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ABSTRACT Detailed knowledge about radiation exposure is crucial for radiology professionals. The conventional calculation of effective dose (ED) for computed tomography (CT) is based on dose

length product (DLP) and population-based conversion factors (k). This is often imprecise and unable to consider individual patient characteristics. We sought to provide more precise and

individual radiation exposure calculation using image based Monte Carlo simulations (MC) in a heterogeneous patient collective and to compare it to phantom based MC provided from the

National Cancer Institute (NCI) as academic reference. Dose distributions were simulated for 22 patients after whole-body CT during Positron Emission Tomography-CT. Based on MC we calculated

individual Lifetime Attributable Risk (LAR) and Excess Relative Risk (ERR) of cancer mortality. EDMC was compared to EDDLP and EDNCI. EDDLP (13.2 ± 4.5 mSv) was higher compared to EDNCI

(9.8 ± 2.1 mSv) and EDMC (11.6 ± 1.5 mSv). Relative individual differences were up to −48% for EDMC and −44% for EDNCI compared to EDDLP. Matching pair analysis illustrates that young age

and gender are affecting LAR and ERR significantly. Because of these uncertainties in radiation dose assessment automated individual dose and risk estimation would be desirable for dose

monitoring in the future. SIMILAR CONTENT BEING VIEWED BY OTHERS VALIDATION OF SSDE CALCULATION IN A MODERN CT SCANNER AND CORRELATION WITH EFFECTIVE DOSE Article Open access 19 February

2025 CHARACTERIZING IMAGING RADIATION RISK IN A POPULATION OF 8918 PATIENTS WITH RECURRENT IMAGING FOR A BETTER EFFECTIVE DOSE Article Open access 14 March 2024 THE DOSE–RESPONSE

CHARACTERISTICS OF FOUR NTCP MODELS: USING A NOVEL CT-BASED RADIOMIC METHOD TO QUANTIFY RADIATION-INDUCED LUNG DENSITY CHANGES Article Open access 29 June 2020 INTRODUCTION The Euratom

council directive (_2013/59_/_Euratom_) emphasizes the need for patient radiation dose monitoring in clinical routine1. Computed tomography (CT) is indispensable for contemporary patient

care, but many studies suggest a relation between low dose protracted radiation exposure and an increased incidence of malignancy based on the linear Non-threshold Dose-Response Model2.

Therefore, detailed knowledge of radiation exposure from CT examinations is crucial for radiology healthcare professionals in clinical routine to detect and address increased risks of

malignancy. Widely used parameters for radiation exposure assessment like the volumetric CT dose index (CTDIvol) and the Dose Length Product (DLP) characterize scanner radiation output, but

are unable to take individual patient characteristics into account3. Conversion to effective dose (EDDLP) is feasible using population-based conversion factors (k) that take the averaged

radiosensitivity in defined anatomic regions into account4. A variety of different k-factors are recommended in the literature for different volumes (e.g. head, thorax, abdomen, pelvis) and

different CT-scanners5,6. These k-factors are mostly derived from phantom models that try to represent average patient anatomy in western populations, but are unable to respect individual

anatomy like missing organs due to aplasia or resection, organ hypo- or hypertrophy, skeletal deformations and metal implants. Nevertheless, these programs, like e.g. the National Cancer

Institute (NCI) dosimetry system for CT, can be considered as current academic reference (EDNCI)7. However, the routinely performed calculation of EDDLP is imprecise and the degree of

difference to the real ED in each individual patient is unknown. Risk estimates that are calculated on these imprecise data could then easily be misinterpreted. Therefore, conventionally

calculated EDDLP can be used for comparison between different CT-scanners or different examination protocols, but should not be used for comparison between different patient collectives or

for radiation risk estimation8,9. Stochastic estimation of radiation exposure from CT is feasible by Monte Carlo (MC) simulations. Romanyukha _et al_. used computational phantoms and

