Comparison of SASSONE Scoring and ADNEX Model in Differentiating Benign and Malignant Ovarian Neoplasm in a University Hospital

INTRODUCTION

Ovarian Cancer is the second most common gynecologic cancer worldwide and the sixth most common cancer in women (3) with approximately 255,000 new cases diagnosed each year (5). It has the highest mortality rate among gynecologic cancers with a general survival rate of less than 50% (8). Most cases are seen in pre-menopausal and post-menopausal women who often remain asymptomatic in their early phase of the disease because of the anatomic location of the ovaries, deep in the pelvis. Thus, most cases are diagnosed at an advanced stage when prognosis is very poor already (4). The presenting signs and symptoms are also vague like bloating, pelvic or abdominal pain, poor appetite and urinary urgency which may be confused with other gastrointestinal and urologic diseases that can present similarly hence the late consult, diagnosis and management.

Ovarian cancer is a heterogeneous disease and composed of different types of tumors derived from various cell lines with diverse behaviors and clinicopathologic characteristics (4). There are three main types which are responsible for almost all malignant tumors: surface epithelial-stromal tumors (90-95%), sex cord-stromal tumors (5-10%) and germ cell tumors (5-10%) (4).

At present, there is no universal protocol for differentiating benign and malignant ovarian masses. Several studies have attempted to use imaging, cytology and tumor markers but no standard method was accepted for pre-operative screening of ovarian cancer (10). 

Ultrasound is the most practical modality for assessment of ovarian tumors. It is non-invasive, readily available, cost-effective and can provide a detailed information in evaluating the characteristic and malignant potential of an ovarian mass. It has an 82% sensitivity and specificity in identifying benign and malignant tumors (8).

Ultrasound correlates the images morphologically with macroscopic pathologic features of tumors such as solid component, thick septations, multiple loculations, and papillary projections (8). Inter-observer difference and extreme variability of macroscopic characteristics of ovarian tumor make an accurate diagnosis difficult by ultrasound alone. Therefore, to offset these limitations, use of ultrasound scoring system was encouraged (8). To aid in the diagnosis as well as to differentiate between benign and malignant ovarian masses. Some of these scoring systems are the SASSONE Scoring and the ADNEX Model where the latter, aside from sonologic findings will be derived from clinical predictors such as age, serum CA-125 level and type of hospital where gynecologic oncology referral is available.

SASSONE Scoring, devised by AM Sassone, is a scoring system that uses traditional gray scale ultrasound to characterize ovarian lesion and composed of four variables such as inner wall structure, wall thickness, septum and echogenicity. Each variable has a corresponding value and a total score of > 9 suggest malignancy.

Assessment of Different NEoplasias in the AdneXa (ADNEX) Model is a scoring program generated by the International Ovarian Tumor Analysis (IOTA)  group. It can be downloaded through the internet (www. iotagroup.org) as a computer or mobile phone application. It contains three clinical and six ultrasound predictors: age, serum CA-125 level, type of center (oncology center vs other hospitals), maximum diameter of lesion, proportion of solid tissue, more than 10 cysts locules, number of papillary projections, acoustic shadows and ascites. Once all parameters are assessed, the application will compute for chances of having a benign tumor, risk of malignancy, risk of metastatic cancer to the adnexa, risk stage II-IV ovarian cancer, risk stage I ovarian cancer, risk of having a borderline tumor.  It is the first risk model created that can differentiate between benign and four subgroups of malignant adnexal tumors (8). Information of the specific type of adnexal pathology pre-operatively has better patient triage and makes it feasible to optimize treatment (2).

The objective of this study is to determine the diagnostic accuracy of the SASSONE scoring and the ADNEX model in terms of their sensitivity, specificity, positive and negative likelihood ratios, positive and negative predictive values in differentiating benign and malignant ovarian neoplasm.

METHODS

This prospective cohort study included all women with a consideration of an ovarian neoplasm by history and physical examination seen at the University of Santo Tomas Hospital, from January to October 2017.

