Improvement in imaging diagnosis technique and modalities for solitary pulmonary nodules: from ground-glass opacity nodules to part-solid and solid nodules

Impact of Voxel Normalization on a Machine Learning-Based Method: A Study on Pulmonary Nodule Malignancy Diagnosis Using Low-Dose Computed Tomography (LDCT)

Lung cancer (LC) stands as the foremost cause of cancer-related fatality rates worldwide. Early diagnosis significantly enhances patient survival rate. Nowadays, low-dose computed tomography (LDCT) is widely employed on the chest as a tool for large-scale lung cancer screening. Nonetheless, a large amount of chest radiographs creates an onerous burden for radiologists. Some computer-aided diagnostic (CAD) tools can provide insight to the use of medical images for diagnosis and can augment diagnostic speed. However, due to the variation in the parameter settings across different patients, substantial discrepancies in image voxels persist. We found that different voxel sizes can create a compromise between model generalization and diagnostic efficacy. This study investigates the performance disparities of diagnostic models trained on original images and LDCT images reconstructed to different voxel sizes while making isotropic. We examined the ability of our method to differentiate between benign and malignant nodules. Using 11 features, a support vector machine (SVM) was trained on LDCT images using an isotropic voxel with a side length of 1.5 mm for 225 patients in-house. The result yields a favorable model performance with an accuracy of 0.9596 and an area under the receiver operating characteristic curve (ROC/AUC) of 0.9855. In addition, to furnish CAD tools for clinical application, future research including LDCT images from multi-centers is encouraged.

Evaluation of the dual vascular supply patterns in ground-glass nodules with a dynamic volume computed tomography

BACKGROUND

In recent years, the detection rate of ground-glass nodules (GGNs) has been improved dramatically due to the popularization of low-dose computed tomography (CT) screening with high-resolution CT technique. This presents challenges for the characterization and management of the GGNs, which depends on a thorough investigation and sufficient diagnostic knowledge of the GGNs. In most diagnostic studies of the GGNs, morphological manifestations are used to differentiate benignancy and malignancy. In contrast, few studies are dedicated to the assessment of the hemodynamics, i.e., perfusion parameters of the GGNs.

AIM

To assess the dual vascular supply patterns of GGNs on different histopathology and opacities.

METHODS

Forty-seven GGNs from 47 patients were prospectively included and underwent the dynamic volume CT. Histopathologic diagnoses were obtained within two weeks after the CT examination. Blood flow from the bronchial artery [bronchial flow (BF)] and pulmonary artery [pulmonary flow (PF)] as well as the perfusion index (PI) = [PF/(PF + BF)] were obtained using first-pass dual-input CT perfusion analysis and compared respectively between different histopathology and lesion types (pure or mixed GGNs) and correlated with the attenuation values of the lesions using one-way ANOVA, student’s t test and Pearson correlation analysis.

RESULTS

Of the 47 GGNs (mean diameter, 8.17 mm; range, 5.3-12.7 mm), 30 (64%) were carcinoma, 6 (13%) were atypical adenomatous hyperplasia and 11 (23%) were organizing pneumonia. All perfusion parameters (BF, PF and PI) demonstrated no significant difference among the three conditions (all P > 0.05). The PFs were higher than the BFs in all the three conditions (all P < 0.001). Of the 30 GGN carcinomas, 14 showed mixed GGNs and 16 pure GGNs with a higher PI in the latter (P < 0.01). Of the 17 benign GGNs, 4 showed mixed GGNs and 13 pure GGNs with no significant difference of the PI between the GGN types (P = 0.21). A negative correlation (r = -0.76, P < 0.001) was demonstrated between the CT attenuation values and the PIs in the 30 GGN carcinomas.

CONCLUSION

The GGNs are perfused dominantly by the PF regardless of its histopathology while the weight of the BF in the GGN carcinomas increases gradually during the progress of its opacification.

Prediction Model for Lung Cancer in High-Risk Nodules Being Considered for Resection: Development and Validation in a Chinese Population

Background

Determining benign and malignant nodules before surgery is very difficult when managing patients with pulmonary nodules, which further makes it difficult to choose an appropriate treatment. This study aimed to develop a lung cancer risk prediction model for predicting the nature of the nodule in patients’ lungs and deciding whether to perform a surgical intervention.

Methods

This retrospective study included patients with pulmonary nodules who underwent lobectomy or sublobectomy at Tianjin Medical University General Hospital between 2017 and 2020. All subjects were further divided into training and validation sets. Multivariable logistic regression models with backward selection based on the Akaike information criterion were used to identify independent predictors and develop prediction models.