MC-simulations to calculate body size-specific conversion factors from regression curves10. Huda _et al_. were able to show that this phantom based technique is also feasible to derive organ

doses and to use these for cancer risk estimation11. Only few studies with small numbers of mostly pediatric patients used MC simulations for individual dose and risk estimation per

patient, probably due to the high computational power needed12,13. The main drawback of MC calculations is that they are limited to the reconstructed body volume. Therefore, overexposure in

z-axis and scattered radiation to the organs outside the directly exposed volume cannot be taken into account. Detailed information about biological effects of low-level ionizing radiation

is available from the National Academy of Sciences report number seven about _Health Risks from Exposure to Low Levels of Ionizing Radiation_ (BEIR VII). The authors of BEIR VII report

follow the linear Non-threshold Dose-Response Model and presume that the risk of cancer incidence and mortality due to medical examinations are mainly based on low-level ionizing radiation

dose, age and gender14. The aim of this study was to provide individual calculations of ED in whole-body exposure derived from MC calculations (EDMC) and to compare them with the

conventional method (EDDLP) and the academic reference (EDNCI) following an equivalence hypothesis in a heterogeneous study collective. Furthermore, cancer risk estimates that are based on

EDMC should allow to evaluate the impact of patient characteristics on individual risk of malignancy. Whole body CT from combined Positron Emission Tomography (PET)-CT examinations were

exclusively selected in order to minimize the indeterminate radiation dose to body parts outside the imaging volume that cannot be assessed by MC. METHODS STUDY DESIGN Twenty-two patients

from a collective of 34 consecutive patients, with clinical indication for whole body PET-CT, were retrospectively included (Fig. 1). Inclusion criteria were the complete and artifact-free

coverage of the scan volume, sufficient intra-venous contrast injection and full availability of individual patient data including age, height, weight and body mass index (BMI). Exclusion

criteria were severe artifacts (n = 0) and examinations without contrast injection (n = 12). Clinical indications were infectious diseases (n = 8), bronchial carcinoma (n = 6), head and neck

cancer (n = 3), cancer of unknown primary (n = 2), malignant melanoma (n = 2) and retroperitoneal fibrosis (n = 1). Written informed consent was obtained and archived for each patient. The

study was approved by the local ethics committee of the Friedrich-Alexander University Erlangen-Nuremberg and complied with the Declaration of Helsinki. IMAGE ACQUISITION All examinations

were performed with continuous volume acquisition using a Siemens Biograph True Point 64 PET-CT (Siemens Healthcare GmbH, Forchheim, Germany). Indication, supervision and image analysis were

performed by senior physicians with extensive experience in PET-CT (>6 years). CT was performed from head to mid-femur by a single spiral acquisition in a cranio-caudal direction using a

tube voltage of 120 kV, anatomic tube current modulation with a reference tube-current time product of 170 mAs, pitch 0.8, gantry rotation time 0.5 seconds and 64 × 0.6 mm slice acquisition

by 32 × 0.6 mm detector collimation and z-axis flying focal spot double sampling15. Unlike in diagnostic CT, where different body parts (e.g. head/neck and thorax/abdomen) are often

examined in multiple contrast phases and different positions, PET-CT uses single spiral acquisitions with only one single bolus injection allowing for complete coverage of all body organs

without overlapping of anatomic regions. Therefore, the effect of scattered radiation to all radiosensitive tissues can be taken into account by MC calculations and the effect of overranging

in the beginning and end of the spiral acquisition can be neglected16. All patients received body weight adapted intravenous injection of 18F-fluorodeoxyglucose (3 MBq/kg). After an average

resting period of 91 (±25.6) minutes iodine containing contrast agent (Ultravist 370®, Bayer-Schering Healthcare, Berlin, Germany) was mechanically administered into an antebrachial cannula

using a power injector (Accutron CT-D, Medtron AG, Saarbruecken, Germany), and the single spiral CT acquisition was started with a fixed delay of 70 s. The PET acquisitions were run

