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

Current Preoperative Management of Vulvar Squamous Cell Carcinoma: An Overview

Vulvar carcinoma is a rare cancer affecting the genital tract, constituting 4% of gynecological tumors. Vulvar squamous cell carcinoma (VSCC) is the most common type. Diagnosis relies on biopsy during vulvoscopy, plus imaging such as ultrasonography (USG), magnetic resonance imaging (MRI) and positron emission tomography (PET). This review aims to lay out a thorough overview as to the current preoperative management of VSCC, both in case of vulvar and lymph node involvement. The data research was conducted using the following databases: MEDLINE, EMBASE, Web of Sciences, Scopus, ClinicalTrial.gov, OVID and Cochrane Library from 2010 to 2024. The selection criteria included only original articles. Seventeen studies were assessed for eligibility. A concordance rate of 62.3% for vHSIL and 65.2% for carcinoma at vulvoscopy, with a sensitivity of 98%, specificity of 40%, PPV (Positive Predictive Value) of 37% and NPV (Negative Predictive Value) of 98% in identifying malignant lesions was found. Regarding the reliability of PET for staging and assessing lymph node involvement, a mean SUV (Standardized Uptake Value) for malignant vulvar lesions of 8.4 (range 2.5-14.7) was reported. In the case of MRI, useful for the evaluation of loco-regional infiltration and lymph node involvement, the ratio of the short-to-long-axis diameter and the reader's diagnostic confidence for the presence of lymph node metastasis yielded accuracy of 84.8% and 86.9%, sensitivity of 86.7% and 87.5%, specificity of 81.3% and 86.2%, PPV of 89.7% and 87.5% and NPV of 76.5% and 86.2%, respectively. A long lymph node axis >10 mm and a short diameter >5.8 mm were found to be predictors of malignancy. At USG, instead, the two main characteristics of potentially malignant lymph nodes are cortical thickness and short axis length; the combination of these ultrasound parameters yielded the highest accuracy in distinguishing between negative and positive lymph nodes. Despite the heterogeneity of the included studies and the lack of randomized clinical trials, this review provides a broad overview of the three imaging tools used for the presurgical management of VSCC. Nowadays, although MRI and PET represent the gold standard, ultrasound evaluation is taking on a growing role, as long as it is carried out by expert sonographer. The management of this rare disease should be always performed by a multidisciplinary team in order to precisely stage the tumor and determine the most suitable treatment approach.

Applying Artificial Intelligence to Gynecologic Oncology: A Review

Importance

Artificial intelligence (AI) will play an increasing role in health care. In gynecologic oncology, it can advance tailored screening, precision surgery, and personalized targeted therapies.

Objective

The aim of this study was to review the role of AI in gynecologic oncology.

Evidence Acquisition

Artificial intelligence publications in gynecologic oncology were identified by searching "gynecologic oncology AND artificial intelligence" in the PubMed database. A review of the literature was performed on the history of AI, its fundamentals, and current applications as related to diagnosis and treatment of cervical, uterine, and ovarian cancers.

Results

A PubMed literature search since the year 2000 showed a significant increase in oncology publications related to AI and oncology. Early studies focused on using AI to interrogate electronic health records in order to improve clinical outcome and facilitate clinical research. In cervical cancer, AI algorithms can enhance image analysis of cytology and visual inspection with acetic acid or colposcopy. In uterine cancers, AI can improve the diagnostic accuracies of radiologic imaging and predictive/prognostic capabilities of clinicopathologic characteristics. Artificial intelligence has also been used to better detect early-stage ovarian cancer and predict surgical outcomes and treatment response.

Conclusions and Relevance

Artificial intelligence has been shown to enhance diagnosis, refine clinical decision making, and advance personalized therapies in gynecologic cancers. The rapid adoption of AI in gynecologic oncology will depend on overcoming the challenges related to data transparency, quality, and interpretation. Artificial intelligence is rapidly transforming health care. However, many physicians are unaware that this technology is being used in their practices and could benefit from a better understanding of the statistics and computer science behind these algorithms. This review provides a summary of AI, its applicability, and its limitations in gynecologic oncology.

Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis

OBJECTIVES

The aim of this study was to compare the accuracy of seven classical Machine Learning (ML) models trained with ultrasound (US) soft markers to raise suspicion of endometriotic bowel involvement.

MATERIALS AND METHODS

Input data to the models was retrieved from a database of a previously published study on bowel endometriosis performed on 333 patients. The following models have been tested: k-nearest neighbors algorithm (k-NN), Naive Bayes, Neural Networks (NNET-neuralnet), Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression. The data driven strategy has been to split randomly the complete dataset in two different datasets. The training dataset and the test dataset with a 67 % and 33 % of the original cases respectively. All models were trained on the training dataset and the predictions have been evaluated using the test dataset. The best model was chosen based on the accuracy demonstrated on the test dataset. The information used in all the models were: age; presence of US signs of uterine adenomyosis; presence of an endometrioma; adhesions of the ovary to the uterus; presence of "kissing ovaries"; absence of sliding sign. All models have been trained using CARET package in R with ten repeated 10-fold cross-validation. Accuracy, Sensitivity, Specificity, positive (PPV) and negative (NPV) predictive value were calculated using a 50 % threshold. Presence of intestinal involvement was defined in all cases in the test dataset with an estimated probability greater than 0.5.

RESULTS

In our previous study from where the inputs were retrieved, 106 women had a final expert US diagnosis of rectosigmoid endometriosis. In term of diagnostic accuracy the best model was the Neural Net (Accuracy, 0.73; sensitivity, 0.72; specificity 0.73; PPV 0.52; and NPV 0.86) but without significant difference with the others.

CONCLUSIONS

The accuracy of ultrasound soft markers in raising suspicion of rectosigmoid endometriosis using Artificial Intelligence (AI) models showed similar results to the logistic model.

Sonographic and Doppler predictors of malignancy in ovarian lesions

Background

To determine the best sonographic (US) and/or Doppler features that the radiologist can use as predictors or risk factors for ovarian malignancy

Results

Among the examined 156 ovarian lesions, there were 53 malignant and 103 benign lesions. Most of the malignant ovarian lesions were noted in older age than in benign lesions p < 0.001. Majority of the malignant lesions had non-hyperechoic solid component (92.5%); it had the highest sensitivity of 92.5%, specificity of 97%, accuracy of 94.8%, positive predictive value of 94%, negative predictive value of 96%, and AUC of 0.94 in discrimination between benign and malignant ovarian lesions. The presence of papillary projection, the absence of wall definitions and thick wall, and thick septation were noted in 83%, 81%, and 53.8% of the malignant ovarian lesions respectively. Color flow Doppler shows neovascularity in 88.7% of the malignant lesions, 73.6% of them has central blood flow. The multivariate regression analysis revealed that the presence of non-hyperechoic solid component, new vascularity with central location of the blood flow, papillary projection, thick septa, and old age were the most significant parameters in predicting ovarian cancer in decreasing order of frequency according to their odds ratio (19.45, 7.55, 4.56, 3.45, and 1.45, respectively).

Conclusions

The non-hyperechoic solid component, new vascularity with central location of the blood flow, papillary projection, and thick septa were the most significant and consistent US and Doppler predictors of ovarian malignancy in addition to one clinical feature which is the old age ≥ 52 years.

