Assessment of radiologist performance in the detection of lung nodules: dependence on the definition of "truth".

RATIONALE AND OBJECTIVES: Studies that evaluate the lung nodule detection performance of radiologists or computerized methods depend on an initial inventory of the nodules within the thoracic images (the "truth"). The purpose of this study was to analyze (1) variability in the "truth" defined by different combinations of experienced thoracic radiologists and (2) variability in the performance of other experienced thoracic radiologists based on these definitions of "truth" in the context of lung nodule detection in computed tomographic (CT) scans. MATERIALS AND METHODS: Twenty-five thoracic CT scans were reviewed by four thoracic radiologists, who independently marked lesions they considered to be nodules >or=3 mm in maximum diameter. Panel "truth" sets of nodules were then derived from the nodules marked by different combinations of two and three of these four radiologists. The nodule detection performance of the other radiologists was evaluated based on these panel "truth" sets. RESULTS: The number of "true" nodules in the different panel "truth" sets ranged from 15 to 89 (mean 49.8 +/- 25.6). The mean radiologist nodule detection sensitivities across radiologists and panel "truth" sets for different panel "truth" conditions ranged from 51.0 to 83.2%; mean false-positive rates ranged from 0.33 to 1.39 per case. CONCLUSIONS: Substantial variability exists across radiologists in the task of lung nodule identification in CT scans. The definition of "truth" on which lung nodule detection studies are based must be carefully considered, because even experienced thoracic radiologists may not perform well when measured against the "truth" established by other experienced thoracic radiologists.

The Lung Image Database Consortium (LIDC): ensuring the integrity of expert-defined "truth".

RATIONALE AND OBJECTIVES: Computer-aided diagnostic (CAD) systems fundamentally require the opinions of expert human observers to establish "truth" for algorithm development, training, and testing. The integrity of this "truth," however, must be established before investigators commit to this "gold standard" as the basis for their research. The purpose of this study was to develop a quality assurance (QA) model as an integral component of the "truth" collection process concerning the location and spatial extent of lung nodules observed on computed tomography (CT) scans to be included in the Lung Image Database Consortium (LIDC) public database. MATERIALS AND METHODS: One hundred CT scans were interpreted by four radiologists through a two-phase process. For the first of these reads (the "blinded read phase"), radiologists independently identified and annotated lesions, assigning each to one of three categories: "nodule >or=3 mm," "nodule <3 mm," or "non-nodule >or=3 mm." For the second read (the "unblinded read phase"), the same radiologists independently evaluated the same CT scans, but with all of the annotations from the previously performed blinded reads presented; each radiologist could add to, edit, or delete their own marks; change the lesion category of their own marks; or leave their marks unchanged. The post-unblinded read set of marks was grouped into discrete nodules and subjected to the QA process, which consisted of identification of potential errors introduced during the complete image annotation process and correction of those errors. Seven categories of potential error were defined; any nodule with a mark that satisfied the criterion for one of these categories was referred to the radiologist who assigned that mark for either correction or confirmation that the mark was intentional. RESULTS: A total of 105 QA issues were identified across 45 (45.0%) of the 100 CT scans. Radiologist review resulted in modifications to 101 (96.2%) of these potential errors. Twenty-one lesions erroneously marked as lung nodules after the unblinded reads had this designation removed through the QA process. CONCLUSIONS: The establishment of "truth" must incorporate a QA process to guarantee the integrity of the datasets that will provide the basis for the development, training, and testing of CAD systems.

The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation.

RATIONALE AND OBJECTIVES: The Lung Image Database Consortium (LIDC) is developing a publicly available database of thoracic computed tomography (CT) scans as a medical imaging research resource to promote the development of computer-aided detection or characterization of pulmonary nodules. To obtain the best estimate of the location and spatial extent of lung nodules, expert thoracic radiologists reviewed and annotated each scan. Because a consensus panel approach was neither feasible nor desirable, a unique two-phase, multicenter data collection process was developed to allow multiple radiologists at different centers to asynchronously review and annotate each CT scan. This data collection process was also intended to capture the variability among readers. MATERIALS AND METHODS: Four radiologists reviewed each scan using the following process. In the first or "blinded" phase, each radiologist reviewed the CT scan independently. In the second or "unblinded" review phase, results from all four blinded reviews were compiled and presented to each radiologist for a second review, allowing the radiologists to review their own annotations together with the annotations of the other radiologists. The results of each radiologist's unblinded review were compiled to form the final unblinded review. An XML-based message system was developed to communicate the results of each reading. RESULTS: This two-phase data collection process was designed, tested, and implemented across the LIDC. More than 500 CT scans have been read and annotated using this method by four expert readers; these scans either are currently publicly available at http://ncia.nci.nih.gov or will be in the near future. CONCLUSIONS: A unique data collection process was developed, tested, and implemented that allowed multiple readers at distributed sites to asynchronously review CT scans multiple times. This process captured the opinions of each reader regarding the location and spatial extent of lung nodules.

The Lung Image Database Consortium (LIDC): a comparison of different size metrics for pulmonary nodule measurements.

RATIONALE AND OBJECTIVES: The goal was to investigate the effects of choosing between different metrics in estimating the size of pulmonary nodules as a factor both of nodule characterization and of performance of computer aided detection systems, because the latter are always qualified with respect to a given size range of nodules. MATERIALS AND METHODS: This study used 265 whole-lung CT scans documented by the Lung Image Database Consortium (LIDC) using their protocol for nodule evaluation. Each inspected lesion was reviewed independently by four experienced radiologists who provided boundary markings for nodules larger than 3 mm. Four size metrics, based on the boundary markings, were considered: a unidimensional and two bidimensional measures on a single image slice and a volumetric measurement based on all the image slices. The radiologist boundaries were processed and those with four markings were analyzed to characterize the interradiologist variation, while those with at least one marking were used to examine the difference between the metrics. RESULTS: The processing of the annotations found 127 nodules marked by all of the four radiologists and an extended set of 518 nodules each having at least one observation with three-dimensional sizes ranging from 2.03 to 29.4 mm (average 7.05 mm, median 5.71 mm). A very high interobserver variation was observed for all these metrics: 95% of estimated standard deviations were in the following ranges for the three-dimensional, unidimensional, and two bidimensional size metrics, respectively (in mm): 0.49-1.25, 0.67-2.55, 0.78-2.11, and 0.96-2.69. Also, a very large difference among the metrics was observed: 0.95 probability-coverage region widths for the volume estimation conditional on unidimensional, and the two bidimensional size measurements of 10 mm were 7.32, 7.72, and 6.29 mm, respectively. CONCLUSIONS: The selection of data subsets for performance evaluation is highly impacted by the size metric choice. The LIDC plans to include a single size measure for each nodule in its database. This metric is not intended as a gold standard for nodule size; rather, it is intended to facilitate the selection of unique repeatable size limited nodule subsets.

The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans.

RATIONALE AND OBJECTIVES: The purpose of this study was to analyze the variability of experienced thoracic radiologists in the identification of lung nodules on computed tomography (CT) scans and thereby to investigate variability in the establishment of the "truth" against which nodule-based studies are measured. MATERIALS AND METHODS: Thirty CT scans were reviewed twice by four thoracic radiologists through a two-phase image annotation process. During the initial "blinded read" phase, radiologists independently marked lesions they identified as "nodule >or=3 mm (diameter)," "nodule <3 mm," or "non-nodule >or=3 mm." During the subsequent "unblinded read" phase, the blinded read results of all four radiologists were revealed to each radiologist, who then independently reviewed their marks along with the anonymous marks of their colleagues; a radiologist's own marks then could be deleted, added, or left unchanged. This approach was developed to identify, as completely as possible, all nodules in a scan without requiring forced consensus. RESULTS: After the initial blinded read phase, 71 lesions received "nodule >or=3 mm" marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. After the unblinded reads, a total of 59 lesions were marked as "nodule >or=3 mm" by at least one radiologist. Twenty-seven (45.8%) of these lesions received such marks from all four radiologists, three (5.1%) were identified as such by three radiologists, 12 (20.3%) were identified by two radiologists, and 17 (28.8%) were identified by only a single radiologist. CONCLUSION: The two-phase image annotation process yields improved agreement among radiologists in the interpretation of nodules >or=3 mm. Nevertheless, substantial variability remains across radiologists in the task of lung nodule identification.

Evaluation of lung MDCT nodule annotation across radiologists and methods.

RATIONALE AND OBJECTIVES: Integral to the mission of the National Institutes of Health-sponsored Lung Imaging Database Consortium is the accurate definition of the spatial location of pulmonary nodules. Because the majority of small lung nodules are not resected, a reference standard from histopathology is generally unavailable. Thus assessing the source of variability in defining the spatial location of lung nodules by expert radiologists using different software tools as an alternative form of truth is necessary. MATERIALS AND METHODS: The relative differences in performance of six radiologists each applying three annotation methods to the task of defining the spatial extent of 23 different lung nodules were evaluated. The variability of radiologists' spatial definitions for a nodule was measured using both volumes and probability maps (p-map). Results were analyzed using a linear mixed-effects model that included nested random effects. RESULTS: Across the combination of all nodules, volume and p-map model parameters were found to be significant at P < .05 for all methods, all radiologists, and all second-order interactions except one. The radiologist and methods variables accounted for 15% and 3.5% of the total p-map variance, respectively, and 40.4% and 31.1% of the total volume variance, respectively. CONCLUSION: Radiologists represent the major source of variance as compared with drawing tools independent of drawing metric used. Although the random noise component is larger for the p-map analysis than for volume estimation, the p-map analysis appears to have more power to detect differences in radiologist-method combinations. The standard deviation of the volume measurement task appears to be proportional to nodule volume.

Lung image database consortium: developing a resource for the medical imaging research community.

To stimulate the advancement of computer-aided diagnostic (CAD) research for lung nodules in thoracic computed tomography (CT), the National Cancer Institute launched a cooperative effort known as the Lung Image Database Consortium (LIDC). The LIDC is composed of five academic institutions from across the United States that are working together to develop an image database that will serve as an international research resource for the development, training, and evaluation of CAD methods in the detection of lung nodules on CT scans. Prior to the collection of CT images and associated patient data, the LIDC has been engaged in a consensus process to identify, address, and resolve a host of challenging technical and clinical issues to provide a solid foundation for a scientifically robust database. These issues include the establishment of (a) a governing mission statement, (b) criteria to determine whether a CT scan is eligible for inclusion in the database, (c) an appropriate definition of the term qualifying nodule, (d) an appropriate definition of "truth" requirements, (e) a process model through which the database will be populated, and (f) a statistical framework to guide the application of assessment methods by users of the database. Through a consensus process in which careful planning and proper consideration of fundamental issues have been emphasized, the LIDC database is expected to provide a powerful resource for the medical imaging research community. This article is intended to share with the community the breadth and depth of these key issues.

The future of medical imaging in the detection of early markers of disease.

Imaging techniques are a combination of a contrast mechanism, exogenous or endogenous, and an instrument to exploit that contrast. This final chapter of these two special issues of this journal points to possible ways to improve the ability of imaging systems to exploit markers of cancer in the early detection of that disease. The aim not only is to find cancer at an earlier, more treatable stage, but to determine whether the disease discovered is dangerous and to indicate the possibilities for successful treatment. These topics are explored for each imaging system, with an emphasis on directions for future improvements.

Animal models for imaging.

Animal models can be used in the study of disease. This chapter discusses imaging animal models to elucidate the process of human disease. The mouse is used as the primary model. Though this choice simplifies many research choices, it necessitates compromises for in vivo imaging. In the future, we can expect improvements in both animal models and imaging techniques.