Radiation

Background: One method for the radiation therapy of rectal cancer is to set patients supine and treat them with volumetric modulated arc therapy (VMAT). The posterior skin dose is of concern due to undesirable bolusing from mounting surfaces the patient lays upon, namely the carbon fiber couch (CFC). The posterior skin dose may be mitigated by positioning the patient on top of a low-density material that separates the patient from the CFC. Purpose: Our objective was to determine the reduction in the posterior surface dose when a mattress or foam board is used to prop the patient away from the CFC. Materials and Methods: Three clinical rectal cancer patient VMAT plans were selected. A solid water phantom with optically stimulated luminescence dosimeters (OSLDs) placed at the posterior surface was mounted using three setups: directly on the CFC, with a mattress on the CFC, and with a 10 cm thick foam board on the CFC. The three VMAT plans were delivered to this phantom, with OSLDs measuring the posterior surface dose with each setup. In the treatment planning system (TPS), the CFC only, mattress, and foam board setups were simulated on the patient’s anatomy with posterior surface doses reported. Results: The OSLD measurements in the phantom showed that the mattress reduced the posterior surface dose on average by 1.3%, and the foam board reduced the dose by 8.3%. The TPS estimates demonstrated that, on average, the mattress reduced the surface dose by 15.8%, and the foam board reduced the dose by 33.0%. It is likely that the TPS had limitations accurately modeling the surface dose, so OSLD measurements were closer to clinical reality. Conclusions: The mattress does not reduce the posterior skin dose enough to warrant its use as a skin sparing device. The CFC produces a bolusing effect that can be reduced by separating the patient from the CFC with a 10 cm thick foam board.

Background: Interpretation of radiographs is prone to diagnostic errors. Artificial intelligence (AI) has shown promising results in fracture detection, although systematic evaluation of software updates remains limited. This study compares the diagnostic performance of two versions of an AI-based fracture detection software in hand and ankle radiographs and assesses the influence of AI output on diagnostic decisions. Methods: This retrospective diagnostic accuracy study included 193 hand and ankle examinations obtained during routine clinical practice at Lillebaelt Hospital, Denmark. Radiographs were analysed using two versions of the same AI software and compared with the diagnostic report as the reference standard. Diagnostic performance of both versions was assessed using diagnostic accuracy metrics. Exploratory subgroup analyses were conducted to further investigate the difference in performance. The influence of AI was evaluated by the proportion of reports revised after review of AI output. Results: The newest software version demonstrated higher diagnostic performance than the older one (accuracy 0.933 vs. 0.824; p < 0.001). Similar improvements were observed across patient subgroups. Excluding radiographs containing casts resulted in only minimal changes in performance (accuracy in version 2: 0.930 vs. 0.933). In 8 of 15 discordant cases, reporting radiographers revised the initial assessment upon reassessment. Conclusions: The newest version demonstrated higher overall diagnostic performance, indicating that software updates can enhance the accuracy of AI-assisted fracture detection. The proportion of revised assessments suggests that radiographers’ decisions may be influenced by AI output.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Pulmonary function tests (PFTs) and quantitative indices (QIs) of computed tomography (CT) are typically used to diagnose COPD. The purpose of this work was to determine the correlation of the vector divergence operator with PFTs and QIs in COPD patients and compare the divergence of normal lung function to that in COPD. Vector divergence is computed for 73 patients with four-dimensional CT scans retrospectively identified as normal (n = 37) and COPD (n = 36), which includes emphysema (n = 13). The divergence is the flux per unit volume at a point in a vector field and reflects the local lung tissue expansion when the data are taken during inspiration. The divergence measures are strongly correlated with both PFTs and QIs of COPD patients and therefore are a useful biomarker in analyzing regional lung function. In physical terms, the divergence shows that there is a significant difference in lung tissue expansion between normal subjects and patients with airflow obstruction as in emphysema and COPD. The divergence analysis also enables new images using color overlays to provide a functional measure (local expansion capability) to the anatomical CT image.