Camilla Nero

Detection of Clinically Significant BRCA Large Genomic Rearrangements in FFPE Ovarian Cancer Samples: A Comparative NGS Study

Background: Copy number variations (CNVs), also referred to as large genomic rearrangements (LGRs), represent a crucial component of BRCA1/2 (BRCA) testing. Next-generation sequencing (NGS) has become an established approach for detecting LGRs by combining sequencing data with dedicated bioinformatics pipelines. However, CNV detection in formalin-fixed paraffin-embedded (FFPE) samples remains technically challenging, and there is the need to implement a robust and optimized analysis strategy for routine clinical practice. Methods: This study evaluated 40 FFPE ovarian cancer (OC) samples from patients undergoing BRCA testing. The performance of the amplicon-based NGS Diatech Myriapod® NGS BRCA1/2 panel (Diatech Pharmacogenetics, Jesi, Italy) was assessed for its ability to detect BRCA CNVs and results were compared to two hybrid capture-based reference assays. Results: Among the 40 analyzed samples (17 CNV-positive and 23 CNV-negative for BRCA genes), the Diatech pipeline showed a good concordance with the reference method—all CNVs were correctly identified in 16 cases with good enough sequencing quality. Only one result was inconclusive due to low sequencing quality. Conclusions: These findings support the clinical utility of NGS-based CNV analysis in FFPE samples when combined with appropriate bioinformatics tools. Integrating visual inspection of CNV plots with automated CNV calling improves the reliability of CNV detection and enhances the interpretation of results from tumor tissue. Accurate CNV detection directly from tumor tissue may reduce the need for additional germline testing, thus shortening turnaround times. Nevertheless, blood-based testing remains mandatory to determine whether detected BRCA CNVs are of hereditary or somatic origin, particularly in cases with a strong clinical suspicion of inherited predisposition due to young age and a personal and/or family history of OC.

Focal lymphovascular space invasion: Friend or foe? A large retrospective analysis on stage I endometrioid endometrial carcinomas

Literature is inconsistent with respect to clinical value of lymphovascular space invasion (LVSI) semiquantitative assessment. We aim to investigate the prognostic role of LVSI extent in stage I endometrioid endometrial carcinomas (ECs) classified by immunohistochemistry (IHC) analysis. Patients with stage I endometrioid EC undergone primary surgery were retrospectively included. Following World Health Organization definition for LVSI pathologic evaluation, subjects were divided into: LVSI-negative; LVSI-focal; LVSI-substantial. An IHC-based model was utilized to classify patients into: p53-aberrant (p53abn); mismatch repair deficient (MMRd); mismatch repair proficient with positive estrogen receptors (MMRp-ERpos); and mismatch repair proficient with negative estrogen receptors (MMRp-ERneg). 2091 subjects were included and divided into: 78.0 % (n:1631) LVSI-negative, 10.6 % (n:221) LVSI-focal, and 11.4 % (n:239) LVSI-substantial. Presence of LVSI (any extent) was associated with older age, larger tumor size and deeper myometrial infiltration. Patients with LVSI-substantial presented with higher incidence of grade 3 tumors, p53abn and MMRd status. Conversely, most LVSI-negative and LVSI-focal cases were MMRp-ERpos. At multivariable regression, LVSI-substantial was independently associated with reduced 5-year disease-free survival (DFS) and overall-survival (OS). LVSI-negative and LVSI-focal groups had similar DFS (p = 0.42) and OS (p = 0.09), whereas comparison with LVSI-substantial demonstrated significantly poorer outcomes for patients with substantial invasion. These findings were confirmed in sub-analyses of cases with grade 1-2 endometrioid and myometrial infiltration, and in the MMRp-ERpos cohort. In stage I endometrioid ECs, LVSI-focal was not associated with reduced oncologic outcomes compared to LVSI-negative. In contrast, LVSI-substantial was associated with aggressive clinicopathologic and molecular features and behaved as an independent prognostic factor for reduced survival. Our results were further confirmed in two low-risk EC settings: grade 1-2 with myometrial infiltration, and the MMRp-ERpos group.

