Evaluating Size-Specific Dose Estimate (SSDE) as an estimate of organ doses from routine CT exams derived from Monte Carlo simulations.

PURPOSE: Size-specific dose estimate (SSDE) is a metric that adjusts CTDIvol to account for patient size. While not intended to be an estimate of organ dose, AAPM Report 204 notes the difference between the patient organ dose and SSDE is expected to be 10-20%. The purpose of this work was therefore to evaluate SSDE against estimates of organ dose obtained using Monte Carlo (MC) simulation techniques applied to routine exams across a wide range of patient sizes. MATERIALS AND METHODS: Size-specific dose estimate was evaluated with respect to organ dose based on three routine protocols taken from Siemens scanners: (a) brain parenchyma dose in routine head exams, (b) lung and breast dose in routine chest exams, and (c) liver, kidney, and spleen dose in routine abdomen/pelvis exams. For each exam, voxelized phantom models were created from existing models or derived from clinical patient scans. For routine head exams, 15 patient models were used which consisted of 10 GSF/ICRP voxelized phantom models and five pediatric voxelized patient models created from CT image data. For all exams, the size metric used was water equivalent diameter (Dw ). For the routine chest exams, data from 161 patients were collected with a Dw range of ~16-44 cm. For the routine abdomen/pelvis exams, data from 107 patients were collected with a range of Dw from ~16 to 44 cm. Image data from these patients were segmented to generate voxelized patient models. For routine head exams, fixed tube current (FTC) was used while tube current modulation (TCM) data for body exams were extracted from raw projection data. The voxelized patient models and tube current information were used in detailed MC simulations for organ dose estimation. Organ doses from MC simulation were normalized by CTDIvol and parameterized as a function of Dw . For each patient scan, the SSDE was obtained using Dw and CTDIvol values of each scan, according to AAPM Report 220 for body scans and Report 293 for head scans. For each protocol and each patient, normalized organ doses were compared with SSDE. A one-sided tolerance limit covering 95% (P = 0.95) of the population with 95% confidence (α = 0.05) was used to assess the upper tolerance limit (TU ) between SSDE and normalized organ dose. RESULTS: For head exams, the TU between SSDE and brain parenchyma dose was observed to be 12.5%. For routine chest exams, the TU between SSDE and lung and breast dose was observed to be 35.6% and 68.3%, respectively. For routine abdomen/pelvis exams, the TU between SSDE and liver, spleen, and kidney dose was observed to be 30.7%, 33.2%, and 33.0%, respectively. CONCLUSIONS: The TU of 20% between SSDE and organ dose was found to be insufficient to cover 95% of the sampled population with 95% confidence for all of the organs and protocols investigated, except for brain parenchyma dose. For the routine body exams, excluding the breasts, a wider threshold difference of ~30-36% would be needed. These results are, however, specific to Siemens scanners.

A comparison of breast and lung doses from chest CT scans using organ-based tube current modulation (OBTCM) vs. Automatic tube current modulation (ATCM).

PURPOSE: The purpose of this work was to estimate and compare breast and lung doses of chest CT scans using organ-based tube current modulation (OBTCM) to those from conventional, attenuation-based automatic tube current modulation (ATCM) across a range of patient sizes. METHODS: Thirty-four patients (17 females, 17 males) who underwent clinically indicated CT chest/abdomen/pelvis (CAP) examinations employing OBTCM were collected from two multi-detector row CT scanners. Patient size metric was assessed as water equivalent diameter (Dw ) taken at the center of the scan volume. Breast and lung tissues were segmented from patient image data to create voxelized models for use in a Monte Carlo transport code. The OBTCM schemes for the chest portion were extracted from the raw projection data. ATCM schemes were estimated using a recently developed method. Breast and lung doses for each TCM scenario were estimated for each patient model. CTDIvol -normalized breast (nDbreast ) and lung (nDlung ) doses were subsequently calculated. The differences between OBTCM and ATCM normalized organ dose estimates were tested using linear regression models that included CT scanner and Dw as covariates. RESULTS: Mean dose reduction from OBTCM in nDbreast was significant after adjusting for the scanner models and patient size (P = 0.047). When pooled with females and male patient, mean dose reduction from OBTCM in nDlung was observed to be trending after adjusting for the scanner model and patient size (P = 0.085). CONCLUSIONS: One specific manufacturer's OBTCM was analyzed. OBTCM was observed to significantly decrease normalized breast relative to a modeled version of that same manufacturer's ATCM scheme. However, significant dose savings were not observed in lung dose over all. Results from this study support the use of OBTCM chest protocols for females only.

