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Objective: To evaluate the feasibility and clinical application of fusion imaging with virtual navigation, combining 18F‐fluorodeoxyglucose (18F‐FDG) positron emission tomography/computed tomography (PET/CT) with real‐time ultrasound imaging, in assessing superficial lymph nodes in breast‐cancer and gynecological‐cancer patients. Methods: This was a pilot study of breast‐ and gynecological‐cancer patients with abnormal uptake of 18F‐FDG by axillary or groin lymph nodes on PET/CT scan, examined at our institution between January 2017 and May 2019. Fusion imaging was performed, uploading preacquired PET/CT DICOM images onto the ultrasound machine and synchronizing them with real‐time ultrasound scanning performed at the lymph‐node site. In the first phase, we assessed the feasibility and reliability of fusion imaging in a series of 10 patients with suspicious lymph nodes on both PET/CT and ultrasound, and with full correspondence between both techniques in terms of size, shape and morphology of the lymph nodes (Group A). In the second phase, we included 20 patients with non‐corresponding findings between PET/CT and ultrasound: 10 patients with lymph nodes that were suspicious or pathological on PET/CT scan but not suspicious on ultrasound assessment (Group B), and 10 patients with suspicious or pathological lymph nodes on both PET/CT and ultrasound but with no correspondence between the two techniques in terms of number of affected lymph nodes (Group C). Results: In the 30 selected patients, fusion imaging was assessed at 30 lymph‐node sites (22 inguinal and eight axillary nodes). In the first phase (Group A), the fusion technique was shown to be feasible in all 10 lymph‐node sites evaluated. In the second phase, fusion imaging was completed successfully in nine of 10 cases in Group B and in all 10 cases in Group C. In all groups, fusion imaging was able to identify the target lymph node, guiding the examiner to perform a core‐needle aspiration biopsy or to inject radiotracer for selective surgical nodal excision, according to the radio‐guided occult lesion localization technique. Conclusion: Fusion imaging with virtual navigation, combining PET/CT and real‐time ultrasound imaging, is technically feasible and able to detect target lymph nodes even when PET/CT and ultrasound findings are inconsistent. Fusion imaging can also be used to guide the performance of core‐needle aspiration biopsy, avoiding further surgical diagnostic procedures, or the injection of radiotracer for selective surgical nodal excision, enabling more sparing, selective surgery. This innovative technique could open up multiple diagnostic and therapeutic opportunities in breast and gynecological oncology. © 2021 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

A mathematical model was developed to predict the lysozyme release kinetics from crosslinked polyvinylalcohol (PVOH) into an aqueous solution. The results indicate that the release kinetic of an antimicrobial agent from a highly hydrophilic polymeric matrix is controlled to a certain extent by adjusting the degree of crosslink of polymeric matrix.

Oncoplastic surgery of the breast (OPS) has generated great excitement over the past years and has become an integrated component of the surgical treatment of breast cancer. Oncoplastic surgical procedures associate the best surgical oncologic principles to achieve wide tumor-free margins with the best principles of plastic surgery to optimize cosmetic outcomes. Thanks to oncoplastic techniques, the role of breast conserving surgery (BCS) has been extended to include a group of patients who would otherwise require mastectomy to achieve adeguate tumor clearance. As OPS continues to gain acceptance and diffusion, an optimal and systematic approach to these techniques is becoming increasingly necessary. This article has the aim to review the essential principles and techniques associated with oncoplastic surgery, based on the data acquired through an extensive search of the PUBMED and MEDLINE database for articles published using the key words "breast cancer oncoplastic surgery". This review analyzes possible the advantages", classifications, indications, and the criteria for a proper selection of oncoplastic techniques to facilitate one's ability to master these procedures and make OPS a safe and an effective procedure. [ABSTRACT FROM AUTHOR]

