Ultrasound and Assessment of Ovarian Cancer Risk

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1 Women s Imaging Review Twickler and Moschos Ultrasound of Ovarian ancer Women s Imaging Review FOUS ON: Diane M. Twickler 1,2 Elysia Moschos 2 Twickler DM, Moschos E Keywords: malignancy, ovarian cancer, ovary, risk prediction, ultrasound DOI: /JR Department of Radiology, University of Texas Southwestern Medical enter, 5323 Harry Hines lvd., Dallas, TX ddress correspondence to D. M. Twickler (diane.twickler@utsouthwestern.edu). 2 Department of Obstetrics and Gynecology, University of Texas Southwestern Medical enter, Dallas, TX. JR 2010; 194: X/10/ merican Roentgen Ray Society WOMEN S IMGING Received ugust 31, 2009; accepted after revision November 17, Ultrasound and ssessment of Ovarian ancer Risk OJETIVE. The purpose of this article is to review the ultrasound characteristics of ovarian and adnexal masses and to discuss the prediction of the likelihood of ovarian cancer based on these characteristics and clinical parameters. ONLUSION. Ultrasound characteristics can be used to diagnose the classic-appearing nonneoplastic entities, benign neoplasms and malignancies. In cases in which the appearance of an ovarian mass is not classic, assignment of relative risk of malignancy using a multiparametric model is appropriate and beneficial for patient management. T he early detection of ovarian carcinoma continues to be a formidable challenge and an elusive task. The risk of a woman developing ovarian cancer is 1 in 71 [1]. ge is a major factor in determining the likelihood of cancer, with age-adjusted rates increasing as age advances [1]. Multiparity and early age at first birth lower the risk, and personal or family histories of breast or ovarian cancer increase the risk [2 4]. Women carrying R1 or R2 genes are at a much higher risk for developing ovarian cancer [5, 6]. The poor prognosis of ovarian cancer is associated with the advanced stages of the disease at the time of diagnosis [7 9]. Since the advent of ultrasound evaluation of the female pelvis, the characteristics of the normal and abnormal ovary have been extensively studied. The most common scenario for this evaluation is in the setting of a clinically suspected pelvic mass, but studies have also investigated the role of ultrasound as part of screening protocols for detection of ovarian cancer [9 12]. The introduction of transvaginal ultrasound has improved visualization of normal ovarian function and ovarian tumors, and much work has been done to define and standardize ovarian tumor characteristics [3, 13 16]. The use of Doppler analysis for the purposes of colorflow mapping and characterization of waveforms has been used to evaluate neovascularity of ovarian neoplasms, often combined with other ultrasound markers [10, 12, 17 20]. n important goal of ovarian evaluation by ultrasound is to determine the differences between normal physiologic findings, inflammatory changes, benign neoplastic processes, and ovarian cancer. Tumor markers, such as -125, have been used to assign a relative risk of malignancy in certain clinical scenarios, but recent articles in the literature suggest the superiority of an ultrasound pattern-based recognition over serum -125 for discrimination between benign and malignant neoplasms [6, 7, 21]. Multiple studies have explored the use of ultrasound screening in populations of women with varying degrees of risk for ovarian cancer in an effort to improve outcomes in women with an early diagnosis of ovarian cancer [22 27]. Is it possible to use a relative risk model to predict the likelihood of ovarian cancer based on ultrasound characteristics and clinical parameters? Ultrasound haracteristics of Ovarian and dnexal Masses Size The large size of an ovarian mass, with the other characteristics being equal, has been found to be a significant factor in predicting ovarian cancer. n early study in postmenopausal women found that tumors exceeding 10 cm were significantly more likely to be associated with malignancy [28]. This finding has been confirmed in several other studies; when single or multiple measurements were performed separately or as part of a multiparametric analysis, larger masses were significantly 322 JR:194, February 2010

2 Ultrasound of Ovarian ancer associated with an increased likelihood of ovarian cancer [15, 16]. Morphologic haracteristics n extensive number of ultrasound studies of ovarian neoplasms promote establishing pattern recognition of the ultrasound features to predict tumor morphology, as classically defined in the Sassone et al. [13] scale and later refined by the International Ovarian Tumor nalysis (IOT) Group [16] among others. Such sonographic features include the cystic and solid tumor compositions as well as the presence and type of septations and papillations. n important goal of the analysis of ovarian and adnexal masses is an attempt to identify nonneoplastic entities, such as functional cysts, tubal and inflammatory diseases, or endometriosis (Figs. 1 and 2). These nonneoplastic entities are usually smaller in size and may display classic ultrasound appearances that are referred to as pathognomonic (13 16). However, each of these entities can have appearances that mimic neoplastic processes as well. If the mass is thought to be neoplastic, one may consider whether it has the classic appearance of the most common benign neoplasm of the ovary, the dermoid tumor. dermoid, or teratoma, has several classic appearances (Fig. 3). Unless their unique characteristics and classic types of ultrasound morphology are appreciated, ovarian dermoids may be misclassified as tumors suspicious for malignancy, such as the with Sassone et al. scale, because of their solid and echogenic appearances [13 16]. Fig. 1 lassic appearances of functional cysts., Simple 20-mm cyst of ovary (calipers) on menstrual day 13 in 26-year-old woman, consistent with dominant follicle., lassic appearance of network of septations in hemorrhagic cyst and peripheral vasculature in 17-year-old woman., lassic appearance of clot retraction within small hemorrhagic cyst (calipers) during luteal phase in 19-year-old woman. If the ovarian or adnexal mass does not fit any of these classic descriptions, further qualification and quantification of the tumor is warranted (Table 1). Many authors have designed quantitative scales or qualitative pattern recognition algorithms that facilitate categorization of a mass on a spectrum of worsening appearance, from simple cyst to a cyst with septations, loculations, then papillations, and ultimately varying degrees of predominantly solid (nondermoid lesions) [2, 3, 12 16]. Most recently, members of the IOT Group [29] have used this qualitative pattern recognition scale, along with other ultrasound and clinical characteristics, to differentiate between benign and malignant tumors in specific subgroups, very similar to the original Sassone et al. [13] data. Doppler Evaluation Doppler examination was once thought to be the key in distinguishing between benign and malignant masses because the vascular characteristics within a malignant neoplasm often differ from those of a benign neoplasm (Table 1). Malignant lesions usually produce a significant increase in color Doppler flow signals secondary to angiogenesis. The color content of the tumor probably reflects tumor vascularity better than any other Doppler parameter (Fig. 4). The overall impression of tumor vascularity reflects both the number and size of tumor vessels and their functional capacity. The IOT Group has suggested the use of such a subjective semiquantitative assessment of flow to describe the vascular features of ovarian masses [16]. color score is used to describe the amount of blood flow for the tumor as a whole: color score 1, no detectable blood flow; score 2, minimal flow; score 3, moderate flow; and score 4, highly vascular. Malignancies often exhibit their increased flow signals not only at the periphery of the mass, as seen with benign lesions, but also in the central regions of the mass, including within septations and solid tumor areas. The neovascularity within malignancies is made up of abnormal vessels, lacking smooth muscle within their walls and containing multiple arteriovenous shunts, resulting in low-impedance flow (pulsatility index < 1.0) and (resistance index < 0.4), high time-averaged maximum velocity (> 15 cm/s), and absence of a diastolic notch in such masses [10, 11]. These observations have led many investigators to evaluate the presence, spatial distribution, and prevalence of flow signals with ovarian masses to differentiate benign from malignant lesions [9 12, 17]. However, because of the overlap of vascular parameters between malignant and benign neoplasms, a firm differential diagnosis based on spectral Doppler evaluation alone is not possible [30]. Review of Multiparametric Ultrasound and linical nalyses Preoperative classification of an ovarian mass as benign or malignant is imperative for appropriate patient triage, referral, and management. lthough it may not determine whether or not to perform surgery, malignancy risk prediction may assist in decisions regarding surgical approach (laparoscopy or laparotomy) and the degree of involvement by the gynecologic oncologists. Thus, many investigators have used myriad sonographic JR:194, February

