Search

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]

Objective: To assess the accuracy of preoperative ultrasound examination for predicting lymph-node (LN) status in patients with vulvar cancer.Methods: This was a single-institution retrospective observational study of all women with a histological diagnosis of vulvar cancer triaged to inguinal surgery within 30 days following ultrasound evaluation between December 2010 and January 2016. For each groin examined, 15 morphological and dimensional sonographic parameters associated with suspicion for LN involvement were examined. A morphometric ultrasound pattern (MUP) was expressed for each groin, classifying the inguinal LN status into five groups (normal; reactive-but-negative; minimally suspicious/probably negative; moderately suspicious; and highly suspicious/positive) according to subjective judgment, followed by stratification as positive or negative for metastasis according to morphometric binomial assessment (MBA). In cases of positive MBA, fine-needle aspiration cytology was performed. Combining the information obtained from MUP and cytologic results, a binomial final overall assessment (FOA) was assigned for each groin. The final histology was considered as the reference standard. Comparison was performed between patients with negative and those with positive LNs on histology, and receiver-operating-characteristics curves were generated for statistically significant variables on univariate analysis, to evaluate their diagnostic ability to predict negative LN status.Results: Of 144 patients included in the analysis, 87 had negative inguinal LNs and 57 had positive LNs on histology. A total of 256 groins were analyzed, of which 171 were negative and 85 showed at least one metastatic LN on histology. The following parameters showed the greatest accuracy, with the best balance between specificity and sensitivity, in predicting negative LN status: cortical (C) thickness of the dominant LN (cut-off, 2.5 mm; sensitivity, 90.0%; specificity, 77.9%); short-axis (S) length of the dominant LN (cut-off, 8.4 mm; sensitivity, 63.9%; specificity, 90.6%); C/medulla (M) thickness ratio of the dominant LN (cut-off, 1.2 mm; sensitivity, 70.4%; specificity, 91.5%), the combination of S length and C/M thickness ratio (sensitivity, 88.9%; specificity, 82.4%); and the FOA analysis (sensitivity, 85.9%; specificity, 84.2%).Conclusions: Preoperative ultrasound assessment, with or without the addition of cytology, has a high accuracy in assessing inguinal LN status in patients with vulvar cancer. In particular, the combination of two ultrasound parameters (S length and C/M thickness ratio) provided the greatest accuracy in discriminating between negative and positive LNs. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd. [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.

Objective: To evaluate the feasibility of fusion of ultrasound imaging and three-dimensional (3D) single-photon-emission computed tomography/computed tomography (SPECT/CT) in detecting sentinel lymph nodes in women with vulvar cancer.Methods: This was a prospective pilot single-center study. Patients with vulvar cancer who were candidates for sentinel lymph-node biopsy were enrolled between December 2018 and February 2019. Fusion imaging virtual navigation using 3D SPECT/CT and ultrasound was performed to investigate the tumor-draining lymph node. All clinical, imaging, surgical and histological information was collected prospectively and entered into a dedicated Excel file. Feasibility and success of fusion imaging virtual navigation and time needed to perform the three steps of fusion imaging were evaluated.Results: Ten lymph-node sites were evaluated in five consecutive women with a histological diagnosis of vulvar cancer. Fusion imaging virtual navigation was feasible and completed successfully for all (10/10) draining sites. Median overall time to perform fusion imaging was 32 (range, 25-40) min and the time decreased from the first to the last examination.Conclusions: The present study demonstrated that fusion imaging virtual navigation using 3D SPECT/CT and ultrasound is feasible and able to detect sentinel lymph nodes in women with vulvar carcinoma. Fusion imaging using ultrasound for detection of sentinel lymph nodes opens up multiple diagnostic and therapeutic opportunities in gynecological oncology. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Objective The study aims were: 1) to verify the role of sentinel node biopsy (SNB) in a subset of patients with clinical N0 (cN0) invasive vulvar cancer (VC) who were still candidates for radical inguinal surgery according to the current guidelines; 2) to investigate whether a preoperative 18 F-FDG PET/CT (PET/CT) evaluation could improve the selection of node negative patients. Methods From July 2013 to July 2016, all patients with VC admitted to our Division were evaluated by standard imaging and clinical exam. Among the patients assessed as cN0 we enrolled those unsuitable for SNB, due to: T > 4 cm, multifocal tumors, complete tumor diagnostic excision, contralateral nodal involvement and local recurrence. A preoperative PET/CT was performed. For each patient surgery included SNB, performed using a combined technique (radiotracer plus blue dye), followed by standard inguino-femoral lymphadenectomy. The reference standard was histopathology. Results Forty-seven patients entered the study for a total of 73 groins. Histopathology revealed 12 metastatic SNs in 9 groins. No false negative SNs were found (NPV 100%). PET/CT showed a negative predictive value of 93%. Conclusions Our data suggest that SNB is accurate and safe even in cN0 patients currently excluded from this procedure, providing that a careful preoperative selection is performed. PET/CT allows a reliable assessment of LN status and may be an effective support for the selection of patients who are safe candidates for SNB. [ABSTRACT FROM AUTHOR]

