Autologous fat grafting after sarcoma surgery : evaluation of oncological safety

Background: The regenerative effectiveness of lipoaspirate procedures relies on the presence of mesenchymal stem cells, but the stromal microenvironment and hormonal secretions of the adipose tissue may be involved in cancer growth. Only few oncological outcome studies of fat grafting at the surgical site of malignant neoplasms of mesenchymal origin are available; none of these studies examined a series of sarcoma cases. Objectives: We analyzed outcome in terms of local or distant spread and overall survival to investigate the oncological safety of fat grafting in patients with sarcoma. Patients and methods: Sixty consecutive patients who had undergone 143 fat grafting procedures after surgical resection of bone and soft tissue sarcomas of the head, trunk, and limbs with clear resection margins were enrolled from 2004 to 2015 in our tertiary care center. A multidisciplinary sarcoma team administered adjuvant therapies. Patients were recurrence free at fat grafting. Results: The overall median follow-up was 7.5 years. At follow-up after fat grafting (2.4 years), one patient had distant metastasis and two had local relapse. Kaplan–Meier analysis showed disease-free survival rate of 95.4% (CI: 89.1–100.0) at 24 months. The risk of local recurrence (LR) within 24 months was 4.6% (CI: 0.0–20.9). The probability of not having LR after fat grafting was ≥ 89.1%. Conclusion: We found no evidence of an increased cancer risk after fat grafting procedures in patients with sarcoma, but a stimulatory role of fat cannot be excluded for bone sarcomas based on the cases reported here, and further studies are therefore needed

Autologous fat grafting after sarcoma surgery : evaluation of oncological safety

Background: The regenerative effectiveness of lipoaspirate procedures relies on the presence of mesenchymal stem cells, but the stromal microenvironment and hormonal secretions of the adipose tissue may be involved in cancer growth. Only few oncological outcome studies of fat grafting at the surgical site of malignant neoplasms of mesenchymal origin are available; none of these studies examined a series of sarcoma cases. Objectives: We analyzed outcome in terms of local or distant spread and overall survival to investigate the oncological safety of fat grafting in patients with sarcoma. Patients and methods: Sixty consecutive patients who had undergone 143 fat grafting procedures after surgical resection of bone and soft tissue sarcomas of the head, trunk, and limbs with clear resection margins were enrolled from 2004 to 2015 in our tertiary care center. A multidisciplinary sarcoma team administered adjuvant therapies. Patients were recurrence free at fat grafting. Results: The overall median follow-up was 7.5 years. At follow-up after fat grafting (2.4 years), one patient had distant metastasis and two had local relapse. Kaplan\u2013Meier analysis showed disease-free survival rate of 95.4% (CI: 89.1\u2013100.0) at 24 months. The risk of local recurrence (LR) within 24 months was 4.6% (CI: 0.0\u201320.9). The probability of not having LR after fat grafting was 65 89.1%. Conclusion: We found no evidence of an increased cancer risk after fat grafting procedures in patients with sarcoma, but a stimulatory role of fat cannot be excluded for bone sarcomas based on the cases reported here, and further studies are therefore needed

Local safety of immediate reconstruction during primary treatment of breast cancer : direct-to-implant versus expander-based surgery

Introduction: After mastectomy, immediate breast reconstruction is paramount. With the growing number of nipple-sparing mastectomies, the chances of successful one-stage reconstruction with implants are also increasing. Local safety is one of the main issues. This study investigated the factors that could lead to major or minor complications after expander-based versus direct-to-implant (DTI) reconstruction. Methods: The studied factors were age, body mass index (BMI), hypertension, smoking, diabetes, type of mastectomy (nipple-sparing/total), implant size, neoadjuvant/adjuvant chemotherapy, and radiotherapy. The study sample included 294 immediate reconstructions over 3 years. The primary outcome was the incidence of complications, major or minor depending on the necessity of revision surgery. For the DTI pocket, we applied a variant of the conventional submuscular technique. Results: In DTI reconstructions (median follow-up 26 months), the complication rate was 17.2% (4.3% major and 12.8% minor) with no significant association with clinical variables. In expander-based reconstructions (median follow-up 19 months), the complication rate was 18.3% (12.5% major and 5.8% minor). Univariate analysis showed a significant association between overall complications and radiotherapy (P = 0.01) as well as between major complications and expander size (P < 0.005), BMI (P < 0.005), and radiotherapy (P < 0.01); radiotherapy and BMI retained significance in multivariate analysis. Neoadjuvant/adjuvant chemotherapy did not affect the complication rate. Conclusions: There was evidence of an association between major complications and clinical variables in the expander-based cohort. Larger expander size was a predictor of failure, especially combined with radiation. Direct-to-implant reconstruction proved to be safe. We describe a reliable method of reconstruction and a safe range of implant sizes even beyond 500 g