Sentinel-node biopsy in apparent early stage ovarian cancer: final results of a prospective multicentre study (SELLY)

To evaluate the sensitivity and specificity of sentinel-lymph-node mapping compared with the gold standard of systematic lymphadenectomy in detecting lymph node metastasis in apparent early stage ovarian cancer. Multicenter, prospective, phase II trial, conducted in seven centers from March 2018 to July 2022. Patients with presumed stage I-II epithelial ovarian cancer planned for surgical staging were eligible. Patients received injection of indocyanine green in the infundibulo-pelvic and, when feasible, utero-ovarian ligaments and sentinel lymph node biopsy followed by pelvic and para-aortic lymphadenectomy was performed. Histopathological examination of all nodes was performed including ultra-staging protocol for the sentinel lymph node. 174 patients were enrolled and 169 (97.1 %) received study interventions. 99 (58.6 %) patients had successful mapping of at least one sentinel lymph node and 15 (15.1 %) of them had positive nodes. Of these, 11 of 15 (73.3 %) had a correct identification of the disease in the sentinel lymph node; 7 of 11 (63.6 %) required ultra-staging protocol to detect nodal metastasis. Four (26.7 %) patients with node-positive disease had a negative sentinel-lymph-node (sensitivity 73.3 % and specificity 100.0 %). In a multicenter setting, identifying sentinel-lymph nodes in apparent early stage epithelial ovarian cancer did not reach the expected sensitivity: 1 of 4 patients might have metastatic lymphatic disease unrecognized by sentinel-lymph-node biopsy. Nevertheless, 35.0 % of node positive patients was identified only thanks to ultra-staging protocol on sentinel-lymph-nodes.

Are all mismatch repair deficient endometrial cancers created equal? A large, retrospective, tertiary center experience

One third of endometrial carcinomas (ECs) presents with mismatch repair deficiency (MMRd). Of these, 70 % are caused by somatic hypermethylation of MLH1 promoter; the remaining cases are determined by Lynch syndrome or double somatic inactivation of MMR genes. Although associated with good-intermediate prognosis, heterogeneity in treatment response and survival has been reported among MMRd ECs. We aim to investigate differences in pathologic aggressiveness and event-free survival (EFS) among three MMRd EC subtypes, classified by immunohistochemistry (IHC) and MLH1 methylation analysis. Subjects undergone surgical staging for EC were retrospectively included. IHC analysis was performed in all patients to assess MMR and p53 status. Methylation analysis was performed in MMRd patients with IHC-negative MLH1. The MMRd population was classified into: 1)MLH1-hypermethylated (MLH1-HyMet); 2)MLH1-unmethylated (MLH1-UnMet); 3)IHC-negative MSH2 and/or MSH6 or PMS2 alone (non-MLH1). Of 1171 patients undergoing surgical staging and IHC assessment, 362 (30.9 %) were classified as MMRd and included in the analysis. Among these, 59.7 % (n = 216) were MLH1-HyMet, 11 % (n = 40) MLH1-UnMet, and 29.3 % (n = 106) non-MLH1. Compared to MLH1-UnMet and non-MLH1, MLH1-HyMet was associated with older age, higher BMI, larger tumor size, deeper myometrial invasion, substantial lymphovascular space invasion, lower frequency of early-stage and low-risk disease. EFS was similar when comparing the MMRd subtypes, even after adjusting for stage and tumor histology. However, a trend of MLH1-HyMet toward poorer prognosis can be observed, particularly in the advanced/metastatic setting. MLH1-hypermethylated MMRd ECs display more aggressive clinicopathologic features compared to the other MMRd subgroups. However, although a suggestive trend toward poorer EFS was observed in the hypermethylated subset, particularly in the advanced setting, no significant differences in prognosis were detected among the MMRd subtypes.

