Lung Nodule Detection and Characterization With Multi-Slice CT

Spatial Resolution Fidelity Comparison Between Energy Integrating and Deep Silicon Photon Counting CT: Implications for Pulmonary Imaging

PURPOSE

We investigated spatial resolution loss away from isocenter for a prototype deep silicon photon-counting detector (PCD) CT scanner and compare with a clinical energy-integrating detector (EID) CT scanner.

MATERIALS AND METHODS

We performed three scans on a wire phantom at four positions (isocenter, 6.7, 11.8, and 17.1 cm off isocenter). The acquisition modes were 120 kV EID CT, 120 kV high-definition (HD) EID CT, and 120 kV PCD CT. HD mode used double the projection view angles per rotation as the "regular" EID scan mode. The diameter of the wire was calculated by taking the full width of half max (FWHM) of a profile drawn over the radial and azimuthal directions of the wire. Change in wire diameter appearance was assessed by calculating the ratio of the radial and azimuthal diameter relative to isocenter. t tests were used to make pairwise comparisons of the wire diameter ratio with each acquisition and mean ratios' difference from unity.

RESULTS

Deep silicon PCD CT had statistically smaller (P<0.05) changes in diameter ratio for both radial and azimuthal directions compared with both regular and HD EID modes and was not statistically different from unity (P<0.05). Maximum increases in FWMH relative to isocenter were 36%, 12%, and 1% for regular EID, HD EID, and deep silicon PCD, respectively.

CONCLUSION

Deep silicon PCD CT exhibits less change in spatial resolution in both the radial and azimuthal directions compared with EID CT.

Identification of Nonaggressive Pulmonary Nodules Using an Optimized Scoring System

Supplemental Digital Content is available in the text.

Purpose:

The purpose of this study was to define the optimal scoring method for identifying benign intrapulmonary lymph nodes.

Materials and Methods:

Subjects for this study were selected from the COPDGene study, a large multicenter longitudinal observational cohort study. A retrospective case-control analysis was performed using identified nodules on a subset of 377 patients who demonstrated 765 pulmonary nodules on their baseline computed tomography (CT) study. Nodule characteristics of 636 benign nodules (which resolved or showed <20% growth rate at 5 y follow-up) were compared with 51 nodules that occurred in the same lobe as a reported malignancy. Two radiologists scored each pulmonary nodule on the basis of intrapulmonary lymph node characteristics. A simple scoring strategy weighing all characteristics equally was compared with an optimized scoring strategy that weighed characteristics on the basis of their relative importance in identifying benign pulmonary nodules.

Results:

A total of 479 of 636 benign pulmonary nodules had the majority of lymph node characteristics, whereas only 1 subpleural nodule with the majority of lymph node characteristics appeared to be malignant. Only 279 of 479 (58%) of benign pulmonary nodules with the majority of lymph node characteristics were intrafissural or subpleural. The optimized scoring strategy showed improved performance compared with the simple scoring strategy with average area under the curve of 0.80 versus 0.55. Optimized cutoff scores showed negative likelihood values for both readers of <0.2. A simulation showed a potential reduction in CT utilization of up to 36% for Fleischner criteria and up to 5% for LUNG-RADS.

Conclusions:

Nodules with the majority of lymph node characteristics, regardless of location, are likely benign, and weighing certain lymph node characteristics greater than others can improve overall performance. Given the potential to reduce CT utilization, lymph node characteristics should be considered when recommending appropriate follow-up.

Morphological classification of pre-invasive lesions and early-stage lung adenocarcinoma based on CT images

OBJECTIVE

To retrospectively analyze the computed tomography (CT) features in patients with pre-invasive lesions and early-stage lung adenocarcinoma and to explore the correlation between tumor morphological changes and pathological diagnoses.

