Retrospective assessment of interobserver agreement and accuracy in classifications and measurements in subsolid nodules with solid components less than 8mm: which window setting is better?

Comparative analysis of three-dimensional and two-dimensional models for predicting the malignancy probability of subsolid nodules

AIM

To construct three-dimensional (3D) and two-dimensional (2D) models to predict the malignancy probability of subsolid nodules (SSNs) and compare their effectiveness.

MATERIALS AND METHODS

A total of 371 SSNs from 332 patients, collected between January 2020 and January 2024, were included in the study. The SSNs were divided into a training set for constructing the models and a test set for validating the models. Models were developed using binary logistic backward regression, based on factors that showed significant differences in univariate analyses. The performance of the models was assessed using the area under the curve (AUC) of the receiver operating characteristic (ROC). The AUCs of different models were compared using the DeLong test.

RESULTS

The AUCs for the two 3D models, one 2D model, and the Brock model were 0.785 (0.733-0.836), 0.776 (0.723-0.829), 0.764 (0.710-0.818), and 0.738 (0.679-0.798) in the training set. In the test set, these AUCs were 0.817 (0.706-0.928), 0.796 (0.679-0.913), 0.771 (0.647-0.895), and 0.790 (0.678-0.903). The two 3D models demonstrated statistically significant differences from the Brock model in the training set (P=0.024 and P=0.046). None of the four models showed significant differences in the test set (all P>0.05).

CONCLUSION

The 3D models outperform both the 2D model and the Brock model in predicting the malignancy probability of SSNs, and the 3D model incorporating volume, mean CT attenuation value, and lobulation as factors performed the best.

A nomogram based on the quantitative and qualitative features of CT imaging for the prediction of the invasiveness of ground glass nodules in lung adenocarcinoma

PURPOSE

Based on the quantitative and qualitative features of CT imaging, a model for predicting the invasiveness of ground-glass nodules (GGNs) was constructed, which could provide a reference value for preoperative planning of GGN patients.

MATERIALS AND METHODS

Altogether, 702 patients with GGNs (including 748 GGNs) were included in this study. The GGNs operated between September 2020 and July 2022 were classified into the training group (n = 555), and those operated between August 2022 and November 2022 were classified into the validation group (n = 193). Clinical data and the quantitative and qualitative features of CT imaging were harvested from these patients. In the training group, the quantitative and qualitative characteristics in CT imaging of GGNs were analyzed by using performing univariate and multivariate logistic regression analyses, followed by constructing a nomogram prediction model. The differentiation, calibration, and clinical practicability in both the training and validation groups were assessed by the nomogram models.

RESULTS

In the training group, multivariate logistic regression analysis disclosed that the maximum diameter (OR = 4.707, 95%CI: 2.06-10.758), consolidation/tumor ratio (CTR) (OR = 1.027, 95%CI: 1.011-1.043), maximum CT value (OR = 1.025, 95%CI: 1.004-1.047), mean CT value (OR = 1.035, 95%CI: 1.008-1.063; P = 0.012), spiculation sign (OR = 2.055, 95%CI: 1.148-3.679), and vascular convergence sign (OR = 2.508, 95%CI: 1.345-4.676) were independent risk parameters for invasive adenocarcinoma. Based on these findings, we established a nomogram model for predicting the invasiveness of GGN, and the AUC was 0.910 (95%CI: 0.885-0.934) and 0.902 (95%CI: 0.859-0.944) in the training group and the validation group, respectively. The internal validation of the Bootstrap method showed an AUC value of 0.905, indicating a good differentiation of the model. Hosmer-Lemeshow goodness of fit test for the training and validation groups indicated that the model had a good fitting effect (P > 0.05). Furthermore, the calibration curve and decision analysis curve of the training and validation groups reflected that the model had a good calibration degree and clinical practicability.

CONCLUSION

Combined with the quantitative and qualitative features of CT imaging, a nomogram prediction model can be created to forecast the invasiveness of GGNs. This model has good prediction efficacy for the invasiveness of GGNs and can provide help for the clinical management and decision-making of GGNs.

Evaluation and Management of Indeterminate Pulmonary Nodules on Chest Computed Tomography in Asymptomatic Subjects: The Principles of Nodule Guidelines

With the rapidly increasing number of chest computed tomography (CT) examinations, the question of how to manage lung nodules found in asymptomatic patients has become increasingly important. Several nodule management guidelines have been developed that can be applied to incidentally found lung nodules (the Fleischner Society guideline), nodules found during lung cancer screening (International Early Lung Cancer Action Program protocol [I-ELCAP] and Lung CT Screening Reporting and Data System [Lung-RADS]), or both (American College of Chest Physicians guideline [ACCP], British Thoracic Society guideline [BTS], and National Comprehensive Cancer Network guideline [NCCN]). As the radiologic nodule type (solid, part-solid, and pure ground glass) and size are significant predictors of a nodule's nature, most guidelines categorize nodules in terms of these characteristics. Various methods exist for measuring the size of nodules, and the method recommended in each guideline should be followed. The diameter can be manually measured as a single maximal diameter or as an average of two-dimensional diameters, and software can be used to obtain volumetric measurements. It is important to properly evaluate and measure nodules and familiarize ourselves with the relevant guidelines to appropriately utilize medical resources and minimize unnecessary radiation exposure to patients.