ENDOESTRO score: Where is the dark side? Evaluation of estrogen receptor expression cutoff in endometrial cancer molecular classification, an ambispective study

Estrogen receptor (ER) expression has prognostic value in endometrial cancer (EC), also in the context of molecular classification. However, a standardized cutoff to define ER positivity is lacking. To identify the ER expression cutoff that best correlates with survival outcomes overall and within each molecular subgroup. We conducted an ambispective study comprising a retrospective phase (Feb 2017-Feb 2022) and a prospective phase (Mar 2022-Jan 2024), including patients with EC who underwent surgery at our institution. In the retrospective cohort, molecular subgroups were defined by immunohistochemistry (IHC): 1) MMRp Among 2084 patients, ER < 10 % was the strongest cutoff for prognosis based on disease-free survival (DFS). Patients with ER < 10 % and < 1 % shared similar unfavorable clinicopathological and molecular features, while ER ≥ 10 % was associated with more favorable profiles. ER < 10 % was an independent adverse prognostic factor in uni- and multivariate analyses for DFS and overall survival, particularly in MMRp ER < 10 % expression is a robust prognostic indicator, associated with worse outcomes and may serve as a clinically meaningful cutoff in endometrial cancer risk stratification. Further prospective validation is warranted.

Recent progress in the use of pharmacotherapy for endometrial cancer

Endometrial cancer (EC) is the most common gynecological cancer in developed countries. The ESGO/ESTRO/ESP updated evidence-based guidelines in 2020, introducing molecular classification to guide EC treatment. The genomic-based approach has identified four prognostic subgroups of EC. Each of these may benefit from a tailored treatment depending on the molecular profile, the histotype, and stage of disease for the adjuvant and the metastatic/recurrent setting. Several clinical trials are now ongoing to identify the best treatment according to the molecular profile of EC. This review analyzes tailored treatment for EC according to the molecular profile, both in the adjuvant and in the metastatic/recurrent setting. The authors review the results of clinical studies and highlight ongoing trials. Several new agents are under evaluation in order to personalize EC treatment according to specific molecular profiles in the adjuvant, advanced, and recurrent settings. Clinical trials investigating the impact of molecular classification have yielded encouraging results. EC can no longer be considered a single tumor entity susceptible to a single treatment modality but rather be split into four distinct types, requiring tailored treatments.

Further refining 2020 ESGO/ESTRO/ESP molecular risk classes in patients with early‐stage endometrial cancer: A propensity score–matched analysis

BackgroundThe integration of molecular features with clinicopathological findings in endometrial cancer classification seems to be able to significantly refine risk assessment. Nevertheless, clinical management remains challenging, and different therapeutic options are available for each class. Further prognostic characterization of the subgroups within each risk class could be helpful in the decision‐making process.MethodsThis study evaluated the role of the 2020 European Society of Gynaecological Oncology (ESGO)/European Society for Radiotherapy and Oncology (ESTRO)/European Society of Pathology (ESP) risk assessment system and the three prognostic profiles adopted in the PORTEC‐4a trial in predicting disease‐free and overall survival in a retrospective study cohort of patients with early‐stage endometrial cancer. Patients were selected according to a 1:2 propensity score matching analysis. Moreover, the sequencing of 29 genes was undertaken for tumor samples.ResultsThe study included 137 patients. No differences in disease‐free or overall survival at 5 years were observed among the 2020 ESGO/ESTRO/ESP risk classes without molecular features (p = .766 and p = .176, respectively). Once molecular features were integrated, the probability of overall survival was significantly different (p = .011). When the three prognostic profiles were applied, the probability of recurrence had a p value of .097, and significant differences were observed in overall survival (p = .004). Among patients experiencing recurrence, 17.6% showed mutations in BRCA1/2, RAD50, BRIP1, and XRCC2, whereas 22.5% had PD‐L1–positive expression and an MUTYH mutation.ConclusionsFurther stratification within each risk class according to the most relevant prognostic features could better define the prognosis of patients with early‐stage endometrial cancer. Nearly half of the patients who experienced recurrence showed a targetable molecular alteration for which dedicated trials should be encouraged.