IOTA Simple Ultrasound Rules for Triage of Adnexal Mass: Experience from South India

Objective

To assess the diagnostic performance of International Ovarian Tumor Analysis (IOTA) simple ultrasound rules to discriminate adnexal masses as benign or malignant.

Methods

A cross-sectional prospective study was conducted on women scheduled for elective surgery due to adnexal masses. Ultrasound examiner systematically assessed the tumors according to the IOTA simple rules to determine the risk of the tumor being malignant. If the simple rules yielded inconclusive result, pattern recognition was used to categorize the mass. Results were then compared with histologic findings after surgery. Diagnostic performance was assessed by calculating sensitivity and specificity.

Results

Two hundred and five women undergoing surgery were included. The rules were applicable in 183 (89.3%) of the tumors; and for these tumors, sensitivity was 92.8% (95% CI 77-99%) and specificity was 92.9% (95% CI 88-96.4%). Of the tumors, 144 were benign and 39 were malignant. The simple rules yielded inconclusive results in 22 masses which were analyzed by pattern recognition.

Conclusion

IOTA simple rules provide excellent discrimination between benign and malignant adnexal masses.

Simultaneous Imaging of Ribonucleic Acid and Hydrogen Sulfide in Living Systems with Distinct Fluorescence Signals Using a Single Fluorescent Probe

Ribonucleic acid (RNA) and hydrogen sulfide (H2S) are important genes and gaseous signal molecules in physiological environment. However, simultaneous investigation of distribution and interrelation of RNA and H2S in living systems is restricted by lack of functional molecular tools. To address this critical challenge, the development of TP-MIVC is described as the first paradigm of the probes that can concurrently report ribonucleic acid and hydrogen sulfide with distinct fluorescence signals in the cancer cells, zebrafish, and living animals. The advantageous features of the probe include high stability, low background fluorescence, high sensitivity, and two-photon imaging property. Significantly, regardless of normal mice or tumor mice, tumor tissues exhibit stronger fluorescence intensity than other organs. More interestingly, it is found that TP-MIVC is capable of distinguishing normal mice and tumor mice by in vivo imaging. This study may open a new pathway for distinguishing malignant and benign tumor by fluorescence imaging of RNA.

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.

[Sonographic diagnosis of presumed benign ovarian tumors]

To discriminate ovarian lesions is of particular importance in gynecological practice. Two main problems need answers: discrimination of benign and malignant adnexal masses and choice of the appropriate surgical treatment if necessary. Nearly 2% of the adnexal masses are ovarian carcinomas or borderline tumors. It is now, well established that ultrasonography is the gold standard for ovarian cyst diagnosis. The purpose of this data was to review the literature and to establish, with the evidence base medicine model, which parameters and existing diagnostic models using ultrasound and Doppler perform best in the evaluation of adnexal masses. Transvaginal sonography has demonstrated considerable advantage over conventional transabdominal sonography. However, transparietal sonography is still useful in large tumors. Definition of the nomenclature and classification was done and should be used. Unilocular ovarian cyst characterization seems easy using sonography and Doppler. In front of complication, discrimination of such functional cyst may be difficult but spontaneous regression confirms usually the expectative management. Dermoid cysts and endometriomas seem to be easier to discriminate from other adnexal masses. Ultrasound and morphologic parameters have a sensitivity of about 90% and a specificity of 80%; that makes this exam the gold standard for ovarian masses diagnosis. Only 50% of ovarian masses are characterized by sonography. Scoring systems help to differentiate benign from malignant masses (sensitivity of about 90%). Logistic regression and models are good methods especially for LR1 and 2 and RMI and may be useful for malignancy prediction but are difficult to use in current practice. Expert diagnosis is a subjective but most important performing parameter. Any suspicious ovarian mass or not easily diagnosed mass requires sonography by an expert, which can first use all the techniques and the different parameters to discriminate benign and malignant tumors. An explicit report will help the physician to define the right attitude for an appropriate management. Six to 16% of adnexial masses are complex or not classified and will result in MRI prescription or surgery.

A new endoscopic ultrasonography image processing method to evaluate the prognosis for pancreatic cancer treated with interstitial brachytherapy

AIM: To develop a fuzzy classification method to score the texture features of pancreatic cancer in endoscopic ultrasonography (EUS) images and evaluate its utility in making prognosis judgments for patients with unresectable pancreatic cancer treated by EUS-guided interstitial brachytherapy.

METHODS: EUS images from our retrospective database were analyzed. The regions of interest were drawn, and texture features were extracted, selected, and scored with a fuzzy classification method using a C++ program. Then, patients with unresectable pancreatic cancer were enrolled to receive EUS-guided iodine 125 radioactive seed implantation. Their fuzzy classification scores, tumor volumes, and carbohydrate antigen 199 (CA199) levels before and after the brachytherapy were recorded. The association between the changes in these parameters and overall survival was analyzed statistically.

RESULTS: EUS images of 153 patients with pancreatic cancer and 63 non-cancer patients were analyzed. A total of 25 consecutive patients were enrolled, and they tolerated the brachytherapy well without any complications. There was a correlation between the change in the fuzzy classification score and overall survival (Spearman test, r = 0.616, P = 0.001), whereas no correlation was found to be significant between the change in tumor volume (P = 0.663), CA199 level (P = 0.659), and overall survival. There were 15 patients with a decrease in their fuzzy classification score after brachytherapy, whereas the fuzzy classification score increased in another 10 patients. There was a significant difference in overall survival between the two groups (67 d vs 151 d, P = 0.001), but not in the change of tumor volume and CA199 level.

CONCLUSION: Using the fuzzy classification method to analyze EUS images of pancreatic cancer is feasible, and the method can be used to make prognosis judgments for patients with unresectable pancreatic cancer treated by interstitial brachytherapy.

IOTA simple rules for discriminating between benign and malignant adnexal masses: prospective external validation

OBJECTIVE

To determine the diagnostic performance of International Ovarian Tumor Analysis (IOTA) 'simple' rules for discriminating between benign and malignant adnexal masses.

METHODS

A prospective study was performed between January 2011 and June 2012. Eligible patients were women diagnosed with a persistent adnexal mass who presented to the participating centers. Four trainees evaluated the adnexal mass by transvaginal ultrasound under the supervision of an expert examiner. The trainee analyzed the mass according to IOTA simple rules and provided a diagnosis of benign, malignant or inconclusive. All women included in the study underwent surgery and tumor removal in the center of recruitment. Diagnostic performance was assessed by calculating sensitivity, specificity and positive (LR+) and negative (LR-) likelihood ratios.

RESULTS

A total of 340 women were included (mean patient age, 42.1 (range, 13-79) years). Of the tumors, 55 (16.2%) were malignant and 285 (83.8%) were benign. The IOTA simple rules could be applied in 270 (79.4%) cases. In these cases, sensitivity was 87.9% (95% CI, 72.4-95.2), specificity 97.5% (95% CI, 94.6-98.8), LR+ 34.7 (95% CI, 15.6-77.3) and LR- 0.12 (95% CI, 0.05-0.31).

CONCLUSIONS

Application of the IOTA simple rules yielded acceptable results in terms of specificity in the hands of non-expert examiners. However, with non-expert examiners there was a 12% false-negative rate, which is relatively high.

Sonographic predictors of ovarian malignancy

PURPOSE

To identify a combination of sonographic features that best predicts ovarian malignancy.