Results

To build and validate the model, 503 and 260 malignant and benign nodules were used. Covariates predicting lung cancer in the current model included female sex, age, smoking history, nodule type (pure ground-glass and part-solid), nodule diameter, lobulation, margin (smooth, or spiculated), calcification, intranodular vascularity, pleural indentation, and carcinoembryonic antigen. The final model of this study showed excellent discrimination and calibration with a concordance index (C-index) of 0.914 (0.890–0.939). In an independent sample used for validation, the C-index for the current model was 0.876 (0.825–0.927) compared with 0.644 (0.559–0.728) and 0.681 (0.605–0.757) for the Mayo and Brock models. The decision curve analysis showed that the current model had higher discriminatory power for malignancy than the Mayo and the Brock models.

Conclusions

The current model can be used in estimating the probability of lung cancer in nodules requiring surgical intervention. It may reduce unnecessary procedures for benign nodules and prompt diagnosis and treatment of malignant nodules.

Clinical Analysis of Video-Assisted Thoracoscopic Surgery for Resection of Solitary Pulmonary Nodules and Influencing Factors in the Diagnosis of Benign and Malignant Nodules

Purpose

This is a retrospective research comparing the clinical outcomes of single-hole versus multi-hole video-assisted thoracoscopic surgical (VATS) resection for solitary pulmonary nodules (SPN) and examining the factors influencing the diagnosis of benign and malignant pulmonary nodules.

Method

We collected the clinical data, surgical status, outcomes, and corresponding imaging features of 317 patients with SPN who were surgically resected by VATS and diagnosed as benign or malignant by pathology in our hospital from January 2019 to December 2021.

Result

Among the 317 patients, 124 (39.12%) underwent single-port VATS and 193 (60.88%) underwent multiple-hole VATS. All patients were grouped according to the different surgical methods, and their postoperative indicators were statistically analyzed. The results showed that neither the single-port VATS group nor the multi-port VATS group had any serious adverse events such as death during the perioperative period. The average operation time, intraoperative blood loss, drainage tube indwelling time, and postoperative hospital stay were significantly lower in the two groups. Statistics of postoperative pathological diagnosis showed that 98 cases (30.91%) of all nodules were benign nodules and 219 cases (69.09%) were malignant nodules, and a further single-multivariate analysis showed that age, nodule maximum diameter, lobular sign, burr sign, vascular cluster sign, and pleural depression sign were independent relevant factors for the diagnosis of benign and malignant nodules.

Conclusion

VATS is less invasive and has fewer complications and is of great clinical value for both diagnosis and treatment of benign and malignant SPN. Age, maximum nodal diameter, lobar sign, burr sign, vascular set sign, and pleural depression sign were independent correlates affecting the diagnosis of benign and malignant SPN, which reminds that great attention should be paid to patients who are older and have risk factors on imaging, and early and timely active treatment or close follow-up should be carried out.

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.

Association Between 18F-FDG PET/CT-Based SUV Index and Malignant Status of Persistent Ground-Glass Nodules

To explore the association between ¹⁸F-FDG PET/CT-based SUV index and malignant risk of persistent ground-glass nodules (GGNs). We retrospectively analyzed a total of 166 patients with GGN who underwent PET/CT examination from January 2012 to October 2019. There were 113 women and 53 men, with an average age of 60.8 ± 9.1 years old. A total of 192 GGNs were resected and confirmed by pathology, including 22 in benign group and 170 in adenocarcinoma group. They were divided into three groups according to SUV index tertiles: Tertile 1 (0.14–0.54), Tertile 2 (0.55–1.17), and Tertile 3 (1.19–6.78), with 64 GGNs in each group. The clinical and imaging data of all patients were collected and analyzed. After adjusting for the potential confounding factors, we found that the malignancy risk of GGN significantly decreased as the SUV index increased (OR, 0.245; 95%CI, 0.119–0.504; P <0.001), the average probability of malignant GGN was 89.1% (95% CI, 53.1–98.3%), 80.5% (95% CI, 36.7–96.7%), and 34.3% (95%CI, 9.5–72.2%) for Tertile 1 to Tertile 3. And the increasing trend of SUV index was significantly correlated with the reduction of malignant risk (OR, 0.099; 95%CI, 0.025–0.394; P = 0.001), especially between Tertile 3 versus Tertile 1 (OR, 0.064; 95%CI, 0.012–0.356; P = 0.002). Curve fitting showed that the SUV index was linearly and negatively correlated with the malignant risk of GGN. SUV index is an independent correlation factor for malignancy risk of GGN, the higher the SUV index, the lower the probability of GGN malignancy.

