Can CT imaging features of ground-glass opacity predict invasiveness? A meta-analysis

CT morphological features and histogram parameters to predict micropapillary or solid components in stage IA lung adenocarcinoma

Objectives

This study aimed to construct prediction models based on computerized tomography (CT) signs, histogram and morphology features for the diagnosis of micropapillary or solid (MIP/SOL) components of stage IA lung adenocarcinoma (LUAC) and to evaluate the models' performance.

Methods

This clinical retrospective study included image data of 376 patients with stage IA LUAC based on postoperative pathology, admitted to Putian First Hospital from January 2019 to June 2023. According to the presence of MIP/SOL components in postoperative pathology, patients were divided into MIP/SOL+ and MIP/SOL- groups. Cases with tumors ≤ 3 cm and ≤ 2 cm were separately analyzed. Each subgroup of patients was then randomly divided into a training set and a test set in a ratio of 7:3. The training set was used to build the prediction model, and the test set was used for internal validation.

Results

For tumors ≤ 3 cm, ground-glass opacity (GGO) [odds ratio (OR) = 0.244; 95% confidence interval (CI): 0.103-0.569; p = 0.001], entropy (OR = 1.748; 95% CI: 1.213-2.577; p = 0.004), average CT value (OR = 1.002; 95% CI: 1.000-1.004; p = 0.002), and kurtosis (OR = 1.240; 95% CI: 1.023-1.513; p = 0.030) were independent predictors of MIP/SOL components of stage IA LUAC. The area under the ROC curve (AUC) of the nomogram prediction model for predicting MIP/SOL components was 0.816 (95% CI: 0.756-0.877) in the training set and 0.789 (95% CI: 0.689-0.889) in the test set. In contrast, for tumors ≤ 2 cm, kurtosis was no longer an independent predictor. The nomogram prediction model had an AUC of 0.811 (95% CI: 0.731-0.891) in the training set and 0.833 (95% CI: 0.733-0.932) in the test set.

Conclusion

For tumors ≤ 3 cm and ≤ 2 cm, GGO, average CT value, and entropy were the same independent influencing factors in predicting MIP/SOL components of stage IA LUAC. The nomogram prediction models have potential diagnostic value for identifying MIP/SOL components of early-stage LUAC.

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.

Bubble-like lucency in pulmonary ground glass nodules on computed tomography: a specific pattern of air-containing space for diagnosing neoplastic lesions

PURPOSE

To investigate the computed tomography (CT) characteristics of air-containing space and its specific patterns in neoplastic and non-neoplastic ground glass nodules (GGNs) for clarifying their significance in differential diagnosis.

MATERIALS AND METHODS

From January 2015 to October 2022, 1328 patients with 1,350 neoplastic GGNs and 462 patients with 465 non-neoplastic GGNs were retrospectively enrolled. Their clinical and CT data were analyzed and compared with emphasis on revealing the differences of air-containing space and its specific patterns (air bronchogram and bubble-like lucency [BLL]) between neoplastic and non-neoplastic GGNs and their significance in differentiating them.

RESULTS

Compared with patients with non-neoplastic GGNs, female was more common (P < 0.001) and lesions were larger (P < 0.001) in those with neoplastic ones. Air bronchogram (30.1% vs. 17.2%), and BLL (13.0% vs. 2.6%) were all more frequent in neoplastic GGNs than in non-neoplastic ones (each P < 0.001), and the BLL had the highest specificity (93.6%) in differentiation. Among neoplastic GGNs, the BLL was more frequently detected in the larger (14.9 ± 6.0 mm vs. 11.4 ± 4.9 mm, P < 0.001) and part-solid (15.3% vs. 10.7%, P = 0.011) ones, and its incidence significantly increased along with the invasiveness (9.5-18.0%, P = 0.001), whereas no significant correlation was observed between the occurrence of BLL and lesion size, attenuation, or invasiveness.

CONCLUSION

The air containing space and its specific patterns are of great value in differentiating GGNs, while BLL is a more specific and independent sign of neoplasms.

Predicting the Invasiveness of Pulmonary Adenocarcinomas in Pure Ground-Glass Nodules Using the Nodule Diameter: A Systematic Review, Meta-Analysis, and Validation in an Independent Cohort

The nodule diameter was commonly used to predict the invasiveness of pulmonary adenocarcinomas in pure ground-glass nodules (pGGNs). However, the diagnostic performance and optimal cut-off values were inconsistent. We conducted a meta-analysis to evaluate the diagnostic performance of the nodule diameter for predicting the invasiveness of pulmonary adenocarcinomas in pGGNs and validated the cut-off value of the diameter in an independent cohort. Relevant studies were searched through PubMed, MEDLINE, Embase, and the Cochrane Library, from inception until December 2022. The inclusion criteria comprised studies that evaluated the diagnostic accuracy of the nodule diameter to differentiate invasive adenocarcinomas (IAs) from non-invasive adenocarcinomas (non-IAs) in pGGNs. A bivariate mixed-effects regression model was used to obtain the diagnostic performance. Meta-regression analysis was performed to explore the heterogeneity. An independent sample of 220 pGGNs (82 IAs and 128 non-IAs) was enrolled as the validation cohort to evaluate the performance of the cut-off values. This meta-analysis finally included 16 studies and 2564 pGGNs (761 IAs and 1803 non-IAs). The pooled area under the curve, the sensitivity, and the specificity were 0.85 (95% confidence interval (CI), 0.82-0.88), 0.82 (95% CI, 0.78-0.86), and 0.73 (95% CI, 0.67-0.78). The diagnostic performance was affected by the measure of the diameter, the reconstruction matrix, and patient selection bias. Using the prespecified cut-off value of 10.4 mm for the mean diameter and 13.2 mm for the maximal diameter, the mean diameter showed higher sensitivity than the maximal diameter in the validation cohort (0.85 vs. 0.72, p < 0.01), while there was no significant difference in specificity (0.83 vs. 0.86, p = 0.13). The nodule diameter had adequate diagnostic performance in differentiating IAs from non-IAs in pGGNs and could be replicated in a validation cohort. The mean diameter with a cut-off value of 10.4 mm was recommended.

