Mature Ovarian Teratoma: Neurological Implications in a Young Woman

Anti-NMDAR encephalitis is an autoimmune syndrome associated with antibodies against NMDA receptors. In some cases, it is associated with various tumors; one of them is ovarian teratoma, which mostly affects women below the age of 30 years. Here, we report a case of ovarian teratoma associated with anti-NMDAR encephalitis treated with both laparoscopic surgery and immunotherapy. Multidisciplinary approach is the cornerstone for the management of this syndrome.

CT-Based Fagotti Scoring System for Non-Invasive Prediction of Cytoreduction Surgery Outcome in Patients with Advanced Ovarian Cancer

Objective

To construct a CT-based Fagotti scoring system by analyzing the correlations between laparoscopic findings and CT features in patients with advanced ovarian cancer.

Materials and Methods

This retrospective cohort study included patients diagnosed with stage III/IV ovarian cancer who underwent diagnostic laparoscopy and debulking surgery between January 2010 and June 2018. Two radiologists independently reviewed preoperative CT scans and assessed ten CT features known as predictors of suboptimal cytoreduction. Correlation analysis between ten CT features and seven laparoscopic parameters based on the Fagotti scoring system was performed using Spearman's correlation. Variable selection and model construction were performed by logistic regression with the least absolute shrinkage and selection operator method using a predictive index value (PIV) ≥ 8 as an indicator of suboptimal cytoreduction. The final CT-based scoring system was internally validated using 5-fold cross-validation.

Results

A total of 157 patients (median age, 56 years; range, 27–79 years) were evaluated. Among 120 (76.4%) patients with a PIV ≥ 8, 105 patients received neoadjuvant chemotherapy followed by interval debulking surgery, and the optimal cytoreduction rate was 90.5% (95 of 105). Among 37 (23.6%) patients with PIV < 8, 29 patients underwent primary debulking surgery, and the optimal cytoreduction rate was 93.1% (27 of 29). CT features showing significant correlations with PIV ≥ 8 were mesenteric involvement, gastro-transverse mesocolon-splenic space involvement, diaphragmatic involvement, and para-aortic lymphadenopathy. The area under the receiver operating curve of the final model for prediction of PIV ≥ 8 was 0.72 (95% confidence interval: 0.62–0.82).

Conclusion

Central tumor burden and upper abdominal spread features on preoperative CT were identified as distinct predictive factors for high PIV on diagnostic laparoscopy. The CT-based PIV prediction model might be useful for patient stratification before cytoreduction surgery for advanced ovarian cancer.

Adding Value of MRI over CT in Predicting Peritoneal Cancer Index and Completeness of Cytoreduction

BACKGROUND

This study aimed to investigate the adding value of MRI over CT for preoperative cytoreductive surgery with hyperthermic intraperitoneal chemotherapies (CRS/HIPEC).

METHODS

Imaging and intraoperative peritoneal cancer index (PCI) were calculated in 62 patients with peritoneal metastasis. Predictive models for the completeness of cytoreductive score using PCI data were established using decision tree algorithms.

RESULTS

In gastric cancer patients, a large discrepancy and poor agreement was appreciated between CT and surgical PCI, and a nonsignificant difference was noted between MRI and surgical PCI. In colon cancer patients, a better agreement and higher correlation with a smaller error was observed in PCI score using MRI than in that using CT. However, the addition of MRI to CT was limited for appendiceal and ovarian cancer patients. For predicting incomplete cytoreduction, CT models yielded inadequate accuracy while MRI models were more accurate with fair discrimination ability.

CONCLUSIONS

CT was suitable for estimating PCI and surgery outcome in appendiceal and ovarian cancer patients, while further MRI in addition to CT was recommended for colon and gastric cancer patients. However, for classifying patients with peritoneal carcinomatosis into complete and incomplete cytoreduction, MRI was more effective than CT.

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.

Anti-N-methyl-D-aspartate receptor encephalitis induced by bilateral ovarian teratomas with distinct histopathologic types: A case report and brief literature review

RATIONALE

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is an autoimmune disorder that is most frequently induced by ovarian teratoma in young females. The condition can be controlled and reversed via ovarian tumor resection and immunotherapy. However, anti-NMDAR encephalitis induced by bilateral ovarian teratomas with distinct histopathologic types is rarely reported in the literature.

PATIENT CONCERNS

A 23-year-old woman presented with seizures.

DIAGNOSES

The diagnosis was anti-NMDAR encephalitis associated with ovarian teratomas based on positive anti-NMDAR antibody tests in both the cerebrospinal fluid and serum, and the detection of bilateral ovarian lesions on pelvic computed tomography. The postoperative histopathologic examination confirmed that the left lesion was an immature teratoma, and the right lesion was a mature teratoma.