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]

To evaluate the feasibility of Fusion of SPECT/CT and ultrasound in detecting sentinel lymph nodes in patients with vulvar cancer. The present study demonstrated that the fusion virtual navigation using SPECT/CT and ultrasound is feasible and it is able to detect sentinel lymph nodes in patients with vulvar carcinoma. [Extracted from the article]

An abstract of the article "Ultrasound morphometric and cytological combined preoperative assessment of inguinal lymph node status in women with invasive vulvar carcinoma," by G. Garganese and colleagues is presented.

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.)

Digital Breast Tomosynthesis (DBT) is a cutting-edge technology introduced in recent years as an in-depth analysis of breast cancer diagnostics. Compared with 2D Full-Field Digital Mammography, DBT has demonstrated greater sensitivity and specificity in detecting breast tumors. This work aims to quantitatively evaluate the impact of the systematic introduction of DBT in terms of Biopsy Rate and Positive Predictive Values for the number of biopsies performed (PPV-3). For this purpose, we collected 69,384 mammograms and 7894 biopsies, of which 6484 were Core Biopsies and 1410 were stereotactic Vacuum-assisted Breast Biopsies (VABBs), performed on female patients afferent to the Breast Unit of the Istituto Tumori "Giovanni Paolo II" of Bari from 2012 to 2021, thus, in the period before, during and after the systematic introduction of DBT. Linear regression analysis was then implemented to investigate how the Biopsy Rate had changed over the 10 year screening. The next step was to focus on VABBs, which were generally performed during in-depth examinations of mammogram detected lesions. Finally, three radiologists from the institute's Breast Unit underwent a comparative study to ascertain their performances in terms of breast cancer detection rates before and after the introduction of DBT. As a result, it was demonstrated that both the overall Biopsy Rate and the VABBs Biopsy Rate significantly decreased following the introduction of DBT, with the diagnosis of an equal number of tumors. Besides, no statistically significant differences were observed among the three operators evaluated. In conclusion, this work highlights how the systematic introduction of DBT has significantly impacted the breast cancer diagnostic procedure, by improving the diagnostic quality and thereby reducing needless biopsies, resulting in a consequent reduction in costs., (© 2023. The Author(s).)

For endocrine-positive Her2 negative breast cancer patients at an early stage, the benefit of adding chemotherapy to adjuvant endocrine therapy is not still confirmed. Several genomic tests are available on the market but are very expensive. Therefore, there is the urgent need to explore novel reliable and less expensive prognostic tools in this setting. In this paper, we shown a machine learning survival model to estimate Invasive Disease-Free Events trained on clinical and histological data commonly collected in clinical practice. We collected clinical and cytohistological outcomes of 145 patients referred to Istituto Tumori "Giovanni Paolo II". Three machine learning survival models are compared with the Cox proportional hazards regression according to time-dependent performance metrics evaluated in cross-validation. The c-index at 10 years obtained by random survival forest, gradient boosting, and component-wise gradient boosting is stabled with or without feature selection at approximately 0.68 in average respect to 0.57 obtained to Cox model. Moreover, machine learning survival models have accurately discriminated low- and high-risk patients, and so a large group which can be spared additional chemotherapy to hormone therapy. The preliminary results obtained by including only clinical determinants are encouraging. The integrated use of data already collected in clinical practice for routine diagnostic investigations, if properly analyzed, can reduce time and costs of the genomic tests., (© 2023. The Author(s).)

Background: A timely diagnosis is essential for improving breast cancer patients' survival and designing targeted therapeutic plans. For this purpose, the screening timing, as well as the related waiting lists, are decisive. Nonetheless, even in economically advanced countries, breast cancer radiology centres fail in providing effective screening programs. Actually, a careful hospital governance should encourage waiting lists reduction programs, not only for improving patients care, but also for minimizing costs associated with the treatment of advanced cancers. Thus, in this work, we proposed a model to evaluate several scenarios for an optimal distribution of the resources invested in a Department of Breast Radiodiagnosis., Materials and Methods: Particularly, we performed a cost-benefit analysis as a technology assessment method to estimate both costs and health effects of the screening program, to maximise both benefits related to the quality of care and resources employed by the Department of Breast Radiodiagnosis of Istituto Tumori "Giovanni Paolo II" of Bari in 2019. Specifically, we determined the Quality-Adjusted Life Year (QALY) for estimating health outcomes, in terms of usefulness of two hypothetical screening strategies with respect to the current one. While the first hypothetical strategy adds one team made up of a doctor, a technician and a nurse, along with an ultrasound and a mammograph, the second one adds two afternoon teams., Results: This study showed that the most cost-effective incremental ratio could be achieved by reducing current waiting lists from 32 to 16 months. Finally, our analysis revealed that this strategy would also allow to include more people in the screening programs (60,000 patients in 3 years)., (© 2023. The Author(s).)

Non-small cell lung cancer (NSCLC) represents 85% of all new lung cancer diagnoses and presents a high recurrence rate after surgery. Thus, an accurate prediction of recurrence risk in NSCLC patients at diagnosis could be essential to designate risk patients to more aggressive medical treatments. In this manuscript, we apply a transfer learning approach to predict recurrence in NSCLC patients, exploiting only data acquired during its screening phase. Particularly, we used a public radiogenomic dataset of NSCLC patients having a primary tumor CT image and clinical information. Starting from the CT slice containing the tumor with maximum area, we considered three different dilatation sizes to identify three Regions of Interest (ROIs): CROP (without dilation), CROP 10 and CROP 20. Then, from each ROI, we extracted radiomic features by means of different pre-trained CNNs. The latter have been combined with clinical information; thus, we trained a Support Vector Machine classifier to predict the NSCLC recurrence. The classification performances of the devised models were finally evaluated on both the hold-out training and hold-out test sets, in which the original sample has been previously divided. The experimental results showed that the model obtained analyzing CROP 20 images, which are the ROIs containing more peritumoral area, achieved the best performances on both the hold-out training set, with an AUC of 0.73, an Accuracy of 0.61, a Sensitivity of 0.63, and a Specificity of 0.60, and on the hold-out test set, with an AUC value of 0.83, an Accuracy value of 0.79, a Sensitivity value of 0.80, and a Specificity value of 0.78. The proposed model represents a promising procedure for early predicting recurrence risk in NSCLC patients., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Bove 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.)