Background and Objective: Detecting patients at high risk of occurrence of an Invasive Disease Event after a first diagnosis of breast cancer, such as recurrence, distant metastasis, contralateral tumor and second tumor, could support clinical decision-making processes in the treatment of this malignancy. Though several machine learning models analyzing both clinical and histopathological information have been developed in literature to address this task, these approaches turned out to be unsuitable for describing this problem., Methods: In this study, we designed a novel artificial intelligence-based approach which converts clinical information into an image-form to be analyzed through Convolutional Neural Networks. Specifically, we predicted the occurrence of an Invasive Disease Event at both 5-year and 10-year follow-ups of 696 female patients with a first invasive breast cancer diagnosis enrolled at IRCCS "Giovanni Paolo II" in Bari, Italy. After transforming each patient, represented by a vector of clinical information, to an image form, we extracted low-level quantitative imaging features by means of a pre-trained Convolutional Neural Network, namely, AlexNET. Then, we classified breast cancer patients in the two classes, namely, Invasive Disease Event and non-Invasive Disease Event, via a Support Vector Machine classifier trained on a subset of significative features previously identified., Results: Both 5-year and 10-year models resulted particularly accurate in predicting breast cancer recurrence event, achieving an AUC value of 92.07% and 92.84%, an accuracy of 88.71% and 88.82%, a sensitivity of 86.83% and 88.06%, a specificity of 89.55% and 89.3%, a precision of 71.93% and 84.82%, respectively., Conclusions: This is the first study proposing an approach which converts clinical information into an image-form to develop a decision support system for identifying patients at high risk of occurrence of an Invasive Disease Event, and then defining personalized oncological therapeutic treatments for breast cancer patients., Competing Interests: The authors declare no competing interests., (Copyright: © 2024 Fanizzi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Tunneled central venous catheter (tCVCs) is a vascular access frequently employed in hemodialysis patients. Catheter-related bloodstream infections (CRBSI) are potentially life-threatening complications., Methods: We performed a retrospective survey regarding tCVCs prevalence as well as the CRBSI incidence and management within five hospitals in the Brescia province belonging to the "East Lombardy Nephrological Network"; this study was based upon 18 queries regarding the years 2020 and 2021., Results: The data collected refer to an overall hemodialysis population of 736 patients in 2020 and 745 patients in 2021. The prevalence of tCVCs was respectively 22.1% and 24.2% with the initial placement being performed with fluoroscopy support in 80% of the centers. CRBSI incidence was respectively 0.88 and 0.77 episodes per 1000 days of tCVC use. When the CRBI was caused by Staphylococcus Aureus (SA) or Pseudomonas, differently from the recommendation of the KDOQI guidelines, the removal or the substitution of the tCVC did not occur immediately at the time of the diagnosis of the infection but only when the specific antibiotic therapy failed. A nose swab aimed at identifying SA carriers was performed in 60% of centers. The policy regarding the referral to other specialists (infectious disease specialist and microbiologist) was heterogenous across the centers according to their specific logistics., Conclusions: This retrospective survey performed by the "East Lombardy Nephrological Network" within the Brescia province describes the prevalence of tCVCs use as well as the incidence and management of CRBSIs in the hemodialysis patients of this area. The clinical impact of the differences in terms of clinical approach detected compared to the KDOQI guidelines will need to be clarified ideally in prospective studies., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Nipple-areolar complex reconstruction is the final stage of breast reconstruction, and it improves quality of life in patients with post-mastectomy breast cancer. We present a case of a patient with breast cancer underwent breast reconstruction and subsequent nipple-areolar complex reconstruction with an innovative biocompatible smooth silicone implant specially designed for a long-lasting restoration of the nipple-areola complex called FixNip NRI. However, to our knowledge, nipple-areolar complex reconstruction with FixNip was not previously reported., Innovative Technique: We present an emerging technique applied on a patient with breast cancer treated with skin-sparing mastectomy and with immediate breast reconstruction using an expander and then exchanged expander to breast implant. FixNip nipple reconstruction implant is implanted for the reconstruction of the areola-nipple complex with local-regional anaesthesia. She did not develop any postoperatively short-term or long-term complications, and her nipple slowly underwent to a gradual and better definition of its profile., Conclusion: This new approach regarding the reconstruction of the nipple-areola complex seems to be very promising in relation to both the degree of aesthetic satisfaction of patients and the ease of use by surgeons., Level of Evidence V: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 ., (© 2024. The Author(s).)

