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341 Artificial intelligence as tool for early diagnosis and precision surgery, in endometriosis-related ovarian cancer (ATENA): the interim analysis
  1. Miriam Santoro1,
  2. Christian Macis1,
  3. Camelia Alexandra Coada2,
  4. Vladislav Zybin3,
  5. Giulia Dondi4,
  6. Marco Di Stanislao4,5,
  7. Antonio De Leo5,6,
  8. Stella Di Costanzo4,
  9. Gloria Ravegnini7,
  10. Dario De Biase6,8,
  11. Luigi Lovato3,
  12. Pierandrea De Iaco4,5,
  13. Anna Myriam Perrone4,5 and
  14. Lidia Strigari1
  1. 1Department of Medical Physics, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
  2. 2Faculty of Medicine, ‘Iuliu Hatieganu’ University of Medicine and Pharmacy, Cluj-Napoca, Romania
  3. 3Pediatric and Adult CardioThoracic and Vascular, Onchoematologic and Emergency Radiology Unit, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
  4. 4Division of Oncologic Gynecology, IRCCS Azienda Ospedaliero Universitaria di Bologna, Bologna, Italy
  5. 5Department of Medical and Surgical Sciences (DIMEC), University of Bologna, Bologna, Italy
  6. 6Solid Tumor Molecular Pathology Laboratory, IRCCS Azienda Ospedaliero-Universitaria di Bologna, Bologna, Italy
  7. 7Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy
  8. 8Department of Pharmacy and Biotechnology (FaBit), University of Bologna, Bologna, Italy


Introduction/Background Women with endometriosis (EMS) present an increased risk of developing EMS-related ovarian carcinoma (EROC). To date, the definitive diagnosis relies on the post-surgical assessment by a pathologist, thus limiting individualized medical approaches due to the lack of screening methods for the timely detection of EROC development. To fill in this unmet clinical need, the purpose of this study was to explore the capacity of radiomic-based machine learning (ML) models to discriminate EROC from EMS lesions and normal ovaries using pre-operative contrast-enhanced CT (CE-CT) images.

Methodology Volumes of interest from both ovaries were semi-automatically contoured by an experienced radiologist. Feature pre-processing, harmonization, and extraction were done using the PyRadiomics library. Three ML models were implemented in R for the binary classification task, employing a 10-fold cross-validation approach with a 70:30 ratio between training and test sets. The ML models were based on different functions for feature reduction (i.e., Boruta, Recursive Feature Elimination, and Least Absolute Shrinkage and Selection Operator) and integrated with generalized linear models. The predictive ability of each classifier was assessed using area under the curve (AUC), sensitivity and specificity.

Results In the 95 prospectively enrolled women (median[range] age: 58[31–79] years) with definitive diagnosis, 45 EROC-related ovaries and 65 non-tumoral controls were delineated, while 80 volumes were excluded being borderlines or with other cancer types. The ML models, each based on 1409 extracted radiomic features (RFs), showed good performances with AUC, sensitivity, and specificity ranging from 0.72 to 0.88, from 0.54 to 0.92, and from 0.84 to 0.89, respectively.

Conclusion ML models based on CE-CT-derived RFs have great potential in differentiating EROC-related patients against controls, thus representing a valuable and objective information to complement the clinical decision in the attempt to implement fertility sparing approaches.

Disclosures The study is funded by the Italian Ministry of Health (ENDO-2021–12371926).

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