Search

Simple Summary: Cancer develops through a complex process involving genetic changes that can lead to uncontrolled cell growth and tumor formation. This research focuses on developing an advanced approach to classify tumors into meaningful subgroups based on somatic mutations. Using machine learning techniques, specifically a deep neural network, and integrating genetic data with known gene interaction networks, we propose a framework for tumor stratification, called D3NS (deep neural network integrated into network-based stratification). This framework identifies patient subtypes predictive for survival and significantly associated with several clinical outcomes (tumor stage, grade and treatment response). We applied D3NS to real-world data from the Cancer Genome Atlas for bladder, ovarian, and kidney cancers. The results demonstrate the potential of this approach to improve cancer stratification, positioning it as a useful base model for cancer research and a promising tool in clinical settings. (1) Background: The identification of tumor subtypes is fundamental in precision medicine for accurate diagnoses and personalized therapies. Cancer development is often driven by the accumulation of somatic mutations that can cause alterations in tissue functions and morphologies. In this work, a method based on a deep neural network integrated into a network-based stratification framework (D3NS) is proposed to stratify tumors according to somatic mutations. (2) Methods: This approach leverages the power of deep neural networks to detect hidden information in the data by combining the knowledge contained in a network of gene interactions, as typical of network-based stratification methods. D3NS was applied using real-world data from The Cancer Genome Atlas for bladder, ovarian, and kidney cancers. (3) Results: This technique allows for the identification of tumor subtypes characterized by different survival rates and significant associations with several clinical outcomes (tumor stage, grade or response to therapy). (4) Conclusion: D3NS can provide a base model in cancer research and could be considered as a useful tool for tumor stratification, offering potential support in clinical settings. [ABSTRACT FROM AUTHOR]

Objective: Distress during SARS‐CoV‐2 outbreak affected also cancer patients' well‐being. Aim of this study was to investigate patient' reactions and behavior (flexible‐adaptive vs. inflexible‐maladaptive) during the SARS‐CoV‐2 outbreak. Methods: A cross‐sectional survey was designed with a self‐report questionnaire, "the ImpACT questionnaire," developed for the study. Regression analysis was performed on data. Results: Four hundred and forty five cancer patients from 17 Italian regions participated in the study. 79.8% of participants were female (mean age of 58 years). 92.6% of participants reported feeling vulnerable to COVID‐19 contagion; 75.6% reported helpless, 62.7% sad, 60.4% anxious, and 52.0% anger. Avoidance of thinking about coronavirus is the principal maladaptive behavior that emerged. Participants who reported feeling anxious were more likely to have fear of staff being infected with COVID‐19 (OR = 3.01; 95% CI = 1.49–6.30) and to have disrupted sleep due to worry (OR = 2.42; 95% CI = 1.23–4.83). Younger participants reported more anxiety (OR = 0.97; 95% CI = 0.94–1.00); men reported feeling calm more than women (OR = 2.60; 95% CI = 1.27–5.43). Conclusions: Majority of cancer patients reported serious concerns regarding SARS‐CoV‐2 infection; reliable information and psychological support must be offers to respond to these needs. [ABSTRACT FROM AUTHOR]

Simple Summary: Operation Phalco is an experimental training protocol for breast cancer survivors implemented through a network of oncologists, sports medicine physicians and kinesiologists. The aim of this research is to evaluate the effects of an online adapted physical activity protocol with respect to a usual care on functional capacity, fatigue and quality of life of breast cancer patients. The protocol was structured by taking into account both cancer-related issues and the presence of comorbidities detected in the enrolled patients. For this reason, the patients recruited in the oncology centers of Rome and Verona were divided into three risk levels based on the risk stratification carried out by a sports medicine physician during a medical examination. The results evidenced the positive impact of a well-adapted physical activity on breast cancer patients' quality of life, suggesting that this program should be included as early as possible in the cancer patient's care process. The international literature emphasizes the importance of physical activity (PA) in the first steps after cancer surgery. The regular practice of physical exercise causes positive adaptations on several functional capacities, with positive consequences on patients' quality of life. This project aims to evaluate the effect of a post-operative training protocol, structured by taking into account both cancer-related issues and the presence of comorbidities, on functional capacities and quality of life of breast cancer survivors. Therefore, it was necessary to create a synergy between oncologists (referring physicians), sport medicine physicians (risk stratification and exercise prescription) and kinesiologists (trainers). Thirty-five post-surgery BC patients decided on a voluntary basis to attend an online Adapted PA (APA) protocol for 4 months, twice a week (APA Group) or Usual Care Group (UC Group). Functional capacity of the APA Group significantly increased, by 13.1% (p = 0.000), whereas perceived exertion decreased by 19.7% (p = 0.020). In the same group, the general health evaluated through the questionnaire EORTC-QLQ-C30 increased (p = 0.050). No differences were found in the UC Group. Operation Phalco, creating a network between oncologists, sports medicine physicians and kinesiologists, confirms the importance of structuring a post-operative path where APA should be included as early as possible in the cancer patient care. [ABSTRACT FROM AUTHOR]