Reference dataset for benchmarking fetal doses derived from Monte Carlo simulations of CT exams.

PURPOSE: Task Group Report 195 of the American Association of Physicists in Medicine contains reference datasets for the direct comparison of results among different Monte Carlo (MC) simulation tools for various aspects of imaging research that employs ionizing radiation. While useful for comparing and validating MC codes, that effort did not provide the information needed to compare absolute dose estimates from CT exams. Therefore, the purpose of this work is to extend those efforts by providing a reference dataset for benchmarking fetal dose derived from MC simulations of clinical CT exams. ACQUISITION AND VALIDATION METHODS: The reference dataset contains the four necessary elements for validating MC engines for CT dosimetry: (a) physical characteristics of the CT scanner, (b) patient information, (c) exam specifications, and (d) fetal dose results from previously validated and published MC simulations methods in tabular form. Scanner characteristics include non-proprietary descriptions of equivalent source cumulative distribution function (CDF) spectra and bowtie filtration profiles, as well as scanner geometry information. Additionally, for the MCNPX MC engine, normalization factors are provided to convert raw simulation results to absolute dose in mGy. The patient information is based on a set of publicly available fetal dose models and includes de-identified image data; voxelized MC input files with fetus, uterus, and gestational sac identified; and patient size metrics in the form of water equivalent diameter (Dw ) z-axis distributions from a simulated topogram (Dw,topo ) and from the image data (Dw,image ). Exam characteristics include CT scan start and stop angles and table and patient locations, helical pitch, nominal collimation and measured beam width, and gantry rotation time for each simulation. For simulations involving estimating doses from exams using tube current modulation (TCM), a realistic TCM scheme is presented that is estimated based upon a validated method. (d) Absolute and CTDIvol -normalized fetal dose results for both TCM and FTC simulations are given for each patient model under each scan scenario. DATA FORMAT AND USAGE NOTES: Equivalent source CDFs and bowtie filtration profiles are available in text files. Image data are available in DICOM format. Voxelized models are represented by a header followed by a list of integers in a text file representing a three-dimensional model of the patient. Size distribution metrics are also given in text files. Results of absolute and normalized fetal dose with associated MC error estimates are presented in tabular form in an Excel spreadsheet. All data are stored on Zenodo and are publicly accessible using the following link: https://zenodo.org/record/3959512. POTENTIAL APPLICATIONS: Similar to the work of AAPM Report 195, this work provides a set of reference data for benchmarking fetal dose estimates from clinical CT exams. This provides researchers with an opportunity to compare MC simulation results to a set of published reference data as part of their efforts to validate absolute and normalized fetal dose estimates. This could also be used as a basis for comparison to other non-MC approaches, such as deterministic approaches, or to commercial packages that provide estimates of fetal doses from clinical CT exams.

Estimating fetal dose from tube current-modulated (TCM) and fixed tube current (FTC) abdominal/pelvis CT examinations.