subsequently in 7–8 steps, depending on patient length and exam volumes. For this study full field of view (500 × 500 mm) images were reconstructed using a smooth filtered back projection

kernel (B31), slice thickness 5.0 mm and increment 5.0 mm. The resulting voxel size was 0.98 × 0.98 × 5.00 mm. CONVENTIONAL CALCULATION OF EFFECTIVE DOSE Tube current time product, CTDIvol

and DLP were recorded for each examination from the PET-CT scan protocol as provided by the scanner. Details about their definition and calculation are reported elsewhere in literature6. The

main drawback of these monitoring techniques is that they are unable to provide information about the biological impact of radiation exposure and should only be taken as an index of

radiation output by the CT system for comparative purposes9,17. The biologically relevant effective dose (EDDLP) was calculated for each examination by multiplication of DLP and the

region-specific conversion factor. For this study kBody (0.015 mSv/mGy∙cm) was used as recommended by the American Association of Physicists in Medicine6. Dose exposition related to

18F-fluorodeoxyglucose (FDG)-PET were not considered for ED calculation. PHANTOM BASED CALCULATION OF EFFECTIVE DOSE Academic reference ED was calculated using a dedicated CT dosimetry

software tool provided by the National Cancer Institute (NCI-CT)7, which combines reference phantoms provided by the International Commission on Radiological Protection (ICRP) and MC

simulations of a reference CT scanner (Somatom Sensation 16, Siemens Healthcare GmbH, Forchheim, Germany). Individual examination parameters, gender, age, height and body weight were used as

input for this mathematical phantom based calculation (EDNCI). Dedicated descriptions about computational methods of this software are available in the literature7. ORGAN SEGMENTATION All

radiosensitive organs or tissues including all remainders, which are mentioned in IRCP report 103 were segmented using a dedicated software package (ITK-SNAP 1.8, Penn Image Computing and

Science Laboratory, Philadelphia, USA). Semi-automatic threshold segmentation with manual corrections was used for the lungs, cortical bone, bone marrow, liver, spleen, brain, heart, muscle

and skin. All other organs or tissues were segmented manually in a slice per slice fashion. Therefore, missing organs due to aplasia or resection, organ hypo- or hypertrophy, skeletal

deformations and metal implants were considered. For hollow organs, only voxels contributing to the wall were considered to contain radiosensitive tissues. Segmentation of lymphatic tissues

was restricted to visible lymphatic nodes (Fig. 2). All segmentations were performed by a specially trained radiology resident (4 years experience) and reviewed from an experienced

board-certified radiologist (9 years experience). MONTE CARLO BASED CALCULATION OF EFFECTIVE DOSE Extensive mathematical models in simulation techniques can be utilized to calculate dose

distribution for each voxel of CT scan volumes using the attenuation values and geometry from DICOM datasets as input18,19. All MC dose simulations were carried out using the software

package ImpactMC (VAMP GmbH, Erlangen, Germany). Software details and information concerning the Monte Carlo calculation algorithms are reported elsewhere18,20. Multiplication of the

resulting relative dose values per voxel with air kerma provides the absorbed dose for each voxel. The air kerma is a scanner specific value, which describes the kinetic energy transferred

in air dependent on geometry and tube settings. It can be considered as measurement of x-ray beam intensity without object, which was 18.3 mGy for this study. The averaged absorbed dose over

an organ volume, defined from segmentations, provides the absorbed organ dose. Equivalent organ dose was calculated by multiplication with the radiation weighting factor for photons (WR = 

