Gender-Specific Radiation Burden and Cancer Risk Assessment in Pediatric Polytrauma: A Four-Year Longitudinal CT Utilization Analysis

Gender-Specific Radiation Burden and Cancer Risk Assessment in Pediatric Polytrauma: A Four-Year Longitudinal CT Utilization Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gender-Specific Radiation Burden and Cancer Risk Assessment in Pediatric Polytrauma: A Four-Year Longitudinal CT Utilization Analysis Juan Chen, Jianjun Zheng, Jingfeng Zhang, Qun Zhang, Qi Dai, Yong Wang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8784170/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background: Computed tomography remains the cornerstone of rapid diagnosis and surgical planning in pediatric polytrauma. Repeated examinations, however, result in substantial cumulative effective doses that significantly increase lifetime radiation-induced cancer risk owing to heightened tissue radiosensitivity and prolonged latency periods in children. Despite increasing recognition of this concern, robust data on cumulative radiation exposure and associated oncogenic risk in Asian pediatric populations remain limited. Methods : We performed a retrospective cohort study of 394 pediatric patients with polytrauma (Injury Severity Score ≥16; median age 6.58 years) who underwent 1817 computed tomography examinations at a tertiary pediatric trauma center in Zhejiang Province, China, from January 2020 to December 2024. Effective dose was derived from the recorded dose-length product using age- and region-specific conversion coefficients based on ICRP Publication 103. Lifetime attributable risks of radiation-induced solid cancer and leukemia were calculated with the BEIR VII Phase 2 risk models. Multivariable regression analysis was used to identify independent predictors of high cumulative exposure and elevated oncogenic risk. Results: The 394 patients underwent a mean of 4.61 CT examinations (range 1–37), with 76.3% receiving repeated scans. While males comprised the majority of the cohort (69.5%) and received higher cumulative doses, female patients exhibited a significantly higher mean lifetime attributable risk (LAR) than males (0.0021 vs. 0.0017, p=0.04). Median cumulative effective dose (CED) was 36.8 mSv (IQR 13.9–93.9 mSv); 10.2% of patients exceeded 50 mSv. Injury Severity Score (ISS) ≥25 was an independent predictor of high exposure (RR 1.09; 95% CI 1.02–1.16). Conclusions: Pediatric polytrauma patients in emergency settings face substantial radiation burdens from recurrent CT, with notable oncogenic risks particularly in children under 5 years. Implementation of selective imaging protocols, automated tube current modulation, and iterative reconstruction algorithms could reduce exposure by up to 20%-40% without compromising diagnostic accuracy. Surgical protocols should prioritize dose optimization and non-ionizing alternatives to balance efficacy and long-term health. Computed tomography cumulative exposure pediatric polytrauma radiation risk BEIR VII model Figures Figure 1 Background Pediatric polytrauma defined as injuries involving two or more anatomical regions with an Injury Severity Score (ISS) ≥ 16[ 1 , 2 ], remains a major global cause of morbidity and mortality among children. Age-specific injury mechanisms include falls in infants and motor vehicle collisions in older children, frequently resulting in severe outcomes such as traumatic brain injury (TBI)[ 3 ]. This condition accounts for approximately 10–20% of all pediatric trauma deaths[ 4 ]. According to the United Nations Inter-agency Group for Child Mortality Estimation (UNIGME) and World Health Organization (WHO), global under-five mortality declined to 4.8 million in 2023; however, unintentional injuries continue to represent a leading cause of death, particularly in low- and middle-income countries[ 5 , 6 ]. The Global Burden of Disease (GBD) 2023 study estimated an age-standardized incidence of pediatric injuries at approximately 8,000–8,500 per 100,000, with a mortality rate of about 20 per 100,000 and 1,800 disability-adjusted life years (DALYs) per 100,000, reflecting only a modest 16% decline in injury-related DALYs since 2010[ 7 ]. In high-income regions such as the United States, unintentional injuries remain the leading cause of death among individuals aged 1–19 years, resulting in approximately 12,000 deaths and 22 million non-fatal cases annually—nearly one-third of the pediatric population[ 8 , 9 ]. Notably, trauma patterns and injury mechanisms often exhibit distinct gender-related variations; adolescent males, for instance, are frequently overrepresented in high-energy trauma cohorts due to behavioral and environmental factors. These disparities in injury frequency and severity necessitate a more nuanced evaluation of subsequent diagnostic interventions. In emergency surgical settings, Computed Tomography (CT) is indispensable for rapid diagnosis and multidisciplinary surgical planning. Its ability to facilitate early detection of life-threatening conditions, such as intracranial hemorrhage and solid organ laceration, has established it as the gold standard for evaluating blunt polytrauma[ 10 ][ 11 – 13 ]. However, the management of severe trauma often requires serial imaging for monitoring clinical progression or post-operative evaluation. This recurrent utilization leads to substantial cumulative effective doses (CED) that may exceed recommended diagnostic reference levels, particularly in pediatric populations where clinical stability is often volatile[ 14 ]. Nevertheless, frequent CT use in pediatric polytrauma especially repeated examinations for hemorrhage monitoring, post-operative evaluation, or clinical deterioration raises growing concerns regarding radiation safety. Children exhibit significantly greater radiosensitivity than adults owing to three critical factors: (1) higher cellular proliferation rates during growth and development, (2) smaller body dimensions resulting in less tissue attenuation and higher organ doses, and(3)extended post-exposure lifespan providing longer latency for malignancy development[ 15 , 16 ]. These biological vulnerabilities collectively elevate lifetime cancer risk following ionizing radiation exposure. Large cohort studies demonstrate a dose-response association; cumulative exposures of 50–60 mGy correlate with 1.5- to 3.5-fold increased risks of leukemia and brain tumors [ 17 , 18 ]. More recent multinational analyses led by UCSF investigators have further confirmed that even limited exposures equivalent to one or two head CT scans (≈ 15–30 mGy) are linked to a 1.8- to 3.5-fold elevation in hematologic cancer risk, suggesting that up to 10% of pediatric hematologic malignancies could be attributable to diagnostic radiation[ 19 ]. Moreover, BEIR VII projection models estimate lifetime attributable risks (LAR) for fatal solid cancers up to 0.18% in young children undergoing abdominal CT[ 20 ]. Children are significantly more radiosensitive than adults due to higher cellular proliferation rates and a longer post-exposure lifespan for malignancy development. Beyond age, biological sex is an increasingly recognized but often under-addressed determinant of radiation-induced risk. Epidemiological data, including the BEIR VII models, suggest that female pediatric patients face higher lifetime attributable risks (LAR) for solid cancers compared to males for equivalent radiation doses, primarily due to the heightened radiosensitivity of breast and thyroid tissues. Recent analyses indicate that even limited exposures from diagnostic CT can lead to a 1.8- to 3.5-fold elevation in hematologic cancer risk[ 21 ]. Consequently, a gender-risk paradox emerges in trauma care: while males may undergo more scans due to trauma prevalence, females may carry a higher biological burden per exposure. Despite the clear intersection of gender and radiological risk, robust data on gender-sensitive radiation practices in Asian pediatric populations remain scarce. Critical differences in patient anthropometry and institutional imaging protocols in regions like China limit the applicability of Western-based guidelines. Therefore, this study aims to systematically quantify cumulative radiation exposure and estimate LAR among pediatric polytrauma patients at a major Chinese tertiary center. By integrating gender-specific LAR modeling with clinical parameters such as ISS and anatomical scan regions, we seek to identify determinants of elevated risk and advocate for gender-sensitive, ALARA-compliant imaging protocols that ensure equitable safety in pediatric trauma management. Methods Design of the study and participants This study employed a retrospective cohort design to evaluate cumulative radiation exposure and associated cancer risk in pediatric polytrauma patients undergoing repeated CT examinations. The study population comprised children under 14 years of age who presented with traumatic injuries at A Level III pediatric trauma center in Zhejiang, China between January 2020 and December 2024. The patient selection process is illustrated in Figure 1. An initial screening identified 2,137 patients. Inclusion criteria were as follows:（1）Trauma involving two or more anatomical regions or organ systems;（2）Availability of Injury Severity Score (ISS) ≥16;（3）Complete radiation dose information, including dose-length product (DLP) and volumetric CT dose index (CTDIvol). Exclusion criteria were:（1）Death during the trauma event;（2）Incomplete imaging data (e.g., missing DICOM metadata);（3）Insufficient clinical information to calculate ISS. Based on these criteria, 1,743 patients were excluded (1,579 with ISS <16, 6 deceased, 158 with incomplete imaging data), resulting in a final cohort of 394 patients with a total of 1,817 CT examinations. Data Collection Imaging data were extracted from the Picture Archiving and Communication System (PACS) and dose-structured reports, including examination type, anatomical region scanned, dose-length product (DLP, mGy·cm), volumetric CT dose index (CTDIvol, mGy), and scan date and time. Clinical data were obtained from electronic medical records and included patient sex, age, mechanism of injury, ISS, medical history, clinical diagnosis, and treatment outcomes. Patients were categorized into three age groups: 10 years. Anatomical regions were standardized as head, chest, abdomen, pelvis, cervical spine, extremities, and other. When multiple independent scan protocols were performed within the same anatomical region, each scan’s DLP was recorded separately to ensure accurate dose estimation. All data were independently entered into Microsoft Excel by two investigators and standardized; discrepancies were resolved through consensus discussion. Ethical statement As a retrospective study using anonymized historical data without patient intervention, informed consent was waived. All data were handled in accordance with patient privacy protection regulations. Radiation Dose Estimation Radiation dose estimation was conducted in accordance with international recommendations from the International Atomic Energy Agency (IAEA)[22,23]. Effective dose (ED) was used to evaluate the overall risk of stochastic effects, such as radiation-induced cancer, as defined by ICRP Publication 103[24]. ED represents the sum of equivalent doses to individual organs and tissues, weighted by tissue-specific sensitivity factors, and is expressed in millisieverts (mSv). Weighting factors assign higher values to radiosensitive tissues, such as breast and bone marrow, enabling comparisons of radiation risks across different sources. However, ED is not intended for individual-level cancer prediction[25,26]. While facilitating comparisons between radiation sources, ED is not intended for individual cancer risk prediction. In clinical practice, ED serves as a standardized metric for radiation dose monitoring and reporting, facilitating comparison with natural background radiation levels. ED was calculated from the dose-length product (DLP) using age- and region-specific conversion coefficients (k-factors)[27], according to the formula: 𝐸𝐷=𝐷𝐿𝑃× k-factor (age, region) DLP is automatically generated by the CT scanner and represents the product of the volumetric CT dose index (CTDIvol, mGy) and scan length (cm), expressed in mGy·cm. K-factors are derived from Monte Carlo simulations based on IAEA SSG-46 guidelines and AAPM Report No. 96, and are adjusted using tissue weighting factors from ICRP 103[28,29]. For example, the k-factor for head CT in children under 5 years is approximately 0.0040 mSv/(mGy·cm), and for chest CT it is approximately 0.0180 mSv/(mGy·cm)[30,31]. This method is widely adopted by contemporary CT dose reporting systems. Multiplying the scanner-generated DLP by the corresponding k-factor provides a practical and standardized estimate of ED. These coefficients are pre-calculated using computational anthropomorphic phantoms and Monte Carlo simulations of CT radiation transport. Multiple studies have reported DLP-to-ED conversion factors for different patient populations. Building on ICRP Publications 60 and 103, Deak et al. updated pediatric and adult k-factor sets through Monte Carlo calculations incorporating standardized tissue weighting[31]. For each scan sequence, DLP values were extracted from PACS and multiplied by the appropriate k-factor to obtain the ED for that individual CT examination. In patients undergoing multiple scans in different anatomical regions or repeated scans in the same region, each ED was calculated separately and then summed to derive the cumulative effective dose (CED) over the treatment period. All calculations were performed in Python using the Pandas library, with precision to 0.1 mSv. Selected k-factors were adaptively adjusted based on Deak et al.’s pediatric cohort to better reflect the anthropometric characteristics of the Chinese pediatric population[31]. Dosimetric Uncertainty and Sensitivity Analysis： Conversion coefficients were applied according to anatomical region and age groups (<1 year, 1-5 years, 6-10 years, 11-14 years), referencing validated pediatric-specific values from Deak et al.[31] for body CT and ICRP Publication 103 for head CT. For examinations lacking complete DLP data (n=23, 1.3%), effective dose was estimated using institution-specific protocols validated through quality assurance phantom measurements. To account for dosimetric uncertainties inherent in conversion coefficient methodologies, we performed sensitivity analyses in which coefficients were varied by ±20% (representing typical inter-individual variations in body habitus and anatomical positioning). Monte Carlo simulations (10,000 iterations) assessed the impact on lifetime attributable risk estimates. Results demonstrated that LAR estimates remained within 15% of base-case values across the tested range, the effective dose was estimated using institution-specific protocols validated through quality-the robustness of primary findings. Cancer Risk Assessment Lifetime cancer risk in the 394 pediatric polytrauma patients was systematically estimated using the BEIR VII (Biological Effects of Ionizing Radiation) model developed by the U.S. National Academies of Sciences[32]. This model is based on the linear no-threshold (LNT) assumption, which posits a linear increase in radiation-induced risk with any exposure below 100 mSv. Although the applicability of the LNT model at low-dose ranges remains a subject of scientific debate, it remains a widely used framework for population-level risk assessment. The BEIR VII model integrates cumulative effective dose, patient age, sex, and tissue-specific radiosensitivity (e.g., bone marrow, breast, lung, and thyroid) using Monte Carlo simulations and epidemiological data to estimate lifetime attributable risk (LAR). This model is based on the linear no-threshold (LNT) assumption, which posits a linear increase in radiation-induced risk with any exposure below 100 mSv. The model integrates epidemiological data to estimate LAR, representing the probability of developing excess cancer attributable to radiation beyond the baseline risk. This metric provides a quantitative assessment of the long-term health impact of medical radiation in high-risk pediatric populations[33]. Statistical Analysis Continuous variables were expressed as mean ± standard deviation (SD) or median with interquartile range (IQR), depending on distribution normality assessed by the Shapiro-Wilk test. Categorical variables were presented as frequencies and percentages. For multivariable analysis identifying predictors of high cumulative radiation exposure (defined as >50 mSv), candidate variables were initially screened using univariate logistic regression (inclusion threshold P<0.10). Variables included: age group (<1, 1-5, 6-10, 11-14 years), sex, ISS category (16-24 vs ≥25), mechanism of injury, number of anatomical regions injured, hospital length of stay, and presence of severe traumatic brain injury. Collinearity was assessed using the variance inflation factor (VIF); variables with VIF>5 were excluded. The final multivariable model was constructed using backward stepwise selection (removal threshold P>0.10). Model diagnostics included assessment of residual distribution, leverage points (Cook's distance >1), and goodness-of-fit using the Hosmer-Lem show test (P>0.05 indicating adequate fit). Relative risks (RR) with 95% confidence intervals (CI) were calculated for LAR outcomes. Subgroup analyses were performed stratified by age groups and ISS categories. Statistical significance was set at two-tailed P<0.05. All analyses were performed using R version 4.3.0 (R Foundation for Statistical Computing). Results Patient Demographics A total of 394 pediatric polytrauma patients (ISS ≥16) were included in this study, accounting for 1,817 CT examinations. The mean age was 6.58 years, with most patients aged 5–9 years (49.5%, 195/394). A significant gender disparity was observed in trauma prevalence, with male patients comprising 69.5% (274/394) of the cohort, while females accounted for 30.5% (120/394). The predominant mechanisms of injury were motor vehicle collisions (43.4%), followed by falls (19.5%), high-altitude falls (11.4%), impact against objects (6.3%), non-accidental trauma/abuse (5.3%), sports-related injuries (5.1%), non-motorized vehicle accidents (4.8%), and crush injuries (2.3%). The mean ISS was 24.3 (range 16–66). The distribution of ISS scores was as follows: 16–20 (69.3%, 273/394), 21–30 (21.1%, 83/394), 31–40 (4.3%, 17/394), 41–50 (4.8%, 19/394), and >50 (0.5%, 2/394). The most frequently injured anatomical region was the head (42.9%, 169/394), followed by the abdomen (20.3%, 80/394), chest (16.5%, 65/394), other regions (12.2%, 48/394), cervical spine (4.8%, 19/394), pelvis (2.3%, 9/394), and extremities (1.0%, 4/394). Regarding the type of visit, 40.9% (161/394) presented to the emergency department, 32.2% (127/394) were admitted as inpatients, and 26.9% (106/394) were outpatients. Patients underwent a mean of 4.61 CT examinations (range 1–37), with 76.3% undergoing repeated scans. Baseline patient characteristics are summarized in Table 1. Table 1 Demographics, injury mechanisms, and injury severity among Pediatric Polytrauma Patients (n=394) Variable N % or Range Age (years) （ Mean ） 6.58 0-4 116 29.4% 5-9 195 49.5% 10-14 83 21.1% Sex Male 274 69.5% Female 120 30.5% Mechanism of Injury Motor Vehicle Collision 171 43.4% Ground-level fall 77 19.5% Fall from height 45 11.4% Impact against object 25 6.3% Non-accidental trauma or abuse 21 5.3% Sports-related injury 20 5.1% Non-motorized vehicle accident 19 4.8% Crush injury 9 2.3% Penetrating injury 2 0.5% Sharp object injury 1 0.3% Injury Severity Score (ISS) 16-20 273 69.3% 21-30 83 21.1% 31-40 17 4.3% 41-50 19 4.8% >51 2 0.5% Injured Region Head 169 42.9% Abdomen 80 20.3% Chest 65 16.5% Other regions 48 12.2% Cervical spine 19 4.8% Pelvis 9 2.3% Extremities 4 1.0% Distribution and Frequency of CT Examinations Age-stratified analysis revealed that patients aged 10 years or older underwent the most CT examinations (727 scans, 40.0%), followed by those aged 5–9 years (636 scans, 35.0%) and those younger than 5 years (454 scans, 25.0%). Across all age groups, the most frequently scanned regions were the head, abdomen, and chest. Notably, the >10-year group had substantially more head CT scans (219) than the <5-year group (137), indicating an age-related increase in head trauma or clinical indications for neuroimaging. When stratified by sex, male patients accounted for a greater total number of CT examinations (999 scans, 54.9%) than female patients (818 scans, 45.1%). Males underwent more scans in the head (300 vs. 247), abdomen (292 vs. 238), and chest (245 vs. 200) regions, respectively. Taken together, older male patients exhibited particularly high frequencies of abdominal and chest CT examinations, identifying them as a subgroup with potentially higher cumulative radiation exposure. Overall, CT utilization was more frequent among older and male pediatric trauma patients, suggesting sex- and age-related differences in imaging needs and trauma patterns. These variations may reflect developmental differences in activity levels, injury mechanisms, and clinical decision-making processes. From a radiological protection perspective, clinicians should carefully balance diagnostic benefits against radiation risks, ensuring that CT indications are age-appropriate and justified to minimize unnecessary exposure and adhere to optimization principles in pediatric imaging. Distribution of Radiation Dose Among the 394 pediatric polytrauma patients, 40 (10.2%) had a cumulative effective dose (ED) exceeding 50 mSv, including 11 patients (2.8%) with exposures above 100 mSv (Table 2). Age-matched distribution of cumulative radiation exposure is shown in Table 2. Table 2. Age-matched patients stratified by cumulative radiation exposure. Cumulative Dose 0-25 mSv 26-50 mSv 51-75 mSv 76-100 mSv >100 mSv Sex Male Female Male Female Male Female Male Female Male Female Mean cumulative dose (mSv) 8.30 7.15 32.94 35.89 60.98 60.41 90.00 87.68 141.83 116.02 Mean lifetime cancer risk 0.00037 0.00039 0.0015 0.0022 0.0027 0.003 0.0037 0.0047 0.0044 0.0079 Mean ISS score 18.82 18.65 24.33 31.10 29.93 31.00 30.75 28.20 28.10 32.00 Mean age (years) 6.51 6.21 6.40 4.50 7.60 8.20 8.75 6.40 11.60 2.00 Number of patients 230.00 99.00 15.00 10.00 15.00 5.00 4.00 5.00 10.00 1.00 Percentage of patients (%) 58.38 25.13 3.81 2.54 3.81 1.27 1.02 1.27 2.54 0.25 Note: Data are from 394 pediatric polytrauma patients who underwent 1,817 CT scans. Cumulative dose is expressed as effective dose (mSv). Lifetime cancer risk was estimated using the BEIR VII model. Percentages are calculated based on the total number of patients. Patients were categorized into five cumulative dose groups: 0–25 mSv, 26–50 mSv, 51–75 mSv, 76–100 mSv, and >100 mSv. The majority (83.5%, 329/394) were within the 0–25 mSv range, whereas the >100 mSv group accounted for the smallest proportion (2.8%, 11/394). Across all dose categories, males predominated, particularly in the high-dose group (>100 mSv), where males represented 2.5% (10/394) and females only 0.25% (1/394). A clear upward trend was observed in mean cumulative ED across increasing dose strata. In the >100 mSv group, the mean cumulative ED was 141.8 mSv in males and 116.0 mSv in females. Correspondingly, the lifetime attributable risk (LAR) for cancer estimated using the BEIR VII model also increased with cumulative dose. The average LAR in the >100 mSv group reached 0.0044 for males and 0.0079 for females, demonstrating a positive dose–risk relationship consistent with the linear no-threshold (LNT) hypothesis. Notably, patients in higher dose groups tended to have greater injury severity and older age, suggesting that repeated scanning in more severely injured cases contributed to elevated cumulative exposure. In the >100 mSv group, the mean age of male patients was 11.6 years, compared with 2.0 years for females. When analyzed by anatomical region, the mean cumulative EDs were 6.0 mSv for the head, 5.0 mSv for the abdomen, and 4.0 mSv for the chest. The median cumulative ED for head scans was 5.5 mSv (IQR: 4.0–7.5 mSv), with some patients exceeding 15 mSv, indicating substantial interindividual variability. Further stratification by age and sex revealed that males younger than 5 years had the highest mean head dose (approximately 6.5 mSv), whereas females older than 10 years had the lowest abdominal dose (approximately 4.0 mSv), highlighting pronounced sex- and age-related differences in exposure to high-dose regions. To provide an overall view of dose distribution, the proportions of males and females across cumulative dose categories. While most patients were in the low-dose group (0–25 mSv, 83.5%), males were overrepresented in the higher dose groups. Specifically, males accounted for 58.4% of the 0–25 mSv group and 2.5% of the >100 mSv group, compared with 25.1% and 0.25% for females, respectively. Collectively, these findings indicate that although the proportion of patients receiving high cumulative doses was small, male and severely injured patients were disproportionately represented in this subgroup, underscoring the need for targeted strategies to mitigate potential long-term radiation risks in high-exposure pediatric populations. Cancer Risk Assessment Overall Lifetime Cancer Risk Using the BEIR VII model, the lifetime attributable risk (LAR) of radiation-induced cancer was systematically estimated for all 394 pediatric polytrauma patients. The mean LAR for the entire cohort was 0.0019 (95% CI: 0.0015–0.0023), corresponding to an excess lifetime cancer risk of approximately 190 cases per 10,000 person·Gy. The median LAR per single CT examination was 112 per 100,000 (IQR: 86–174 per 100,000), while the median cumulative LAR per patient reached 521 per 100,000. Solid cancer risk (LARsolid) accounted for approximately 85% of the total radiation-induced risk, with a median value of 95 per 100,000, whereas leukemia risk (LARleukemia) contributed around 15% (median 17 per 100,000). These findings indicate that solid tumors constitute the predominant component of radiation-related cancer burden in pediatric trauma populations. Stratified Analysis by Age, Sex, and Injury Severity Age stratification: A clear inverse association was observed between age and LAR, with younger patients demonstrating higher susceptibility to radiation-induced malignancies. The 10-year group (0.0012, 95% CI: 0.0009–0.0015), with statistically significant intergroup differences (ANOVA, p<0.001). This pattern reflects the biological characteristics of early childhood, where rapid cell proliferation, immature organ development, and longer expected lifespan confer greater radiosensitivity and thus higher lifetime risk. Sex stratification: Female patients exhibited a slightly higher mean LAR than males (0.0021 vs. 0.0017, p=0.04). This difference is likely attributable to the greater contribution of radiosensitive organs, such as the breast and thyroid, which are more prevalent sites of radiation-induced malignancy in females. Injury severity: The LAR demonstrated a positive correlation with injury severity, as measured by the Injury Severity Score (ISS) Patients with ISS 16–24 had a mean LAR of 0.0017, while those with ISS ≥25 exhibited an increased mean LAR of 0.0023 (p=0.01). This association suggests that patients with more severe trauma, often requiring repeated imaging for diagnostic clarification and clinical monitoring, accumulate substantially higher radiation doses, leading to a marked elevation in cumulative cancer risk. Dose Dependence and Anatomical Distribution To assess the dose–response relationship between radiation exposure and lifetime cancer risk, a dose and dose-rate effectiveness factor (DDREF) of 1.5 was applied for risk adjustment. Patients were stratified by cumulative effective dose (ED) into five categories (0–25, 26–50, 51–75, 76–100, and >100 mSv), and the corresponding lifetime attributable risk for solid cancers (LAR solid )was calculated. LAR solid demonstrated a linear increase with escalating cumulative doses. In the >100 mSv group, the mean LAR solid was 137.8 per 10,000 person·Gy in males and 91.8 per 10,000 person·Gy in females. Although females exhibited a steeper increase in risk, the difference between sexes was not statistically significant due to overlapping confidence intervals (p > 0.05). Anatomical site–specific analysis revealed that chest CT examinations yielded the highest LAR solid (73.1 per 10,000 person·Gy), followed by abdominal (50.8 per 10,000 person·Gy) and pelvic scans (45.6 per 10,000 person·Gy). This elevated chest risk primarily reflects the high tissue weighting factors assigned to radiosensitive organs such as the lungs and breasts. In contrast, LAR for leukemia (LAR leukemia) remained relatively uniform across anatomical regions (3.6–5.2 per 10,000 person·Gy), consistent with the distributed nature of active bone marrow exposure throughout the body. Sex Differences and Dose Distribution Patterns Sex-stratified analysis of anatomical site–specific effective doses revealed that the median abdominal dose was the highest (79.49 mSv in males vs. 47.47 mSv in females), followed by the pelvic (79.49 mSv in males vs. 47.47 mSv in females) and chest regions (43.96 mSv in males vs. 48.69 mSv in females). The interquartile range (IQR) for abdominal and pelvic doses in males was wide (Q1: 34.32 mSv, Q3: 142.34 mSv), indicating substantial interindividual variability and the presence of outliers (maximum: 254.65 mSv), likely due to repeated scanning. The mean cumulative effective dose (ED) was slightly higher in males (34.67 ± 35.19 mSv) than in females (26.43 ± 23.06 mSv), though the difference was not statistically significant (p > 0.05). Analysis of LAR by anatomical region across 1,817 CT scans demonstrated that the chest contributed the highest LAR solid (73.1 per 10,000 person·Gy), attributable to the high tissue weighting factors of radiosensitive organs such as the lungs and breasts, followed by the abdomen (50.8 per 10,000 person·Gy) and pelvis (45.6 per 10,000 person·Gy). LAR leukemia remained relatively uniform across regions (3.6–5.2 per 10,000 person·Gy), reflecting the homogeneous distribution of active bone marrow. In the high-dose group (ED > 100 mSv), patients aged > 10 years (n = 15, 1.36%) showed a LAR solid of 140.9 per 10,000 person·Gy, whereas those < 5 years (n = 5, 0.45%) had 120.5 per 10,000 person·Gy, suggesting a synergistic effect between age and cumulative dose[34]. These findings emphasize the need to prioritize dose optimization in chest and abdominal CT, particularly among younger children, whose anatomical proportions and tissue radiosensitivity confer elevated risk. Compared with previous studies, the median abdominal dose in this cohort (79.49 mSv in males, 47.47 mSv in females) exceeded the 15 mSv reported by Pearce et al.