Diagnostic formula for the differentiation of adnexal tumors by transvaginal sonography

Unexpected malignancy at the time of hysterectomy performed for a benign indication: A retrospective review

Objective

To determine the proportion of patients undergoing hysterectomy for a benign indication who have unexpected malignancy (UM) on postoperative pathology and characterize the nature of UMs.

Methods

This was a multi-center, retrospective study of patients undergoing hysterectomy for a benign indication from July 2016 to December 2019 at 7 Ontario, Canada hospitals (4 academic, 3 community). Hysterectomies for invasive placentation, malignant, and premalignant indications were excluded. Primary outcome was rate of unexpected malignancy as defined by the number of patients with malignancy on final pathology divided by the total number of hysterectomy cases. Data was extracted from health records and electronic charts. Patient, surgical, and surgeon characteristics were compared between benign and UM groups using bivariate methods. Associations between UM status and perioperative variables were assessed using bivariate logistic regression.

Results

In the study period, 2779 hysterectomies were performed. UM incidence was 1.8% (51 malignancies/2779 cases), with one patient having two malignancies (total UMs = 52). The most common UM types were endometrial (27/52, 51.9%) and sarcoma (13/52, 25%). Patients with UM were older (57.2 ± 11.4 years vs. 52.8 ± 12.5 years, p = .015), had more previous laparotomies (2 (1.25, 2.0) vs. 1 (1.0, 1.0), p < .001), and higher BMI (29.7 ± 7.2 kg/m² vs. 28.0 ± 5.9 kg/m², p = .049) and ASA class (p < .028). Regarding surgical factors, patients with UM had more adhesions (p = .001), transfusions (p = .020), and blood loss (p = .006) compared to those with benign pathology. Patient characteristics most strongly associated with UM were age (OR 2.57, 95% CI 1.78–3.72, p < .001) and preoperative diagnosis of pelvic mass (OR 2.76, 95% CI 1.11–6.20, p = .019).

Conclusion

Incidence of UM at hysterectomy for benign indication was 1.8%. Several perioperative variables are associated with an increased chance of UM.

Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis

BACKGROUND Characterizing ovarian pathology is fundamental to optimizing management in both pre- and post-menopausal women. Inappropriate referral to oncology services can lead to unnecessary surgery or overly radical interventions compromising fertility in young women, whilst the consequences of failing to recognize cancer significantly impact on prognosis. By reflecting on recent developments of new diagnostic tests for preoperative identification of malignant disease in women with adnexal masses, we aimed to update a previous systematic review and meta-analysis. METHODS An extended search was performed in MEDLINE (PubMed) and EMBASE (OvidSp) from March 2008 to October 2013. Eligible studies provided information on diagnostic test performance of models, designed to predict ovarian cancer in a preoperative setting, that contained at least two variables. Study selection and extraction of study characteristics, types of bias, and test performance was performed independently by two reviewers. Quality was assessed using a modified version of the QUADAS assessment tool. A bivariate hierarchical random effects model was used to produce summary estimates of sensitivity and specificity with 95% confidence intervals or plot summary ROC curves for all models considered. RESULTS Our extended search identified a total of 1542 new primary articles. In total, 195 studies were eligible for qualitative data synthesis, and 96 validation studies reporting on 19 different prediction models met the predefined criteria for quantitative data synthesis. These models were tested on 26 438 adnexal masses, including 7199 (27%) malignant and 19 239 (73%) benign masses. The Risk of Malignancy Index (RMI) was the most frequently validated model. The logistic regression model LR2 with a risk cut-off of 10% and Simple Rules (SR), both developed by the International Ovarian Tumor Analysis (IOTA) study, performed better than all other included models with a pooled sensitivity and specificity, respectively, of 0.92 [95% CI 0.88-0.95] and 0.83 [95% CI 0.77-0.88] for LR2 and 0.93 [95% CI 0.89-0.95] and 0.81 [95% CI 0.76-0.85] for SR. A meta-analysis of centre-specific results stratified for menopausal status of two multicentre cohorts comparing LR2, SR and RMI-1 (using a cut-off of 200) showed a pooled sensitivity and specificity in premenopausal women for LR2 of 0.85 [95% CI 0.75-0.91] and 0.91 [95% CI 0.83-0.96] compared with 0.93 [95% CI 0.84-0.97] and 0.83 [95% CI 0.73-0.90] for SR and 0.44 [95% CI 0.28-0.62] and 0.95 [95% CI 0.90-0.97] for RMI-1. In post-menopausal women, sensitivity and specificity of LR2, SR and RMI-1 were 0.94 [95% CI 0.89-0.97] and 0.70 [95% CI 0.62-0.77], 0.93 [95% CI 0.88-0.96] and 0.76 [95% CI 0.69-0.82], and 0.79 [95% CI 0.72-0.85] and 0.90 [95% CI 0.84-0.94], respectively. CONCLUSIONS An evidence-based approach to the preoperative characterization of any adnexal mass should incorporate the use of IOTA Simple Rules or the LR2 model, particularly for women of reproductive age.