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.

The value of preoperative positron emission tomography/computed tomography in differentiating the invasive degree of hypometabolic lung adenocarcinoma

OBJECTIVES

To investigate the value of preoperative positron emission tomography/computed tomography (PET/CT) in differentiating the invasive degree of hypometabolic lung adenocarcinoma.

METHODS

We retrospectively analyzed the data of patients who underwent PET/CT examination, high-resolution computed tomography, and surgical resection for low-metabolism lung adenocarcinoma in our hospital between June 2016 and December 2021. We also investigated the relationship between the preoperative PET/CT findings and the pathological subtype of hypometabolic lung adenocarcinoma.

RESULTS

A total of 128 lesions were found in 113 patients who underwent resection for lung adenocarcinoma, including 20 minimally invasive adenocarcinomas (MIA) and 108 invasive adenocarcinomas (IAC), whose preoperative PET/CT showed low metabolism. There were significant differences in the largest diameter (Dmax), lesion type, maximum standard uptake value (SUVmax), SUVindex (the ratio of SUVmax of lesion to SUVmax of contralateral normal lung paranchyma), fasting blood glucose, lobulation, spiculation, and pleura indentation between the MIA and IAC groups (p < 0.05). Multivariate logistic regression analysis showed that the Dmax (odds ratio (OR) = 1.413, 95% confidence interval (CI: 1.155-1.729, p = 0.001)) and SUVmax (OR = 12.137, 95% CI: 1.068-137.900, p = 0.044) were independent risk factors for predicting the hypometabolic IAC (p < 0.05). Receiver operating characteristic (ROC) curve analysis showed that the Dmax ≥ 10.5 mm and SUVmax ≥ 0.85 were the cut-off values for differentiating MIA from IAC, with high sensitivity (84.3% and 75.9%, respectively) and specificity (84.5% and 85.0%, respectively), the Combined Diagnosis showed higher sensitivity (91.7%) and specificity (85.0%).

CONCLUSIONS

The PET/CT findings correlated with the subtype of hypometabolic lung adenocarcinoma. The parameters Dmax and SUVmax were independent risk factors for predicting IAC, and the sensitivity of Combined Diagnosis prediction is better.

The use of the mean computed-tomography value to predict the invasiveness of ground-glass nodules: A meta-analysis

The invasiveness of ground-glass nodules (GGNs) is difficult to characterize through morphological examination. Multiple studies have independently detected a close relationship between mean computed tomography value and invasiveness of GGNs, however, their relative diagnostic accuracy is uncertain. Here, we performed a meta-analysis to validate whether the mean computed tomography value can predict the invasiveness of GGNs. Briefly, we searched the Web of Science, Embase, PubMed, Cochrane, Google Scholar, CNKI, VIP, Wanfang and SinoMed databases. The sensitivity, specificity, 95% confidence interval (CI), symmetric receiver operating characteristic curve (SROC curve) and the area under curve (AUC) were obtained using STATA 16.0 to evaluate the predictive value of the mean computed tomography value for GGNs. The presence of heterogeneity was assessed using fixed effects sensitivity analysis and I² statistics. We used the Deek's funnel plot to evaluate the possibility of publication bias. Thirteen studies encompassing 1564 GGNs were included in our meta-analysis. Six of these studies revealed that using the mean computed tomography value for the diagnosis of pre-invasive and invasive lesions had a sensitivity and specificity of 0.75 (95% CI: 0.61-0.85) and 0.81 (95% CI: 0.74-0.86), respectively. The optimal critical value was -557 Hu. Later, eight studies were examined for the use of the mean CT value for patients with minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC); the results showed that the sensitivity was 0.78 (95% CI: 0.66-0.86) and the specificity was 0.81 (95% CI: 0.68-0.89), and the optimal critical value was -484 Hu. Therefore, the mean computed tomography value assessed via CT scan could be a significant predictor of the invasiveness of GGNs as well as a good surgical treatment guide in patients diagnosed with lung cancer. PROSPERO REGISTRATION NUMBER: CRD42020177125.

Spectral Dual-Layer Computed Tomography Can Predict the Invasiveness of Ground-Glass Nodules: A Diagnostic Model Combined with Thymidine Kinase-1

OBJECTIVES

Few studies have explored the use of spectral dual-layer detector-based computed tomography (SDCT) parameters, thymidine kinase-1 (TK1), and tumor abnormal protein (TAP) for the detection of ground-glass nodules (GGNs). Therefore, we aimed to evaluate the quantitative and qualitative parameters generated from SDCT for predicting the pathological subtypes of GGN-featured lung adenocarcinoma combined with TK1 and TAP.