Introduction: Malignant pleural mesothelioma (MPM) is a poor-prognosis disease. Owing to the recent availability of new therapeutic options, there is a need to better assess prognosis. The initial clinical response could represent a useful parameter., Methods: We proposed a transfer learning approach to predict an initial treatment response starting from baseline CT scans of patients with advanced/unresectable MPM undergoing first-line systemic therapy. The therapeutic response has been assessed according to the mRECIST criteria by CT scan at baseline and after two to three treatment cycles. We used three slices of baseline CT scan as input to the pre-trained convolutional neural network as a radiomic feature extractor. We identified a feature subset through a double feature selection procedure to train a binary SVM classifier to discriminate responders (partial response) from non-responders (stable or disease progression)., Results: The performance of the prediction classifiers was evaluated with an 80:20 hold-out validation scheme. We have evaluated how the developed model was robust to variations in the slices selected by the radiologist. In our dataset, 25 patients showed an initial partial response, whereas 13 patients showed progressive or stable disease. On the independent test, the proposed model achieved a median AUC and accuracy of 86.67% and 87.50%, respectively., Conclusions: The proposed model has shown high performance even by varying the reference slices. Novel tools could help to improve the prognostic assessment of patients with MPM and to better identify subgroups of patients with different therapeutic responsiveness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Fanizzi, Catino, Bove, Comes, Montrone, Sicolo, Signorile, Perrotti, Pizzutilo, Galetta and Massafra.)

Background: Risk stratification and treatment benefit prediction models are urgent to improve negative sentinel lymph node (SLN-) melanoma patient selection, thus avoiding costly and toxic treatments in patients at low risk of recurrence. To this end, the application of artificial intelligence (AI) could help clinicians to better calculate the recurrence risk and choose whether to perform adjuvant therapy., Methods: We made use of AI to predict recurrence-free status (RFS) within 2-years from diagnosis in 94 SLN- melanoma patients. In detail, we detected quantitative imaging information from H&E slides of a cohort of 71 SLN- melanoma patients, who registered at Istituto Tumori "Giovanni Paolo II" in Bari, Italy (investigational cohort, IC). For each slide, two expert pathologists firstly annotated two Regions of Interest (ROIs) containing tumor cells alone (TUMOR ROI) or with infiltrating cells (TUMOR + INF ROI). In correspondence of the two kinds of ROIs, two AI-based models were developed to extract information directly from the tiles in which each ROI was automatically divided. This information was then used to predict RFS. Performances of the models were computed according to a 5-fold cross validation scheme. We further validated the prediction power of the two models on an independent external validation cohort of 23 SLN- melanoma patients (validation cohort, VC)., Results: The TUMOR ROIs have revealed more informative than the TUMOR + INF ROIs. An Area Under the Curve (AUC) value of 79.1% and 62.3%, a sensitivity value of 81.2% and 76.9%, a specificity value of 70.0% and 43.3%, an accuracy value of 73.2% and 53.4%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the IC cohort, respectively. An AUC value of 76.5% and 65.2%, a sensitivity value of 66.7% and 41.6%, a specificity value of 70.0% and 55.9%, an accuracy value of 70.0% and 56.5%, were achieved on the TUMOR and TUMOR + INF ROIs extracted for the VC cohort, respectively., Conclusions: Our approach represents a first effort to develop a non-invasive prognostic method to better define the recurrence risk and improve the management of SLN- melanoma patients., (© 2024. The Author(s).)

Background: Oncology nurses support cancer patients in meeting their self-care needs, often neglecting their own emotions and self-care needs. This study aims to investigate the variations in the five facets of holistic mindfulness among Italian oncology nurses based on gender, work experience in oncology, and shift work., Method: A cross-sectional study was carried out in 2023 amongst all registered nurses who were employed in an oncology setting and working in Italy., Results: There were no significant differences in all five facets of holistic mindfulness ( p ≥ 0.05) according to gender, work experience in the oncology field, and shift work., Conclusion: Could holistic mindfulness be defined as an intrinsic individual characteristic? Surely, more insights will be necessary to better define the holistic trend in oncology nursing.

