Dose-response modeling and treatment plan assessment with a python software toolkit.

This software assistant aims at calculating the dose-response relations of tumors and normal tissues, or clinically assessing already determined values by other researchers. It can also indicate the optimal dose prescription by optimizing the expected treatment outcome. The software is developed solely in python programming language, and it employs PSFL license for its Graphical User Interface (GUI), NUMPY, MATPLOTLIB, and SCIPY libraries. It comprises of two components. The first is the Dose-response relations derivation component, which takes as input the dose volume histograms (DVHs) of patients and their recorded responses regarding a given clinical endpoint to determine the parameters of different tumor control probability (TCP) or normal tissue complication probability (NTCP) models. The second is the Treatment Plan Assessment component, which uses the DVHs of a plan and the dose-response parameters values of the involved tumors and organs at risk (OARs) to calculate their expected responses. Additionally, the overall probabilities of benefit (PB), injury (PI) and complication-free tumor control (P+) are calculated. The software calculates rapidly the corresponding generalized equivalent uniform doses (gEUD) and biologically effective uniform doses (D‾‾) for the Lyman-Kutcher-Burman (LKB), parallel volume (PV) and relative seriality (RS) models respectively, determining the model parameters. In the Dose-Response Relations Derivation component, the software plots the dose-response curves of the irradiated organ with the relevant confidence internals along with the data of the patients with and without toxicity. It also calculates the odds ratio (OR) and the area under the curve (AUC) of different dose metrics or model parameter values against the individual patient outcomes to determine their discrimination capacity. It also performs a goodness-of-fit evaluation of any model parameter set. The user has the option of viewing plots like Scatter, 3D surfaces, and Bootstrap plots. In the Treatment Plan Assessment part, the software calculates the TCP and NTCP values of the involved tumors and OARs, respectively. Furthermore, it plots the dose-response curves of the TCPs, NTCPs, PB, PI, and P+ for a range of prescription doses for different treatment plans. The presented software is ideal for efficiently conducting studies of radiobiological modeling. Furthermore, it is ideal for performing treatment plan assessment, comparison, and optimization studies.

NTCP modelling of xerostomia after radiotherapy for oropharyngeal cancer using the PRO-CTCAE and CTCAE scoring systems at different time-points post-RT.

PURPOSE: This study aims at determining the parameter values of three normal tissue complication probability (NTCP) models for the contralateral parotid gland, contralateral submandibular gland (SMG) and contralateral salivary glands regarding the endpoint of xerostomia 6-24 months after radiotherapy for oropharynx cancer. METHODS: The treatment and outcome data of 231 patients with favorable risk, HPV-associated oropharyngeal squamous cell carcinoma are analyzed. 60 Gy intensity modulated radiotherapy was delivered to all the patients. The presence and severity of xerostomia was recorded (pre- and post- radiotherapy) by the PRO-CTCAE and the CTCAE scoring systems. In both scoring systems, patients with a change in symptom severity (from baseline) of ≥ 2 were considered responders. RESULTS: Xerostomia was observed in 61.3 %, 39.2 %, 28.6 % and 27.0 % of the patients based on the PRO-CTCAE scoring system at 6-, 12-, 18- and 24-months post-RT, respectively. The AUCs of the contralateral salivary glands ranged between 0.58-0.64 in the LKB model with the gEUD ranging between 20.3 Gy and 24.7 Gy. CONCLUSIONS: Based on the PRO-CTCAE scores, mean dose < 22 Gy, V50 < 10 % for the contralateral salivary glands and mean dose < 18 Gy, V45 < 10 % for the contralateral parotid were found to significantly reduce by a factor of 2-3 the risk for radiation induced xerostomia that is observed at 6-24 months post-RT, respectively. Also, gEUD < 22 Gy to the contralateral salivary glands and < 18 Gy to the contralateral parotid was found to significantly reduce the risk for radiation induced xerostomia that is observed at 6-24 months post-RT by 2.0-2.3 times.

