDT meeting allocated 213 women (51%) to pre-COVA 1, 136 (33%) to pre-COVA 2, and 52 (12%) to pre-COVA 3. At the end of surgery, 168 (40%) were classified as COVA 1, 158 (38%) were classified as COVA 2, and 28 (7%) were classified as COVA 3. Traced individually, 212 (51%) patients were correctly preclassified at the MDT meeting and distributed into 110 (52%) COVA 1, 71 (52%) COVA 2, and 17 (32%) COVA 3. Analyzing the subgroup of patients with cancer, 164 (47%) were correctly preclassified. Regarding the International Federation of Gynecology and Obstetrics (FIGO) stages, the pre-COVA classification predicted the actual COVA group in 79 (49%) FIGO stages I to IIIB and in 85 (45%) FIGO stages IIIC to IV.

Conclusions

The COVA classification system is a simple and useful tool in the MDT setting where specialists make treatment decisions based on advanced technology. The use of pre-COVA classification facilitates well-organized patient care–relevant procedures to be undertaken. Pre-COVA accurately predicts the final COVA in 51% classified women.

High-Density Lipoprotein Cholesterol, Blood Urea Nitrogen, and Serum Creatinine Can Predict Severe Acute Pancreatitis

BACKGROUND AND AIMS

Early prediction of disease severity of acute pancreatitis (AP) would be helpful for triaging patients to the appropriate level of care and intervention. The aim of the study was to develop a model able to predict Severe Acute Pancreatitis (SAP).

METHODS

A total of 647 patients with AP were enrolled. The demographic data, hematocrit, High-Density Lipoprotein Cholesterol (HDL-C) determinant at time of admission, Blood Urea Nitrogen (BUN), and serum creatinine (Scr) determinant at time of admission and 24 hrs after hospitalization were collected and analyzed statistically.

RESULTS

Multivariate logistic regression indicated that HDL-C at admission and BUN and Scr at 24 hours (hrs) were independently associated with SAP. A logistic regression function (LR model) was developed to predict SAP as follows: -2.25-0.06 HDL-C (mg/dl) at admission + 0.06 BUN (mg/dl) at 24 hours + 0.66 Scr (mg/dl) at 24 hours. The optimism-corrected c-index for LR model was 0.832 after bootstrap validation. The area under the receiver operating characteristic curve for LR model for the prediction of SAP was 0.84.

CONCLUSIONS

The LR model consists of HDL-C at admission and BUN and Scr at 24 hours, representing an additional tool to stratify patients at risk of SAP.

Prediction of incomplete primary debulking surgery in patients with advanced ovarian cancer: An external validation study of three models using computed tomography

OBJECTIVE

To test the ability of three prospectively developed computed tomography (CT) models to predict incomplete primary debulking surgery in patients with advanced (International Federation of Gynecology and Obstetrics stages III-IV) ovarian cancer.

METHODS

Three prediction models to predict incomplete surgery (any tumor residual >1cm in diameter) previously published by Ferrandina (models A and B) and by Gerestein were applied to a validation cohort consisting of 151 patients with advanced epithelial ovarian cancer. All patients were treated with primary debulking surgery in the Eastern part of the Netherlands between 2000 and 2009 and data were retrospectively collected. Three individual readers evaluated the radiographic parameters and gave a subjective assessment. Using the predicted probabilities from the models, the area under the curve (AUC) was calculated which represents the discriminative ability of the model.

RESULTS

The AUC of the Ferrandina models was 0.56, 0.59 and 0.59 in model A, and 0.55, 0.60 and 0.59 in model B for readers 1, 2 and 3, respectively. The AUC of Gerestein's model was 0.69, 0.61 and 0.69 for readers 1, 2 and 3, respectively. AUC values of 0.69 and 0.63 for reader 1 and 3 were found for subjective assessment.

CONCLUSIONS

Models to predict incomplete surgery in advanced ovarian cancer have limited predictive ability and their reproducibility is questionable. Subjective assessment seems as successful as applying predictive models. Present prediction models are not reliable enough to be used in clinical decision-making and should be interpreted with caution.