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Structured reporting contributes to the completeness of radiology reports and improves quality. Both the content and the structure are essential for successful implementation of structured reporting. Contextual structured reporting is tailored to a specific scenario and can contain information retrieved from the context. Critical findings detected by imaging need urgent communication to the referring physician. According to guidelines, the occurrence of this communication should be documented in the radiology reports and should contain when, to whom and how was communicated. In free-text reporting, one or more of these required items might be omitted. We developed a contextual structured reporting template to ensure complete documentation of the communication of critical findings. The WHEN and HOW items were included automatically, and the insertion of the WHO-item was facilitated by the template. A pre- and post-implementation study demonstrated a substantial improvement in guideline adherence. The template usage improved in the long-term post-implementation study compared with the short-term results. The two most often occurring categories of critical findings are "infection / inflammation" and "oncology", corresponding to the a large part of urgency level 2 (to be reported within 6 h) and level 3 (to be reported within 6 days), respectively. We conclude that contextual structured reporting is feasible for required elements in radiology reporting and for automated insertion of context-dependent data. Contextual structured reporting improves guideline adherence for communication of critical findings. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: To retrospectively evaluate whether baseline nodule density or changes in density or nodule features could be used to discriminate between benign and malignant solid indeterminate nodules. Materials and methods: Solid indeterminate nodules between 50 and 500mm3 (4.6–9.8mm) were assessed at 3 and 12 months after baseline lung cancer screening (NELSON study). Nodules were classified based on morphology (spherical or non-spherical), shape (round, polygonal or irregular) and margin (smooth, lobulated, spiculated or irregular). The mean CT density of the nodule was automatically generated in Hounsfield units (HU) by the Lungcare© software. Results: From April 2004 to July 2006, 7310 participants underwent baseline screening. In 312 participants 372 solid purely intra-parenchymal nodules were found. Of them, 16 (4%) were malignant. Benign nodules were 82.8mm3 (5.4mm) and malignant nodules 274.5mm3 (8.1mm) (p =0.000). Baseline CT density for benign nodules was 42.7HU and for malignant nodules −2.2HU (p =ns). The median change in density for benign nodules was −0.1HU and for malignant nodules 12.8HU (p <0.05). Compared to benign nodules, malignant nodules were more often non-spherical, irregular, lobulated or spiculated at baseline, 3-month and 1-year follow-up (p <0.0001). In the majority of the benign and malignant nodules there was no change in morphology, shape and margin during 1 year of follow-up (p =ns). Conclusion: Baseline nodule density and changes in nodule features cannot be used to discriminate between benign and malignant solid indeterminate pulmonary nodules, but an increase in density is suggestive for malignancy and requires a shorter follow-up or a biopsy. [Copyright &y& Elsevier]

Purpose: To evaluate prospectively the value of size, shape, margin and density in discriminating between benign and malignant CT screen detected solid non-calcified pulmonary nodules. Material and Methods: This study was institutional review board approved. For this study 405 participants of the NELSON lung cancer screening trial with 469 indeterminate or potentially malignant solid pulmonary nodules (>50mm3) were selected. The nodules were classified based on size, shape (round, polygonal, irregular) and margin (smooth, lobulated, spiculated). Mean nodule density and nodule volume were automatically generated by software. Analyses were performed by univariate and multivariate logistic regression. Results were presented as likelihood ratios (LR) with 95% confidence intervals (CI). Receiver operating characteristic analysis was performed for mean density as predictor for lung cancer. Results: Of the 469 nodules, 387 (83%) were between 50 and 500mm3, 82 (17%) >500mm3, 59 (13%) malignant, 410 (87%) benign. The median size of the nodules was 103mm3 (range 50-5486mm3). In multivariate analysis lobulated nodules had LR of 11 compared to smooth; spiculated nodules a LR of 7 compared to smooth; irregular nodules a LR of 6 compared to round and polygonal; volume a LR of 3. The mean nodule CT density did not predict the presence of lung cancer (AUC 0.37, 95% CI 0.32-0.43). Conclusion: In solid non-calcified nodules larger than 50mm3, size and to a lesser extent a lobulated or spiculated margin and irregular shape increased the likelihood that a nodule was malignant. Nodule density had no discriminative power. [ABSTRACT FROM AUTHOR]

Abstract: Purpose: To evaluate prospectively the value of size, shape, margin and density in discriminating between benign and malignant CT screen detected solid non-calcified pulmonary nodules. Material and methods: This study was institutional review board approved. For this study 405 participants of the NELSON lung cancer screening trial with 469 indeterminate or potentially malignant solid pulmonary nodules (>50mm3) were selected. The nodules were classified based on size, shape (round, polygonal, irregular) and margin (smooth, lobulated, spiculated). Mean nodule density and nodule volume were automatically generated by software. Analyses were performed by univariate and multivariate logistic regression. Results were presented as likelihood ratios (LR) with 95% confidence intervals (CI). Receiver operating characteristic analysis was performed for mean density as predictor for lung cancer. Results: Of the 469 nodules, 387 (83%) were between 50 and 500mm3, 82 (17%) >500mm3, 59 (13%) malignant, 410 (87%) benign. The median size of the nodules was 103mm3 (range 50–5486mm3). In multivariate analysis lobulated nodules had LR of 11 compared to smooth; spiculated nodules a LR of 7 compared to smooth; irregular nodules a LR of 6 compared to round and polygonal; volume a LR of 3. The mean nodule CT density did not predict the presence of lung cancer (AUC 0.37, 95% CI 0.32–0.43). Conclusion: In solid non-calcified nodules larger than 50mm3, size and to a lesser extent a lobulated or spiculated margin and irregular shape increased the likelihood that a nodule was malignant. Nodule density had no discriminative power. [Copyright &y& Elsevier]