Computed tomography density is not associated with pathological tumor invasion for pure ground-glass nodules

Exploration of CT-based discrimination and diagnosis of various pathological types of ground glass nodules in the lungs

PURPOSE

This study aims to examine the diagnostic usefulness of CT imaging in distinguishing between various pathological forms of lung ground-glass nodules (GGNs).

METHODS

We conducted a retrospective analysis on 210 patients with lung ground-glass nodules (GGNs) who received diagnosis and treatment at our hospital between January 2021 and May 2024. Every patient had comprehensive imaging and pathology investigations. Lesion size, three-dimensional ratio, two-dimensional ratio, size of solid components, form, spiculation, lobulation, and cavitation were studied across several pathological kinds of pulmonary ground-glass nodules (GGNs).

RESULTS

Of the 210 patients, 51 were diagnosed with benign conditions, while 159 had malignant lesions distributed across AIS, MIA, and IAC. The imaging data revealed that pulmonary ground-glass nodules (GGNs) exhibiting spiculation, lobulation, cavitation, pleural indentation, irregular shape, and fuzzy borders were considerably more prevalent in the inflammatory group, atypical adenomatous hyperplasia (AAH) group, adenocarcinoma in situ (AIS) group, minimally invasive adenocarcinoma (MIA) group, and invasive adenocarcinoma (IAC) group. These differences were statistically significant (P < 0.05). Significant variations in lesion size and size of solid components were observed among the groups, with the inflammatory group having the smallest size, followed by the AAH group, AIS group, MIA group, and finally the IAC group (P < 0.05). Nevertheless, there were no statistically significant disparities in the three-dimensional ratio and two-dimensional ratio across the five groups (P > 0.05). The calculated areas under the curve for distinguishing pre-invasive lesions from MIA and MIA from IAC, depending on the size of solid components, were 0.705 and 0.814, respectively. These values indicate a high diagnostic accuracy.

CONCLUSION

A thorough examination of the CT imaging characteristics of ground-glass nodules is crucial for accurately distinguishing between various pathological forms of pulmonary GGNs.

Radiomics integration based on intratumoral and peritumoral computed tomography improves the diagnostic efficiency of invasiveness in patients with pure ground-glass nodules: a machine learning, cross-sectional, bicentric study

BACKGROUND

Radiomics has shown promise in the diagnosis and prognosis of lung cancer. Here, we investigated the performance of computed tomography-based radiomic features, extracted from gross tumor volume (GTV), peritumoral volume (PTV), and GTV + PTV (GPTV), for predicting the pathological invasiveness of pure ground-glass nodules present in lung adenocarcinoma.

METHODS

This was a retrospective, cross-sectional, bicentric study with data collected from January 1, 2018, to June 1, 2022. We divided the dataset into a training cohort (n = 88) from one center and an external validation cohort (n = 59) from another center. Radiomic signatures (rad-scores) were obtained after features were selected through correlation and least absolute shrinkage and selection operator analysis. Three machine learning models, a support vector machine model, a random forest model, and a generalized linear model, were then applied to build radiomic models.

RESULTS

Invasive adenocarcinoma had a higher rad-score (P<0.001) in the GTV and GPTV. The area under the curves (AUC) of GTV, PTV, and GPTV were 0.839, 0.809, and 0.855 in the training cohort and 0.755, 0.777, and 0.801 in the external validation cohort, respectively. The GPTV model had higher AUCs for predicting pathological invasiveness. The random forest model had the best validity and fit for the proposed machine learning approach, suggesting that it may be the most appropriate model.

CONCLUSIONS

GPTV had the highest diagnostic efficiency for predicting pathological invasiveness in patients with pure ground-grass nodules, and the random forest model outperformed other predictive models.

Prediction and analysis of the tumor invasiveness of pulmonary ground-glass nodules based on metabolomics

In recent years, the incidence of ground-glass nodular lung adenocarcinoma has gradually increased. Preoperative evaluation of the tumor invasiveness is very important, but there is a lack of effective methods. Plasma samples of ground-glass nodular lung adenocarcinoma and healthy volunteers were collected. Pulmonary nodules with different densities were compared by metabolomics. Different invasive degrees of lung adenocarcinoma were contrasted as well. Multivariate statistical methods were applied to search for significant metabolites from comparisons between two groups. The common metabolites among the different comparisons were selected and then assessed by various indices. Five metabolites were discovered for lung adenocarcinoma with different invasive degrees. Significant metabolites were selected for pulmonary nodules with different densities as well. When these metabolites were cross-compared, only the level of lysoPC(18:3) was significantly lower in ground-glass nodular lung adenocarcinoma than healthy population, as opposed to other metabolites. After identifying the invasive degree of pulmonary ground-glass nodules, lysoPC(18:3) showed a satisfactory sensitivity and specificity, both greater than 0.85. Metabolomics analysis has favorable advantages in the study of ground-glass nodular lung adenocarcinoma. LysoPC(18:3) may have the potential to differentiate precancerous lesions from invasive lung cancer, which could help clinicians to make proper judgment before surgery.

The value of interlobar fissure semilunar sign based on multifactor joint analysis in predicting the invasiveness of ground glass nodules with interlobar fissure attachment in the lungs

BACKGROUND

This study explores the value of interlobar fissure semilunar sign(IFSS) based on multifactor joint analysis in predicting the invasiveness of ground glass nodules(GGNs) with interlobar fissure attachment in the lungs.

METHODS

This was a retrospective analysis of clinical data and CT images of 203 GGNs attached to the interlobar fissures confirmed by surgery and pathology. According to pathological results, those GGNs were divided into three groups: glandular precursor lesion (atypical adenomatous hyperplasia/adenocarcinoma in situ), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC). Various quantitative and qualitative parameters were analyzed.

RESULTS

Patient age, maximum diameter, mean size, maximum CT value, and mean CT value differed significantly among the three groups and between with the other group (P < 0.05). The types of GGNs, IFSS, lobulation, spiculation, cavity sign, air bronchogram sign, bronchial changes, and vascular changes had varying degrees of significance in the comparison of each group of lesions. Logistic regression analysis showed that IFSS is one of the important factors in predicting whether GGN is invasive. The regression model I was Logit (P) 1 = -3.578 + 0.272 × 2 + 2.253 × 5, with the area under curve (AUC) for diagnosis of MIA = 0.762. Model III was Logit (P) 3 = -4.494 + 0.376 × 2 + 2.363 × 5, with the AUC for diagnosis of MIA/IAC = 0.881. The sensitivity and specificity of IFSS in model III were 0.961 and 0.458, respectively.

CONCLUSIONS

The absence of IFSS in GGNs attached to the interlobar fissure suggests noninvasive lesions. The logistic regression model based on multi factor joint analysis IFSS and maximum diameter can better predict whether the GGN attached to the interlobar fissure pleura is invasive.

Phantom evaluation of feasibility and applicability of artificial intelligence based pulmonary nodule detection in chest radiographs

The aim of our study was to evaluate the specific performance of an artificial intelligence (AI) algorithm for lung nodule detection in chest radiography for a larger number of nodules of different sizes and densities using a standardized phantom approach. A total of 450 nodules with varying density (d1 to d3) and size (3, 5, 8, 10 and 12 mm) were inserted in a Lungman phantom at various locations. Radiographic images with varying projections were acquired and processed using the AI algorithm for nodule detection. Computed tomography (CT) was performed for correlation. Ground truth (detectability) was established through a human consensus reading. Overall sensitivity and specificity of 0.978 and 0.812, respectively, were achieved for nodule detection. The false-positive rate was low with an overall rate of 0.19. The overall accuracy was calculated as 0.84 for all nodules. While most studies evaluating AI performance in the detection of pulmonary nodules have evaluated a mix of varying nodules, these are the first results of a controlled phantom-based study using a balanced number of nodules of all sizes and densities. To increase the radiologist's diagnostic performance and minimize the risk of decision bias, such algorithms have an obvious benefit in a clinical scenario.

Quantification of Intratumoral Heterogeneity: Distinguishing Histological Subtypes in Clinical T1 Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules on Computed Tomography

RATIONALE AND OBJECTIVES

To quantify intratumor heterogeneity (ITH) in clinical T1 stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGN) on computed tomography, assessing its value in distinguishing histological subtypes.

MATERIALS AND METHODS

An ITH score was developed for quantitative measurement by integrating local radiomics features and global pixel distribution patterns. Diagnostic efficacy in distinguishing histological subtypes was evaluated using receiver operating characteristic curve analysis and area under the curve (AUC) values. The ITH score's performance was compared to those of conventional radiomics (C-radiomics), and radiological assessments conducted by experienced radiologists.

RESULTS

The ITH score demonstrated excellent performance in distinguishing lepidic-predominant adenocarcinoma (LPA) from other histological subtypes of clinical T1 stage lung adenocarcinoma presenting as pGGN. It outperformed both C-radiomics and radiological findings, exhibiting higher AUCs of 0.784 (95% confidence interval [CI]: 0.742-0.826) and 0.801 (95% CI: 0.739-0.863) in the training and validation cohorts, respectively. The AUCs of C-radiomics were 0.764 (95% CI: 0.718-0.810, DeLong test, p = 0.025) and 0.760 (95% CI: 0.692-0.829, p = 0.023) and those of radiological findings were 0.722 (95% CI: 0.673-0.771, p = 0.003) and 0.754 (95% CI: 0.684-0.823, p = 0.016) in the training and validation cohorts, respectively. Subgroup analysis revealed varying diagnostic efficacy across clinical T1 stages, with the highest efficacy in the T1a stage, followed by the T1b stage, and lowest in the T1c stage.

CONCLUSION

The ITH score presents a superior method for evaluating histological subtypes and distinguishing LPA from other subtypes in clinical T1 stage lung adenocarcinoma presenting as pGGN.

Radiologic Parameters Predicting the Histologic Invasiveness of Pure Ground-Glass Nodules

Background

This study aimed to investigate the diagnostic performance of combined computed tomography (CT) and fluorine-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) for predicting histologic invasiveness of pure ground-glass nodules (pGGNs).