Background: International guidelines recommend a pathway for preferable nursing handling in a specific cancer topic, like chemotherapy toxicity, low adhesion in toxicity reported with a consequential increase in adverse events (AEs) frequency, poorer QoL outcomes, and increased use of healthcare service until death. Unpredictability, postponed reports, and incapability to access healthcare services can compromise toxicity-related effects by including patients' safety. In this scenario, a more attentive nursing intervention can improve patients' outcomes and decrease costs for healthcare services, respectively. The present scoping review aims to describe and synthesize scientific care nursing evidence assessment in oncology patients., Methods: PubMed, Embase, Nursing & Allied Health Database, and British Nursing were the databases examined. Keywords used and associated with Boolean operators were assessment, care, nursing, immune disorder, oncology, and patient. Research articles considered were published between 2013-2023. All systematic processes were performed according to the PRISMA procedure in order to reach all manuscripts considered in the present scoping review., Results: The Embase database showed a total of 25 articles, PubMed displayed 77, the Nursing & Allied Health Database evidenced a total of 74, and the British Nursing database showed 252 records. Then, after a first revision in each database by considering the inclusion criteria, the abovementioned titles and abstracts were selected and, 336 records were removed, and 92 studies remained. Of these, 65 manuscripts were excluded after verifying abstracts. Finally, a total of 7 articles were carefully analysed and selected for this scoping review. Specifically, 2 articles belonged to the British Nursing Database, 3 articles belonged to Embase, 1 to the Nursing & Allied Health Database and one related to PubMed., Conclusion: Oncology nursing should consider several aspects, such as therapy-related toxicity and its related morbidity and mortality, worsening levels of quality of life, and increasing duty by the healthcare organization or endorsements for the principal symptoms and signs which may anticipate few diseases and worst clinical conditions, too. Therefore, careful monitoring may allow prompt recognition and subsequent earlier management in the treatment efficacy., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Several studies have emphasised how positive and negative human papillomavirus (HPV+  and HPV-, respectively) oropharyngeal squamous cell carcinoma (OPSCC) has distinct molecular profiles, tumor characteristics, and disease outcomes. Different radiomics-based prediction models have been proposed, by also using innovative techniques such as Convolutional Neural Networks (CNNs). Although some of these models reached encouraging predictive performances, there evidence explaining the role of radiomic features in achieving a specific outcome is scarce. In this paper, we propose some preliminary results related to an explainable CNN-based model to predict HPV status in OPSCC patients. We extracted the Gross Tumor Volume (GTV) of pre-treatment CT images related to 499 patients (356 HPV+ and 143 HPV-) included into the OPC-Radiomics public dataset to train an end-to-end Inception-V3 CNN architecture. We also collected a multicentric dataset consisting of 92 patients (43 HPV+ , 49 HPV-), which was employed as an independent test set. Finally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) technique to highlight the most informative areas with respect to the predicted outcome. The proposed model reached an AUC value of 73.50% on the independent test. As a result of the Grad-CAM algorithm, the most informative areas related to the correctly classified HPV+ patients were located into the intratumoral area. Conversely, the most important areas referred to the tumor edges. Finally, since the proposed model provided additional information with respect to the accuracy of the classification given by the visualization of the areas of greatest interest for predictive purposes for each case examined, it could contribute to increase confidence in using computer-based predictive models in the actual clinical practice., (© 2024. The Author(s).)

