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. 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 .

Assessing the cost-effectiveness of waiting list reduction strategies for a breast radiology department: a real-life case study.

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).

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. Data collected included: sex, years of work experience in oncology field and 10 items assessing participants' self-perceptions of their attitudes in clinical trials. Linear regression was used to assess associations between work experience and self-perceived preparedness. RESULTS: A total of 338 Italian oncology nurses were enrolled. Most nurses declared not receiving any specific training in oncology clinical trials (23.1%). No significant associations were reported between self- perceived attitudes in clinical trial involvement in the oncology setting and both work experience and clinical trial involvement. CONCLUSIONS: Cancer centers are improving cancer nursing research in supplying clinical care. But very few centers are involved in training oncology nurses. This gap seems to be very deep in taking into consideration the oncology nursing research in clinical trials, too.

A Machine Learning Approach for Predicting Capsular Contracture after Postmastectomy Radiotherapy in Breast Cancer Patients.

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.