Management of The Elderly Cancer Patients Complexity: The Radiation Oncology Potential.

Radiation oncology has the potential to be an excellent option for the frail elderly cancer patients because of its limited systemic toxicities. It can be effective for curative, prophylactic, disease control or palliative purposes. Currently about 60% of all cancer patients undergoing active treatment at some point receive radiation treatment. However, though widely used, there are limited clinical trials strictly designed for the elderly. This paper will review the key points in the assessment and treatment of elderly cancer patient including quality of life, active life expectancy, cognitive performance, frailty, sarcopenia and how the new technologies can help to reach the key goal of maintaining autonomy and independence for the elderly cancer patient.

kOBCS©: a novel software calculator program of the Objective Breast Cosmesis Scale (OBCS).

PURPOSE: The Objective Breast Cosmesis Scale (OBCS) is an objective method that documents the aesthetic changes in breast cancer patients. This work evaluates the kOBCS© software (http://www.kobcs.info) which simplifies the estimation of the OBCS values. METHODS: Five schematic drawings were photographed and imported into the kOBCS©. Thirty photos of breast cancer patients were imported into kOBCS©; 20 users (experts and non-experts) evaluated the photographs on two different settings. Subjective evaluation was performed using the Harvard breast cosmesis scale. RESULTS: There was a highly significant correlation between the OBCS values based on hand measurements and the values estimated by kOBCS© (r = 0.997, P < 0.001). Agreement among the users using the kOBCS© was strong with high statistical significance (ICC = 0.846, P < 0.001, 95% CI 0.774-0.910, Cronbach's alpha = 0.991). Results of the subjective analyses and mean OBCS values as estimated by kOBCS© correlated significantly (r = 0.961, P < 0.001). CONCLUSIONS: The kOBCS© is a reliable and reproducible easy-to-use software for reporting breast cosmesis following breast-conserving therapy.

ENT COBRA ONTOLOGY: the covariates classification system proposed by the Head & Neck and Skin GEC-ESTRO Working Group for interdisciplinary standardized data collection in head and neck patient cohorts treated with interventional radiotherapy (brachytherapy).

PURPOSE: Clinical data collecting is expensive in terms of time and human resources. Data can be collected in different ways; therefore, performing multicentric research based on previously stored data is often difficult. The primary objective of the ENT COBRA (COnsortium for BRachytherapy data Analysis) ontology is to define a specific terminological system to standardized data collection for head and neck (H&N) cancer patients treated with interventional radiotherapy. MATERIAL AND METHODS: ENT-COBRA is a consortium for standardized data collection for H&N patients treated with interventional radiotherapy. It is linked to H&N and Skin GEC-ESTRO Working Group and includes 11 centers from 6 countries. Its ontology was firstly defined by a multicentric working group, then evaluated by the consortium followed by a multi-professional technical commission involving a mathematician, an engineer, a physician with experience in data storage, a programmer, and a software expert. RESULTS: Two hundred and forty variables were defined on 13 input forms. There are 3 levels, each offering a specific type of analysis: 1. Registry level (epidemiology analysis); 2. Procedures level (standard oncology analysis); 3. Research level (radiomics analysis). The ontology was approved by the consortium and technical commission; an ad-hoc software architecture ("broker") remaps the data present in already existing storage systems of the various centers according to the shared terminology system. The first data sharing was successfully performed using COBRA software and the ENT COBRA Ontology, automatically collecting data directly from 3 different hospital databases (Lübeck, Navarra, and Rome) in November 2017. CONCLUSIONS: The COBRA Ontology is a good response to the multi-dimensional criticalities of data collection, retrieval, and usability. It allows to create a software for large multicentric databases with implementation of specific remapping functions wherever necessary. This approach is well-received by all involved parties, primarily because it does not change a single center's storing technologies, procedures, and habits.

A new standardized data collection system for interdisciplinary thyroid cancer management: Thyroid COBRA.

