Faculty Bibliography

OBJECTIVE: The objective of our study was to evaluate the impact of computer-aided detection (CAD) on the identification of subsolid and solid lung nodules on thin- and thick-section CT. MATERIALS AND METHODS: For 46 chest CT examinations with ground-glass opacity (GGO) nodules, CAD marks computed using thin data were evaluated in two phases. First, four chest radiologists reviewed thin sections (reader(thin)) for nodules and subsequently CAD marks (reader(thin) + CAD(thin)). After 4 months, the same cases were reviewed on thick sections (reader(thick)) and subsequently with CAD marks (reader(thick) + CAD(thick)). Sensitivities were evaluated. Additionally, reader(thick) sensitivity with assessment of CAD marks on thin sections was estimated (reader(thick) + CAD(thin)). RESULTS: For 155 nodules (mean, 5.5 mm; range, 4.0-27.5 mm)-74 solid nodules, 22 part-solid (part-solid nodules), and 59 GGO nodules-CAD stand-alone sensitivity was 80%, 95%, and 71%, respectively, with three false-positives on average (0-12) per CT study. Reader(thin) + CAD(thin) sensitivities were higher than reader(thin) for solid nodules (82% vs 57%, p < 0.001), part-solid nodules (97% vs 81%, p = 0.0027), and GGO nodules (82% vs 69%, p < 0.001) for all readers (p < 0.001). Respective sensitivities for reader(thick), reader(thick) + CAD(thick), reader(thick) + CAD(thin) were 40%, 58% (p < 0.001), and 77% (p < 0.001) for solid nodules; 72%, 73% (p = 0.322), and 94% (p < 0.001) for part-solid nodules; and 53%, 58% (p = 0.008), and 79% (p < 0.001) for GGO nodules. For reader(thin), false-positives increased from 0.64 per case to 0.90 with CAD(thin) (p < 0.001) but not for reader(thick); false-positive rates were 1.17, 1.19, and 1.26 per case for reader(thick), reader(thick) + CAD(thick), and reader(thick) + CAD(thin), respectively. CONCLUSION: Detection of GGO nodules and solid nodules is significantly improved with CAD. When interpretation is performed on thick sections, the benefit is greater when CAD marks are reviewed on thin rather than thick sections.

Purpose: To assess whether automated texture analysis of CT images enables discrimination among pathologic classes of lung adenocarcinomas, and thus serves as an in vivo biomarker of lung cancer prognosis. Materials and Methods: Chest CTs of 30 nodules in 30 patients with resected adenocarcinomas were evaluated by a pulmonary pathologist who classified each resected cancer according to the International Association for the Study of Lung Cancer (IASLC) system. The categories included adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), lepidic-predominant adenocarcinoma (LPA), and other invasive adenocarcinomas (INV). 3D volumes of interest (VOIs) and 2D regions of interest (ROIs) were then constructed for each nodule. A comprehensive set of N=279 texture parameters were computed for both 3D and 2D regions. Clustering and classification of these parameters were performed with linear discriminant analysis (LDA) using features determined by optimal subsets. Results: Of the 30 adenocarcinomas, there were 13 INV, 11 LPA, 3 MIA, and 3 AIS. AIS and MIA groups were analyzed together. With all 3 classes, LDA classified 17 of 30 nodules correctly using the nearest neighbor (k=1) method. When only the two largest classes (INV and LPA) were used, 21 of 24 nodules were classified correctly. With 3 classes and 2D texture analysis, and when using only the two largest groups, LDA was able to correctly classify all nodules. Conclusion: CT texture parameters determined by optimal subsets allows for effective clustering of adenocarcinoma classes. These results suggest the potential use of automated (or computer-assisted) CT image analysis to predict the invasive pathologic character of lung nodules. Our approach overcomes the limitations of current radiologic interpretation, such as subjectivity, interand intra-observer variability, and the effect of reader experience

The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 >/= 4 and 50 >/= 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for >/=3 mm, 48 to 61 % for >/=4 mm, and 44 to 60 % for >/=5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules >/=3, >/=4, and >/=5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.

The radiologic evaluation and management of the indeterminate solitary pulmonary nodule provide common diagnostic dilemma. With continued technologic advancements in multidetector computed tomography leading to higher spatial resolution and greater overall sensitivity of computed tomography scanners, increasing numbers of indeterminate solitary pulmonary nodules are being detected. Malignant and benign solitary pulmonary nodules have similar imaging features. Clinical management of these incidental nodules relies not only on imaging characteristics but also on malignancy risk factors, along with the risks and benefits of further investigation.

OBJECTIVE: The purpose of this study was to assess the impact of an automated program on improvement in lung nodule matching efficiency. MATERIALS AND METHODS: Four thoracic radiologists independently reviewed two serial chest CT examinations from each of 57 patients. Each radiologist performed timed manual lung nodule matching. After 6 weeks, all radiologists independently repeated the timed matching portion using an automated nodule matching program. The time required for manual and automated matching was compared. The impact of nodule size and number on matching efficiency was determined. RESULTS: An average of 325 (range, 244-413) noncalcified solid pulmonary nodules was identified. Nodule matching was significantly faster with the automated program irrespective of the interpreting radiologist (p < 0.0001 for each). The maximal time saved with automated matching was 11.4 minutes (mean, 2.3 +/- 2.0 minutes). Matching was faster in 56 of 57 cases (98.2%) for three readers and in 46 of 57 cases (80.7%) for one reader. There were no differences among readers with respect to the mean time saved per matched nodule (p > 0.5). The automated program achieved 90%, 90%, 79%, and 92% accuracy for the four readers. The improvement in efficiency for a given patient using the automated technique was proportional to the number of matched nodules (p < 0.0001) and inversely proportional to nodule size (p < 0.05). CONCLUSION: Use of the automated lung nodule matching program significantly improves diagnostic efficiency. The time saved is proportionate to the number of nodules identified and inversely proportional to nodule size. Adoption of such a program should expedite CT examination interpretation and improve report turnaround time.