INTERVENTIONS

We performed surgical resection of the ovarian teratomas and administered immunotherapy for the control of anti-NMDAR encephalitis. Chemotherapy was administered for the immature teratoma.

OUTCOMES

The patient recovered without any postoperative complications. She has been confirmed to be in complete clinical remission, and has not had a recurrence during 18 months of follow-up.

LESSONS

Anti-NMDAR encephalitis induced by bilateral ovarian teratomas of differing histopathologic types (1 immature and 1 mature) is rare. Early diagnosis and treatment with tumor resection, immunotherapy, and chemotherapy are critical for a good prognosis.

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.

Predicting surgical outcome in patients with International Federation of Gynecology and Obstetrics stage III or IV ovarian cancer using computed tomography: a systematic review of prediction models

OBJECTIVE

Maximal cytoreduction to no residual disease is an important predictor of prognosis in patients with advanced-stage epithelial ovarian cancer. Preoperative prediction of outcome of surgery should guide treatment decisions, for example, primary debulking or neoadjuvant chemotherapy followed by interval debulking surgery. The objective of this study was to systematically review studies evaluating computed tomography imaging based models predicting the amount of residual tumor after cytoreductive surgery for advanced-stage epithelial ovarian cancer.

METHODS

We systematically searched the literature for studies investigating multivariable models that predicted the amount of residual disease after cytoreductive surgery in advanced-stage epithelial ovarian cancer using computed tomography imaging. Detected studies were scored for quality and classified as model derivation or validation studies. We summarized their performance in terms of discrimination when possible.

RESULTS

We identified 11 studies that described 13 models. The 4 models that were externally validated all had a poor discriminative capacity (sensitivity, 15%-79%; specificity, 32%-64%). The only internal validated model had an area under the receiver operating characteristic curve of 0.67. Peritoneal thickening, mesenterial and diaphragm disease, and ascites were most often used as predictors in the final models. We did not find studies that assessed the impact of prediction model on outcomes.

CONCLUSIONS

Currently, there are no external validated studies with a good predictive performance for residual disease. Studies of better quality are needed, especially studies that focus on predicting any residual disease after surgery.

A preoperative personalized risk assessment calculator for elderly ovarian cancer patients undergoing primary cytoreductive surgery

OBJECTIVE

Cytoreductive surgery for ovarian cancer has higher rates of postoperative complication than neoadjuvant chemotherapy followed by surgery. If patients at high risk of postoperative complication were identified preoperatively, primary therapy could be tailored. Our objective was to develop a predictive model to estimate the risk of major postoperative complication after primary cytoreductive surgery among elderly ovarian cancer patients.

METHODS

Patients who underwent primary surgery for ovarian cancer between 2005 and 2013 were identified from the National Surgical Quality Improvement Project. Patients were selected using primary procedure CPT codes. Major complications were defined as grade 3 or higher complications on the validated Claviden-Dindo scale. Using logistic regression, we identified demographic and clinical characteristics predictive of postoperative complication.

RESULTS

We identified 2101 ovarian cancer patients of whom 35.9% were older than 65. Among women older than 65, the rate of major postoperative complication was 16.4%. Complications were directly associated with preoperative laboratory values (serum creatinine, platelets, white blood cell count, hematocrit), ascites, white race, and smoking status, and indirectly associated with albumin. Our predictive model had an area under receiver operating characteristic curve of 0.725. In order to not deny patients necessary surgery, we chose a 50% population rate of postoperative complication which produced model sensitivity of 9.8% and specificity of 98%.

DISCUSSION

Our predictive model uses easily and routinely obtained objective preoperative factors to estimate the risk of postoperative complication among elderly ovarian cancer patients. This information can be used to assess risk, manage postoperative expectations, and make decisions regarding initial treatment.

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

OBJECTIVES

To construct a tool, using computed tomography (CT) imaging and preoperative clinical variables, to estimate successful primary cytoreduction for advanced epithelial ovarian cancer (EOC).

METHODS

Women who underwent primary cytoreductive surgery for stage IIIC/IV EOC at Mayo Clinic between 1/2/2003 and 12/30/2011 and had preoperative CT images of the abdomen and pelvis within 90days prior to their surgery available for review were included. CT images were reviewed for large-volume ascites, diffuse peritoneal thickening (DPT), omental cake, lymphadenopathy (LP), and spleen or liver involvement. Preoperative factors included age, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS), American Society of Anesthesiologists (ASA) score, albumin, CA-125, and thrombocytosis. Two prediction models were developed to estimate the probability of (i) complete and (ii) suboptimal cytoreduction (residual disease (RD) >1cm) using multivariable logistic analysis with backward and stepwise variable selection methods. Internal validation was assessed using bootstrap resampling to derive an optimism-corrected estimate of the c-index.