1) and used for subsequent effective organ dose calculations (EDOrgan) by multiplication with the dedicated tissue weighting factor (WT) as recommended in ICRP report 10321. The sum of all

effective organ doses provides EDMC for each individual (Fig. 3). The relative contribution of the respective EDOrgan to the EDMC was calculated following Eq. 1:

$${{\rm{C}}}_{{\rm{Organ}}}={{\rm{ED}}}_{{\rm{Organ}}}/{{\rm{ED}}}_{{\rm{MC}}}$$ (1) Individual conversion factors (kMC) were calculated using Eq. 2 to illustrate the differences compared to

the conventional calculation method using DLP and kbody. $${{\rm{k}}}_{{\rm{MC}}}={{\rm{ED}}}_{{\rm{MC}}}/{\rm{DLP}}$$ (2) RADIATION RISK ASSESSMENT Lifetime Attributable Risk (LAR) is

defined as risk of disease of an exposed cohort in comparison to a non-exposed cohort. Calculations of LAR for cancer mortality were based on report VII about Biologic Effects of Ionizing

Radiation (BEIR VII). The BEIRV VII report refers to an individual radiation dose exposure of 100 mGy. Assuming a linear risk distribution between decennium age intervals LARMC was derived

from EDMC by linear interpolation between the younger (Ny) and older (NO) cohort as shown in equation 3 using patient age (Ap) and the age of the younger cohort (Ay) as input values22.

Equation 3: Calculation of individual LAR $$LA{R}_{MC}=\left({N}_{y}-\left(({N}_{y}-{{\rm{N}}}_{0})\ast \frac{{A}_{p}-{A}_{Y}}{10\,{\rm{years}}}\right)\right)\ast

\frac{E{D}_{Organ}}{100\,{\rm{mGy}}}$$ The Excess Relative Risk (ERRMC), as a measurement of the exceeding risk of an exposed person compared to a non-exposed person, was calculated using

the solid cancer mortality in the United States as baseline (female: 17500/100000; male: 22100/100000) using equation 423. Equation 4: Calculation of individual ERR

$$ER{R}_{MC}=LA{R}_{MC}/LA{R}_{baseline}$$ PAIRWISE PATIENT COMPARISON Because of the rather small patient collective in this study, mainly due to the extensive effort needed for

segmentation of all radiosensitive tissues, pairwise patient comparisons were selected to highlight the influence of individual patient characteristics on the radiation dose parameters and

radiation risk estimation. Matching patient pairs, each with two comparable values and one variable parameter, were found for the parameters age, sex and BMI. STATISTICAL ANALYSIS All

statistical analyses were performed using the software package SPSS Statistics Version 21 (IBM, Somers, NY, USA). Normal distribution of the data was tested by Kolmogorov-Smirnov and

Shapiro-Wilk test. Normally distributed data is presented as mean ± standard deviation. Median and range are provided if no normal distribution was assumed. Illustration is provided as

Bland-Altman plots. Spearman’s rank order test was used to test for correlations between tube current, BMI, EDDLP and EDMC. The significance level was defined as p < 0.05. RESULTS

DEMOGRAPHICS OF THE STUDY COLLECTIVE Five out of 22 patients (23%) were female and 17 male (77%). The mean age was 57.3 ± 14.3 years and the mean BMI was 26.0 ± 5.9 kg/m². Three patients

(13.6%) were younger than 40 years. The female patient group was younger (53.6 ± 17.6 years) compared to the male patient group (58.4 ± 13.6 years). Female patients had a lower mean BMI

(21.2 ± 2.5 kg/m²) compared to male patients (27.4 ± 5.88 kg/m2). From the male subgroup 9 patients (52.9%) suffered from overweight (BMI > 25), and three patients (17.6%) had severe

overweight (BMI > 30). One female and one male patient were considered as underweight (BMI < 19). No overweight patient was found in the female subgroup. Detailed patient

characteristics are shown in Table 1. ORGAN SPECIFIC RADIATION DOSE EXPOSURE Mean equivalent organ dose was 13.0 ± 3.5 mGy. Highest equivalent organ dose values were found in high

