Multi-detector spiral CT study of the relationships between pulmonary ground-glass nodules and blood vessels

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.

Histogram analysis combined with morphological characteristics to discriminate adenocarcinoma in situ or minimally invasive adenocarcinoma from invasive adenocarcinoma appearing as pure ground-glass nodule

OBJECTIVE

To construct a predictive model to discriminate adenocarcinoma in situ (AIS) or minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) appearing as pure ground-glass nodules (pGGNs) using computed tomography (CT) histogram analysis combined with morphological characteristics and to evaluate its diagnostic performance.

MATERIALS AND METHODS

Two hundred eighty-nine patients with surgically resected solitary pGGN and pathologically diagnosed with AIS, MIA, or IAC in our institution from January 2014 to May 2018 were enrolled in our study. Two hundred twenty-six pGGNs (79 AIS, 84 MIA, and 63 IAC) were randomly selected and assigned to a model-development cohort, and the remaining 63 pGGNs (11 AIS, 29 MIA and 23 IAC) were assigned to a validation cohort. The morphological characteristics were established as model A and histogram parameters as model B. The diagnostic performances of model A, model B, and model A + B were evaluated and compared via receiver operating curve (ROC) analysis and logistic regression analysis.

RESULTS

Entropy (odd ratio [OR] = 23.25, 95%CI: 6.83-79.15, p < 0.001), microvascular sign (OR = 8.62, 95%CI: 3.72-19.98, p < 0.001) and the maximum diameter (OR = 4.37, 95%CI: 2.44-7.84, p < 0.001) were identified as independent predictors in the IAC group. The area under the ROC (Az value), accuracy, sensitivity and specificity of model A + B were 0.896, 88.1%, 79.4% and 91.4%, respectively, exhibiting a significantly higher Az value than either model A or model B alone (0.785 vs 0.896, p < 0.001; 0.849 vs 0.896, p = 0.029). Model A + B also conveyed a good diagnostic performance in the validation cohort, with an Az value of 0.851.

CONCLUSION

Histogram analysis combined with morphological characteristics exhibit a superior diagnostic performance in discriminating AIS-MIA from IAC appearing as pGGNs.

Influences of scan-position on clinical ultra-high-resolution CT scanning: a preliminary study

The aim of this study is to access influences of scan-position on clinical ultra-high-resolution CT scanning. We proposed a breath-hold assisted ultra-high-resolution scanning technology (scan scheme G) and compared with scan scheme A (regular CT plain scan) and scheme B (1024 ultra-high-resolution scan with patients stay in supine position). A total of 30 patients with fGGO were included in this study. Three highly experienced chest imaging doctors were employed to score the image and to select regions of interest (ROIs) for CT value and signal-to-noise ratio (SNR) calculation. In comparison with scan A and B, this new scan scheme G shows more clear CT images and higher SNRs at overall lung field (the p-values of A versus G and B versus G are 0.041 and 0.065, respectively). These findings suggest that scan-G provides a better image quality and contributes significantly to clinical detection accuracy of fGGO.

Development and validation of a radiomics nomogram for identifying invasiveness of pulmonary adenocarcinomas appearing as subcentimeter ground-glass opacity nodules

The aim of the present study was to develop and validate a radiomics-based nomogram for differentiation of pre-invasive lesions from invasive lesions that appearing as ground-glass opacity nodules (GGNs) ≤10 mm (sub-centimeter) in diameter at CT. A total of 542 consecutive patients with 626 pathologically confirmed pulmonary subcentimeter GGNs were retrospectively studied from October 2011 to September 2017. All the GGNs were divided into a training set (n = 334) and a validation set (n = 292). Researchers extracted 475 radiomics features from the plain CT images; a radiomics signature was constructed with the least absolute shrinkage and selection operator (LASSO) based on multivariable regression in the training set. Based on the multivariable logistic regression model, a radiomics nomogram was developed in the training set. The performance of the nomogram was evaluated with respect to its calibration, discrimination, and clinical-utility and this was assessed in the validation set. The constructed radiomics signature, which consisted of 15 radiomics features, was significantly associated with the invasiveness of subcentimeter GGNs (P < 0.0001 for both training set and validation set). To build the nomogram model, radiomics signature and mean CT value were used. The nomogram model demonstrated good discrimination and calibration in both training set (C-index, 0.716 [95% CI, 0.632 to 0.801]) and validation set (C-index, 0.707 [95% CI, 0.625 to 0.788]). Decision curve analysis (DCA) indicated that radiomics-based nomogram was clinically useful. A radiomics-based nomogram that incorporates both radiomics signature and mean CT value is constructed in the study, which can be conveniently used to facilitate the preoperative individualized prediction of the invasiveness in patients with subcentimeter GGNs.