In recent years, immediate breast reconstruction after mastectomy surgery has steadily increased in the treatment pathway of breast cancer (BC) patients due to its potential impact on both the morpho-functional and aesthetic type of the breast and the quality of life. Although recent studies have demonstrated how recent radiotherapy techniques have allowed a reduction of adverse events related to breast reconstruction, capsular contracture (CC) remains the main complication after post-mastectomy radio-therapy (PMRT). In this study, we evaluated the association of the occurrence of CC with some clinical, histological and therapeutic parameters related to BC patients. We firstly performed bivariate statistical tests and we then evaluated the prognostic predictive power of the collected data by using machine learning techniques. Out of a sample of 59 patients referred to our institute, 28 patients (i.e., 47%) showed contracture after PMRT. As a result, only estrogen receptor status (ER) and molecular subtypes were significantly associated with the occurrence of CC after PMRT. Different machine learning models were trained on a subset of clinical features selected by a feature importance approach. Experimental results have shown that collected features have a non-negligible predictive power. The extreme gradient boosting classifier achieved an area under the curve (AUC) value of 68% and accuracy, sensitivity, and specificity values of 68%, 64%, and 74%, respectively. Such a support tool, after further suitable optimization and validation, would allow clinicians to identify the best therapeutic strategy and reconstructive timing.

Mastectomy skin-flap necrosis (MSFN) is one of the most feared complications of immediate implant-based breast reconstruction (IIBR). Traditionally, mastectomy skin-flap viability was based only on surgeons' clinical experience. Even though numerous studies have already addressed the patients' risk factors for MSFN, few works have focused on assessing quality of breast envelope. This review investigates mastectomy's flap viability-assessment methods, both preoperative (PMFA) and intraoperative (IMFA), to predict MSFN and its sequalae. Between June and November 2022, we conducted a systematic review of Pubmed/MEDLINE and Cochrane electronic databases. Only English studies regarding PMFA and IMFA applied to IIBR were selected. The use of digital mammography, ultrasound, magnetic resonance imaging, and a combination of several methods before surgery was shown to be advantageous by several authors. Indocyanine performed better than other IMFA, however both thermal imaging and spectroscopy demonstrated novel and promising results. Anyway, the best prediction comes when preoperative and intraoperative values are combined. Particularly in prepectoral reconstruction, when mastectomy flaps are essential to determine a successful breast reconstruction, surgeons' clinical judgment is insufficient in assessing the risk of MSFN. Preoperative and intraoperative assessment techniques play an emerging key role in MSFN prediction. However, although there are several approaches to back up the surgeon's processing choice, there is still a dearth of pertinent literature on the subject, and more research is required., Competing Interests: Disclosure The authors have stated that they have no conflicts of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Introduction: Recently, accurate machine learning and deep learning approaches have been dedicated to the investigation of breast cancer invasive disease events (IDEs), such as recurrence, contralateral and second cancers. However, such approaches are poorly interpretable., Methods: Thus, we designed an Explainable Artificial Intelligence (XAI) framework to investigate IDEs within a cohort of 486 breast cancer patients enrolled at IRCCS Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Using Shapley values, we determined the IDE driving features according to two periods, often adopted in clinical practice, of 5 and 10 years from the first tumor diagnosis., Results: Age, tumor diameter, surgery type, and multiplicity are predominant within the 5-year frame, while therapy-related features, including hormone, chemotherapy schemes and lymphovascular invasion, dominate the 10-year IDE prediction. Estrogen Receptor (ER), proliferation marker Ki67 and metastatic lymph nodes affect both frames., Discussion: Thus, our framework aims at shortening the distance between AI and clinical practice., 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 Massafra, Fanizzi, Amoroso, Bove, Comes, Pomarico, Didonna, Diotaiuti, Galati, Giotta, La Forgia, Latorre, Lombardi, Nardone, Pastena, Ressa, Rinaldi, Tamborra, Zito, Paradiso, Bellotti and Lorusso.)