attenuating structures such as the cortical bone and the thyroid gland. Lowest values were found in profound organs like the extra-thoracic (ET) respiratory region and the uterus. Many

superficial organs like muscles (11.5 ± 2.0 mGy), breast tissue (10.9 ± 0.8 mGy) and salivary glands (14.3 ± 2.8 mGy) also had rather low equivalent organ dose values. Despite its profound

position well perfused kidneys had very high equivalent organ dose values (17.0 ± 2.1 mGy). Effective organ dose was mainly influenced by WT, nicely demonstrated by the thyroid gland. It has

the second highest mean equivalent organ dose (21.2 ± 5.4 mGy), but is only ranked 7th highest effective organ dose (8% of total organ dose distribution) because of the rather low WT

(0.04). In contrast, the lungs ranked only 7th in equivalent organ dose (13.4 ± 1.9 mGy), but received highest effective organ dose levels in female and male patients (12% and 14% of the

total EDMC) due to high WT (0.12). Colon, lungs and stomach contributed to more than 50% of the total EDMC in male patients and to 48% in female patients (Fig. 4). Differences between WT and

the relative contribution of the effective organ dose to whole body EDMC (COrgan) reflect the influence of individual radiation dose distribution on each organ dose. Highest positive

differences between WT and COrgan were found for the bone surface (♀: +102.1%; ♂: +134.8%), the thyroid gland (♀: +89.1%; ♂: +78.1%) and the kidneys (♀: + 49.4%; ♂: +47.1%). The male gonads

also had substantially higher COrgan than expected from its rather low WT ( + 38.2%), but the female gonads had the highest negative difference of all organs (−21.8%). Other organs with high

negative differences were the adrenal glands (♀: −18.3%; ♂: −11.8%). Detailed organ dose information is shown in Table 2 and organ-based LAR of cancer mortality is provided for all organs

listed in BEIR VII. Considering the similar effective organ doses of the lung (1.48 ± 0.15 mSv) and the breast (1.30 ± 0.09 mSv) the LAR for pulmonary malignant disease (13.25 ± 

4.24/100.000) was remarkably higher compared to the LAR for breast cancer mortality (2.46 ± 1.62/100.000). EFFECTIVE DOSE AND INDIVIDUAL RADIATION RISK ASSESSMENT EDDLP (13.2 ± 4.52 mSv) was

higher than EDMC (11.6 ± 1.47 mSv) and EDNCI (9.8 ± 2.1 mSv). Particularly high differences between EDMC and EDDLP were found for patients with relatively high or low radiation dose

exposure. Mean difference between both methods was −1.7 mSv (Fig. 5a). The same tendency was found for the comparison of EDNCI and EDDLP but with higher mean negative differences −3.4 mSv

(Fig. 5b). A comparison between the three calculation methods is illustrated as boxplot (Fig. 6). The range of radiation dose was substantially smaller in both advanced methods (EDMC: 5.6 

mSv, EDNCI: 10.2 mSv) compared to the conventional technique (EDDLP: 19.3 mSv). Effective mAs and DLP linearly increased with higher BMI in Spearman’s rank order test (rs = 0.961 and 0.949).

This effect should be mainly due to the anatomy-based tube current modulation algorithm. Therefore, also BMI and EDDLP had a high correlation coefficient (rs = 0.949). The correlation

between EDMC and BMI (rs = 0.644) was less and differences between EDDLP and the advanced techniques was especially high in over- and underweight patients. For example, the highest EDDLP of

26.3 mSv was found in a 77-year-old man suffering from adiposity grade 3 (BMI 40.0 kg/m²) while EDNCI (17.3 mSv) and EDMC (13.9 mSv) were substantially lower (34% and 47% less). Lowest EDDLP

of 7.0 mSv was calculated for a 30-year-old underweight man (BMI 16.8 kg/m²), which was close to EDNCI (7.1 mSv), but substantially lower than EDMC (8.5 mSv, 21% higher). Consequently, the

individually calculated conversion factors (kMC) have a range from 53% to 140% when referred to kBody from literature. The mean kMC (0.014 ± 0.004 mSv/mGy cm) approximately reflects the

established value (0.015 mSv/mGy cm), which therefore seems to be suitable for regular weight patients. However, only one fourth of the patients in this study (n = 6, 27.2%) had less than

