Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening

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.

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.

Feasibility and safety of computed tomography-guided intrapulmonary injection of indocyanine green for localization of peripheral pulmonary ground-glass nodules

Background

The early surgical intervention for pulmonary ground-glass nodules (GGNs) has become increasingly important, but accurate identification of these nodules during thoracoscopic surgery poses challenges due to the need for sublobar resections and reliance on visual and tactile perception alone. The prognosis of the procedure is closely tied to the use of precise positioning technology. Thus, it is crucial to develop an accurate positioning technology that can improve patient prognosis.

Methods

Clinical data from the cardiothoracic department of a tertiary hospital in Shanghai were collected and analyzed between January 2020 and December 2021. The patients were categorized into 2 groups: an indocyanine green (ICG) group and a hook-wire group. Outcome measures including success rate, complications, procedure time, localization-related pain, and interval time were assessed. Adverse events and reactions were reported and compared between the 2 groups.

Results

A total of 62 patients (17 males and 45 females, aged 50.5±13.2 years) were in the ICG group, while 66 patients (23 males and 43 females, aged 48.4±12.9 years) were localized in the hook-wire group. The success rate was comparable between the 2 groups. However, the ICG group showed significant advantages over the hook-wire group in terms of procedure time (22.6±4.4 vs. 24.1±4.9 min; P=0.012), localization-related pain (P<0.001), and interval time [median and interquartile range (IQR): 3 (0.7, 104.9) vs. 1.2 (0.5, 3.3) h; P<0.001]. In the ICG group, there were 11 cases of pneumothorax, 4 cases of hemothorax, and 2 cases of ICG diffusion. In the hook-wire group, there were 24 cases of pneumothorax, 25 cases of hemothorax, and 2 cases of dislodgement. The ICG group had fewer complications, including pneumothorax (P=0.018) and hemothorax (P=0.007), compared to the hook-wire group.

Conclusions

Computed tomography (CT)-guided intrapulmonary injection of ICG for preoperative localization of peripheral pulmonary GGNs is a practical and safe technique. It offers advantages in terms of reduced procedure time, localization-related pain, and interval time compared to the hook-wire method. Moreover, the ICG technique results in fewer complications, making it a valuable preoperative localization technique worthy of popularization.

Percutaneous microwave ablation for lung tumors: a retrospective case-control study of conventional CT and C-arm CT guidance

Background

Although conventional computed tomography (cCT) is the mainstream guidance equipment for lung microwave ablation (MWA), C-arm CT can provide 3-dimensional (3D) CT-like images reconstructed from 2-dimensional (2D) digital subtraction angiography (DSA) information within 8 seconds, highlighting its utility as a new guidance tool. This retrospective case-control study was performed to evaluate the clinical performance of percutaneous MWA for lung tumors using cCT and C-arm CT guidance.

Methods

From April 2015 to April 2020, 101 consecutive patients with solitary lung tumors who underwent percutaneous MWA at our single center (Zhengzhou, China) were divided into 2 groups: the cCT group (n=56), with unarmed puncture, and the C-arm CT group (n=45), with iGuide navigation-assisted puncture. The primary endpoints were technical success, technical efficacy, puncture scoring (PS), and complete ablation (CA) rate. The secondary endpoints were complications, median progression-free survival (mPFS), and median overall survival (mOS).

Results

The technical success rates were 100% in both the C-arm CT group and cCT group. The technical efficacies were 93.3% and 91.1% in the C-arm CT group and cCT group, respectively, with no statistical difference (P=0.67). The PS (2.9 vs. 2.5, P=0.02), total procedure time (TPT; 39.3 vs. 50.0 min, P<0.001), puncture time (PT; 12.6 vs. 15.7 min, P=0.001), and irradiation effective dose (ED; 15.2 vs. 20.9 mSV, P<0.001) showed significances between patients in the C-arm CT and those in the cCT group. The ablation time (AT; 9.1 vs. 9.6 min, P=0.36), CA rate (93.3% vs. 92.9%, P=0.93), local tumor progression (LTP) rate (11.1% vs. 8.9%, P=0.98), complications, mPFS (9.5 vs. 10.1 months, P=0.52), and mOS (37.9 vs. 38.8 months, P=0.67) showed no statistically significant difference between the 2 groups.