MATERIALS AND METHODS

CT morphological characteristics in 2106 patients with pre-invasive (stage 0) and early-stage (stage I) lung adenocarcinoma were analyzed; lesions were confirmed by surgical pathology. Based on the morphological characteristics, the lesions were divided into eight types: I (cotton ball, ground-glass nodules), II (solid fill), III (granular), IV (dendriform), V (bubble-like lucencies), VI (alveolate or honeycomb), VII (scar-like), and VIII (notched or umbilication). The different distributions of eight morphological types in pathological types of the lesions and subtypes of invasive adenocarcinoma were analyzed by chi-squared or Fisher's exact test. Correlation between the percentage of ground-glass opacity in the lesions and pathology types were analyzed by two-tailed Pearson's test.

RESULTS

A negative correlation was observed between the pathological types and proportion of ground-glass component in the lesions (p < 0.001 and r = - 0.583). Significant differences in morphological characteristics among various pathological types of pre-invasive lesions and early lung adenocarcinomas were observed (p < 0.05). Furthermore, among the different pathological subtypes of stage I invasive adenocarcinoma, the differences in their manifestation as morphological types I, II, III, and VI were statistically significant (p < 0.05).

CONCLUSION

The eight types of morphological classification of pre-invasive lesions and early-stage (stage 0 or stage I) lung adenocarcinoma has different pathological bases, and morphological classification may be useful for the diagnosis and differential diagnosis of lung adenocarcinoma.

KEY POINTS

• CT morphological classification of pre-invasive lesions and lung adenocarcinoma is intuitive. • CT morphological classification characterizes morphological changes of the entire lesion. • Different pathological types of lung adenocarcinoma have different morphological features.

Clinical utility of quantitative dual-energy CT iodine maps and CT morphological features in distinguishing small-cell from non-small-cell lung cancer

AIM

To evaluate the clinical usefulness of quantitative dual-energy (DE) computed tomography (CT) iodine enhancement metrics combined with morphological CT features in distinguishing small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

One hundred and six untreated lung cancer patients who underwent DECT before biopsy or surgery were prospectively enrolled. Twenty-seven routine CT descriptors, including tumour location, size, shape, margin, enhancement heterogeneity, and internal and surrounding structures, and associated findings were assessed and DECT parameters were measured in all patients. Multiple logistic regression analyses were applied to identify independent predictors of SCLC. The area under the receiver operating characteristic curve was compared between CT features combined with DECT metrics and CT features alone for distinguishing SCLC from NSCLC.

RESULTS

Histology revealed NSCLC in 80 and SCLC in 26 patients. In univariate analysis, 12 morphological CT features and two DECT metrics differed significantly between NSCLC and SCLC. When DECT parameters were combined with CT features for multivariate analysis, the independent predictors of SCLC were large tumour size, central location, confluent mediastinal lymphadenopathy, homogeneous enhancement, absence of coarse spiculation, and lower iodine density and iodine ratio (all p<0.05). The area under the receiver operating characteristic curve was improved from 0.908 to 0.981 after adding DECT metrics compared with CT features alone (p=0.007).

CONCLUSION

The combination of DECT measures and CT morphological features can be used to distinguish SCLC from NSCLC, with higher diagnostic performance compared with CT morphological features alone.

Can MRI contribute to pulmonary nodule analysis?

BACKGROUND

There is no accurate method distinguishing different types of pulmonary nodules.

PURPOSE

To investigate whether multiparametric 3T MRI biomarkers can distinguish malignant from benign pulmonary nodules, differentiate different types of neoplasms, and compare MRI-derived measurements with values from commonly used noninvasive imaging modalities.

STUDY TYPE

Prospective.

SUBJECTS

Sixty-eight adults with pulmonary nodules undergoing resection.

SEQUENCES

Respiratory triggered diffusion-weighted imaging (DWI), periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) fat saturated T₂ -weighted imaging, T₁ -weighted 3D volumetric interpolated breath-hold examination (VIBE) using CAIPIRINHA (controlled aliasing in parallel imaging results in a higher acceleration).