[35], reflecting the cumulative effect of repeated scans in trauma settings. The proportion of whole-body CT examinations was notably higher in the 10–<15 year age group, consistent with increasing injury complexity with age. However, the median single-scan ED was substantially higher in the < 5 year group (approximately 9 mSv/scan), and when combined with their longer life expectancy, this group exhibited the highest cumulative lifetime cancer risk (median 166.1 per 100,000 vs. 102.9 per 100,000 for the 10–<15 year group). Age-dependent parameters higher CTDI and DLP in older children, but greater DLP-to-ED conversion coefficients in younger children explain these differences in dose distribution. Anatomical characteristics such as compliant thoracic structures in younger patients, which predispose to internal injuries often necessitate chest and abdominal CT, yet underscore the importance of dose optimization. By integrating dose, age, sex, and anatomical site variables, this analysis identified high ISS and younger age as key predictors of elevated radiation risk, supporting the continued application of the ALARA principle in pediatric trauma imaging. Future studies employing Monte Carlo simulations could further refine organ-specific dose estimations and validate long-term clinical outcomes. Discussion This study provides a comprehensive Asian cohort analysis quantifying the CT radiation burden in pediatric polytrauma, revealing three principal findings with immediate clinical implications: (1) substantial cumulative radiation exposures (median 36.8 mSv), with 10.2% of patients exceeding the 50 mSv threshold; (2) a distinct "gender-risk paradox", where female patients exhibit significantly higher oncogenic risk despite receiving lower average cumulative doses than males ; and (3) the identification of Injury Severity Score (ISS) ≥25 and age under 5 years as independent predictors of elevated radiation burden. We observed substantial cumulative radiation exposure in pediatric polytrauma patients, with a median dose of 36.8 mSv and more than 10% of patients exceeding 50 mSv. These exposure levels are notably higher than those reported in North American pediatric trauma cohorts, where median or mean cumulative doses typically range between 22 and 28 mSv[37]. Such differences likely reflect variations in injury severity, CT utilization patterns, imaging protocols, and resource availability across healthcare systems. Importantly, cumulative doses above 50–100 mSv fall within a range where epidemiological studies have demonstrated increased risks of radiation-induced malignancies, underscoring the long-term relevance of radiation protection in this vulnerable population[19,36]. A cornerstone of our findings is the significant disparity between radiation exposure and biological cost between sexes. While male patients predominated the cohort (69.5%) and were more likely to fall into the highest dose category (≥100 mSv) , our results indicate that the biological impact of ionizing radiation is disproportionately higher in females. The mean Lifetime Attributable Risk (LAR) was significantly elevated in female patients (0.0021 vs. 0.0017 in males, p=0.04). This phenomenon becomes even more pronounced in the high-exposure tier, where the average LAR reached 0.0079for females compared to 0.0044 for males in the >100 mSv group. This paradox is primarily attributable to sex-specific tissue radiosensitivity rather than differences in imaging frequency alone. Chest CT examinations routinely performed for thoracic injury assessment in polytrauma contributed the highest solid cancer risk in our cohort. The elevated tissue weighting factors assigned to the breast and thyroid, organs with higher baseline susceptibility to radiation-induced malignancies in females, translate equivalent or lower physical doses into substantially greater biological cost. These findings challenge the prevailing “one-size-fits-all” approach to trauma imaging and indicate that cumulative dose alone may underestimate risk in female pediatric patients. The identification of this gender-specific risk profile provides actionable guidance for the "As Low As Reasonably Achievable" (ALARA) principle in emergency settings. Given their higher biological sensitivity, female pediatric patients should be prioritized for advanced dose-reduction technologies. While the urgent need for rapid diagnosis in high-ISS cases remains paramount, the implementation of automated tube current modulation and iterative reconstruction algorithms which can reduce exposure by 20%–40% without compromising diagnostic accuracy,is particularly critical for female thoracic and abdominal imaging. Furthermore, for stable female patients, our data support a more conservative approach toward repeat CTs, favoring ultrasound (e.g., FAST) or MRI for follow-up to minimize cumulative biological costs. Beyond sex-specific vulnerability, our findings demonstrate that age and injury severity jointly define a high-risk radiation exposure profile in pediatric polytrauma. Children under 5 years of age exhibited approximately threefold higher lifetime attributable cancer risks compared with adolescents. This age-dependent susceptibility reflects both higher per-examination doses—due to technical and anatomical factors—and heightened tissue radiosensitivity during early developmental stages. These observations reinforce existing radiobiological evidence and highlight the critical importance of rigorously age-specific dose optimization strategies in young children. In parallel, injury severity emerged as an independent predictor of cumulative radiation burden. Patients with an Injury Severity Score (ISS) ≥25 had a significantly increased risk of higher cumulative exposure, with more than 15% exceeding 100 mSv. In this subgroup, repeated CT examinations are often clinically unavoidable due to the complexity and multisystem nature of injuries. Consequently, ISS-based risk stratification offers a pragmatic framework for identifying patients who may benefit most from intensified dose monitoring, protocol optimization, and multidisciplinary imaging decision-making. Furthermore, these findings diverge from prior Western reports, underscoring regional variations in CT utilization and radiation practices. Consequently, they necessitate a revision of current imaging algorithms in Asian pediatric trauma centers, prioritizing evidence-based strategies such as selective scanning and advanced dose-reduction technologies to align with the ALARA principle. ISS, age, hospitalization length, and anatomical scan region were identified as reliable predictors of radiation exposure and cancer risk[37]. Our findings are consistent with the American College of Radiology (ACR) Appropriateness Criteria for pediatric trauma CT, highlighting opportunities to refine imaging decisions using structured frameworks such as RAND/UCLA or GRADE[38]. Our median cumulative ED (36.8 mSv) substantially exceeds values reported from North American and European pediatric trauma centers[39–41], Howard et al. [37]documented mean exposures of 28.5 mSv in UK polytrauma cohorts, while Brinkman et al.[42]reported median 22.3 mSv among transferred US pediatric trauma patients. Similarly, a German multicenter study by Harrieder et al. [43]found mean cumulative doses of 31.2 mSv in comparable ISS-matched populations. Several factors may explain these discrepancies: Our cohort exhibited higher median ISS (21 vs 18-19 in Western studies[37] [42], reflecting referral patterns to our tertiary center serving a population of 6.2 million. Imaging Frequency： Patients in our cohort underwent more frequent surveillance scans. This may reflect institutional protocols that emphasize close monitoring or the limited availability of alternative modalities, such as ultrasound surveillance. Technological Factors： During 2020-2022, our institution utilized predominantly conventional reconstruction algorithms. Implementation of iterative reconstruction (2023-2024) reduced per-scan doses by approximately 28%, contributing to the declining trend in anual cumulative exposures observed in our temporal analysis. Protocol Standardization： Absence of universally adopted pediatric trauma CT protocols in China[21]may result in greater inter-institutional variability compared to regions with established guidelines (e.g.ACR Appropriateness Criteria). Importantly, despite higher absolute exposures, our LAR estimates align closely with BEIR VII projections and recent Korean data from Han et al.[41], suggesting consistent dose-risk relationships across Asian populations. This validates the applicability of BEIR VII models to Asian pediatric cohorts and highlights the urgent need for dose-optimization interventions. Previous reports also indicate that radiation doses among patients treated in non-pediatric trauma centers are approximately twice those in specialized centers[44], and substantial inter-institutional variation persists[43]. Our study did not differentiate between hospital types; thus, future research should investigate the contribution of protocol heterogeneity, equipment calibration, and staff training to dose variation. The LAR estimates derived from the BEIR VII model were consistent with prior research. For example, a 2025 U.S. projection estimated approximately 103,000 future cancer cases attributable to CT imaging annually, including pediatric cases[45]. In our cohort, the additional LAR was approximately 0.5 per 10,000 person·Gy (solid cancers: 49.65 per 10,000 person·Gy; leukemia: 3.56 per 10,000 person·Gy), corresponding to an estimated lifetime cancer incidence of 2,047 per 100,000 males and 2,452 per 100,000 females an overall increase of 0.56%. Compared with Raelson et al.[46] (male: 890.6 per 100,000; female: 1,222.5 per 100,000), our risk estimates are higher, likely due to the increased frequency of repeat imaging in polytrauma patients. The UNSCEAR 2020/2021 report similarly emphasized elevated LAR in children even under low-dose CT conditions, corroborating our findings[17]. Implementing the “As Low As Reasonably Achievable” (ALARA) principle in pediatric polytrauma imaging presents unique challenges, but our findings provide actionable guidance. High-risk subgroups (e.g., children <5 years or with ISS ≥25) should be prioritized for dose optimization given their disproportionate LAR elevation. Current guidelines recommend against routine whole-body CT in high ISS cases, favoring selective imaging to avoid unnecessary exposure[47,48]. The key challenge lies in balancing the urgent need for rapid diagnosis such as identifying abdominal hemorrhage within minutes with dose minimization. Standard CT protocols may inadvertently lead to overexposure, particularly when referring institutions fail to adhere to ALARA principles[42]. Technological solutions include automated exposure control (AEC) and low tube voltage (80–100 kVp) protocols, which have been shown to reduce doses by 30–40% without compromising diagnostic quality[49,50]. Tube current modulation (mA modulation) dynamically adjusts exposure to patient size and remains among the most effective dose reduction techniques in pediatric trauma CT, achieving 20–50% reductions in ED[51]. Iterative reconstruction algorithms (e.g., ASiR, iDose) further enhance image quality while maintaining >95% diagnostic accuracy in head and abdominal imaging[52]. Alternative imaging modalities play an important complementary role. Ultrasound, particularly the FAST (Focused Assessment with Sonography in Trauma) protocol, achieves 85–95% sensitivity for abdominal injuries in stable patients but is limited by operator dependence and interference from bowel gas or bone[53]. MRI, while radiation-free and effective for neurological follow-up, is constrained by prolonged scan duration (20–60 minutes vs. ~5 minutes for CT), limited availability, challenges in vital sign monitoring, and the frequent need for sedation in children[54]. For stable patients, prioritizing ultrasound or MRI over repeat CT can substantially reduce exposure; in high-ISS cohorts, hybrid strategies combining initial CT with ultrasound follow-up may reduce cumulative ED by up to 40%[55]. The novelty of this study lies in its focus on pediatric polytrauma a population often underrepresented in radiation risk research. By systematically analyzing scan regions, ISS, cranial injury status, and repeat imaging, we identified key predictors of increased exposure and LAR, thereby offering practical insights for clinical decision-making. In contrast to Western cohorts such as EPI-CT, this study fills an epidemiologic gap in China by accounting for regional anthropometric characteristics and generating locally relevant data. Integrating BEIR VII modeling with clinical parameters provides a framework for developing national guidelines. It paves the way for artificial intelligence–assisted dose prediction models (e.g., PyTorch-based pretrained imaging networks) to enhance radiation protection. This study has several limitations warranting careful consideration: First, the retrospective single-center design may introduce incomplete documentation or selection bias, potentially underestimating radiation exposure in the most severely injured patients who died at the trauma scene or within the first hour (estimated 6-8% of severe polytrauma cases). Our tertiary referral center primarily serves higher-severity cases, which limits generalizability to community trauma centers. Furthermore, radiation doses and protocols may vary substantially across Chinese institutions, particularly between urban tertiary facilities and rural hospitals equipped with older-generation scanners. Second, we used population-based