Pulmonary adenocarcinomas with ground-glass attenuation on thin-section CT: Quantification by three-dimensional image analyzing method

Unsupervised contrastive learning based transformer for lung nodule detection

Objective.Early detection of lung nodules with computed tomography (CT) is critical for the longer survival of lung cancer patients and better quality of life. Computer-aided detection/diagnosis (CAD) is proven valuable as a second or concurrent reader in this context. However, accurate detection of lung nodules remains a challenge for such CAD systems and even radiologists due to not only the variability in size, location, and appearance of lung nodules but also the complexity of lung structures. This leads to a high false-positive rate with CAD, compromising its clinical efficacy.Approach.Motivated by recent computer vision techniques, here we present a self-supervised region-based 3D transformer model to identify lung nodules among a set of candidate regions. Specifically, a 3D vision transformer is developed that divides a CT volume into a sequence of non-overlap cubes, extracts embedding features from each cube with an embedding layer, and analyzes all embedding features with a self-attention mechanism for the prediction. To effectively train the transformer model on a relatively small dataset, the region-based contrastive learning method is used to boost the performance by pre-training the 3D transformer with public CT images.Results.Our experiments show that the proposed method can significantly improve the performance of lung nodule screening in comparison with the commonly used 3D convolutional neural networks.Significance.This study demonstrates a promising direction to improve the performance of current CAD systems for lung nodule detection.

Lung nodules: size still matters

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

Lepidic Predominant Pulmonary Lesions (LPL): CT-based Distinction From More Invasive Adenocarcinomas Using 3D Volumetric Density and First-order CT Texture Analysis

RATIONALE AND OBJECTIVES

This study aimed to differentiate pathologically defined lepidic predominant lesions (LPL) from more invasive adenocarcinomas (INV) using three-dimensional (3D) volumetric density and first-order texture histogram analysis of surgically excised stage 1 lung adenocarcinomas.

MATERIALS AND METHODS

This retrospective study was institutional review board approved and Health Insurance Portability and Accountability Act compliant. Sixty-four cases of pathologically proven stage 1 lung adenocarcinoma surgically resected between September 2006 and October 2015, including LPL (n = 43) and INV (n = 21), were evaluated using high-resolution computed tomography. Quantitative measurements included nodule volume, percent solid volume (% solid), and first-order texture histogram analysis including skewness, kurtosis, entropy, and mean nodule attenuation within each histogram quartile. Binomial logistic regression models were used to identify the best set of parameters distinguishing LPL from INV.

RESULTS

Univariate analysis of 3D volumetric density and histogram features was statistically significant between LPL and INV groups (P < .05). Accuracy of a binomial logistic model to discriminate LPL from INV based on size and % solid was 85.9%. With optimized probability cutoff, the model achieves 81% sensitivity, 76.7% specificity, and area under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.821-0.973). An additional model based on size and mean nodule attenuation of the third quartile (Hu_Q3) of the histogram achieved similar accuracy of 81.3% and area under the receiver operating characteristic curve of 0.877 (95% confidence interval, 0.790-0.964).

CONCLUSIONS

Both 3D volumetric density and first-order texture analysis of stage 1 lung adenocarcinoma allow differentiation of LPL from more invasive adenocarcinoma with overall accuracy of 85.9%-81.3%, based on multivariate analyses of either size and % solid or size and Hu_Q3, respectively.