3 Twickler and Moschos Fig. 2 lassic appearances of adnexal masses., Hydrosalpinx with classic tubular appearance in 22-year-old woman., Pyosalpinx with complex fluid in rounded tube and paratubal vessels in 25-year-old woman., ogwheel appearance of thickened folds of fallopian tubes in chronic salpingitis in 32-year-old woman. D, Homogeneous echoes in endometrioma with associated acoustic enhancement of classic chocolate cyst in 29-year-old woman. variables in an attempt to predict malignancy (Fig. 5). In a landmark study, Granberg et al. [31] reported that the gross morphology of adnexal masses could be used to predict the likelihood of malignancy. They also established that ultrasound images of tumors predicted their gross morphology, and therefore they concluded that it should be possible to estimate the risk of malignancy on the basis of ultrasound morphology. Subsequently, Sassone et al. [13] devised a scale for such morphologic ovarian characteristics, including inner wall structure, wall thickness, the presence of septa, and echogenicity of the mass, and were able to distinguish benign from malignant masses with specificity of 83%, sensitivity of 100%, and positive and negative predictive values of 37% and 100%, respectively. This concept of pattern recognition has since been repeatedly confirmed, establishing that subjective assessment of gray-scale and Doppler ultrasound features by an experienced sonologist is an excellent method for discriminating between benign and malignant pelvic masses [8, 19, 20]. In fact, in a large multicenter study, pattern recognition was superior to serum -125 in the diagnosis of benign and malignant ad nexal masses [21]. More recently, building on the concept of pattern recognition, scoring systems were developed to more accurately discriminate between benign and malignant neoplasms [7, 14, 15]. In 1999, Twickler et al. [15] incorporated the patient s age, ovarian volume, Doppler velocimetry and vessel location, and echogenic predominance of the mass (suggestive of a dermoid) with the morphology scale of Sassone et al. [13] to compute the ovarian tumor index, a calculated probability of malignancy based on the weighting of each of the listed parameters. The ovarian tumor index was found to be discriminating for predicting ovarian malignancy in the clinical scenario of a suspected adnexal mass, with a receiver operating characteristic (RO) of Website is available to calculate the cancer risk at www4.utsouthwestern.edu/oti, and an example of an epithelial ovarian cancer case submission is shown in ppendix 1. The major weakness of these scoring systems was that they were each developed in their respective institutional centers, and when they were externally validated in a new population, they did not perform as well. Then in 1999, a prospective, multicenter study was begun that included nine centers from five European countries. The purpose of this IOT study was to minimize the limitations of previous research by prospectively collecting the demographic and sonographic data of more than 1,000 patients with persistent adnexal masses and by following a standardized protocol of terms, definitions, and qualitative and quantitative end points to describe the ultrasound features of adnexal tumors [16]. From this data, a mathematic model was developed to calculate the risk of malignancy in an adnexal mass, with an area under the RO of 0.96 [32]. Now there are myriad scoring systems [3], logistic regression models [33 35], neural networks [36, 37], and relevance vector machines [38] to aid in the preoperative diagnosis of an D 324 JR:194, February 2010

4 Ultrasound of Ovarian ancer D TLE 1: Ultrasound and linical Variables for Ovarian ancer From Selected Studies Variable Sassone et al. [13],1991 Fleischer et al. [9, 10], 1991, 1993 Study E Fig. 3 lassic appearances of dermoid tumors., Hyperechoic mass with acoustic attenuation in 23-year-old woman., Hyperechoic fat fluid level (calipers) in 34-yearold woman., Hyperechoic Rokitansky nodule with acoustic attenuation nodule (calipers) in large dermoid in 28-year-old woman. D, Hyperechoic Rokitansky nodule with acoustic attenuation and fat fluid level in 34-year-old woman. E, Hyperechoic lines and punctuate dots representing hair in dermoid tumor (calipers) in 21-year-old woman. Kurjak and Predanic [12],1992 Twickler et al. [15],1999 IOT Group [16, 29], 2000, 2009 Morphology Wall thickness Wall thickness Unilocular Septa Septa Irregular cyst walls, multilocular Papillaries Papillaries Papillation Echogenicity Echogenicity Solid scites Intense echo of dermoid coustic shadows Doppler imaging Pulsatility index, lowest Resistance index Pulsatility index olor score of intratumoral blood flow Vessel location Vessel location Vessel location lood flow in solid papillary projection Notch Size Volume Maximum diameter of lesion Maximum diameter of solid component ge ge ge linical findings Menstrual cycle stage History of ovarian cancer Hormonal therapy Pain JR:194, February