PURPOSE: The purpose of this work was to estimate scanner-independent CTDIvol -to-fetal-dose coefficients for tube current-modulated (TCM) and fixed tube current (FTC) computed tomography (CT) examinations of pregnant patients of various gestational ages undergoing abdominal/pelvic CT examinations. METHODS: For 24 pregnant patients of gestational age from <5 to 36 weeks who underwent clinically indicated CT examinations, voxelized models of maternal and fetal (or embryo) anatomy were created from abdominal/pelvic image data. Absolute fetal dose (Dfetus ) was estimated using Monte Carlo (MC) simulations of helical scans covering the abdomen and pelvis for TCM and FTC scans. Estimated TCM schemes were generated for each patient model using a validated method that accounts for patient attenuation and scanner output limits for one scanner model and were incorporated into MC simulations. FTC scans were also simulated for each patient model with multidetector row CT scanners from four manufacturers. Normalized fetal dose estimates, nDfetus , was obtained by dividing Dfetus from the MC simulations by CTDIvol . Patient size was described using water equivalent diameter (Dw ) measured at the three-dimensional geometric centroid of the fetus. Fetal depth (DEf ) was measured from the anterior skin surface to the anterior part of the fetus. nDfetus and Dw were correlated using an exponential model to develop equations for fetal dose conversion coefficients for TCM and FTC abdominal/pelvic CT examinations. Additionally, bivariate linear regression was performed to analyze the correlation of nDfetus with Dw and fetal depth (DEf ). For one scanner model, nDfetus from TCM was compared to FTC and the size-specific dose estimate (SSDE) conversion coefficients (f-factors) from American Association of Physicists in Medicine (AAPM) Report 204. nDfetus from FTC simulations was averaged across all scanners for each patient ( n D fetus ¯ ) . n D fetus ¯ was then compared with SSDE f-factors and correlated with Dw using an exponential model and with Dw and DEf using a bivariate linear model. RESULTS: For TCM, the coefficient of determination (R2 ) of nDfetus and Dw was observed to be 0.73 using an exponential model. Using the bivariate linear model with Dw and DEf , an R2 of 0.78 was observed. For the TCM technology modeled, TCM yielded nDfetus values that were on average 6% and 17% higher relative to FTC and SSDE f-factors, respectively. For FTC, the R2 of n D fetus ¯ with respect to Dw was observed to be 0.64 using an exponential model. Using the bivariate linear model, an R2 of 0.75 was observed for n D fetus ¯ with respect to Dw and DEf . A mean difference of 0.4% was observed between n D fetus ¯ and SSDE f-factors. CONCLUSION: Good correlations were observed for nDfetus from TCM and FTC scans using either an exponential model with Dw or a bivariate linear model with both Dw and DEf . These results indicate that fetal dose from abdomen/pelvis CT examinations of pregnant patients of various gestational ages may be reasonably estimated with models that include (a) scanner-reported CTDIvol and (b) Dw as a patient size metric, in addition to (c) DEf if available. These results also suggest that SSDE f-factors may provide a reasonable (within ±25%) estimate of nDfetus for TCM and FTC abdomen/pelvis CT exams.

Guidance on Communication of Uncertainty in Scientific Assessments.

This document provides guidance for communicators on how to communicate the various expressions of uncertainty described in EFSA's document: 'Guidance on uncertainty analysis in scientific assessments'. It also contains specific guidance for assessors on how best to report the various expressions of uncertainty. The document provides a template for identifying expressions of uncertainty in scientific assessments and locating the specific guidance for each expression. The guidance is structured according to EFSA's three broadly defined categories of target audience: 'entry', 'informed' and 'technical' levels. Communicators should use the guidance for entry and informed audiences, while assessors should use the guidance for the technical level. The guidance was formulated using evidence from the scientific literature, grey literature and two EFSA research studies, or based on judgement and reasoning where evidence was incomplete or missing. The limitations of the evidence sources inform the recommendations for further research on uncertainty communication.

Guidance on harmonised methodologies for human health, animal health and ecological risk assessment of combined exposure to multiple chemicals.

This Guidance document describes harmonised risk assessment methodologies for combined exposure to multiple chemicals for all relevant areas within EFSA's remit, i.e. human health, animal health and ecological areas. First, a short review of the key terms, scientific basis for combined exposure risk assessment and approaches to assessing (eco)toxicology is given, including existing frameworks for these risk assessments. This background was evaluated, resulting in a harmonised framework for risk assessment of combined exposure to multiple chemicals. The framework is based on the risk assessment steps (problem formulation, exposure assessment, hazard identification and characterisation, and risk characterisation including uncertainty analysis), with tiered and stepwise approaches for both whole mixture approaches and component-based approaches. Specific considerations are given to component-based approaches including the grouping of chemicals into common assessment groups, the use of dose addition as a default assumption, approaches to integrate evidence of interactions and the refinement of assessment groups. Case studies are annexed in this guidance document to explore the feasibility and spectrum of applications of the proposed methods and approaches for human and animal health and ecological risk assessment. The Scientific Committee considers that this Guidance is fit for purpose for risk assessments of combined exposure to multiple chemicals and should be applied in all relevant areas of EFSA's work. Future work and research are recommended.