Methods

The study analyzed 91 patients who underwent resection of pGGNs and examined the correlation of pathologic invasiveness with preoperative CT and FDG PET findings.

Results

Overall, 24, 36, and 31 patients had adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAD), respectively. Compared with AIS and MIA, IAD was significantly correlated with larger CT size (P = .001), maximum CT value (P = .026), and high maximum standardized uptake value (SUVmax; P < .001). Multivariable logistic analyses revealed that CT size (odds ratio [OR], 3.848; P = .019) and SUVmax (OR, 4.968; P = .009) were independent predictors of histologic invasiveness. Receiver operating characteristic curve analysis revealed that a cutoff CT size value of 18 mm predicted histologic invasiveness with a sensitivity and specificity of 65% and 80%, respectively; similarly, a cutoff SUVmax value of 1.5 predicted histologic invasiveness with a sensitivity and specificity of 61% and 90%, respectively. Of 20 lesions with CT size ≥18 mm and SUVmax ≥1.5, 16 (80%) were IAD. Of 54 lesions with CT size <18 mm and SUVmax <1.5, 46 (85%) were non-IAD lesions. Furthermore, all pGGNs with SUVmax ≥2.5 were IAD.

Conclusions

CT size and SUVmax were significantly correlated with the histologic invasiveness of pGGNs. These factors may aid in determining optimal surgical procedures.

Peritumoral radiomics increases the efficiency of classification of pure ground-glass lung nodules: a multicenter study

PURPOSE

We aimed to evaluate the efficiency of computed tomography (CT) radiomic features extracted from gross tumor volume (GTV) and peritumoral volumes (PTV) of 5, 10, and 15 mm to identify the tumor grades corresponding to the new histological grading system proposed in 2020 by the Pathology Committee of the International Association for the Study of Lung Cancer (IASLC).

METHODS

A total of 151 lung adenocarcinomas manifesting as pure ground-glass lung nodules (pGGNs) were included in this randomized multicenter retrospective study. Four radiomic models were constructed from GTV and GTV + 5/10/15-mm PTV, respectively, and compared. The diagnostic performance of the different models was evaluated using receiver operating characteristic curve analysis RESULTS: The pGGNs were classified into grade 1 (117), 2 (34), and 3 (0), according to the IASLC grading system. In all four radiomic models, pGGNs of grade 2 had significantly higher radiomic scores than those of grade 1 (P < 0.05). The AUC of the GTV and GTV + 5/10/15-mm PTV were 0.869, 0.910, 0.951, and 0.872 in the training cohort and 0.700, 0.715, 0.745, and 0.724 in the validation cohort, respectively.

CONCLUSIONS

The radiomic features we extracted from the GTV and PTV of pGGNs could effectively be used to differentiate grade-1 and grade-2 tumors. In particular, the radiomic features from the PTV increased the efficiency of the diagnostic model, with GTV + 10 mm PTV exhibiting the highest efficacy.

A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study

OBJECTIVE

To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance.

METHODS

The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %.

CONCLUSION

The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.

Development and validation of a risk prediction model for invasiveness of pure ground-glass nodules based on a systematic review and meta-analysis

BACKGROUND

Assessing the aggressiveness of pure ground glass nodules early on significantly aids in making informed clinical decisions.

OBJECTIVE

Developing a predictive model to assess the aggressiveness of pure ground glass nodules in lung adenocarcinoma is the study's goal.

METHODS

A comprehensive search for studies on the relationship between computed tomography(CT) characteristics and the aggressiveness of pure ground glass nodules was conducted using databases such as PubMed, Embase, Web of Science, Cochrane Library, Scopus, Wanfang, CNKI, VIP, and CBM, up to December 20, 2023. Two independent researchers were responsible for screening literature, extracting data, and assessing the quality of the studies. Meta-analysis was performed using Stata 16.0, with the training data derived from this analysis. To identify publication bias, Funnel plots and Egger tests and Begg test were employed. This meta-analysis facilitated the creation of a risk prediction model for invasive adenocarcinoma in pure ground glass nodules. Data on clinical presentation and CT imaging features of patients treated surgically for these nodules at the Third Affiliated Hospital of Kunming Medical University, from September 2020 to September 2023, were compiled and scrutinized using specific inclusion and exclusion criteria. The model's effectiveness for predicting invasive adenocarcinoma risk in pure ground glass nodules was validated using ROC curves, calibration curves, and decision analysis curves.

RESULTS

In this analysis, 17 studies were incorporated. Key variables included in the model were the largest diameter of the lesion, average CT value, presence of pleural traction, and spiculation. The derived formula from the meta-analysis was: 1.16×the largest lesion diameter + 0.01 × the average CT value + 0.66 × pleural traction + 0.44 × spiculation. This model underwent validation using an external set of 512 pure ground glass nodules, demonstrating good diagnostic performance with an ROC curve area of 0.880 (95% CI: 0.852-0.909). The calibration curve indicated accurate predictions, and the decision analysis curve suggested high clinical applicability of the model.

CONCLUSION

We established a predictive model for determining the invasiveness of pure ground-glass nodules, incorporating four key radiological indicators. This model is both straightforward and effective for identifying patients with a high likelihood of invasive adenocarcinoma.

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.

Intratumoural and peritumoural CT-based radiomics for diagnosing lepidic-predominant adenocarcinoma in patients with pure ground-glass nodules: a machine learning approach

AIM

To develop and validate a diagnostic model utilising machine-learning algorithms that differentiates lepidic predominant adenocarcinoma (LPA) from other pathological subtypes in patients with pure ground-glass nodules (pGGNs).

MATERIALS AND METHODS

This bicentric study was conducted across two medical centres and included 151 patients diagnosed with lung adenocarcinoma based on histopathological confirmation of pGGNs. The training cohort consisted of 99 patients from Institution 1, while the test cohort included 52 patients from Institution 2. Radiomics features were extracted from both tumours and the 2 mm peritumoural parenchyma. The tumoural and peritumoural radiomics were designated as Modeltumoural and Modelperitumoural, respectively. The diagnostic efficacy of various models was evaluated through the receiver operating characteristic (ROC) curve analysis. Subsequently, a machine-learning-based prediction model that combined Modeltumoural, Modelperitumoural, and Modelclinical-radiological was developed to differentiate LPA from other pathological subtypes in patients with pGGNs.

RESULTS

Modeltumoural achieved area under the curve (AUC) values of 0.762 and 0.783 in the training and validation sets, respectively. Modelperitumoural attained AUCs of 0.742 and 0.667, and Modelclinical-radiological generated an AUC of 0.727 and 0.739 in the training and validation sets, respectively. Among the machine-learning models evaluated, gradient boosting machines demonstrated the best diagnostic efficacy, with accuracy, AUC, F1 score, and log loss values of 0.885, 0.956, 0.943, and 0.260, respectively.

CONCLUSION

The combined model based on machine learning that incorporated tumour and peritumoural parenchyma, as well as clinical and imaging characteristics, may offer benefits in assessing the pathological subtype of pGGNs.

Enhancing the prediction of the invasiveness of pulmonary adenocarcinomas presenting as pure ground-glass nodules: Integrating intratumor heterogeneity score with clinical-radiological features via machine learning in a multicenter study

Objective

The invasiveness of lung adenocarcinoma significantly impacts clinical decision-making. However, assessing this invasiveness preoperatively, especially when it manifests as pure ground-glass nodules (pGGN) on CT scans, poses challenges. This study aims to quantify intratumor heterogeneity (ITH) and determine whether the ITH score can enhance the accuracy of invasiveness predictions.

Methods

A total of 524 patients with lung adenocarcinomas presenting as pGGN were enrolled in the study, with 177 (33.78%) receiving a pathologic diagnosis of invasiveness. Four diagnostic approaches were developed to predict the invasiveness of lung adenocarcinoma presenting as pGGN: (1) conventional lesion size, (2) ITH score, (3) clinical-radiological features (ClinRad), and (4) integration of the ITH score with ClinRad. ClinRad alone or in combination with the ITH score served as the input for 11 machine learning approaches. The trained models were evaluated in an independent validation cohort, and the area under the curve (AUC) was calculated to assess classification performance.

Results

The conventional lesion size showed the lowest performance, with an AUC of 0.826 (95% confidence interval [CI]: 0.758-0.894), while the ITH score outperformed it with an AUC of 0.846 (95% CI: 0.787-0.905). The CatBoost model performed best when the ITH score and ClinRad were both used as input features, leading to the development of an ITH-ClinRad-guided CatBoost classifier. CatBoost also excelled with ClinRad alone, resulting in a ClinRad-guided CatBoost classifier with an AUC of 0.830 (95% CI: 0.764-0.896), surpassed by the ITH-ClinRad-guided CatBoost classifier with an AUC of 0.871 (95% CI: 0.818-0.924).

Conclusion

The ITH-ClinRad-guided CatBoost classifier emerges as a promising tool with significant potential to revolutionize the management of lung adenocarcinomas presenting as pGGNs.

Intra- and Peritumoral-Based Radiomics for Preoperatively Assessing the Pathological Subtype of T1-Stage Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules

Purpose: To evaluate the diagnostic performance of CT radiomic features extracted from tumor and peritumoral regions in identifying pathological subtypes of T1-stage lung adenocarcinoma presenting as pure ground-glass nodules (pGGNs). Methods: A retrospective analysis was conducted on the data of T1-stage lung adenocarcinoma patients who underwent surgical resection and whose preoperative CT scans revealed pGGNs from June 2020 to June 2023 in our hospital. 3D Slicer was used to extract radiomic features of the intratumoral (VOI entire) and peritumoral regions (VOI +2 mm), and Rad-scores were calculated from the coefficients of features obtained after dimensionality reduction with LASSO regression. Results: A total of 131 patients with T1-stage lung adenocarcinoma presenting as pGGNs were included in this study; of these, 84 were pathologically diagnosed with the lepidic-predominant (LPA) subtype, and 47 were diagnosed with non-LPA. The diagnostic performance of the VOI entire and VOI +2 mm features for the pathological subtype of pGGN was superior to that of conventional features, with the VOI +2 mm features showing the best performance: the area under the curve, sensitivity, specificity, and accuracy in the training set were 0.883, 0.964, 0.667, and 0.761, respectively. Conclusion: Intra- and especially peritumoral-based radiomic features have high diagnostic performance for the pathological subtype of T1-stage lung adenocarcinoma presenting as pGGNs.

Meta-analysis of the correlation between CT-based features and invasive properties of pure ground-glass nodules

Several studies have revealed that computed tomography (CT) features can make a distinction in the invasive properties of pure ground-glass nodules (pGGNs). However, imaging parameters related to the invasive properties of pGGNs are unclear. This meta-analysis was designed to decipher the correlation between the invasiveness of pGGNs and CT-based features, and ultimately to be conducive to making rational clinical decisions. We searched a series of databases, including PubMed, Embase, Web of Science, Cochrane Library, Scopus, wanfang, CNKI, VIP, as well as CBM databases, until September 20, 2022, for the eligible publications only in Chinese or English. This meta-analysis was implemented with the Stata 16.0 software. Ultimately, 17 studies published between 2017 and 2022 were included. According to the meta-analysis, we observed a larger maximum size of lesions in invasive adenocarcinoma (IAC) versus that in preinvasive lesions (PIL) [SMD = 1.37, 95% CI (1.07-1.68), P < 0.05]. Meanwhile, there were also increased mean CT values of IAC [SMD = 0.71, 95% CI (0.35, 1.07), P < 0.05], the incidence of pleural traction sign [OR = 1.94, 95% CI (1.24, 3.03), P < 0.05], the incidence of IAC spiculation [OR = 1.55, 95% CI (1.05, 2.29), P < 0.05] in comparison to those of PIL. Nevertheless, IAC and PIL exhibited no significant differences in vacuole sign, air bronchogram, regular shape, lobulation and vascular convergence sign (all P > 0.05). Therefore, IAC and PIL manifested different CT features of pGGNs. The maximum diameter of lesions, mean CT value, pleural traction sign and spiculation are important indicators to distinguish IAC and PIL. Reasonable use of these features can be helpful to the treatment of pGGNs.

Ten-Year Follow-up Results of Pure Ground-Glass Opacity-Featured Lung Adenocarcinomas After Surgery

BACKGROUND

Previously, we have demonstrated that the 5-year recurrence-free survival after surgery of pure ground-glass opacity (GGO)-featured lung adenocarcinoma is 100%. This study aimed to reveal the long-term outcomes of these patients 10 years after surgery.

METHODS

Lung adenocarcinoma patients who underwent surgery between December 2007 and December 2013 were reviewed. Patients with pure GGO-featured lung adenocarcinoma were enrolled. Postoperative survival and the risk of developing second primary lung cancer were analyzed.

RESULTS

Overall, 308 cases of pure GGO-featured lung adenocarcinomas were included. Of these patients, 226 (73.4%) were female, 268 (87.0%) were nonsmokers, and 187 (60.7%) underwent sublobar resection. The median follow-up period after surgery was 112 months. The 10-year recurrence-free survival rate of these patients was 100%, and 10-year overall survival rate was 96.9%. Both 5-year and 10-year lung cancer-specific survival were 100%. There was no difference in 10-year recurrence-free survival rates between patients who underwent lobectomy or sublobar resection (P = .697). EGFR mutations were detected in 55.6% (84 of 151) of patients who underwent mutational analysis. The risk of developing secondary primary lung cancer for pure GGO-featured lung adenocarcinoma patients at 10 years after resection was 2.4%, and was not correlated with EGFR mutation status (P = .452).

CONCLUSIONS

No recurrence was observed in patients with pure GGO-featured lung adenocarcinomas 10 years after surgery, even when pathologically evaluated as invasive adenocarcinoma. Pure GGO can be cured by surgery. Surgery is recommended for the appropriate time window with the view to cure. Our study emphasizes that radiologic pure GGO-featured lung adenocarcinomas should be distinguished from other lung adenocarcinomas.

Comparative genomics between matched solid and lepidic portions of semi-solid lung adenocarcinomas

BACKGROUND

Genetic changes that drive the transition from lepidic to invasive cancer development within a radiographic ground glass or semi-solid lung lesion (SSL) are not well understood. Biomarkers to predict the transition to solid, invasive cancer within SSL are needed.

METHODS

Patients with surgically resected SSL were identified retrospectively from a surgical database. Clinical characteristics and survival were compared between stage I SSL (n = 65) and solid adenocarcinomas (n = 120) resected during the same time period. Areas of normal lung, in situ lepidic, and invasive solid tumor were microdissected from within the same SSL specimens and next generation sequencing (NGS) and Affymetrix microarray of gene expression were performed.

