Mapping LIDC, RadLex™, and Lung Nodule Image Features

Abstract

Ideally, an image should be reported and interpreted in the same way (e.g., the same perceived likelihood of malignancy) or similarly by any two radiologists; however, as much research has demonstrated, this is not often the case. Various efforts have made an attempt at tackling the problem of reducing the variability in radiologists’ interpretations of images. The Lung Image Database Consortium (LIDC) has provided a database of lung nodule images and associated radiologist ratings in an effort to provide images to aid in the analysis of computer-aided tools. Likewise, the Radiological Society of North America has developed a radiological lexicon called RadLex. As such, the goal of this paper is to investigate the feasibility of associating LIDC characteristics and terminology with RadLex terminology. If matches between LIDC characteristics and RadLex terms are found, probabilistic models based on image features may be used as decision-based rules to predict if an image or lung nodule could be characterized or classified as an associated RadLex term. The results of this study were matches for 25 (74%) out of 34 LIDC terms in RadLex. This suggests that LIDC characteristics and associated rating terminology may be better conceptualized or reduced to produce even more matches with RadLex. Ultimately, the goal is to identify and establish a more standardized rating system and terminology to reduce the subjective variability between radiologist annotations. A standardized rating system can then be utilized by future researchers to develop automatic annotation models and tools for computer-aided decision systems.

Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods

“…However, we observe that there are a number of published articles that employ these physician-quantified labelings of spiculation and lobulation from the LIDC dataset, but none mention the possible mislabelings in the dataset nor the exclusion of these 399 cases from their studies. [6][7][8]10,11,18,22,32,[38][39][40][41] Leaving out these 399 cases, we are left with 4384 nodule annotations that were consistently labeled. The number of annotations used is further reduced from 4384 to 2817 to exclude indeterminate cases (as described in Sec.…”

Quantitative Imaging in Cancer Evolution and Ecology

“…In the past several years, radiology research has increasingly focused on quantifying these imaging variations (19)(20)(21) and to medical outcomes (22)(23)(24)(25)(26)(27). Researchers are currently working to develop both a standardized lexicon to describe tumor features (28,29) and a standard method to convert these descriptors into quantitative mineable data (30,31) (Fig 3). Several recent articles underscore the potential power of feature analysis.…”

Standardized representation of the LIDC annotations using DICOM

“…We have also utilized the terms defined by the Imaging Biomarker Standardization Initiative (IBSI) 28 , which defines those in the context of radiomics feature extraction. Furthermore, we consulted the earlier report by Opulencia et al 29 mapping LIDC concepts to RadLex and refined our selection accordingly. Where matches were identified, standard codes were used.…”