conversion coefficients rather than patient-specific Monte Carlo simulations. While sensitivity analyses confirmed robustness (±15% variation), individual-level dosimetry could provide ±20% uncertainty in ED estimates due to variations in patient size, positioning, and anatomical differences[56,57]. Future studies employing personalized Monte Carlo dosimetry would enhance precision. The dose metrics used (DLP and ED) cannot precisely represent organ-specific absorbed doses, which are more directly linked to cancer risk (e.g., bone marrow exposure and leukemia incidence)[58]. Third, the BEIR VII model’s linear no-threshold (LNT) assumption for lifetime attributable risk (LAR) calculations may overestimate risks at low doses (<100 mSv), a topic of ongoing debate. These assumptions include lifetime follow-up without accounting for competing mortality risks from severe trauma and constant baseline cancer incidence rates, potentially overestimating absolute cancer burdens in high-mortality trauma populations. Nonetheless, they provide conservative, clinically useful estimates consistent with ICRP and BEIR VII recommendations[59]. Fourth, while our sample size (n=394) met primary analytical goals, it may lack statistical power to detect smaller effect sizes or variable interactions, potentially underestimating subtle predictors of radiation risk. Prospective multicenter studies incorporating patient-specific Monte Carlo dosimetry, long-term cancer surveillance (≥20 years), dose-reduction intervention trials, and economic analyses of optimization strategies are needed to validate these findings and establish evidence-based regional imaging guidelines. Conclusion This four-year cohort study of 394 pediatric polytrauma patients identifies a substantial cumulative radiation burden (median CED 36.8 mSv, with 10.2% exceeding 50 mSv) and reveals a "gender-risk paradox": while male patients receive higher scanning frequencies and cumulative doses, female patients face a significantly higher lifetime attributable risk (LAR) for cancer (0.0021 vs. 0.0017, p=0.04) due to the heightened radiosensitivity of organs such as the breast and thyroid. These findings advocate for a shift toward gender-sensitive radiation protection, prioritizing dose-optimization technologies and non-ionizing alternatives for female and young pediatric patients to balance diagnostic necessity with long-term biological safety. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Ningbo No.2 Hospital (Approval No.: SL-NBEY-KY-2026-030-01). The approval was granted on September 28, 2025, for the research project titled "Cumulative Radiation Exposure and Cancer Risk in Pediatric Polytrauma: A Four-Year CT Utilization Analysis". The study was conducted in accordance with the 1964 Helsinki Declaration and its later amendments. As this research involved a retrospective analysis of anonymized historical data, the requirement for informed consent was waived by the Ethics Committee. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to hospital policy and patient privacy protection. However, the anonymized datasets are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Ningbo Key Laboratory of Digital Imaging and Medical-Engineering Interdisciplinarity； Project of National Key Clinical Specialty (Department of Medical Imaging, No. 2024017) Authors' contributions Juan Chen (JC): Conceptualization, Methodology, Software (Python/Pandas), and Writing - original draft. Jianjun Zheng (JJZ): Supervision, Project administration, Funding acquisition, and Writing - review & editing. Qi Dai (QD) and Jingfeng Zhang (JFZ): Data curation (PACS/DICOM extraction) and Investigation. Qun Zhang (QZ) and Yong Wang (YW): Formal analysis (using R software) and Validation. GR and SWQ were responsible for data collection and clinical record retrieval. Han Zhang (HZ) and Shuoyi Qian (SQ): Methodology and final manuscript review. All authors read and approved the final manuscript. References Ciorba MC, Maegele M. Polytrauma in Children—Epidemiology, Acute Diagnostic Evaluation, and Treatment. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8784170","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633675423,"identity":"ef995fb5-2cdd-4220-8932-1a27a095e554","order_by":0,"name":"Juan Chen","email":"","orcid":"","institution":"Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Chen","suffix":""},{"id":633675425,"identity":"8ecc4d8b-3003-417a-8d5c-25c5eed4728d","order_by":1,"name":"Jianjun 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Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Qun","middleName":"","lastName":"Zhang","suffix":""},{"id":633675429,"identity":"b1d9028f-2c76-4ad0-aeaa-685a6ee5d9c6","order_by":4,"name":"Qi Dai","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Dai","suffix":""},{"id":633675430,"identity":"715ed93a-440c-4c8d-afc2-be868eeb0b13","order_by":5,"name":"Yong Wang","email":"","orcid":"","institution":"Ningbo Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Wang","suffix":""},{"id":633675431,"identity":"1ef073b9-851f-42f7-825f-ed282f0c2ee2","order_by":6,"name":"Han Zhang","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Zhang","suffix":""},{"id":633675432,"identity":"9f3b446a-c124-43eb-ae75-ac3a733f27b2","order_by":7,"name":"Wenqian Sha","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenqian","middleName":"","lastName":"Sha","suffix":""},{"id":633675433,"identity":"41c48bf9-fc99-4f53-8ad4-b28a27caeae9","order_by":8,"name":"Rong Guo","email":"","orcid":"","institution":"Ningbo No. 2 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Guo","suffix":""},{"id":633675434,"identity":"2e593ace-73ef-4f56-b1ca-87eecd1233dc","order_by":9,"name":"Shuoyi Qian","email":"","orcid":"","institution":"Hangzhou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Shuoyi","middleName":"","lastName":"Qian","suffix":""}],"badges":[],"createdAt":"2026-02-04 09:09:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8784170/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8784170/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108822476,"identity":"0ebdaa26-fb1a-4e75-a1ca-589aec9e9c6d","added_by":"auto","created_at":"2026-05-08 16:48:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":303986,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram illustrating patient selection and radiation risk assessment\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8784170/v1/2c91e3c8b31130a380cdb2e8.png"},{"id":108823178,"identity":"d41d753e-b495-4eba-8011-4568485f1bf3","added_by":"auto","created_at":"2026-05-08 16:52:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":616882,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8784170/v1/2c7f2023-ab9c-4904-a3d3-001ae4bb5dbe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gender-Specific Radiation Burden and Cancer Risk Assessment in Pediatric Polytrauma: A Four-Year Longitudinal CT Utilization Analysis","fulltext":[{"header":"Background","content":"\u003cp\u003ePediatric polytrauma defined as injuries involving two or more anatomical regions with an Injury Severity Score (ISS)\u0026thinsp;\u0026ge;\u0026thinsp;16[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], remains a major global cause of morbidity and mortality among children. Age-specific injury mechanisms include falls in infants and motor vehicle collisions in older children, frequently resulting in severe outcomes such as traumatic brain injury (TBI)[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This condition accounts for approximately 10\u0026ndash;20% of all pediatric trauma deaths[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the United Nations Inter-agency Group for Child Mortality Estimation (UNIGME) and World Health Organization (WHO), global under-five mortality declined to 4.8\u0026nbsp;million in 2023; however, unintentional injuries continue to represent a leading cause of death, particularly in low- and middle-income countries[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The Global Burden of Disease (GBD) 2023 study estimated an age-standardized incidence of pediatric injuries at approximately 8,000\u0026ndash;8,500 per 100,000, with a mortality rate of about 20 per 100,000 and 1,800 disability-adjusted life years (DALYs) per 100,000, reflecting only a modest 16% decline in injury-related DALYs since 2010[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In high-income regions such as the United States, unintentional injuries remain the leading cause of death among individuals aged 1\u0026ndash;19 years, resulting in approximately 12,000 deaths and 22\u0026nbsp;million non-fatal cases annually\u0026mdash;nearly one-third of the pediatric population[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Notably, trauma patterns and injury mechanisms often exhibit distinct gender-related variations; adolescent males, for instance, are frequently overrepresented in high-energy trauma cohorts due to behavioral and environmental factors. These disparities in injury frequency and severity necessitate a more nuanced evaluation of subsequent diagnostic interventions.\u003c/p\u003e \u003cp\u003eIn emergency surgical settings, Computed Tomography (CT) is indispensable for rapid diagnosis and multidisciplinary surgical planning. Its ability to facilitate early detection of life-threatening conditions, such as intracranial hemorrhage and solid organ laceration, has established it as the gold standard for evaluating blunt polytrauma[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, the management of severe trauma often requires serial imaging for monitoring clinical progression or post-operative evaluation. This recurrent utilization leads to substantial cumulative effective doses (CED) that may exceed recommended diagnostic reference levels, particularly in pediatric populations where clinical stability is often volatile[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNevertheless, frequent CT use in pediatric polytrauma especially repeated examinations for hemorrhage monitoring, post-operative evaluation, or clinical deterioration raises growing concerns regarding radiation safety. Children exhibit significantly greater radiosensitivity than adults owing to three critical factors: (1) higher cellular proliferation rates during growth and development, (2) smaller body dimensions resulting in less tissue attenuation and higher organ doses, and(3)extended post-exposure lifespan providing longer latency for malignancy development[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These biological vulnerabilities collectively elevate lifetime cancer risk following ionizing radiation exposure.\u003c/p\u003e \u003cp\u003eLarge cohort studies demonstrate a dose-response association; cumulative exposures of 50\u0026ndash;60 mGy correlate with 1.5- to 3.5-fold increased risks of leukemia and brain tumors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. More recent multinational analyses led by UCSF investigators have further confirmed that even limited exposures equivalent to one or two head CT scans (\u0026asymp;\u0026thinsp;15\u0026ndash;30 mGy) are linked to a 1.8- to 3.5-fold elevation in hematologic cancer risk, suggesting that up to 10% of pediatric hematologic malignancies could be attributable to diagnostic radiation[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, BEIR VII projection models estimate lifetime attributable risks (LAR) for fatal solid cancers up to 0.18% in young children undergoing abdominal CT[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eChildren are significantly more radiosensitive than adults due to higher cellular proliferation rates and a longer post-exposure lifespan for malignancy development. Beyond age, biological sex is an increasingly recognized but often under-addressed determinant of radiation-induced risk. Epidemiological data, including the BEIR VII models, suggest that female pediatric patients face higher lifetime attributable risks (LAR) for solid cancers compared to males for equivalent radiation doses, primarily due to the heightened radiosensitivity of breast and thyroid tissues. Recent analyses indicate that even limited exposures from diagnostic CT can lead to a 1.8- to 3.5-fold elevation in hematologic cancer risk[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Consequently, a gender-risk paradox emerges in trauma care: while males may undergo more scans due to trauma prevalence, females may carry a higher biological burden per exposure.\u003c/p\u003e \u003cp\u003eDespite the clear intersection of gender and radiological risk, robust data on gender-sensitive radiation practices in Asian pediatric populations remain scarce. Critical differences in patient anthropometry and institutional imaging protocols in regions like China limit the applicability of Western-based guidelines. Therefore, this study aims to systematically quantify cumulative radiation exposure and estimate LAR among pediatric polytrauma patients at a major Chinese tertiary center. By integrating gender-specific LAR modeling with clinical parameters such as ISS and anatomical scan regions, we seek to identify determinants of elevated risk and advocate for gender-sensitive, ALARA-compliant imaging protocols that ensure equitable safety in pediatric trauma management.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eDesign of the study and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a retrospective cohort design to evaluate cumulative radiation exposure and associated cancer risk in pediatric polytrauma patients undergoing repeated CT examinations. The study population comprised children under 14 years of age who presented with traumatic injuries at A Level III pediatric trauma center in Zhejiang, China between January 2020 and December 2024. The patient selection process is illustrated in Figure 1.\u003c/p\u003e\n\u003cp\u003eAn initial screening identified 2,137 patients. Inclusion criteria were as follows:（1）Trauma involving two or more anatomical regions or organ systems;（2）Availability of Injury Severity Score (ISS) \u0026ge;16;（3）Complete radiation dose information, including dose-length product (DLP) and volumetric CT dose index (CTDIvol).\u003c/p\u003e\n\u003cp\u003eExclusion criteria were:（1）Death during the trauma event;（2）Incomplete imaging data (e.g., missing DICOM metadata);（3）Insufficient clinical information to calculate ISS.\u003c/p\u003e\n\u003cp\u003eBased on these criteria, 1,743 patients were excluded (1,579 with ISS \u0026lt;16, 6 deceased, 158 with incomplete imaging data), resulting in a final cohort of 394 patients with a total of 1,817 CT examinations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImaging data were extracted from the Picture Archiving and Communication System (PACS) and dose-structured reports, including examination type, anatomical region scanned, dose-length product (DLP, mGy\u0026middot;cm), volumetric CT dose index (CTDIvol, mGy), and scan date and time. Clinical data were obtained from electronic medical records and included patient sex, age, mechanism of injury, ISS, medical history, clinical diagnosis, and treatment outcomes.