Conclusions

C-arm CT guidance is as feasible and effective as cCT for lung tumor MWA, which can increase PS and decrease TPT.

An uncertainty-aware self-training framework with consistency regularization for the multilabel classification of common computed tomography signs in lung nodules

Background

Computed tomography (CT) signs of lung nodules play an important role in indicating lung nodules' malignancy, and accurate automatic classification of these signs can help doctors improve their diagnostic efficiency. However, few relevant studies targeting multilabel classification (MLC) of nodule signs have been conducted. Moreover, difficulty in obtaining labeled data also restricts this avenue of research to a large extent. To address these problems, a multilabel automatic classification system for nodule signs is proposed, which consists of a 3-dimensional (3D) convolutional neural network (CNN) and an efficient new semi-supervised learning (SSL) framework.

Methods

Two datasets were used in our experiments: Lung Nodule Analysis 16 (LUNA16), a public dataset for lung nodule classification, and a private dataset of nodule signs. The private dataset contains 641 nodules, 454 of which were annotated with 6 important signs by radiologists. Our classification system consists of 2 main parts: a 3D CNN model and an SSL method called uncertainty-aware self-training framework with consistency regularization (USC). In the system, supervised training is performed with labeled data, and simultaneously, an uncertainty-and-confidence-based strategy is used to select pseudo-labeled samples for unsupervised training, thus jointly realizing the optimization of the model.

Results

For the MLC of nodule signs, our proposed 3D CNN achieved satisfactory results with a mean average precision (mAP) of 0.870 and a mean area under the curve (AUC) of 0.782. In semi-supervised experiments, compared with supervised learning, our proposed SSL method could increase the mAP by 7.6% (from 0.730 to 0.806) and the mean AUC by 8.1% (from 0.631 to 0.712); it thus efficiently utilized the unlabeled data and achieved superior performance improvement compared to the recently advanced methods.

Conclusions

We realized the optimal MLC of lung nodule signs with our proposed 3D CNN. Our proposed SSL method can also offer an efficient solution for the insufficiency of labeled data that may exist in the MLC tasks of 3D medical images.

Correlation Between Intranodular Vessels and Tumor Invasiveness of Lung Adenocarcinoma Presenting as Ground-glass Nodules: A Deep Learning 3-Dimensional Reconstruction Algorithm-based Quantitative Analysis on Noncontrast Computed Tomography Images

PURPOSE

To evaluate the role of quantitative features of intranodular vessels based on deep learning in distinguishing pulmonary adenocarcinoma invasiveness.

MATERIALS AND METHODS

This retrospective study included 512 confirmed ground-glass nodules from 474 patients with 241 precursor glandular lesions (PGL), 126 minimally invasive adenocarcinomas (MIA), and 145 invasive adenocarcinomas (IAC). The pulmonary blood vessels were reconstructed on noncontrast computed tomography images using deep learning-based region-segmentation and region-growing techniques. The presence of intranodular vessels was evaluated based on the automatic calculation of vessel prevalence, vascular categories, and vessel volume percentage. Further comparisons were made between different invasive groups by the Mantel-Haenszel χ 2 test, χ 2 test, and analysis of variance.

RESULTS

The detection rate of intranodular vessels in PGL (33.2%) was significantly lower than that of MIA (46.8%, P = 0.011) and IAC (55.2%, P < 0.001), while the vascular categories were similar (all P > 0.05). Vascular changes were more common in IAC and MIA than in PGL, mainly in increased vessel volume percentage (12.4 ± 19.0% vs. 6.3 ± 13.1% vs. 3.9 ± 9.4%, P < 0.001). The average intranodular artery and vein volume percentage of IAC (7.5 ± 14.0% and 5.0 ± 10.1%) was higher than that of PGL (2.1 ± 6.9% and 1.7 ± 5.8%) and MIA (3.2 ± 9.1% and 3.1 ± 8.7%), with statistical significance (all P < 0.05).

CONCLUSIONS

The quantitative analysis of intranodular vessels on noncontrast computed tomography images demonstrated that the ground-glass nodules with increased internal vessel prevalence and volume percentages had higher possibility of tumor invasiveness.