Background: Accurate characterization of newly diagnosed a solid adnexal lesion is a key step in defining the most appropriate therapeutic approach. Despite guidance from the International Ovarian Tumor Analyzes Panel, the evaluation of these lesions can be challenging. Recent studies have demonstrated how machine learning techniques can be applied to clinical data to solve this diagnostic problem. However, ML models can often consider as black-boxes due to the difficulty of understanding the decision-making process used by the algorithm to obtain a specific result., Aims: For this purpose, we propose an Explainable Artificial Intelligence model trained on clinical characteristics and qualitative ultrasound indicators to predict solid adnexal masses diagnosis., Materials & Methods: Since the diagnostic task was a three-class problem (benign tumor, invasive cancer, or ovarian metastasis), we proposed a waterfall classification model: a first model was trained and validated to discriminate benign versus malignant, a second model was trained to distinguish nonmetastatic versus metastatic malignant lesion which occurs when a patient is predicted to be malignant by the first model. Firstly, a stepwise feature selection procedure was implemented. The classification performances were validated on Leave One Out scheme., Results: The accuracy of the three-class model reaches an overall accuracy of 86.36%, and the precision per-class of the benign, nonmetastatic malignant, and metastatic malignant classes were 86.96%, 87.27%, and 77.78%, respectively., Discussion: SHapley Additive exPlanations were performed to visually show how the machine learning model made a specific decision. For each patient, the SHAP values expressed how each characteristic contributed to the classification result. Such information represents an added value for the clinical usability of a diagnostic system., Conclusions: This is the first work that attempts to design an explainable machine-learning tool for the histological diagnosis of solid masses of the ovary., (© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.)

Background: So far, baseline Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) has played a key role for the application of sophisticated artificial intelligence-based models using Convolutional Neural Networks (CNNs) to extract quantitative imaging information as earlier indicators of pathological Complete Response (pCR) achievement in breast cancer patients treated with neoadjuvant chemotherapy (NAC). However, these models did not exploit the DCE-MRI exams in their full geometry as 3D volume but analysed only few individual slices independently, thus neglecting the depth information., Method: This study aimed to develop an explainable 3D CNN, which fulfilled the task of pCR prediction before the beginning of NAC, by leveraging the 3D information of post-contrast baseline breast DCE-MRI exams. Specifically, for each patient, the network took in input a 3D sequence containing the tumor region, which was previously automatically identified along the DCE-MRI exam. A visual explanation of the decision-making process of the network was also provided., Results: To the best of our knowledge, our proposal is competitive than other models in the field, which made use of imaging data alone, reaching a median AUC value of 81.8%, 95%CI [75.3%; 88.3%], a median accuracy value of 78.7%, 95%CI [74.8%; 82.5%], a median sensitivity value of 69.8%, 95%CI [59.6%; 79.9%] and a median specificity value of 83.3%, 95%CI [82.6%; 84.0%], respectively. The median and CIs were computed according to a 10-fold cross-validation scheme for 5 rounds., Conclusion: Finally, this proposal holds high potential to support clinicians on non-invasively early pursuing or changing patient-centric NAC pathways., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: We present the case of a recurrent malignant phyllodes tumor of the breast, after mastectomy and radiotherapy, in which electrochemotherapy (ECT) was applied to the tumor bed, to achieve better local control., Case Report: A 66-year-old woman with a large malignant phyllodes tumor of the right breast with a size of 40 cm underwent right radical mastectomy and right axillary lymph node sampling. One month after surgery, with histologically clear margins, the woman presented with multiple small oval masses in the upper portion of the chest wall, indicating rapid disease progression. A second radical excision with clear margins was performed, followed by adjuvant radiotherapy. Two months after the end of treatment, a new 3-cm mass was present in the right axillary extension. The patient underwent a third extensive debulking surgery. At the end of the resection, ECT was applied on the tumor bed along the extensive skin flaps and resection margins. After eight months of follow-up, breast magnetic resonance imaging and total body computed tomography showed disease recurrence in the anterior portion of the right serratus muscle and in the lungs bilaterally. The area undergoing previous ECT showed no disease recurrence. The patient received two lines of palliative chemotherapy. She died 28 months after diagnosis. At the time of death, the large area treated with ECT was geometrically spared from local disease progression., Conclusion: This case report suggests the potential efficacy of ECT at the operating bedside to increase local control in aggressive malignancies., (Copyright © 2024 International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Non-Small cell lung cancer (NSCLC) is one of the most dangerous cancers, with 85% of all new lung cancer diagnoses and a 30-55% of recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients during diagnosis could be essential to drive targeted therapies preventing either overtreatment or undertreatment of cancer patients. The radiomic analysis of CT images has already shown great potential in solving this task; specifically, Convolutional Neural Networks (CNNs) have already been proposed providing good performances. Recently, Vision Transformers (ViTs) have been introduced, reaching comparable and even better performances than traditional CNNs in image classification. The aim of the proposed paper was to compare the performances of different state-of-the-art deep learning algorithms to predict cancer recurrence in NSCLC patients. In this work, using a public database of 144 patients, we implemented a transfer learning approach, involving different Transformers architectures like pre-trained ViTs, pre-trained Pyramid Vision Transformers, and pre-trained Swin Transformers to predict the recurrence of NSCLC patients from CT images, comparing their performances with state-of-the-art CNNs. Although, the best performances in this study are reached via CNNs with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.91, 0.89, 0.85, 0.90, and 0.78, respectively, Transformer architectures reach comparable ones with AUC, Accuracy, Sensitivity, Specificity, and Precision equal to 0.90, 0.86, 0.81, 0.89, and 0.75, respectively. Based on our preliminary experimental results, it appears that Transformers architectures do not add improvements in terms of predictive performance to the addressed problem., (© 2023. The Author(s).)

