Identification of factors influencing the Whole Body Absorption Rate using statistical analysis

To protect people from Electromagnetic Fields (EMF), ICNIRP has defined limits. The fundamental ones are the Basic Restrictions (BRs) [1]. The BRs determine the maximum values (averaged over the whole body and averaged over 10 grams of tissues) of Specific Absorption Rate (SAR). Since BRs can be complex to assess ICNIRP has also defined derived value: the reference levels (RLs). These RLs were established to guaranty the compliance to the BRs. Several studies with human model voxels (a.k.a. phantoms) show that even below the RLs, the WBSAR (Whole Body average SAR) may exceed the BRs due to the variability of human morphology [2]. In this paper we will identify the morphological factors influencing the WBSAR in the case of a frontal plane wave exposure at the frequency of 2100MHz in isolated conditions and vertical polar. The method is based on the construction of a model that makes it possible to estimate the statistical distribution of the WBSAR for a given human population

Identification des facteurs morphologiques impactant le Débit d'Absorption Spécifique du Corps Entier

Les systèmes fondés sur des technologies liées aux champs électromagnétiques (EM) sont de plus en plus répandus. La question des effets possibles sur la santé dus à ces technologies sont devenues une préoccupation publique. Afin de limiter l'exposition des personnes aux ondes EM, des niveaux de protection appelés restrictions de bases, ont été définis par l'ICNIRP [1]. Ces niveaux fixent des valeurs de Débit d'Absorption Spécifique (Specific Absorption Rate : SAR) à ne pas dépasser. Des niveaux de références ont été dérivés des restrictions de base de façon conservative. Plusieurs études menées avec des modèles numériques d'humains [2,3] (fantômes) montrent que, pour certaines configurations, le WBSAR (Whole Body averged Specific Absorption Rate) est très proche des restrictions de base. D'autres études ont souligné la variabilité du WBSAR due à la variabilité de la morphologie humaine [2]. L'objectif de ce papier est d'identifier les facteurs morphologiques (internes et externes) qui ont un impact sur le WBSAR pour des fantômes exposés à une onde plane à une fréquence fixée à 2100MHz et une densité de puissance de 1W/m²

Bayesian experiment planning applied to numerical dosimetry

To protect people from electromagnetic field, Basic Restrictions (BR) are defined [1]. These BR fix a limit to be not exceeded. The metric associated with these BR is the Specific Absorption Rate (SAR). Reference Levels (RL) are also defined since the BR are difficult to check in situ. These RL set the maximum allowed electromagnetic field. The compliance to RL guaranties the compliance to BR. To evaluate the SAR in the human body, some anatomical models (phantoms) and numerical methods are used (e.g. Finite Difference in Time Domain). Based on this, studies show that for some configurations the Whole Body SAR (WBSAR) is close to BR. Other studies stressed the variability of the WBSAR due to the variability of human morphology [2]. Despite the computing resources development, the number of the phantoms is very limited. This limited number of phantoms does not allow using usual method such as Monte Carlo to assess the maximal threshold of the WBSAR for a given population. Hence the construction of a model of the WBSAR as a function of morphology is required. Nevertheless, the WBSAR is impacted by the external morphology (height and weight) and the internal morphology (proportion of fat, proportion of muscles...). But there is no statistical data concerning the internal ones. In this paper, the external morphology is focused and the internal morphology is released by considering homogeneous phantoms. A Bayesian sequential experiment planning is proposed. This method consists in refining the region of interest of the WBSAR statistical distribution for a given population. This region of interest is the threshold of the WBSAR at 95% (WBSAR95). This study is conducted in the case of a plane wave vertically polarized and frontally oriented on phantoms. The incident power is equal to 1W/m². The frequency is fixed at 2.1GHz

Analyse statistique de la puissance absorbée par le corps entier en radiofréquence

Dans ce papier, nous proposons une identification des facteurs morphologiques qui peuvent impacter le Débit d'Absorption Spécifique (DAS) du corps entier dans le cas d'une onde plan. Cette étude compare différents modèles mathématiques et conclue l'analyse des données par des tests statistiques. Sous certaines hypothèses aussi une approche permettant de quantifier le quantile à 95% du DAS pour le corps entier est enfin proposée

Plan d'expériences séquentiel appliqué à la dosimétrie numérique

Dans ce papier nous allons proposer une méthodologie consistant à trouver la valeur du Débit d'Absorption Spécifique du Corps Entier (DAS_CE) qui couvre 95% d'une population donnée. Cette méthode repose d'une part sur de l'Inférence Bayesienne et d'autre part sur un modèle paramétrique de prédiction du DAS_CE en fonction de la morphologie ainsi que des outils de simulations numériques

A generalized model for monitor units determination in ocular proton therapy using machine learning:A proof-of-concept study

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.</p

A generalized model for monitor units determination in ocular proton therapy using machine learning:A proof-of-concept study

