Volumetric Measurements of Lung Nodules with Multi-Detector Row CT: Effect of Changes in Lung Volume

OBJECTIVE: To evaluate how changes in lung volume affect volumetric measurements of lung nodules using a multi-detector row CT. MATERIALS AND METHODS: Ten subjects with asthma or chronic bronchitis who had one or more lung nodules were included. For each subject, two sets of CT images were obtained at inspiration and at expiration. A total of 33 nodules (23 nodules > or =3 mm) were identified and their volume measured using a semiautomatic volume measurement program. Differences between nodule volume on inspiration and expiration were compared using the paired t-test. Percent differences, between on inspiration and expiration, in nodule attenuation, total lung volume, whole lung attenuation, and regional lung attenuation, were computed and compared with percent difference in nodule volume determined by linear correlation analysis. RESULTS: The difference in nodule volume observed between inspiration and expiration was significant (p or =3 mm. The volume of nodules was measured to be larger on expiration CT than on inspiration CT (28 out of 33 nodules; 19 out of 23 nodules > or =3 mm). A statistically significant correlation was found between the percent difference of lung nodule volume and lung volume or regional lung attenuation (p or =3 mm. CONCLUSION: Volumetric measurements of pulmonary nodules were significantly affected by changes in lung volume. The variability in this respiration-related measurement should be considered to determine whether growth has occurred in a lung nodule.Supported by in part NIH NHLBI, RO1 HL 69149 and by a grant from Electronics and Telecommunications Research Institute

A New Method of Measuring the Amount of Soft Tissue in Pulmonary Ground-Glass Opacity Nodules: a Phantom Study

OBJECTIVE: To devise a new method to measure the amount of soft tissue in pulmonary ground-glass opacity nodules, and to compare the use of this method with a previous volumetric measurement method by use of a phantom study. MATERIALS AND METHODS: Phantom nodules were prepared with material from fixed normal swine lung. Forty nodules, each with a diameter of 10 mm, were made with a variable mean attenuation. The reference-standard amount of soft tissue in the nodules was obtained by dividing the weight by the specific gravity. The imaging data on the phantom nodules were acquired with the use of a 16-channel multidetector CT scanner. The CT-measured amount of soft tissue of the nodules was calculated as follows: soft tissue amount = volume x (1 + mean attenuation value / 1,000). The relative percentage error (RPE) between the CT-measured amount of the soft tissue and the reference-standard amount of the soft tissue was also measured. The RPEs determined with use of the new method were compared with the RPEs determined with the current volumetric measurement method by the use of the paired t test. RESULTS: The CT-measured amount of soft tissue showed a strong correlation with the reference-standard amount of soft tissue (R(2) = 0.996, p < 0.01). The mean RPE of the CT-measured amount of soft tissue in the nodules was -7.79 +/- 1.88%. The mean RPE of the CT-measured volume was 114.78 +/- 51.02%, which was significantly greater than the RPE of the CT-measured amount of soft tissue (p < 0.01). CONCLUSION: The amount of soft tissue measured by the use of CT reflects the reference-standard amount of soft tissue in the ground-glass opacity nodules much more accurately than does the use of the CT-measured volume

Lung nodule detection on thoracic computed tomography images: Preliminary evaluation of a computerâ aided diagnosis system

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135039/1/mp5762.pd

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 ≥ 3mm,” “nodule < 3mm,” or “non-nodule ≥ 3mm.” 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 marks, 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 (1) identification of potential errors introduced during the complete image annotation process (such as two marks on what appears to be a single lesion or an incomplete nodule contour) and (2) 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. CONCLUSION: 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

Evaluating computer‐aided detection algorithms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134784/1/mp6289.pd

18F-fluoro-deoxy-glucose focal uptake in very small pulmonary nodules: fact or artifact? Case reports

ABSTRACT: BACKGROUND: F-fluoro-deoxy-glucose (18F-FDG) positron emission tomography integrated/combined with computed tomography (PET-CT) provides the best diagnostic results in the metabolic characterization of undetermined solid pulmonary nodules. The diagnostic performance of 18F-FDG is similar for nodules measuring at least 1 cm and for larger masses, but few data exist for nodules smaller than 1 cm. CASE PRESENTATION: We report five cases of oncologic patients showing focal lung 18F-FDG uptake on PET-CT in nodules smaller than 1 cm. We also discuss the most common causes of 18F-FDG false-positive and false-negative results in the pulmonary parenchyma. In patient 1, contrast-enhanced CT performed 10 days before PET-CT did not show any abnormality in the site of uptake; in patient 2, high-resolution CT performed 1 month after PET showed a bronchiole filled with dense material interpreted as a mucoid impaction; in patient 3, contrast-enhanced CT performed 15 days before PET-CT did not identify any nodules; in patients 4 and 5, contrast-enhanced CT revealed a nodule smaller than 1 cm which could not be characterized. The 18F-FDG uptake at follow-up confirmed the malignant nature of pulmonary nodules smaller than 1 cm which were undetectable, misinterpreted, not recognized or undetermined at contrast-enhanced CT. CONCLUSION: In all five oncologic patients, 18F-FDG was able to metabolically characterize as malignant those nodules smaller than 1 cm, underlining that: 18F-FDG uptake is not only a function of tumor size but it is strongly related to the tumor biology; functional alterations may precede morphologic abnormalities. In the oncologic population, especially in higher-risk patients, PET can be performed even when the nodules are smaller than 1 cm, because it might give an earlier characterization and, sometimes, could guide in the identification of alterations missed on CT

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 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 ≥ 3mm (diameter),” “nodule < 3mm,” or “non-nodule ≥ 3mm.” During the subsequent “unblinded read” phase, the blinded read results of all radiologists were revealed to each of the four radiologists, 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, a total of 71 lesions received “nodule ≥ 3mm” marks from at least one radiologist; however, all four radiologists assigned such marks to only 24 (33.8%) of these lesions. Following the unblinded reads, a total of 59 lesions were marked as “nodule ≥ 3 mm” by at least one radiologist. 27 (45.8%) of these lesions received such marks from all four radiologists, 3 (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 ≥ 3mm. Nevertheless, substantial variabilty remains across radiologists in the task of lung nodule identification