We report our experience in direct-to-implant breast reconstruction with prepectoral polyurethane implants, with a focus on intraoperative mastectomy flap thickness compared to preoperative data (flap thickness ratio) as a reliable predictive variable of ischemic complications and reconstructive outcomes (satisfaction with breast)., Background: The optimization of nipple sparing mastectomy and implant-based reconstruction techniques led to an increase in the popularity of prepectoral reconstruction. The aim of this study is to explore the ratio between the intraoperative and preoperative breast tissue coverage assessment as reliable tool in order to predict the risk of ischemic complications in prepectoral reconstruction., Methods: We analyzed 124 preoperative digital mammograms of 100 patients who underwent prepectoral implant-based reconstruction. We applied a Rancati modified score for breast tissue coverage classification, adding 4 measurements on the craniocaudal view. The intraoperative mastectomy flap thickness was measured using an intraoperative ultrasound assessment. We investigated the differences between the groups with and without ischemic complications related to the preoperative, intraoperative, and flap thickness ratio data., Results: The flap thickness ratio was lower in ischemic complication group compared to no ischemic complication group (0.4 vs. 0.8) with statistically significant differences for all ischemic complication subgroups: major mastectomy flap necrosis (P = .000), minor mastectomy flap necrosis (P = .005), partial nipple areola complex necrosis (P = .007), and implant exposure (P = .001)., (Copyright © 2022 Elsevier Inc. All rights reserved.)

The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival (DFS) in stage I-III melanoma patients is crucial to optimize patient management. In this study, we designed a deep learning-based model with the aim of learning prognostic biomarkers from WSIs to predict 1-year DFS in cutaneous melanoma patients. First, WSIs referred to a cohort of 43 patients (31 DF cases, 12 non-DF cases) from the Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) public database were firstly annotated by our expert pathologists and then automatically split into crops, which were later employed to train and validate the proposed model using a fivefold cross-validation scheme for 5 rounds. Then, the model was further validated on WSIs related to an independent test, i.e. a validation cohort of 11 melanoma patients (8 DF cases, 3 non-DF cases), whose data were collected from Istituto Tumori 'Giovanni Paolo II' in Bari, Italy. The quantitative imaging biomarkers extracted by the proposed model showed prognostic power, achieving a median AUC value of 69.5% and a median accuracy of 72.7% on the public cohort of patients. These results remained comparable on the validation cohort of patients with an AUC value of 66.7% and an accuracy value of 72.7%, respectively. This work is contributing to the recently undertaken investigation on how treat features extracted from raw WSIs to fulfil prognostic tasks involving melanoma patients. The promising results make this study as a valuable basis for future research investigation on wider cohorts of patients referred to our Institute., (© 2022. The Author(s).)

Background and Purpose: Although the latest breakthroughs in radiotherapy (RT) techniques have led to a decrease in adverse event rates, these techniques are still associated with substantial toxicity, including xerostomia. Imaging biomarkers could be useful to predict the toxicity risk related to each individual patient. Our preliminary work aims to develop a radiomic-based support tool exploiting pre-treatment CT images to predict late xerostomia risk in 3 months after RT in patients with oropharyngeal cancer (OPC)., Materials and Methods: We performed a multicenter data collection. We enrolled 61 patients referred to three care centers in Apulia, Italy, out of which 22 patients experienced at least mild xerostomia 3 months after the end of the RT cycle. Pre-treatment CT images, clinical and dose features, and alcohol-smoking habits were collected. We proposed a transfer learning approach to extract quantitative imaging features from CT images by means of a pre-trained convolutional neural network (CNN) architecture. An optimal feature subset was then identified to train an SVM classifier. To evaluate the robustness of the proposed model with respect to different manual contouring practices on CTs, we repeated the same image analysis pipeline on "fake" parotid contours., Results: The best performances were achieved by the model exploiting the radiomic features alone. On the independent test, the model reached median AUC, accuracy, sensitivity, and specificity values of 81.17, 83.33, 71.43, and 90.91%, respectively. The model was robust with respect to diverse manual parotid contouring procedures., Conclusion: Radiomic analysis could help to develop a valid support tool for clinicians in planning radiotherapy treatment, by providing a risk score of the toxicity development for each individual patient, thus improving the quality of life of the same patient, without compromising patient care., 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 © 2022 Fanizzi, Scognamillo, Nestola, Bambace, Bove, Comes, Cristofaro, Didonna, Di Rito, Errico, Palermo, Tamborra, Troiano, Parisi, Villani, Zito, Lioce and Massafra.)