Applying ecosystem services for pre-market environmental risk assessments of regulated stressors.

Ecosystem services (ES) are the benefits that people obtain from ecosystems. Investigating the environment through an ES framework has gained wide acceptance in the international scientific community and is applied by policymakers to protect biodiversity and safeguard the sustainability of ecosystems. This approach can enhance the ecological and societal relevance of pre-market/prospective environmental risk assessments (ERAs) of regulated stressors by: (1) informing the derivation of operational protection goals; (2) enabling the integration of environmental and human health risk assessments; (3) facilitating horizontal integration of policies and regulations; (4) leading to more comprehensive and consistent environmental protection; (5) articulating the utility of, and trade-offs involved in, environmental decisions; and (6) enhancing the transparency of risk assessment results and the decisions based upon them. Realisation of these advantages will require challenges that impede acceptance of an ES approach to be overcome. Particularly, there is concern that, if biodiversity only matters to the extent that it benefits humans, the intrinsic value of nature is ignored. Moreover, our understanding of linkages among ecological components and the processes that ultimately deliver ES is incomplete, valuing ES is complex, and there is no standard ES lexicon and limited familiarity with the approach. To help overcome these challenges, we encourage: (1) further research to establish biodiversity-ES relationships; (2) the development of approaches that (i) quantitatively translate responses to chemical stressors by organisms and groups of organisms to ES delivery across different spatial and temporal scales, (ii) measure cultural ES and ease their integration into ES valuations, and (iii) appropriately value changes in ES delivery so that trade-offs among different management options can be assessed; (3) the establishment of a standard ES lexicon; and (4) building capacity in ES science and how to apply ES to ERAs. These development needs should not prevent movement towards implementation of an ES approach in ERAs, as the advantages we perceive of using this approach render it more than worthwhile to tackle those challenges. Society and the environment stand to benefit from this shift in how we conduct the ERA of regulated stressors.

EFSA's third Scientific Conference 'Science, Food, Society': concluding remarks.

From 18-21 September 2018, EFSA hosted its third Scientific Conference on Science, Food and Society in Parma, Italy. This paper summarises the overall recommendations on future actions and research priorities of the conference and complements the earlier multi-authored papers in this issue. The conference started and closed with the plenary sessions 'where science meets society: putting risk assessment in context' and 'staying relevant in a changing world', respectively. In between, there were seven breakout sessions five of which aimed at advancing risk assessment science in the areas of human health, the environment, biological hazards, nutrition and managing evidence, and two of which were dedicated to the societal aspects of risk assessment: engaging with society and envisioning the expertise of the future. During the 4 days of the event, participants addressed the complex interplay between science, risk assessment, policy and society, and explored how to advance food safety risk assessment to address the challenges of a changing world and ensure preparedness. Acknowledging that good science alone is no longer sufficient to ensure fit-for-purpose food safety risk assessments, EFSA must further build on its current five Strategic Objectives. To ensure that its risk assessments remain scientifically robust and sound, EFSA should strive for robust and fit-for-purpose risk assessments; consider food in the context of safety, nutrition and sustainability; and explore further how EFSA can work with other organisations to achieve the One Health/One Environment goals. In addition, EFSA should base scientific risk assessments on reliable science while capitalising on scientific advances; address scientific uncertainties; and fully publish the evidence and data used. In line with societal expectations, EFSA, in coordination with risk managers, should frame risk assessments through clear policy goals and problem formulation; be explicit about value judgements; communicate clearly and consistently and in coordination with risk assessors and risk managers; involve society; avoid conflicts of interest; and follow trustworthy processes.

Estimating a size-specific dose for helical head CT examinations using Monte Carlo simulation methods.

