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Purpose of ReviewBreast cancer (BC) is the most common cancer diagnosed in women in the West World. Coping with cancer is cause of extreme stress for patients and their family. The purpose of this review is to evaluate possible approaches to follow to control those situations that can impact on quality of life (QoL) and compliance to treatments.Recent FindingsAnxiety, distress, depression, and posttraumatic stress disorder are the most frequent psychological disorders in BC patients. Cognitive disorders and sexual dysfunction can also be important in affecting QoL both in younger and older patients. Younger and older patients show different characteristics of these disorders and different strategies of managing them.SummarySeveral psychotherapeutic and supportive approaches have proven effective in managing psychological disorders in BC patients. Every BC patient should be supported with these techniques during her entire oncological history, in order to increase QoL and compliance to treatments.

Cancer care is moving from a disease‐focused management toward a patient‐centered tailored approach. Multidisciplinary management that aims to define individual, optimal treatment strategies through shared decision making between healthcare professionals and patient is a fundamental aspect of high‐quality cancer care and often includes radiation oncology. Advances in technology and radiobiological research allow to deliver ever more tailored radiation treatments in an ever easier and faster way, thus improving the efficacy, safety, and accessibility of radiation therapy. While these changes are improving quality of cancer care, they are also enormously increasing complexity of decision making, thus challenging the ability to deliver quality affordable cancer care. In this review, we provide an updated outline of the role of radiation oncology in the modern multidisciplinary treatment of cancer. Particularly, we focus on the way some developments in key areas of cancer management are challenging multidisciplinary cancer care in the different clinical settings of early, locally advanced, and metastatic disease, thus highlighting some priority areas of research. Cancer care is moving from a disease‐focused management toward a patient‐centered tailored approach. A huge technology‐driven revolution is taking place and innovative technologies are increasingly entering the mainstream of clinical practice to personalize cancer treatment. Here, we provide an outline of the role of modern radiation oncology in contemporary multidisciplinary treatment of cancer focusing on the main areas of innovation that are contributing to a shift toward an increasingly tailored use of radiotherapy.

Background Different studies have proved in recent years that hypofractionated radiotherapy (RT) improves overall survival of patients affected by locally advanced, unresectable, pancreatic cancer. The clinical management of these patients generally leads to poor results and is considered very challenging, due to different factors, heavily influencing treatment delivery and its outcomes. Firstly, the dose prescribed to the target is limited by the toxicity that the highly radio-sensitive organs at risk (OARs) surrounding the disease can develop. Treatment delivery is also complicated by the significant inter-fractional and intra-fractional variability of therapy volumes, mainly related to the presence of hollow organs and to the breathing cycle. Main body of the abstract The recent introduction of magnetic resonance guided radiotherapy (MRgRT) systems leads to the opportunity to control most of the aforementioned sources of uncertainty influencing RT treatment workflow in pancreatic cancer. MRgRT offers the possibility to accurately identify radiotherapy volumes, thanks to the high soft-tissue contrast provided by the Magnetic Resonance imaging (MRI), and to monitor the tumour and OARs positions during the treatment fraction using a high-temporal cine MRI. However, the main advantage offered by the MRgRT is the possibility to online adapt the RT treatment plan, changing the dose distribution while the patient is still on couch and successfully addressing most of the sources of variability. Short conclusion Aim of this study is to present and discuss the state of the art, the main pitfalls and the innovative opportunities offered by online adaptive MRgRT in pancreatic cancer treatment.

Despite the recent advanced developments in radiation therapy planning, treatment planning for head-neck and pelvic cancers remains challenging due to large concave target volumes, multiple dose prescriptions and numerous organs at risk close to targets. Inter-institutional studies highlighted that plan quality strongly depends on planner experience and skills. Automated optimization of planning procedure may improve plan quality and best practice. We performed a comprehensive dosimetric and clinical evaluation of the Pinnacle 3 AutoPlanning engine, comparing automatically generated plans (AP) with the historically clinically accepted manually-generated ones (MP). Thirty-six patients (12 for each of the following anatomical sites: head-neck, high-risk prostate and endometrial cancer) were re-planned with the AutoPlanning engine. Planning and optimization workflow was developed to automatically generate “dual-arc” VMAT plans with simultaneously integrated boost. Various dose and dose-volume parameters were used to build three metrics able to supply a global Plan Quality Index evaluation in terms of dose conformity indexes, targets coverage and sparing of critical organs. All plans were scored in a blinded clinical evaluation by two senior radiation oncologists. Dose accuracy was validated using the PTW Octavius-4D phantom together with the 1500 2D-array. Autoplanning was able to produce high-quality clinically acceptable plans in all cases. The main benefit of Autoplanning strategy was the improvement of overall treatment quality due to significant increased dose conformity and reduction of integral dose by 6–10%, keeping similar targets coverage. Overall planning time was reduced to 60–80 minutes, about a third of time needed for manual planning. In 94% of clinical evaluations, the AP plans scored equal or better to MP plans. Despite the increased fluence modulation, dose measurements reported an optimal agreement with dose calculations with a γ-pass-rate greater than 95% for 3%(global)-2 mm criteria. Autoplanning engine is an effective device enabling the generation of VMAT high quality treatment plans according to institutional specific planning protocols.

There is a large variability regarding the definition and choice of primary endpoints in phase 2 and phase 3 multimodal rectal cancer trials, resulting in inconsistency and difficulty of data interpretation. Also, surrogate properties of early and intermediate endpoints have not been systematically assessed. We provide a comprehensive review of clinical and surrogate endpoints used in trials for non-metastatic rectal cancer. The applicability, advantages, and disadvantages of these endpoints are summarised, with recommendations on clinical endpoints for the different phase trials, including limited surgery or non-operative management for organ preservation. We discuss how early and intermediate endpoints, including patient-reported outcomes and involvement of patients in decision making, can be used to guide trial design and facilitate consistency in reporting trial results in rectal cancer.