OBJECTIVE: The objective of our study was to identify the significance of lung nodules reported on abdominal CT. MATERIALS AND METHODS: Abdominal CT reports from a 1-year period were reviewed for the terms "nodule," "nodular," or "mass" in reference to the lung bases. Patients with prior chest or abdominal CT examinations were excluded; the study population included patients with an initial abdominal CT study and at least one follow-up chest or abdominal CT examination. Two thoracic radiologists characterized nodules in consensus. Radiology and clinical records were reviewed for nodule growth and clinical diagnoses. RESULTS: The term "nodule," "nodular," or "mass" in reference to the lung bases was reported in 364 of 12,287 abdominal CT studies (3%). Of 125 patients with no prior CT examination, 42 had undergone follow-up chest CT, abdominal CT, or both. Common imaging indications included abdominal pain (13/42, 31%) and preexisting malignancy (n = 7, 16.7%). Regardless of the indication for imaging, 16 (38.1%) had malignancy that was known (n = 13) or newly diagnosed (n = 3) on the initial abdominal CT. Three of 42 patients (7.1%) had malignant nodules representing metastatic disease: Nodule growth was seen in one patient with preexisting colon cancer, one patient with newly diagnosed metastatic pancreatic cancer, and a third with known bladder cancer. The latter patient had suspected lung metastases that were confirmed on chest CT 1 day later. Three of the 16 patients (18.8%) with preexisting or newly diagnosed cancer had malignant nodules. No malignant nodules were identified without such history. Six patients (14.3%) had an infection. CONCLUSION: Lung nodules incidentally detected on abdominal CT were rarely malignant and were seen only in the setting of an underlying abdominal malignancy. Knowledge of such history is of critical importance to both the clinician and the radiologist. Dedicated chest CT is most useful when assessing pulmonary nodules in patients with localized malignancy.

PURPOSE: To determine the precision of a three-dimensional (3D) method for measuring the growth rate of solid and subsolid nodules and its ability to detect abnormal growth rates. MATERIALS AND METHODS: This study was approved by the Institutional Research Board and was HIPAA compliant. Informed consent was waived. The growth rates of 123 lung nodules in 59 patients who had undergone lung cancer screening computed tomography (CT) were measured by using a 3D semiautomated computer-assisted volume method. Clinical stability was established with long-term CT follow-up (mean, 6.4 years+/-1.9 [standard deviation]; range, 2.0-8.5 years). A mean of 4.1 CT examinations per patient+/-1.2 (range, two to seven CT examinations per patient) was analyzed during 2.4 years+/-0.5 after baseline CT. Nodule morphology, attenuation, and location were characterized. The analysis of standard deviation of growth rate in relation to time between scans yielded a normative model for detecting abnormal growth. RESULTS: Growth rate precision increased with greater time between scans. Overall estimate for standard deviation of growth rate, on the basis of 939 growth rate determinations in clinically stable nodules, was 36.5% per year. Peripheral location (P=.01; 37.1% per year vs 25.6% per year) and adjacency to pleural surface (P=.05; 38.9% per year vs 34.0% per year) significantly increased standard deviation of growth rate. All eight malignant nodules had an abnormally high growth rate detected. By using 3D volumetry, growth rate-based diagnosis of malignancy was made at a mean of 183 days+/-158, compared with radiologic or clinical diagnosis at 344 days+/-284. CONCLUSION: A normative model derived from the variability of growth rates of nodules that were stable for an average of 6.4 years may enable identification of lung cancer.

BACKGROUND AND PURPOSE: To determine the optimal method of targeting breast and regional nodes in selected breast cancer patients after axillary dissection, we compared the results of IMRT versus no IMRT, and CT-informed versus clinically-placed fields, in supine and prone positions. MATERIALS AND METHODS: Twelve consecutive breast cancer patients simulated both prone and supine provided the images for this study. Four techniques were used to target breast, level III axilla, and supraclavicular fossa in either position: a traditional three-field three-dimensional conformal radiotherapy (3DCRT) plan, a four-field 3DCRT plan using a posterior axillary boost field, and two techniques using a CT-informed target volume consisting of an optimized 3DCRT plan (CT-planned 3D) and an intensity-modulated radiotherapy (IMRT) plan. The prescribed dose was 50Gy in 25 fractions. RESULTS: CT-planned 3D and IMRT techniques improved nodal PTV coverage. Supine, mean nodal PTV V50 was 50% (3-field), 59% (4-field), 92% (CT-planned 3D), and 94% (IMRT). Prone, V50 was 29% (3-field), 42% (4-field), 97% (CT-planned 3D), and 95% (IMRT). Prone positioning, compared to supine, and IMRT technique, compared to 3D, lowered ipsilateral lung V20. CONCLUSIONS: Traditional 3DCRT plans provide inadequate nodal coverage. Prone IMRT technique resulted in optimal target coverage and reduced ipsilateral lung V20