PURPOSE: Size-specific dose estimates (SSDE) conversion factors have been determined by AAPM Report 204 to adjust CTDIvol to account for patient size but were limited to body CT examinations. The purpose of this work was to determine conversion factors that could be used for an SSDE for helical, head CT examinations for patients of different sizes. METHODS: Validated Monte Carlo (MC) simulation methods were used to estimate dose to the center of the scan volume from a routine, helical head examination for a group of patient models representing a range of ages and sizes. Ten GSF/ICRP voxelized phantom models and five pediatric voxelized patient models created from CT image data were used in this study. CT scans were simulated using a Siemens multidetector row CT equivalent source model. Scan parameters were taken from the AAPM Routine Head protocols for a fixed tube current (FTC), helical protocol, and scan lengths were adapted to the anatomy of each patient model. MC simulations were performed using mesh tallies to produce voxelized dose distributions for the entire scan volume of each model. Three tally regions were investigated: (1) a small 0.6 cc volume at the center of the scan volume, (2) 0.8-1.0 cm axial slab at the center of the scan volume, and (3) the entire scan volume. Mean dose to brain parenchyma for all three regions was calculated. Mean bone dose and a mass-weighted average dose, consisting of brain parenchyma and bone, were also calculated for the slab in the central plane and the entire scan volume. All dose measures were then normalized by CTDIvol for the 16 cm phantom (CTDIvol,16 ). Conversion factors were determined by calculating the relationship between normalized doses and water equivalent diameter (Dw ). RESULTS: CTDIvol,16 -normalized mean brain parenchyma dose values within the 0.6 cc volume, 0.8-1.0 cm central axial slab, and the entire scan volume, when parameterized by Dw , had an exponential relationship with a coefficient of determination (R2 ) of 0.86, 0.84, and 0.88, respectively. There was no statistically significant difference between the conversion factors resulting from these three different tally regions. Exponential relationships between CTDIvol,16 -normalized mean bone doses had R2 values of 0.83 and 0.87 for the central slab and for the entire scan volume, respectively. CTDIvol,16 -normalized mass-weighted average doses had R2 values of 0.39 and 0.51 for the central slab and for the entire scan volume, respectively. CONCLUSIONS: Conversion factors that describe the exponential relationship between CTDIvol,16 -normalized mean brain dose and a size metric (Dw ) for helical head CT examinations have been reported for two different interpretations of the center of the scan volume. These dose descriptors have been extended to describe the dose to bone in the center of the scan volume as well as a mass-weighted average dose to brain and bone. These may be used, when combined with other efforts, to develop an SSDE dose coefficients for routine, helical head CT examinations.

Estimating lung, breast, and effective dose from low-dose lung cancer screening CT exams with tube current modulation across a range of patient sizes.

PURPOSE: The purpose of this study was to estimate the radiation dose to the lung and breast as well as the effective dose from tube current modulated (TCM) lung cancer screening (LCS) scans across a range of patient sizes. METHODS: Monte Carlo (MC) methods were used to calculate lung, breast, and effective doses from a low-dose LCS protocol for a 64-slice CT that used TCM. Scanning parameters were from the protocols published by AAPM's Alliance for Quality CT. To determine lung, breast, and effective doses from lung cancer screening, eight GSF/ICRP voxelized phantom models with all radiosensitive organs identified were used to estimate lung, breast, and effective doses. Additionally, to extend the limited size range provided by the GSF/ICRP phantom models, 30 voxelized patient models of thoracic anatomy were generated from LCS patient data. For these patient models, lung and breast were semi-automatically segmented. TCM schemes for each of the GSF/ICRP phantom models were generated using a validated method wherein tissue attenuation and scanner limitations were used to determine the TCM output as a function of table position and source angle. TCM schemes for voxelized patient models were extracted from the raw projection data. The water equivalent diameter, Dw, was used as the patient size descriptor. Dw was estimated for the GSF/ICRP models. For the thoracic patient models, Dw was extracted from the DICOM header of the CT localizer radiograph. MC simulations were performed using the TCM scheme for each model. Absolute organ doses were tallied and effective doses were calculated using ICRP 103 tissue weighting factors for the GSF/ICRP models. Metrics of scanner radiation output were determined based on each model's TCM scheme, including CTDIvol , dose length product (DLP), and CTDIvol, Low Att , a previously described regional metric of scanner output covering most of the lungs and breast. All lung and breast doses values were normalized by scan-specific CTDIvol and CTDIvol, Low Att . Effective doses were normalized by scan-specific CTDIvol and DLP. Absolute and normalized doses were reported as a function of Dw. RESULTS: Lung doses normalized by CTDIvol, Low Att were modeled as an exponential relationship with respect to Dw with coefficients of determination (R2 ) of 0.80. Breast dose normalized by CTDIvol, Low Att was modeled with an exponential relationship to Dw with an R2 of 0.23. For all eight GSF/ICRP phantom models, the effective dose using TCM protocols was below 1.6 mSv. Effective doses showed some size dependence but when normalized by DLP demonstrated a constant behavior. CONCLUSION: Lung, breast, and effective doses from LCS CT exams with TCM were estimated with respect to patient size. Normalized lung dose can be reasonably estimated with a measure of a patient size such as Dw and regional metric of CTDIvol covering the thorax such as CTDIvol, Low Att , while normalized breast dose can also be estimated with a regional metric of CTDIvol but with a larger degree of variability than observed for lung. Effective dose normalized by DLP can be estimated with a constant multiplier.