RESULTS

279 patients met inclusion criteria: 143 had complete cytoreduction, 26 had suboptimal cytoreduction (RD>1cm), and 110 had measurable RD ≤1cm. On multivariable analysis, age, absence of ascites, omental cake, and DPT on CT imaging independently predicted complete cytoreduction (c-index=0.748). Conversely, predictors of suboptimal cytoreduction were ECOG PS, DPT, and LP on preoperative CT imaging (c-index=0.685).

CONCLUSIONS

The generated models serve as preoperative evaluation tools that may improve counseling and selection for primary surgery, but need to be externally validated.

Nomogram for predicting incomplete cytoreduction in advanced ovarian cancer patients

OBJECTIVE

Accurately predicting cytoreducibility in advanced-ovarian cancer is needed to establish preoperative plans, consider neoadjuvant chemotherapy, and improve clinical trial protocols. We aimed to develop a positron-emission tomography/computed tomography-based nomogram for predicting incomplete cytoreduction in advanced-ovarian cancer patients.

METHODS

Between 2006 and 2012, 343 consecutive advanced-ovarian cancer patients underwent positron-emission tomography/computed tomography before primary cytoreduction: 240 and 103 patients were assigned to the model development or validation cohort, respectively. After reviewing the detailed surgical documentation, incomplete cytoreduction was defined as a remaining gross residual tumor. We evaluated each individual surgeon's surgical aggressiveness index (number of high-complex surgeries/total number of surgeries). Possible predictors, including surgical aggressiveness index and positron-emission tomography/computed tomography features, were analyzed using logistic regression modeling. A nomogram based on this model was developed and externally validated.

RESULTS

Complete cytoreduction was achieved in 120 patients (35%). Surgical aggressiveness index and five positron-emission tomography/computed tomography features were independent predictors of incomplete cytoreduction. Our nomogram predicted incomplete cytoreduction by incorporating these variables and demonstrated good predictive accuracy (concordance index = 0.881; 95% CI = 0.838-0.923). The predictive accuracy of our validation cohort was also good (concordance index = 0.881; 95% CI = 0.790-0.932) and the predicted probability was close to the actual observed outcome. Our model demonstrated good performance across surgeons with varying degrees of surgical aggressiveness.

CONCLUSION

We have developed and validated a nomogram for predicting incomplete cytoreduction in advanced-ovarian cancer patients which may help stratify patients for clinical trials, establish meticulous preoperative plans, and determine if neoadjuvant chemotherapy is warranted.

Role of minimally invasive surgery in ovarian cancer

The standard treatment of ovarian cancer includes upfront surgery with intent to accurately diagnose and stage the disease and to perform maximal cytoreduction, followed by chemotherapy in most cases. Surgical staging of ovarian cancer traditionally has included exploratory laparotomy with peritoneal washings, hysterectomy, salpingo-oophorectomy, omentectomy, multiple peritoneal biopsies, and possible pelvic and para-aortic lymphadenectomy. In the early 1990s, pioneers in laparoscopic surgery used minimally invasive techniques to treat gynecologic cancers, including laparoscopic staging of early ovarian cancer and primary and secondary cytoreduction in advanced and recurrent disease in selected cases. Since then, the role of minimally invasive surgery in gynecologic oncology has been continually expanding, and today advanced laparoscopic and robotic-assisted laparoscopic techniques are used to evaluate and treat cervical and endometrial cancer. However, the important question about the place of the minimally invasive approach in surgical treatment of ovarian cancer remains to be evaluated and answered. Overall, the potential role of minimally invasive surgery in treatment of ovarian cancer is as follows: i) laparoscopic evaluation, diagnosis, and staging of apparent early ovarian cancer; ii) laparoscopic assessment of feasibility of upfront surgical cytoreduction to no visible disease; iii) laparoscopic debulking of advanced ovarian cancer; iv) laparoscopic reassessment in patients with complete remission after primary treatment; and v) laparoscopic assessment and cytoreduction of recurrent disease. The accurate diagnosis of suspect adnexal masses, the safety and feasibility of this surgical approach in early ovarian cancer, the promise of laparoscopy as the most accurate tool for triaging patients with advanced disease for surgery vs upfront chemotherapy or neoadjuvant chemotherapy, and its potential in treatment of advanced cancer have been documented and therefore should be incorporated in the surgical methods of every gynecologic oncology unit and in the training programs in gynecologic oncology.