Solitary ground-glass opacity nodules of stage IA pulmonary adenocarcinoma: combination of 18F-FDG PET/CT and high-resolution computed tomography features to predict invasive adenocarcinoma

To investigate the performance of combined 18F-FDG Positron Emission Tomography/Computed Tomography with high-resolution CT for differentiating invasive adenocarcinoma from adenocarcinoma in situ (pre-invasive lesion) or minimally invasive adenocarcinoma in stage IA lung cancer patients with solitary ground-glass opacity nodules. This retrospective study enrolled 58 consecutive stage IA pulmonary adenocarcinoma patients with solitary ground-glass opacity nodules. The characteristics and measurements of the ground-glass opacity nodules as pure ground-glass opacity nodules and mixed ground-glass opacity nodules in the pre-invasive or minimally invasive adenocarcinoma and invasive adenocarcinoma groups on Positron Emission Tomography/Computed Tomography and high-resolution CT were compared and analyzed. Ground-glass opacity nodules in the pre-invasive or minimally invasive adenocarcinoma group preferentially manifested as pure ground-glass opacity nodule (p < 0.01) compared to the invasive adenocarcinoma group. While cystic appearance was more common in the invasive adenocarcinoma group (p < 0.05). Significant differences were found in the diameter of the ground-glass opacity nodule itself and its solid component, and consolidation/tumor ratio between the two groups. The sensitivity in predicting invasive adenocarcinoma was higher with a combined consolidation/tumor ratio > 0.38 and SUV_max > 1.46 in mixed ground-glass opacity nodule when compared to those of SUV_max > 0.95 alone or consolidation/tumor ratio> 0.39 alone (both p > 0.05). For a mixed ground-glass opacity nodule combined consolidation/tumor ratio > 0.38 and SUV_max > 1.46 appears to better predict invasive adenocarcinoma in stage IA lung cancer patients with solitary ground-glass opacity nodules.

Stage I lung adenocarcinoma: the value of quantitative CT in differentiating pathological subtypes and predicting growth of subsolid nodules

The aim of this study was to investigate feasibility of quantitative computed tomography (CT) measurements in predicting invasiveness and growth of nodular ground glass opacities (nGGOs).A set of 203 patients (group A) with nGGOs that were confirmed stage-I adenocarcinomas and 79 patients (group B) with nGGOs that were completely followed up were included. Lesions diameters, volume (VOL), maximum (MAX), mean (MEN), and standard deviation (STD) of CT attenuation were measured. P53 labeling index (LI) was evaluated through immunohistochemistry in group-A patients. Multivariate linear stepwise regressions were performed based on group-A lesions to calculate P53-LI prediction from CT measurements. The receiver operating characteristic (ROC) curve analyses were performed to assess the performance of P53-LI prediction in predicting invasiveness and growth of nGGOs. The Cox regression analysis was conducted to identify correlation between P53-LI Prediction and volume doubling time (VDT) of lesions in group B.Diameter, VOL, MEN, STD, and the P53 LI showed significant differences between lesions of different pathological invasiveness (P < .01). By multivariate linear regressions, MEN and STD were identified as independent variables indicating P53 LI (P < .001); thus, an equation was established to calculate P53-LI Prediction as: P53LI Prediction = 0.013 ×  MEN + 0.024 × STD + 9.741 (R square = 0.411, P < .001). The P53-LI Prediction showed good performance, similar as the actual one, in differentiating pathological invasiveness of nGGOs. In addition, the P53-LI Prediction demonstrated excellent performance in predicting growth of nGGOs (AUC = 0.833, P < .001) and independently forecasted VDT of nGGOs (β = 1.773, P < .001).The P53-LI Prediction that was calculated from preoperative quantitative CT measurements of nGGOs indicates lesions' invasiveness and allows for predicting growth of nGGOs.

Three-Dimensional Ground Glass Opacity Ratio in CT Images Can Predict Tumor Invasiveness of Stage IA Lung Cancer

PURPOSE

We investigated the relationship between various parameters, including volumetric parameters, and tumor invasiveness according to the International Association for the Study of Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) classification.

MATERIALS AND METHODS

We retrospectively reviewed 99 patients with completely resected stage IA lung adenocarcinoma. The correlation between several parameters [one-dimensional ground glass opacity (1D GGO) ratio, two-dimensional (2D) GGO ratio, three-dimensional (3D) GGO ratio, 1D solid size, 2D solid size, and 3D solid size] and tumor invasiveness according to IASLC/ATS/ERS classification was investigated using receiver operating characteristic (ROC) analysis. Adenocarcinoma in situ and minimally invasive adenocarcinoma were referred to as noninvasive adenocarcinoma.