Differentiation of primary lung cancer from solitary lung metastasis in patients with colorectal cancer: a retrospective cohort study

Background

This study aimed to evaluate the computed tomography (CT) features of solitary pulmonary nodule (SPN), which can be a non-invasive diagnostic tool to differentiate between primary lung cancer (LC) and solitary lung metastasis (LM) in patients with colorectal cancer (CRC).

Methods

This retrospective study included SPNs resected in CRC patients between January 2011 and December 2019. The diagnosis of primary LC or solitary LM was based on histopathologic report by thoracoscopic wedge resection. Chest CT images were assessed by two thoracic radiologists, and CT features were identified by consensus. Predictive parameters for the discrimination of primary LC from solitary LM were evaluated using multivariate logistic regression analysis.

Results

We analyzed CT data of 199 patients (mean age, 65.95 years; 131 men and 68 women). The clinical characteristic of SPNs suggestive of primary LC rather than solitary LM was clinical stages I–II CRC (P < 0.001, odds ratio [OR] 21.70). The CT features of SPNs indicative of primary LC rather than solitary LM were spiculated margin (quantitative) (P = 0.020, OR 8.34), sub-solid density (quantitative) (P < 0.001, OR 115.56), and presence of an air bronchogram (quantitative) (P = 0.032, OR 5.32).

Conclusions

Quantitative CT features and clinical characteristics of SPNs in patients with CRC could help differentiate between primary LC and solitary LM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02131-7.

A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules

This study aims to predict the histological invasiveness of pulmonary adenocarcinoma spectrum manifesting with subsolid nodules ≦ 3 cm using the preoperative CT-based radiomic approach. A total of 186 patients with 203 SSNs confirmed with surgically pathologic proof were retrospectively reviewed from February 2016 to March 2020 for training cohort modeling. The validation cohort included 50 subjects with 57 SSNs confirmed with surgically pathologic proof from April 2020 to August 2020. CT-based radiomic features were extracted using an open-source software with 3D nodular volume segmentation manually. The association between CT-based conventional features/selected radiomic features and histological invasiveness of pulmonary adenocarcinoma status were analyzed. Diagnostic models were built using conventional CT features, selected radiomic CT features and experienced radiologists. In addition, we compared diagnostic performance between radiomic CT feature, conventional CT features and experienced radiologists. In the training cohort of 203 SSNs, there were 106 invasive lesions and 97 pre-invasive lesions. Logistic analysis identified that a selected radiomic feature named GLCM_Entropy_log10 was the predictor for histological invasiveness of pulmonary adenocarcinoma spectrum (OR: 38.081, 95% CI 2.735–530.309, p = 0.007). The sensitivity and specificity for predicting histological invasiveness of pulmonary adenocarcinoma spectrum using the cutoff value of CT-based radiomic parameter (GLCM_Entropy_log10) were 84.8% and 79.2% respectively (area under curve, 0.878). The diagnostic model of CT-based radiomic feature was compared to those of conventional CT feature (morphologic and quantitative) and three experienced radiologists. The diagnostic performance of radiomic feature was similar to those of the quantitative CT feature (nodular size and solid component, both lung and mediastinal window) in prediction invasive pulmonary adenocarcinoma (IPA). The AUC value of CT radiomic feature was higher than those of conventional CT morphologic feature and three experienced radiologists. The c-statistic of the training cohort model was 0.878 (95% CI 0.831–0.925) and 0.923 (0.854–0.991) in the validation cohort. Calibration was good in both cohorts. The diagnostic performance of CT-based radiomic feature is not inferior to solid component (lung and mediastinal window) and nodular size for predicting invasiveness. CT-based radiomic feature and nomogram could help to differentiate IPA lesions from preinvasive lesions in the both independent training and validation cohorts. The nomogram may help clinicians with decision making in the management of subsolid nodules.

Effects of kernel and window setting combinations on assessments of small and complicated vasculature in computed tomography angiographic images of dogs with and without tumors

OBJECTIVE

To evaluate the effect of kernel and window settings on the assessment of small and complicated vasculature in CT angiographic (CTA) images of kidneys, jejunum with mesentery, and tumors in dogs.

ANIMALS

20 healthy dogs and 20 dogs with tumors.