Designing targeted treatments for breast cancer patients after primary tumor removal is necessary to prevent the occurrence of invasive disease events (IDEs), such as recurrence, metastasis, contralateral and second tumors, over time. However, due to the molecular heterogeneity of this disease, predicting the outcome and efficacy of the adjuvant therapy is challenging. A novel ensemble machine learning classification approach was developed to address the task of producing prognostic predictions of the occurrence of breast cancer IDEs at both 5- and 10-years. The method is based on the concept of voting among multiple models to give a final prediction for each individual patient. Promising results were achieved on a cohort of 529 patients, whose data, related to primary breast cancer, were provided by Istituto Tumori "Giovanni Paolo II" in Bari, Italy. Our proposal greatly improves the performances returned by the baseline original model, i.e., without voting, finally reaching a median AUC value of 77.1% and 76.3% for the IDE prediction at 5-and 10-years, respectively. Finally, the proposed approach allows to promote more intelligible decisions and then a greater acceptability in clinical practice since it returns an explanation of the IDE prediction for each individual patient through the voting procedure., Competing Interests: The authors have declared that no competing interests exist.

Objective: To determine the efficacy of ultrasound in assessing the inguinal lymph nodes in patients with vulvar cancer., Methods: A systematic review of published research up to October 2020 that compares the results of ultrasound to determine groin node status with histology was conducted. All study types that reported primary data on the role of ultrasound in the evaluation of groin lymph nodes in vulvar cancer were included in the systematic review. Data retrieved from the included studies were pooled in random-effects meta-analyses., Results: After the screening and selection process, eight articles were deemed pertinent for inclusion in the systematic review and meta-analysis. The random-effects model showed a pooled Se of 0.85 (95% CI: 0.81-0.89), Sp of 0.86 (95% CI: 0.81-0.91), PPV of 0.65 (95% CI: 0.54-0.79) and NPV of 0.92 (95% CI: 0.91-0.94). There was a pooled LR+ and LR- of 6.44 (95% CI: 3.72-11.4) and 0.20 (95% CI: 0.14-0.27), respectively. The pooled accuracy was 0.85 (95% CI: 0.80-0.91)., Conclusions: Although the studies had small sample sizes, this review represents the best summary of the data so far. Ultrasound has revealed high sensitivity and high negative predictive value in the assessment of nodal status in vulvar cancer.

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori "Giovanni Paolo II" in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.

Immune checkpoint inhibitors (ICIs) have made a breakthrough in the systemic treatment for metastatic triple-negative breast cancer (TNBC) patients. However, results of phase II and III clinical trials assessing ICIs plus chemotherapy as neoadjuvant treatment were controversial and conflicting. We performed a meta-analysis aimed at assessing the Odds Ratio (OR) of the pathological complete response (pCR) rate in trials assessing neoadjuvant chemoimmunotherapy in TNBC. According to our results, the use of neoadjuvant chemoimmunotherapy was associated with higher pCR (OR 1.95; 95% Confidence Intervals, 1.27-2.99). In addition, we highlighted that this benefit was observed regardless of PD-L1 status since the analysis reported a statistically significant and clinically meaningful benefit in both PD-L1 positive and PD-L1 negative patients. These findings further support the exploration of the role of ICIs plus chemotherapy in early-stage TNBC, given the potentially meaningful clinical impact of these agents. Further studies aimed at more deeply investigating this emerging topic in breast cancer immunotherapy are warranted.