RESULTS

The areas under the curve (AUC) to predict invasive adenocarcinoma for the 1D, 2D, and 3D GGO ratios were 0.962, 0.967, and 0.971, respectively. The optimal cut-off values for the 1D, 2D, and 3D GGO ratios were 38%, 62%, and 74%, respectively. The AUC values for 1D, 2D, and 3D solid sizes to predict invasive adenocarcinoma were 0.933, 0.944, and 0.903, respectively. The optimal cut-off values for 1D, 2D, and 3D solid sizes were 1.2 cm, 1.5 cm², and 0.7 cm³, respectively. The difference in the ROC curves for 3D GGO ratio and 3D solid size was significant (p=0.01).

CONCLUSION

Computed tomography image-related parameters based on GGO were well correlated with and predictive of invasiveness according to IASLC/ATS/ERS classification. 3D GGO ratio was more strongly correlated with pathologic invasiveness than 3D solid size.

Pulmonary ground-glass nodules diagnosis: mean change rate of peak CT number as a discriminative factor of pathology during a follow-up

OBJECTIVE

We aimed to analyse the peak CT number (PEAK) in CT number histogram of ground-glass nodules (GGN), meaning the most frequent density of pixels in the image of pulmonary nodule, based on three-dimensional (3D) reconstructive model pre-operatively, and the mean rate of PEAK change (V-PEAK) during a follow-up of GGN for differential diagnosis between pre-invasive adenocarcinoma (PIA) and invasive adenocarcinoma (IAC).

METHODS

CT number histogram of pixels in GGN was made automatically by 3D measurement software. Diameter, total volume, PEAK and V-PEAK were measured from CT data sets of different groups classified by pathology, subtype and number of GGN, respectively.

RESULTS

Among all 102 cases, 47 were PIA, including atypical adenomatous hyperplasia (n = 29) and adenocarcinoma in situ (n = 18), and 55 were IAC, including minimally IAC (MIA, n = 4). By Wilcoxon test, PEAK of IAC was significantly higher than that of PIA (p < 0.001). By receiver operating curve analysis, area under the curve (AUC) was 0.857 and threshold -820.50 Hounsfield units (HU) for differentiation between PIA and IAC. V-PEAK of IAC was unexpectedly remarkably smaller than that of PIA (p < 0.001) with AUC and threshold being 0.810 and -0.829 HU day(-1), respectively.

CONCLUSION

Pre-operative PEAK and V-PEAK, which interpret and evaluate the change of volume and density of pulmonary nodule simultaneously from both exterior and interior perspectives, can help to distinguish IAC from PIA.

ADVANCES IN KNOWLEDGE

This study provided researchers of GGN another perspective, taking both volume and density of nodules into consideration for pathological evaluation.

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.

Clinical impact of high-attenuation and cystic areas on computed tomography in fibrotic idiopathic interstitial pneumonias

Background

Quantitative computed tomography (CT) analysis has been proposed as a means of objectively assessing fibrotic interstitial pneumonia (IP) including idiopathic pulmonary fibrosis (IPF). We investigated whether percentages of high-attenuation areas (HAA%) and cystic areas (CA%) quantified from CT images were useful as indices of fibrotic IP.

Methods

CT images of 74 patients with fibrotic idiopathic interstitial pneumonias (IPF, 36; non-specific interstitial pneumonia, 9; unclassifiable idiopathic interstitial pneumonia, 29) were analyzed via in-house computer software, which automatically calculated HAA%, CA%, mean lung density (MLD), standard deviation of lung density (SD-LD), kurtosis, and skewness from CT attenuation histograms. These indices were compared in each instance with physiologic measures, visual fibrosis score, clinical diagnosis, radiologic CT pattern, and prognosis.