10% difference between kMC (0.0135–0.0165 mSv/mGy cm) and kBody (mean BMI 22.95 ± 1.58, range 20.7–25.0 kg/m²). The values of LAR were especially high in young female patients. The highest

value (60/100.000) was calculated for a 30-year-old, normal weight woman (BMI 21.6 kg/m²). In contrast, low LAR values were calculated for older, predominantly male patients. The lowest

value (23/100.000) was found for a 76-year-old, overweight man (BMI 29.8 kg/m²). Extent of EDMC, EDDLP and LAR often differed considerably when compared between young and old or underweight

and obese patients, while the interaction between these individual parameters was rather difficult to predict. Detailed results of radiation dose and risk calculations are provided in Table 

3. PAIRWISE PATIENT COMPARISON Two male patients with matching constitution (BMI 24 vs. 23 kg/m²) but different age (36 versus 67 years) had comparable DLP (783 vs. 724 mGy · cm, −7.5%),

EDDLP (11.8 vs. 10.9 mSv, −7.5%), EDNCI (9.4 vs. 9.0 mSv, −4.3%) and EDMC (10.9 vs 10.0 mSv, −8.4%). Nevertheless, calculations for LAR of cancer mortality (41.3 vs. 27.1/100.000; −34.4%)

and ERR (0.19 vs. 0.12%, −36.8%) differed considerably. The young age seems to be the decisive factor among all the considered factors, accounting for about 50% higher risk estimates (Fig. 

7a). Two male patients with matching age (76 and 77 years) but different BMI (29.7 vs. 40.0 kg/m², +34.7%) were exposed to considerably different DLP (998 vs. 1751 mGy·cm, +75.4%) based on

anatomic tube current modulation. Therefore, EDDLP calculations provided very high values for the obese patient (15.0 vs. 26.3 mSv, +75.5%). Dose differences calculated with EDNCI (10.5 vs.

17.3 mSv, +64.5%) were also considerably high, while EDMC values ware nearly comparable (11.9 vs. 13.8 mSv; +15.9%). Differences in LARMC (22.8 vs. 25.1/100.000; +9.6%) and ERR (0.10 vs

0.11%; +10.0%) were even less (Fig. 7b). BMI seems to have only a minor influence on effective dose and risk estimation, while conventional methods would have resulted in an overestimation

of almost 100%. Two patients with matching age (56 and 54 years) and BMI (21.5 vs 21.0 kg/m²) but different sex had similar DLP (634 vs. 630 mGy·cm; +0.63%). ED was the same using the

conventional method in both patients (EDDLP = 9.5 mSv) and almost the same with the NCI method (EDNCI = 8.7 vs 9.0 mSv; −3.3%). EDMC calculation was slightly higher for the female patient

(12.3 vs. 10.3 mSv; +19.4%), but LAR (53.1 vs. 35.4/100000; +50.2%) and ERR (0.3% vs. 0.16%; +87.5%) were substantially higher. Sex seems to have a dominant impact on the risk estimation.