Invasive lobular carcinoma is the second most common histologic form of breast cancer, representing 5% to 15% of all invasive breast cancers. Due to an insidious proliferative pattern, invasive lobular carcinoma remains clinically and radiologically elusive in many cases. Breast magnetic resonance imaging (MR) is considered the most accurate imaging modality in detecting and staging invasive lobular carcinoma and it is strongly recommended in pre-operative planning for all ILC. Contrast-enhanced spectral mammography (CESM) is a new diagnostic method that enables the accurate detection of malignant breast lesions similar to that of breast MR. CESM is also a promising breast imaging method for planning surgeries. In this study, we compare the ability of contrast-enhanced spectral mammography (CESM) with breast MR in the preoperative assessment of the extent of invasive lobular carcinoma. All patients with proven invasive lobular carcinoma treated in our breast cancer center underwent preoperative breast MRI and CESM. Images were reviewed by two dedicated breast radiologists and results were compared to the reference standard histopathology. CESM was similar and in some cases more accurate than breast MR in assessing the extent of disease in invasive lobular cancers. Further evaluation in larger prospective randomized trials is needed to validate our preliminary results.

In breast cancer patients, an accurate detection of the axillary lymph node metastasis status is essential for reducing distant metastasis occurrence probabilities. In case of patients resulted negative at both clinical and instrumental examination, the nodal status is commonly evaluated performing the sentinel lymph-node biopsy, that is a time-consuming and expensive intraoperative procedure for the sentinel lymph-node (SLN) status assessment. The aim of this study was to predict the nodal status of 142 clinically negative breast cancer patients by means of both clinical and radiomic features extracted from primary breast tumor ultrasound images acquired at diagnosis. First, different regions of interest (ROIs) were segmented and a radiomic analysis was performed on each ROI. Then, clinical and radiomic features were evaluated separately developing two different machine learning models based on an SVM classifier. Finally, their predictive power was estimated jointly implementing a soft voting technique. The experimental results showed that the model obtained by combining clinical and radiomic features provided the best performances, achieving an AUC value of 88.6%, an accuracy of 82.1%, a sensitivity of 100% and a specificity of 78.2%. The proposed model represents a promising non-invasive procedure for the SLN status prediction in clinically negative patients., (© 2022. The Author(s).)

Characterization of breast cancer into intrinsic molecular profiles has allowed women to live longer, undergoing personalized treatments. With the aim of investigating the relation between different values of ki67 and the predisposition to develop a breast cancer-related IDE at different ages, we enrolled 900 patients with a first diagnosis of invasive breast cancer, and we partitioned the dataset into two sub-samples with respect to an age value equal to 50 years. For each sample, we performed a Kaplan−Meier analysis to compare the IDE-free survival curves obtained with reference to different ki67 values. The analysis on patients under 50 years old resulted in a p-value < 0.001, highlighting how the behaviors of patients characterized by a ki67 ranging from 10% to 20% and greater than 20% were statistically significantly similar. Conversely, patients over 50 years old characterized by a ki67 ranging from 10% to 20% showed an IDE-free survival probability significantly greater than patients with a ki67 greater than 20%, with a p-value of 0.01. Our work shows that the adoption of two different ki67 values, namely, 10% and 20%, might be discriminant in designing personalized treatments for patients under 50 years old and over 50 years old, respectively.