Results

HAA% correlated significantly with physiologic measures and visual fibrosis score to a moderate extent (%forced vital capacity, r_s = −0.59; % carbon monoxide diffusion capacity, r_s = −0.43; fibrosis score, r_s = 0.23). Densitometric parameters (MLD, SD-LD, kurtosis, and skewness) correlated significantly with physiologic measures and fibrosis score (|r_s| = 0.28-0.59). CA% showed no association with pulmonary functions but differed significantly between IPF and other interstitial pneumonias (IPs) (1.50 ± 2.41 % vs. 0.41 ± 0.80 %; P < 0.01) and between the definite usual interstitial pneumonia (UIP) pattern and other patterns (1.48 ± 2.38 % vs. 0.55 ± 1.19 %; P < 0.01). On univariate analysis, HAA%, MLD, SD-LD, kurtosis, skewness, fibrosis score, and definite UIP pattern all correlated with survival, with kurtosis alone identified as a significant predictor of mortality on multivariate analysis (hazard ratio = 0.67; 95 % CI, 0.44-0.96; P = 0.03).

Conclusion

CA% and HAA% are novel quantitative CT indices with differing properties in fibrotic IP evaluations. HAA% largely reflects physiologic impairments, whereas CA% corresponds with diagnosis and HRCT pattern. Of the CT indices examined, kurtosis constituted the strongest predictor of mortality.

The association between baseline clinical-radiological characteristics and growth of pulmonary nodules with ground-glass opacity

INTRODUCTION

Pulmonary nodules with ground-glass opacity (GGO) are frequently encountered; there is little consensus on appropriate monitoring of them. The purpose of this study was to clarify which baseline clinical and radiological characteristics were associated with growth of these nodules.

METHODS

We retrospectively studied patients with pulmonary nodules that met the following criteria: (1) lesion diameter of ≤3 cm, (2) GGO proportion of ≥50%, and (3) observation without treatment in the prior 6 months. Between 1999 and 2013, 120 pulmonary lesions in 67 patients fulfilled inclusion criteria. We evaluated changes in lesion size on serial computed tomography. Two endpoints, "time to 2-mm growth" and "incidence of 2-mm growth", were analyzed using Cox proportional hazards and logistic regression models, respectively.

RESULTS

At the median observation period of 4.2 years, 34 lesions exhibited growth by ≥2 mm, whereas 86 remained unchanged. Smoking history and initial lesion diameter were statistically significant variables in both time-to-event and regression analyses. Hazard ratio (HR) for smoking history was 3.67 (P<0.01). Compared with those ≤1 cm, HRs for 1.1-2 cm and 2.1-3 cm lesions were 2.23 (P=0.08) and 5.08 (P=0.04), respectively. Odds ratio (OR) for smoking history was 6.51 (P<0.01); OR for lesion diameter of 1.1-3 cm (versus ≤1 cm) was 4.06 (P=0.02).

CONCLUSION

Smoking history and initial lesion diameter are robustly associated with GGO growth. These results suggest that large GGOs, especially in smokers, warrant close follow-up to accurately monitor lesion growth.

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.

Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society

This report is to complement the original Fleischner Society recommendations for incidentally detected solid nodules by proposing a set of recommendations specifically aimed at subsolid nodules. The development of a standardized approach to the interpretation and management of subsolid nodules remains critically important given that peripheral adenocarcinomas represent the most common type of lung cancer, with evidence of increasing frequency. Following an initial consideration of appropriate terminology to describe subsolid nodules and a brief review of the new classification system for peripheral lung adenocarcinomas sponsored by the International Association for the Study of Lung Cancer (IASLC), American Thoracic Society (ATS), and European Respiratory Society (ERS), six specific recommendations were made, three with regard to solitary subsolid nodules and three with regard to multiple subsolid nodules. Each recommendation is followed first by the rationales underlying the recommendation and then by specific pertinent remarks. Finally, issues for which future research is needed are discussed. The recommendations are the result of careful review of the literature now available regarding subsolid nodules. Given the complexity of these lesions, the current recommendations are more varied than the original Fleischner Society guidelines for solid nodules. It cannot be overemphasized that these guidelines must be interpreted in light of an individual's clinical history. Given the frequency with which subsolid nodules are encountered in daily clinical practice, and notwithstanding continuing controversy on many of these issues, it is anticipated that further refinements and modifications to these recommendations will be forthcoming as information continues to emerge from ongoing research.