Establishment of a Predictive Model for Surgical Resection of Ground-Glass Nodules

PURPOSE

To establish a predictive model for surgical resection of invasive pulmonary adenocarcinoma (IPA) presenting as ground-glass nodules (GGNs) based on a radiomics nomogram.

METHODS

The CT images of 239 patients with GGNs were collected, of which 160 cases were included in the training set to construct the predictive model and 79 cases were included in the validation set to verify the established predictive model. The least absolute shrinkage and selection operator algorithm was used to select the radiomic features and construct the radiomics tagging. The predictive model for the surgical resection of IPA was constructed using the radiomics nomogram.

RESULTS

The presence of IPA showed significant correlations with seven radiomics features (P < .01), which were the independent predictors. The predictive model constructed using the radiomics features performed well on the training set (area under the curve [AUC] 0.792, 95% confidence interval [CI]: 0.720-0.864) and the validation set (AUC 0.773, 95% CI: 0.668-0.877). The predictive model constructed using the clinical information alone was relatively less effective (AUC 0.711, 95% CI: 0.634-0.787). The predictive model constructed by integrating the radiomics features into the clinical information using the radiomics nomogram showed the best predictive ability and calibration in the training set (AUC 0.831, 95% CI: 0.765-0.897) and the validation set (AUC 0.816, 95% CI: 0.724-0.909). Decision curve analysis showed that radiomics nomogram has a certain clinical value.

CONCLUSION

The predictive model for surgical resection of IPA constructed by integrating the radiomics features and the clinical information based on the radiomics nomogram can help clinicians control the operative node and reduce the occurrence of overtreatment.

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.

The morphological changes of bronchovascular bundles within subsolid nodules on HRCT correlate with the new IASLC classification of adenocarcinoma

AIM

To observe the morphological changes of bronchovascular bundles within subsolid nodules on high-resolution (HR) computed tomography (CT) and analyse the correlation with the new adenocarcinoma classification.

MATERIALS AND METHODS

Two hundred and sixteen lesions (absent consolidation on mediastinal window) were reviewed retrospectively. CT features including dimensions, contour, morphological changes of the blood vessels, and bronchi/bronchioles, vacuole signs, and their correlation with histopathology were evaluated.

RESULTS

Excluding nine non-cancerous lesions, 34 pre-invasive lesions (PILs) including 15 atypical adenomatous hyperplasias (AAHs) and 19 adenocarcinomas in situ (AISs), 21 minimally invasive adenocarcinomas (MIAs), and 152 invasive adenocarcinomas (IACs) were analysed. Lepidic, acinar, and papillary patterns were identified in this cohort of adenocarcinomas. IACs were grouped into three types: type I (lepidic pattern ≥80%, n=47), type II (lepidic pattern ≥50%, <80%, n=67), and type III (lepidic pattern <50%, n=38). The contour of lesions, and morphological changes in vessels and bronchi/bronchioles significantly correlated with the classification of PIL, MIA, and IACs (p=0.000, p=0.000, and p=0.017, respectively). In IACs, the prevalence of vascular abnormalities on HRCT significantly correlated with (p=0.000) the proportion of non-lepidic pattern (23.40% in type I, 58.21% in type II, and 76.32% in type III); the prevalence of bronchial/bronchiolar abnormalities was higher (p=0.008) in type II/III (20.95%) compared with type I (6.38%).

CONCLUSIONS

The morphological changes of vessels and bronchi/bronchioles within the subsolid nodules on HRCT help to differentiate IAC from PIL and MIA, and are more common in non-lepidic predominant adenocarcinomas.

HRCT morphological characteristics distinguishing minimally invasive pulmonary adenocarcinoma from invasive pulmonary adenocarcinoma appearing as subsolid nodules with a diameter of ≤3 cm

AIM

To differentiate retrospectively the morphological characteristics at high-resolution computed tomography (CT) between minimally invasive pulmonary adenocarcinoma (MIA) and invasive pulmonary adenocarcinoma (IAC) appearing as subsolid nodules (SNs) with a diameter of ≤3 cm and to provide information to help operative decision-making.

MATERIALS AND METHODS

The patient notes of 260 patients with SNs of ≤3 cm in diameter (98 with MIA and 162 with IAC) confirmed at surgery and histopathology from September 2008 to June 2012 were reviewed retrospectively at the Department of Radiology, Weifang Respiratory Disease Hospital. Sixty-seven patients had pure ground-glass nodules (PGGNs) and 193 had mixed ground-glass nodules (MGGNs). Patients were grouped according to the final pathology: minimally invasive MIA and IAC. The HRCT characteristics were compared between the two groups.