Background: About 15%-20% of breast cancer (BC) cases is classified as Human Epidermal growth factor Receptor type 2 (HER2) positive. The Neoadjuvant chemotherapy (NAC) was initially introduced for locally advanced and inflammatory BC patients to allow a less extensive surgical resection, whereas now it represents the current standard for early-stage and operable BC. However, only 20%-40% of patients achieve pathologic complete response (pCR). According to the results of practice-changing clinical trials, the addition of trastuzumab to NAC brings improvements to pCR, and recently, the use of pertuzumab plus trastuzumab has registered further statistically significant and clinically meaningful improvements in terms of pCR. The goal of our work is to propose a machine learning model to predict the pCR to NAC in HER2-positive patients based on a subset of clinical features., Method: First, we evaluated the significant association of clinical features with pCR on the retrospectively collected data referred to 67 patients afferent to Istituto Tumori "Giovanni Paolo II." Then, we performed a feature selection procedure to identify a subset of features to be used for training a machine learning-based classification algorithm. As a result, pCR to NAC was associated with ER status, Pgr status, and HER2 score., Results: The machine learning model trained on a subgroup of essential features reached an AUC of 73.27% (72.44%-73.66%) and an accuracy of 71.67% (71.64%-73.13%). According to our results, the clinical features alone are not enough to define a support system useful for clinical pathway., Conclusion: Our results seem worthy of further investigation in large validation studies and this work could be the basis of future study that will also involve radiomics analysis of biomedical images., (© 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

Introduction: It has been estimated that 19,880 new cases of ovarian cancer had been diagnosed in 2022. Most epithelial ovarian cancer are sporadic, while in 15%-25% of cases, there is evidence of a familial or inherited component. Approximately 20%-25% of high-grade serous carcinoma cases are caused by germline mutations in the BRCA1 and BRCA2 genes. However, owing to a lack of effective early detection methods, women with BRCA mutations are recommended to undergo bilateral risk-reducing salpingo-oophorectomy (RRSO) after childbearing. Determining the right timing for this procedure is a difficult decision. It is crucial to find a clinical signature to identify high-risk BRCA-mutated patients and determine the appropriate timing for performing RRSO., Methods: In this work, clinical data referred to a cohort of 184 patients, of whom 7.6% were affected by adnexal tumors including invasive carcinomas and intraepithelial lesions after RSSO has been analyzed. Thus, we proposed an explainable machine learning (ML) ensemble approach using clinical data commonly collected in clinical practice to early identify BRCA-mutated patients at high risk of ovarian cancer and consequentially establish the correct timing for RRSO., Results: The ensemble model was able to handle imbalanced data achieving an accuracy value of 83.2%, a specificity value of 85.3%, a sensitivity value of 57.1%, a G-mean value of 69.8%, and an AUC value of 71.1%., Discussion: In agreement with the promising results achieved, the application of suitable ML techniques could play a key role in the definition of a BRCA-mutated patient-centric clinical signature for ovarian cancer risk and consequently personalize the management of these patients. As far as we know, this is the first work addressing this task from an ML perspective., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Comes, Arezzo, Cormio, Bove, Calabrese, Fanizzi, Kardhashi, La Forgia, Legge, Romagno, Loizzi and Massafra.)