PROCEDURES

Images from CTA performed previously in dogs were reconstructed with 3 different combinations of kernel and window settings (soft kernel with soft tissue window, soft kernel with bone window, and sharp kernel with bone window), and reconstructed images of the left kidney and the jejunum with the mesentery in healthy dogs and tumors in affected dogs were evaluated by reviewers blinded to the settings.

RESULTS

For images of kidney and jejunum with mesentery, reviewers' scores for the conspicuity of vascularity in the arterial phase and the differentiation of the organs from the adjacent structures were significantly higher when viewed in bone window (vs soft tissue window) regardless of kernel setting. For images of head and gastrointestinal tumors, reviewers' scores for differentiation of intratumoral vasculature were higher when viewed in sharp kernel with bone window versus other setting combinations. However, the conspicuity of gastrointestinal, hepatic, or splenic tumoral vessels from the adjacent structures had higher reviewer scores for images in soft kernel with soft tissue window, compared with other setting combinations.

CONCLUSIONS AND CLINICAL RELEVANCE

Results indicated that reconstruction of CTA images with sharp kernel combined with bone window settings might have clinical utility in evaluating and planning treatments for dogs with various tumors; however, additional research is warranted to further identify effects of various kernel and window setting combinations on assessments of small and complicated vasculature in larger and more diverse populations of dogs with and without tumors.

Clinical T category for lung cancer staging: A pragmatic approach for real-world practice

BACKGROUND

To determine which components should be measured and which window settings are appropriate for computerized tomography (CT) size measurements of lung adenocarcinoma (ADC) and to explore interobserver agreement and accuracy according to the eighth edition of TNM staging.

METHODS

A total of 165 patients with surgically resected lung ADC earlier than stage 3A were included in this study. One radiologist and two pulmonologists independently measured the total and solid sizes of components of tumors on different window settings and assessed solidity. CT measurements were compared with pathologic size measurements.

RESULTS

In categorizing solidity, 25% of the cases showed discordant results among observers. Measuring the total size of a lung adenocarcinoma predicted pathologic invasive components to a degree similar to measuring the solid component. Lung windows were more accurate (intraclass correlation [ICC] = 0.65-0.81) than mediastinal windows (ICC = 0.20-0.72) at predicting pathologic invasive components, especially in a part-solid nodule. Interobserver agreements for measurement of solid components were good with little significant difference (lung windows, ICC = 0.89; mediastinal windows, ICC = 0.91). A high level of interobserver agreement was seen between the radiologist and pulmonologists and between residents (from the division of pulmonology and critical care) versus a fellow (from the division of pulmonology and critical care) on different windows.

CONCLUSIONS

A considerable percentage (25%) of discrepancies was encountered in categorizing the solidity of lesions, which may decrease the accuracy of measurements. Lung window settings may be superior to mediastinal windows for measuring lung ADCs, with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components.

KEY POINTS

SIGNIFICANT FINDINGS OF THE STUDY: Lung window settings are better for evaluating part-solid lung adenocarcinoma (ADC), with comparable interobserver agreement and moderate accuracy for predicting pathologic invasive components. The considerable percentage (25%) of discrepancies in categorizing solidity of the lesions may also have decreased the accuracy of measurements.

WHAT THIS STUDY ADDS

For accurate measurement and categorization of lung ADC, robust quantitative analysis is needed rather than a simple visual assessment.

Assessing invasiveness of subsolid lung adenocarcinomas with combined attenuation and geometric feature models

The aim of this study was to develop and test multiclass predictive models for assessing the invasiveness of individual lung adenocarcinomas presenting as subsolid nodules on computed tomography (CT). 227 lung adenocarcinomas were included: 31 atypical adenomatous hyperplasia and adenocarcinomas in situ (class H1), 64 minimally invasive adenocarcinomas (class H2) and 132 invasive adenocarcinomas (class H3). Nodules were segmented, and geometric and CT attenuation features including functional principal component analysis features (FPC1 and FPC2) were extracted. After a feature selection step, two predictive models were built with ordinal regression: Model 1 based on volume (log) (logarithm of the nodule volume) and FPC1, and Model 2 based on volume (log) and Q.875 (CT attenuation value at the 87.5% percentile). Using the 200-repeats Monte-Carlo cross-validation method, these models provided a multiclass classification of invasiveness with discriminative power AUCs of 0.83 to 0.87 and predicted the class probabilities with less than a 10% average error. The predictive modelling approach adopted in this paper provides a detailed insight on how the value of the main predictors contribute to the probability of nodule invasiveness and underlines the role of nodule CT attenuation features in the nodule invasiveness classification.

Radiological-pathological correlation of subsolid pulmonary nodules: A single centre retrospective evaluation of the 2011 IASLC adenocarcinoma classification system

INTRODUCTION

The 2011 IASLC classification system proposes guidelines for radiologists and pathologists to classify adenocarcinomas spectrum lesions as preinvasive, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). IA portends the worst clinical prognosis, and the imaging distinction between MIA and IA is controversial.