MATERIAL AND METHODS

Between July 2021 and September 2022, 238 patients with GGNs were retrospectively enrolled in this study. SDCT and tests for TK1 and TAP were performed preoperatively, and the lesions were divided into glandular precursor lesions (PGL), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), according to the pathological results. A receiver operating characteristic (ROC) curve was used to compare the diagnostic performance of these parameters. Multivariate logistic regression analysis was performed to construct a joint diagnostic model and create a nomogram.

RESULTS

This study included 238 GGNs, including 41 atypical adenomatous hyperplasias (AAH), 62 adenocarcinomas in situ (AIS), 49 MIA, and 86 IAC, with a high proportion of women, non-smokers, and pure ground-glass nodule (pGGN). CT100 keV (a/v), electronic density (EDW) (a/v), Daverage, Dsolid, TK1, and TAP of MIA and IAC were higher than those of PGL. The effective atomic number (Zeff (a/v)) was lower in MIA and IAC than in PGL (all p < 0.05). Logistic regression analysis showed that Zeff (a), EDW (a), TK1, Daverage, and internal bronchial morphology were crucial factors in predicting the aggressiveness of GGN. Zeff (a) had the highest diagnostic performance with an area under the ROC curve (AUC) = 0.896, followed by EDW (a) (AUC = 0.838) and CT100 keVa (AUC = 0.819). The diagnostic model and nomogram constructed using these five parameters (Zeff (a) + EDW (a) + CT100 keVa + Daverage + TK1) had an AUC = 0.933, which was higher than the individual parameters (p < 0.05).

CONCLUSIONS

Multiple quantitative and functional parameters can be selected based on SDCT, especially Zeff (a) and EDW (a), which have high sensitivity and specificity for predicting GGNs' invasiveness. Additionally, the combination of TK1 can further improve diagnostic performance, and using a nomogram is helpful for individualized predictions.

[Advances in the Study of Invasive Non-mucinous Adenocarcinoma with Different Pathological Subtypes]

Lung cancer is the leading cause of cancer death in the world today, and adenocarcinoma is the most common histopathological type of lung cancer. In May 2021, World Health Organization (WHO) released the 5th edition of the WHO classification of thoracic tumors, which classifies invasive non-mucinous adenocarcinoma (INMA) into lepidic adenocarcinoma, acinar adenocarcinoma, papillary adenocarcinoma, solid adenocarcinoma, and micropapillary adenocarcinoma based on its histological characteristics. These five pathological subtypes differ in clinical features, treatment and prognosis. A complete understanding of the characteristics of these subtypes is essential for the clinical diagnosis, treatment options, and prognosis predictions of patients with lung adenocarcinoma, including recurrence and progression. This article will review the grading system, morphology, imaging prediction, lymph node metastasis, surgery, chemotherapy, targeted therapy and immunotherapy of different pathological subtypes of INMA.
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Clinical and radiomic factors for predicting invasiveness in pulmonary ground‑glass opacity

Patients with preinvasive or invasive pulmonary ground-glass opacity (GGO) often face different clinical treatments and prognoses. The present study aimed to identify the invasiveness of pulmonary GGO by analysing clinical and radiomic features. Patients with pulmonary GGOs who were treated between January 2014 and February 2019 were included. Clinical features were collected, while radiomic features were extracted from computed tomography records using the three-dimensional Slicer software. Predictors of GGO invasiveness were selected by least absolute shrinkage and selection operator logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model. A total of 194 patients with pulmonary GGOs were included in the present study. The maximum diameter of the solid component, waveletHLL_ngtdm_Coarseness (P=0.03), waveletLHH_firstorder_Maximum (P<0.01) and waveletLLH_glrlm_LongRunEmphasis (P<0.01) were significant predictors of invasive lung GGOs. The area under the ROC curve (AUC) for the prediction models of clinical features and radiomic features was 0.755 and 0.719, respectively, whereas the AUC for the combined prediction model was 0.864 (95% CI, 0.802-0.926). Finally, a nomogram was established for individualized prediction of invasiveness. The combination of radiomic and clinical features can enable the differentiation between preinvasive and invasive GGOs. The present results can provide some basis for the best choice of treatment in patients with lung GGOs.

Invasive Prediction of Ground Glass Nodule Based on Clinical Characteristics and Radiomics Feature

Objective: To explore the diagnostic value of CT radiographic images and radiomics features for invasive classification of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT).

Methods: A total of 312 GGNs were enrolled in this retrospective study. All GGNs were randomly divided into training set (n = 219) and test set (n = 93). Univariate and multivariate logistic regressions were used to establish a clinical model, while the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct the radiomics model. A combined model was finally built by combining these two models. The performance of these models was assessed in both training and test set. A combined nomogram was developed based on the combined model and evaluated with its calibration curves and C-index.

Results: Diameter [odds ratio (OR), 1.159; p ＜ 0.001], lobulation (OR, 2.953; p = 0.002), and vascular changes (OR, 3.431; p ＜ 0.001) were retained as independent predictors of the invasive adenocarcinoma (IAC) group. Eleven radiomics features were selected by mRMR and LASSO method to established radiomics model. The clinical model and radiomics mode showed good predictive ability in both training set and test set. When two models were combined, the diagnostic area under the curve (AUC) value was higher than the single clinical or radiomics model (training set: 0.86 vs. 0.83 vs. 0.82; test set: 0.80 vs. 0.78 vs. 0.79). The constructed combined nomogram could effectively quantify the risk degree of 3 image features and Rad score with a C-index of 0.855 (95%: 0.805∼0.905).