Objective. Determining and verifying the number of monitor units is crucial to achieving the desired dose distribution in radiotherapy and maintaining treatment efficacy. However, current commercial treatment planning system(s) dedicated to ocular passive eyelines in proton therapy do not provide the number of monitor units for patient-specific plan delivery. Performing specific pre-treatment field measurements, which is time and resource consuming, is usually gold-standard practice. This proof-of-concept study reports on the development of a multi-institutional-based generalized model for monitor units determination in proton therapy for eye melanoma treatments. Approach. To cope with the small number of patients being treated in proton centers, three European institutes participated in this study. Measurements data were collected to address output factor differences across the institutes, especially as function of field size, spread-out Bragg peak modulation width, residual range, and air gap. A generic model for monitor units prediction using a large number of 3748 patients and broad diversity in tumor patterns, was evaluated using six popular machine learning algorithms: (i) decision tree; (ii) random forest, (iii) extra trees, (iv) K-nearest neighbors, (v) gradient boosting, and (vi) the support vector regression. Features used as inputs into each machine learning pipeline were: Spread-out Bragg peak width, range, air gap, fraction and calibration doses. Performance measure was scored using the mean absolute error, which was the difference between predicted and real monitor units, as collected from institutional gold-standard methods. Main results. Predictions across algorithms were accurate within 3% uncertainty for up to 85.2% of the plans and within 10% uncertainty for up to 98.6% of the plans with the extra trees algorithm. Significance. A proof-of-concept of using machine learning-based generic monitor units determination in ocular proton therapy has been demonstrated. This could trigger the development of an independent monitor units calculation tool for clinical use.</p

Comparison of stereotactic radiotherapy and protons for uveal melanoma patients

Background and purpose: Uveal melanoma (UM) is the most common primary ocular malignancy. We compared fractionated stereotactic radiotherapy (SRT) with proton therapy, including toxicity risks for UM patients. Materials and methods: For a total of 66 UM patients from a single center, SRT dose distributions were compared to protons using the same planning CT. Fourteen dose-volume parameters were compared in 2-Gy equivalent dose per fraction (EQD2). Four toxicity profiles were evaluated: maculopathy, optic-neuropathy, visual acuity impairment (Profile I); neovascular glaucoma (Profile II); radiation-induced retinopathy (Profile III); and dry-eye syndrome (Profile IV). For Profile III, retina Mercator maps were generated to visualize the geographical location of dose differences. Results: In 9/66 cases, (14 %) proton plans were superior for all dose-volume parameters. Higher T stages benefited more from protons in Profile I, especially tumors located within 3 mm or less from the optic nerve. In Profile II, only 9/66 cases resulted in a better proton plan. In Profile III, better retina volume sparing was always achievable with protons, with a larger gain for T3 tumors. In Profile IV, protons always reduced the risk of toxicity with a median RBE-weighted EQD2 reduction of 15.3 Gy. Conclusions: This study reports the first side-by-side imaging-based planning comparison between protons and SRT for UM patients. Globally, while protons appear almost always better regarding the risk of optic-neuropathy, retinopathy and dry-eye syndrome, for other toxicity like neovascular glaucoma, a plan comparison is warranted. Choice would depend on the prioritization of risks.</p

Gender-related and geographic trends in interactions between radiotherapy professionals on Twitter.

BACKGROUND AND PURPOSE Twitter presence in academia has been linked to greater research impact which influences career progression. The purpose of this study was to analyse Twitter activity of the radiotherapy community around ESTRO congresses with a focus on gender-related and geographic trends. MATERIALS AND METHODS Tweets, re-tweets and replies, here designated as interactions, around the ESTRO congresses held in 2012-2021 were collected. Twitter activity was analysed temporally and, for the period 2016-2021, the geographical span of the ESTRO Twitter network was studied. Tweets and Twitter users collated during the 10 years analysed were ranked based on number of 'likes', 're-tweets' and followers, considered as indicators of leadership/influence. Gender representation was assessed for the top-end percentiles. RESULTS Twitter activity around ESTRO congresses was multiplied by 60 in 6 years growing from 150 interactions in 2012 to a peak of 9097 in 2018. In 2020, during the SARS-CoV-2 pandemic, activity dropped by 60 % to reach 2945 interactions and recovered to half the pre-pandemic level in 2021. Europe, North America and Oceania were strongly connected and remained the main contributors. While overall, 58 % of accounts were owned by men, this proportion increased towards top liked/re-tweeted tweets and most-followed profiles to reach up to 84 % in the top-percentiles. CONCLUSION During the SARS-CoV-2 pandemic, Twitter activity around ESTRO congresses substantially decreased. Men were over-represented on the platform and in most popular tweets and influential accounts. Given the increasing importance of social media presence in academia the gender-based biases observed may help in understanding the gender gap in career progression