METHODS

Subjects included 249 women who had a transvaginal sonogram for a pelvic mass at Brigham and Women's Hospital between December 2005 and February 2010. Subjects underwent surgery for removal of the mass and pathologic diagnosis was available. Images were reviewed retrospectively by one sonologist blinded to diagnosis and clinical information. Twelve sonographic features were scored for each mass. The dataset was divided into training (n = 149) and testing (n = 100) sets. Within the training set, a stepwise logistic regression was used to weigh each variable and combination of features to identify those associated with malignancies. Using the results from the logistic regression analyses, we created a three-level risk stratification that was applied to the sonograms of subjects in the testing set to assess its ability to distinguish benign lesions from invasive and borderline cancers.

RESULTS

High risk lesions included all masses with internal vascularity. In our testing set, this feature was present in 9 out of 12 (75%) invasive cancers, 1 out of 6 (16.7%) borderline lesions, and 9 out of 82 (11%) benign masses. The intermediate risk level included lesions with a thick wall or thick septa without internal blood flow. This combination of features identified one additional invasive cancer and 5 out of 6 (83.3%) borderline tumors. Masses with low risk features had a 2/49 (4.0%) incidence of malignancy.

CONCLUSIONS

In the absence of high or intermediate risk sonographic features, the risk of malignancy is low.

Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test

Background

Differentiating pancreatic cancer (PC) from normal tissue by computer-aided diagnosis of EUS images were quite useful. The current study was designed to investigate the feasibility of using computer-aided diagnostic (CAD) techniques to extract EUS image parameters for the differential diagnosis of PC and chronic pancreatitis (CP).

Methodology/Principal Findings

This study recruited 262 patients with PC and 126 patients with CP. Typical EUS images were selected from the sample sets. Texture features were extracted from the region of interest using computer-based techniques. Then the distance between class algorithm and sequential forward selection (SFS) algorithm were used for a better combination of features; and, later, a support vector machine (SVM) predictive model was built, trained, and validated. Overall, 105 features of 9 categories were extracted from the EUS images for pattern classification. Of these features, the 16 were selected as a better combination of features. Then, SVM predictive model was built and trained. The total cases were randomly divided into a training set and a testing set. The training set was used to train the SVM, and the testing set was used to evaluate the performance of the SVM. After 200 trials of randomised experiments, the average accuracy, sensitivity, specificity, the positive and negative predictive values of pancreatic cancer were 94.2±0.1749%,96.25±0.4460%, 93.38±0.2076%, 92.21±0.4249% and 96.68±0.1471%, respectively.

Conclusions/Significance

Digital image processing and computer-aided EUS image differentiation technologies are highly accurate and non-invasive. This technology provides a kind of new and valuable diagnostic tool for the clinical determination of PC.

Value of three-dimensional contrast-enhanced ultrasound in the diagnosis of small adnexal masses

The main purpose of this study was to determine whether three-dimensional contrast-enhanced ultrasound (3D-CEUS) can provide useful information to distinguish malignant from benign adnexal masses (≤4 cm). Forty-seven patients with 51 adnexal masses were examined with 3D-CEUS. The sonographic features of masses were analyzed. All diagnoses were confirmed by surgical pathology and long-term follow-up results. The 51 masses included 43 benign and 8 malignant lesions. On 3D-CEUS images, benign lesions appeared as round structures formed by sparse and straight capillary vessels. Malignant lesions showed irregular stereo structures with dense and tortuous vascular distribution. A 3D-CEUS scoring system was established. There were no statistically significant differences in scores at each time point between the 20th and 70th seconds, and the area under the receiver operating characteristic curve for this time period was the largest (0.995). A cut-off score of 8 was established, with scores ≥8 being suggestive of malignancy. The 3D-CEUS scoring system had a high sensitivity (100%) and specificity (98%). 3D-CEUS is likely to be the new tool to distinguish malignant from benign small adnexal masses and diagnose early ovarian cancer.

Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies

In order to ensure that ovarian cancer patients access appropriate treatment to improve the outcome of this disease, accurate characterization before any surgery on ovarian pathology is essential. The International Ovarian Tumor Analysis (IOTA) collaboration has standardized the approach to the ultrasound description of adnexal pathology. A prospectively collected large database enabled previously developed prediction models like the risk of malignancy index (RMI) to be tested and novel prediction models to be developed and externally validated in order to determine the optimal approach to characterize adnexal pathology preoperatively. The main IOTA prediction models (logistic regression model 1 (LR1) and logistic regression model 2 (LR2)) have both shown excellent diagnostic performance (area under the curve (AUC) values of 0.96 and 0.95, respectively) and outperform previous diagnostic algorithms. Their test performance almost matches subjective assessment by experienced examiners, which is accepted to be the best way to classify adnexal masses before surgery. A two-step strategy using the IOTA simple rules supplemented with subjective assessment of ultrasound findings when the rules do not apply, also reached excellent diagnostic performance (sensitivity 90%, specificity 93%) and misclassified fewer malignancies than did the RMI. An evidence-based approach to the preoperative characterization of ovarian and other adnexal masses should include the use of LR1, LR2 or IOTA simple rules and subjective assessment by an experienced examiner.

Key findings from the International Ovarian Tumor Analysis (IOTA) study: an approach to the optimal ultrasound based characterisation of adnexal pathology

The principal aim of the IOTA project has been to develop approaches to the evaluation of adnexal pathology using ultrasound that can be transferred to all examiners. Creating models that use simple, easily reproducible ultrasound characteristics is one approach.

Management of a suspicious adnexal mass: a clinical practice guideline

QUESTIONS

What is the optimal strategy for preoperative identification of the adnexal mass suspicious for ovarian cancer? What is the most appropriate surgical procedure for a woman who presents with an adnexal mass suspicious for malignancy?

PERSPECTIVES

In Canada in 2010, 2600 new cases of ovarian cancer were estimated to have been diagnosed, and of those patients, 1750 were estimated to have died, making ovarian cancer the 7th most prevalent form of cancer and the 5th leading cause of cancer death in Canadian women. Women with ovarian cancer typically have subtle, nonspecific symptoms such as abdominal pain, bloating, changes in bowel frequency, and urinary or pelvic symptoms, making early detection difficult. Thus, most ovarian cancer cases are diagnosed at an advanced stage, when the cancer has spread outside the pelvis. Because of late diagnosis, the 5-year relative survival ratio for ovarian cancer in Canada is only 40%. Unfortunately, because of the low positive predictive value of potential screening tests (cancer antigen 125 and ultrasonography), there is currently no screening strategy for ovarian cancer. The purpose of this document is to identify evidence that would inform optimal recommended protocols for the identification and surgical management of adnexal masses suspicious for malignancy.

OUTCOMES

Outcomes of interest for the identification question included sensitivity and specificity. Outcomes of interest for the surgical question included optimal surgery, overall survival, progression-free or disease-free survival, reduction in the number of surgeries, morbidity, adverse events, and quality of life.

METHODOLOGY

After a systematic review, a practice guideline containing clinical recommendations relevant to patients in Ontario was drafted. The practice guideline was reviewed and approved by the Gynecology Disease Site Group and the Report Approval Panel of the Program in Evidence-based Care. External review by Ontario practitioners was obtained through a survey, the results of which were incorporated into the practice guideline.