Comparison between CT volume measurement and histopathological assessment of response to neoadjuvant therapy in rectal cancer

OBJECTIVES

The aim of this study was to compare volume measurements on computed tomography (CT) images with histopathological assessments of chemoradiotherapy (CRT)-induced tumor regression in locally advanced rectal cancer (RC).

METHODS

In 25 patients (13 males, 12 females; median age, 63 years; age range, 44-79 years) with locally advanced RC treated with preoperative CRT and surgery, two radiologists measured tumor volume on CT images before and after CRT. CT-based tumor volumetry and the modified response evaluation criteria in solid tumors (mRECISTs) were compared with T and N downstaging after CRT, and with the tumor regression grade (TRG).

RESULTS

Tumor volumes were significantly smaller on CT images after CRT. The tumors regressed in 52% (13/25), 36% (9/25) and 40% (10/25) of patients, based on T downstaging, TRG and mRECIST findings, respectively. In terms of T downstaging, the pre- and post-CRT tumor volumes of responders and non-responders to the treatment differed statistically, while their tumor volume reduction rates and volume reductions according to the 65% mRECIST threshold did not. In terms of N downstaging and TRG, the differences between the responders' and the non-responders' pre- and post-CRT tumor volumes, tumor volume reduction rates, and mRECIST thresholds were never statistically significant.

CONCLUSION

Measuring tumor size on CT images is of limited value in predicting the histopathological response to preoperative CRT in RC patients, so it may be unwise to select surgical treatment strategies based on CT volumetry.

Quantitative classification based on CT histogram analysis of non-small cell lung cancer: correlation with histopathological characteristics and recurrence-free survival

PURPOSE

Quantification of the CT appearance of non-small cell lung cancer (NSCLC) is of interest in a number of clinical and investigational applications. The purpose of this work is to present a quantitative five-category (α, β, γ, δ, and ɛ) classification method based on CT histogram analysis of NSCLC and to determine the prognostic value of this quantitative classification.

METHODS

Institutional review board approval and informed consent were obtained at the National Cancer Center Hospital. A total of 454 patients with NSCLC (maximum lesion size of 3 cm) were enrolled. Each lesion was measured using multidetector CT at the same tube voltage, reconstruction interval, beam collimation, and reconstructed slice thickness. Two observers segmented NSCLC nodules from the CT images by using a semi-automated three-dimensional technique. The two observers classified NSCLCs into one of five categories from the visual assessment of CT histograms obtained from each nodule segmentation result. Interobserver variability in the classification was computed with Cohen's κ statistic. Any disagreements were resolved by consensus between the two observers to define the gold standard of the classification. Using a classification and regression tree (CART), the authors obtained a decision tree for a quantitative five-category classification. To assess the impact of the nodule segmentation on the classification, the variability in classifications obtained by two decision trees for the nodule segmentation results was also calculated with the Cohen's κ statistic. The authors calculated the association of recurrence with prognostic factors including classification, sex, age, tumor diameter, smoking status, disease stage, histological type, lymphatic permeation, and vascular invasion using both univariate and multivariate Cox regression analyses.

