Kurtosis and skewness assessments of solid lung nodule density histograms: differentiating malignant from benign nodules on CT

Abstract

Kurtosis and skewness assessments of density histograms may be useful for differentiating malignant from benign nodules.

Recommendations for Measuring Pulmonary Nodules at CT: A Statement from the Fleischner Society

“…This can be explained by the increased partial volume averaging effect for small nodules when thicker sections are used, whereas the same effect is less severe with larger nodules. From a practical perspective, these findings support the use of contiguous thin (1.5 mm) sections for the purpose been used to differentiate adenocarcinoma subtypes, evaluate progression, and predict prognosis, notably in partsolid nodules (38)(39)(40)(41)(42). These studies provide promising preliminary insights into the potential of attenuation measurement as a tool to assess pulmonary nodules more accurately.…”

Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis

“…The advantage of imaging intralesional heterogeneity is its noninvasiveness and the fact that it can evaluate overall lesion spatial complexity or identify the subregions reflecting unique, spatially explicit biological processes, whereas random sampling or biopsy is invasive and limited to a local extent of the entire lesion (22,23). Chest CT texture analysis is emerging as a useful technique for assessing the properties of several pulmonary diseases including pulmonary nodules (10,11,(24)(25)(26). However, to our knowledge, there has been a limited number of studies using textural heterogeneity difference as an imaging biomarker in assessment of pulmonary nodules and masses.…”

“…Nevertheless, texture analysis on radiological data has nowadays become an integral part of an emerging field call radiomics, a high-throughput process in which large amounts of advanced quantitative imaging features are extracted and integrated for predictive or prognostic purpose (6)(7)(8). Some research groups have developed texture analysis methods to quantify lesion heterogeneity on lung CT (9)(10)(11).…”

Three-dimensional substructure measurements for the differential diagnosis of ground glass nodules

“…Some authors have already investigated the average CT number for GGNs that allow pathologic differentiation. Although others have studied the histogram peak CT numbers of GGNs, they have shown great interest in peak patterns, 5 to 95th percentile CT numbers, skewedness, and kurtosis [21–30]; furthermore, all of these studies focused on the CT number for the whole GGN.…”