A decision support system based on radiomics and machine learning to predict the risk of malignancy of ovarian masses from transvaginal ultrasonography and serum CA-125

European Radiology Experimental

Dominique Ronzulli¹  Christian Salvatore²  Matteo Interlenghi²  Giorgio Bogani³  Valentina Chiappa³  Giuseppe Sarpietro³  Francesca Bertolina³  Francesco Raspagliesi³  Mauro Signorelli³  Isabella Castiglioni⁴ 

[1]Clinical Trial Center, Fondazione IRCCS Istituto Nazionale Tumori di Milano, Milan, Italy

[2]DeepTrace Technologies S.R.L., Milan, Italy

[3]Department of Gynecologic Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Via Venezian 1, 20133, Milan, Italy

[4]Dipartimento di Fisica G. Occhialini, University of Milan-Bicocca, Milan, Italy

关键词: Artificial intelligence;    Machine learning;    CA-125 antigen;    Ovarian neoplasms;    Ultrasonography;   

DOI  :  10.1186/s41747-021-00226-0

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY