The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

Dir Journal Diagnostic and Interventional Radiology

Diagnostic and Interventional Radiology

Rationale: Reliable outcome prediction in patients with fibrotic lung disease using baseline high-resolution computed tomography (HRCT) data remains challenging. Objectives: To evaluate the prognostic accuracy of a deep learning algorithm (SOFIA [Systematic Objective Fibrotic Imaging Analysis Algorithm]), trained and validated in the identification of usual interstitial pneumonia (UIP)-like features on HRCT (UIP probability), in a large cohort of well-characterized patients with progressive fibrotic lung disease drawn from a national registry. Methods: SOFIA and radiologist UIP probabilities were converted to Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED)-based UIP probability categories (UIP not included in the differential, 0–4%; low probability of UIP, 5–29%; intermediate probability of UIP, 30–69%; high probability of UIP, 70–94%; and pathognomonic for UIP, 95–100%), and their prognostic utility was assessed using Cox proportional hazards modeling. Measurements and Main Results: In multivariable analysis adjusting for age, sex, guideline-based radiologic diagnosis, anddisease severity (using total interstitial lung disease [ILD] extent on HRCT, percent predicted FVC, Dl_CO, or the composite physiologic index), only SOFIA UIP probability PIOPED categories predicted survival. SOFIA-PIOPED UIP probability categories remained prognostically significant in patients considered indeterminate (n = 83) by expert radiologist consensus (hazard ratio, 1.73; P < 0.0001; 95% confidence interval, 1.40–2.14). In patients undergoing surgical lung biopsy (n = 86), after adjusting for guideline-based histologic pattern and total ILD extent on HRCT, only SOFIA-PIOPED probabilities were predictive of mortality (hazard ratio, 1.75; P < 0.0001; 95% confidence interval, 1.37–2.25). Conclusions: Deep learning–based UIP probability on HRCT provides enhanced outcome prediction in patients with progressive fibrotic lung disease when compared with expert radiologist evaluation or guideline-based histologic pattern. In principle, this tool may be useful in multidisciplinary characterization of fibrotic lung disease. The utility of this technology as a decision support system when ILD expertise is unavailable requires further investigation.

OBJECTIVE. The objectives of our study were to assess the prevalence of pulmonary embolism incidentally detected on routine MDCT of the chest and to determine whether the use of wide window settings can improve detection of unsuspected pulmonary embolism.MATERIALS AND METHODS. A retrospective review of routine contrast-enhanced MDCT scans of the chest obtained in 589 patients was undertaken. CT angiograms obtained for suspected pulmonary embolism or thoracic aorta disease were not considered. Image evaluation was performed on a dedicated workstation during two separate review sessions using different window settings: standard, with a width of 400 H and level of 40 H; and wide, with a width of 600 H and level of 100–150 H. The quality of vascular enhancement was recorded.RESULTS. Eight examinations were excluded because of poor quality. Unsuspected pulmonary embolism was present in 20 (3.4%) of 581 patients with an inpatient prevalence of 4.0% (19/474) and outpatient prevalence of 0.9% (1/107). Fourteen pa...

Three-dimensional (3D) conformal radiation therapy (CRT) and stereotactic body radiation therapy (SBRT) are designed to deliver the maximum therapeutic radiation dose to the tumor, allowing improved local disease control, while minimizing irradiation of surrounding normal structures. The complex configuration of the multiple beams that deliver the radiation dose to the tumor in 3D CRT and SBRT produces patterns of lung injury that differ in location and extent from those seen after conventional radiation therapy. Radiation-induced changes in lung tissue after 3D CRT and SBRT occur within the radiation portals. The imaging appearance of irradiated tissues varies according to the time elapsed after the completion of therapy, with acute-phase changes of radiation pneumonitis represented by ground-glass opacities and consolidation and with late-phase changes of radiation fibrosis manifesting as volume loss, consolidation, and traction bronchiectasis. Knowledge of treatment timelines and radiation field locations, as well as familiarity with the full spectrum of possible radiation-induced lung injuries after 3D CRT and SBRT, is important to correctly interpret the abnormalities that may be seen at computed tomography (CT). Differential diagnoses in this context might include infections, lymphangitic carcinomatosis, local recurrence of malignancy, and radiation-induced tumors. The integration of morphologic information obtained at CT with metabolic information obtained at positron emission tomography is helpful in distinguishing radiation-induced parenchymal abnormalities from residual, recurrent, and new cancers. Thus, multimodality follow-up imaging may lead to substantial changes in disease management.