The influence of inspiratory effort and emphysema on pulmonary nodule volumetry reproducibility

Factors influencing the outcome of volumetry tools for pulmonary nodule analysis: a systematic review and attempted meta-analysis

Health systems worldwide are implementing lung cancer screening programmes to identify early-stage lung cancer and maximise patient survival. Volumetry is recommended for follow-up of pulmonary nodules and outperforms other measurement methods. However, volumetry is known to be influenced by multiple factors. The objectives of this systematic review (PROSPERO CRD42022370233) are to summarise the current knowledge regarding factors that influence volumetry tools used in the analysis of pulmonary nodules, assess for significant clinical impact, identify gaps in current knowledge and suggest future research. Five databases (Medline, Scopus, Journals@Ovid, Embase and Emcare) were searched on the 21st of September, 2022, and 137 original research studies were included, explicitly testing the potential impact of influencing factors on the outcome of volumetry tools. The summary of these studies is tabulated, and a narrative review is provided. A subset of studies (n = 16) reporting clinical significance were selected, and their results were combined, if appropriate, using meta-analysis. Factors with clinical significance include the segmentation algorithm, quality of the segmentation, slice thickness, the level of inspiration for solid nodules, and the reconstruction algorithm and kernel in subsolid nodules. Although there is a large body of evidence in this field, it is unclear how to apply the results from these studies in clinical practice as most studies do not test for clinical relevance. The meta-analysis did not improve our understanding due to the small number and heterogeneity of studies testing for clinical significance. CRITICAL RELEVANCE STATEMENT: Many studies have investigated the influencing factors of pulmonary nodule volumetry, but only 11% of these questioned their clinical relevance in their management. The heterogeneity among these studies presents a challenge in consolidating results and clinical application of the evidence. KEY POINTS: • Factors influencing the volumetry of pulmonary nodules have been extensively investigated. • Just 11% of studies test clinical significance (wrongly diagnosing growth). • Nodule size interacts with most other influencing factors (especially for smaller nodules). • Heterogeneity among studies makes comparison and consolidation of results challenging. • Future research should focus on clinical applicability, screening, and updated technology.

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

Morphologic Characterization of Pulmonary Nodules With Ultrashort TE MRI at 3T

OBJECTIVE

Ultrashort TE (UTE) MRI has been shown to deliver high-resolution images comparable to CT images. Here we evaluate the potential of UTE-MRI for precise lung nodule characterization.

SUBJECTS AND METHODS

Fifty-one patients (mean [± SD] age, 68.7 ± 10.8 years) with 119 nodules or masses (mean size, 17.4 ± 16.3 mm; range, 4-88 mm) prospectively underwent CT (1-mm slice thickness) and UTE-MRI (TE, 192 μs; 1 mm³ resolution). Two radiologists assessed nodule dimensions and morphologic features (i.e., attenuation, margins, and internal lucencies), in consensus for CT and in a blinded fashion for UTE-MRI. Sensitivity, specificity, and kappa statistics were calculated in reference to CT.

RESULTS

Readers 1 and 2 underestimated the nodules' long axial diameter with UTEMRI by 1.2 ± 3.4 and 2.1 ± 4.2 mm, respectively (p < 0.001). The sensitivity and specificity of UTE-MRI for subsolid attenuation were 95.9% and 70.3%, respectively, for reader 1 and 97.1% and 71.4%, respectively, for reader 2 (κ = 0.71 and 0.68). With regard to margin characteristics, for lobulation, sensitivity was 70.6% and 54.9%, and specificity was 93.2% and 96.3% for readers 1 and 2, respectively; for spiculation, sensitivity was 61.5% and 48.0%, and specificity was 95.2% and 95.0%; and for pleural tags, sensitivity was 87.0% and 73.3%, and specificity was 93.8% and 95.0%. Finally, for internal lucencies, sensitivity was 72.7% and 61.3%, and specificity was 96.1% and 97.3% for readers 1 and 2, respectively (κ = 0.64-0.81 for reader 1 and 0.48-0.72 for reader 2). Interreader agreement for attenuation, margin characteristics, and lucencies was substantial to almost perfect with few exceptions (κ = 0.51-0.90).

CONCLUSION

UTE-MRI systematically underestimated dimension measurements by approximately 1-2 mm but otherwise showed high diagnostic properties and interreader agreement, yet unprecedented by MRI, for nodule morphologic assessment.

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.