Sentinel node biopsy (SNB) is the standard of care in women with breast cancer (BC) and clinically nonsuspicious axillary lymph nodes (LNs), due to its high negative predictive value (NPV) in the assessment of nodal status. SNB has significantly reduced complications related to the axillary lymph node dissection, such as lymphedema and upper limb dysfunction. The gold standard technique for SNB is the blue dye (BD) and technetium labelled nanocolloid (Tc-99m) double technique. However, nuclear medicine is not available in all Institutions and several new tracers and devices have been proposed, such as indocyanine green (ICG) and superparamagnetic iron oxides (SPIO). All these techniques show an accuracy and detection rate not inferior to that of the standard technique, with different specific pros and cons. The choice of how to perform a SNB primarily depends on the surgeon's confidence with the procedure, the availability of nuclear medicine and the economic resources of the Institutions. In this setting, new tracers, hybrid tracers and imaging techniques are being evaluated in order to improve the detection rate of sentinel lymph nodes (SNs) and minimize the number of unnecessary axillary surgeries through an accurate preoperative assessment of nodal status and to guide new minimally invasive diagnostic procedures of SNs. In particular, the contrast-enhanced ultrasound (CEUS) is an active field of research but cannot be recommended for clinical use at this time. The ICG fluorescence technique was superior in terms of DR, as well as having the lowest FNR. The DR descending order was SPIO, Tc, dual modality (Tc/BD), CEUS and BD. This paper is a narrative review of the most common SNB techniques in BC with a focus on recent innovations.

Background : Worldwide, breast cancer (BC) is the most common malignancy in the female population. In recent years, its diagnosis in young women has increased, together with a growing desire to become pregnant later in life. Although there is evidence about the detrimental effect of chemotherapy (CT) on the menses cycle, a practical tool to measure ovarian reserve is still missing. Recently, anti-Mullerian hormone (AMH) has been considered a good surrogate for ovarian reserve. The main objective of this paper is to evaluate the effect of CT on AMH value., Methods: A systematic review and meta-analysis were conducted on the PubMed and Scopus electronic databases on articles retrieved from inception until February 2021. Trials evaluating ovarian reserves before and after CT in BC were included. We excluded case reports, case-series with fewer than ten patients, reviews (narrative or systematic), communications and perspectives. Studies in languages other than English or with polycystic ovarian syndrome (PCOS) patients were also excluded. AMH reduction was the main endpoint. Egger's and Begg's tests were used to assess the risk of publication bias., Results: Eighteen trials were included from the 833 examined. A statistically significant decline in serum AMH concentration was found after CT, persisting even after years, with an overall reduction of -1.97 (95% CI: -3.12, -0.82). No significant differences in ovarian reserve loss were found in the BRCA1/2 mutation carriers compared to wild-type patients., Conclusions: Although this study has some limitations, including publication bias, failure to stratify the results by some important factors and low to medium quality of the studies included, this metanalysis demonstrates that the level of AMH markedly falls after CT in BC patients, corresponding to a reduction in ovarian reserve. These findings should be routinely discussed during oncofertility counseling and used to guide fertility preservation choices in young women before starting treatment.

The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.

Contrast-enhanced spectral mammography (CESM) is an advanced instrument for breast care that is still operator dependent. The aim of this paper is the proposal of an automated system able to discriminate benign and malignant breast lesions based on radiomic analysis. We selected a set of 58 regions of interest (ROIs) extracted from 53 patients referred to Istituto Tumori "Giovanni Paolo II" of Bari (Italy) for the breast cancer screening phase between March 2017 and June 2018. We extracted 464 features of different kinds, such as points and corners of interest, textural and statistical features from both the original ROIs and the ones obtained by a Haar decomposition and a gradient image implementation. The features data had a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme for dimension reduction was needed. Specifically, a principal component analysis (PCA) dimension reduction technique that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, we trained three different classifiers, that is a random forest, a naïve Bayes and a logistic regression, on each sub-set of principal components (PC) selected by a sequential forward algorithm. Moreover, we focused on the starting features that contributed most to the calculation of the related PCs, which returned the best classification models. The method obtained with the aid of the random forest classifier resulted in the best prediction of benign/malignant ROIs with median values for sensitivity and specificity of 88.37% and 100%, respectively, by using only three PCs. The features that had shown the greatest contribution to the definition of the same were almost all extracted from the LE images. Our system could represent a valid support tool for radiologists for interpreting CESM images.