The study was approved by the Institutional Review Board. Exclusion criteria included those patients with previous history of ovarian malignancy. A minimum of 68 subjects was required for this study based on a level of significance of 5%, a prevalence of 32.14%, sensitivity of 94% with a half-width of the confidence interval of 0.10. The values for the prevalence of malignant ovarian mass and sensitivity were based from the study by Shende et al., 2016 (1). A written informed consent was obtained from all subjects.

Each patient underwent ultrasound to characterize the ovarian neoplasm:  (transvaginal ultrasound for subjects with previous sexual contact, transrectal ultrasound for patients with no history of sexual contact, transabdominal ultrasound if applicable for patients with huge ovarian neoplasm). SASSONE Scoring (Table 1) and ADNEX model (Table 2) were applied and computed based on the sonographic descriptions. However, serum CA-125 was excluded as part of the parameters of ADNEX Model due to the expenses it entails to the investigator and subjects. This parameter will only decrease the distinction between stage II-IV invasive tumors but the application could still differentiate the ovarian mass as benign or malignant. Sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of the SASSONE Scoring and the ADNEX Model in discriminating benign and malignant neoplasm were computed.

The gold standard for the diagnosis was the histopathologic examination of the specimen obtained from laparotomy of the adnexal mass. Borderline tumors were categorized as malignant.

Descriptive statistics were used to summarize the general and clinical characteristics of the  subjects. Frequency and proportion were used for nominal variables, median and range for ordinal variables, and mean and standard deviation for interval/ratio variables.

All valid data was included in the analysis. Missing variables were neither replaced nor estimated. Null hypothesis was rejected at 0.05 α-level of significance. STATA 15.0 was used for data analysis.

RESULTS

The study included 68  women with ovarian neoplasms. The mean age of the women was 38.06 ± 12.57 years and their BMI was 25 kg/m2 (Table 3). Majority were nulligravid (52%) and nulliparous (57%), with only 19% using contraceptives (OCP/injectables). The three most common presenting complaints were abdominal pain (41%), irregular menses (23%), and abdominal mass (14%).

Comparing those who were benign versus malignant on histopathology, we found that the benign group was significantly younger (36.8 versus 47.5 years, p = 0.023), and consequently had a higher proportion of menstruating patients (83% versus 37.5%, p = 0.003) (Table 4).

The study assessed the diagnostic accuracy of SASSONE score in relation to the histopathologic results (Table 7) and found it to have an overall accuracy of 88% (95% CI 78.1-94.8%). It has a high negative predictive value: that is, patients who are classified as benign by the SASSONE score (less than 9) has a 94.82% probability of having benign histopathology. Similarly, it was highly specific: where among those who have truly benign histopathologic results, there is a 91.67% probability that their test will turn benign by SASSONE as well. Sensitivity and positive predictive values were equivocal, with point estimates and confidence intervals approaching 50%. Patients who were malignant by SASSONE is 7.5 times more likely to have a malignant neoplasm on histopathology as compared to patients with benign histopathology (Table 5).

In comparison, the ADNEX model (Table 7) has a slightly higher overall accuracy compared to SASSONE score at 89% (95% CI 79.9-95.8%). It also has a higher specificity of 96% (95% CI 88.5-99.6%), albeit a lower sensitivity at 37% (95% CI 8.52-75.51%). Similar to SASSONE, ADNEX had good specificity and NPV. It had high negative predictive value: that is, patients who are classified as benign by the ADNEX score has a 92.06% probability of having benign histopathology. It was also highly specific: where among those who have truly benign histopathologic results, there is a 96.67% probability that their test will turn benign by ADNEX as well. Sensitivity and positive predictive values were non-conclusive, with point estimates and confidence intervals approaching 50%. Patients who were malignant by ADNEX were 11.25 times more likely to have a malignant neoplasm on histopathology compared to patients with benign histopathology (Table 6).

Malignant cases in the study by histopathology were compared using SASSONE Scoring and ADNEX model. SASSONE Scoring was more sensitive in screening of ovarian lesions but ADNEX Model was more specific as to what type of ovarian malignancy (Table 8).