RESULTS

There were more never smokers, Asian patients, and sub-lobar resections among SSL but no difference in 5-year survival between SSL and solid adenocarcinoma. Driver mutations found in both lepidic and solid invasive portion were EGFR (43%), KRAS (21%), and DNMT3A (5%). CEACAM5 was the most upregulated gene found in solid, invasive portions of SSL. Lepidic and invasive solid areas had many similarities in gene expression, however there were some significant differences with the gene SPP1 being a unique biomarker for the invasive component of a SSL.

CONCLUSIONS

Common lung cancer driver mutations are present in in situ lepidic as well as invasive solid portions of a SSL, suggesting early development of driver mutations. CEACAM5 and SPP1 emerged as promising biomarkers of invasive potential in semi-solid lesions. Other studies have shown both genes to correlate with poor prognosis in lung cancer and their role in evolution of semi-solid lung lesions warrants further study.

Computed tomography-based radiomics machine learning models for prediction of histological invasiveness with sub-centimeter subsolid pulmonary nodules: a retrospective study

To improve the accuracy of preoperative diagnoses and avoid over- or undertreatment, we aimed to develop and compare computed tomography-based radiomics machine learning models for the prediction of histological invasiveness using sub-centimeter subsolid pulmonary nodules. Three predictive models based on radiomics were built using three machine learning classifiers to discriminate the invasiveness of the sub-centimeter subsolid pulmonary nodules. A total of 203 sub-centimeter nodules from 177 patients were collected and assigned randomly to the training set (n = 143) or test set (n = 60). The areas under the curve of the predictive models were 0.743 (95% confidence interval CI [0.661-0.824]) for the logistic regression, 0.828 (95% CI [0.76-0.896]) for the support vector machine, and 0.917 (95% CI [0.869-0.965]) for the XGBoost classifier models in the training set, and 0.803 (95% CI [0.694-0.913]), 0.726 (95% CI [0.598-0.854]), and 0.874 (95% CI [0.776-0.972]) in the test set, respectively. In addition, the decision curve showed that the XGBoost model added more net benefit within the range of 0.06 to 0.93.

Dual-layer spectral detector CT (SDCT) can improve the detection of mixed ground-glass lung nodules

BACKGROUND

Mixed ground-glass lung nodules are a high-risk factor for lung adenocarcinoma. This study aimed to analyze the value of SDCT electron density imaging in the detection of mixed ground-glass lung nodules (GGNs).

METHOD

150 patients with GGNs confirmed by chest SDCT and surgical pathology were retrospectively analyzed. GGNs were screened by two senior radiologists by the double-blind method based on conventional CT and SDCT electron density images. Average CT values and electron density (ED) values of GGNs were measured for all, solid and ground-glass.

RESULT

Thirty pGGN cases determined by conventional CT were found to be mGGN on electron density images, including 23 in the invasive adenocarcinoma group (detection rate of 35.38%), which was significantly higher than that of the PGL group (14.89%, P < 0.05). In electron density images, average CT values and ED values in the PGL and invasive adenocarcinoma groups with pGGNs were no difference. The average CT value and ED value were significantly higher in the mGGN invasive adenocarcinoma group compared with the PGL group (P < 0.05). Meanwhile, ROC curve analysis of average CT value and ED value revealed AUC values for mGGN infiltration of 0.759 and 0.752.

CONCLUSION

SDCT can improve GGN visualization and increase the detection rate of mGGN compared with conventional CT. Attention should be paid to invasive adenocarcinoma for lung GGNs detected as mGGNs with high average CT value or ED value.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists’ readings

Background

Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature.

Purpose

To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT).

Material and Methods

In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland–Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT.

Results

The radiologist and DLA measurements exhibited good agreement with a Bland–Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 ( P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group ( P < 0.001).

Conclusion

Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

Application of three-dimensional (3D) reconstruction in the treatment of video-assisted thoracoscopic complex segmentectomy of the lower lung lobe: A retrospective study

Background

An increasing number of lung ground-glass nodules (GGNs) have been detected ever since low-dose computer tomography started growing in popularity. Three-dimensional (3D) reconstruction technology plays a critical role in lung resection, especially in segmentectomy. In this study, we explore the role of 3D reconstruction in thoracoscopic complex segmentectomy of lower lung lobe.

Methods

A total of 97 patients who underwent complex segmentectomy of lower lung lobe from January 2021 to March 2022 were retrospectively analyzed. We divided these patients into a 3D group (n = 42) and a routine group (n = 55) based on preoperative 3D reconstruction or without this procedure. The demographics of patients and GGNs were collected and perioperative outcomes were compared between the two groups.

Results

All of the baseline characteristics between the groups were comparable (all P > 0.05). There was no 30-day postoperative mortality and conversion in the two groups. The operation time of the 3D group was significantly shorter than that of the routine group (111.4 ± 20.8 min vs. 127.1 ± 32.3 min, P = 0.007). The number of stapler reloads during surgery in the 3D group was less than that in the routine group (9.0 ± 2.2 vs. 10.4 ± 2.6, P = 0.009). The rate of air leakage on postoperative days 1–3 was lower in the 3D group (11.9% vs. 30.9%, P = 0.027). In addition, the resection margins of all patients in the 3D group were adequate, while four patients in the routine group had inadequate resection margins, although there was no statistically significant difference (P = 0.131). Intraoperative blood loss, postoperative drainage, postoperative hospital stay, pneumonia/atelectasis, and hemoptysis were similar between the two groups.