Conclusion: Radiographic and radiomics features show high accuracy in the invasive diagnosis of GGNs, and their combined analysis can improve the diagnostic efficacy of IAC manifesting as GGNs. The nomogram, serving as a noninvasive and accurate predictive tool, can help judge the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.

Identification of pathological subtypes of early lung adenocarcinoma based on artificial intelligence parameters and CT signs

Objective: To explore the value of quantitative parameters of artificial intelligence (AI) and computed tomography (CT) signs in identifying pathological subtypes of lung adenocarcinoma appearing as ground-glass nodules (GGNs). Methods: CT images of 224 GGNs from 210 individuals were collected retrospectively and classified into atypical adenomatous hyperplasia (AAH)/adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) groups. AI was used to identify GGNs and to obtain quantitative parameters, and CT signs were recognized manually. The mixed predictive model based on logistic multivariate regression was built and evaluated. Results: Of the 224 GGNs, 55, 93, and 76 were AAH/AIS, MIA, and IAC, respectively. In terms of AI parameters, from AAH/AIS to MIA, and IAC, there was a gradual increase in two-dimensional mean diameter, three-dimensional mean diameter, mean CT value, maximum CT value, and volume of GGNs (all P<0.0001). Except for the CT signs of the location, and the tumor–lung interface, there were significant differences among the three groups in the density, shape, vacuolar signs, air bronchogram, lobulation, spiculation, pleural indentation, and vascular convergence signs (all P<0.05). The areas under the curve (AUC) of predictive model 1 for identifying the AAH/AIS and MIA and model 2 for identifying MIA and IAC were 0.779 and 0.918, respectively, which were greater than the quantitative parameters independently (all P<0.05). Conclusion: AI parameters are valuable for identifying subtypes of early lung adenocarcinoma and have improved diagnostic efficacy when combined with CT signs.

Maximum Standardized Uptake Value of 18F-deoxyglucose PET Imaging Increases the Effectiveness of CT Radiomics in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules

To investigate whether the maximum standardized uptake value (SUVmax) of ¹⁸F-deoxyglucose (FDG) PET imaging can increase the diagnostic efficiency of CT radiomics-based prediction model in differentiating benign and malignant pulmonary ground-glass nodules (GGNs). We retrospectively collected 190 GGNs from 165 patients who underwent ¹⁸F-FDG PET/CT examination from January 2012 to March 2020. Propensity score matching (PSM) was performed to select GGNs with similar baseline characteristics. LIFEx software was used to extract 49 CT radiomic features, and the least absolute shrinkage and selection operator (LASSO) algorithm was used to select parameters and establish the Rad-score. Logistic regression analysis was performed combined with semantic features to construct a CT radiomics model, which was combined with SUVmax to establish the PET + CT radiomics model. Receiver operating characteristic (ROC) was used to compare the diagnostic efficacy of different models. After PSM at 1:4, 190 GGNs were divided into benign group (n = 23) and adenocarcinoma group (n = 92). After texture analysis, the Rad-score with three CT texture features was constructed for each nodule. Compared with the Rad-score and CT radiomics model (AUC: 0.704 (95%CI: 0.562-0.845) and 0.908 (95%CI: 0.842-0.975), respectively), PET + CT radiomics model had the best diagnostic efficiency (AUC: 0.940, 95%CI: 0.889-0.990), and there was significant difference between each two of them (P = 0.001-0.030). SUVmax can effectively improve CT radiomics model performance in the differential diagnosis of benign and malignant GGNs. PET + CT radiomics might become a noninvasive and reliable method for differentiating of GGNs.

Application of dual-stream 3D convolutional neural network based on 18F-FDG PET/CT in distinguishing benign and invasive adenocarcinoma in ground-glass lung nodules

Purpose

This work aims to train, validate, and test a dual-stream three-dimensional convolutional neural network (3D-CNN) based on fluorine 18 (¹⁸F)-fluorodeoxyglucose (FDG) PET/CT to distinguish benign lesions and invasive adenocarcinoma (IAC) in ground-glass nodules (GGNs).

Methods

We retrospectively analyzed patients with suspicious GGNs who underwent ¹⁸F-FDG PET/CT in our hospital from November 2011 to November 2020. The patients with benign lesions or IAC were selected for this study. According to the ratio of 7:3, the data were randomly divided into training data and testing data. Partial image feature extraction software was used to segment PET and CT images, and the training data after using the data augmentation were used for the training and validation (fivefold cross-validation) of the three CNNs (PET, CT, and PET/CT networks).

