Features of the Nurse-Patient Relationship: Insights from a Qualitative Review Using Artificial Intelligence Interpretation.

Introduction: This qualitative literature review explored the intersection of art, creativity, and the nurse-patient relationship in the context of oncology nursing. It delved into the perceptions and reflections of nurses as captured by Generative Artificial Intelligence (GAI) analysis from two specialized nursing databases.

Methods: The protocol was registered on the Open Science Framework (OSF) Platform.

Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients.

Objectives: Endometrial carcinosarcoma is a rare, aggressive high-grade endometrial cancer, accounting for about 5% of all uterine cancers and 15% of deaths from uterine cancers. The treatment can be complex, and the prognosis is poor. Its increasing incidence underscores the urgent requirement for personalized approaches in managing such challenging diseases.

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

Background: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-acquired pre- and mid-treatment quantitatively capture information about tumor heterogeneity as potential earlier indicators of pathological complete response (pCR) to NAC in breast cancer.

Aims: This study aimed to develop an ensemble deep learning-based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre- and mid-treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC.

Prediction of breast cancer Invasive Disease Events using transfer learning on clinical data as image-form.

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.

Sex differences in adverse events among cancer patients receiving immune checkpoint inhibitors: the MOUSEION-07 systematic review and meta-analysis.

Immune checkpoint inhibitors have revolutionized cancer treatment, but they are associated with a range of immune-related adverse events (irAEs), and emerging evidence suggests significant sex differences in the incidence, type, and severity of these toxicities, suggesting an influential factor and understanding sex-related differences in irAEs as crucial for optimizing patient care and improving clinical outcomes. In MOUSEION-07 study, we aimed to assess the association between sex and treatment-related adverse events in cancer patients treated with immunotherapy through a large up-to-date meta-analysis of available clinical trials. The present systematic review and meta-analysis was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis.

Immunobiology of biliary tract cancer and recent clinical findings in approved and upcoming immune checkpoint inhibitors.

Introduction: Recently, immunotherapy has offered new hope for treating biliary tract cancer (BTC). However, several issues are to be considered, including the lack of validated predictive biomarkers that could help to identify patient groups which are most likely to benefit from such therapeutic approaches.

Areas Covered: In the current article, we will provide an overview of recent results and ongoing and future research directions of immunotherapy in BTC, with a special focus on recently published, practice-changing data, and ongoing active and recruiting clinical trials.

Self-perceived attitudes of Italian oncology nurses towards clinical trial involvement: A cohort observational study.

Background: Literature is lacking when it comes to oncology nursing attitudes in clinical trial involvement.

Objective: To assess how Italian oncology nurses perceived their attitudes in clinical trials involvement.

Methods: An on-line cohort observational study was carried out.

Robust machine learning challenge: An AIFM multicentric competition to spread knowledge, identify common pitfalls and recommend best practice.

Purpose: A novel and unconventional approach to a machine learning challenge was designed to spread knowledge, identify robust methods and highlight potential pitfalls about machine learning within the Medical Physics community.

Methods: A public dataset comprising 41 radiomic features and 535 patients was employed to assess the potential of radiomics in distinguishing between primary lung tumors and metastases. Each participant developed two classification models using: (i) all features (base model); (ii) only robust features (robust model).

Nipple-Areola Complex Reconstruction Using FixNip NRI Implant after Mastectomy: An Innovative Technique.

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.

Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.

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.

An artificial intelligence-based model exploiting H&E images to predict recurrence in negative sentinel lymph-node melanoma patients.

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.

Developing an ensemble machine learning study: Insights from a multi-center proof-of-concept study.

Background: To address the numerous unmeet clinical needs, in recent years several Machine Learning models applied to medical images and clinical data have been introduced and developed. Even when they achieve encouraging results, they lack evolutionary progression, thus perpetuating their status as autonomous entities. We postulated that different algorithms which have been proposed in the literature to address the same diagnostic task, can be aggregated to enhance classification performance.

Generative Artificial Intelligence (AI) to Uncover Insights From Breast Cancer Patients' Perceptions to Mindfulness-Based Stress Reduction (MBSR) Interventions.

The study's central objective is to harness the power of generative Artificial Intelligence (AI), in particular based on Large Language Models, as a valuable resource for delving deeper into the insights offered by patients with breast cancer (BC) who actively participated in a Mindfulness-Based Stress Reduction (MBSR) program. In a 6-week MBSR program, each session lasted 2 hours and encompassed a range of techniques, including sitting meditation, body scan, Hatha yoga, and walking meditation. A total of 25 participants were enrolled in the study.

Variations in the Five Facets of Mindfulness in Italian Oncology Nurses according to Sex, Work Experience in Oncology, and Shift Work.

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.

Care Nursing in Immune Disorder Assessment among Adult Oncology Patients: A Scoping Review.

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 role of immune checkpoint inhibitors in the first-line treatment for patients with advanced biliary tract cancer: a systematic review and meta-analysis of randomized trials.

Background: We performed a systematic review and meta-analysis to further explore the impact of the addition of immunotherapy to gemcitabine-cisplatin as first-line treatment for advanced biliary tract cancer (BTC) patients.

Methods: Literature research was performed, and hazard ratio values and 95% confidence intervals were calculated. Heterogeneity among studies was assessed using the tau-squared estimator .

Nursing Coaching Can Improve the Quality of Life and Immune-Endocrine Condition in Hospitalized Cancer Patients.

Introduction: Any cancer diagnosis induces fear and shocking emotional experiences accompanied by anxiety, depression, unpredictability, and distress. The emotional effect of a cancer diagnosis and the rigidity of cancer treatment negatively impact the quality of life (QoL) of patients, and this may continue after treatment. Additionally, emotional distress induces neuroendocrine stress activation systems and raises stress hormone secretion by causing immunological dysfunctions.

Attitudes, Knowledge and Clinical Practice of Health Professionals towards Psychological Disorders in Cancer Patients: An Observational Study.

Background: The suffering associated with a cancer diagnosis can find different channels to express itself: sleep disorders, psychiatric disorders, sexuality. These are not always analyzed by health professionals, but they have an impact on the patient's quality of life and on the outcome of the disease.

Methods: An observational study was conducted in order to investigate attitudes, knowledge and clinical practice towards psychological symptoms in cancer patients.

Breast Cancer Prevention Practices and Knowledge in Italian and Chinese Women in Italy: Clinical Checkups, Free NHS Screening Adherence, and Breast Self-Examination (BSE).

Breast cancer remains a significant global concern, underscoring the critical need for early detection and prevention strategies. Primary and secondary preventive measures, such as routine screenings and behaviors like breast self-examination (BSE), play a crucial role in facilitating early diagnosis. While the National Health System (NHS) in Italy offers free regular screenings for women aged 50-69, there is a lack of clarity regarding the participation of both Italian and Chinese women residing in Italy in these screening programs.

An explainable machine learning model to solid adnexal masses diagnosis based on clinical data and qualitative ultrasound indicators.

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.

Sex Differences in Anxiety and Depression Conditions among Cancer Patients: A Systematic Review and Meta-Analysis.

(1) Background: Evidence suggested inconsistent results in anxiety and depression scores among female and male cancer patients. The present systematic review and meta-analysis aimed to assess how anxiety and depression conditions among cancer patients vary according to sex. (2) Methods: This systematic review and meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA).