Evaluating a digital tool for supporting breast cancer patients: a randomized controlled trial protocol (ADAPT)

Use of Telemedicine to Improve Cognitive Functions and Psychological Well-Being in Patients with Breast Cancer: A Systematic Review of the Current Literature

The diagnosis and side effects of breast cancer (BC) treatments greatly affect the everyday lives of women suffering from this disease, with relevant psychological and cognitive consequences. Several studies have reported the psychological effects of receiving a diagnosis of BC. Moreover, women undergoing anticancer therapies may exhibit cognitive impairment as a side effect of the treatments. The access to cognitive rehabilitation and psychological treatment for these patients is often limited by resources; women of childbearing age often encounter difficulties in completing rehabilitation programs requiring access to care institutions. Telemedicine, which provides health services using information and communication technologies, is a useful tool to overcome these limitations. In particular, telemedicine may represent an optimal way to guarantee cognitive rehabilitation, psychological support, and recovery to BC patients. Previous studies have reviewed the use of telemedicine to improve psychological well-being in BC patients, and a few have investigated the effect of telerehabilitation on cognitive deficits. This study systematically reviewed the evidence on the cognitive and psychological effects of telemedicine in BC patients. Current evidence suggests that telemedicine may represent a promising tool for the management of some psychological problems experienced by breast cancer patients, but more controlled studies are needed to clarify its effectiveness, especially for cognitive deficits. The results are also discussed in light of the intervening and modulating factors that may mediate both side effect occurrence and the success of the interventions.

Mobile Applications Available in Germany Supporting Breast Cancer Patients During Treatment and Aftercare: a Systematic Review

Purpose Systematic evaluation of health apps designed to support and aid remote monitoring of patients during breast cancer treatment and aftercare. Method A systematic search and assessment of apps was conducted using search terms: breast cancer; breast cancer therapy; and breast cancer aftercare. Evaluation criteria were user assessments, scientifically published benefits, user-friendliness, data protection, app individualization, motivation, and financial aspects. Up to two points (P) could be awarded per criterion. The lowest possible score was 0P and the maximum was 28P. Three examiners from different institutions independently assessed the apps according to the specified criteria. Reference value was defined as the average value given by the examiners. Apps with > 18P were classified as "recommended"; ≥ 11-≤ 18P as "partially recommended" and ≤ 10P as "not recommended". Results A total of 776 apps (n = 24 from the Apple App Store, n = 752 from the Google Play Store) were identified via search query. After applying exclusion criteria, 36 apps (n = 1 from the Apple App Store; n = 35 from the Google Play Store) were evaluated. Using the mean point values of the examiners, 20 apps were classified as not recommended and 12 as partially recommended (≥ 11-≤ 18P). Four apps were rated partially recommended by two examiners and recommended by one examiner. Three apps were rated as recommended by all examiners. Conclusion Only a small minority of available apps meet recommendation criteria. Use of these apps may benefit breast cancer patients.

Measuring Clinical, Biological, and Behavioral Variables to Elucidate Trajectories of Patient (Reported) Outcomes: The PROFILES Registry

To take cancer survivorship research to the next level, it's important to gain insight in trajectories of changing patient (reported) outcomes and impaired recovery after cancer. This is needed as the number of survivors is increasing and a large proportion is confronted with changing health after treatment. Mechanistic research can facilitate the development of personalized risk-stratified follow-up care and tailored interventions to promote healthy cancer survivorship. We describe how these trajectories can be studied by taking the recently extended Dutch population-based PROFILES (Patient Reported Outcomes Following Initial treatment and Long term Evaluation of Survivorship) registry as an example. PROFILES combines longitudinal assessment of patient-reported outcomes with novel, ambulatory and objective measures (e.g., activity trackers; blood draws; hair samples; online food diaries; online cognitive tests; weighing scales; online symptoms assessment), and cancer registry and pharmacy databases. Furthermore, we discuss methods to optimize the use of a multidomain data collection like return of individual results to participants which may not only improve patient empowerment but also long-term cohort retention. Also, advanced statistical methods are needed to handle high-dimensional longitudinal data (with missing values) and provide insight into trajectories of changing patient (reported) outcomes after cancer. Our coded data can be used by academic researchers around the world. Registries like PROFILES, that go beyond boundaries of disciplines and institutions, will contribute to better predictions of who will experience changes and why. This is needed to prevent and mitigate long-term and late effects of cancer (treatment) and to identify new interventions to promote health.

A survey on Machine learning based Medical Assistive systems in Current Oncological Sciences

BACKGROUND

Cancer is one of the life threatening disease which is affecting a large number of population worldwide. The cancer cells multiply inside the body without showing much symptoms on the surface of the skin thereby making it difficult to predict and detect at the onset of disease. Many organizations are working towards automating the process of cancer detection with minimal false detection rates.

INTRODUCTION

The machine learning algorithms serve to be a promising alternative to support health care practitioners to rule out the disease and predict the growth with various imaging and statistical analysis tools. The medical practitioners are utilizing the output of these algorithms to diagnose and design the course of treatment. These algorithms are capable of finding out the risk level of the patient and can reduce the mortality rate concerning to cancer disease.

METHOD

This article presents the existing state of art techniques for identifying cancer affecting human organs based on machine learning models. The supported set of imaging operations are also elaborated for each type of Cancer.

CONCLUSION

The CAD tools are the aid for the diagnostic radiologists for preliminary investigations and detecting the nature of tumor cells.