Results

A total of 23 benign nodules and 92 IAC nodules from 106 patients were included in this study. In the training set, the performance of PET network (accuracy, sensitivity, and specificity of 0.92 ± 0.02, 0.97 ± 0.03, and 0.76 ± 0.15) was better than the CT network (accuracy, sensitivity, and specificity of 0.84 ± 0.03, 0.90 ± 0.07, and 0.62 ± 0.16) (especially accuracy was significant, P-value was 0.001); in the testing set, the performance of both networks declined. However, the accuracy and sensitivity of PET network were still higher than that of CT network (0.76 vs. 0.67; 0.85 vs. 0.70). For dual-stream PET/CT network, its performance was almost the same as PET network in the training set (P-value was 0.372–1.000), while in the testing set, although its performance decreased, the accuracy and sensitivity (0.85 and 0.96) were still higher than both CT and PET networks. Moreover, the accuracy of PET/CT network was higher than two nuclear medicine physicians [physician 1 (3-year experience): 0.70 and physician 2 (10-year experience): 0.73].

Conclusion

The 3D-CNN based on ¹⁸F-FDG PET/CT can be used to distinguish benign lesions and IAC in GGNs, and the performance is better when both CT and PET images are used together.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-021-00423-1.

Características clinicopatológicas de nódulos pulmonares: Experiencia en Clínica Reina Sofía, Bogotá, Colombia

Introducción. El cáncer de pulmón es la primera causa de mortalidad por cáncer a nivel mundial, lo que hace que sea considerado un problema de salud pública. Existen diferentes hallazgos imagenológicos que hacen sospechar la presencia de cáncer de pulmón, uno de los cuales son los nódulos pulmonares; sin embargo, estos también pueden verse en entidades benignas. Métodos. Se incluyeron 66 pacientes con biopsia de nódulo pulmonar en la Clínica Reina Sofía, en la ciudad de Bogotá, D.C., Colombia, entre el 1° de marzo del 2017 y el 28 de febrero del 2020. Se analizaron las características demográficas de los pacientes, las características morfológicas e histopatológicas de los nódulos pulmonares y la correlación entre sus características imagenológicas e histopatológicas. Resultados. El 69,2 % de los nódulos estudiados tenían etiología maligna, de estos el 55,5 % era de origen metástasico y el 44,5 % eran neoplasias primarias de pulmón, con patrón sólido en el 70,6 % de los casos. El patrón histológico más frecuente fue adenocarcinoma. Respecto a las características radiológicas, en su mayoría los nódulos malignos medían de 1 a 2 cm, de morfología lisa y distribución múltiple, localizados en lóbulos superiores. Conclusiones. La caracterización de los nódulos pulmonares brinda información relevante que orienta sobre los diagnósticos más frecuentes en nuestro medio, cuando se estudian nódulos sospechosos encontrados incidentalmente o en el seguimiento de otro tumor. Como el nódulo es la manifestación del cáncer temprano del pulmón, establecer programas de tamización que permitan el diagnóstico oportuno, es hoy día una imperiosa necesidad, para reducir la mortalidad.

Investigating the association between ground-glass nodules glucose metabolism and the invasive growth pattern of early lung adenocarcinoma

Background

To explore the association between the glucose metabolism level of lung ground-glass nodules (GGNs), as revealed by ¹⁸F-flurodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) imaging, and the invasive pathological growth pattern of early lung adenocarcinoma.

Methods

We retrospectively analyzed patients who underwent PET/CT examination and surgical resection due to persistent GGNs, which were confirmed to be early lung adenocarcinoma by postoperative pathology examination. After adjusting for confounding factors and performing stratified analysis, we explored the association between the maximum standard uptake value of PET (SUVmax) and the invasive pathological growth pattern of early stage lung adenocarcinoma.

Results

The proportions of invasive adenocarcinoma (INV) in the SUVmax of Tertile 1, Tertile 2, and Tertile 3 were 52.7%, 73.3%, and 87.1%, respectively. After adjusting for potential confounding factors, the risk of INV gradually increased as the GGN SUVmax increased [odds ratio (OR): 1.520, 95% confidence interval (CI): 1.044-2.213, P=0.029]. This trend was statistically significant (OR: 1.678, 95% CI: 1.064-2.647, P=0.026), especially in Tertile 3 vs. Tertile 1 (OR: 4.879, 95% CI: 1.349-17.648, P=0.016). Curve fitting showed that the SUVmax and INV risk were linearly and positively associated. The association was consistent in different subgroups based on GGN number, type, shape, edge, bronchial sign, vacuole sign, pleural depression sign, diameters, and consolidation-to-tumor ratio, suggesting that there was no significant interaction between different grouping parameters and the association (P for interaction range = 0.129-0.909).

Conclusions

In FDG PET, the glucose metabolism level (SUVmax) of lung GGNs is independently associated with INV risk, and this association is linear and positive.

Establishment and verification of a prediction model based on clinical characteristics and positron emission tomography/computed tomography (PET/CT) parameters for distinguishing malignant from benign ground-glass nodules

Background

To develop and verify a prediction model for distinguishing malignant from benign ground-glass nodules (GGNs) combined with clinical characteristics and ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET/CT) parameters.

Methods

We retrospectively analyzed 170 patients (56 males and 114 females) with GGNs who underwent PET/CT and high-resolution CT examination in our hospital from November 2011 to December 2019. The clinical and imaging data of all patients were collected, and the nodules were randomly divided into a derivation set and a validation set. For the derivation set, we used multivariate logistic regression to develop a prediction model for distinguishing benign from malignant GGNs. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the model, and the data in the validation set were used to verify the prediction model.

Results

Among the 170 patients, 197 GGNs were confirmed via postoperative pathological examination or clinical follow-up. There were 21 patients with 27 GGNs in the benign group and 149 patients with 170 GGNs in the adenocarcinoma group. A total of five parameters, including the patient's sex, nodule location, margin, pleural indentation, and standardized uptake value (SUV) index (the ratio of nodule SUVmax to liver SUVmean), were selected to develop a prediction model for distinguishing benign from malignant GGNs. The area under the curve (AUC) of the model was 0.875 in the derivation set, with a sensitivity of 0.702 and a specificity of 0.923. The positive likelihood ratio was 9.131, and the negative likelihood ratio was 0.322. In the validation set, the AUC of the model was 0.874, which was not significantly different from the derivation set (P=0.989).

Conclusions

This study developed and validated a prediction model based on ¹⁸F-FDG PET/CT imaging and clinical characteristics for distinguishing malignant from benign GGNs. The model showed good diagnostic efficacy and high specificity, which can improve the preoperative diagnosis of high-risk GGNs.

Predicting Pathological Invasiveness of Lung Adenocarcinoma Manifesting as GGO-Predominant Nodules: A Combined Prediction Model Generated From DECT

RATIONALE AND OBJECTIVES

To evaluate qualitative and quantitative indicators generated from Dual-energy computed tomography (DECT) for preoperatively differentiating between invasive adenocarcinoma (IAC) and preinvasive or minimally invasive adenocarcinoma (MIA) lesions manifesting as ground-glass opacity-predominant (GGO-predominant) nodules.

MATERIALS AND METHODS

We retrospectively enrolled 143 cases of completely resected GGO-predominant lung adenocarcinoma with DECT examinations between December 2017 and July 2019. Qualitative and quantitative parameters of GGO-predominant nodules were compared after grouping nodules into IAC and preinvasive-MIA groups. A multivariate logistic regression models were used for analyzing these parameters. The diagnostic performance of different parameters was compared by receiver operating characteristic (ROC) curves and Z tests.

RESULTS

This study included 137 patients (58 years ± 11; male: female = 52:91) with 143 GGO-predominant nodules. The proportion of margins, internal dilated/distorted/cut-off bronchi, internal thickened/stiff/distorted vasculature, pleural indentation, and vascular convergence were higher in the IAC group than in the preinvasive-MIA group, as were the maximum diameter (D_max), the diameter of the solid component (D_solid) and the enhanced monochromatic CT value at 40 keV-190 keV (CT_{40 keV-190 keV}) (p range: 0.001-0.019). Logistic regression analyses revealed that margin, D_max, and CT_{60 keV} values were independent predictors of the IAC group. The area under the curve (AUC) for the combination of margin, D_max, and CT_{60 keV} was 0.896 (90.2% sensitivity, 70.7% specificity, 84.6% accuracy), which was significantly higher than that for each two of them (all p < 0.05).

CONCLUSION

The combined prediction model generated from DECT allows for effective preoperative differentiation between IAC and preinvasive-MIA in GGO-predominant lung adenocarcinomas.

How Does the Delta-Radiomics Better Differentiate Pre-Invasive GGNs From Invasive GGNs?

Purpose: This study aimed to explore the role of delta-radiomics in differentiating pre-invasive ground-glass nodules (GGNs) from invasive GGNs, compared with radiomics signature.

Materials and Methods: A total of 464 patients including 107 pre-invasive GGNs and 357 invasive GGNs were embraced in radiomics signature analysis. 3D regions of interest (ROIs) were contoured with ITK software. By means of ANOVA/MW, correlation analysis, and LASSO, the optimal radiomic features were selected. The logistic classifier of radiomics signature was constructed and radiomic scores (rad-scores) were calculated. A total of 379 patients including 48 pre-invasive GGNs and 331 invasive GGNs with baseline and follow-up CT examinations before surgeries were enrolled in delta-radiomics analysis. Finally, the logistic classifier of delta-radiomics was constructed. The receiver operating characteristic curves (ROCs) were built to evaluate the validity of classifiers.

Results: For radiomics signature analysis, six features were selected from 396 radiomic features. The areas under curve (AUCs) of logistic classifiers were 0.865 (95% CI, 0.823–0.900) in the training set and 0.800 (95% CI, 0.724–0.863) in the testing set. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. As the follow-up interval went on, more and more delta-radiomic features became statistically different. The AUC of the delta-radiomics logistic classifier was 0.901 (95% CI, 0.867–0.928), which was higher than that of the radiomics signature.

Conclusion: The radiomics signature contributes to distinguish pre-invasive and invasive GGNs. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. More and more delta-radiomic features appeared to be statistically different as follow-up interval prolonged. Delta-radiomics is superior to radiomics signature in differentiating pre-invasive and invasive GGNs.

Value of 18F-FDG PET/CT-based radiomics model to distinguish the growth patterns of early invasive lung adenocarcinoma manifesting as ground-glass opacity nodules

Background

To establish and validate ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT-based radiomics model and use it to predict the intermediate-high risk growth patterns in early invasive adenocarcinoma (IAC).

Methods

Ninety-three ground-glass nodules (GGNs) from 91 patients with stage I who underwent a preoperative ¹⁸F-FDG PET/CT scan and histopathological examination were included in this study. The LIFEx software was used to extract 52 PET and 49 CT radiomic features. The least absolute shrinkage and selection operator (LASSO) algorithm was used to select radiomic features and develop radiomics signatures. We used the receiver operating characteristics curve (ROC) to compare the predictive performance of conventional CT parameters, radiomics signatures, and the combination of these two. Also, a nomogram based on conventional CT indicators and radiomics signature score (rad-score) was developed.

