Tina Pasciuto

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.

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.

Laparoscopic vs. robotic-assisted laparoscopy in endometrial cancer staging: large retrospective single-institution study

The aim of this study is to analyze and draw the potential differences between the robotic-assisted surgery (RS) and the laparoscopy (LPS) in endometrial cancer staging. In this single-institution retrospective study we enrolled 1,221 consecutive clinical stage I-III endometrial cancer patients undergone minimally invasive surgical staging. We compared patients treated by LPS and by RS, on the basis of perioperative and oncological outcomes (disease-free survival [DFS] and overall survival [OS]). A sub-analysis of the high-risk endometrial cancer population was performed in the 2 cohorts. The 2 cohorts (766 treated by LPS and 455 by RS) were homogeneous in terms of perioperative and pathological data. We recorded differences in number of relapse/progression (11.7% in LPS vs. 7% in RS, p=0.008) and in number of deaths (9.8% in LPS vs. 4.8% in RS, p=0.002). Whereas, univariate and multivariate analyses according to DFS and OS confirmed that the surgical approach did not influence the DFS or the OS. In the multivariable analysis the association of the age and grading was significant for DFS and OS. In the sub-analysis of the 426 high risk EC patients (280 in LPS and 146 in RS) the univariate and the multivariate confirmed the influence of the age in DFS and OS, independently of the minimally invasive approach. In our large retrospective analysis, we confirmed that the RS and LPS have similar efficacy and safety for endometrial cancer staging also for the high-risk endometrial cancer patients.

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.

Gene actionability according to the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT) in No Specific Molecular Profile (NSMP) endometrial cancer

The No Specific Molecular Profile (NSMP) subtype accounts for ∼30%-40% of endometrial cancer (EC), comprising a heterogeneous group of EC. The primary outcome of this study was the prevalence of actionable genomic alterations in NSMP EC, classified according to the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets (ESCAT). Oncogenic and likely oncogenic alterations, pathways, and co-mutation patterns were reported. The analysis was stratified by risk group according to the European Society of Gynaecological Oncology (ESGO)-ESMO-European SocieTy for Radiotherapy and Oncology (ESTRO) guidelines. Patients with NSMP EC enrolled in the FPG500 comprehensive cancer genome profiling program (NCT06020625) were included. Two hundred and fifty-three patients with NSMP EC of any International Federation of Gynecology and Obstetrics (FIGO) stage were enrolled between 1 January 2022 and 31 December 2023. Median age was 62 years, and the most frequent histotype was endometrioid (97%). Ninety-five percent of patients were estrogen receptor positive. Two hundred and thirty-three patients (92%) had at least one ESCAT tier I-III alteration. The most frequent variants were in PTEN [88%, 95% confidence interval (CI) 84% to 92%], PIK3CA (42%, 95% CI 36% to 49%), FGFR2 (15%, 95% CI 11% to 20%), and AKT1 (6%, 95% CI 3% to 10%); 4% (95% CI 2% to 8%) of patients had an ESR1 variant, while KRAS G12C was found in 3% (95% CI 1% to 6%) of patients. The majority of PTEN variants were on the R130 hotspot. More frequent PIK3CA hotspot variants were H1047R (9%), E545D/K/Q/A (6%), and E542K (4%). In the overall population, PIK3CA with PIK3R1 (odds ratio [OR] = 0.07, P value = 4.25 × 10 Our findings highlight potentially actionable alterations in NSMP EC patients, supporting the exploration of tailored molecular-matched therapies according to risk groups.

An exploratory assessment of early and delta PET radiomic features for outcome prediction in locally advanced cervical cancer

This study investigated whether radiomic features extracted from [ We retrospectively included LACC patients referred to our Institution from 2010 to 2016. [ 95 patients were included. With a median follow-up of 76.0 months (95% CI: 59.5-82.1), 31.6% of patients had recurrence/progression and 20.0% died of disease. None of the models could predict DFS (C-indices ≤ 0.72). Model performances for OS yielded slightly better results, with mean C-indices of 0.75 for both the radiomic and combined model based on early features, 0.79 and 0.78 for the radiomic and combined model derived from delta features, and 0.76 for the clinical models. [

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.