Moreover, the unfavorable combination of age and sex leads to the highest values in the entire study collective, despite average ED values (Fig. 7c). DISCUSSION Individual radiation dose

assessment and risk calculation is feasible by image based Monte Carlo simulations and organ segmentations in an adult clinical routine collective that underwent full body exposure in a

single spiral acquisition. This is in good agreement with the findings of Li _et al_. and Tian _et al_. who evaluated comparable techniques for radiation dose estimation in small pediatric

collectives (n = 2 and n = 42)16,24. A comparable approach for cancer risk estimation in adults based on anthropomorphic phantoms was described by Huda _et al_.11. In contrast to these prior

studies, we provide a combination of individual radiation dose analysis, voxel-based organ segmentation and cancer risk estimation in direct comparison to such phantom based estimation and

the DLP method. The need for such an advanced dose monitoring method, due to several uncertainties with the conventional techniques, has been raised elsewhere in literature20,25. In our

study high equivalent organ doses were strongly related to radiodensity (e.g. bone surface and high contrast medium uptake in the kidneys, the thyroid gland and the extra-thoracic

respiratory region), which seems to be a much stronger predictor for equivalent organ dose than anatomical organ position. This is in good agreement with the contrast media related increase

of DNA double-strand break foci after radiation exposure reported in a prior study for CT26. WT is the strongest predictor of effective organ dose. For example, the thyroid gland and the

kidneys have the second and third highest equivalent organ dose (21.2 ± 3.2 mSv and 17.0 ± 2.1 mSv). However, due to low tissue weighting factors (WT = 0.04 and 0.0092) effective organ dose

of the thyroid gland and the kidneys were only sixth and twelfth highest. Our results confirm that individual patient characteristics have a considerable impact on radiation dose

calculation, which is underrepresented by the conventional method. The calculated error in our adult collective (−48 to 39%) is comparable to the previously published study results for

children (−63 to 28%)19. Individually calculated kMC from our study reveals that the routinely used kbody from literature can be applied to larger clinical collectives, but are limited in

their value for individual risk assessment6. The DLP-method slightly overestimates the effective dose in general, but it seems to be appropriate in regular weight male patients. However,

especially in female and underweight patients, underestimation of the effective dose may be critical for risk perception in the clinical setting. Moreover, its overestimation in obese

patients may lead to restrained use of high exposure parameters, and then to poor or insufficient image quality. Individual anatomic characteristics (e.g. missing organs due to aplasia or

resection, organ hypo- or hypertrophy, skeletal deformations, metal implants) may lead to considerable changes of radiation dose distribution and consequently of individual risk. Even

phantom based estimation methods, like EDNCI in this study, are unable to take these individual properties into account7. Bland-Altman analysis demonstrated that these phantom based

estimates have the tendency to underestimate ED in general, while the effect of only moderate increase of ED in obese patients despite the very high energy exposure is confirmed. The

influence of personal risk profiles on cancer risk estimates are not yet represented in standard radiation dose reporting systems. It can further increase the error of perceived and true

risk of radiation dose from CT examinations. The combination of individual dose distributions and risk assessment has been presented for a few other examinations in literature, such as

absorptiometry and spine radiographs, but not for spiral CT examinations27. Pairwise patient comparisons provide a comprehensive overview of the extent of error with regard to age, BMI and

sex. Especially younger patients are prone to higher LAR and ERR since more active cell division and longer life expectancy after radiation exposure is presumed28. In our example the ERR of

a patient in his mid-thirties was 1.6-fold the ERR of a patient in his mid-sixties and the ERR for a female patient in her mid-fifties was almost doubled compared to a matching male patient.

The increasing tube current by anatomy-based modulation in obese patients seems to be of less importance to the patients’ cancer risk. CTDI and EDDLP were 1.8-fold higher for a high-grade

obese patient compared to an overweight patient with matching age and sex, while the ERR was only 10% higher. Therefore, future dose assessment strategies should not only focus on absolute

dose values. Furthermore, reporting LAR or ERR seems to be much more appropriate in clinical routine and a more comprehensible value for the patient-physician interaction. Some limitations

have to be considered while interpreting this study. First, the study population consisted of a heterogeneous, small and retrospectively selected patient collective with an imbalanced ratio

of female to male patients. The small number of patients is mainly due to the high computing power required for MC calculations and the work intense manual organ segmentation that we used

for this study. We estimate that the increasing computing power and automated organ segmentations by the application of artificial intelligence could overcome this limitation in the near

future29,30. Second, only full body dose exposure is reported in this study. This avoids indeterminate scattered radiation to radiosensitive structures and over-ranging effects, but limits

the findings to this clinically rather rare indication. Further evaluations for limited examination volumes could become feasible with retrospective simulations from these data in larger

collectives by future studies. Third, all examinations were conducted on the same scanner. The findings of this study can therefore not automatically be transferred to other CT systems5,24.