PRACTICE GUIDELINE

These recommendations apply to adult women presenting with a suspicious adnexal mass, either symptomatic or asymptomatic. IDENTIFICATION OF AN ADNEXAL MASS SUSPICIOUS FOR OVARIAN CANCER: Sonography (particularly 3-dimensional sonography), magnetic resonance imaging (mri), and computed tomography (ct) imaging are each recommended for differentiating malignant from benign ovarian masses. However, the working group offers the following further recommendations, based on their expert consensus opinion and a consideration of availability, access, and harm: Where technically feasible, transvaginal sonography should be the modality of first choice in patients with a suspicious isolated ovarian mass.To help clarify malignant potential in patients in whom ultrasonography may be unreliable, mri is the most appropriate test.In cases in which extra-ovarian disease is suspected or needs to be ruled out, ct is the most useful technique.Evaluation of an adnexal mass by Doppler technology alone is not recommended. Doppler technology should be combined with a morphology assessment.Ultrasonography-based morphology scoring systems can be used to differentiate benign from malignant adnexal masses. These scoring systems are based on specific ultrasound parameters, each with several scores base on determined features. All evaluated scoring systems were found to have an acceptable level of sensitivity and specificity; the choice of scoring system may therefore be made based on clinician preference.As a standalone modality, serum cancer antigen 125 is not recommended for distinguishing between benign and malignant adnexal masses.Frozen sections for the intraoperative diagnosis of a suspicious adnexal mass is recommended in settings in which availability and patient preference allow. SURGICAL PROCEDURES FOR AN ADNEXAL MASS SUSPICIOUS FOR MALIGNANCY: To improve survival, comprehensive surgical staging with lymphadenectomy is recommended for the surgical management of patients with early-stage ovarian cancer. Laparoscopy is a reasonable alternative to laparotomy, provided that appropriate surgery and staging can be done. The choice between laparoscopy and laparotomy should be based on patient and clinician preference. Discussion with a gynecologic oncologist is recommended. Fertility-preserving surgery is an acceptable alternative to more extensive surgery in patients with low-malignant-potential tumours and those with well-differentiated surgical stage i ovarian cancer. Discussion with a gynecologic oncologist is recommended.

Pediatric risk of malignancy index for preoperative evaluation of childhood ovarian tumors

PURPOSE

This study aimed to develop and provisionally validate a novel scoring index for preoperative cancer-risk prediction in childhood ovarian tumors.

METHODS

Fifty-five girls aged 18 and below underwent surgery for ovarian masses between 2004 and 2009. Benign or non-benign histological diagnoses (the latter including all malignant and borderline tumors and tumors containing immature components) were correlated with clinical and biochemical parameters, and blinded scores of ultrasound and computed-tomography using multivariate logistic regression. Regression coefficients were used as weighting factors to create an additive index. This index was validated prospectively against 23 consecutive adnexal masses operated in 2010.

RESULTS

In total, 67 tumors were benign and 11 non-benign. Non-benign diagnosis was independently associated with the maximum diameter of the largest solid component (score = value in cm), the presence of sex hormone-related symptoms (score = +6), and enhancement or flow in a septum or solid papillary projection (score = +4). The novel scoring index was calculated as the total score of these three parameters. A cutoff score of 7 gave a specificity of 97.9% and sensitivity of 87.5% for the training data set, and specificity and sensitivity of 100% for the pilot testing set.

CONCLUSION

The novel pediatric risk-of-malignancy index is able to accurately discriminate between benign and non-benign ovarian tumors in children and adolescents. Its preoperative application may guide surgical management decisions before the availability of histological confirmation.

Differential diagnosis of adnexal masses: sequential use of the risk of malignancy index and HistoScanning, a novel computer-aided diagnostic tool

OBJECTIVE

To assess the value of ovarian Histo-Scanning(™) , a novel computerized technique for interpreting ultrasound data, in combination with the risk of malignancy index (RMI) in improving triage for women with adnexal masses.

METHODS

RMI indices were assessed in 199 women enrolled in a prospective study to investigate the use of HistoScanning. Ultrasound scores were obtained by blinded analysis of archived images. The following sequential test was developed: HistoScanning was modeled as a second-line test for RMI between a lower cut-off and an upper cut-off. The optimal combination of these cut-offs that together maximized the Youden index (Sensitivity + Specificity - 1) was determined.

RESULTS

Using RMI at the standard cut-off value of 250 resulted in a sensitivity of 74% and a specificity of 86%. When RMI was combined with HistoScanning, the highest accuracy was achieved by using HistoScanning as a sequential second-line test for patients with RMI values between 105 and 2100. At these cut-off values, sequential use of RMI and HistoScanning resulted in mean sensitivity and specificity estimates of 88% and 95%, respectively.

CONCLUSIONS

Our data suggest that HistoScanning may have the potential to improve the diagnostic accuracy of RMI, which could result in better triage for women with adnexal masses. Further prospective validation is warranted.

Adnexal masses suspected to be benign treated with laparoscopy

OBJECTIVE

Guidelines for referring women with pelvic masses suspicious for ovarian cancer to gynecologic oncologists have been developed by the American College of Obstetrician Gynecologists (ACOG). We set out to evaluate the negative predictive value of these guidelines and to assess a modified algorithm involving minimally invasive surgery in the treatment of women with masses suspected to be benign.

METHODS

257 consecutive patients with adnexal masses of 8cm to 13cm on preoperative ultrasound examination meeting Triage Criteria set forth in ACOG Committee Opinion 280. Patients meeting the selection criteria were scheduled for operative laparoscopy, washings, adnexectomy, bagging, and colpotomy. A total of 240 patients successfully completed intended treatment (93.38%), and 234 of these did not require admission (97.5%). There was a low incidence of significant complications: 97.50% of women were successfully treated as outpatients, 97.92% of surgeries lasted <136 minutes, and <97.08% had blood loss <200mL. The negative predictive value of ACOG Committee Opinion 280 Triage Criteria as a deselector for having invasive ovarian malignancy in our population was 95.57% for premenopausal and 90.91% for postmenopausal women.

CONCLUSIONS

Laparoscopic adnexectomy, bagging, and colpotomy is a desirable goal for patients with ovarian masses in the 8cm to 13cm range meeting selection criteria affording a minimally invasive approach with attendant benefits including outpatient treatment (97.5%), few complications, low likelihood of iatrogenic rupture of the ovarian capsule (1.25%), and low necessity for reoperation after final pathology is evaluated (6.03%). Negative predictive value of ACOG Committee Opinion 280 is confirmed in a community gynecology practice and is recommended to form the basis of a new treatment algorithm for women with adnexal masses.

External validation of diagnostic models to estimate the risk of malignancy in adnexal masses

PURPOSE

To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses.

EXPERIMENTAL DESIGN

We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and likelihood ratios (LR(+), LR(-)).

RESULTS

Seven hundred and forty-two (74%) benign and 255 (26%) malignant masses were included. The IOTA models did better than the non-IOTA models (AUCs between 0.941 and 0.956 vs. 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 [95% confidence interval (CI), 0.011-0.044]. The AUC of the RMI was 0.911 (difference with the best IOTA model, 0.044; 95% CI, 0.024-0.064). The superior performance of the IOTA models was most pronounced in premenopausal patients but was also observed in postmenopausal patients. IOTA models were better able to detect stage I ovarian cancer.

CONCLUSION

External validation shows that the IOTA models outperform other models, including the current reference test RMI, for discriminating between benign and malignant adnexal masses.