Results

GGNs were divided into lepidic group (n = 18) and acinar-papillary group (n = 75). Four radiomic features (2 for PET and 2 for CT) were selected to calculate the rad-score, and the area under the curve (AUC) of rad-score was 0.790, which was not significantly different as the attenuation value of the ground-glass opacity component on CT (CT_GGO) (0.675). When rad-score was combined with edge (joint model), the AUC increased to 0.804 (95% CI [0.699–0.895]), but which was not significantly higher than CT_GGO (P = 0.109). Furthermore, the decision curve of joint model showed higher clinical value than rad-score and CT_GGO, especially under the purpose of screening for intermediate-high risk growth patterns.

Conclusion

PET/CT-based radiomics model shows good performance in predicting intermediate-high risk growth patterns in early IAC. This model provides a useful method for risk stratification, clinical management, and personalized treatment.

Feasibility of electromagnetic navigation bronchoscopy-guided lung resection for pulmonary ground-glass opacity nodules

Background

Recent advances in imaging modalities and recommended low-dose computed tomography screening programs have made it easier to diagnose early lung cancer. However, the diagnosis of small ground-glass nodules (GGNs) has been problematic due to inappropriate specimen procurement and failure of conventional percutaneous core needle biopsy. Thus, we aimed to evaluate the usefulness of electromagnetic navigation bronchoscopy (ENB)-guided video-assisted lung resection for not only the diagnosis but also treatment of GGNs.

Methods

From 2017 to 2019, 110 patients with suspicious lung cancer lesions that were not diagnosed by conventional procedure underwent ENB-guided lung resection. Among 35 cases of GGNs, 33 cases of localization were included in this study (two cup biopsy cases were excluded). We used SuperDimension™ for the ENB procedure. After general anesthesia, indigo carmine (0.3–0.5 mL) was injected, and GGNs were resected through video-assisted thoracoscopic surgery.

Results

Of the 33 GGNs, 16 were pure (2 adenocarcinomas in situ, 5 minimally invasive adenocarcinomas (MIAs), 3 adenocarcinomas, and 6 benign lesions) and 17 were mixed (1 MIA, 11 adenocarcinomas, and 5 benign lesions). The mean size of all lesions was 11.2±7.78 mm, mean distance to the pleura was 11.2±14.2 mm, and mean ENB procedure time was 18.8±8.88 minutes. Dye localization and surgical resection of GGN were successful in all cases. There was no procedure-related complication.

Conclusions

ENB is a feasible and highly accurate localization method for minimally invasive lung resection of small GGNs.

Role of PET/CT in Management of Early Lung Adenocarcinoma

OBJECTIVE. The purpose of this study was to explore the value of FDG PET combined with high-resolution CT (HRCT) in predicting the pathologic subtypes and growth patterns of early lung adenocarcinoma. MATERIALS AND METHODS. A retrospective analysis was conducted on the PET/CT data on ground-glass nodules (GGNs) resected from patients with stage IA lung adenocarcinoma. The efficacy of PET maximum standardized uptake value (SUV_max) combined with HRCT signs in prediction of histopathologic subtype and growth pattern of lung adeno-carcinoma was evaluated. RESULTS. SUV_max was significantly higher in GGNs with invasive HRCT signs. The diameter of GGN (odds ratio, 1.660; p = 0.000) and the difference in attenuation value (odds ratio, 1.012; p = 0.011) between ground-glass components and adjacent lung tissues were independent predictors of FDG uptake by GGNs. SUV_max was higher in invasive adenocarcinoma than in adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) (median SUV_max, 2.0 vs 1.1; p = 0.008). An SUV_max of 2.0 was the optimal cutoff value for differentiating invasive adenocarcinoma from AIS-MIA. Acinar-papillary adenocarcinoma had a higher SUV_max than lepidic adenocarcinoma (median SUV_max, 2.1 vs 1.3; p = 0.037). An SUV_max of 1.4 was the optimal cutoff value for differentiating the growth pattern of adenocarcinoma. Use of PET/CT with HRCT significantly improved efficacy for differentiating invasive adeno-carcinoma from AIS-MIA. However, use of HRCT cannot significantly improve the diagnostic efficacy of FDG PET in the evaluation of tumors with different growth patterns. CONCLUSION. FDG PET can be used to predict the histopathologic subtypes and growth patterns of early lung adenocarcinoma. Combined with HRCT, it has value for predicting invasive histopathologic subtypes but no significance for predicting invasive growth patterns.

A simple prediction model using fluorodeoxyglucose-PET and high-resolution computed tomography for discrimination of invasive adenocarcinomas among solitary pulmonary ground-glass opacity nodules

OBJECTIVE

To analyze the FDG-PET and high-resolution computed tomography (HRCT) features of early lung adenocarcinoma manifesting as solitary ground-glass opacity nodules (GGNs), and to establish a new risk model for predicting the invasiveness of early lung adenocarcinoma.