5 Twickler and Moschos D D G E E Fig. 4 Variables of Doppler analysis in ovarian neoplasms., Spectral Doppler waveform of low impedance flow in 57-year-old woman., Spectral Doppler waveform in peripheral vessel with high impedance and diastolic notch in 27-yearold woman., olor Doppler image of vascularity in solid papillary projection in 49-year-old woman. D, Very strong intratumoral blood flow on color Doppler image in 54-year-old woman. E, Septal blood flow on color Doppler image in 38-year-old woman. Fig. 5 Variables of tumor characteristics. and, Very large, complex solid cystic mass (calipers) in 48-year-old woman obtained in sagittal () and transverse () planes, with large calculated volume and maximal diameter., Measurement of thickened septa greater than 3 mm (calipers) in 58-year-old woman. D, Large predominantly cystic mass with single large papillation in 50-year-old woman. E, Solid tumor with cursor measurements in 47-year-old woman. F, ystic mass with internal echoes and multiple papillaries in 36-year-old woman. G, scites manifesting as fluid in posterior cul-de-sac and surrounding uterus in 63-year-old woman. F 326 JR:194, February 2010

6 Ultrasound of Ovarian ancer TLE 2: Selected Historical omparisons of Ultrasound and linical riteria for ancer Prediction Study No. of Patients haracteristics ccuracy Sassone et al. [13] 143 Gray-scale characteristics score Sensitivity, 100%; specificity 83%; PPV, 37%; NPV, 100% Fleischer et al. [9] 43 Pulsatility index Pulsatility index; sensitivity, 100%; specificity, 82%; PPV, 73%; NPV, 100% Fleischer et al. [10] 50 olor and spectral Doppler characteristics Other variables significant OTI [15] 244 ombined gray-scale, Doppler characteristics, and age UKTOS [27] 50,640 Multitechnique screening (ultrasound and serum -125) IOT [16] 1,573 ombined gray-scale and color mapping, age, and other clinical variables adnexal mass. In 2007, the IOT group tested the accuracy of these previously published various mathematic models and RO curves were constructed to compare the performance of the models [39]. They found that simple morphologic scoring systems performed the least well overall, whereas multitechnique risk of malignancy index models performed better and similar to that of most logistic regression and artificial neural network models. The most accurate results were obtained with a relevance vector machine model, with use of these complex mathematic models resulting in the correct diagnosis of a significant number of additional malignancies. Research is also ongoing with regard to the use of ultrasound in ovarian cancer screening. In pril 2009, results of the prevalence screen of the United Kingdom ollaborative Trial of Ovarian ancer Screening were published [27]. This study, the largest randomized controlled trial of ovarian cancer to date, randomly assigned more than 200,000 postmenopausal women to one of three screening arms: no screening (control group because this is the current standard of care), ultrasound screening only, and annual multitechnique screening with transvaginal ultrasound and a serum -125 assay. oth screening techniques performed well. The annual multitechnique screening strategy had a significantly better specificity (99.8%) than did the ultrasound screening only strategy (98.2%), resulting in fewer repeat tests and less surgery. The sensitivity for the detection of primary epithelial cancers of the ovaries and fallopian tubes was better with the annual multitechnique screening (89.4%) than with the ultrasound screening only (84.9%) method, but the difference was not statistically significant. Overdiagnosis of borderline ovarian cancers was more of a problem using the ultrasound only method than with the multitechnique method. The results of this study illustrate that both a based and ultrasound-based screening strategy are feasible on a large scale. However, because the data on the mortality rates in the screening groups and the control group are not yet available, conclusions about the effects of such ovarian cancer screening cannot be drawn. summary of the findings is reviewed in Table 2. The important considerations of these models to date include size, appearance, color-flow characteristics, and appreciation of some of the classic appearances of benign findings such as dermoid tumors. Weighted multiparametric ultrasound analysis and clinical information are used as summarized in Table 1. relative risk for cancer may be based on the percentage likelihood, or a discriminatory zone from the created RO may be generated. cutoff number can be assigned based on the desired outcome, with the knowledge that a high sensitivity will result in a high false-positive rate. In the case of the ovarian tumor index, an arbitrary cutoff from the receiver operator curve of greater than 35 and less than 40 results in a 6% likelihood of cancer, with sensitivity, specificity, positive