Germline reflex BRCA1/2 testing following tumor-only comprehensive genomic profiling: why, when, and how

Abstract The majority of tumor comprehensive genomic profiling (CGP) currently does not include a matched normal control. The use of a tumor-only CGP approach needs the development of a strategy to refine germline pathogenic/likely pathogenic variants (gP/LPVs) calls, so as to limit the performance of unnecessary germline reflex tests and instead successfully identify patients who are carriers of likely gP/LPVs. Guidelines have been developed for the identification of gP/LPVs in BRCA1/2 genes on the basis of tumor-only CGP results and for the evaluation of the appropriateness of performing germline reflex BRCA1/2 testing. In this study, an algorithm to assist decision-making for germline reflex testing of BRCA1/2 variants following tumor-only CGP is proposed.

Radiomics analysis of ultrasound images to discriminate between benign and malignant adnexal masses with solid morphology on ultrasound

ABSTRACT Objective The primary aim was to identify radiomics ultrasound features that can distinguish between benign and malignant adnexal masses with solid ultrasound morphology, and between primary malignant (including borderline and primary invasive) and metastatic solid ovarian masses, and to develop ultrasound‐based machine learning models that include radiomics features to discriminate between benign and malignant solid adnexal masses. The secondary aim was to compare the discrimination performance of our newly developed radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and that of subjective assessment by an experienced ultrasound examiner. Methods This was a retrospective, observational single‐center study conducted at Fondazione Policlinico Universitario A. Gemelli IRCC, in Rome, Italy. Included were patients with a histological diagnosis of an adnexal tumor with solid morphology according to International Ovarian Tumor Analysis (IOTA) terminology at preoperative ultrasound examination performed in 2014–2020, who were managed with surgery. The patient cohort was split randomly into training and validation sets at a ratio of 70:30 and with the same proportion of benign and malignant tumors in the two subsets, with malignant tumors including borderline, primary invasive and metastatic tumors. We extracted 68 radiomics features, belonging to two different families: intensity‐based statistical features and textural features. Models to predict malignancy were built based on a random forest classifier, fine‐tuned using 5‐fold cross‐validation over the training set, and tested on the held‐out validation set. The variables used in model‐building were patient age and radiomics features that were statistically significantly different between benign and malignant adnexal masses and assessed as not redundant based on the Pearson correlation coefficient. We evaluated the discriminative ability of the models and compared it to that of the ADNEX model and that of subjective assessment by an experienced ultrasound examiner using the area under the receiver‐operating‐characteristics curve (AUC) and classification performance by calculating sensitivity and specificity. Results In total, 326 patients were included and 775 preoperative ultrasound images were analyzed. Of the 68 radiomics features extracted, 52 differed statistically significantly between benign and malignant tumors in the training set, and 18 uncorrelated features were selected for inclusion in model‐building. The same 52 radiomics features differed significantly between benign, primary malignant and metastatic tumors. However, the values of the features manifested overlapped between primary malignant and metastatic tumors and did not differ significantly between them. In the validation set, 25/98 (25.5%) tumors were benign and 73/98 (74.5%) were malignant (6 borderline, 57 primary invasive, 10 metastatic). In the validation set, a model including only radiomics features had an AUC of 0.80, sensitivity of 0.78 and specificity of 0.76 at an optimal cut‐off for risk of malignancy of 68%, based on Youden's index. The corresponding results for a model including age and radiomics features were AUC of 0.79, sensitivity of 0.86 and specificity of 0.56 (cut‐off 60%, based on Youden's index), while those of the ADNEX model were AUC of 0.88, sensitivity of 0.99 and specificity of 0.64 (at a 20% risk‐of‐malignancy cut‐off). Subjective assessment had a sensitivity of 0.99 and specificity of 0.72. Conclusions Our radiomics model had moderate discriminative ability on internal validation and the addition of age to this model did not improve its performance. Even though our radiomics models had discriminative ability inferior to that of the ADNEX model, our results are sufficiently promising to justify continued development of radiomics analysis of ultrasound images of adnexal masses. © 2024 The Author(s). Ultrasound in Obstetrics &amp; Gynecology published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Oregovomab: an investigational agent for the treatment of advanced ovarian cancer