ASSESSMENT/STATISTICS

Apparent diffusion coefficient (ADC), T₁ , T₂ , T₁ and T₂ normalized to muscle (T₁ /M and T₂ /M), and dynamic contrast enhancement (DCE) values were compared with histology to determine whether they could distinguish malignant from benign nodules and discern primary from secondary malignancies using logistic regression. Predictability of primary neoplasm types was assessed using two-sample t-tests. MRI values were compared with positron emission tomography / computed tomography (PET/CT) to examine if they correlated with standardized uptake value (SUV) or CT Hounsfield unit (HU). Intra- and interreader agreements were assessed using intraclass correlations.

RESULTS

Forty-nine of 74 nodules were malignant. There was a significant association between ADC and malignancy (odds ratio 4.47, P < 0.05). ADC ≥1.3 μm² /ms predicted malignancy. ADC, T₁ , and T₂ together predicted malignancy (P = 0.003). No MRI parameter distinguished primary from metastatic neoplasms. T₂ predicted PET positivity (P = 0.016). T₂ and T₁ /M correlated with SUV (P < 0.05). Of 18 PET-negative malignant nodules, 12 (67%) had an ADC ≥1.3 μm² /ms. With the exception of T₂ , all noncontrast MRI parameters distinguished adenocarcinomas from carcinoid tumors (P < 0.05). T₁ , T₂ , T₁ /M, and T₂ /M correlated with HU and therefore can predict nodule density. Combined with ADC, washout enhancement, arrival time (AT), peak enhancement intensity (PEI), K^trans , K_ep , V_e collectively were predictive of malignancy (P = 0.012). Combined washin, washout, time to peak (TTP), AT, and PEI values predicted malignancy (P = 0.043). There was good observer agreement for most noncontrast MRI biomarkers.

DATA CONCLUSION

MRI can contribute to pulmonary nodule analysis. Multiparametric MRI might be better than individual MRI biomarkers in pulmonary nodule risk stratification.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

CT characterization of different pathological types of subcentimeter pulmonary ground-glass nodular lesions

OBJECTIVE

To explore the CT characteristics of small lung nodules and improve the diagnosis of pulmonary ground-glass nodules less than 10  mm in size.

METHODS

We retrospectively analyzed CT images of 161 pulmonary nodules (less than 10  mm in size) with spiculation, lobulation, vacuoles, and pleural indentation and compared these images with pathological results or follow-up CT images. The relationships between the ground-glass nodules (GGNs) and blood vessels were observed. The GGN-vessel relationship was divided into four types, Type I (pass-by), Type II (pass-through), Type III (distorted/dilated), Type IV (complicated). The vessels traveling through a GGN were divided into three categories, category A (arteries), category B (veins), category C (arteries and veins).

RESULTS

161 GGNs were divided into three groups (benign group, pre-invasive group, and adenocarcinoma group) according to their pathological diagnosis. Significant differences in density of nodules were observed among the three different groups (p < 0.05). Significant differences in the shape (round/round-like or not) of the nodules were observed between the benign group and the pre-invasive group and between the pre-invasive group and the adenocarcinoma group (p < 0.05). No significant differences in the presence of vacuoles were observed between the benign group and the pre-invasive group or between the pre-invasive group and the adenocarcinoma group (p ＞0.05), but a significant difference was observed between the benign group and the adenocarcinoma group (p < 0.05). The differences in the vascularization of the lesions among the three groups were statistically significant (p < 0.05). No significant differences or correlations were observed between vascular categories and GGN groups (p > 0.05).

CONCLUSION

For subcentimeter nodules, mixed GGNs with vacuoles, well-defined border, combined with Type III or Type IV GGN-vessel relationship may strongly suggest malignant.