MATERIALS AND METHODS

Subsolid pulmonary nodules resected by microcoil localization over a three-year period were retrospectively reviewed by three chest radiologists and a pulmonary pathologist. Nodules were classified radiologically based on preoperative computed tomography (CT), with the solid nodule component measured on mediastinal windows applied to high-frequency lung kernel reconstructions, and pathologically according to 2011 IASLC criteria. Radiology interobserver and radiological-pathological variability of nodule classification, and potential reasons for nodule classification discordance were assessed.

RESULTS

Seventy-one subsolid nodules in 67 patients were included. The average size of invasive disease focus at histopathology was 5 mm (standard deviation 5 mm). Radiology interobserver agreement of nodule classification was good (Cohen's Kappa = 0.604, 95 % CI: 0.447 to 0.761). Agreement between consensus radiological interpretation and pathological category was fair (Cohen's Kappa = 0.236, 95 % CI: 0.054-0.421). Radiological and pathological nodule classification were concordant in 52 % (37 of 71) of nodules. The IASLC proposed CT solid component cut-off of 5 mm to distinguish MIA and IA yielded a sensitivity of 59 % and specificity of 80 %. Common reasons for nodule classification discordance included multiple solid components within a nodule on CT, scar and stromal collapse at pathology, and measurement variability.

CONCLUSION

Solid component(s) within persistent part-solid pulmonary nodules raise suspicion for invasive adenocarcinoma. Preoperative imaging classification is frequently discordant from final pathology, reflecting interpretive and technical challenges in radiological and pathological analysis.

CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules

OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm²; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm²), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.

Utility of FDG PET/CT for Preoperative Staging of Non-Small Cell Lung Cancers Manifesting as Subsolid Nodules With a Solid Portion of 3 cm or Smaller

OBJECTIVE. The objective of our study was to investigate the utility of FDG PET/CT for the preoperative staging of subsolid non-small cell lung cancers (NSCLCs) with a solid portion size of 3 cm or smaller. MATERIALS AND METHODS. We retrospectively enrolled 855 patients with pathologically proven NSCLCs manifesting as subsolid nodules with a solid portion of 3 cm or smaller on CT. We then compared the diagnostic performances of FDG PET/CT and chest CT for detecting lymph node (LN), intrathoracic, or distant metastases in patients who underwent preoperative chest CT and FDG PET/CT. After propensity score matching, we compared the diagnostic performance of FDG PET/CT in the group who underwent both chest CT and FDG PET/CT with that of chest CT in patients who did not undergo FDG PET/CT. RESULTS. There were LN metastases in 25 of 765 patients (3.3%) who underwent surgical LN dissection or biopsy and intrathoracic or distant metastasis in two of 855 patients (0.2%). For LN staging, FDG PET/CT showed a sensitivity of 44.0%, specificity of 81.5%, positive predictive value of 9.6%, negative predictive value of 97.0%, and accuracy of 79.9%, which were lower than those of chest CT for accuracy (p < 0.0001). FDG PET/CT could not accurately detect any intrathoracic or distant metastasis. After propensity score matching, the diagnostic accuracy for LN staging of FDG PET/CT in the group who underwent both CT and FDG PET/CT was lower than that of chest CT in the group who did not undergo FDG PET/CT (p = 0.002), and the diagnostic accuracy for intrathoracic and distant metastases was not different (p > 0.999). CONCLUSION. FDG PET/CT has limited utility in preoperatively detecting LN or distant metastasis in patients with subsolid NSCLCs with a solid portion size of 3 cm or smaller.

A Novel Algorithm to Differentiate Between Multiple Primary Lung Cancers and Intrapulmonary Metastasis in Multiple Lung Cancers With Multiple Pulmonary Sites of Involvement

INTRODUCTION

Differentiating between multiple primary lung cancer (MPLC) and intrapulmonary metastasis (IPM) is critical for developing a therapeutic strategy to treat multiple lung cancers with multiple pulmonary sites of involvement.

METHODS

We retrospectively included 252 lesions (126 pairs) from 126 patients with surgically resected multiple lung adenocarcinomas. Each pair was classified as MPLC or IPM based on histopathologic findings as the reference standard. A novel algorithm was established with four sequential decision steps based on the combination of computed tomography (CT) lesion types (step 1), CT lesion morphology (step 2), difference of maximal standardized uptake values on positron-emission tomography/CT (step 3), and presence of N2/3 lymph node metastasis or distant metastasis (step 4). The diagnostic accuracy of the algorithm was analyzed. Performances of 11 observers were assessed without and with knowledge of algorithm.

RESULTS

Among 126 pairs, 90 (71.4%) were classified as MPLCs and 36 (28.6%) as IPMs. On applying the diagnostic algorithm, the overall accuracy for diagnosis of IPM among conclusive cases up to step 4 was 88.9%, and 65 and 44 pairs were correctly diagnosed based on step 1 and step 2, respectively. Specificity and positive predictive value for diagnosis of IPM increased significantly in all observers compared with reading rounds without the algorithm.

CONCLUSIONS

Application of the algorithm based on comprehensive information on clinical and imaging variables can allow differentiation between MPLCs and IPMs. When both of two suspected malignant lesions appear as solid predominant lesions without spiculation or air-bronchogram on CT, IPM should be considered.

Implementation of lung cancer screening at the national level: Polish example

In Poland the national demonstration lung cancer screening program is about to be started in 2019. We share our concerns and discussing most important topics to be resolved while preparing such a program. The decisions made are virtually based on available scientific data and the results of two randomized controlled trials but also on the personal experience gained during the lung cancer screening studies performed in Poland. The most important and comprehensive guidelines and statements, both European and American, have been searched to find an optimal solution adjusted to the Polish national circumstances-as we assume that should be done in each country implementing such a program.

Qualitative CT Criterion for Subsolid Nodule Subclassification: Improving Interobserver Agreement and Pathologic Correlation in the Adenocarcinoma Spectrum

RATIONALE AND OBJECTIVES

The main aim of this study was to evaluate the clinical validity and correlation with pathologic invasiveness in the pulmonary adenocarcinoma spectrum based on the novel qualitative computed tomography criterion for subsolid nodule (SSN) classification, which classified SSN into pure ground-glass nodule, heterogeneous ground-glass nodule, and part-solid nodule. In addition, we compared the performance of the conventional and novel classifications.

MATERIALS AND METHODS

The computed tomography images of 41 SSN nodules were interpreted by six observers independently, and the SSN characteristics were classified according to both the conventional and the novel classification systems. Each observer assessed 41 nodules in two different classifications separated by a minimum of 8 weeks. The kappa (κ) coefficient test was used to determine the reliability. The correlation between pulmonary adenocarcinoma spectrum and the SSN classification was analyzed with Spearman correlation coefficients.

RESULTS

Interobserver agreement (κ) was 0.702 (range 0.42-0.89) and 0.707 (range 0.58-0.88) for the conventional and the novel classifications for SSN, respectively, and intraobserver agreement (κ) was 0.92 and 0.88 for the conventional and the novel classifications for SSN, respectively. The novel SSN classification (correlation coefficient range 0.622-0.732) is more strongly correlated with the pathologic invasiveness degree of lesions in adenocarcinoma spectrum than the conventional SSN classification (correlation coefficient range 0.458-0.644).

CONCLUSIONS

The agreement between observers on the novel SSN classification system was good and had better correlation with pathologic invasiveness than the conventional SSN classification. Further studies are needed to confirm these results on interobserver agreement.

A simple prediction model using size measures for discrimination of invasive adenocarcinomas among incidental pulmonary subsolid nodules considered for resection

OBJECTIVES

To develop and validate a concise prediction model using simple size measures for the discrimination of invasive pulmonary adenocarcinomas (IPAs) among incidentally detected subsolid nodules (SSNs) considered for resection and to compare its diagnostic performance with the Brock model.

METHODS

This retrospective institutional review board-approved study included 427 surgically resected SSNs (121 preinvasive lesions/minimally invasive adenocarcinomas [MIAs] and 306 IPAs) from 407 patients. After stratified random splitting of the study population into the training and validation sets (3:1), a simple logistic model was constructed using nodule size, solid proportion, and type for the differentiation of IPAs. Diagnostic performance of this model was compared with the original and modified Brock models using the DeLong method for area under the receiver-operating characteristic curve (AUC) and McNemar test for diagnostic sensitivity and specificity.

RESULTS

Our proposed model had an AUC of 0.859 in the validation set, while the original Brock model showed an AUC of 0.775 (p = 0.035) and the modified Brock model exhibited an AUC of 0.787 (p = 0.006). At equally high specificity of 90%, our proposed model exhibited significantly higher sensitivity (65.8%) than the original and modified Brock models (38.2% and 50.0%; p < 0.001 and 0.008, respectively).

CONCLUSIONS

Our study results demonstrated that the proposed concise model outperformed both Brock models, demonstrating its potential to be utilized as a specific tool to differentiate IPAs from preinvasive lesions and MIAs, which were considered for resection. External validation studies are warranted for the population with incidentally detected SSNs including small SSNs to confirm our observations.

KEY POINTS

• Size measures provided sufficient information for the risk stratification of surgical candidate incidental subsolid nodules. • Our proposed concise model showed higher diagnostic performance than the Brock model for incidentally detected subsolid nodules. • Our proposed model can specifically differentiate invasive adenocarcinomas among incidentally detected subsolid nodules and reduce overtreatment for indolent subsolid nodules.

Implication of total tumor size on the prognosis of patients with clinical stage IA lung adenocarcinomas appearing as part-solid nodules: Does only the solid portion size matter?

OBJECTIVES

The aim was to investigate the effect of clinico-radiologic variables, including total tumor (T_total) size and clinical T category, on the prognosis of patients with stage IA (T1N0M0) lung adenocarcinomas appearing as part-solid nodules (PSNs).

METHODS

This institutional review board-approved retrospective study included 506 patients (male:female = 200:306; median age, 62 years) with PSNs of the adenocarcinoma spectrum in clinical stage IA who underwent standard lobectomy at a single tertiary medical center. Prognostic stratification of the patients in terms of disease-free survival was analyzed with variables including age, sex, T_total size, solid portion size, clinical T category, and tumor location using univariate and subsequent multivariate Cox regression analysis. Subgroup analysis was performed to reveal the effect of the T_total size at each clinical T category.

RESULTS

Multivariate Cox regression analysis demonstrated that T_total size*cT1b [interaction term; hazard ratio (HR) = 1.091; 95% confidence interval (CI): 1.015, 1.173; p = 0.019] and cT1c (HR = 68.436; 95% CI: 2.797, 1674.415; p = 0.010) were independent risk factors for the tumor recurrence. When patients with cT1b were dichotomized based on a T_total size cutoff of 3.0 cm, PSNs with T_total > 3.0 cm showed a significantly worse outcome (HR = 3.796; 95% CI: 1.006, 14.317; p = 0.049). No significant difference was observed in the probability of recurrence between cT1b with T_total > 3.0 cm and cT1c (p = 0.915).

CONCLUSIONS

T_total size is a significant prognostic factor in adenocarcinoma patients in cT1b without lymph node or distant metastasis. PSNs in cT1b with T_total > 3.0 cm have a comparable risk of lung cancer recurrence to those in cT1c.

KEY POINTS

• Current T descriptor was a powerful prognostic factor in stage IA adenocarcinomas appearing as part-solid nodules. • Total tumor size further stratified risk of recurrence of adenocarcinomas in cT1b. • Upstaging of tumors in cT1b with total tumor size > 3.0 cm may be more appropriate.

Effect of CT window settings on size measurements of the solid component in subsolid nodules: evaluation of prediction efficacy of the degree of pathological malignancy in lung adenocarcinoma

OBJECTIVE

To investigate the predictive value of size measurements of the solid components in pulmonary subsolid nodules with different CT window settings and to evaluate the degree of pathological malignancy in lung adenocarcinoma.  Methods: The preoperative chest CT images and pathological data of 125 patients were retrospectively evaluated. The analysis included 127 surgically resected lung adenocarcinomas that manifested as subsolid nodules. All subsolid nodules were divided into two groups: 69 in group A, including 22 adenocarcinomas in situ (AIS) and 47 minimally invasive adenocarcinomas (MIA); 58 in group B that included invasive pulmonary adenocarcinomas (IPA). The size of the solid component in the pulmonary subsolid nodules were calculated in one dimensional, two dimensional and three dimensional views using lung and mediastinal windows that were recorded as 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW and 3D-SCMW, respectively. Furthermore, the volume of solid component with a threshold of -300HU was measured using lung window (3D-SCT). All the quantitative features were evaluated by the Mann-Whitney U test. Multivariate analysis was used to identify the significant predictor of the degree of pathological malignancy.  Results: The 1D-SCLW, 2D-SCLW, 3D-SCLW, 1D-SCMW, 2D-SCMW, 3D-SCMW and 3D-SCT views of group B were significantly larger than those of group A (p < 0.001). The multivariate logistic regression analysis indicated that 3D-SCT (OR = 1.018, 95%CI: 1.005 ~ 1.03, p <0.05＝was the independent predictive factor. The larger SCT was significantly associated with IPAs.  Conclusion: 3D-SCT of subsolid nodules during preoperative CT can be used to predict the degree of pathological malignancy in lung adenocarcinoma, which may provide a more objective and convenient selection criterion for clinical application.  Advances in knowledge:  Applying threshold of -300 HU with lung window setting would be better than other window setting for the evaluation of solid component in subsolid nodules. Computer-aided volumetry of the solid component in subsolid nodules can more accurately predict the degree of pathological malignancy than the other dimensional measurements.

Evaluation of T categories for pure ground-glass nodules with semi-automatic volumetry: is mass a better predictor of invasive part size than other volumetric parameters?

OBJECTIVES

This study aimed to investigate the diagnostic advantage of nodule mass in differentiating invasive pulmonary adenocarcinomas (IPAs) among pure ground-glass nodules (pGGNs) over other volumetric measurements. Another aim of this study was to analyse the correlation between volumetric measurements on computed tomography (CT) scans and the pathological invasive component size.

METHODS

This Institutional Review Board-approved retrospective study included 117 patients (men:women = 53:64; mean age, 57.3 years) with 117 pGGNs. Semi-automatic segmentation was performed for all nodules, and volumetric measurements, such as nodule volume, attenuation, mass, two-dimensional (2D) average diameter and three-dimensional (3D) longest diameter, were obtained. Receiver operating characteristic (ROC) curve analyses were performed to evaluate the diagnostic performances of the volumetric parameters in discriminating IPAs. Spearman correlation coefficients were calculated between the volumetric measurements and the invasive component size.

RESULTS

Area under the ROC curve for mass was 0.792 (95% CI, 0.691-0.872) in non-enhanced CT and 0.730 (95% CI, 0.607-0.832) in contrast-enhanced CT. Nodule mass was not superior to 2D average diameter for the differentiation of IPAs in both non-enhanced (0.792 vs 0.780; p = 0.501) CT and contrast-enhanced CT scans (0.730 vs 0.700; p = 0.319). The correlation between the volumetric measurements (mass, 3D longest diameter and 2D average diameter) and the invasive component size was moderate (Spearman's rho, 0.401-0.422) in non-enhanced CT and weak (Spearman's rho, 0.276-0.310) in contrast-enhanced CT.

CONCLUSIONS

Nodule mass measurement had no strength over other volumetric parameters for the prediction of pathological invasiveness in the diagnosis of pGGNs.

KEY POINTS

• Mass is not superior to other volumetric measurements for the diagnosis of pure ground-glass nodules. • Mass and two-dimensional average diameter exhibited comparable performance for the discrimination of invasive adenocarcinomas among pure ground-glass nodules. • The diagnostic performance of volumetric measurements was lower on contrast-enhanced CT scans. • The correlation between the volumetric measurements and the invasive component size was moderate on non-enhanced CT scans and weak on contrast-enhanced CT scans.

Diameter of the Solid Component in Subsolid Nodules on Low-Dose Unenhanced Chest Computed Tomography: Measurement Accuracy for the Prediction of Invasive Component in Lung Adenocarcinoma

Objective

To determine if measurement of the diameter of the solid component in subsolid nodules (SSNs) on low-dose unenhanced chest computed tomography (CT) is as accurate as on standard-dose enhanced CT in prediction of pathological size of invasive component of lung adenocarcinoma.

Materials and Methods

From February 2012 to October 2015, 114 SSNs were identified in 105 patients that underwent low-dose unenhanced and standard-dose enhanced CT pre-operatively. Three radiologists independently measured the largest diameter of the solid component. Intraclass correlation coefficients (ICCs) were used to assess inter-reader agreement. We estimated measurement differences between the size of solid component and that of invasive component. We measured diagnostic accuracy of the prediction of invasive adenocarcinoma using a size criterion of a solid component ≥ 6 mm, and compared them using a generalized linear mixed model.

Results

Inter-reader agreement was excellent (ICC, 0.84.0.89). The mean ± standard deviation of absolute measurement differences between the solid component and invasive component was 4 ± 4 mm in low-dose unenhanced CT and 5 ± 4 mm in standard-dose enhanced CT. Diagnostic accuracy was 81.3% (95% confidence interval, 76.7.85.3%) in low-dose unenhanced CT and 76.6% (71.8.81.0%) in standard-dose enhanced CT, with no statistically significant difference (p = 0.130).

Conclusion

Measurement of the diameter of the solid component of SSNs on low-dose unenhanced chest CT was as accurate as on standard-dose enhanced CT for predicting the invasive component. Thus, low-dose unenhanced CT may be used safely in the evaluation of patients with SSNs.

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.

Pulmonary subsolid nodules: value of semi-automatic measurement in diagnostic accuracy, diagnostic reproducibility and nodule classification agreement

OBJECTIVES

We hypothesized that semi-automatic diameter measurements would improve the accuracy and reproducibility in discriminating preinvasive lesions and minimally invasive adenocarcinomas from invasive pulmonary adenocarcinomas appearing as subsolid nodules (SSNs) and increase the reproducibility in classifying SSNs.

METHODS

Two readers independently performed semi-automatic and manual measurements of the diameters of 102 SSNs and their solid portions. Diagnostic performance in predicting invasive adenocarcinoma based on diameters was tested using logistic regression analysis with subsequent receiver operating characteristic curves. Inter- and intrareader reproducibilities of diagnosis and SSN classification according to Fleischner's guidelines were investigated for each measurement method using Cohen's κ statistics.

