Risk Factors for the Occurrence of Interpretative Errors in Radiology

Background. The results of radiological examinations largely determine the subsequent treatment strategy. Errors occur in 20–40% of cases and can have serious negative consequences for patients. It is crucial to understand the causes of these errors, which may impact the possibility of reducing their incidence. Objective: to identify the main risk factors for interpretative errors in radiology when analyzing radiation studies.

Material and methods. A retrospective analysis of 89 imaging studies with interpretative errors was conducted. The largest share had computed tomography (73%), followed by magnetic resonance imaging (15.7%), and radiography (11.3%). Errors were classified by prognostic factors from Category 1 (minimal changes) to Category 5 (critical). The time from initial examination to erroneous conclusions ranged from 0 to 2162 days, with an average of 80.6 days.

Results. The most common errors were detected in studies of brain (33.7%), chest (24.7%), and abdomen (15.7%). Incorrect interpretations included neoplastic lesions (39.3%), inflammatory changes (28.1%), and vascular pathology (22.5%). The most frequently observed errors were those with prognostic Category 4, requiring changes in treatment tactics (69.7%). A number of risk factors for the occurrence of interpretative errors were identified: the leading one was “incomplete analysis of semiotic features” (42.7%), followed by “anamnesis” (19.1%) and “insufficient knowledge” (12.4%). However, in 18% of cases, no risk factors were identified.

Conclusion. Identification of risk factors for the occurrence of diagnostic errors in the analysis of radiological studies is necessary for the subsequent development of preventive measures that should reduce the incidence of interpretative errors and improve the quality of radiological diagnostics in general.

1. Pesapane F, Gnocchi G, Quarrella C, et al. Errors in radiology: a standard review. J Clin Med. 2024; 13(15): 4306. https://doi.org/10.3390/jcm13154306.

2. Nechaev VA, Vasiliev AYu. Approaches to the classification of errors in radiology: a review. Diagnostic Radiology and Radiotherapy. 2024; 15(2): 19–24 (in Russ). https://doi.org/10.22328/2079-5343-2024-15-2-19-24.

3. Onder O, Yarasir Y, Azizova A, et al. Errors, discrepancies and underlying bias in radiology with case examples: a pictorial review. Insights Imaging. 2021; 12(1): 51. https://doi.org/10.1186/s13244-021-00986-8.

4. Ivanovic V, Broadhead K, Beck R, et al. Factors associated with neuroradiologic diagnostic errors at a large tertiarycare academic medical center: a case-control study. AJR Am J Roentgenol. 2023; 221(3): 355–62. https://doi.org/10.2214/AJR.22.28925.

5. Zhang L, Wen X, Li JW, et al. Diagnostic error and bias in the department of radiology: a pictorial essay. Insights Imaging. 2023; 14(1): 163. https://doi.org/10.1186/s13244-023-01521-7.

6. Patel AG, Pizzitola VJ, Johnson CD, et al. Radiologists make more errors interpreting off-hours body CT studies during overnight assignments as compared with daytime assignments. Radiology. 2020; 297(2): 374–9. https://doi.org/10.1148/radiol.2020201558.

7. Lee CS, Nagy PG, Weaver SJ, Newman-Toker DE. Cognitive and system factors contributing to diagnostic errors in radiology. AJR Am J Roentgenol. 2013; 201(3): 611–7. https://doi.org/10.2214/AJR.12.10375.

8. McBee M, McBee L. The importance of providing clinical history for radiology studies in the urgent care setting. J Urgent Care Med. 2024; 18(3): 17–9.

9. Vasil’ev A.Yu., Nechaev V.A. Evaluation of the influence of clinical information on the interpretation of digital chest radiographs. Radiology – Practice. 2022; 3: 67–73 (in Russ). https://doi.org/10.52560/2713-0118-2022-3-67-73.

10. Nechaev VA, Vasiliev AYu. Risk factors for development of the perceptual errors in radiology. Vestnik SurGU. Meditsina. 2024; 17(4): 14–22 (in Russ). https://doi.org/10.35266/2949-3447-2024-4-2.

11. Lange MB, Petersen LJ, Lausen M, et al. Influence of prior imaging information on diagnostic accuracy for focal skeletal processes – a retrospective analysis of the consistency between biopsy-verified imaging diagnoses. Diagnostics. 2022; 12(7): 1735. https://doi.org/10.3390/diagnostics12071735.