RESULTS

There were statistically significant differences in the pattern, shape, diameter of solid components, proportion of solid components, CT radiodensity values of the ground-glass and solid components, borders, margins, air bronchograms, microvascular signs, and pleural indentations of the nodules between the two groups (all p<0.05). Multivariate and receiver operating characteristic (ROC) analyses indicated significant predictors of MIAs were as follows: small lesion diameter (≤14.7 mm), solid components ≤7 mm, <50% of solid components, low CT radiodensity values of the solid components (≤-107 HU), air bronchograms in the ground-glass opacity components, and microvascular signs.

CONCLUSION

The morphological characteristics at high-resolution CT can be used to differentiate between MIAs and IACs appearing as SNs with a diameter of ≤3 cm and provide information to help operative decision-making.

Vasculature surrounding a nodule: A novel lung cancer biomarker

PURPOSE

To investigate whether the vessels surrounding a nodule depicted on non-contrast, low-dose computed tomography (LDCT) can discriminate benign and malignant screen detected nodules.

MATERIALS AND METHODS

We collected a dataset consisting of LDCT scans acquired on 100 subjects from the Pittsburgh Lung Screening study (PLuSS). Fifty subjects were diagnosed with lung cancer and 50 subjects had suspicious nodules later proven benign. For the lung cancer cases, the location of the malignant nodule in the LDCT scans was known; while for the benign cases, the largest nodule in the LDCT scan was used in the analysis. A computer algorithm was developed to identify surrounding vessels and quantify the number and volume of vessels that were connected or near the nodule. A nonparametric receiver operating characteristic (ROC) analysis was performed based on a single nodule per subject to assess the discriminability of the surrounding vessels to provide a lung cancer diagnosis. Odds ratio (OR) were computed to determine the probability of a nodule being lung cancer based on the vessel features.

RESULTS

The areas under the ROC curves (AUCs) for vessel count and vessel volume were 0.722 (95% CI=0.616-0.811, p<0.01) and 0.676 (95% CI=0.565-0.772), respectively. The number of vessels attached to a nodule was significantly higher in the lung cancer group 9.7 (±9.6) compared to the non-lung cancer group 4.0 (±4.3) CONCLUSION: Our preliminary results showed that malignant nodules are often surrounded by more vessels compared to benign nodules, suggesting that the surrounding vessel characteristics could serve as lung cancer biomarker for indeterminate nodules detected during LDCT lung cancer screening using only the information collected during the initial visit.

The halo sign: HRCT findings in 85 patients

Objective:

The halo sign consists of an area of ground-glass opacity surrounding pulmonary lesions on chest CT scans. We compared immunocompetent and immunosuppressed patients in terms of halo sign features and sought to identify those of greatest diagnostic value.

Methods:

This was a retrospective study of CT scans performed at any of seven centers between January of 2011 and May of 2015. Patients were classified according to their immune status. Two thoracic radiologists reviewed the scans in order to determine the number of lesions, as well as their distribution, size, and contour, together with halo thickness and any other associated findings.

Results:

Of the 85 patients evaluated, 53 were immunocompetent and 32 were immunosuppressed. Of the 53 immunocompetent patients, 34 (64%) were diagnosed with primary neoplasm. Of the 32 immunosuppressed patients, 25 (78%) were diagnosed with aspergillosis. Multiple and randomly distributed lesions were more common in the immunosuppressed patients than in the immunocompetent patients (p < 0.001 for both). Halo thickness was found to be greater in the immunosuppressed patients (p < 0.05).

Conclusions:

Etiologies of the halo sign differ markedly between immunocompetent and immunosuppressed patients. Although thicker halos are more likely to occur in patients with infectious diseases, the number and distribution of lesions should also be taken into account when evaluating patients presenting with the halo sign.

Can we differentiate minimally invasive adenocarcinoma and non-invasive neoplasms based on high-resolution computed tomography features of pure ground glass nodules?

Objective

The purpose of our study was to assess the differentially diagnostic value of radiographic characteristics of pure ground glass nodules (GGNs) between minimally invasive adenocarcinoma and non-invasive neoplasm.

Methods

Sixty-seven pure GGNs (28 minimally invasive adenocarcinomas (MIA) and 39 pre-invasive lesions) were analyzed from June 2012 to June 2015. Pre-invasive lesions consisted of 15 atypical adenomatous hyperplasia (AAH) and 24 adenocarcinomas in situ (AIS). High-resolution computed tomography (HRCT) features and volume of MIA and pre-invasive lesions were assessed. Fisher exact test, independent sample t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were performed.