The principles and methods behind EFSA's Guidance on Uncertainty Analysis in Scientific Assessment.

To meet the general requirement for transparency in EFSA's work, all its scientific assessments must consider uncertainty. Assessments must say clearly and unambiguously what sources of uncertainty have been identified and what is their impact on the assessment conclusion. This applies to all EFSA's areas, all types of scientific assessment and all types of uncertainty affecting assessment. This current Opinion describes the principles and methods supporting a concise Guidance Document on Uncertainty in EFSA's Scientific Assessment, published separately. These documents do not prescribe specific methods for uncertainty analysis but rather provide a flexible framework within which different methods may be selected, according to the needs of each assessment. Assessors should systematically identify sources of uncertainty, checking each part of their assessment to minimise the risk of overlooking important uncertainties. Uncertainty may be expressed qualitatively or quantitatively. It is neither necessary nor possible to quantify separately every source of uncertainty affecting an assessment. However, assessors should express in quantitative terms the combined effect of as many as possible of identified sources of uncertainty. The guidance describes practical approaches. Uncertainty analysis should be conducted in a flexible, iterative manner, starting at a level appropriate to the assessment and refining the analysis as far as is needed or possible within the time available. The methods and results of the uncertainty analysis should be reported fully and transparently. Every EFSA Panel and Unit applied the draft Guidance to at least one assessment in their work area during a trial period of one year. Experience gained in this period resulted in improved guidance. The Scientific Committee considers that uncertainty analysis will be unconditional for EFSA Panels and staff and must be embedded into scientific assessment in all areas of EFSA's work.

Guidance on Uncertainty Analysis in Scientific Assessments.

Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. It is therefore relevant in all EFSA's scientific assessments and also necessary, to ensure that the assessment conclusions provide reliable information for decision-making. The form and extent of uncertainty analysis, and how the conclusions should be reported, vary widely depending on the nature and context of each assessment and the degree of uncertainty that is present. This document provides concise guidance on how to identify which options for uncertainty analysis are appropriate in each assessment, and how to apply them. It is accompanied by a separate, supporting opinion that explains the key concepts and principles behind this Guidance, and describes the methods in more detail.

Guidance on risk assessment of the application of nanoscience and nanotechnologies in the food and feed chain: Part 1, human and animal health.

The European Food Safety Authority has produced this Guidance on human and animal health aspects (Part 1) of the risk assessment of nanoscience and nanotechnology applications in the food and feed chain. It covers the application areas within EFSA's remit, e.g. novel foods, food contact materials, food/feed additives and pesticides. The Guidance takes account of the new developments that have taken place since publication of the previous Guidance in 2011. Potential future developments are suggested in the scientific literature for nanoencapsulated delivery systems and nanocomposites in applications such as novel foods, food/feed additives, biocides, pesticides and food contact materials. Therefore, the Guidance has taken account of relevant new scientific studies that provide more insights to physicochemical properties, exposure assessment and hazard characterisation of nanomaterials. It specifically elaborates on physicochemical characterisation of nanomaterials in terms of how to establish whether a material is a nanomaterial, the key parameters that should be measured, the methods and techniques that can be used for characterisation of nanomaterials and their determination in complex matrices. It also details the aspects relating to exposure assessment and hazard identification and characterisation. In particular, nanospecific considerations relating to in vivo/in vitro toxicological studies are discussed and a tiered framework for toxicological testing is outlined. It describes in vitro degradation, toxicokinetics, genotoxicity as well as general issues relating to testing of nanomaterials. Depending on the initial tier results, studies may be needed to investigate reproductive and developmental toxicity, immunotoxicity, allergenicity, neurotoxicity, effects on gut microbiome and endocrine activity. The possible use of read-across to fill data gaps as well as the potential use of integrated testing strategies and the knowledge of modes/mechanisms of action are also discussed. The Guidance proposes approaches to risk characterisation and uncertainty analysis, and provides recommendations for further research in this area.