Conclusions

For performing complex segmentectomy of the lower lung lobe, the procedure of 3D reconstruction may shorten the operation time, decrease the number of stapler reloads, prevent postoperative air leakage, and guarantee a safe surgical margin. Therefore, 3D reconstruction is recommended for complex segmentectomy of the lower lung lobe.

Radiomics for identifying lung adenocarcinomas with predominant lepidic growth manifesting as large pure ground-glass nodules on CT images

Purpose

To explore the value of radiomics in the identification of lung adenocarcinomas with predominant lepidic growth in pure ground-glass nodules (pGGNs) larger than 10 mm.

Methods

We retrospectively analyzed CT images of 204 patients with large pGGNs (≥ 10 mm) pathologically diagnosed as minimally invasive adenocarcinomas (MIAs), lepidic predominant adenocarcinomas (LPAs), and non-lepidic predominant adenocarcinomas (NLPAs). All pGGNs in the two groups (MIA/LPA and NLPA) were randomly divided into training and test cohorts. Forty-seven patients from another center formed the external validation cohort. Baseline features, including clinical data and CT morphological and quantitative parameters, were collected to establish a baseline model. The radiomics model was built with the optimal radiomics features. The combined model was developed using the rad_score and independent baseline predictors. The performance of the models was evaluated using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test. The differential diagnosis performance of the models was compared with three radiologists (with 20+, 10+, and 3 years of experience) in the test cohort.

Results

The radiomics (training AUC: 0.833; test AUC: 0.804; and external validation AUC: 0.792) and combined (AUC: 0.849, 0.820, and 0.775, respectively) models performed better for discriminating than the baseline model (AUC: 0.756, 0.762, and 0.725, respectively) developed by tumor location and mean CT value of the whole nodule. The DeLong test showed that the AUCs of the combined and radiomics models were significantly increased in the training cohort. The highest AUC value of the radiologists was 0.600.

Conclusion

The application of CT radiomics improved the identification performance of lung adenocarcinomas with predominant lepidic growth appearing as pGGNs larger than 10 mm.

Quantifying invasiveness of clinical stage IA lung adenocarcinoma with computed tomography texture features

OBJECTIVES

The study objectives were to establish and validate a nomogram for pathological invasiveness prediction in clinical stage IA lung adenocarcinoma and to help identify those potentially unsuitable for sublobar resection-based computed tomography texture features.

METHOD

Patients with clinical stage IA lung adenocarcinoma who underwent surgery at Guangdong Provincial People's Hospital between January 2015 and October 2018 were retrospectively reviewed. All surgically resected nodules were pathologically classified into less-invasive and invasive cohorts. Each nodule was manually segmented, and its computerized texture features were extracted. Clinicopathological and computed tomographic texture features were compared between 2 cohorts. A nomogram for distinguishing the pathological invasiveness was established and validated.

RESULTS

Among 428 enrolled patients, 249 were diagnosed with invasive pathological subtypes. Smoking status (odds ratio, 2.906; 95% confidence interval, 1.285-6.579; P = .011), mean computed tomography attenuation value (odds ratio, 1.005, 95% confidence interval, 1.002-1.007; P < .001), and entropy (odds ratio, 8.536, 95% confidence interval, 3.478-20.951; P < .001) were identified as independent predictors for pathological invasiveness by multivariate logistics regression analysis. The nomogram showed good calibration (P = .182) with an area under the curve of 0.849 when validated with testing set data. Decision curve analysis indicated the potentially clinical usefulness of the model with respect to treat-all or treat-none scenario. Compared with intraoperative frozen-section, the nomogram performed better in pathological invasiveness diagnosis (area under the curve, 0.815 vs 0.670; P = .00095).

CONCLUSIONS

We established and validated a nomogram to compute the probability of invasiveness of clinical stage IA lung adenocarcinoma with great calibration, which may contribute to decisions related to resection extent.

Management of Ground-Glass Nodules: When and How to Operate?

Simple Summary

An increasing number of lung cancer screening programs have detected the frequent occurrence of small pulmonary ground-glass nodules (GGNs). If GGN is an incidental finding, it should be followed according to the guidelines. A multidisciplinary team discussion should be initiated if a new solid component develops or the solid portion grows on follow-up CT. Preoperative attempts to biopsy solid components in part-solid GGNs are often not feasible and not helpful. If malignancy is suspected, a surgical biopsy with the guidance of various localization methods is recommended. Once the GGN is confirmed to be malignant, sub-lobar resection may be reasonable in the majority of cases, and the extent of lung resection should be determined based on the CT finding or intraoperative frozen section examination using special inflation technique. Although rare, the recurrence in the remaining lobe can occur especially in patients with high risk histologic features, which currently cannot accurately diagnosed either pre- or intra-operatively.

