Abstract
BackgroundTo evaluate the performance of a decision support system (DSS) based on radiomics and machine learning in predicting the risk of malignancy of ovarian masses (OMs) from transvaginal ultrasonography (TUS) and serum CA-125.MethodsA total of 274 consecutive patients who underwent TUS (by different examiners and with different ultrasound machines) and surgery, with suspicious OMs and known CA-125 serum level were used to train and test a DSS. The DSS was used to predict the risk of malignancy of these masses (very low versus medium-high risk), based on the US appearance (solid, liquid, or mixed) and radiomic features (morphometry and regional texture features) within the masses, on the shadow presence (yes/no), and on the level of serum CA-125. Reproducibility of results among the examiners, and performance accuracy, sensitivity, specificity, and area under the curve were tested in a real-world clinical setting.ResultsThe DSS showed a mean 88% accuracy, 99% sensitivity, and 77% specificity for the 239 patients used for training, cross-validation, and testing, and a mean 91% accuracy, 100% sensitivity, and 80% specificity for the 35 patients used for independent testing.ConclusionsThis DSS is a promising tool in women diagnosed with OMs at TUS, allowing to predict the individual risk of malignancy, supporting clinical decision making.
Highlights
To evaluate the performance of a decision support system (DSS) based on radiomics and machine learning in predicting the risk of malignancy of ovarian masses (OMs) from transvaginal ultrasonography (TUS) and serum cancer antigen-125 (CA-125)
Guidelines support specialised clinicians to classify the risk of cancer of ovarian masses on the bases of only ultrasound features as is for the International Federation of Gynecology and Obstetrics (FIGO) classification [9] or with a combination of ultrasound and biological markers, as is for the “risk of malignancy index” (RMI) [10] or “assessment of different neoplasias in the adnexa” (ADNEX) [11]
In the case of Ovarian cancer (OC), we have recently developed a classification model based on radiomics applied to ultrasonography to automatically classify lesions with proven benign versus malignant histopathology on surgical specimens, allowing a stratification of low versus high risk of cancer with a mean 84% accuracy, 79% sensitivity, and 86% specificity [18]
Summary
Study design and study population This is a single-centre, observational, retrospective, and prospective clinical study. The DSS The DSS was based on an ensemble of three radiomic machine learning models, designed to classify the risk level for (i) fully solid OMs (“solid masses”), (ii) fully liquid OMs (“cystic masses”), and (iii) OMs with solid and liquid components (“mixed masses”) Such radiomic machine learning models have been previously proposed and tested on the first patient cohort in [18]. Robust radiomic features are automatically calculated and selected by the software for the manipulated segmented mass on the TUS image according to the specific mass type as described in our previous work [18] (269 features for solid masses, 278 features for cystic masses, and 306 features for mixed masses) Such radiomic features are used by one of the three machine learning models, according to the mass type, to predict the risk of malignancy of the mass based only on the TUS radiomic features within the mass. Malignant), with 95% confidence intervals (CI), calculated according to the binomial distribution
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