Adnexal masses difficult to classify as benign or malignant using subjective assessment of gray-scale and Doppler ultrasound findings: logistic regression models do not help

OBJECTIVE

To develop a logistic regression model that can discriminate between benign and malignant adnexal masses perceived to be difficult to classify by subjective evaluation of gray-scale and Doppler ultrasound findings (subjective assessment) and to compare its diagnostic performance with that of subjective assessment, serum CA 125 and the risk of malignancy index (RMI).

METHODS

We used data from the 3511 patients with an adnexal mass included in the International Ovarian Tumor Analysis (IOTA) studies. All patients had been examined using transvaginal gray-scale and Doppler ultrasound following a standardized research protocol carried out by an experienced ultrasound examiner using a high-end ultrasound system. In addition to prospectively collecting information on > 40 clinical and ultrasound variables, the ultrasound examiner classified each mass as certainly or probably benign, unclassifiable, or certainly or probably malignant. A logistic regression model to discriminate between benignity and malignancy was developed for the unclassifiable masses (n = 244, i.e. 7% of all tumors) using a training set (160 tumors, 45 malignancies) and then tested on a test set (84 tumors, 28 malignancies). The gold standard was the histological diagnosis of the surgically removed adnexal mass. The area under the receiver-operating characteristics curve (AUC), sensitivity, specificity, positive likelihood ratio (LR+) and negative likelihood ratio (LR-) were used to describe diagnostic performance and were compared between subjective assessment, CA 125, the RMI and the logistic regression model created.

RESULTS

One variable was retained in the logistic regression model: the largest diameter (in mm) of the largest solid component of the tumor (odds ratio (OR) = 1.04; 95% CI, 1.02-1.06). The model had an AUC of 0.68 (95% CI, 0.59-0.78) on the training set and an AUC of 0.65 (95% CI, 0.53-0.78) on the test set. On the test set, a cut-off of 25% probability of malignancy (corresponding to the largest diameter of the largest solid component of 23 mm) resulted in a sensitivity of 64% (18/28), a specificity of 55% (31/56), an LR+ of 1.44 and an LR- of 0.65. The corresponding values for subjective assessment were 68% (19/28), 59% (33/56), 1.65 and 0.55. On the test set of patients with available CA 125 results, the LR+ and LR- of the logistic regression model (cut-off = 25% probability of malignancy) were 1.29 and 0.73, of subjective assessment were 1.45 and 0.63, of CA 125 (cut-off = 35 U/mL) were 1.24 and 0.84 and of RMI (cut-off = 200) were 1.21 and 0.92.

CONCLUSIONS

About 7% of adnexal masses that are considered appropriate for surgical removal cannot be classified as benign or malignant by experienced ultrasound examiners using subjective assessment. Logistic regression models to estimate the risk of malignancy, CA 125 measurements and the RMI are not helpful in these masses.

A nomogram for estimating the probability of ovarian cancer

OBJECTIVE

Accurate preoperative estimates of the probability of malignancy in women with adnexal masses are essential for ensuring optimal care. This study presents a new statistical model for combining predictive information and a graphic decision support tool for calculating risk of malignancy.

METHODS

The study included 153 women treated with definitive surgery for adnexal mass between 2001 and 2007 with preoperative ultrasound testing and a serum CA125. Multivariable logistic regression was used to develop a statistical model for estimating the probability of ovarian cancer as a function of age, ultrasound score, and CA125 value, with adjustments for nonlinear and interactive relationships.

RESULTS

A total of 20 cases of pathologically confirmed cancer (13 invasive malignancies, and 7 tumors of low malignant potential) were identified (20/153=13%). The model obtained excellent discrimination (ROC area=0.87), explained nearly half of the observed variation in the risk of malignancy (R²=0.43), and was well calibrated across the full range of malignancy probabilities. The model equation is represented in the form of a nomogram, which can be used to calculate preoperative probability of malignancy. At a 5% risk of malignancy threshold, the model has a sensitivity of 90% and a specificity of 73%.

CONCLUSIONS

Statistical models for estimating the probability of adnexal mass malignancy are substantially improved by including adjustments for non-linear relationships among key variables. A clinically relevant nomogram provides an objective tool to further aid clinicians in counseling patients and ensuring proper referral to surgical sub-specialists when indicated.

Mathematical Models to Discriminate Between Benign and Malignant Adnexal Masses: Potential Diagnostic Improvement Using Ovarian HistoScanning

Purpose:

Accurate preoperative clinical assessment of adnexal masses can optimize outcomes by ensuring appropriate and timely surgery. This article addresses whether a new technology, ovarian HistoScanning, has an additional diagnostic value in mathematical models developed for the differential diagnosis of adnexal masses.

Patients and Methods:

Transvaginal sonography-based morphological variables were obtained through blinded analysis of archived images in 199 women enrolled in a prospective study to assess the performance of ovarian HistoScanning. Logistic regression (LR) and neural network (NN) models including these variables and clinical and patient data along with the HistoScanning score (HSS) (range, 0-125; based on mathematical algorithms) were developed in a learning set (60% patients). The remaining 40% patients (evaluation set) were used to assess model performance.

Results:

Of all morphological and clinical variables tested, serum CA-125, presence of a solid component, and HSS were most significant and used to develop the LR model. The NN model included all variables. The novel variable, HSS, offered significant improvement in the LR and NN models' performance. The LR and NN models in an independent evaluation set were found to have area under the receiver operating characteristic curve = 0.97 (95% confidence interval [CI], 94-99) and 0.93 (95% CI, 88-98), sensitivities = 83% (95% CI, 71%-91%) and 80% (95% CI, 67%-89%), and specificities = 98% (95% CI, 89%-99%) and 86% (95% CI, 72%-95%), respectively. In addition, these models showed an improved performance when compared with 3 other existing models (allP< 0.05).

Conclusions:

This initial report shows a clear benefit of including ovarian HistoScanning into mathematical models used for discriminating benign from malignant ovarian masses. These models may be specifically helpful to the less experienced examiner. Future research should assess performance of these models in prospective clinical trials in different populations.

Simple ultrasound rules to distinguish between benign and malignant adnexal masses before surgery: prospective validation by IOTA group

OBJECTIVES

To prospectively assess the diagnostic performance of simple ultrasound rules to predict benignity/malignancy in an adnexal mass and to test the performance of the risk of malignancy index, two logistic regression models, and subjective assessment of ultrasonic findings by an experienced ultrasound examiner in adnexal masses for which the simple rules yield an inconclusive result.

DESIGN

Prospective temporal and external validation of simple ultrasound rules to distinguish benign from malignant adnexal masses. The rules comprised five ultrasonic features (including shape, size, solidity, and results of colour Doppler examination) to predict a malignant tumour (M features) and five to predict a benign tumour (B features). If one or more M features were present in the absence of a B feature, the mass was classified as malignant. If one or more B features were present in the absence of an M feature, it was classified as benign. If both M features and B features were present, or if none of the features was present, the simple rules were inconclusive.

SETTING

19 ultrasound centres in eight countries.

PARTICIPANTS

1938 women with an adnexal mass examined with ultrasound by the principal investigator at each centre with a standardised research protocol. Reference standard Histological classification of the excised adnexal mass as benign or malignant.

MAIN OUTCOME MEASURES

Diagnostic sensitivity and specificity.