Update: use of the benchmark dose approach in risk assessment.

The Scientific Committee (SC) reconfirms that the benchmark dose (BMD) approach is a scientifically more advanced method compared to the NOAEL approach for deriving a Reference Point (RP). Most of the modifications made to the SC guidance of 2009 concern the section providing guidance on how to apply the BMD approach. Model averaging is recommended as the preferred method for calculating the BMD confidence interval, while acknowledging that the respective tools are still under development and may not be easily accessible to all. Therefore, selecting or rejecting models is still considered as a suboptimal alternative. The set of default models to be used for BMD analysis has been reviewed, and the Akaike information criterion (AIC) has been introduced instead of the log-likelihood to characterise the goodness of fit of different mathematical models to a dose-response data set. A flowchart has also been inserted in this update to guide the reader step-by-step when performing a BMD analysis, as well as a chapter on the distributional part of dose-response models and a template for reporting a BMD analysis in a complete and transparent manner. Finally, it is recommended to always report the BMD confidence interval rather than the value of the BMD. The lower bound (BMDL) is needed as a potential RP, and the upper bound (BMDU) is needed for establishing the BMDU/BMDL per ratio reflecting the uncertainty in the BMD estimate. This updated guidance does not call for a general re-evaluation of previous assessments where the NOAEL approach or the BMD approach as described in the 2009 SC guidance was used, in particular when the exposure is clearly smaller (e.g. more than one order of magnitude) than the health-based guidance value. Finally, the SC firmly reiterates to reconsider test guidelines given the expected wide application of the BMD approach.

Clarification of some aspects related to genotoxicity assessment.

The European Commission requested EFSA to provide advice on the following: (1) the suitability of the unscheduled DNA synthesis (UDS) in vivo assay to follow-up positive results in in vitro gene mutation tests; (2) the adequacy to demonstrate target tissue exposure in in vivo studies, particularly in the mammalian erythrocyte micronucleus test; (3) the use of data in a weight-of-evidence approach to conclude on the genotoxic potential of substances and the consequent setting of health-based guidance values. The Scientific Committee concluded that the first question should be addressed in both a retrospective and a prospective way: for future assessments, it is recommended no longer performing the UDS test. For re-assessments, if the outcome of the UDS is negative, the reliability and significance of results should be carefully evaluated in a weight-of-evidence approach, before deciding whether more sensitive tests such as transgenic assay or in vivo comet assay would be needed to complete the assessment. Regarding the second question, the Scientific Committee concluded that it should be addressed in lines of evidence of bone marrow exposure: toxicity to the bone marrow in itself provides sufficient evidence to allow concluding on the validity of a negative outcome of a study. All other lines of evidence of target tissue exposure should be assessed within a weight-of-evidence approach. Regarding the third question, the Scientific Committee concluded that any available data that may assist in reducing the uncertainty in the assessment of the genotoxic potential of a substance should be taken into consideration. If the overall evaluation leaves no concerns for genotoxicity, health-based guidance values may be established. However, if concerns for genotoxicity remain, establishing health-based guidance values is not considered appropriate.

Scientific motivations and criteria to consider updating EFSA scientific assessments.