Forth, all tissue weighting factors that are provided in literature so far are averaged for sex and age, which may probably limit their applicability for patient specific risk estimation.

Fifth, the linear Non-threshold Dose-Response Model itself is discussed controversially among experts for diagnostic dose levels31,32. Sixth, LAR and ERR calculations in this study are only

related to low dose radiation exposure. They illustrate the individual radiation dose related risk to develop cancer. Obviously, the overall individual cancer risk for a primary or even a

secondary cancer and also the life expectancy is potentially much more influenced by age, genetic make-up, efficiency of DNA damage repair, therapy-related adverse effects and many other

influencing factors. The importance of patient specific dose surveillance is illustrated by this study. EDDLP can be used for radiation dose assessments in larger collectives, but individual

considerations require advanced techniques like EDMC. Conventional methods tend to underestimate radiation dose in underweight and female patients. Therefore, these patients have to be

evaluated with special care. The influence of young patient age and female gender on cancer risk estimates is very high. Thus, radiation dose assessment should not only provide whole body or

organ dose measurements but also individual risk calculations, which could be included in future surveillance programs. DATA AVAILABILITY The datasets generated during and analyzed during

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AFFILIATIONS * Department of Radiology, University Hospital Erlangen, Erlangen, Germany Markus Kopp, Wolfgang Wuest, Michael Brand, Matthias Wetzl, Wolfram Nitsch, Michael Uder & 

Matthias May * Institute of Medical Microbiology and Hygiene, University of Technology Dresden, Dresden, Germany Tobias Loewe * Department of Nuclear Medicine, University Hospital Erlangen,

Erlangen, Germany Daniela Schmidt & Michael Beck * Siemens Healthineers GmbH, Forchheim, Germany Bernhard Schmidt Authors * Markus Kopp View author publications You can also search for

this author inPubMed Google Scholar * Tobias Loewe View author publications You can also search for this author inPubMed Google Scholar * Wolfgang Wuest View author publications You can also

search for this author inPubMed Google Scholar * Michael Brand View author publications You can also search for this author inPubMed Google Scholar * Matthias Wetzl View author publications

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author publications You can also search for this author inPubMed Google Scholar * Michael Beck View author publications You can also search for this author inPubMed Google Scholar * Bernhard

Schmidt View author publications You can also search for this author inPubMed Google Scholar * Michael Uder View author publications You can also search for this author inPubMed Google

Scholar * Matthias May View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS Contributed to the writing of the manuscript: M.K., M.M., T.L. and

W.W. Study design, measurements and statistical analysis: M.K., M.M., W.W., M.Br., M.W., T.L., W.N., B.S., D.S., M.Be. and M.U. All authors reviewed the manuscript. CORRESPONDING AUTHOR

Correspondence to Markus Kopp. ETHICS DECLARATIONS COMPETING INTERESTS M.K., M.M., W.W. and M.U. are members of the Siemens Speaker’s Bureau. B.S. is an employee of Siemens Healthineers

GmbH. M.W., T.L., M.Br., W.N., D.S. and M.Be. declare no competing financial interests. All authors declare no competing non-financial interests. ADDITIONAL INFORMATION PUBLISHER’S NOTE

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Effective Dose and Risk of Malignancy Based on Monte Carlo Simulations after Whole Body Computed Tomography. _Sci Rep_ 10, 9475 (2020). https://doi.org/10.1038/s41598-020-66366-2 Download

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