The accuracy of risk scores in predicting ovarian malignancy: a systematic review

OBJECTIVE

To perform a systematic review of the literature on the accuracy of prediction models in the preoperative assessment of adnexal masses.

DATA SOURCES

Studies were identified through the MEDLINE and EMBASE databases from inception to March 2008. The MEDLINE search was performed using the keywords ["ovarian neoplasms"[MeSH] NOT "therapeutics"[MeSH] AND "model"] and ["ovarian neoplasms"[MeSH] NOT "therapeutics"[MeSH] AND "prediction"]. The Embase search was performed using the keywords [ovary tumor AND prediction], [ovary tumor AND Mathematical model], and [ovary tumor AND statistical model].

METHODS OF STUDY SELECTION

The search detected 1,161 publications; from the cross-references, another 116 studies were identified. Language restrictions were not applied. Eligible studies contained data on the accuracy of models predicting the risk of malignancy in ovarian masses. Models were required to combine at least two parameters.

TABULATION, INTEGRATION, AND RESULTS

Two independent reviewers selected studies and extracted study characteristics, study quality, and test accuracy. There were 109 accuracy studies that met the selection criteria. Accuracy data were used to form two-by-two contingency tables of the results of the risk score compared with definitive histology. We used bivariate meta-analysis to estimate pooled sensitivities and specificities and to fit summary receiver operating characteristic curves.Studies included in our analysis reported on 83 different prediction models. The model developed by Sassone was the most evaluated prediction model. All models has acceptable sensitivity and specificity. However, the Risk of Malignancy Index I and the Risk of Malignancy Index II, which use the product of the serum CA 125 level, an ultrasound scan result, and the menopausal state, were the best predictors. When 200 was used as the cutoff level, the pooled estimate for sensitivity was 78% for a specificity of 87%.

CONCLUSION

Based on our review, the Risk of Malignancy Index should be the prediction model of choice in the preoperative assessment of the adnexal mass.

External validation of mathematical models to distinguish between benign and malignant adnexal tumors: a multicenter study by the International Ovarian Tumor Analysis Group

PURPOSE

Several scoring systems have been developed to distinguish between benign and malignant adnexal tumors. However, few of them have been externally validated in new populations. Our aim was to compare their performance on a prospectively collected large multicenter data set.

EXPERIMENTAL DESIGN

In phase I of the International Ovarian Tumor Analysis multicenter study, patients with a persistent adnexal mass were examined with transvaginal ultrasound and color Doppler imaging. More than 50 end point variables were prospectively recorded for analysis. The outcome measure was the histologic classification of excised tissue as malignant or benign. We used the International Ovarian Tumor Analysis data to test the accuracy of previously published scoring systems. Receiver operating characteristic curves were constructed to compare the performance of the models.

RESULTS

Data from 1,066 patients were included; 800 patients (75%) had benign tumors and 266 patients (25%) had malignant tumors. The morphologic scoring system used by Lerner gave an area under the receiver operating characteristic curve (AUC) of 0.68, whereas the multimodal risk of malignancy index used by Jacobs gave an AUC of 0.88. The corresponding values for logistic regression and artificial neural network models varied between 0.76 and 0.91 and between 0.87 and 0.90, respectively. Advanced kernel-based classifiers gave an AUC of up to 0.92.

CONCLUSION

The performance of the risk of malignancy index was similar to that of most logistic regression and artificial neural network models. The best result was obtained with a relevance vector machine with radial basis function kernel. Because the models were tested on a large multicenter data set, results are likely to be generally applicable.

Estimation of probability of malignancy using a logistic model combining color Doppler ultrasonography, serum CA125 level in women with a pelvic mass

The goal of this study was to develop a scoring system using combination of Doppler characterization of pelvic/ovarian lesions and serum CA125 level. Our purpose was to maximize the preoperative discrimination between benign and malignant entities. In a prospective study, a total of 101 patients were evaluated preoperatively using a standard transvaginal ultrasound and color Doppler imaging with pulse spectral analysis and serum CA125 level within a week prior to surgery. The variables that were analyzed by the multivariate logistic regression method are as follows: tumor structure, ascites, presence of septum, the peak systolic velocity (PSV), the resistance index (RI), and serum CA125 level. Of the 101 patients qualified for the study, 48 patients were diagnosed with benign (47.5%) and 53 (52.5%) with malignant tumors. Each criterion used alone provides statistically significant discrimination between benign and malignant tumors. Four criteria could be combined in a malignancy score which is calculated using the product of the serum CA125 level (1 if CA125 > or =40 U/mL and 0 if CA125 <40 U/mL), the result of sonography for presence of septum in tumor (1 if there was septum > or =3 mm, 0 if there was no septum or <3 mm), result of Doppler flow imaging as RI (1 if RI < or =0.5 and 0 if RI >0.5) and the PSV (1 if PSV > or =40 cm/s and 0 if PSV <40 cm/s). This scoring system devised was statistically more effective discriminator between cancer and benign lesions than formal methods. Using malignancy score cutoff level of two, the sensitivity was 98% (CI 88.62-99.9.), the specificity was 85% (CI 71.62-93.45), the positive predictive value was 87.5%, and the negative predictive value was 97.6%. Area under curve of receiver operative characteristic curves was 0.987 (CI 0.971-1.004). These values were statistically more significant than those obtained from the independent use of RI, PSV, or serum CA125 level at their optimum decision values (P < 0.05). There is a need for a prospective evaluation of this score using a larger sample of patients.

