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Background: Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning., Methods: A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS)., Results: Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %-89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927)., Conclusion: Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: To compare the demographic characteristics of active physicians, trainees, medical school clinical sciences faculty, and department chairs in radiology with those in other medical specialties., Methods: An analysis was conducted using publicly available deidentified aggregate data from the Association of American Medical Colleges (AAMC). Our data collection included information from the 2022 AAMC Physician Specialty Data Report, the 2022 AAMC Report on Residents, and the 2022 AAMC Faculty Roster. We examined factors such as graduation country, gender, and self-identified race/ethnicity. MedCalc software was used for the analyses., Results: Compared to other specialties, active radiologists exhibited a significantly lower percentage of females, International Medical Graduates (IMGs), individuals of American Indian/Alaska Native (AIAN) descent, Black/African-American individuals, and individuals of Hispanic/Latino/Spanish origin. Conversely, the proportion of White active radiologists was higher. Among radiology trainees, there was a notably lower percentages of females, IMGs, individuals of Black/African-American descent, and individuals of Hispanic/Latino/Spanish origin, while the percentage of Asians was significantly higher. Furthermore, medical school radiology faculty showed a significant lower proportion of females, Black/African-American individuals, Hispanic/Latino/Spanish individuals, and individuals categorized under the white race/ethnicity, with Asians having a higher representation. As radiology department chairs, Asians were noted at significantly lower percentages compared to their proportions among medical school radiology faculty, while Black/African-American individuals were observed at significantly higher percentages in the same comparison., Conclusion: This study revealed a notable underrepresentation of females, individuals of Black/African-American descent, and those of Hispanic/Latino/Spanish origin among active radiologists, radiology trainees, and medical school radiology faculty when compared to their counterparts in other medical specialties. Given these findings, further investigation into the underlying causes of these disparities is warranted., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose: To quantitatively synthesize and report the frequency and category of incidental findings on Computed Tomography (CT) scans in pediatric trauma patients., Methods: A thorough literature search was carried out in PubMed, Scopus, and Web of Science databases until March 6, 2023, in adherence to the preferred reporting items for systematic review and meta-analyses (PRISMA) guidelines. Studies describing incidental findings on CT scans in trauma patients ≤21 years were included. Incidental findings were grouped into three categories: Category 1 (requiring immediate or urgent evaluation or treatment), Category 2 (likely benign but which may require outpatient follow-up), and Category 3 (benign anatomic variants or pathologic findings that do not require follow-up or intervention)., Results: Seven studies were included in this study, which revealed a combined rate of 27.10 % of incidental findings with notable heterogeneity among the studies. Aggregated frequencies were 10.15 % for Category 1, 32.18 % for Category 2 and 51.44 % for Category 3. Subgroup meta-analysis on abdominal CT scans showed a higher pooled incidence of incidental findings at 47.17 %, but with lower heterogeneity than the general meta-analysis., Conclusion: The study underscores the prevalence of incidental findings in pediatric trauma patients undergoing CT scans. The categorization of these findings provides useful information for clinicians in determining appropriate follow-up and management strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)