\u003c/p\u003e\n\u003cp\u003ePatients were categorized into three age groups: \u0026lt;5 years, 5\u0026ndash;10 years, and \u0026gt;10 years. Anatomical regions were standardized as head, chest, abdomen, pelvis, cervical spine, extremities, and other. When multiple independent scan protocols were performed within the same anatomical region, each scan\u0026rsquo;s DLP was recorded separately to ensure accurate dose estimation. All data were independently entered into Microsoft Excel by two investigators and standardized; discrepancies were resolved through consensus discussion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs a retrospective study using anonymized historical data without patient intervention, informed consent was waived. All data were handled in accordance with patient privacy protection regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRadiation Dose Estimation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRadiation dose estimation was conducted in accordance with international recommendations from the International Atomic Energy Agency (IAEA)[22,23].\u0026nbsp;Effective dose (ED) was used to evaluate the overall risk of stochastic effects, such as radiation-induced cancer, as defined by ICRP Publication 103[24]. ED represents the sum of equivalent doses to individual organs and tissues, weighted by tissue-specific sensitivity factors, and is expressed in millisieverts (mSv). Weighting factors assign higher values to radiosensitive tissues, such as breast and bone marrow, enabling comparisons of radiation risks across different sources. However, ED is not intended for individual-level cancer prediction[25,26].\u0026nbsp;While facilitating comparisons between radiation sources, ED is not intended for individual cancer risk prediction. In clinical practice, ED serves as a standardized metric for radiation dose monitoring and reporting, facilitating comparison with natural background radiation levels.\u003c/p\u003e\n\u003cp\u003eED was calculated from the dose-length product (DLP) using age- and region-specific conversion coefficients (k-factors)[27], according to the formula:\u003c/p\u003e\n\u003cp\u003e𝐸𝐷=𝐷𝐿𝑃\u0026times;\u003cem\u003ek-factor\u003csub\u003e(age, region)\u003c/sub\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDLP is automatically generated by the CT scanner and represents the product of the volumetric CT dose index (CTDIvol, mGy) and scan length (cm), expressed in mGy\u0026middot;cm. K-factors are derived from Monte Carlo simulations based on IAEA SSG-46 guidelines and AAPM Report No. 96, and are adjusted using tissue weighting factors from ICRP 103[28,29].\u0026nbsp;For example, the k-factor for head CT in children under 5 years is approximately 0.0040 mSv/(mGy\u0026middot;cm), and for chest CT it is approximately 0.0180 mSv/(mGy\u0026middot;cm)[30,31].\u003c/p\u003e\n\u003cp\u003eThis method is widely adopted by contemporary CT dose reporting systems. Multiplying the scanner-generated DLP by the corresponding k-factor provides a practical and standardized estimate of ED. These coefficients are pre-calculated using computational anthropomorphic phantoms and Monte Carlo simulations of CT radiation transport. Multiple studies have reported DLP-to-ED conversion factors for different patient populations. Building on ICRP Publications 60 and 103, Deak et al. updated pediatric and adult k-factor sets through Monte Carlo calculations incorporating standardized tissue weighting[31].\u003c/p\u003e\n\u003cp\u003eFor each scan sequence, DLP values were extracted from PACS and multiplied by the appropriate k-factor to obtain the ED for that individual CT examination. In patients undergoing multiple scans in different anatomical regions or repeated scans in the same region, each ED was calculated separately and then summed to derive the cumulative effective dose (CED) over the treatment period. All calculations were performed in Python using the Pandas library, with precision to 0.1 mSv. Selected k-factors were adaptively adjusted based on Deak et al.\u0026rsquo;s pediatric cohort to better reflect the anthropometric characteristics of the Chinese pediatric population[31].\u003c/p\u003e\n\u003cp\u003eDosimetric Uncertainty and Sensitivity Analysis：\u003c/p\u003e\n\u003cp\u003eConversion coefficients were applied according to anatomical region and age groups (\u0026lt;1 year, 1-5 years, 6-10 years, 11-14 years), referencing validated pediatric-specific values from Deak et al.[31] for body CT and ICRP Publication 103 for head CT. For examinations lacking complete DLP data (n=23, 1.3%), effective dose was estimated using institution-specific protocols validated through quality assurance phantom measurements. To account for dosimetric uncertainties inherent in conversion coefficient methodologies, we performed sensitivity analyses in which coefficients were varied by \u0026plusmn;20% (representing typical inter-individual variations in body habitus and anatomical positioning). Monte Carlo simulations (10,000 iterations) assessed the impact on lifetime attributable risk estimates. Results demonstrated that LAR estimates remained within 15% of base-case values across the tested range, the effective dose was estimated using institution-specific protocols validated through quality-the robustness of primary findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer Risk Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLifetime cancer risk in the 394 pediatric polytrauma patients was systematically estimated using the BEIR VII (Biological Effects of Ionizing Radiation) model developed by the U.S. National Academies of Sciences[32]. This model is based on the linear no-threshold (LNT) assumption, which posits a linear increase in radiation-induced risk with any exposure below 100 mSv. Although the applicability of the LNT model at low-dose ranges remains a subject of scientific debate, it remains a widely used framework for population-level risk assessment. The BEIR VII model integrates cumulative effective dose, patient age, sex, and tissue-specific radiosensitivity (e.g., bone marrow, breast, lung, and thyroid) using Monte Carlo simulations and epidemiological data to estimate lifetime attributable risk (LAR). This model is based on the linear no-threshold (LNT) assumption, which posits a linear increase in radiation-induced risk with any exposure below 100 mSv. The model integrates epidemiological data to estimate LAR, representing the probability of developing excess cancer attributable to radiation beyond the baseline risk. This metric provides a quantitative assessment of the long-term health impact of medical radiation in high-risk pediatric populations[33].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContinuous variables were expressed as mean \u0026plusmn; standard deviation (SD) or median with interquartile range (IQR), depending on distribution normality assessed by the Shapiro-Wilk test. Categorical variables were presented as frequencies and percentages. For multivariable analysis identifying predictors of high cumulative radiation exposure (defined as \u0026gt;50 mSv), candidate variables were initially screened using univariate logistic regression (inclusion threshold P\u0026lt;0.10). Variables included: age group (\u0026lt;1, 1-5, 6-10, 11-14 years), sex, ISS category (16-24 vs \u0026ge;25), mechanism of injury, number of anatomical regions injured, hospital length of stay, and presence of severe traumatic brain injury. Collinearity was assessed using the variance inflation factor (VIF); variables with VIF\u0026gt;5 were excluded. The final multivariable model was constructed using backward stepwise selection (removal threshold P\u0026gt;0.10). Model diagnostics included assessment of residual distribution, leverage points (Cook\u0026apos;s distance \u0026gt;1), and goodness-of-fit using the Hosmer-Lem show test (P\u0026gt;0.05 indicating adequate fit). Relative risks (RR) with 95% confidence intervals (CI) were calculated for LAR outcomes. Subgroup analyses were performed stratified by age groups and ISS categories. Statistical significance was set at two-tailed P\u0026lt;0.05. All analyses were performed using R version 4.3.0 (R Foundation for Statistical Computing).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePatient Demographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 394 pediatric polytrauma patients (ISS \u0026ge;16) were included in this study, accounting for 1,817 CT examinations. The mean age was 6.58 years, with most patients aged 5\u0026ndash;9 years (49.5%, 195/394). A significant gender disparity was observed in trauma prevalence, with male patients comprising 69.5% (274/394) of the cohort, while females accounted for 30.5% (120/394).\u003c/p\u003e\n\u003cp\u003eThe predominant mechanisms of injury were motor vehicle collisions (43.4%), followed by falls (19.5%), high-altitude falls (11.4%), impact against objects (6.3%), non-accidental trauma/abuse (5.3%), sports-related injuries (5.1%), non-motorized vehicle accidents (4.8%), and crush injuries (2.3%).\u003c/p\u003e\n\u003cp\u003eThe mean ISS was 24.3 (range 16\u0026ndash;66). The distribution of ISS scores was as follows: 16\u0026ndash;20 (69.3%, 273/394), 21\u0026ndash;30 (21.1%, 83/394), 31\u0026ndash;40 (4.3%, 17/394), 41\u0026ndash;50 (4.8%, 19/394), and \u0026gt;50 (0.5%, 2/394). The most frequently injured anatomical region was the head (42.9%, 169/394), followed by the abdomen (20.3%, 80/394), chest (16.5%, 65/394), other regions (12.2%, 48/394), cervical spine (4.8%, 19/394), pelvis (2.3%, 9/394), and extremities (1.0%, 4/394).\u003c/p\u003e\n\u003cp\u003eRegarding the type of visit, 40.9% (161/394) presented to the emergency department, 32.2% (127/394) were admitted as inpatients, and 26.9% (106/394) were outpatients. Patients underwent a mean of 4.61 CT examinations (range 1\u0026ndash;37), with 76.3% undergoing repeated scans. Baseline patient characteristics are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographics, injury mechanisms, and injury severity among Pediatric Polytrauma Patients (n=394)\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e% or Range\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e6.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; 0-4\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e29.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; 5-9 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e49.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; 10-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMechanism of Injury\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Motor Vehicle Collision\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e43.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Ground-level fall\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e19.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Fall from height\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e11.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Impact against object\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e6.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Non-accidental trauma or abuse\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Sports-related injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e5.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Non-motorized vehicle accident\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Crush injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Penetrating injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Sharp object injury\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjury Severity Score (ISS)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; 16-20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e69.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; 21-30\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; 31-40\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; 41-50\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026gt;51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e0.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInjured Region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Head\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Abdomen\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e20.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Chest\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e16.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Other regions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e12.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Cervical spine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e4.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Pelvis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e2.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp; Extremities\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 25px;\"\u003e\n \u003cp\u003e1.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution and Frequency of CT Examinations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge-stratified analysis revealed that patients aged 10 years or older underwent the most CT examinations (727 scans, 40.0%), followed by those aged 5\u0026ndash;9 years (636 scans, 35.0%) and those younger than 5 years (454 scans, 25.0%). Across all age groups, the most frequently scanned regions were the head, abdomen, and chest. Notably, the \u0026gt;10-year group had substantially more head CT scans (219) than the \u0026lt;5-year group (137), indicating an age-related increase in head trauma or clinical indications for neuroimaging.\u003c/p\u003e\n\u003cp\u003eWhen stratified by sex, male patients accounted for a greater total number of CT examinations (999 scans, 54.9%) than female patients (818 scans, 45.1%). Males underwent more scans in the head (300 vs. 247), abdomen (292 vs. 238), and chest (245 vs. 200) regions, respectively. Taken together, older male patients exhibited particularly high frequencies of abdominal and chest CT examinations, identifying them as a subgroup with potentially higher cumulative radiation exposure.\u003c/p\u003e\n\u003cp\u003eOverall, CT utilization was more frequent among older and male pediatric trauma patients, suggesting sex- and age-related differences in imaging needs and trauma patterns. These variations may reflect developmental differences in activity levels, injury mechanisms, and clinical decision-making processes. From a radiological protection perspective, clinicians should carefully balance diagnostic benefits against radiation risks, ensuring that CT indications are age-appropriate and justified to minimize unnecessary exposure and adhere to optimization principles in pediatric imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of Radiation Dose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 394 pediatric polytrauma patients, 40 (10.2%) had a cumulative effective dose (ED) exceeding 50 mSv, including 11 patients (2.8%) with exposures above 100 mSv (Table 2). Age-matched distribution of cumulative radiation exposure is shown in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Age-matched patients stratified by cumulative radiation exposure.