Artificial inTelligence in eNdometriosis-related ovArian Cancer and Precision Surgery in eNdometriosis-related ovArian Cancer

Study Overview
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Brief Summary
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Endometriosis (EMS) is a chronic, invaliding, inflammatory gynaecological condition affecting 10-15% of women in reproductive age. EMS is characterized by lesions of endometrial-like tissue outside the uterus involving pelvic peritoneum and ovaries. In addition, distant foci are sometimes observed. Unfortunately, the aetiology of the EMS is little known. Although non-malignant, EMS shares similar features with cancer, such as development of local and distant foci, resistance to apoptosis and invasion of other tissues with subsequent damage to the target organs. Moreover, patients with EMS (particularly ovarian EMS) showed high risk (about 3 to 10 times) of developing epithelial ovarian cancer (EOC). Epidemiologic, morphological and molecular studies reported endometrioma as the precursor of EOC, including clear cell (CCC) endometrioid carcinoma which are both called EMS-related ovarian carcinoma (EROC). To date, it remains unclear why benign EMS causes malignant transformation. This multi-step process, unlike high-grade serous carcinomas, offers the possibility to identify the carcinoma precursors enabling an early diagnosis and in the early stages of the disease. EOC is the most lethal female gynecological cancer with 25% 5-year overall survival (OS), due to the lack of effective screening tools, and rapidly spreads over the entire peritoneal surface (carcinosis) thus involving all abdominal organs. Diagnosis and clinical staging of EOC is currently performed by qualitative image evaluation although the sensitivity/specificity is suboptimal. To date, diagnostic, staging, and prognostic factors are strongly correlated with subjective assessment training and clinician experience. Genomic analysis based on Next Generation Sequencing (NGS) has revealed the presence of cancer-associated gene mutations in EMS. Moreover, the chronic inflammatory process of EMS involves many factors, such as hormones, cytokines, glycoproteins, and angiogenic factors, which are expected to become early EMS biomarkers. A promising new branch of cancer research is the use of artificial intelligence (AI) to recognize new image patterns and texture and/or detecting novel biomarkers to improve the early identification of EROC patients. AI has never been used for EROC and we want to investigate whether these methods/techniques can support and even improve current diagnostics and risk assessment. AI will be used to construct a new 3D risk assessment model based on images and volume of interest

Official Title
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Artificial inTelligence as Tool for Early Diagnosis and Precision Surgery in eNdometriosis-related ovArian Cancer

Conditions
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Patients With Suspected Ovarian Carcinoma, Non Oncological Patients or With Endometriosis

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: age>18 Suspected diagnosis of epithelial ovarian cancer Patients eligible for surgery radiological imaging available informed consent Exclusion Criteria: Patients with previous different malignancies Patients with previous chemotherapeutic treatment Patients with previous pelvic radiotherapeutic treatment

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 90 Years

Sexes Eligible for Study
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Female

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| group 1<br>200 patients with suspected ovarian cancer | |
| group 2<br>40 non oncological patients of witch 20 with endometriosis | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| development of a diagnostic and prognostic model based on the use of artificial intelligence | development of a diagnostic and prognostic model based on the use of artificial intelligence in patients suffering from ovarian cancer related to endometriosis through the collection of all available information (clinical, pathological, molecular, genetic, radiomic data) | 2 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Correlation of specific features with clinical characteristic | Correlation of the histopathological features, immuno-phenotypic and molecular alterations present in epithelial ovarian tumors, in particular in associated endometriosis related- ovarian tumors, using an immunohistochemical profile and an NGS panel evaluation of the miRNA expression profile in endometriosis related- ovarian tumors identification and validation of radiomic features indicative of endometriosis related- ovarian tumors build a three-dimensional map of the lesions in order to distinguish the tumor areas to be removed during surgery while preserving the organs not affected by the tumor pathology | 2 years | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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endometriosis, ovarian cancer, artificial intelligence