The solid component within part-solid nodules: 3-dimensional quantification, correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas, and comparisons with 2-dimentional measures and semantic features in low-dose computed tomography

BACKGROUND

There is no consensus on 3-dimensional (3D) quantification method for solid component within part-solid nodules (PSNs). This study aimed to find the optimal attenuation threshold for the 3D solid component proportion in low-dose computed tomography (LDCT), namely the consolidation/tumor ratio of volume (CTRV), basing on its correlation with the malignant grade of nonmucinous pulmonary adenocarcinomas (PAs) according to the 5th edition of World Health Organization classification. Then we tested the ability of CTRV to predict high-risk nonmucinous PAs in PSNs, and compare its performance with 2-dimensional (2D) measures and semantic features.

METHODS

A total of 313 consecutive patients with 326 PSNs, who underwent LDCT within one month before surgery and were pathologically diagnosed with nonmucinous PAs, were retrospectively enrolled and were divided into training and testing cohorts according to scanners. The CTRV were automatically generated by setting a series of attenuation thresholds from - 400 to 50 HU with an interval of 50 HU. The Spearman's correlation was used to evaluate the correlation between the malignant grade of nonmucinous PAs and semantic, 2D, and 3D features in the training cohort. The semantic, 2D, and 3D models to predict high-risk nonmucinous PAs were constructed using multivariable logistic regression and validated in the testing cohort. The diagnostic performance of these models was evaluated by the area under curve (AUC) of receiver operating characteristic curve.

RESULTS

The CTRV at attenuation threshold of -250 HU (CTRV_- 250HU) showed the highest correlation coefficient among all attenuation thresholds (r = 0.655, P < 0.001), which was significantly higher than semantic, 2D, and other 3D features (all P < 0.001). The AUCs of CTRV_- 250HU to predict high-risk nonmucinous PAs were 0.890 (0.843-0.927) in the training cohort and 0.832 (0.737-0.904) in the testing cohort, which outperformed 2D and semantic models (all P < 0.05).

CONCLUSIONS

The optimal attenuation threshold was - 250 HU for solid component volumetry in LDCT, and the derived CTRV_- 250HU might be valuable for the risk stratification and management of PSNs in lung cancer screening.

Enhanced ultrasound-guided versus non-enhanced ultrasound-guided percutaneous needle biopsy in tissue cellularity of lung malignancies: a propensity score matched study

BACKGROUND

Though ultrasound-guided percutaneous lung needle biopsy (US-PLNB) is a first-line small biopsy method for peripheral lung lesions, quality of cellularity in specimens obtained via US-PLNB is uncertain. This study investigated the accuracy, sensitivity, and cellularity of US-PLNB. It examined the ability of contrast-enhanced ultrasound (CEUS) to improve the effectiveness of US-PLNB.

METHODS

We retrospectively analyzed all data of patients with subpleural lung lesions who underwent US-PLNB. The cellularity of US-PLNB from malignant lesions included the tumor cell number and proportion. The definition of high-quality cellularity (HQC) was concurrently achieving a tumor cell number ≥400 and a proportion ≥20%. The sensitivity, the actual numbers of tumor cell number/proportion, and the rate of HQC were calculated and compared between the CEUS and non-enhanced US groups after propensity score matching (PSM) with subgroup analyses by lesion size (small lesion ≤30 mm and large lesion >30 mm).

RESULTS

A total of 345 patients undergoing 345 US-PLNBs were evaluated, with 3.7±1.1 of punctures on average. There were 201 malignant and 144 benign lesions with a mean size of 43.8±24.1 mm. Among the 201 malignant lesions, 124 cases underwent CEUS and 77 underwent non-enhanced US. The quantity of tumor cells, the proportion of tumor cells, and the rate of HQC in 201 cases of US-PLNB from malignant lesions were 2,862.1±2,288.0, 44.6%±24.5%, and 82.1% [95% confidence interval (CI): 76.6% to 87.1%], respectively. The quantity of tumor cells, the proportion of tumor cells, and rate of HQC were significantly higher in the CEUS group than that in the non-enhanced US group, both in the analysis of overall malignant lesions and in large malignant lesions (all P<0.05).