Diagnostic accuracy of frozen section diagnosis of the adnexal mass: a metaanalysis

BACKGROUND

Frozen section diagnosis is a diagnostic procedure for the assessment of the adnexal mass during surgery. The purpose of the present study was to perform a systematic review of the literature on the accuracy of frozen section diagnosis in the assessment of the adnexal mass.

METHODS

We performed a computerized Medline and EMBASE search to identify all registered articles published between January 1966 and June 2003, comparing frozen section diagnosis of ovarian pathology to the final histopathological diagnosis. For each study, we calculated the prevalence of malignant and borderline tumors, and the sensitivity and specificity of the frozen section diagnosis using the final histopathological diagnosis as reference. We performed the calculations in two ways. In the first analysis, tumors that were found to be borderline were considered as malignant in the 2 x 2 table. In the second analysis, tumors that were found to be borderline were considered as benign in the 2 x 2 table.

RESULTS

Eighteen studies were included for analysis. When the diagnosis borderline was classified as malignant, the sensitivity of frozen section diagnosis varied between 65% and 97%, and the specificity between 97% and 100%. When the diagnosis borderline was considered to be benign, the sensitivity varied between 71% and 100%, for a specificity varying between 98.3% and 100%.

CONCLUSION

The accuracy of frozen section diagnosis for the assessment of the ovarian mass is good, with acceptable sensitivities for almost perfect specificities. Future studies on patient preferences for the different outcomes as well as economic analysis are needed for definite position of this diagnostic technique.

Distinguishing the benign and malignant adnexal mass: an external validation of prognostic models

OBJECTIVE

Because external validation of the present models has not been reported, the purpose of the present study was to assess existing diagnostic models that are used to distinguish malignant from benign masses.

METHODS

We tested the performance of existing models in a prospectively assembled data set of 170 patients with an adnexal mass. Twenty-one models that have been reported previously were assessed. The models were based on combinations of ultrasound findings, color Doppler tests, CA-125 measurement, age, and/or menopausal status. For each model, we constructed ROC curves and calculated an area under the ROC curve.

RESULTS

Of the 170 adnexal masses that were operated on, 30 (18%) were malignant. The area under the ROC curve of 21 models that were externally validated varied between 0.69 and 0.90. We found the performance of the existing models to be inferior to the performance reported in the initial studies. Even models that incorporated multiple diagnostic tools and that were developed using logistic regression models or neural networks had an area under the ROC curve of 0.86 at maximum. In the case where we focused on almost perfect sensitivity, the highest specificities varied between 0.45 and 0.60.

CONCLUSION

Although diagnostic models might be of value in the preoperative assessment of the adnexal mass, their diagnostic performance is not as good as that reported in the original publications.

Pretreatment evaluation of adnexal tumors predicting ovarian cancer

Kobal B, Rakar S, Ribic-Pucelj M, Tomazevie T, Zaletel-Kragelj L. Pretreatment evaluation of adnexal tumors predicting ovarian cancer. The objective of this study was to determine the ability of tumor marker assessment, gray-scale transvaginal with color Doppler ultrasonography to predict ovarian malignancy. One hundred thirty-four subjects with ovarian masses who entered the study prospectively underwent pelvic examination, tumor marker assessment and gray-scale transvaginal with color flow Doppler ultrasonography preoperatively. Malignancy predictors were statistically evaluated with stepwise multiple logistic regression, and the scores from the model were transformed to probability for having a malignant disease. The presence of neovascularization, intracystic papillary projections, elevated serum CA 125, and age over 45 years were significant predictors for malignancy. Positive predictive value (PPV) for the regression model was 89.0%, and negative predictive value (NPV) was 96.8%. Probability for malignancy ranged from 0.004 to 0.991 depending on which covariates were included. Logistic regression analysis of pretreatment diagnostic gray-scale and color Doppler ultrasonographic characteristics, together with CA 125 enabled a creation of probability assessment scale for individual estimation of ovarian mass, which may contribute to final clinical decision.