Management of incidental pulmonary nodules: current strategies and future perspectives

Introduction: Detection and characterization of pulmonary nodules is an important issue, because the process is the first step in the management of lung cancers.Areas covered: Literature review was performed on May 15 2019 by using the PubMed, US National Library of Medicine National Institutes of Health, and the National Center for Biotechnology information. CT features helping identify the druggable mutations and predict the prognosis of malignant nodules were presented. Technical advancements in MRI and PET/CT were introduced for providing functional information about malignant nodules. Advances in various tissue biopsy techniques enabling molecular analysis and histologic diagnosis of indeterminate nodules were also presented. New techniques such as radiomics, deep learning (DL) technology, and artificial intelligence showing promise in differentiating between malignant and benign nodules were summarized. Recently, updated management guidelines for solid and subsolid nodules incidentally detected on CT were described. Risk stratification and prediction models for indeterminate nodules under active investigation were briefly summarized.Expert opinion: Advancement in CT knowledge has led to a better correlation between CT features and genomic alterations or tumor histology. Recent advances like PET/CT, MRI, radiomics, and DL-based approach have shown promising results in the characterization and prognostication of pulmonary nodules.

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.

Advances in intelligent diagnosis methods for pulmonary ground-glass opacity nodules

Pulmonary nodule is one of the important lesions of lung cancer, mainly divided into two categories of solid nodules and ground glass nodules. The improvement of diagnosis of lung cancer has significant clinical significance, which could be realized by machine learning techniques. At present, there have been a lot of researches focusing on solid nodules. But the research on ground glass nodules started late, and lacked research results. This paper summarizes the research progress of the method of intelligent diagnosis for pulmonary nodules since 2014. It is described in details from four aspects: nodular signs, data analysis methods, prediction models and system evaluation. This paper aims to provide the research material for researchers of the clinical diagnosis and intelligent analysis of lung cancer, and further improve the precision of pulmonary ground glass nodule diagnosis.

Value of window technique in diagnosis of the ground glass opacities in patients with non-small cell pulmonary cancer

The aim of the present study was to examine the value of window technique in qualitative diagnosis of the ground glass opacities (GGO) in patients with non-small cell pulmonary cancer. A total of 124 clinically suspected pulmonary cancer patients were analyzed retrospectively. The lesions were affirmed by puncture biopsy, and were GGO on pulmonary window while were invisible on mediastinal window. Sixty-four multi-detector spiral computed tomography with the window width and window level of 1,500 Hounsfield units (HU) and −450 HU on pulmonary window, while the window width and window level of 400 and 40 HU on mediastinal window, was used in the study. The window adjustment technique was used to analyze the window width and window level of lesion on pulmonary window and mediastinal window, for searching invisible threshold on 3-megapixel medical displays. The diagnostic accuracy and the cut-off value were compared on receiver operating characteristic (ROC) curve. The results showed that the window width and window level on pulmonary window and mediastinal window of malignant lesions were significantly less than those of benign ones (P<0.05). The cut-off value on pulmonary window was the window width and window level of 1,300 and −220 HU, the area under the ROC was 0.830 [sensitivity was 72.5%, specificity was 84.3%; 95% confidence interval (CI), 0.712–0.945]. The cut-off value on mediastinal window was the window width and window level of 360 and 30 HU, and the area under the ROC was 0.623 (was 62.0%, specificity was 55.7%; 95% CI, 0.541–0.745). In conclusion, the window technique has high sensitivity and accuracy in qualitative diagnosis of the GGO.

Differential diagnosis of solitary pulmonary nodules with dual-source spiral computed tomography

The aim of the present study was to analyze the value of applying dual-source 64-layer spiral computed tomography (CT) in the differential diagnosis of solitary pulmonary nodules (SPNs). Mediastinal windows from 45 cases were selected to study SPNs (maximum diameter, ≤3 cm), and the pathological nature of lesions was determined by clinical and pathological diagnosis. Conventional 64-layer spiral CT scanning, local enhancement and 3D recombination technologies were used to determine the occurrence rate, lesion diameter, degree of enhancement, lobular sign, spicule sign, pleural indentation sign, vessel convergence sign and bronchus sign. The final diagnoses indicated 34 cases of malignant SPNs (75.6%) and 11 benign cases (24.4%). When the nodule diameter in the malignant group was compared with that of the benign group, the difference was not statistically significant (P>0.05). Nodules in the malignant group showed inhomogeneous enhancement while nodules in the benign group showed homogeneous enhancement. The enhanced CT values in the malignant group were higher than those in the benign group, and the difference was statistically significant (P<0.05). The proportion of nodules with lobular sign in the malignant group was significantly higher than that in the benign group (P<0.05). The proportion of nodules with calcification, vessel convergence sign and bronchus sign in the malignant group were significantly higher than those in the benign group, and the differences were statistically significant (P<0.05). A comparison of vacuole sign, pleural indentation sign, spiculate protuberance and fat occurrence between the two groups yielded no statistically significant differences (P>0.05). The sensitivity of CT enhancement was 85.6%, specificity was 79.6%, positive predicated value was 92.3%, and the negative predicted value was 85.2%. In conclusion, SPNs diagnosed by CT enhancement manifested with enhancement degree, lobular sign, calcification, vessel convergence sign and bronchus sign with high diagnostic accuracy.