Abstract

With the increased popularity of low-dose computed tomography (LDCT), many patients present with pulmonary ground-glass nodules (GGNs), and the appropriate diagnostic and management strategy of those lesions make physicians be on the horn of the clinical dilemma. As there is not enough data available to set universally acceptable guidelines, the management of GGNs may be different. If GGN is an incidental finding through LDCT, the lesion should be followed according to the current guidelines. We recommend a multidisciplinary team discussion to be initiated if a new solid component develops or the solid portion size grows on follow-up CT as the risk of malignancy is high. Attempts to preoperatively biopsy solid components in part-solid GGNs are often not feasible and not helpful in clinical settings. Currently, if malignancy is suspected, a surgical biopsy with the guidance of various localization methods is recommended. If malignancy is confirmed, sub-lobar resection may provide an excellent oncologic outcome.

Prognostic Impact of the Histologic Lepidic Component in Pathologic Stage IA Adenocarcinoma

INTRODUCTION

Because several articles have reported a prognostic association with the radiologic features of ground-glass opacity, we explored whether the histologic presence of a lepidic component had similar significance.

METHODS

We retrospectively evaluated 380 consecutive surgically resected lung adenocarcinomas (ADCs) of pathologic (p)stage IA. The tumors were classified into lepidic-positive and lepidic-negative ADCs. Clinicopathologic characteristics, radiographic ground-glass opacity status, and disease-free survival were compared between lepidic-positive and lepidic-negative ADCs and between part-solid and solid nodules on computed tomography images.

RESULTS

Of the 380 cases, 176 (46.3%) were lepidic-positive ADCs. Of the overall patients with pT1, lepidic-positive ADCs were found to have significantly better recurrence-free survival (5 y, 95.4% versus 87.0%, p = 0.005), but this significance was not reproduced in pT1 subcategories (pT1a, pT1b, and pT1c). Furthermore, the presence of the lepidic component was not an independent prognostic factor in the multivariate analysis (hazard ratio = 0.46 [95% confidence interval: 0.19-1.14], p = 0.09). We also analyzed the extent of the lepidic component with 10% incremental valuables. Although we found that a 10% or greater extent of lepidic component made the recurrence-free survival difference the largest, a clear prognostic impact was not obtained with this cutoff point.

CONCLUSIONS

Although lepidic-positive ADCs tended to have a favorable outcome, the lepidic component was not a clear independent prognostic factor in pstage I ADC.

Predicting the histological invasiveness of pulmonary adenocarcinoma manifesting as persistent pure ground-glass nodules by ultra-high-resolution CT target scanning in the lateral or oblique body position

Background

Ultra-high-resolution computed tomography (U-HRCT) has improved image quality for displaying the detailed characteristics of disease states and lung anatomy. The purpose of this study was to retrospectively examine whether U-HRCT target scanning in the lateral or oblique body position (protocol G scan) could predict histological invasiveness of pulmonary adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).

Methods

From January 2015 to December 2016, 260 patients with 306 pathologically confirmed pGGNs who underwent preoperative protocol G scans were retrospectively reviewed and analyzed. The U-HRCT findings of preinvasive lesions [atypical adenomatous hyperplasias (AAH) and adenocarcinomas in situ (AIS)] and invasive pulmonary adenocarcinomas [minimally invasive adenocarcinomas (MIA) and invasive adenocarcinomas (IAC)] were manually compared and analyzed using orthogonal multiplanar reformation (MPR) images. The logistic regression model was established to determine variables that could predict the invasiveness of pGGNs. Receiver operating characteristic (ROC) curve analysis was performed to evaluate their diagnostic performance.

Results

There were 213 preinvasive lesions (59 AAHs and 154 AISs) and 93 invasive pulmonary adenocarcinomas (53 MIAs and 40 IACs). Compared with the preinvasive lesions, invasive adenocarcinomas exhibited a larger diameter (13.5 vs. 9.3 mm, P=0.000), higher mean attenuation (-571 vs. -613 HU, P=0.002), higher representative attenuation (-475 vs. -547 HU, P=0.000), lower relative attenuation (-339 vs. -292 HU, P=0.000) and greater frequencies of heterogeneity (P=0.001), air bronchogram (P=0.000), bubble lucency (P=0.000), and pleural indentation (P=0.000). Multiple logistic analysis revealed that larger diameter [odds ratio (OR), 1.328; 95% CI: 1.208-1.461; P=0.000] and higher representative attenuation (OR, 1.005; 95% CI: 1.003-1.007; P=0.000) were significant predictive factors of invasive pulmonary adenocarcinomas from preinvasive lesions. The optimal cut-off value of the maximum diameter for invasive pulmonary adenocarcinomas was larger than 10 mm (sensitivity, 66.7%; specificity, 72.8%).

Conclusions

The imaging features based on protocol G scanning can effectively help predict the histological invasiveness of pGGNs. The maximum diameter and representative attenuation are important parameters for predicting invasiveness.