Results

Inter-observer agreement indexes for the diameter, mean HRCT attenuations and volume of pure GGNs were all high (ICC>0.75). Univariate analyses showed that lesion diameter, mean HRCT attenuation, and volume value differed significantly between two groups. Among HRCT findings, GGN shape as round or oval (F = 13.456, P = 0.002) and lesion borders as smooth or notched (F = 15.742, P = 0.001) frequently appeared in pre-invasive lesions in comparison with MIA. Type II and type III of the relationship between blood vessels and pure GGNs suggested higher possibility of malignancy than type I.

Conclusions

HRCT features of pure GGNs can help to differentiate MIA from non-invasive neoplasms.

CT and histopathologic characteristics of lung adenocarcinoma with pure ground-glass nodules 10 mm or less in diameter

OBJECTIVE

To evaluate CT and histopathologic features of lung adenocarcinoma with pure ground-glass nodule (pGGN) ≤10 mm in diameter.

METHODS

CT appearances of 148 patients (150 lesions) who underwent curative resection of lung adenocarcinoma with pGGN ≤10 mm (25 atypical adenomatous hyperplasias, 42 adenocarcinoma in situs, 38 minimally invasive adenocarcinomas, and 45 invasive pulmonary adenocarcinomas) were analyzed for lesion size, density, bubble-like sign, air bronchogram, vessel changes, margin, and tumour-lung interface. CT characteristics were compared among different histopathologic subtypes. Univariate and multivariate analysis were used to assess the relationship between CT characteristics of pGGN and lesion invasiveness, respectively.

RESULTS

There were statistically significant differences among histopathologic subtypes in lesion size, vessel changes, and tumour-lung interface (P<0.05). Univariate analysis revealed significant differences of vessel changes, margin and tumour-lung interface between preinvasive and invasive lesions (P<0.05). Logistic regression analysis showed that the vessel changes, unsmooth margin and clear tumour-lung interface were significant predictive factors for lesion invasiveness, with odds ratios (95% CI) of 2.57 (1.17-5.62), 1.83 (1.25-2.68) and 4.25 (1.78-10.14), respectively.

CONCLUSION

Invasive lesions are found in 55.3% of subcentimeter pGGNs in our cohort. Vessel changes, unsmooth margin, and clear lung-tumour interface may indicate the invasiveness of lung adenocarcinoma with subcentimeter pGGN.

KEY POINTS

• Invasive lesions were found in 55.3% of lung adenocarcinomas with subcentimeter pGGNs • Lesion size, vessel changes, and tumour-lung interface showed different among histopathologic subtypes • Vessel changes, unsmooth margin and clear tumour-lung interface were predictors for lesion invasiveness.

HRCT features distinguishing pre-invasive from invasive pulmonary adenocarcinomas appearing as ground-glass nodules

OBJECTIVE

To investigate the high-resolution computed tomography (HRCT) features that distinguish lung adenocarcinomas in situ (AISs) and minimally invasive adenocarcinomas (MIAs) from invasive adenocarcinomas (IACs) appearing as ground-glass nodules (GGNs), and to select candidates for sublobar resection.

METHODS

Two hundred and twenty-nine patients with 237 GGNs of less than 2 cm (139 AIS-MIA nodules and 98 IAC nodules) confirmed by surgery and pathology were retrospectively reviewed. The HRCT features of the AIS-MIAs and IACs were analysed and compared. Receiver operating characteristic (ROC) analyses were conducted to determine the cutoff values for the qualitative variables and their diagnostic performances.

RESULTS

Significant differences were found in the density, nodule and solid component diameters, CT values of the ground-glass and solid components, lobulated shape, spiculated margin, abnormal pulmonary vein and artery, air bronchogram, and pleural indentation of the GGNs between the two groups. Multivariate and ROC analyses revealed that larger diameter of nodules (≥12.2 mm) and solid components (≥6.7 mm), and higher CT values of the solid components (≥ -192 HU) in the GGNs with air bronchogram were significantly associated with IACs.

CONCLUSIONS

HRCT can identify distinguishing morphological features between AIS-MIAs and IACs, and is helpful for selecting candidates for sublobar resection.

KEY POINTS

• IACs appearing as GGNs were often ≥ 12.2 mm in diameter. • IACs were often ≥ 6.7 mm in solid component diameter. • The solid components of the IACs often exhibited ≥ -192 HU. • IACs exhibited air bronchogram more frequently than AIS-MIAs.

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.