Infiltrating T lymphocytes and programmed cell death protein-1/programmed death-ligand 1 expression in endometriosis-associated ovarian cancer

To characterize T lymphocyte infiltration and programmed cell death protein-1 (PD-1)/programmed death-ligand 1 (PD-L1) expression in early-stage endometriosis-associated ovarian cancer (EAOC), ovarian endometriosis (OE), atypical endometriosis (AE), and deep endometriosis (DE). Case-control, retrospective study. Research University Hospital. A total of 362 patients with a histologic diagnosis of EAOC, OE, AE, or DE were identified between 2000 and 2019 from Fondazione Policlinico Universitario Agostino Gemelli IRCCS and Gemelli Molise SpA tissue data banks. A 1:1 propensity score-matched method yielded matched pairs of 55 subjects with EAOC, 55 patients with OE, 12 patients with AE, and 42 patients with DE, resulting in no differences in family history of cancer, parity, and use of oral contraceptives. Immunohistochemistry assays using the following primary antibodies: CD3+; CD4+; CD8+; PD-1; and PD-L1. To characterize T lymphocyte infiltration and PD-1/PD-L1 expression in 4 different endometriosis-related diseases. Endometriosis-associated ovarian cancer cases displayed significantly higher levels of PD-1/PD-L1 expression compared with all other endometriosis-related diseases (vs. OE vs. AE vs. DE). Moreover, a significantly lower count of infiltrating T lymphocytes was observed in EAOC cases compared with OE ones. Finally, one-third of OE cases showed a cancer-like PD-1/PD-L1 expression profile. Endometriosis-associated ovarian cancer is characterized by higher levels of PD-1/PD-L1 expression compared with benign endometriosis-related diseases. This profile was found in one-third of clinically benign cases, suggesting that it develops early in the carcinogenesis process.

Deep-Learning to Predict BRCA Mutation and Survival from Digital H&amp;E Slides of Epithelial Ovarian Cancer

BRCA 1/2 genes mutation status can already determine the therapeutic algorithm of high grade serous ovarian cancer patients. Nevertheless, its assessment is not sufficient to identify all patients with genomic instability, since BRCA 1/2 mutations are only the most well-known mechanisms of homologous recombination deficiency (HR-d) pathway, and patients displaying HR-d behave similarly to BRCA mutated patients. HRd assessment can be challenging and is progressively overcoming BRCA testing not only for prognostic information but more importantly for drugs prescriptions. However, HR testing is not already integrated in clinical practice, it is quite expensive and it is not refundable in many countries. Selecting patients who are more likely to benefit from this assessment (BRCA 1/2 WT patients) at an early stage of the diagnostic process, would allow an optimization of genomic profiling resources. In this study, we sought to explore whether somatic BRCA1/2 genes status can be predicted using computational pathology from standard hematoxylin and eosin histology. In detail, we adopted a publicly available, deep-learning-based weakly supervised method that uses attention-based learning to automatically identify sub regions of high diagnostic value to accurately classify the whole slide (CLAM). The same model was also tested for progression free survival (PFS) prediction. The model was tested on a cohort of 664 (training set: n = 464, testing set: n = 132) ovarian cancer patients, of whom 233 (35.1%) had a somatic BRCA 1/2 mutation. An area under the curve of 0.7 and 0.55 was achieved in the training and testing set respectively. The model was then further refined by manually identifying areas of interest in half of the cases. 198 images were used for training (126/72) and 87 images for validation (55/32). The model reached a zero classification error on the training set, but the performance was 0.59 in terms of validation ROC AUC, with a 0.57 validation accuracy. Finally, when applied to predict PFS, the model achieved an AUC of 0.71, with a negative predictive value of 0.69, and a positive predictive value of 0.75. Based on these analyses, we have planned further steps of development such as proving a reference classification performance, exploring the hyperparameters space for training optimization, eventually tweaking the learning algorithms and the neural networks architecture for better suiting this specific task. These actions may allow the model to improve performances for all the considered outcomes.