RESULTS

Of the 1938 patients with an adnexal mass, 1396 (72%) had benign tumours, 373 (19.2%) had primary invasive tumours, 111 (5.7%) had borderline malignant tumours, and 58 (3%) had metastatic tumours in the ovary. The simple rules yielded a conclusive result in 1501 (77%) masses, for which they resulted in a sensitivity of 92% (95% confidence interval 89% to 94%) and a specificity of 96% (94% to 97%). The corresponding sensitivity and specificity of subjective assessment were 91% (88% to 94%) and 96% (94% to 97%). In the 357 masses for which the simple rules yielded an inconclusive result and with available results of CA-125 measurements, the sensitivities were 89% (83% to 93%) for subjective assessment, 50% (42% to 58%) for the risk of malignancy index, 89% (83% to 93%) for logistic regression model 1, and 82% (75% to 87%) for logistic regression model 2; the corresponding specificities were 78% (72% to 83%), 84% (78% to 88%), 44% (38% to 51%), and 48% (42% to 55%). Use of the simple rules as a triage test and subjective assessment for those masses for which the simple rules yielded an inconclusive result gave a sensitivity of 91% (88% to 93%) and a specificity of 93% (91% to 94%), compared with a sensitivity of 90% (88% to 93%) and a specificity of 93% (91% to 94%) when subjective assessment was used in all masses.

CONCLUSIONS

The use of the simple rules has the potential to improve the management of women with adnexal masses. In adnexal masses for which the rules yielded an inconclusive result, subjective assessment of ultrasonic findings by an experienced ultrasound examiner was the most accurate diagnostic test; the risk of malignancy index and the two regression models were not useful.

Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images

BACKGROUND

EUS can detect morphologic abnormalities of pancreatic cancer with high sensitivity but with limited specificity.

OBJECTIVE

To develop a classification model for differential diagnosis of pancreatic cancer by using a digital imaging processing (DIP) technique to analyze EUS images of the pancreas.

DESIGN

A retrospective, controlled, single-center design was used.

SETTING

The study took place at the Second Military Medical University, Shanghai, China.

PATIENTS

There were 153 pancreatic cancer and 63 noncancer patients in this study.

INTERVENTION

All patients underwent EUS-guided FNA and pathologic analysis.

MAIN OUTCOME MEASUREMENTS

EUS images were obtained and correlated with cytologic findings after FNA. Texture features were extracted from the region of interest, and multifractal dimension vectors were introduced in the feature selection to the frame of the M-band wavelet transform. The sequential forward selection process was used for a better combination of features. By using the area under the receiver operating characteristic curve and other texture features based on separability criteria, a predictive model was built, trained, and validated according to the support vector machine theory.

RESULTS

From 67 frequently used texture features, 20 better features were selected, resulting in a classification accuracy of 99.07% after being added to 9 other features. A predictive model was then built and trained. After 50 random tests, the average accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for the diagnosis of pancreatic cancer were 97.98 ± 1.23%, 94.32 ± 0.03%, 99.45 ± 0.01%, 98.65 ± 0.02%, and 97.77 ± 0.01%, respectively.

LIMITATIONS

The limitations of this study include the small sample size and that the support vector machine was not performed in real time.

CONCLUSION

The classification of EUS images for differentiating pancreatic cancer from normal tissue by DIP is quite useful. Further refinements of such a model could increase the accuracy of EUS diagnosis of tumors.

Polytomous diagnosis of ovarian tumors as benign, borderline, primary invasive or metastatic: development and validation of standard and kernel-based risk prediction models

Background

Hitherto, risk prediction models for preoperative ultrasound-based diagnosis of ovarian tumors were dichotomous (benign versus malignant). We develop and validate polytomous models (models that predict more than two events) to diagnose ovarian tumors as benign, borderline, primary invasive or metastatic invasive. The main focus is on how different types of models perform and compare.

Methods

A multi-center dataset containing 1066 women was used for model development and internal validation, whilst another multi-center dataset of 1938 women was used for temporal and external validation. Models were based on standard logistic regression and on penalized kernel-based algorithms (least squares support vector machines and kernel logistic regression). We used true polytomous models as well as combinations of dichotomous models based on the 'pairwise coupling' technique to produce polytomous risk estimates. Careful variable selection was performed, based largely on cross-validated c-index estimates. Model performance was assessed with the dichotomous c-index (i.e. the area under the ROC curve) and a polytomous extension, and with calibration graphs.

Results

For all models, between 9 and 11 predictors were selected. Internal validation was successful with polytomous c-indexes between 0.64 and 0.69. For the best model dichotomous c-indexes were between 0.73 (primary invasive vs metastatic) and 0.96 (borderline vs metastatic). On temporal and external validation, overall discrimination performance was good with polytomous c-indexes between 0.57 and 0.64. However, discrimination between primary and metastatic invasive tumors decreased to near random levels. Standard logistic regression performed well in comparison with advanced algorithms, and combining dichotomous models performed well in comparison with true polytomous models. The best model was a combination of dichotomous logistic regression models. This model is available online.

Conclusions

We have developed models that successfully discriminate between benign, borderline, and invasive ovarian tumors. Methodologically, the combination of dichotomous models was an interesting approach to tackle the polytomous problem. Standard logistic regression models were not outperformed by regularized kernel-based alternatives, a finding to which the careful variable selection procedure will have contributed. The random discrimination between primary and metastatic invasive tumors on temporal/external validation demonstrated once more the necessity of validation studies.

Ovarian cancer prediction in adnexal masses using ultrasound-based logistic regression models: a temporal and external validation study by the IOTA group

OBJECTIVES

The aims of the study were to temporally and externally validate the diagnostic performance of two logistic regression models containing clinical and ultrasound variables in order to estimate the risk of malignancy in adnexal masses, and to compare the results with the subjective interpretation of ultrasound findings carried out by an experienced ultrasound examiner ('subjective assessment').

METHODS

Patients with adnexal masses, who were put forward by the 19 centers participating in the study, underwent a standardized transvaginal ultrasound examination by a gynecologist or a radiologist specialized in ultrasonography. The examiner prospectively collected information on clinical and ultrasound variables, and classified each mass as benign or malignant on the basis of subjective evaluation of ultrasound findings. The gold standard was the histology of the mass with local clinicians deciding whether to operate on the basis of ultrasound results and the clinical picture. The models' ability to discriminate between malignant and benign masses was assessed, together with the accuracy of the risk estimates.

RESULTS

Of the 1938 patients included in the study, 1396 had benign, 373 had primary invasive, 111 had borderline malignant and 58 had metastatic tumors. On external validation (997 patients from 12 centers), the area under the receiver-operating characteristics curve (AUC) for a model containing 12 predictors (LR1) was 0.956, for a reduced model with six predictors (LR2) was 0.949 and for subjective assessment was 0.949. Subjective assessment gave a positive likelihood ratio of 11.0 and a negative likelihood ratio of 0.14. The corresponding likelihood ratios for a previously derived probability threshold (0.1) were 6.84 and 0.09 for LR1, and 6.36 and 0.10 for LR2. On temporal validation (941 patients from seven centers), the AUCs were 0.945 (LR1), 0.918 (LR2) and 0.959 (subjective assessment).

CONCLUSIONS

Both models provide excellent discrimination between benign and malignant masses. Because the models provide an objective and reasonably accurate risk estimation, they may improve the management of women with suspected ovarian pathology.