predictive value, and negative predictive value of 96%, 66%, 35%, and 99%, respectively [15]. This cutoff can be arbitrarily changed if specificity is desired over sensitivity. It seems reasonable to conclude in this indeterminate cancer risk group that an assignment of relative U = 0.91 with relative cancer risk assignment (0 99%); ovarian tumor index of > 35 40: 8% cancer likelihood; sensitivity, 96%; specificity, 66%; PPV, 29%; NPV, 99% and ovarian tumor index of > 60 65: 33% cancer likelihood; sensitivity, 74%; specificity, 95%; PPV, 69%; NPV, 96% Sensitivity, 89%; specificity, 99%; PPV, 35%; NPV, 99% U = ; pattern recognition: sensitivity, 90%; specificity, 88% and logistic regression: sensitivity, 95%; specificity, 74% and subgroup category: sensitivity, 88%, specificity 90% Note OTI = ovarian tumor index, UKTOS = United Kingdom ollaborative Trial of Ovarian ancer Screening, IOT = International Ovarian Tumor nalysis Group, PPV = positive predictive value, NPV = negative predictive value, U = area under receiver operating characteristic curve. risk would be preferred over an imperfect discriminatory zone. onclusion Ultrasound characteristics can be used to categorize ovarian and adnexal masses, and pattern recognition can accurately diagnose some of the classic-appearing nonneoplastic entities, benign neoplasms, and malignancies. Often, however, the sonographic appearance of an ovarian mass is not pathognomonic. It is in these indeterminate cases that an assignment of a relative risk of malignancy is beneficial for patient care. Features that have been found to contribute to malignancy risk include clinical issues such as age and cancer history, morphology and size of the mass, and Doppler parameters. Thus, a multiparametric model for risk assessment is appropriate and more accurate in distinguishing between benign and malignant ovarian masses; however, the optimal model has yet to be developed. The ultimate approach to prospectively predicting ovarian malignancy by ultrasound should include a universal consensus of the clinical and sonographic risk parameters among radiologists and gynecologists and gynecologic oncologists with a multiparametric model that has an organized, coordinated template that is generally used, easily applied, and offers clear interpretations of relative risk. t this juncture, the IOT ultrasound and clinical multiparametric analyses and the subgroup analysis are most recent, with the best prediction of malignancy in the largest series to date, and combine the best predictors of previous studies (Table 1) with age and clinical variables. n ultrasound data JR:194, February

7 Twickler and Moschos entry system of quantifiable variables, similar to or consisting of the IOT data, that assigns a relative malignancy risk with the ease of Web-based data entry, similar to the ovarian tumor index of ppendix 1, would seem to be a desired goal. The ability to track outcomes within this data set would allow ongoing evaluation and identification of ultrasound and clinical parameters in the future. References 1. Horner MJ, Ries LG, Krapcho M, et al. SEER cancer statistics review, , National ancer Institute. SEER Website. seer.cancer.gov/ csr/1975_2006. ased on November 2008 SEER data submission. Published May 29, ccessed December 3, Timor-Trish IE, Lerner JP, Monteagudo, Santos R. Transvaginal ultrasonographic characterization of ovarian masses by means of color-flow-directed Doppler measurements and a morphologic scoring system. m J Obstet Gynecol 1993; 168: Lerner JP, Timor-Trish LE, Federman, bramovich G. 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8 Ultrasound of Ovarian ancer 2007; 29: ampbell S. Sonographic prediction of malignancy in adnexal masses using an artificial neural 36. Timmerman D, Verrelst H, ourne TH, et al. rtificial neural network models for the preoperative network. r J Obstet Gynaecol 1999; 106:21 30 discrimination between malignant and benign adnexal masses. Ultrasound Obstet Gynecol 1999; Vergote I, Timmerman D. Preoperative prediction 38. Lu, Van Gestel T, Suykens JK, Van Huffel S, 13:17 25 of malignancy of ovarian tumor using least 37. Tailor, Jurkovic D, ourne TH, ollins WP, squares support vector machines. rtif Intell Med PPENDIX 1: Ovarian Tumor Index [15] 2003; 28: Van Holsbeke, Van alster, Valentin L, et al. External validation of mathematical models to distinguish between benign and malignant adnexal tumors: a multicenter study by the International Ovarian Tumor nalysis group. lin ancer Res 2007; 13: Note Example of case of epithelial cancer parameters using retrospectively derived data from authors patient population. alculated volume = 166.8, calculated morphology scale = 12, pulsatility index =.75, calculated ovarian tumor index = 74, and calculated malignancy probability = 52.2%. JR:194, February