Screen-detected solid nodules: from detection of nodule to structured reporting

Discrimination of Lung Cancer and Benign Lung Diseases Using BALF Exosome DNA Methylation Profile

Benign lung diseases are common and often do not require specific treatment, but they pose challenges in the distinguishing of them from lung cancer during low-dose computed tomography (LDCT). This study presents a comprehensive methylation analysis using real-time PCR for minimally invasive diagnoses of lung cancer via employing BALF exosome DNA. A panel of seven epigenetic biomarkers was identified, exhibiting specific methylation patterns in lung cancer BALF exosome DNA. This panel achieved an area under the curve (AUC) of 0.97, with sensitivity and specificity rates of 88.24% and 97.14%, respectively. Each biomarker showed significantly higher mean methylation levels (MMLs) in both non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC) compared to non-cancer groups, with fold changes from 1.7 to 13.36. The MMLs of the biomarkers were found to be moderately elevated with increasing patient age and smoking history, regardless of sex. A strong correlation was found between the MMLs and NSCLC stage progression, with detection sensitivities of 79% for early stages and 92% for advanced stages. In the validation cohort, the model demonstrated an AUC of 0.95, with 94% sensitivity and specificity. Sensitivity for early-stage NSCLC detection improved from 88.00% to 92.00% when smoking history was included as an additional risk factor.

Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm

RATIONALE AND OBJECTIVES

We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm.

MATERIALS AND METHODS

The study patients (n = 719) were divided into internal training, internal validation, and external validation cohorts; all had small SPNs and had undergone preoperative chest CTs and surgical resection. We developed five DL models incorporating features of the nodule and five different peri-nodular regions with the Multiscale Dual Attention Network (MDANet) to differentiate benign and malignant SPNs. We selected the best-performing model, which was then compared to four conventional algorithms (VGG19, ResNet50, ResNeXt50, and DenseNet121). Furthermore, another five DL models were constructed using MDANet to distinguish benign tumors from inflammatory nodules and the one performed best was selected out.

RESULTS

Model 4, which incorporated the nodule and 15 mm peri-nodular region, best differentiated benign and malignant SPNs. The model had an area under the curve (AUC), accuracy, recall, precision, and F1-score of 0.730, 0.724, 0.711, 0.705, and 0.707 in the external validation cohort. Model 4 also performed better than the other four conventional algorithms. Model 8, which incorporated the nodule and 10 mm peri-nodular region, was the best model for distinguishing benign tumors from inflammatory nodules. The model had an AUC, accuracy, recall, precision, and F1-score of 0.871, 0.938, 0.863, 0.904, and 0.882 in the external validation cohort.

CONCLUSION

The study concludes that CT-based DL models built with MDANet can accurately discriminate among small benign and malignant SPNs, benign tumors and inflammatory nodules.

Absolute ground truth-based validation of computer-aided nodule detection and volumetry in low-dose CT imaging

PURPOSE

To validate the performance of computer-aided detection (CAD) and volumetry software using an anthropomorphic phantom with a ground truth (GT) set of 3D-printed nodules.

METHODS

The Kyoto Kaguku Lungman phantom, containing 3D-printed solid nodules including six diameters (4 to 9 mm) and three morphologies (smooth, lobulated, spiculated), was scanned at varying CTDIvol levels (6.04, 1.54 and 0.20 mGy). Combinations of reconstruction algorithms (iterative and deep learning image reconstruction) and kernels (soft and hard) were applied. Detection, volumetry and density results recorded by a commercially available AI-based algorithm (AVIEW LCS + ) were compared to the absolute GT, which was determined through µCT scanning at 50 µm resolution. The associations between image acquisition parameters or nodule characteristics and accuracy of nodule detection and characterization were analyzed with chi square tests and multiple linear regression.

RESULTS

High levels of detection sensitivity and precision (minimal 83 % and 91 % respectively) were observed across all acquisitions. Neither reconstruction algorithm nor radiation dose showed significant associations with detection. Nodule diameter however showed a highly significant association with detection (p < 0.0001). Volumetric measurements for nodules > 6 mm were accurate within 10 % absolute range from volumeGT, regardless of dose and reconstruction. Nodule diameter and morphology are major determinants of volumetric accuracy (p < 0.001). Density assignment was not significantly influenced by any parameters.

CONCLUSIONS

Our study confirms the software's accurate performance in nodule volumetry, detection and density characterization with robustness for variations in CT imaging protocols. This study suggests the incorporation of similar phantom setups in quality assurance of CAD tools.

Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets

BACKGROUND

Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images.

METHODS

In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules.

RESULTS

The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%.

CONCLUSION

A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.

A comprehensive nomogram combining CT-based radiomics with clinical features for differentiation of benign and malignant lung subcentimeter solid nodules

Objective

To establish a nomogram based on non-enhanced computed tomography(CT) imaging radiomics and clinical features for use in predicting the malignancy of sub-centimeter solid nodules (SCSNs).

Materials and methods

Retrospective analysis was performed of records for 198 patients with SCSNs that were surgically resected and examined pathologically at two medical institutions between January 2020 and June 2021. Patients from Center 1 were included in the training cohort (n = 147), and patients from Center 2 were included in the external validation cohort (n = 52). Radiomic features were extracted from chest CT images. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomic feature extraction and computation of radiomic scores. Clinical features, subjective CT findings, and radiomic scores were used to build multiple predictive models. Model performance was examined by evaluating the area under the receiver operating characteristic curve (AUC). The best model was selected for efficacy evaluation in a validation cohort, and column line plots were created.

Results

Pulmonary malignant nodules were significantly associated with vascular alterations in both the training (p &lt; 0.001) and external validation (p &lt; 0.001) cohorts. Eleven radiomic features were selected after a dimensionality reduction to calculate the radiomic scores. Based on these findings, three prediction models were constructed: subjective model (Model 1), radiomic score model (Model 2), and comprehensive model (Model 3), with AUCs of 0.672, 0.888, and 0.930, respectively. The optimal model with an AUC of 0.905 was applied to the validation cohort, and decision curve analysis indicated that the comprehensive model column line plot was clinically useful.

Conclusion

Predictive models constructed based on CT-based radiomics with clinical features can help clinicians diagnose pulmonary nodules and guide clinical decision making.

Computed Tomography Imaging Features of Lung Cancer under Artificial Intelligence Algorithm and Its Correlation with Pathology

This study aims to investigate the relationship between the detection performance of an artificial intelligence (AI) algorithm and pathology in chest computed tomography (CT) images. In this study, a new pulmonary nodule (PN) detection algorithm was designed and developed on the three-dimensional (3D) connected domain algorithm. The appropriate grayscale threshold of CT images was selected, the CT images were converted into black-and-white images, and the useless images were removed. Then, the remaining lung images were formed into a 3D black-and-white pixel matrix. Labeling statistics was carried out, and the size, property, and location of PN could be measured and determined. A self-built database of PNs undergoing chest multislice spiral CT examination was retrospectively selected, and 150 cases were randomly selected by SPSS 22.0. Image processing was performed according to the algorithm and compared with the PN detected by radiologists; finally, the detection results were counted. There were 560 benign and malignant PNs, 312 malignant, and 248 benign. The algorithm detected 498 cases, of which 478 cases were detected accurately, and the sensitivity was 95.98%. The radiologist detected 424 cases, 364 cases were accurate, and the sensitivity was 85.85%. Compared with the detection results of radiologists, the algorithm detection results of nodules in solid nodules and ground glass nodules were more accurate. The detection results of nodules in the pleural connection type, peripheral type, central type, and hilar type were more accurate and statistically significant ( $P<0.05$ ). The malignancy, size, property, and location of different nodules could be accurately determined through CT images under this algorithm. It provided important support for the pathological research of lung cancer and prejudged the future development of PN in patients more accurately.

Quantitative parameters of enhanced dual-energy computed tomography for differentiating lung cancers from benign lesions in solid pulmonary nodules

Objectives

This study aimed to investigate the ability of quantitative parameters of dual-energy computed tomography (DECT) and nodule size for differentiation between lung cancers and benign lesions in solid pulmonary nodules.

Materials and Methods

A total of 151 pathologically confirmed solid pulmonary nodules including 78 lung cancers and 73 benign lesions from 147 patients were consecutively and retrospectively enrolled who underwent dual-phase contrast-enhanced DECT. The following features were analyzed: diameter, volume, Lung CT Screening Reporting and Data System (Lung-RADS) categorization, and DECT-derived quantitative parameters including effective atomic number (Zeff), iodine concentration (IC), and normalized iodine concentration (NIC) in arterial and venous phases. Multivariable logistic regression analysis was used to build a combined model. The diagnostic performance was assessed by area under curve (AUC) of receiver operating characteristic curve, sensitivity, and specificity.

Results

The independent factors for differentiating lung cancers from benign solid pulmonary nodules included diameter, Lung-RADS categorization of diameter, volume, Zeff in arterial phase (Zeff_A), IC in arterial phase (IC_A), NIC in arterial phase (NIC_A), Zeff in venous phase (Zeff_V), IC in venous phase (IC_V), and NIC in venous phase (NIC_V) (all P &lt; 0.05). The IC_V, NIC_V, and combined model consisting of diameter and NIC_V showed good diagnostic performance with AUCs of 0.891, 0.888, and 0.893, which were superior to the diameter, Lung-RADS categorization of diameter, volume, Zeff_A, and Zeff_V (all P &lt; 0.001). The sensitivities of IC_V, NIC_V, and combined model were higher than those of IC_A and NIC_A (all P &lt; 0.001). The combined model did not increase the AUCs compared with IC_V (P = 0.869) or NIC_V (P = 0.633).

Conclusion

The DECT-derived IC_V and NIC_V may be useful in differentiating lung cancers from benign lesions in solid pulmonary nodules.