A Cybersecurity Culture Survey Targeting Healthcare Critical Infrastructures.

Recent studies report that cybersecurity breaches noticed in hospitals are associated with low levels of personnel's cybersecurity awareness. This work aims to assess the cybersecurity culture in healthcare institutions from middle- to low-income EU countries. The evaluation process was designed and performed via anonymous online surveys targeting individually ICT (internet and communication technology) departments and healthcare professionals. The study was conducted in 2019 for a health region in Greece, with a significant number of hospitals and health centers, a large hospital in Portugal, and a medical clinic in Romania, with 53.6% and 6.71% response rates for the ICT and healthcare professionals, respectively. Its findings indicate the necessity of establishing individual cybersecurity departments to monitor assets and attitudes while underlying the importance of continuous security awareness training programs. The analysis of our results assists in comprehending the countermeasures, which have been implemented in the healthcare institutions, and consequently enhancing cybersecurity defense, while reducing the risk surface.

Hospitals' Cybersecurity Culture during the COVID-19 Crisis.

The coronavirus pandemic led to an unprecedented crisis affecting all aspects of the concurrent reality. Its consequences vary from political and societal to technical and economic. These side effects provided fertile ground for a noticeable cyber-crime increase targeting critical infrastructures and, more specifically, the health sector; the domain suffering the most during the pandemic. This paper aims to assess the cybersecurity culture readiness of hospitals' workforce during the COVID-19 crisis. Towards that end, a cybersecurity awareness webinar was held in December 2020 targeting Greek Healthcare Institutions. Concepts of cybersecurity policies, standards, best practices, and solutions were addressed. Its effectiveness was evaluated via a two-step procedure. Firstly, an anonymous questionnaire was distributed at the end of the webinar and voluntarily answered by attendees to assess the comprehension level of the presented cybersecurity aspects. Secondly, a post-evaluation phishing campaign was conducted approximately four months after the webinar, addressing non-medical employees. The main goal was to identify security awareness weaknesses and assist in drafting targeted assessment campaigns specifically tailored to the health domain needs. This paper analyses in detail the results of the aforementioned approaches while also outlining the lessons learned along with the future scientific routes deriving from this research.

Toolkit for determination of dose-response relations, validation of radiobiological parameters and treatment plan optimization based on radiobiological measures.

Accurately determined dose-response relations of the different tumors and normal tissues should be estimated and used in the clinic. The aim of this study is to demonstrate developed tools that are necessary for determining the dose-response parameters of tumors and normal tissues, for clinically verifying already published parameter sets using local patient materials and for making use of all this information in the optimization and comparison of different treatment plans and radiation techniques. One of the software modules (the Parameter Determination Module) is designed to determine the dose-response parameters of tumors and normal tissues. This is accomplished by performing a maximum likelihood fitting to calculate the best estimates and confidence intervals of the parameters used by different radiobiological models. Another module of this software (the Parameter Validation Module) concerns the validation and compatibility of external or reported dose-response parameters describing tumor control and normal tissue complications. This is accomplished by associating the expected response rates, which are calculated using different models and published parameter sets, with the clinical follow-up records of the local patient population. Finally, the last module of the software (the Radiobiological Plan Evaluation Module) is used for estimating and optimizing the effectiveness a treatment plan in terms of complication-free tumor control, P(+). The use of the Parameter Determination Module is demonstrated by deriving the dose-response relation of proximal esophagus from head and neck cancer radiotherapy. The application of the Parameter Validation Module is illustrated by verifying the clinical compatibility of those dose-response parameters with the examined treatment methodologies. The Radiobiological Plan Evaluation Module is demonstrated by evaluating and optimizing the effectiveness of head and neck cancer treatment plans. The results of the radiobiological evaluation are compared against dosimetric criteria. The presented toolkit appears to be very convenient and efficient for clinical implementation of radiobiological modeling. It can also be used for the development of a clinical data and health information database for assisting the performance of epidemiological studies and the collaboration between different institutions within research and clinical frameworks.