Lung nodules: size still matters
Anna Rita Larici,Alessandra Farchione,Paola Franchi,Mario Ciliberto,Giuseppe Cicchetti,Lucio Calandriello,Annemilia Del Ciello,Lorenzo Bonomo +7 more
TL;DR: 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.
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Abstract: 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.
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Figures

FIGURE 2 Disagreement in measuring the solid portion of a part-solid nodule when using different reconstruction algorithms and window settings. A part-solid nodule in the apical segment of left lower lobe is shown. a) By using a high-spatial frequency algorithm and the lung window, the measured maximum axial diameter of the solid portion of the nodule corresponds to 20.3 mm; b) by using a smooth algorithm and the mediastinal window, the measured maximum axial diameter of the solid portion of the nodule corresponds to 16 mm. 2D: two-dimensional. 
FIGURE 3 Volume evaluation during follow-up allows the detection of nodule growth over a shorter period of time compared to diameter estimation. a) Computed tomography (CT) axial image shows the same nodule located in the right lower lobe as reported in figure 1c; b) a 3-month follow-up axial CT image demonstrates minimal change in nodule diameters; c) conversely, nodule volume calculation using a three-dimensional (3D) volumetric method demonstrates a significant increase in volume within the range of malignancy. Histopathology revealed a carcinoid tumour. 2D: two-dimensional; TV: total volume; DT: volume doubling time; %G: volume increase; scan inter: scan interval. Squares in the nodule represent the starting points of the 3D analysis. 
FIGURE 1 Limitations of two-dimensional (2D) measurements. The axial diameter may not be the maximum one in the evaluation of lung nodules. a) A small part-solid nodule in the apico-posterior segment of the left upper lobe, with a maximum axial diameter of 12×12.2 mm; b) the sagittal multiplanar reconstruction shows that the largest diameter of the same nodule is the sagittal one of 24.7 mm. The multiplanar evaluation of nodule diameter is especially important to document asymmetrical growth of nodules. c), d) The low level of agreement when measuring small nodules: for the same nodule in the right lower lobe two different diameter values have been reported by two readers. Considering the nearest whole diameter of the two values, it results in 1 mm difference in the maximum diameter, a significant difference when considering small nodules.
Citations
Radiomics Improves Cancer Screening and Early Detection
TL;DR: The inexorable improvements in radiomics to build more robust classifier models and the significant limitations to this development, including access to well-annotated databases, and biological descriptors of the imaged feature data are discussed.
Lung-RADS Version 1.1: Challenges and a Look Ahead, From the AJR Special Series on Radiology Reporting and Data Systems
Lydia Chelala,Rydhwana Hossain,Ella A. Kazerooni,Jared D. Christensen,Debra S. Dyer,Charles S. White +5 more
TL;DR: Lung-RADS as discussed by the authors provides a common lexicon and standardized nodule follow-up management paradigm for use when reporting lung cancer screening (LCS) low-dose CT (LDCT) chest examinations and serves as a quality assurance and outcome monitoring tool.
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Diagnosis and management of peripheral lung nodule
TL;DR: A solitary pulmonary nodule (SPN) is a well-defined radiographic opacity up to 3 cm in diameter that is surrounded by unaltered aerated lung that is an incidental finding on chest radiographs and chest CT scans.
56
Application of Radiomics in Predicting the Malignancy of Pulmonary Nodules in Different Sizes.
TL;DR: The radiomic T1a model showed superior prediction performance to the T1b and T1c models, and the best performance in terms of AUC and sensitivity was found for predicting the malignancy of T1A PN.
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TL;DR: Codes for the primary tumor categories of AIS and minimally invasive adenocarcinoma (MIA) and a uniform way to measure tumor size in part‐solid tumors for the eighth edition of the tumor, node, and metastasis classification of lung cancer are proposed.
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