Value of Clinical Information on Radiology Reports in Oncological Imaging.
Felix SchönRebecca SinzigFelix WaltherChristoph Georg RadosaHeiner NebelungMaria Eberlein-GonskaRalf-Thorsten HoffmannJens-Peter KühnSophia Freya Ulrike BlumPublished in: Diagnostics (Basel, Switzerland) (2022)
Radiological reporting errors have a direct negative impact on patient treatment. The purpose of this study was to investigate the contribution of clinical information (CI) in radiological reporting of oncological imaging and the dependence on the radiologists' experience level (EL). Sixty-four patients with several types of carcinomas and twenty patients without tumors were enrolled. Computed tomography datasets acquired in primary or follow-up staging were independently analyzed by three radiologists (R) with different EL (R1: 15 years; R2: 10 years, R3: 1 year). Reading was initially performed without and 3 months later with CI. Overall, diagnostic accuracy and sensitivity for primary tumor detection increased significantly when receiving CI from 77% to 87%; p = 0.01 and 73% to 83%; p = 0.01, respectively. All radiologists benefitted from CI; R1: 85% vs. 92%, p = 0.15; R2: 77% vs. 83%, p = 0.33; R3: 70% vs. 86%, p = 0.02. Overall, diagnostic accuracy and sensitivity for detecting lymphogenous metastases increased from 80% to 85% ( p = 0.13) and 42% to 56% ( p = 0.13), for detection of hematogenous metastases from 85% to 86% ( p = 0.61) and 46% to 60% ( p = 0.15). Specificity remained stable (>90%). Thus, CI in oncological imaging seems to be essential for correct radiological reporting, especially for residents, and should be available for the radiologist whenever possible.
Keyphrases
- adverse drug
- artificial intelligence
- high resolution
- computed tomography
- rectal cancer
- radical prostatectomy
- robot assisted
- end stage renal disease
- ejection fraction
- lymph node
- magnetic resonance imaging
- prostate cancer
- case report
- magnetic resonance
- healthcare
- high grade
- real time pcr
- deep learning
- quality improvement
- social media
- image quality
- quantum dots
- drug induced