Preoperative diagnosis of ovarian tumors using pelvic contrast-enhanced sonography

OBJECTIVE

The purpose of this study was to assess the feasibility of using a contrast agent for the sonographic examination of adnexal tumors and identify discriminating parameters in the preoperative diagnosis of malignant tumors.

METHODS

We conducted a prospective descriptive monocenter study that analyzed validated echographic criteria and parameters of the enhancement curve obtained by sonographic contrast agent injection. Patients included were referred for a second opinion after the discovery of a suspicious ovarian image. The final diagnosis was reached after surgery and an anatomopathologic examination.

RESULTS

Fifty-two tumors were analyzed. Morphologic and Doppler criteria analyses were conducted as described in the literature. The significant parameters of the enhancement curve were the time-intensity curve total area and the duration of activity of the contrast agent during the first phase of decay (P < .002). The performance of the contrast agent was lower than that of the examiner's subjective diagnosis, with an area under the receiver operating characteristic curve (AUC) of 0.78 versus 0.80. When borderline tumors were eliminated, there was an inversion of this, with an AUC of 0.85 versus 0.73. The inclusion of contrast results in the examiner's diagnosis in the context of a bivariate model comparing malignant and borderline tumors with benign tumors provided an AUC of 0.88.

CONCLUSIONS

Contrast-enhanced sonography improves preoperative diagnosis of ovarian tumors parameters. The significant parameters of the enhancement curve were significantly different for malignant and benign tumors. Borderline tumors contribute to a reduction of the discriminating capacity of the contrast agent.

Triple-negative breast cancer: present challenges and new perspectives

Triple-negative breast cancers (TNBC), characterized by absence of estrogen receptor (ER), progesterone receptor (PR) and lack of overexpression of human epidermal growth factor receptor 2 (HER2), are typically associated with poor prognosis, due to aggressive tumor phenotype(s), only partial response to chemotherapy and present lack of clinically established targeted therapies. Advances in the design of individualized strategies for treatment of TNBC patients require further elucidation, by combined 'omics' approaches, of the molecular mechanisms underlying TNBC phenotypic heterogeneity, and the still poorly understood association of TNBC with BRCA1 mutations. An overview is here presented on TNBC profiling in terms of expression signatures, within the functional genomic breast tumor classification, and ongoing efforts toward identification of new therapy targets and bioimaging markers. Due to the complexity of aberrant molecular patterns involved in expression, pathological progression and biological/clinical heterogeneity, the search for novel TNBC biomarkers and therapy targets requires collection of multi-dimensional data sets, use of robust multivariate data analysis techniques and development of innovative systems biology approaches.

Failure of institutionally derived predictive models of conversion in laparoscopic colorectal surgery to predict conversion outcomes in an independent data set of 998 laparoscopic colorectal procedures

OBJECTIVE

The aim of this study was to perform an external validation of 2 institutionally derived predictive models of laparoscopic conversion in colorectal surgery using the Mayo Clinic, Rochester (MCR) laparoscopic colon and rectal surgery experience.

SUMMARY OF BACKGROUND DATA

Two different predictive scoring systems of conversion in laparoscopic colorectal surgery were developed and published based upon single institution experiences. Neither model was validated on an independent data set. Thus, the utility of these models outside of their respective institutions is unknown.

METHODS

A prospectively collected data set of 998 laparoscopic colorectal procedures from MCR was analyzed. All patient-, procedure-, and surgeon-related factors used in both models were present in our data set. Logistic regression was used to evaluate their ability to predict conversion in our cohort. Model effectiveness was assessed by area under the curve from the logistic regression model, 95% confidence intervals for the observed number of conversions, and a goodness-of-fit test to compare the observed number of conversions with the predicted conversion rates for each score.

RESULTS

The cohort mean age of 552 women was 53, with a median body mass index of 25.2 kg/m. There were 382 right-sided, 251 left-sided, 46 rectal resections, and 151 proctocolectomies. Major diagnoses were inflammatory bowel disease 34%, cancer 18%, polyps 17%, and diverticular disease 13%. The overall MCR conversion rate was 15%. Several variables from the models were statistically significant predictors of conversion in our data set. However, both models performed similarly with an area under the curve of 0.62, suggesting that these models are of limited predictive value in our independent cohort with a performance closer to chance. The numbers of actual conversions were significantly different from the predicted number for both scoring systems.

CONCLUSION

Patient and clinical factors associated with laparoscopic conversion in colorectal surgery may be institution dependent. This finding cautions surgeons on the applicability of institution-based surgical predictive models. Independent data set validation is recommended before surgical predictive models are applied to general clinical practice.

Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

MOTIVATION

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.

RESULTS

The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes.

CONTACT

dargenio@bmsr.usc.edu

SUPPLEMENTARY INFORMATION

Supplementary material is available at Bioinformatics online.

Prospective internal validation of mathematical models to predict malignancy in adnexal masses: results from the international ovarian tumor analysis study

PURPOSE

To prospectively test the mathematical models for calculation of the risk of malignancy in adnexal masses that were developed on the International Ovarian Tumor Analysis (IOTA) phase 1 data set on a new data set and to compare their performance with that of pattern recognition, our standard method.

METHODS

Three IOTA centers included 507 new patients who all underwent a transvaginal ultrasound using the standardized IOTA protocol. The outcome measure was the histologic classification of excised tissue. The diagnostic performance of 11 mathematical models that had been developed on the phase 1 data set and of pattern recognition was expressed as area under the receiver operating characteristic curve (AUC) and as sensitivity and specificity when using the cutoffs recommended in the studies where the models had been created. For pattern recognition, an AUC was made based on level of diagnostic confidence.

RESULTS

All IOTA models performed very well and quite similarly, with sensitivity and specificity ranging between 92% and 96% and 74% and 84%, respectively, and AUCs between 0.945 and 0.950. A least squares support vector machine with linear kernel and a logistic regression model had the largest AUCs. For pattern recognition, the AUC was 0.963, sensitivity was 90.2%, and specificity was 92.9%.

CONCLUSION

This internal validation of mathematical models to estimate the malignancy risk in adnexal tumors shows that the IOTA models had a diagnostic performance similar to that in the original data set. Pattern recognition used by an expert sonologist remains the best method, although the difference in performance between the best mathematical model is not large.

Laparoscopy versus laparotomy for benign ovarian tumour

BACKGROUND

Over the last 10 years laparoscopy and minilaparotomy have become increasingly common approaches for the surgical removal of benign ovarian tumours. However, in the event that a tumour is found to be malignant, laparotomy is the appropriate procedure. Careful preoperative assessment including transvaginal ultrasound with morphological scoring, colour doppler assessment of vascular quality, and serum cancer antigen 125 (CA 125) level is desirable.

OBJECTIVES

To determine the benefits, harms, and cost of laparoscopy or minilaparotomy compared with laparotomy in women with benign ovarian tumours.

SEARCH STRATEGY

We searched electronic databases, trial registers, and reference lists of published trial reports. Reference lists from trials and review articles were searched.

SELECTION CRITERIA

All randomised controlled trials comparing either laparoscopy or minilaparotomy with laparotomy for benign ovarian tumours.

DATA COLLECTION AND ANALYSIS

Eight review authors independently assessed the eligibility and quality of each study and extracted the data.