RESULTS

The κ values for interobserver agreement of the classification using two nodule segmentation results were 0.921 (P < 0.001) and 0.903 (P < 0.001), respectively. The κ values for the variability in the classification task using two decision trees were 0.981 (P < 0.001) and 0.981 (P < 0.001), respectively. All the NSCLCs were classified into one of five categories (type α, n = 8; type β, n = 38; type γ, n = 103; type δ, n = 112; type ɛ, n = 193) by using a decision tree. Using a multivariate Cox regression analysis, the classification (hazard ratio 5.64; P = 0.008) and disease stage (hazard ratio 8.33; P < 0.001) were identified as being associated with an increased recurrence risk.

CONCLUSIONS

The quantitative five-category classifier presented here has the potential to provide an objective classification of NSCLC nodules that is strongly correlated with prognostic factors.

Measurement of focal ground-glass opacity diameters on CT images: interobserver agreement in regard to identifying increases in the size of ground-glass opacities

PURPOSE

To evaluate interobserver agreement in regard to measurements of focal ground-glass opacities (GGO) diameters on computed tomography (CT) images to identify increases in the size of GGOs.

MATERIALS AND METHODS

Approval by the institutional review board and informed consent by the patients were obtained. Ten GGOs (mean size, 10.4 mm; range, 6.5-15 mm), one each in 10 patients (mean age, 65.9 years; range, 58-78 years), were used to make the diameter measurements. Eleven radiologists independently measured the diameters of the GGOs on a total of 40 thin-section CT images (the first [n = 10], the second [n = 10], and the third [n = 10] follow-up CT examinations and remeasurement of the first [n = 10] follow-up CT examinations) without comparing time-lapse CT images. Interobserver agreement was assessed by means of Bland-Altman plots.

RESULTS

The smallest range of the 95% limits of interobserver agreement between the members of the 55 pairs of the 11 radiologists in regard to maximal diameter was -1.14 to 1.72 mm, and the largest range was -7.7 to 1.7 mm. The mean value of the lower limit of the 95% limits of agreement was -3.1 ± 1.4 mm, and the mean value of their upper limit was 2.5 ± 1.1 mm.

CONCLUSION

When measurements are made by any two radiologists, an increase in the length of the maximal diameter of more than 1.72 mm would be necessary in order to be able to state that the maximal diameter of a particular GGO had actually increased.

Ground-glass nodules on chest CT as imaging biomarkers in the management of lung adenocarcinoma

OBJECTIVE

The purpose of this article is to review the clinical significance of ground-glass nodules (GGNs) in the management of lung adenocarcinoma.

CONCLUSION

GGNs can serve as imaging biomarkers that represent the bronchioloalveolar carcinoma component in adenocarcinoma on histology and indicate a better prognosis in patients with lung adenocarcinoma. The evolution of GGNs reflects the multistep progression of adenocarcinoma. Despite the high probability of malignancy of GGNs, the possibility of overdiagnosis should be considered in the management of GGNs.

[Localized pure or mixed ground-glass lung opacities]

Localized ground-glass opacities (GGOs) have been recently individualized and account for between 2.9% and 19% of all pulmonary nodules detected in high-risk patients included in CT screening series for lung cancer. These opacities, nodular, lobular or flat, correspond to benign lesions (localised infectious and inflammatory diseases, focal interstitial fibrosis, and atypical alveolar hyperplasia) or malignant lesions (bronchioloalveolar carcinoma, early-stage adenocarcinoma and sometimes metastases). Localized GGOs are more likely to be malignant than solid nodules and prognosis is related to the percentage of the ground-glass component. However, doubling time of pure localized malignant GGOs is longer than mixed localized malignant GGOs and even longer than the doubling time of solid malignant nodules. Therefore, localized GGOs warrant a dedicated diagnostic workup.

CT characteristics of resolving ground-glass opacities in a lung cancer screening programme

PURPOSE

This study aimed at evaluating the computed tomography (CT) characteristics of resolving localized ground-glass opacities (GGOs) in a screening programme for lung cancer.

