Evaluation of Normal Anatomy and Anatomical Variations of Foramen Tranversarium by Multidetector Computated Tomography

Background: The foramen transversarium (FT) is a critical anatomical structure of the cervical vertebrae, transmitting the vertebral artery, vein, and sympathetic nerve fibers. Variations in its morphology can have significant clinical implications, particularly in surgical and radiological contexts. This study aimed to assess the normal anatomy and anatomical variations of the FT using multidetector computed tomography (MDCT). Material and Methods: This cross-sectional observational study was conducted over one year at Indira Gandhi Institute of Medical Sciences (IGIMS), Patna. A total of 100 adult patients aged 18–70 years underwent cervical spine MDCT for various clinical indications. The images were reviewed independently by two radiologists to classify FT morphology at cervical levels C1–C7. FT variations were documented based on symmetry, accessory foramina, and dimensions. Descriptive statistics were applied to assess prevalence, while chi-square tests and Cohen’s Kappa analysis evaluated demographic correlations and interobserver agreement. Findings Normal bilateral FT was observed in 68% of cases, unilateral variations in 19%, and bilateral variations in 8%. Accessory FT was detected in 5%, predominantly at C6–C7. The mean FT diameter decreased progressively from C1 (5.8 ± 0.9 mm) to C7 (3.8 ± 0.6 mm). No significant correlations were found between FT variations and demographic variables (p > 0.05). Interobserver agreement analysis yielded a Cohen’s Kappa score of -0.071, indicating poor agreement and highlighting the subjectivity in FT classification. FT variations are relatively common, especially in the lower cervical spine, with potential clinical and surgical implications. The study underscores the importance of standardized radiological assessment criteria and the need for AI-assisted diagnostic tools to improve accuracy and interobserver reliability. Future research should explore larger population studies and machine learning-based FT classification approaches.