RESULTS

Semi-automatic effective diameter measurements were superior to manual average diameters for the diagnosis of invasive adenocarcinoma (AUC, 0.905-0.923 for semi-automatic measurement and 0.833-0.864 for manual measurement; p<0.05). Reproducibility of diagnosis between the readers also improved with semi-automatic measurement (κ=0.924 for semi-automatic measurement and 0.690 for manual measurement, p=0.012). Inter-reader SSN classification reproducibility was significantly higher with semi-automatic measurement (κ=0.861 for semi-automatic measurement and 0.683 for manual measurement, p=0.022).

CONCLUSIONS

Semi-automatic effective diameter measurement offers an opportunity to improve diagnostic accuracy and reproducibility as well as the classification reproducibility of SSNs.

KEY POINTS

• Semi-automatic effective diameter measurement improves the diagnostic accuracy for pulmonary subsolid nodules. • Semi-automatic measurement increases the inter-reader agreement on the diagnosis for subsolid nodules. • Semi-automatic measurement augments the inter-reader reproducibility for the classification of subsolid nodules.

Effect of computed tomography window settings and reconstruction plane on 8th edition T-stage classification in patients with lung adenocarcinoma manifesting as a subsolid nodule

PURPOSE

To assess the effect of window settings and reconstruction plane on clinical T-stage determined by solid portion size within subsolid nodules (SSNs), based on 8th-edition TNM standards.

MATERIALS AND METHODS

This retrospective study included 247 SSNs from 221 patients who underwent surgery for lung adenocarcinomas between Feb 2012 and Oct 2015. Two radiologists independently measured the diameter of the solid portion on axial, coronal, and sagittal planes using lung- and mediastinal-window. The largest diameter among the measurements on the three planes was referred to as multiplanar measurement. Inter-reader agreement as well as the correlation between the CT and pathologic measurements were calculated using intra-class correlation coefficients (ICCs). The proportions of disagreement in clinical T-stage on different measurement methods were measured. The κ values for agreement between clinical- and pathological T-stage were measured.

RESULTS

Inter-reader agreement was moderate-to-excellent (ICC confidence interval [CI] range, 0.51-0.92) in lung-window, while it was good-to-excellent (0.77-0.95) in mediastinal-window. The correlation between the CT and pathologic measurements was good-to-excellent (ICC CI range, 0.63-0.82) in lung-window and fair-to-good (0.25-0.78) in mediastinal-window. The proportions of disagreement between clinical T-stages using mediastinal- and lung-window were 32.0%-41.7% and 33.6%-49.0% with axial and multiplanar measurement, respectively. Multiplanar measurement resulted in upstaging in 12.6%-15.8% and 19.0%-24.3% of cases with mediastinal- and lung-window, respectively, when compared with axial measurement alone. The κ values for agreement between clinical T-stage and pathological T-stage ranged from 0.53 to 0.69.

CONCLUSIONS

Mediastinal-window was a more stable method in the aspect of the inter-reader agreement, but the correlation between the CT and pathologic measurement was better in lung-window. The clinical T-stage varied in up to one-half of the cases according to the window setting, and multiplanar measurement resulted in upstaging in up to one-fourth of the cases.

Semiquantative Visual Assessment of Sub-solid Pulmonary Nodules ≦3 cm in Differentiation of Lung Adenocarcinoma Spectrum

We aimed to analyze CT features of persistent subsolid nodules (SSN) ≦3 cm diagnosed pathologically as adenocarcinoma spectrum to investigate whether parameters enable distinction between invasive pulmonary adenocarcinomas (IPAs) and pre-invasive lesions. A total of 129 patients with 141 SSNs confirmed with surgically pathologic proof were retrospectively reviewed. Of 141 SSNs, there were 57 pure ground-glass nodules (GGNs), 22 heterogeneous GGNs, and 62 part-solid nodules. SSN subclassification showed a significant linear trend with invasive degree of the adenocarcinoma spectrum (pure GGNs 7%; heterogeneous GGNs 36.4%; part-solid nodules 85.5%, P for trend <0.0001). For IPA detection in 141 SSNs, a solid part of ≧3 mm was the most specificity (sensitivity, 76.9%; specificity, 94.7%), followed by air-bronchogram sign (sensitivity, 53.8%; specificity, 89.5%), SSN subclassification (sensitivity, 81.5%; specificity, 88.2%), and a lesion size ≧12 mm (sensitivity, 84.6%; specificity, 76.3%). For IPA detection in 79 pure or heterogeneous GGNs, the heterogeneous GGN sign was the most useful finding, with most specificity (sensitivity, 66.7%; specificity, 79.1%), followed by CT attenuation (HU) of ≧-493 (sensitivity, 75%; specificity, 74.6%) and a lesion size ≧10 mm (sensitivity, 83.3%; specificity, 70.1%). In conclusion, this simple combined visual and semiquantitative analysis of CT features helps distinguish IPAs from pre-invasive lesions.