Estimating Relapse Free Survival as a Net Probability : Regression Models and Graphical Representation : An Application of a Large Breast Cancer Case Series

In most clinical studies, the evaluation of the effect of a therapy and the impact of prognostic factors is based on relapse-free survival. Relapse free is a net survival, since it is interpreted as the relapsefree probability that would be observed if all patients experienced relapse sooner or later. Death without evidence of relapse prevents the subsequent observation of relapse, acting in a semi-competing risks framework. Relapse free survival is often estimated by standard regression models after censoring times to death. The association between relapse and death is thus accounted for. However, to better estimate relapse free survival, a bivariate distribution of times to events needs to be considered, for example by means of copula models. We concentrate here on the copula graphic estimator, for which a pertinent regression model has been developed. No direct parametric estimation of the regression coefficient for the covariates is available and the evaluation of the impact of covariates on relapse free survival is based on graphical representation for each covariate singularly. The advantage of this approach is based on the relationship between net survival, and crude cumulative incidences. Regression models can be fitted for the latter quantities and the estimates can be used to compute net survival through a copula structure. Our proposal is based on flexible regression transformation model on crude cumulative incidences based on pseudo-values. An overall view of the joint association among covariates and relapse free survival is obtained through Multiple Correspondence Analysis. Moreover cluster analysis on MCA coordinates was used to synthesize covariate patterns and to estimates the corresponding relapse free survival curve. This approach has been applied to a large \u201chistorical\u201d case series of patients with breast cancer

Bimodal mortality dynamics for uveal melanoma : a cue for metastasis development traits?

Background: The study estimates mortality dynamics (event-specific hazard rates over a follow-up time interval) for uveal melanoma. Methods: Three thousands six hundred seventy two patients undergoing radical or conservative treatment for unilateral uveal melanoma, whose yearly follow-up data were reported in three published datasets, were analysed. Mortality dynamics was studied by estimating with the life-table method the discrete hazard rate for death. Smoothed curves were obtained by a Kernel-like smoothing procedure and a piecewise exponential regression model. The ratio deaths/patients at risk per year was the main outcome measure. Results: The three explored hazard rate curves display a common bimodal pattern, with a sudden increase peaking at about three years, followed by reduction until the sixth-seventh year and a second surge peaking at about nine years after treatment. Conclusions: The bimodal pattern of mortality indicates that uveal melanoma metastatic development cannot be explained by a continuous growth model. Similar metastasis dynamics have been reported for other tumours, including early breast cancer, for which it supported a paradigm shift to an interrupted growth model, the implications of which are episodes of 'tumour dormancy'. We propose that the concepts of tumour homeostasis, tumour dormancy and enhancement of metastasis growth related to primary tumour removal, convincingly explaining the clinical behaviour of breast cancer, may be used for uveal melanoma as well. To confirm this proposition, a careful analysis of uveal melanoma metastasis dynamics is strongly warranted. \ua9 2014 Demicheli et al.; licensee BioMed Central Ltd

Cancer profiles by Affinity Propagation

The Affinity Propagation algorithm is applied to various problems of breast and cutaneous tumours subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. Well know breast cancer case series and cutaneous melanoma were used to compare the results of the Affinity Propagation with the results obtained with standard algorithms and indexes for the optimal choice of the number of clusters.Results from Affinity Propagation are consistent with the results already obtained having the advantage of providing an indication about the number of clusters

Modeling provincial Covid-19 epidemic data in Italy using an adjusted time-dependent SIRD model

In this paper we develop a predictive model for the spread of COVID-19 infection at a provincial (i.e. EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and from local newspaper websites. This integration is mainly concerned with COVID-19 cause specific death data which are not available at NUTS-3 level from open official data data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic, specifically the number of susceptible, infected, deceased and recovered people. Predictive model performance is evaluated using comparison with real data

Heterogeneity of covid-19 outbreak in italy

An outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) started in December 2019 in China and was declared a pandemic on 11.03.2020 by WHO. Italy is one of the most afflicted Country by this epidemic with 136,110 confirmed cases and 16,654 deaths on 9.4.2020 (at the same date, the Ministry of Health was reporting 143,626 cases). During these few months the National Health Service have made a great effort to cope with the increasing request of intensive care beds and all the elective activities in hospital have been suspended. Data from the different Italian regions shows different patterns of positive and dead for this syndrome. Moreover, striking differences of the observed lethality of the infections among different areas were immediately evident from the epidemic reports. It will be of critical relevance to understand the expected evolution of the first lock-down phase, driving the exhaustion of the Covid-19 outbreak