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative Dose\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-25 mSv\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e26-50 mSv\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e51-75 mSv\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e76-100 mSv\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;100 mSv\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean cumulative dose (mSv)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e60.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e90.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e87.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e141.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e116.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean lifetime cancer risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.00039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0079\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean ISS score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e24.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e31.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMean age (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNumber of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e230.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e99.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePercentage of patients (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e58.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Data are from 394 pediatric polytrauma patients who underwent 1,817 CT scans. Cumulative dose is expressed as effective dose (mSv). Lifetime cancer risk was estimated using the BEIR VII model. Percentages are calculated based on the total number of patients.\u003c/p\u003e\n\u003cp\u003ePatients were categorized into five cumulative dose groups: 0\u0026ndash;25 mSv, 26\u0026ndash;50 mSv, 51\u0026ndash;75 mSv, 76\u0026ndash;100 mSv, and \u0026gt;100 mSv. The majority (83.5%, 329/394) were within the 0\u0026ndash;25 mSv range, whereas the \u0026gt;100 mSv group accounted for the smallest proportion (2.8%, 11/394). Across all dose categories, males predominated, particularly in the high-dose group (\u0026gt;100 mSv), where males represented 2.5% (10/394) and females only 0.25% (1/394).\u003c/p\u003e\n\u003cp\u003eA clear upward trend was observed in mean cumulative ED across increasing dose strata. In the \u0026gt;100 mSv group, the mean cumulative ED was 141.8 mSv in males and 116.0 mSv in females. Correspondingly, the lifetime attributable risk (LAR) for cancer estimated using the BEIR VII model also increased with cumulative dose. The average LAR in the \u0026gt;100 mSv group reached 0.0044 for males and 0.0079 for females, demonstrating a positive dose\u0026ndash;risk relationship consistent with the linear no-threshold (LNT) hypothesis. Notably, patients in higher dose groups tended to have greater injury severity and older age, suggesting that repeated scanning in more severely injured cases contributed to elevated cumulative exposure. In the \u0026gt;100 mSv group, the mean age of male patients was 11.6 years, compared with 2.0 years for females.\u003c/p\u003e\n\u003cp\u003eWhen analyzed by anatomical region, the mean cumulative EDs were 6.0 mSv for the head, 5.0 mSv for the abdomen, and 4.0 mSv for the chest. The median cumulative ED for head scans was 5.5 mSv (IQR: 4.0\u0026ndash;7.5 mSv), with some patients exceeding 15 mSv, indicating substantial interindividual variability. Further stratification by age and sex revealed that males younger than 5 years had the highest mean head dose (approximately 6.5 mSv), whereas females older than 10 years had the lowest abdominal dose (approximately 4.0 mSv), highlighting pronounced sex- and age-related differences in exposure to high-dose regions.\u003c/p\u003e\n\u003cp\u003eTo provide an overall view of dose distribution, the proportions of males and females across cumulative dose categories. While most patients were in the low-dose group (0\u0026ndash;25 mSv, 83.5%), males were overrepresented in the higher dose groups. Specifically, males accounted for 58.4% of the 0\u0026ndash;25 mSv group and 2.5% of the \u0026gt;100 mSv group, compared with 25.1% and 0.25% for females, respectively. Collectively, these findings indicate that although the proportion of patients receiving high cumulative doses was small, male and severely injured patients were disproportionately represented in this subgroup, underscoring the need for targeted strategies to mitigate potential long-term radiation risks in high-exposure pediatric populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCancer Risk Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOverall Lifetime Cancer Risk\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the BEIR VII model, the lifetime attributable risk (LAR) of radiation-induced cancer was systematically estimated for all 394 pediatric polytrauma patients. The mean LAR for the entire cohort was 0.0019 (95% CI: 0.0015\u0026ndash;0.0023), corresponding to an excess lifetime cancer risk of approximately 190 cases per 10,000 person\u0026middot;Gy. The median LAR per single CT examination was 112 per 100,000 (IQR: 86\u0026ndash;174 per 100,000), while the median cumulative LAR per patient reached 521 per 100,000.\u003c/p\u003e\n\u003cp\u003eSolid cancer risk (LAR\u0026lt;sub\u0026gt;solid\u0026lt;/sub\u0026gt;) accounted for approximately 85% of the total radiation-induced risk, with a median value of 95 per 100,000, whereas leukemia risk (LAR\u0026lt;sub\u0026gt;leukemia\u0026lt;/sub\u0026gt;) contributed around 15% (median 17 per 100,000). These findings indicate that solid tumors constitute the predominant component of radiation-related cancer burden in pediatric trauma populations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStratified Analysis by Age, Sex, and Injury Severity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAge stratification: A clear inverse association was observed between age and LAR, with younger patients demonstrating higher susceptibility to radiation-induced malignancies. The \u0026lt;5-year group showed the highest mean LAR (0.0025, 95% CI: 0.0020\u0026ndash;0.0030), followed by the 5\u0026ndash;9-year group (0.0018, 95% CI: 0.0014\u0026ndash;0.0022) and the \u0026gt;10-year group (0.0012, 95% CI: 0.0009\u0026ndash;0.0015), with statistically significant intergroup differences (ANOVA, p\u0026lt;0.001). This pattern reflects the biological characteristics of early childhood, where rapid cell proliferation, immature organ development, and longer expected lifespan confer greater radiosensitivity and thus higher lifetime risk.\u003c/p\u003e\n\u003cp\u003eSex stratification: Female patients exhibited a slightly higher mean LAR than males (0.0021 vs. 0.0017, p=0.04). This difference is likely attributable to the greater contribution of radiosensitive organs, such as the breast and thyroid, which are more prevalent sites of radiation-induced malignancy in females.\u003c/p\u003e\n\u003cp\u003eInjury severity: The LAR demonstrated a positive correlation with injury severity, as measured by the Injury Severity Score (ISS) Patients with ISS 16\u0026ndash;24 had a mean LAR of 0.0017, while those with ISS \u0026ge;25 exhibited an increased mean LAR of 0.0023 (p=0.01). This association suggests that patients with more severe trauma, often requiring repeated imaging for diagnostic clarification and clinical monitoring, accumulate substantially higher radiation doses, leading to a marked elevation in cumulative cancer risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDose Dependence and Anatomical Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the dose\u0026ndash;response relationship between radiation exposure and lifetime cancer risk, a dose and dose-rate effectiveness factor (DDREF) of 1.5 was applied for risk adjustment. Patients were stratified by cumulative effective dose (ED) into five categories (0\u0026ndash;25, 26\u0026ndash;50, 51\u0026ndash;75, 76\u0026ndash;100, and \u0026gt;100 mSv), and the corresponding lifetime attributable risk for solid cancers (LAR\u003csub\u003esolid\u003c/sub\u003e)was calculated. LAR\u003csub\u003esolid\u0026nbsp;\u003c/sub\u003edemonstrated a linear increase with escalating cumulative doses. In the \u0026gt;100 mSv group, the mean LAR\u003csub\u003esolid\u0026nbsp;\u003c/sub\u003ewas 137.8 per 10,000 person\u0026middot;Gy in males and 91.8 per 10,000 person\u0026middot;Gy in females.\u003c/p\u003e\n\u003cp\u003eAlthough females exhibited a steeper increase in risk, the difference between sexes was not statistically significant due to overlapping confidence intervals (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eAnatomical site\u0026ndash;specific analysis revealed that chest CT examinations yielded the highest LAR\u003csub\u003esolid\u003c/sub\u003e (73.1 per 10,000 person\u0026middot;Gy), followed by abdominal (50.8 per 10,000 person\u0026middot;Gy) and pelvic scans (45.6 per 10,000 person\u0026middot;Gy). This elevated chest risk primarily reflects the high tissue weighting factors assigned to radiosensitive organs such as the lungs and breasts. In contrast, LAR for leukemia (LAR\u003csub\u003eleukemia)\u003c/sub\u003e remained relatively uniform across anatomical regions (3.6\u0026ndash;5.2 per 10,000 person\u0026middot;Gy), consistent with the distributed nature of active bone marrow exposure throughout the body.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSex Differences and Dose Distribution Patterns\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSex-stratified analysis of anatomical site\u0026ndash;specific effective doses revealed that the median abdominal dose was the highest (79.49 mSv in males vs. 47.47 mSv in females), followed by the pelvic (79.49 mSv in males vs. 47.47 mSv in females) and chest regions (43.96 mSv in males vs. 48.69 mSv in females). The interquartile range (IQR) for abdominal and pelvic doses in males was wide (Q1: 34.32 mSv, Q3: 142.34 mSv), indicating substantial interindividual variability and the presence of outliers (maximum: 254.65 mSv), likely due to repeated scanning. The mean cumulative effective dose (ED) was slightly higher in males (34.67 \u0026plusmn; 35.19 mSv) than in females (26.43 \u0026plusmn; 23.06 mSv), though the difference was not statistically significant (p \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eAnalysis of LAR by anatomical region across 1,817 CT scans demonstrated that the chest contributed the highest LAR\u003csub\u003esolid\u003c/sub\u003e (73.1 per 10,000 person\u0026middot;Gy), attributable to the high tissue weighting factors of radiosensitive organs such as the lungs and breasts, followed by the abdomen (50.8 per 10,000 person\u0026middot;Gy) and pelvis (45.6 per 10,000 person\u0026middot;Gy). LAR\u003csub\u003eleukemia\u0026nbsp;\u003c/sub\u003eremained relatively uniform across regions (3.6\u0026ndash;5.2 per 10,000 person\u0026middot;Gy), reflecting the homogeneous distribution of active bone marrow. In the high-dose group (ED \u0026gt; 100 mSv), patients aged \u0026gt; 10 years (n = 15, 1.36%) showed a LAR\u003csub\u003esolid\u003c/sub\u003e of 140.9 per 10,000 person\u0026middot;Gy, whereas those \u0026lt; 5 years (n = 5, 0.45%) had 120.5 per 10,000 person\u0026middot;Gy, suggesting a synergistic effect between age and cumulative dose[34]. These findings emphasize the need to prioritize dose optimization in chest and abdominal CT, particularly among younger children, whose anatomical proportions and tissue radiosensitivity confer elevated risk.\u003c/p\u003e\n\u003cp\u003eCompared with previous studies, the median abdominal dose in this cohort (79.49 mSv in males, 47.47 mSv in females) exceeded the 15 mSv reported by Pearce et al.[35], reflecting the cumulative effect of repeated scans in trauma settings. The proportion of whole-body CT examinations was notably higher in the 10\u0026ndash;\u0026lt;15 year age group, consistent with increasing injury complexity with age. However, the median single-scan ED was substantially higher in the \u0026lt; 5 year group (approximately 9 mSv/scan), and when combined with their longer life expectancy, this group exhibited the highest cumulative lifetime cancer risk (median 166.1 per 100,000 vs. 102.9 per 100,000 for the 10\u0026ndash;\u0026lt;15 year group). Age-dependent parameters higher CTDI and DLP in older children, but greater DLP-to-ED conversion coefficients in younger children explain these differences in dose distribution. Anatomical characteristics such as compliant thoracic structures in younger patients, which predispose to internal injuries often necessitate chest and abdominal CT, yet underscore the importance of dose optimization.\u003c/p\u003e\n\u003cp\u003eBy integrating dose, age, sex, and anatomical site variables, this analysis identified high ISS and younger age as key predictors of elevated radiation risk, supporting the continued application of the ALARA principle in pediatric trauma imaging. Future studies employing Monte Carlo simulations could further refine organ-specific dose estimations and validate long-term clinical outcomes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides a comprehensive Asian cohort analysis quantifying the CT radiation burden in pediatric polytrauma, revealing three principal findings with immediate clinical implications: (1) substantial cumulative radiation exposures (median 36.8 mSv), with 10.2% of patients exceeding the 50 mSv threshold; (2) a distinct \u0026quot;gender-risk paradox\u0026quot;, where female patients exhibit significantly higher oncogenic risk despite receiving lower average cumulative doses than males ; and (3) the identification of Injury Severity Score (ISS) \u0026ge;25 and age under 5 years as independent predictors of elevated radiation burden.\u003c/p\u003e\n\u003cp\u003eWe observed substantial cumulative radiation exposure in pediatric polytrauma patients, with a median dose of 36.8 mSv and more than 10% of patients exceeding 50 mSv. These exposure levels are notably higher than those reported in North American pediatric trauma cohorts, where median or mean cumulative doses typically range between 22 and 28 mSv[37]. Such differences likely reflect variations in injury severity, CT utilization patterns, imaging protocols, and resource availability across healthcare systems. Importantly, cumulative doses above 50\u0026ndash;100 mSv fall within a range where epidemiological studies have demonstrated increased risks of radiation-induced malignancies, underscoring the long-term relevance of radiation protection in this vulnerable population[19,36].