The big data approach offers a powerful alternative to Evidence-based medicine. This approach could guide cancer management thanks to machine learning application to large-scale data. Aim of the Thyroid CoBRA (Consortium for Brachytherapy Data Analysis) project is to develop a standardized web data collection system, focused on thyroid cancer. The Metabolic Radiotherapy Working Group of Italian Association of Radiation Oncology (AIRO) endorsed the implementation of a consortium directed to thyroid cancer management and data collection. The agreement conditions, the ontology of the collected data and the related software services were defined by a multicentre ad hoc working-group (WG). Six Italian cancer centres were firstly started the project, defined and signed the Thyroid COBRA consortium agreement. Three data set tiers were identified: Registry, Procedures and Research. The COBRA-Storage System (C-SS) appeared to be not time-consuming and to be privacy respecting, as data can be extracted directly from the single centre's storage platforms through a secured connection that ensures reliable encryption of sensible data. Automatic data archiving could be directly performed from Image Hospital Storage System or the Radiotherapy Treatment Planning Systems. The C-SS architecture will allow "Cloud storage way" or "distributed learning" approaches for predictive model definition and further clinical decision support tools development. The development of the Thyroid COBRA data Storage System C-SS through a multicentre consortium approach appeared to be a feasible tool in the setup of complex and privacy saving data sharing system oriented to the management of thyroid cancer and in the near future every cancer type.

ENT COBRA (Consortium for Brachytherapy Data Analysis): interdisciplinary standardized data collection system for head and neck patients treated with interventional radiotherapy (brachytherapy).

PURPOSE: Aim of the COBRA (Consortium for Brachytherapy Data Analysis) project is to create a multicenter group (consortium) and a web-based system for standardized data collection. MATERIAL AND METHODS: GEC-ESTRO (Groupe Européen de Curiethérapie - European Society for Radiotherapy & Oncology) Head and Neck (H&N) Working Group participated in the project and in the implementation of the consortium agreement, the ontology (data-set) and the necessary COBRA software services as well as the peer reviewing of the general anatomic site-specific COBRA protocol. The ontology was defined by a multicenter task-group. RESULTS: Eleven centers from 6 countries signed an agreement and the consortium approved the ontology. We identified 3 tiers for the data set: Registry (epidemiology analysis), Procedures (prediction models and DSS), and Research (radiomics). The COBRA-Storage System (C-SS) is not time-consuming as, thanks to the use of "brokers", data can be extracted directly from the single center's storage systems through a connection with "structured query language database" (SQL-DB), Microsoft Access(®), FileMaker Pro(®), or Microsoft Excel(®). The system is also structured to perform automatic archiving directly from the treatment planning system or afterloading machine. The architecture is based on the concept of "on-purpose data projection". The C-SS architecture is privacy protecting because it will never make visible data that could identify an individual patient. This C-SS can also benefit from the so called "distributed learning" approaches, in which data never leave the collecting institution, while learning algorithms and proposed predictive models are commonly shared. CONCLUSIONS: Setting up a consortium is a feasible and practicable tool in the creation of an international and multi-system data sharing system. COBRA C-SS seems to be well accepted by all involved parties, primarily because it does not influence the center's own data storing technologies, procedures, and habits. Furthermore, the method preserves the privacy of all patients.

Standardized data collection to build prediction models in oncology: a prototype for rectal cancer.

The advances in diagnostic and treatment technology are responsible for a remarkable transformation in the internal medicine concept with the establishment of a new idea of personalized medicine. Inter- and intra-patient tumor heterogeneity and the clinical outcome and/or treatment's toxicity's complexity, justify the effort to develop predictive models from decision support systems. However, the number of evaluated variables coming from multiple disciplines: oncology, computer science, bioinformatics, statistics, genomics, imaging, among others could be very large thus making traditional statistical analysis difficult to exploit. Automated data-mining processes and machine learning approaches can be a solution to organize the massive amount of data, trying to unravel important interaction. The purpose of this paper is to describe the strategy to collect and analyze data properly for decision support and introduce the concept of an 'umbrella protocol' within the framework of 'rapid learning healthcare'.