ADVANCES IN KNOWLEDGE

Previous studies mainly focused on CT diagnosis of pulmonary nodules (≤ 3  cm in diameter), but this study focused on ground-glass nodules less than 10  mm in diameter, which had not been fully studied. For subcentimeter nodules, mixed GGNs with vacuoles, well-defined border, especially the GGN-vessel relationship manifest as Type III (distorted/dilated) or Type IV (complicated) may strongly suggest malignant.

High-resolution CT with new model-based iterative reconstruction with resolution preference algorithm in evaluations of lung nodules: Comparison with conventional model-based iterative reconstruction and adaptive statistical iterative reconstruction

OBJECTIVE

To compare the image quality of high-resolution computed tomography (HRCT) for evaluating lung nodules reconstructed with the new version of model-based iterative reconstruction and spatial resolution preference algorithm (MBIRn) vs. conventional model-based iterative reconstruction (MBIRc) and adaptive statistical iterative reconstruction (ASIR).

MATERIALS AND METHODS

This retrospective clinical study was approved by our institutional review board and included 70 lung nodules in 58 patients (mean age, 71.2±10.9years; 34 men and 24 women). HRCT of lung nodules were reconstructed using MBIRn, MBIRc and ASIR. Objective image noise was measured by placing the regions of interest on lung parenchyma. Two blinded radiologists performed subjective image analyses.

RESULTS

Significant improvements in the following points were observed in MBIRn compared with ASIR (p<0.005): objective image noise (24.4±8.0 vs. 37.7±10.4), subjective image noise, streak artifacts, and adequateness for evaluating internal characteristics and borders of nodules. The sharpness of small vessels and bronchi and diagnostic acceptability with MBIRn were significantly better than with MBIRc and ASIR (p<0.008).

CONCLUSION

HRCT reconstructed with MBIRn provides diagnostically more acceptable images for the detailed analyses of lung nodules compared with MBIRc and ASIR.

Significance of pulmonary nodules in multi-detector computed tomography scan of noncancerous patients

Background:

Computed tomography (CT) scan is one the most useful devices in chest imaging. CT scan can be used in mediastinal abnormality, lungs, and pleural evaluations. According to the high prevalence and different causes of pulmonary nodules, we designed this study to evaluate the prevalence and the types of pulmonary nodules in noncancerous patients who underwent chest multi-detector CT (MDCT) scan.

Materials and Methods:

This was a cross-sectional study which was in our hospital to evaluate the prevalence of pulmonary nodules in noncancerous patients who underwent MDCT. A checklist was used for data collection containing number, location, size, and shape of pulmonary nodules if present in CT scan, and we also included patient's age and history of smoking. We analyzed the data with Statistical Program for Social Sciences software (version 18).

Results:

In this study, 115 patients (40%) had a pulmonary nodule. The mean number of a total nodule in each patient was 0.8 ± 0.07. Mean number of intra-parenchymal, sub pleural, and perivascular nodules were 0.34 ± 0.04, 0.31 ± 0.04, and 0.14 ± 0.02, respectively. The mean number of calcified nodules was 0.13 ± 0.02. There was no significant correlation between age and nodule characteristics (P > 0.05).

Conclusion:

The prevalence of pulmonary nodules was quite frequent in MDCT scan of noncancerous cases. So, it should not be overvalued in noncancerous cases.

Differentiating between Subsolid and Solid Pulmonary Nodules at CT: Inter- and Intraobserver Agreement between Experienced Thoracic Radiologists

PURPOSE

To quantify the reproducibility and accuracy of experienced thoracic radiologists in differentiating between subsolid and solid pulmonary nodules at CT.

MATERIALS AND METHODS

The institutional review board of Beth Israel Deaconess Medical Center approved this multicenter study. Six thoracic radiologists, with a mean of 21 years of experience in thoracic radiology (range, 17-22 years), selected images of 10 solid and 10 subsolid nodules to create a database of 120 nodules; this selection served as the reference standard. Each radiologist then interpreted 120 randomly ordered nodules in two different sessions that were separated by a minimum of 3 weeks. The radiologists classified whether or not each nodule was subsolid. Inter- and intraobserver agreement was assessed with a κ statistic. The number of correct classifications was calculated and correlated with nodule size by using Bland-Altman plots. The relationship between disagreement and nodule morphologic characteristics was analyzed by calculating the intraclass correlation coefficient.