MATERIAL AND METHODS

280 patients at high-risk for lung cancer (221 men, 59 women; mean age, 58.6 years), divided into four groups (lung cancer history (n = 83), head and neck cancer history (n = 63), symptomatic (n = 88) and asymptomatic (n = 46) cigarette smokers), were included in a prospective trial with annual low-dose CT for lung cancer screening. We retrospectively reviewed all localized GGOs, analyzed the CT characteristics on initial CT scans and changes during follow-up (median 29.1 months). Variables associated with resolution of GGOs were tested using chi-square or Mann-Whitney tests.

RESULTS

A total of 75 GGOs were detected in 37 patients; 54.7% were present at baseline and 45.3% appeared on annual CT. During follow-up, 56.2% persisted and 43.8% disappeared. The resolving localized GGOs were significantly more often lobular GGOs (p = 0.006), polygonal in shape (p = 0.02), mixed (p = 0.003) and larger (p < 0.0001) than non-resolving localized GGOs.

CONCLUSION

Localized GGOs are frequent and many disappeared on follow-up. CT characteristics of resolving GGOs show significant differences compared to persistent ones. This study emphasizes the importance of short-term CT follow-up in subjects with localized GGOs.

Volumetric measurement pulmonary ground-glass opacity nodules with multi-detector CT: effect of various tube current on measurement accuracy--a chest CT phantom study

RATIONALE AND OBJECTIVES

The purpose of this study was to evaluate the effect of various tube currents on the accuracy of volumetric measurements of ground-glass opacity (GGO) nodules using a chest phantom.

MATERIALS AND METHODS

A chest phantom containing 13 artificial GGO nodules with known volumes was scanned using a 64-slice computed tomographic scanner at different tube currents (30, 60, 90, 120, 150, 180, and 210 mA). Volumetric measurements were performed using software. The relative percentage error and the absolute percentage error between the volume measures on computed tomography and the reference-standard volumes were calculated. Correlations between the mean absolute percentage error and the mean attenuation of nodules and between the ratio of solid component and the mean attenuation of nodules were analyzed.

RESULTS

The relative percentage errors showed that there was substantial underestimation of nodule volumes at 30, 60, and 90 mA and substantial overestimation of volumes at 120, 150, 180, and 210 mA, but there was no statistically significant difference in absolute percentage errors (P = .876). Pearson's correlation coefficient of the mean absolute percentage errors of nodules on volumetric measurement versus the mean attenuation value of nodules showed a negative correlation, and the ratio of solid component to whole nodule versus the mean attenuation of nodules showed a positive correlation.

CONCLUSION

Volume measurement is a promising method for the quantification of GGO nodule volume. It is important to know that different tube currents can affect the accuracy of volumetric measurements.

Early changes in tumor size in patients treated for advanced stage nonsmall cell lung cancer do not correlate with survival

BACKGROUND

In clinical trials, change in tumor size is used to stratify patients into response categories. The objective of the current study was to: 1) determine whether early change in the tumor size were correlated with survival in patients with advanced nonsmall cell lung cancer (NSCLC) using modified response categories from the Response Evaluation Criteria in Solid Tumors (RECIST), and 2) to determine whether there was an optimal percentage change in tumor size that could be used to define a partial response that also correlated with survival.

METHODS

A total of 99 consecutive patients presenting for the treatment of advanced NSCLC during the year 2003 who had computed tomography (CT) scans before and after treatment available for review were included in the study. The largest target thoracic lesion was measured on CT before treatment, and again 2 months to 3 months after the initiation of treatment. Percent change in tumor size was calculated. The relation between tumor response and patient survival was investigated.

RESULTS

There was no definite relation noted between early tumor response and patient survival (P = .754). Patients who had any initial reduction in tumor size were not found to have a significantly different survival compared with patients with initial disease progression (P = .580). In addition, there was no particular percent reduction in tumor size that was found to optimally correlate with survival.

CONCLUSIONS

There is no evidence of a relation between early changes in tumor size and survival among patients with advanced stage NSCLC. To predict survival in patients with advanced NSCLC, response criteria other than change in lesion size are needed.