CONCLUSIONS

The US-PLNB has high sensitivity and thereby obtains HQC samples for subpleural lung malignant lesions. The CEUS helps improve the rate of HQC and tissue cellularity of lung malignancies.

Diagnostic value of double low-dose targeted perfusion CT imaging for the diagnosis of invasive and preinvasive pulmonary ground-glass nodules: systematic review and meta-analysis

Background

This study aimed to systematically evaluate and compare the diagnostic value of bubble lucency, interface, lobulated margin and spiculation in distinguishing early invasive and preinvasive intrapulmonary ground-glass nodules (GGNs) using evidence-based meta-analysis methods. Dual low-dose targeted perfusion computed tomography (CT) imaging is controversial in the diagnosis of invasive and preinvasive ground-glass nodules. Different studies have different views and opinions. Therefore, it is necessary to conduct a systematic review of this subject in the form of meta-analysis to guide clinical diagnosis and treatment.

Methods

PubMed, Web of Science, Cochrane library and Embase were searched for recent documentation on the diagnostic value of different signs in invasive and preinvasive pulmonary GGNs. CT imaging signs of bubble lucency, speculation, interface, lobulated margin, and spiculation were used as diagnostic references to discriminate pre-invasive and invasive disease. The sensitivity, specificity, summary receiver operating characteristic (SROC) curves, and the area under the SROC curve (AUC) were calculated to evaluate diagnostic efficiency.

Results

The diagnostic sensitivity and specificity using bubble lucency as a reference of invasive ground-glass opacity (GGO) discrimination was 0.33 (0.24-0.44) and 0.74 (0.62-0.83) respectively. For interface, lobulated margin, and speculation, the diagnostic sensitivity were 0.30 (0.21-0.41), 0.49 (0.39-0.60) and 0.22 (0.14-0.33); and the specificity were 0.83 (0.74-0.89), 0.66 (0.49-0.80) and 0.86 (0.67-0.95). The pooled ROC curve was drawn by sensitivity against 1-specificity using Stata version 15.0. The area under the ROC curve (AUC) values were 0.53, 0.60, 0.58, and 0.43 for bubble lucency, speculation, lobulated margin, and pleural indentation of GGO for discriminating pre-invasive and invasive disease.

Conclusions

The diagnostic value of a single CT imaging sign of GGO, such as bubble lucency, speculation, interface, lobulated margin, and spiculation is limited for discriminating pre-invasive and invasive disease because of low sensitivity, specificity, and AUC.

18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma

Background

Microvascular invasion (MVI) is a critical risk factor for early recurrence of hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The aim of this study was to explore the contribution of ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomic features for the preoperative prediction of HCC and ICC classification and MVI.

Methods

In this retrospective study, 127 (HCC: ICC =76:51) patients with suspected MVI accompanied by either HCC or ICC were included (In HCC group, MVI positive: negative =46:30 in ICC group, MVI positive: negative =31:20). Results-driven feature engineering workflow was used to select the most predictive feature combinations. The prediction model was based on supervised machine learning classifier. Ten-fold cross validation on training cohort and independent test cohort were constructed to ensure stability and generalization ability of models.

Results

For HCC and ICC classification, radiomics predictors composed of two PET and one CT feature achieved area under the curve (AUC) of 0.86 (accuracy, sensitivity, specificity was 0.82, 0.78, 0.88, respectively) on test cohort. For MVI prediction, in HCC group, our MVI prediction model achieved AUC of 0.88 (accuracy, sensitivity, specificity was 0.78, 0.88, 0.60 respectively) with three PET features associated with tumor stage on test cohort. In ICC group, the phenotype composed of two PET features and carbohydrate antigen 19-9 (CA19-9) achieved AUC of 0.90 (accuracy, sensitivity, specificity was 0.77, 0.75, 0.80, respectively).

Conclusions

¹⁸F-FDG PET/CT radiomic features integrating clinical factors have potential in HCC and ICC classification and MVI prediction, while PET features have dominant predictive power in model performance. The prediction model has value in providing a non-invasive biomarker for an earlier indication and comprehensive quantification of primary liver cancers.