RESULTS

Interobserver agreement (κ) was 0.619 (range, 0.469-0.745; 95% confidence interval (CI): 0.576, 0.663) and 0.670 (range, 0.440-0.839; 95% CI: 0.608, 0.733) for interpretation sessions 1 and 2, respectively. Intraobserver agreement (κ) was 0.792 (95% CI: 0.750, 0.833). Averaged for interpretation sessions, correct classification was achieved by all radiologists for 58% (70 of 120) of nodules. Radiologists agreed with their initial determination (the reference standard) in 77% of cases (range, 45%-100%). Nodule size weakly correlated with correct classification (long axis: Spearman rank correlation coefficient, rs = 0.161 and P = .049; short axis: rs = 0.128 and P = .163).

CONCLUSION

The reproducibility and accuracy of thoracic radiologists in classifying whether or not a nodule is subsolid varied in the retrospective study. This inconsistency may affect surveillance recommendations and prognostic determinations.

Using the CT features to differentiate invasive pulmonary adenocarcinoma from pre-invasive lesion appearing as pure or mixed ground-glass nodules

OBJECTIVE

To differentiate pre-invasive lesion from invasive pulmonary adenocarcinoma (IPA) appearing as ground-glass nodules (GGNs) using CT features.

METHODS

149 GGNs were enrolled in this study, with 74 pure GGNs (p-GGNs) and 75 mixed GGNs (m-GGNs). Firstly, univariate analysis was used to analyse the difference of CT features between pre-invasive lesion and IPA. Then, multivariate analysis was conducted to identify variables that could independently differentiate pre-invasive lesion from IPA. Receiver operating characteristic curve analysis was performed to evaluate the differentiating value of identified variables.

RESULTS

In the p-GGNs, multivariate analysis showed that the amount of blood vessels was an independent risk factor. Using the amount of blood vessels "≥1" as the diagnostic criterion, we could diagnose IPA with a sensitivity of 100%. Using the amount of blood vessels "=0" as the diagnostic criterion, we could diagnose pre-invasive lesions with a specificity of 100%. In the m-GGNs, multivariate analysis showed that the volume of solid portion (VSolid) and pleural indentation were two independent risk factors. One further model was constructed using these two variables: model = 2.508 × (VSolid + 1.407) × (pleural indentation - 1.016). Using the new model, improved diagnostic ability was achieved compared with using VSolid or pleural indentation alone.

CONCLUSION

The amount of blood vessels through the p-GGNs would be an important criterion during clinical management, while VSolid and pleural indentation seemed important for m-GGNs. Moreover, the new model could further improve the differentiating value for m-GGNs.

ADVANCES IN KNOWLEDGE

CT features are useful in differentiating pre-invasive lesion from IPA appearing as GGNs.

Preoperative flexible bronchoscopy in patients with persistent ground-glass nodule