\u003c/p\u003e\n\u003cp\u003eA cornerstone of our findings is the significant disparity between radiation exposure and biological cost between sexes. While male patients predominated the cohort (69.5%) and were more likely to fall into the highest dose category (\u0026ge;100 mSv) , our results indicate that the biological impact of ionizing radiation is disproportionately higher in females. The mean Lifetime Attributable Risk (LAR) was significantly elevated in female patients (0.0021 vs. 0.0017 in males, p=0.04). This phenomenon becomes even more pronounced in the high-exposure tier, where the average LAR reached 0.0079for females compared to 0.0044 for males in the \u0026gt;100 mSv group.\u003c/p\u003e\n\u003cp\u003eThis paradox is primarily attributable to sex-specific tissue radiosensitivity rather than differences in imaging frequency alone. Chest CT examinations routinely performed for thoracic injury assessment in polytrauma contributed the highest solid cancer risk in our cohort. The elevated tissue weighting factors assigned to the breast and thyroid, organs with higher baseline susceptibility to radiation-induced malignancies in females, translate equivalent or lower physical doses into substantially greater biological cost. These findings challenge the prevailing \u0026ldquo;one-size-fits-all\u0026rdquo; approach to trauma imaging and indicate that cumulative dose alone may underestimate risk in female pediatric patients.\u003c/p\u003e\n\u003cp\u003eThe identification of this gender-specific risk profile provides actionable guidance for the \u0026quot;As Low As Reasonably Achievable\u0026quot; (ALARA) principle in emergency settings. Given their higher biological sensitivity, female pediatric patients should be prioritized for advanced dose-reduction technologies. While the urgent need for rapid diagnosis in high-ISS cases remains paramount, the implementation of automated tube current modulation and iterative reconstruction algorithms which can reduce exposure by 20%\u0026ndash;40% without compromising diagnostic accuracy,is particularly critical for female thoracic and abdominal imaging. Furthermore, for stable female patients, our data support a more conservative approach toward repeat CTs, favoring ultrasound (e.g., FAST) or MRI for follow-up to minimize cumulative biological costs.\u003c/p\u003e\n\u003cp\u003eBeyond sex-specific vulnerability, our findings demonstrate that age and injury severity jointly define a high-risk radiation exposure profile in pediatric polytrauma. Children under 5 years of age exhibited approximately threefold higher lifetime attributable cancer risks compared with adolescents. This age-dependent susceptibility reflects both higher per-examination doses\u0026mdash;due to technical and anatomical factors\u0026mdash;and heightened tissue radiosensitivity during early developmental stages. These observations reinforce existing radiobiological evidence and highlight the critical importance of rigorously age-specific dose optimization strategies in young children.\u003c/p\u003e\n\u003cp\u003eIn parallel, injury severity emerged as an independent predictor of cumulative radiation burden. Patients with an Injury Severity Score (ISS) \u0026ge;25 had a significantly increased risk of higher cumulative exposure, with more than 15% exceeding 100 mSv. In this subgroup, repeated CT examinations are often clinically unavoidable due to the complexity and multisystem nature of injuries. Consequently, ISS-based risk stratification offers a pragmatic framework for identifying patients who may benefit most from intensified dose monitoring, protocol optimization, and multidisciplinary imaging decision-making.\u003c/p\u003e\n\u003cp\u003eFurthermore, these findings diverge from prior Western reports, underscoring regional variations in CT utilization and radiation practices. Consequently, they necessitate a revision of current imaging algorithms in Asian pediatric trauma centers, prioritizing evidence-based strategies such as selective scanning and advanced dose-reduction technologies to align with the ALARA principle.\u003c/p\u003e\n\u003cp\u003eISS, age, hospitalization length, and anatomical scan region were identified as reliable predictors of radiation exposure and cancer risk[37].\u0026nbsp;Our findings are consistent with the American College of Radiology (ACR) Appropriateness Criteria for pediatric trauma CT, highlighting opportunities to refine imaging decisions using structured frameworks such as RAND/UCLA or GRADE[38]. Our median cumulative ED (36.8 mSv) substantially exceeds values reported from North American and European pediatric trauma centers[39\u0026ndash;41], Howard et al.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[37]documented mean exposures of 28.5 mSv in UK polytrauma cohorts, while Brinkman et al.[42]reported median 22.3 mSv among transferred US pediatric trauma patients. Similarly, a German multicenter study by Harrieder et al.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[43]found mean cumulative doses of 31.2 mSv in comparable ISS-matched populations.\u0026nbsp;Several factors may explain these discrepancies:\u003c/p\u003e\n\u003cp\u003eOur cohort exhibited higher median ISS (21 vs 18-19 in Western studies[37]\u003csup\u003e\u0026nbsp;\u003c/sup\u003e[42], reflecting referral patterns to our tertiary center serving a population of 6.2 million. Imaging Frequency：\u0026nbsp;Patients in our cohort underwent more frequent surveillance scans. This may reflect institutional protocols that emphasize close monitoring or the limited availability of alternative modalities, such as ultrasound surveillance. Technological Factors：\u0026nbsp;During 2020-2022, our institution utilized predominantly conventional reconstruction algorithms. Implementation of iterative reconstruction (2023-2024) reduced per-scan doses by approximately 28%, contributing to the declining trend in anual cumulative exposures observed in our temporal analysis. Protocol Standardization：\u0026nbsp;Absence of universally adopted pediatric trauma CT protocols in China[21]may result in greater inter-institutional variability compared to regions with established guidelines (e.g.ACR Appropriateness Criteria). Importantly, despite higher absolute exposures, our LAR estimates align closely with BEIR VII projections and recent Korean data from Han et al.[41], suggesting consistent dose-risk relationships across Asian populations. This validates the applicability of BEIR VII models to Asian pediatric cohorts and highlights the urgent need for dose-optimization interventions.\u003c/p\u003e\n\u003cp\u003ePrevious reports also indicate that radiation doses among patients treated in non-pediatric trauma centers are approximately twice those in specialized centers[44], and substantial inter-institutional variation persists[43]. Our study did not differentiate between hospital types; thus, future research should investigate the contribution of protocol heterogeneity, equipment calibration, and staff training to dose variation.\u003c/p\u003e\n\u003cp\u003eThe LAR estimates derived from the BEIR VII model were consistent with prior research. For example, a 2025 U.S. projection estimated approximately 103,000 future cancer cases attributable to CT imaging annually, including pediatric cases[45]. In our cohort, the additional LAR was approximately 0.5 per 10,000 person\u0026middot;Gy (solid cancers: 49.65 per 10,000 person\u0026middot;Gy; leukemia: 3.56 per 10,000 person\u0026middot;Gy), corresponding to an estimated lifetime cancer incidence of 2,047 per 100,000 males and 2,452 per 100,000 females an overall increase of 0.56%. Compared with Raelson et al.[46] (male: 890.6 per 100,000; female: 1,222.5 per 100,000), our risk estimates are higher, likely due to the increased frequency of repeat imaging in polytrauma patients. The UNSCEAR 2020/2021 report similarly emphasized elevated LAR in children even under low-dose CT conditions, corroborating our findings[17].\u003c/p\u003e\n\u003cp\u003eImplementing the \u0026ldquo;As Low As Reasonably Achievable\u0026rdquo; (ALARA) principle in pediatric polytrauma imaging presents unique challenges, but our findings provide actionable guidance. High-risk subgroups (e.g., children \u0026lt;5 years or with ISS \u0026ge;25) should be prioritized for dose optimization given their disproportionate LAR elevation. Current guidelines recommend against routine whole-body CT in high ISS cases, favoring selective imaging to avoid unnecessary exposure[47,48]. The key challenge lies in balancing the urgent need for rapid diagnosis such as identifying abdominal hemorrhage within minutes with dose minimization. Standard CT protocols may inadvertently lead to overexposure, particularly when referring institutions fail to adhere to ALARA principles[42]. Technological solutions include automated exposure control (AEC) and low tube voltage (80\u0026ndash;100 kVp) protocols, which have been shown to reduce doses by 30\u0026ndash;40% without compromising diagnostic quality[49,50]. Tube current modulation (mA modulation) dynamically adjusts exposure to patient size and remains among the most effective dose reduction techniques in pediatric trauma CT, achieving 20\u0026ndash;50% reductions in ED[51].\u0026nbsp;Iterative reconstruction algorithms (e.g., ASiR, iDose) further enhance image quality while maintaining \u0026gt;95% diagnostic accuracy in head and abdominal imaging[52].\u003c/p\u003e\n\u003cp\u003eAlternative imaging modalities play an important complementary role. Ultrasound, particularly the FAST (Focused Assessment with Sonography in Trauma) protocol, achieves 85\u0026ndash;95% sensitivity for abdominal injuries in stable patients but is limited by operator dependence and interference from bowel gas or bone[53]. MRI, while radiation-free and effective for neurological follow-up, is constrained by prolonged scan duration (20\u0026ndash;60 minutes vs. ~5 minutes for CT), limited availability, challenges in vital sign monitoring, and the frequent need for sedation in children[54]. For stable patients, prioritizing ultrasound or MRI over repeat CT can substantially reduce exposure; in high-ISS cohorts, hybrid strategies combining initial CT with ultrasound follow-up may reduce cumulative ED by up to 40%[55].\u003c/p\u003e\n\u003cp\u003eThe novelty of this study lies in its focus on pediatric polytrauma a population often underrepresented in radiation risk research. By systematically analyzing scan regions, ISS, cranial injury status, and repeat imaging, we identified key predictors of increased exposure and LAR, thereby offering practical insights for clinical decision-making. In contrast to Western cohorts such as EPI-CT, this study fills an epidemiologic gap in China by accounting for regional anthropometric characteristics and generating locally relevant data. Integrating BEIR VII modeling with clinical parameters provides a framework for developing national guidelines. It paves the way for artificial intelligence\u0026ndash;assisted dose prediction models (e.g., PyTorch-based pretrained imaging networks) to enhance radiation protection.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations warranting careful consideration: First, the retrospective single-center design may introduce incomplete documentation or selection bias, potentially underestimating radiation exposure in the most severely injured patients who died at the trauma scene or within the first hour (estimated 6-8% of severe polytrauma cases). Our tertiary referral center primarily serves higher-severity cases, which limits generalizability to community trauma centers. Furthermore, radiation doses and protocols may vary substantially across Chinese institutions, particularly between urban tertiary facilities and rural hospitals equipped with older-generation scanners. Second, we used population-based conversion coefficients rather than patient-specific Monte Carlo simulations. While sensitivity analyses confirmed robustness (\u0026plusmn;15% variation), individual-level dosimetry could provide \u0026plusmn;20% uncertainty in ED estimates due to variations in patient size, positioning, and anatomical differences[56,57]. Future studies employing personalized Monte Carlo dosimetry would enhance precision. The dose metrics used (DLP and ED) cannot precisely represent organ-specific absorbed doses, which are more directly linked to cancer risk (e.g., bone marrow exposure and leukemia incidence)[58]. Third, the BEIR VII model\u0026rsquo;s linear no-threshold (LNT) assumption for lifetime attributable risk (LAR) calculations may overestimate risks at low doses (\u0026lt;100 mSv), a topic of ongoing debate. These assumptions include lifetime follow-up without accounting for competing mortality risks from severe trauma and constant baseline cancer incidence rates, potentially overestimating absolute cancer burdens in high-mortality trauma populations. Nonetheless, they provide conservative, clinically useful estimates consistent with ICRP and BEIR VII recommendations[59]. Fourth, while our sample size (n=394) met primary analytical goals, it may lack statistical power to detect smaller effect sizes or variable interactions, potentially underestimating subtle predictors of radiation risk.\u003c/p\u003e\n\u003cp\u003eProspective multicenter studies incorporating patient-specific Monte Carlo dosimetry, long-term cancer surveillance (\u0026ge;20 years), dose-reduction intervention trials, and economic analyses of optimization strategies are needed to validate these findings and establish evidence-based regional imaging guidelines.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis four-year cohort study of 394 pediatric polytrauma patients identifies a substantial cumulative radiation burden (median CED 36.8 mSv, with 10.2% exceeding 50 mSv) and reveals a \"gender-risk paradox\": while male patients receive higher scanning frequencies and cumulative doses, female patients face a significantly higher lifetime attributable risk (LAR) for cancer (0.0021 vs. 0.0017, p=0.04) due to the heightened radiosensitivity of organs such as the breast and thyroid. These findings advocate for a shift toward gender-sensitive radiation protection, prioritizing dose-optimization technologies and non-ionizing alternatives for female and young pediatric patients to balance diagnostic necessity with long-term biological safety.