DISCUSSION

Ovarian neoplasm represents a common problem in clinical practice. About 10% of women will undergo exploratory laparotomy for evaluation of ovarian tumors during their lifetime10. Early identification of ovarian malignancies and referral to gynecologic oncologist can improve patient’s prognosis.

This study compared the accuracy of SASSONE scoring and ADNEX model in differentiating benign and malignant neoplasm.  Since there is no standard protocol yet for screening of ovarian malignancies. Histopathologic diagnosis of the ovarian mass during surgery remains to be the gold standard.

Our analysis showed that there is no significant difference in distinguishing benign from malignant neoplasm sonographically, between Sassone Scoring and ADNEX Model, their accuracy rate are 88% and 89% respectively.

Sassone Scoring had a 91.67 % specificity and 62.5% sensitivity. This scoring system takes into consideration the inner wall structure, wall thickness, septa and echogenicity of the mass.  It is a more useful tool in screening ovarian lesions due to its high sensitivity. Study done by Vikram Shende et al. which concluded that the sonographic scoring system has a 94% specificity and 88% sensitivity rate1. They had a 88% negative predictive value and 94% positive predictive value in contrast with our findings of negative and positive predictive value of 94.83 % and 50% respectively. The difference was probably due to our small number of malignant cases as compared to benign by histopathology.

The ADNEX Model is the first risk assessing ultrasound scoring system. It distinguishes between benign and four subgroups of malignant adnexal tumors. It consists of three clinical predictors  (age, type of center where patient has been referred for ultrasound examination and serum CA-125) and six ultrasound predictors (maximal diameter of the lesion, maximal diameter of the largest solid part, presence of more than 10 locules, number of papillations, presence of acoustic shadow, presence of ascites). Aside from discriminating between benign and malignant neoplasm, it can detect whether the malignancy is primary or metastatic. Such information of the type of adnexal pathology before surgery can improve patient triage and maximize treatment options8. In turn, this may result in reduction of morbidity and lead to enhanced survival from the various types of ovarian malignancy.

The ADNEX model can differentiate well between benign tumors, stage I cancers and advanced stages, advanced primary cancer and secondary metastatic cancers (8). In our study, it has a 96.67% specificity and 37.5% sensitivity.  In contrast, the study done by Ben Van Calster showed that ADNEX model has 71.3% specificity and 96.5% sensitivity (8). It has a 60% positive predictive value and 92.06% negative predictive value similar to that of SASSONE scoring.

The type of referral center is one predictor of the ADNEX model based on the perception that patients with masses that look suspicious are more frequently referred to a specialized center for assessment and treatment such as UST Hospital. Based on the study by Van Calster that malignancy rates are higher in specialized center with 22-66% rates as compared to other centers with 0-30% rates (8). 

Serum CA-125 is also an important factor for discrimination between stage II-IV and stage I and secondary metastatic cancer. However in our study, it was excluded as part of the parameters. Its utilization will decrease the distinction between stage II-IV invasive tumors but the ADNEX application still differentiated the ovarian masses as benign, borderline and malignant. Though the model seems complicated, it gives a more detailed differentiation of the type of ovarian neoplasm. In our study, the ADNEX model has poor sensitivity that decreases its utility for screening but its high specificity makes it a better tool for predicting ovarian malignancy and better planning options for management.

CONCLUSION

In conclusion, our study showed no significant difference in using SASSONE and ADNEX model to predict benign from malignant ovarian neoplasm. Both may be used as an ultrasound scoring system to distinguish pre-operatively between benign and malignant ovarian masses.  However, in cases of suspicious tumors, ADNEX model is more useful in discriminating the type and stage of malignancy. Knowing the type and stage of ovarian malignancy is useful to both the clinicians and the patient. For the clinicians, early referral of the patient to a gynecologic oncologist aids in early proper staging and intervention. Consequently, this leads to avoidance of unnecessary cost and morbidity on the part of the patient.