Locally advanced rectal cancer is usually treated with preoperative chemoradiation. After chemoradiation and surgery, 15–27% of the patients have no residual viable tumour at pathological examination, a pathological complete response (pCR). This study established whether patients with pCR have better long-term outcome than do those without pCR. In PubMed, Medline, and Embase we identified 27 articles, based on 17 different datasets, for long-term outcome of patients with and without pCR. 14 investigators agreed to provide individual patient data. All patients underwent chemoradiation and total mesorectal excision. Primary outcome was 5-year disease-free survival. Kaplan-Meier survival functions were computed and hazard ratios (HRs) calculated, with the Cox proportional hazards model. Subgroup analyses were done to test for effect modification by other predicting factors. Interstudy heterogeneity was assessed for disease-free survival and overall survival with forest plots and the Q test. 484 of 3105 included patients had a pCR. Median follow-up for all patients was 48 months (range 0–277). 5-year crude disease-free survival was 83·3% (95% CI 78·8–87·0) for patients with pCR (61/419 patients had disease recurrence) and 65·6% (63·6–68·0) for those without pCR (747/2263; HR 0·44, 95% CI 0·34–0·57; p<0·0001). The Q test and forest plots did not suggest significant interstudy variation. The adjusted HR for pCR for failure was 0·54 (95% CI 0·40–0·73), indicating that patients with pCR had a significantly increased probability of disease-free survival. The adjusted HR for disease-free survival for administration of adjuvant chemotherapy was 0·91 (95% CI 0·73–1·12). The effect of pCR on disease-free survival was not modified by other prognostic factors. Patients with pCR after chemoradiation have better long-term outcome than do those without pCR. pCR might be indicative of a prognostically favourable biological tumour profile with less propensity for local or distant recurrence and improved survival. None.

BackgroundLocal excision might represent an alternative to total mesorectal excision for patients with locally advanced rectal cancer who achieve a major or complete clinical response after neoadjuvant chemoradiotherapy.MethodsBetween August 2005 and July 2011, 63 patients with mid-low rectal adenocarcinoma who had a major/complete clinical response after neoadjuvant chemoradiotherapy were enrolled in a multicenter prospective phase 2 trial and underwent transanal full thickness local excision. The main endpoint of this study was to evaluate the 5- and 10-year overall, relapse-free, local, and distant relapse-free survival, which were calculated by applying the Kaplan–Meier method. The rate of patients with rectum preserved and without stoma were also calculated.ResultsOf 63 patients, 38 (60%) were male and 25 (40%) were female, with a median (range) age of 64 (25–82) years. At baseline, the following clinical stages were found: cT2, n = 21 (33.3%); cT3, n = 42 (66.6%), 39 (61.9%) patients were cN+. At a median (range) follow-up of 108 (32–166) months, the estimated cumulative 5- and 10-year overall survival, relapse-free survival, local recurrence-free survival, and distant recurrence-free survival were 87% (95% CI 76–93) and 79% (95% CI 66–87), 89% (95% CI 78–94) and 82% (95% CI 66–91), both 91% (95% CI 81–96), and 90% (95% CI 80–95) and 86% (95% CI 73–93), respectively. Overall, 49 (77.8%) patients had their rectum preserved, and 54 (84.1%) were stoma-free.ConclusionIn highly selected patients, the local excision approach after neoadjuvant chemoradiotherapy is associated with excellent long-term outcomes, high rates of rectum preservation and absence of permanent stoma.

Purpose of ReviewRecognize which are the elements that predict why a person is aging faster or slower and which intervention we can arrange to slow down the process, which permits to prevent or delay the progression of multimorbidity and disability.Recent FindingsAging is a complex process that leads to changes in all the systems of the body and all the functions of the person; however, aging develops at different rates in different people, and chronological age is not always consistent with biological age.SummaryGerontologists are focused not only on finding the best theory able to explain aging but also on identifying one or more markers, which are able to describe aging processes. These biomarkers are necessary to better define the aging-related pathologies, manage multimorbidity, and improve the quality of life. The aim of this paper is to review the most recent evidence on aging biomarkers and the clusters related to them for personalization of treatments.

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home.

Radiogenomics is a specific application of radiomics where imaging features are linked to genomic profiles. We aim to develop a radiogenomics model based on ovarian US images for predicting germline BRCA1/2 gene status in women with healthy ovaries. From January 2013 to December 2017 a total of 255 patients addressed to germline BRCA1/2 testing and pelvic US documenting normal ovaries, were retrospectively included. Feature selection for univariate analysis was carried out via correlation analysis. Multivariable analysis for classification of germline BRCA1/2 status was then carried out via logistic regression, support vector machine, ensemble of decision trees and automated machine learning pipelines. Data were split into a training (75%) and a testing (25%) set. The four strategies obtained a similar performance in terms of accuracy on the testing set (from 0.54 of logistic regression to 0.64 of the auto-machine learning pipeline). Data coming from one of the tested US machine showed generally higher performances, particularly with the auto-machine learning pipeline (testing set specificity 0.87, negative predictive value 0.73, accuracy value 0.72 and 0.79 on training set). The study shows that a radiogenomics model on machine learning techniques is feasible and potentially useful for predicting g BRCA1/2 status in women with healthy ovaries.