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Ningbo No.2 Hospital (Approval No.: SL-NBEY-KY-2026-030-01). The approval was granted on September 28, 2025, for the research project titled \u0026quot;Cumulative Radiation Exposure and Cancer Risk in Pediatric Polytrauma: A Four-Year CT Utilization Analysis\u0026quot;. The study was conducted in accordance with the 1964 Helsinki Declaration and its later amendments. As this research involved a retrospective analysis of anonymized historical data, the requirement for informed consent was waived by the Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to hospital policy and patient privacy protection. However, the anonymized datasets are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Ningbo Key Laboratory of Digital Imaging and Medical-Engineering Interdisciplinarity；\u0026nbsp;Project of National Key Clinical Specialty (Department of Medical Imaging,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo. 2024017)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJuan Chen (JC): Conceptualization, Methodology, Software (Python/Pandas), and Writing - original draft. Jianjun Zheng (JJZ): Supervision, Project administration, Funding acquisition, and Writing - review \u0026amp; editing. Qi Dai (QD) and Jingfeng Zhang (JFZ): Data curation (PACS/DICOM extraction) and Investigation. Qun Zhang (QZ) and Yong Wang (YW): Formal analysis (using R software) and Validation. GR and SWQ were responsible for data collection and clinical record retrieval. Han Zhang (HZ) and Shuoyi Qian (SQ): Methodology and final manuscript review. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCiorba MC, Maegele M. Polytrauma in Children\u0026mdash;Epidemiology, Acute Diagnostic Evaluation, and Treatment. Dtsch \u0026Auml;rztebl int. 2024;121:291\u0026ndash;7. https://doi.org/10.3238/arztebl.m2024.0036\u003c/li\u003e\n \u003cli\u003eLindner AK, Luger AK, Fritz J, St\u0026auml;blein J, Radmayr C, Aigner F, et al. Do we need repeated CT imaging in uncomplicated blunt renal injuries? Experiences of a high-volume urological trauma centre. 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Eur Radiol. 2020;30:1825\u0026ndash;7. https://doi.org/10.1007/s00330-019-06625-7\u003c/li\u003e\n \u003cli\u003eDeak PD, Smal Y, Kalender WA. Multisection CT Protocols: Sex- and Age-specific Conversion Factors Used to Determine Effective Dose from Dose-Length Product. Radiology. 2010;257:158\u0026ndash;66. https://doi.org/10.1148/radiol.10100047\u003c/li\u003e\n \u003cli\u003eOhene-Botwe B, Schandorf C, Inkoom S, Faanu A. Estimation of organ-specific cancer and mortality risks associated with common indication-specific CT examinations of the abdominopelvic region. J Med Imaging Radiat Sci. Elsevier; 2023;54:135\u0026ndash;44. https://doi.org/10.1016/j.jmir.2022.12.003\u003c/li\u003e\n \u003cli\u003eUnited Nations, editor. Sources and effects of ionizing radiation: United Nations Scientific Committee on the Effects of Atomic Radiation: UNSCEAR 2000 report to the General Assembly, with scientific annexes. New York: United Nations; 2000.\u003c/li\u003e\n \u003cli\u003eKanal KM, Butler PF, Chatfield MB, Wells J, Samei E, Simanowith M, et al. U.S. Diagnostic Reference Levels and Achievable Doses for 10 Pediatric CT Examinations. Radiology. 2022;302:164\u0026ndash;74. https://doi.org/10.1148/radiol.2021211241\u003c/li\u003e\n \u003cli\u003ePearce MS, Salotti JA, Little MP, McHugh K, Lee C, Kim KP, et al. Radiation exposure from CT scans in childhood and subsequent risk of leukaemia and brain tumours: a retrospective cohort study. Lancet. 2012;380:499\u0026ndash;505. https://doi.org/10.1016/S0140-6736(12)60815-0\u003c/li\u003e\n \u003cli\u003eZadeh PT, Mahmoudi F, Rezaeian A, Gholami M. Over-scanning in pediatric head CT: Prevalence, dosimetric impact, and associated cancer risks. Eur J Radiol [Internet]. 2026 [cited 2025 Nov 9];194. https://doi.org/10.1016/j.ejrad.2025.112471\u003c/li\u003e\n \u003cli\u003eHoward A, West RM, Iball G, Panteli M, Baskshi MS, Pandit H, et al. Should Radiation Exposure be an Issue of Concern in Children With Multiple Trauma? Annals of Surgery. 2022;275:596\u0026ndash;601. https://doi.org/10.1097/SLA.0000000000004204\u003c/li\u003e\n \u003cli\u003eExpert Panel on Pediatric Imaging, Ryan ME, Pruthi S, Desai NK, Falcone RA, Glenn OA, et al. ACR Appropriateness Criteria\u0026reg; Head Trauma-Child. J Am Coll Radiol. 2020;17:S125\u0026ndash;37. https://doi.org/10.1016/j.jacr.2020.01.026\u003c/li\u003e\n \u003cli\u003eAloufi KM. Estimation of lifetime attributable cancer risk from abdominal-pelvic pediatric CT procedures. J Radiat Res Appl Sci. Elsevier; 2025;18:101261. https://doi.org/10.1016/j.jrras.2024.101261\u003c/li\u003e\n \u003cli\u003eSmith-Bindman R, Chu PW, Azman Firdaus H, Stewart C, Malekhedayat M, Alber S, et al. Projected Lifetime Cancer Risks From Current Computed Tomography Imaging. JAMA Intern Med. 2025;185:710\u0026ndash;9. https://doi.org/10.1001/jamainternmed.2025.0505\u003c/li\u003e\n \u003cli\u003eHan S, Soh J, Nah S, Han K, Jung J-H, Park J, et al. Pediatric computed tomography scan and subsequent risk of malignancy: a nationwide population-based cohort study in Korea using National Cancer Institute dosimetry system for computed tomography (NCICT). BMC Med. 2025;23:355. https://doi.org/10.1186/s12916-025-04235-3\u003c/li\u003e\n \u003cli\u003eBrinkman AS, Gill KG, Leys CM, Gosain A. Computed Tomography-Related Radiation Exposure in Children Transferred to a Level 1 Pediatric Trauma Center. J Trauma Acute Care Surg. 2015;78:1134\u0026ndash;7. https://doi.org/10.1097/TA.0000000000000645\u003c/li\u003e\n \u003cli\u003eHarrieder A, Geyer L, K\u0026ouml;rner M, Deak Z, Wirth S, Reiser M, et al. [Evaluation of radiation dose in 64-row whole-body CT of multiple injured patients compared to 4-row CT]. R\u0026ouml;Fo - Fortschr auf Geb R\u0026ouml;ntgenstrahlen bildgeb Verfahr. 2012;184:443\u0026ndash;9. https://doi.org/10.1055/s-0031-1299099\u003c/li\u003e\n \u003cli\u003eInjured Children Receive Twice the Radiation Dose at Nonpediatric Trauma Centers Compared With Pediatric Trauma Centers. Journal of the American College of Radiology. Elsevier; 2018;15:58\u0026ndash;64. https://doi.org/10.1016/j.jacr.2017.06.035\u003c/li\u003e\n \u003cli\u003eProjected Lifetime Cancer Risks From Current Computed Tomography Imaging | Less is More | JAMA Internal Medicine | JAMA Network [Internet]. [cited 2025 Oct 8]. https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2832778. Accessed 8 Oct 2025\u003c/li\u003e\n \u003cli\u003eRaelson CA, Kanal KM, Vavilala MS, Rivara FP, Kim LJ, Stewart BK, et al. Radiation Dose and Excess Risk of Cancer in Children Undergoing Neuroangiography. American Journal of Roentgenology. American Roentgen Ray Society; 2009;193:1621\u0026ndash;8. https://doi.org/10.2214/AJR.09.2352\u003c/li\u003e\n \u003cli\u003eHan S, Soh J, Nah S, Han K, Jung J-H, Park J, et al. Pediatric computed tomography scan and subsequent risk of malignancy: a nationwide population-based cohort study in Korea using National Cancer Institute dosimetry system for computed tomography (NCICT). BMC Med. 2025;23:355. https://doi.org/10.1186/s12916-025-04235-3\u003c/li\u003e\n \u003cli\u003eMarin JR, Lyons TW, Claudius I, Fallat ME, Aquino M, Ruttan T, et al. Optimizing Advanced Imaging of the Pediatric Patient in the Emergency Department: Policy Statement. Pediatrics. 2024;154:e2024066854. https://doi.org/10.1542/peds.2024-066854\u003c/li\u003e\n \u003cli\u003ePadole A, Ali Khawaja RD, Kalra MK, Singh S. CT Radiation Dose and Iterative Reconstruction Techniques. Am J Roentgenol. American Roentgen Ray Society; 2015;204:W384\u0026ndash;92. https://doi.org/10.2214/AJR.14.13241\u003c/li\u003e\n \u003cli\u003eRisk of hematological malignancies from CT radiation exposure in children, adolescents and young adults | Nature Medicine [Internet]. [cited 2025 Oct 11]. https://www.nature.com/articles/s41591-023-02620-0. Accessed 11 Oct 2025\u003c/li\u003e\n \u003cli\u003ePapadakis AE, Damilakis J. Automatic Tube Current Modulation and Tube Voltage Selection in Pediatric Computed Tomography. Invest Radiol. 2019;54:265\u0026ndash;72. https://doi.org/10.1097/RLI.0000000000000537\u003c/li\u003e\n \u003cli\u003eDeep Learning\u0026ndash;based Reconstruction for Lower-Dose Pediatric CT: Technical Principles, Image Characteristics, and Clinical Implementations | RadioGraphics [Internet]. [cited 2025 Oct 9]. https://pubs.rsna.org/doi/full/10.1148/rg.2021210105. Accessed 9 Oct 2025\u003c/li\u003e\n \u003cli\u003eLi X, Liu X, Shi M, Zhang M, Wang P, Zhang X. The emerging application of ultrasound technology in pediatric bone fractures: Clinical application, related issues and development prospect. Pediatr Discov. 2024;2:e69. https://doi.org/10.1002/pdi3.69\u003c/li\u003e\n \u003cli\u003eThippeswamy PB, Rajasekaran RB. Imaging in polytrauma \u0026ndash; Principles and current concepts. J Clin Orthop Trauma. 2021;16:106\u0026ndash;13. https://doi.org/10.1016/j.jcot.2020.12.006\u003c/li\u003e\n \u003cli\u003eAziz MM, Onyejesi C, Pyala R, Alattar O, Abdul AA, Alagarswamy K, et al. Reducing radiation exposure in pediatric CT imaging: strategies and alternatives in emergency medicine\u0026mdash;a narrative review. J Emerg Crit Care Med. AME Publishing Company; 2025;9:12\u0026ndash;12. https://doi.org/10.21037/jeccm-24-102\u003c/li\u003e\n \u003cli\u003eMassoumi R, Wertz J, Duong T, Tseng C-H, Jen HC-H. Variation in pediatric cervical spine imaging across trauma centers-A cause for concern? J Trauma Acute Care Surg. 2021;91:641\u0026ndash;8. https://doi.org/10.1097/TA.0000000000003344\u003c/li\u003e\n \u003cli\u003eChu PW, Kofler C, Mahendra M, Wang Y, Chu CA, Stewart C, et al. Dose length product to effective dose coefficients in children. Pediatr Radiol. 2023;53:1659\u0026ndash;68. https://doi.org/10.1007/s00247-023-05638-1\u003c/li\u003e\n \u003cli\u003eJansen JTM, Shrimpton PC. Development of Monte Carlo simulations to provide scanner-specific organ dose coefficients for contemporary CT. Phys Med Biol. 2016;61:5356\u0026ndash;77. https://doi.org/10.1088/0031-9155/61/14/5356\u003c/li\u003e\n \u003cli\u003eO\u0026rsquo;Connor MK. Risk of low-dose radiation and the BEIR VII report: A critical review of what it does and doesn\u0026rsquo;t say. Physica Med. 2017;43:153\u0026ndash;8. https://doi.org/10.1016/j.ejmp.2017.07.016\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Computed tomography, cumulative exposure, pediatric polytrauma, radiation risk, BEIR VII model","lastPublishedDoi":"10.21203/rs.3.rs-8784170/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8784170/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eComputed tomography remains the cornerstone of rapid diagnosis and surgical planning in pediatric polytrauma. Repeated examinations, however, result in substantial cumulative effective doses that significantly increase lifetime radiation-induced cancer risk owing to heightened tissue radiosensitivity and prolonged latency periods in children. Despite increasing recognition of this concern, robust data on cumulative radiation exposure and associated oncogenic risk in Asian pediatric populations remain limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We performed a retrospective cohort study of 394 pediatric patients with polytrauma (Injury Severity Score ≥16; median age 6.58 years) who underwent 1817 computed tomography examinations at a tertiary pediatric trauma center in Zhejiang Province, China, from January 2020 to December 2024. Effective dose was derived from the recorded dose-length product using age- and region-specific conversion coefficients based on ICRP Publication 103. Lifetime attributable risks of radiation-induced solid cancer and leukemia were calculated with the BEIR VII Phase 2 risk models. Multivariable regression analysis was used to identify independent predictors of high cumulative exposure and elevated oncogenic risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The 394 patients underwent a mean of 4.61 CT examinations (range 1–37), with 76.3% receiving repeated scans. While males comprised the majority of the cohort (69.5%) and received higher cumulative doses, female patients exhibited a significantly higher mean lifetime attributable risk (LAR) than males (0.0021 vs. 0.0017, p=0.04). Median cumulative effective dose (CED) was 36.8 mSv (IQR 13.9–93.9 mSv); 10.2% of patients exceeded 50 mSv. Injury Severity Score (ISS) ≥25 was an independent predictor of high exposure (RR 1.09; 95% CI 1.02–1.16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003ePediatric polytrauma patients in emergency settings face substantial radiation burdens from recurrent CT, with notable oncogenic risks particularly in children under 5 years. Implementation of selective imaging protocols, automated tube current modulation, and iterative reconstruction algorithms could reduce exposure by up to 20%-40% without compromising diagnostic accuracy. Surgical protocols should prioritize dose optimization and non-ionizing alternatives to balance efficacy and long-term health.\u003c/p\u003e","manuscriptTitle":"Gender-Specific Radiation Burden and Cancer Risk Assessment in Pediatric Polytrauma: A Four-Year Longitudinal CT Utilization Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 16:16:52","doi":"10.21203/rs.3.rs-8784170/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-07T08:09:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T19:01:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143270959906019105749852549132067576998","date":"2026-05-06T08:50:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253792284513351182145875286507782947202","date":"2026-04-29T16:21:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-27T23:58:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86994248764089900485823820073157641543","date":"2026-04-27T23:49:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85809547252263634963896217196561464387","date":"2026-04-27T19:37:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-23T16:47:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T08:40:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-05T08:38:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Imaging","date":"2026-02-04T08:39:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c1609a39-5daf-4218-bd8a-90bfd50ff138","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-07T08:09:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T19:01:43+00:00","index":41,"fulltext":""},{"type":"reviewerAgreed","content":"143270959906019105749852549132067576998","date":"2026-05-06T08:50:00+00:00","index":40,"fulltext":""},{"type":"reviewerAgreed","content":"253792284513351182145875286507782947202","date":"2026-04-29T16:21:25+00:00","index":36,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T16:16:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 16:16:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8784170","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8784170","identity":"rs-8784170","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}