Verification of the prognostic precision of the new 2023 FIGO staging system in endometrial cancer patients – An international pooled analysis of three ESGO accredited centres

Recently, the new 2023 International Federation of Gynecology and Obstetrics (FIGO) staging system for endometrial cancer (EC) critically integrating new pathological and molecular features was published. The present study evaluated the clinical impact of the new 2023 FIGO staging system by comparing it to the previous 2009 system. This is an international, pooled retrospective study of 519 EC patients who underwent primary treatment (and molecular characterisation) at three European Society of Gynaecological Oncology (ESGO) accredited centres in Austria/Italy. Patients were categorised according to the 2009 and the 2023 FIGO staging systems. Stage shifts were analysed and (sub)stage specific 5-year progression-free (PFS) and overall survival (OS) rates were calculated and compared. Different statistical tests were applied to evaluate the prognostic precision of the two FIGO staging systems and to compare them to each other. (Sub)stage shifts occurred in 143/519 (27.6%) patients: 123 upshifts (23.7%) and 20 (3.9%) downshifts. 2023 FIGO staging system identified a stage I cohort with a notably higher 5-year PFS rate compared to 2009 (93.0% versus 87.4%, respectively). For stage II disease, the 5-year PFS rate was similar in the 2023 and the 2009 FIGO staging systems (70.2% versus 71.2%, respectively). The two new molecularly defined 2023 FIGO substages IAm The new 2023 FIGO stating system led to a substantial stage shift in about one quarter of patients leading to a higher prognostic precision. In early stage disease, the new substages added further prognostic granularity and identified treatment relevant subgroups.

Developing and validating ultrasound‐based machine‐learning models incorporating radiomics features to predict malignancy in adnexal masses