METHODS

We retrospectively analyzed the data of clinical stage IA lung adenocarcinoma patients who received preoperative PET/CT and HRCT examination. Patients were divided into invasive adenocarcinoma (IVA) group and preinvasive minimally invasive adenocarcinoma (MIA) group. The correlations between FDG-PET parameters, HRCT parameters and histopathological invasiveness, and their predictive efficacy were analyzed. A mathematical model for predicting histopathological invasiveness of early lung adenocarcinoma was established and assessed.

RESULTS

This study enrolled 56 patients, 48 were in IVA group and 8 were in preinvasive MIA group. Compared with those in preinvasive MIA group, GGNs in IVA group showed larger diameter, higher ground-glass opacity (GGO) density and more pleural indentation signs (70.8%) on HRCT; they also showed higher maximum standardized uptake value (SUV) and SUV index on FDG-PET (P = 0.001-0.037). Logistic regression analysis found a risk model for predicting IVA of solitary GGNs that were established by CTGGO and SUV index. Receiver operating characteristic curves showed that this model had the highest area under the curve (AUC), sensitivity, specificity and accuracy (AUC, 0.948; sensitivity, 95.8%; specificity, 87.5%; accuracy, 94.6%).

CONCLUSION

Using HRCT combined with FDG-PET to establish the corresponding mathematical prediction model has the potential to identify IVA in early lung adenocarcinoma preoperatively.

Metabolic profiles of serum samples from ground glass opacity represent potential diagnostic biomarkers for lung cancer

Background

Lung cancer is a leading cause of cancer deaths worldwide. Low-dose computed tomography (LDCT) screening trials indicated that LDCT is effective for the early detection of lung cancer, but the findings were accompanied by high false positive rates. Therefore, the detection of lung cancer needs complementary blood biomarker tests to reduce false positive rates.

Methods

In order to evaluate the potential of metabolite biomarkers for diagnosing lung cancer and increasing the effectiveness of clinical interventions, serum samples from subjects participating in a low-dose CT-scan screening were analyzed by using untargeted liquid chromatography-hybrid quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). Samples were acquired from 34 lung patients with ground glass opacity diagnosed lung cancer and 39 healthy controls.

Results

In total, we identified 9 metabolites in electron spray ionization (ESI)(+) mode and 7 metabolites in ESI(-) mode. L-(+)-gulose, phosphatidylethanolamine (PE)(22:2(13Z,16Z)/15:0), cysteinyl-glutamine, S-japonin, threoninyl-glutamine, chlorate, 3-oxoadipic acid, dukunolide A, and malonic semialdehyde levels were observed to be elevated in serum samples of lung cancer cases when compared to those of healthy controls. By contrast, 1-(2-furanylmethyl)-1H-pyrrole, 2,4-dihydroxybenzoic acid, monoethyl carbonate, guanidinosuccinic acid, pseudouridine, DIMBOA-Glc, and 4-feruloyl-1,5-quinolactone levels were lower in serum samples of lung cancer cases compared with those of healthy controls.

Conclusions

This study demonstrates evidence of early metabolic alterations that can possibly distinguish malignant ground glass opacity from benign ground glass opacity. Further studies in larger pools of samples are warranted.

Lung Adenocarcinoma Manifesting as Ground-Glass Opacity Nodules 3 cm or Smaller: Evaluation With Combined High-Resolution CT and PET/CT Modality

OBJECTIVE. The purpose of this study is to evaluate high-resolution CT (HRCT) combined with PET/CT for preoperative differentiation of invasive adenocarcinoma (IAC) from preinvasive lesions and minimally invasive adenocarcinoma (MIA) (the combination of which is hereafter referred to as preinvasive-MIA) in lung adenocarcinoma manifesting as ground-glass opacity nodules (GGNs) 3 cm or smaller. MATERIALS AND METHODS. We retrospectively analyzed the data of patients with lung adenocarcinoma with GGNs that were 3 cm or smaller between November 2011 and November 2018. The HRCT and PET/CT parameters for GGNs were compared to differentiate between IAC and preinvasive-MIA. Qualitative and quantitative parameters were analyzed using univariate and multivariate logistic regression models. The diagnostic performance of different parameters was compared using ROC curves and the McNemar test. RESULTS. The study enrolled 89 patients (24 men and 65 women) with lung adenocarcinoma who had a mean (± SD) age of 60.1 ± 8.1 years (range, 36-78 years). The proportions of mixed GGN type, polygonal or irregular shape, lobulated or spiculated edge, and dilated, distorted, or cutoff bronchial sign were higher for IAC GGNs than for preinvasive-MIA GGNs, and the attenuation value of the ground-glass opacity component on CT (CT_GGO), maximum standardized uptake value, and the standardized uptake value (SUV) index (i.e., the ratio of the tumor maximum SUV to the liver mean SUV) for IAC GGNs were also higher (p = 0.001-0.022). Logistic regression analyses showed that the CT_GGO and SUV index were independent predictors for IAC GGNs. The accuracy of CT_GGO in combination with the SUV index for predicting IAC was 81.4% on a per-GGN basis and 85.4% on a per-patient basis. The combined HRCT and PET/CT modality had higher sensitivity and accuracy than did morphologic features, HRCT, and PET/CT measurement parameters alone (p < 0.001). CONCLUSION. The combined HRCT and PET/CT modality is an effective method to preoperatively identify IAC in lung adenocarcinoma manifesting as GGNs 3 cm or smaller.