There are no accurate data on the diagnostic value of preoperative flexible bronchoscopy (FB) for persistent ground-glass nodule (GGN) of the lung. We evaluated the value of preoperative FB in patients with suspected GGN-type lung cancer. We retrospectively searched a database for subjects who had ‘ground-glass opacity’, ‘non-solid nodule’, ‘part-solid nodule’, or ‘sub-solid nodule’ on chest computed tomography reports between February 2004 and March 2012. Patients who had infiltrative ground-glass opacity lesions, mediastinal lymphadenopathy, or pleural effusion, focal ground-glass opacity lesions >3 cm, and were lost to follow-up were excluded. We assessed the diagnostic value of preoperative FB in patients with persistent GGNs who underwent surgical resection. In total, 296 GGNs were evaluated by FB in 264 patients with persistent GGNs who underwent preoperative FB and surgical resection. The median size of the GGNs was 18 mm; 135 (46%) were pure GGN and 161 (54%) were part-solid GGN. No visible tumor or unsuspected endobronchial metastasis was identified by preoperative FB. Only 3 (1%, 3/208) GGNs were identified preoperatively as malignant by bronchial washing cytology; all were part-solid GGNs. No other etiology was identified by FB. Of the GGNs, 271 (91%) were subsequently confirmed as malignant and 25 (9%) were confirmed as benign at surgical resection. Consequently, the overall diagnostic sensitivity and negative predictive value of preoperative FB on a per-nodule basis was 1% (3/271) and 8% (25/293), respectively. The preoperative FB did not change the surgical strategy. Preoperative FB did not add much to the evaluation of persistent GGNs of the lung. Routine preoperative FB may have limited value in surgical candidates with small persistent pure GGNs.

Thin-section CT findings in peripheral lung cancer of 3 cm or smaller: are there any characteristic features for predicting tumor histology or do they depend only on tumor size?

BACKGROUND

Ground-glass opacity (GGO) is reported to be characteristic to lepidic growth of neoplasm in subsolid nodules. In solid nodules of lung cancer, however, there is no characteristic feature to be reported.

PURPOSE

To study if there are any thin-section CT findings characteristic to tumor histology or if they are only related to tumor size in solid nodules of the lung cancer.

MATERIAL AND METHODS

This study included 106 solid peripheral lung cancers of 3 cm or smaller (56 adenocarcinomas, 33 squamous cell carcinomas, and 17 small cell carcinomas) in which 16-slice CT with 1 mm collimation was performed before surgery. Six morphologic findings (presence or absence of lobulation, coarse spiculation, air bronchogram, cavity, pleural tag, and pleural-based lesion) and four measurements (ratio of the greatest transverse and vertical diameter to the shortest transverse diameter and density of lobulation and coarse spiculation) on thin-section CT images were evaluated. Density of lobulation (coarse spiculation) was defined as the ratio of lobulation (coarse spiculation) number to the greatest transverse diameter of a nodule.

RESULTS

Air bronchogram (P < 0.01) was the only significant factor for predicting lung adenocarcinoma. The prevalence of air bronchogram was significantly greater in adenocarcinoma than in squamous cell carcinoma (P < 0.01) or small cell carcinoma (P < 0.01). As the tumor size advanced, significantly positive linear trends were seen in the prevalence of lobulation (P < 0.01), coarse spiculation (P < 0.01), and pleural tag (P < 0.01), and the mean values of density of lobulation (P < 0.01) and coarse spiculation (P < 0.01), while the significant negative linear trend was seen in the ratio of vertical diameter to the shortest transverse (P = 0.02).

CONCLUSION

Air bronchogram on thin-section CT is characteristic feature of solid adenocarcinoma of the lung. However, other thin-section CT findings are irrelevant to tumor histology and related only to tumor size.

Nodule characterization: subsolid nodules

In this review, we focus on the radiologic, clinical, and pathologic aspects primarily of solitary subsolid pulmonary nodules. Particular emphasis will be placed on the pathologic classification and correlative computed tomography (CT) features of adenocarcinoma of the lung. The capabilities of fluorodeoxyglucose positron emission tomography-CT and histologic sampling techniques, including CT-guided biopsy, endoscopic-guided biopsy, and surgical resection, are discussed. Finally, recently proposed management guidelines by the Fleischner Society and the American College of Chest Physicians are reviewed.

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.

Persistent Pulmonary Nodular Ground-Glass Opacity at Thin-Section CT: Histopathologic Comparisons

PURPOSE

To retrospectively compare pure pulmonary ground-glass opacity (GGO) nodules observed on thin-section computed tomography (CT) images with histopathologic findings.