ABSTRACT Objective The primary aim of this study was to develop and internally validate ultrasound‐based radiomics models to discriminate between all types of benign and malignant adnexal masses. The secondary aim was to compare the performance of the radiomics models with that of the Assessment of Different NEoplasias in the adneXa (ADNEX) model. Methods This was a retrospective, observational, single‐center study, for which all patients with an adnexal mass that were included in the ongoing International Ovarian Tumor Analysis phase‐5 and phase‐7 studies and were examined using ultrasound between January 2012 and December 2023 at Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy, were eligible for inclusion. Inclusion criteria were: adnexal mass detected by ultrasound; surgical removal of the adnexal mass within 180 days after the ultrasound examination; histological confirmation of an adnexal mass; and absence of a synchronous malignant tumor. Patients without digital ultrasound images saved in DICOM format were excluded. The patient cohort was split randomly into training and validation sets using a stratified split with a ratio of 70:30, to preserve the proportion of benign and malignant cases in the two sets. Two machine‐learning models for discriminating between benign and malignant adnexal masses were built using one image per tumor, with 5‐fold cross‐validation for hyperparameter tuning, and were tested on the validation set. The variables used in model building were patient age, serum CA 125 level and the radiomics features that differed significantly between benign and malignant tumors (determined using the Mann–Whitney U ‐test with Benjamini–Hochberg correction) and were not redundant based on Pearson correlation analysis. Histology was the reference standard. We assessed the discriminative performance of the radiomics models using the area under the receiver‐operating‐characteristics curve (AUC) and classification performance using sensitivity and specificity at the optimal cut‐off of each model to classify the mass as malignant, as determined by Youden's index. The diagnostic performance of the developed radiomics models was compared with that of the ADNEX model (AUC, sensitivity and specificity at the 10% risk‐of‐malignancy cut‐off, which is the recommended threshold for clinical use of the ADNEX model). Results In total, 4501 patients met the inclusion criteria. Among these, 2428 patients were excluded owing to an absence of ultrasound images or images unsuitable for radiomics analysis. Overall, a total of 2073 patients were included in the analysis, of whom 803 (38.7%) had a histologically confirmed malignant tumor. In the validation set ( n  = 622, including 254 malignancies), the clinical–radiomics model trained using the eXtreme Gradient Boosting algorithm, including age, serum CA 125 level and 14 selected radiomics features, achieved the highest performance, with an AUC of 0.89 (95% CI, 0.86–0.92), sensitivity of 0.83 (95% CI, 0.79–0.88) and specificity of 0.81 (95% CI, 0.77–0.85) at the optimal cut‐off (31% risk of malignancy, based on Youden's index). At a 10% risk‐of‐malignancy cut‐off, it had a sensitivity of 0.94 (95% CI, 0.91–0.97) and specificity of 0.48 (95% CI, 0.42–0.53). The ADNEX model had an AUC of 0.95 (95% CI, 0.93–0.97), sensitivity of 0.97 (95% CI, 0.95–0.99) and specificity of 0.72 (95% CI, 0.68–0.77) at the 10% risk‐of‐malignancy cut‐off in the validation set. Conclusions Our results support further exploration of radiomics analysis for distinguishing between benign and malignant adnexal masses in larger study populations. Future studies should consider using multiple images per tumor and testing alternative model‐building methods, and should perform external validation to assess the generalizability of the radiomics models. © 2026 The Author(s). Ultrasound in Obstetrics &amp; Gynecology published by John Wiley &amp; Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Patent Blue SLN in Early Ovarian Cancer Prospective Study (FIGO I-II) Evaluating Patent Blue SLN Mapping. Injection Into IP/UO Ligaments in Situ. Goals: Assess Feasibility and Accuracy vs Standard Lymphadenectomy to Minimize Surgical Morbidity

the standard of care in case of early ovarian cancer (stage I or II) is a complete surgery. This surgery includes : hysterectomy (remove of the uterus), bilateral salpingo-oophorectomy (remove of the adnexa), omentectomy (remove of the epiploon), bilateral pelvic lymphadenectomy (remove of pelvic lymph nodes) and para-aortic lymphadenectomy (remove of para-aortic lymph nodes). This procedure is diagnostic, curative and prognostic surgery. In fact, it allows us provider care giver to stratify the stage of the cancer, hence we give the appropriate adjuvant therapy. However, this surgery, especially the extended lymphadenectomy, is associated with some risks: lymphocele, vessel injury, blood loss, morbidity, long recovery period ... In order to reduce these risks, we propose a sentinel lymph node biopsy. This intervention allows us to detect first lymph node relay whether pelvic or para-aortic. In our study, we chose the patent blue dye as a tracer. This tracer is widely used in oncologic surgery (for example in breast cancer) and approved but not in ovarian cancer yet. During surgery for early stage ovarian cancer, we will inject the patent blue dye on both side of the ovarian tumor. Then, we will check for first colorful lymph node, in both pelvic and para-aortic regions. We will send these dissected lymph node to pathology for analysis. Finally, we will continue the procedure as the standard of care. Our objective is to compare the results between the sentinel lymph node and the complete lymphadenectomy and to study the technique of sentinel lymph node biopsy using the blue patent dye as tracer.