MAIN RESULTS

The results of nine randomised controlled trials (N = 482 women) showed that laparoscopic surgery was associated with fewer adverse events of surgery (surgical injury or postoperative complications including fever or infection) (OR 0.3, 95% CI 0.2 to 0.5), less postoperative pain (VAS score WMD -2.4, 95% CI -2.7 to -2.0), greater likelihood of being pain free after two days (OR 7.42, 95% CI 4.86 to 11.33), and fewer days in hospital (WMD -2.88, 95% CI -3.1 to -2.7) than with laparotomy.In one study that reported costs, laparoscopy was associated with a significant reduction in costs compared to laparotomy (WMD - USD 1045, 95% CI -1348 to -742) in 1993. Very high levels of heterogeneity made it inappropriate to pool data on duration of surgery.Three RCTs compared laparoscopy versus minilaparotomy and found that laparoscopy was associated with reduced odds of any adverse event (surgical injury or postoperative complications) (OR 0.10, 95% CI 0 to 0.8) and lower VAS scores for pain (WMD -1.0, 95% CI -1.6 to -0.45). Duration of hospital stay ranged between 1 and 2.2 days, with substantial heterogeneity.

AUTHORS' CONCLUSIONS

In women undergoing surgery for benign ovarian tumours, laparoscopy was associated with a reduction in fever, urinary tract infection, postoperative complications, postoperative pain, number of days in hospital, and total cost. These findings should be interpreted with caution since only a small number of studies were identified. These included a total of only 769 women and not all of the important outcomes were reported in each study.

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.

Comparison of the risk of malignancy index and self-constructed logistic regression models in preoperative evaluation of adnexal masses

OBJECTIVE

The aim of this study was to evaluate women with adnexal masses in the preoperative period by creating 2 logistic regression models, 1 including sonographic morphologic characteristics and the other including both morphologic and color Doppler characteristics, to compare the diagnostic accuracy of these 2 models with the risk of malignancy index (RMI).

METHODS

This prospective study included 38 malignant, 7 borderline, and 244 benign ovarian masses. The menopausal status, presence of septa, presence of papillary projections, location of the tumor, presence of ascites, presence of metastases, cancer antigen 125 level, tumor volume, septa thickness, and percentage of the solid component were included in the initial analysis. A second regression analysis was performed with the addition of Doppler parameters (location of blood flow and lowest resistive index) in the data set. Diagnostic performance of the 2 regression models and RMI were described and compared by generating receiver operating characteristic curves for each model.

RESULTS

The area under the curve values for the morphologic model (model 1), Doppler model (model 2), and RMI were 0.907, 0.971, and 0.889, respectively. Significance levels of model 1 and the RMI were similar (P = .23), whereas model 2 had a significantly higher area under the curve compared with both model 1 (P = .037) and the RMI (P = .018).

CONCLUSIONS

The addition of Doppler parameters in the regression model significantly increases the predictive performance. Nevertheless, in low-resource settings, the RMI remains the method of choice for distinguishing adnexal masses and referral to gynecologic oncology clinics.

Contribution of morphological assessment of the vessel tree by three-dimensional ultrasound to a correct diagnosis of malignancy in ovarian masses

OBJECTIVE

To determine whether subjective evaluation of the morphology of the vessel tree of ovarian tumors, as depicted by three-dimensional (3D) power Doppler ultrasound, can discriminate between benign and malignant ovarian tumors, and whether it improves characterization compared with using gray-scale ultrasound imaging alone.

METHODS

A consecutive series of 104 women scheduled for surgical removal of an ovarian mass were examined with transvaginal two-dimensional (2D) gray-scale and 3D power Doppler ultrasound. Predetermined vessel characteristics, e.g. density of vessels, branching, caliber changes and tortuosity, were evaluated in 360 degrees rotating 3D images of the vessel tree of the tumor. Ultrasound results were compared with those of the histology of the surgical specimens. Univariate and multivariate logistic regression were used.

RESULTS

There were 77 benign tumors, six borderline tumors and 21 invasive malignancies. All vascular features differed significantly between benign and malignant tumors. The areas under their receiver-operating characteristics (ROC) curves (AUCs) were in the range 0.61-0.83. The AUC of a logistic regression model containing three gray-scale ultrasound variables was 0.98. This model correctly classified all malignancies, with a false-positive rate of 10% (8/77). Adding branching of vessels in the whole tumor to the gray-scale model yielded an AUC of 0.99 and resulted in all malignancies and an additional four benign tumors being correctly classified.

CONCLUSIONS

Subjective evaluation of the morphology of the vessel tree, as depicted by 3D power Doppler ultrasound, can be used to discriminate between benign and malignant ovarian tumors, but adds little to gray-scale ultrasound imaging in an ordinary population of tumors.

Laparoscopy versus laparotomy for benign ovarian tumours

BACKGROUND

Over the last ten years laparoscopy has become an increasingly common approach for the surgical removal of benign ovarian tumours. There remains uncertainty as to the value of this intervention. This review has been undertaken to assess the available evidence for the benefits and harms of laparoscopic surgery for benign ovarian tumours compared to laparotomy.

OBJECTIVES

To determine the efficacy, safety and cost of laparoscopic surgery compared with laparotomy in women with ovarian tumours assumed to be benign.

SEARCH STRATEGY

We searched electronic databases, trials registers and reference lists of published trial reports. Review articles were also searched.

SELECTION CRITERIA

All randomised controlled trials comparing laparoscopy versus laparotomy for benign ovarian tumours.

DATA COLLECTION AND ANALYSIS

Three reviewers independently assessed each study's eligibility and quality and extracted data.

MAIN RESULTS

Six randomised controlled trials were identified involving 324 patients. Three subgroups of ovarian tumours were considered: any histological type of benign ovarian tumour, dermoid cysts and endometriomata. Surgical outcomes: The mean duration of surgery was longer in the laparoscopy group compared to the laparotomy group overall (WMD 11.39; 95% CI 0.57 to 22.22). However, heterogeneity was present with substantial inconsistency (I(2)=87%) . The heterogeneity found in these analyses was likely to reflect differences in the patient populations. Adverse effects of surgery: The pooled estimate for febrile morbidity decreased for laparoscopy compared to laparotomy (Peto OR 0.34; 95% CI 0.13 to 0.88). The odds of any adverse effect of surgery (total number of complications - surgical injury and/or post operative complications) were decreased after laparoscopic procedures (Peto OR 0.26; 95% CI 0.12 to 0.55). Short-term recovery: VAS pain scores favoured laparoscopy (WMD -2.36; 95% CI -2.07 to -2.03) andt he odds of being pain free were significantly greater for the laparoscopy group compared to laparotomy (Peto OR 7.35; 95% CI 4.3 to 12.56). Mean length of hospital stay was shorter in the laparoscopy group with reduction 2.79 days (95% CI -2.95 to -2.62) compared to laparotomy. Economic outcomes: There was a significant reduction of US$1045 (95% CI -1361 to -726.97) in the laparoscopy group compared to the laparotomy group in one trial of women with any type of benign ovarian tumour.

AUTHORS' CONCLUSIONS

In women undergoing surgery for benign ovarian tumours, laparoscopy is associated with a reduction in the following; febrile morbidity, urinary tract infection, post operative complications, post operative pain, days in hospital and total cost. These findings should be interpreted with caution as only a small number of studies were identified including a total of only 324 women and not all of the important outcomes were reported in each study.