MATERIALS AND METHODS

The institutional review board approved this study and waived informed consent. Histopathologic specimens were obtained from 53 GGO nodules in 49 patients. CT scans were assessed in terms of nodule size, shape, contour, internal characteristics, and the presence of a pleural tag. The findings obtained were compared with histopathologic results. Differences in thin-section CT findings according to histopathologic diagnoses were analyzed by using the Kruskal-Wallis test or Fisher exact test.

RESULTS

Of 53 nodules in 49 patients (20 men, 29 women; mean age, 54 years; range, 29-78 years), 40 (75%) proved to be broncholoalveolar cell carcinoma (BAC) (n=36) or adenocarcinoma with predominant BAC component (n=4), three (6%) atypical adenomatous hyperplasia, and 10 (19%) nonspecific fibrosis or organizing pneumonia. No significant differences in morphologic findings on thin-section CT scans were found among the three diseases (all P>0.05). A polygonal shape (25%, 10 of 40 nodules) and a lobulated or spiculated margin (45%, 18 of 40) in BAC or adenocarcinoma with predominant BAC component were caused by interstitial fibrosis or infiltrative tumor growth. A polygonal shape and a lobulated or spiculated margin were observed in two (20%) and three (30%) of 10 nodules, respectively, in organizing pneumonia/fibrosis were caused by granulation tissue aligned in a linear manner in perilobular regions with or without interlobular septal thickening.

CONCLUSION

About 75% of persistent pulmonary GGO nodules are attributed to BAC or adenocarcinoma with predominant BAC component, and at thin-section CT, these nodules do not manifest morphologic features that distinguish them from other GGO nodules with different histopathologic diagnoses.

Detección y caracterización del nódulo pulmonar por tomografía computarizada multicorte

Pulmonary nodules are a common finding in routine chest studies. Although there are no pathognomic clinical or radiological signs that enable the exact nature of a pulmonary nodule to be determined, the clinical context and the appropriate characterization of the pulmonary nodule make it possible to reach the correct diagnosis in most cases. This article discusses the most important aspects involved in the use of multislice computed tomography in the noninvasive detection and characterization of pulmonary nodules.

Solitary Pulmonary Nodules: Detection, Characterization, and Guidance for Further Diagnostic Workup and Treatment

OBJECTIVE

The purpose of our study is to improve radiologists' understanding of the clinical issues involved in making a diagnosis and to guide further diagnostic workup and treatment of solitary pulmonary nodules (SPNs).

CONCLUSION

Information on the morphologic and hemodynamic characteristics of SPNs provided by dynamic helical CT, with high specificity and reasonably high accuracy, can be used for initial assessment. PET/CT is more sensitive at detecting malignancy than dynamic helical CT, and all malignant nodules may be potentially diagnosed as malignant by both techniques. Therefore, PET/CT may be selectively performed to characterize SPNs that show indeterminate results at dynamic helical CT.

A KNOWLEDGE-BASED LUNG NODULE DETECTION SYSTEM FOR HELICAL CT IMAGES

In this paper, we describe a knowledge-based system for segmenting and labeling lung nodule on helical CT images. The system was developed under a blackboard environment that incorporates a lung knowledge model, image processing model, inference engine and a blackboard. Lung model, which contains both analogical and propositional knowledge about lung in the form of semantic networks, was used to guide the interpretation process. The system works in a hierarchical structure, from large structures to the final nodule candidates by focusing on the interested region step by step. The symbolic variables, introduced to accomplish high-level inference, were defined by fuzzy confidence functions in the lung model. Composite fuzzy functions were applied to evaluate the plausibility of the mapping between the image and lung model objects. Anatomical lung segments knowledge was embedded in the system to direct 3D validation of suspicious objects. Structures were identified and abnormal objects were reported. The experimental results obtained demonstrate the proof of concept and the potential of the automated knowledge-based lung nodule detection system.