EFSA is committed to assess and communicate the risks occurring in the food and feed chain from farm to fork and to provide other forms of scientific advice. This work, carried out by EFSA since its inception, has resulted in the adoption of thousands of scientific assessments. EFSA is obliged to re-assess past assessments in specific regulatory contexts such as those on food and feed additives, active substances in plant protection products and genetically modified food and feed. In other sectors, the consideration for updating past EFSA scientific assessments is taken on an ad hoc basis mainly depending on specific requests by risk managers or on EFSA self-tasking. If safety is potentially at stake in any area within EFSA's remit, the readiness to update past scientific assessments is important to keep EFSA at the forefront of science and to promote an effective risk assessment. Although this task might be very complex and resource demanding, it is fundamental to EFSA's mission. The present EFSA Scientific Committee opinion deals with scientific motivations and criteria to contribute to the timely updating of EFSA scientific assessments. It is recognised that the decision for updating should be agreed following careful consideration of all the relevant elements by the EFSA management, in collaboration with risk managers and stakeholders. The present opinion addresses the scientific approaches through which it would be possible for EFSA to increase the speed and effectiveness of the acquisition of new data, as well as, to improve the consequent evaluations to assess the relevance and reliability of new data in the context of contributing to the better definition of whether to update past scientific assessments.

Guidance on the risk assessment of substances present in food intended for infants below 16 weeks of age.

Following a request from the European Commission to EFSA, the EFSA Scientific Committee (SC) prepared a guidance for the risk assessment of substances present in food intended for infants below 16 weeks of age. In its approach to develop this guidance, the EFSA SC took into account, among others, (i) an exposure assessment based on infant formula as the only source of nutrition; (ii) knowledge of organ development in human infants, including the development of the gut, metabolic and excretory capacities, the brain and brain barriers, the immune system, the endocrine and reproductive systems; (iii) the overall toxicological profile of the substance identified through the standard toxicological tests, including critical effects; (iv) the relevance for the human infant of the neonatal experimental animal models used. The EFSA SC notes that during the period from birth up to 16 weeks, infants are expected to be exclusively fed on breast milk and/or infant formula. The EFSA SC views this period as the time where health-based guidance values for the general population do not apply without further considerations. High infant formula consumption per body weight is derived from 95th percentile consumption. The first weeks of life is the time of the highest relative consumption on a body weight basis. Therefore, when performing an exposure assessment, the EFSA SC proposes to use the high consumption value of 260 mL/kg bw per day. A decision tree approach is proposed that enables a risk assessment of substances present in food intended for infants below 16 weeks of age. The additional information needed when testing substances present in food for infants below 16 weeks of age and the approach to be taken for the risk assessment are on a case-by-case basis, depending on whether the substance is added intentionally to food and is systemically available.

Guidance on the assessment of the biological relevance of data in scientific assessments.

EFSA requested its Scientific Committee to prepare a guidance document providing generic issues and criteria to consider biological relevance, particularly when deciding on whether an observed effect is of biological relevance, i.e. is adverse (or shows a beneficial health effect) or not. The guidance document provides a general framework for establishing the biological relevance of observations at various stages of the assessment. Biological relevance is considered at three main stages related to the process of dealing with evidence: Development of the assessment strategy. In this context, specification of agents, effects, subjects and conditions in relation to the assessment question(s): Collection and extraction of data; Appraisal and integration of the relevance of the agents, subjects, effects and conditions, i.e. reviewing dimensions of biological relevance for each data set. A decision tree is developed to assist in the collection, identification and appraisal of relevant data for a given specific assessment question to be answered.

Guidance on the use of the weight of evidence approach in scientific assessments.

EFSA requested the Scientific Committee to develop a guidance document on the use of the weight of evidence approach in scientific assessments for use in all areas under EFSA's remit. The guidance document addresses the use of weight of evidence approaches in scientific assessments using both qualitative and quantitative approaches. Several case studies covering the various areas under EFSA's remit are annexed to the guidance document to illustrate the applicability of the proposed approach. Weight of evidence assessment is defined in this guidance as a process in which evidence is integrated to determine the relative support for possible answers to a question. This document considers the weight of evidence assessment as comprising three basic steps: (1) assembling the evidence into lines of evidence of similar type, (2) weighing the evidence, (3) integrating the evidence. The present document identifies reliability, relevance and consistency as three basic considerations for weighing evidence.