Pulmonary Ground-glass Opacity Nodules Research Articles

Diagnostic accuracy of low-dose dual-input computed tomography perfusion in the differential diagnosis of pulmonary benign and malignant ground-glass nodules

This study aimed to evaluate the value of low-dose dual-input computed tomography perfusion (CTP) imaging in the differential diagnosis of benign and malignant pulmonary ground-glass opacity nodules (GGO). A retrospective study was conducted in patients with GGO who underwent CTP in our hospital from January 2021 to October 2023. All nodules were confirmed via pathological analysis or disappeared during follow-up. Postprocessing analysis was conducted using the dual-input perfusion mode (pulmonary artery and bronchial artery) of the body perfusion software to measure the perfusion parameters of the pulmonary GGOs. A total of 101 patients with pulmonary GGOs were enrolled in this study, including 43 benign and 58 malignant nodules. The dose length product of the CTP (348 mGy.cm) was < 75% of the diagnostic reference level of the unenhanced chest CT (470 mGy.cm). The effective radiation dose was 4.872 mSV. The blood flow (BF), blood volume (BV), mean transit time (MTT), and flow extraction product (FEP) of malignant nodules were higher than those of the benign nodules (p < 0.05). The FEP had the highest accuracy for the diagnosis of malignant nodules (area under the curve [AUC] = 0.821, 95% confidence interval [CI]: 0.735–0.908) followed by BV (AUV = 0.713, 95% CI 0.608–0.819), BF (AUC = 0.688, 95% CI 0.587–0.797), and MTT (AUC = 0.616, 95% CI 0.506–0.726). When the FEP was ≥ 19.12 mL/100 mL/min, the sensitivity was 91.5% and the specificity was 62.8%. To distinguish between benign nodules and malignant nodules, the AUC of the combination of BV and FEP was 0.816 (95% CI 0.728–0.903), whereas the AUC of the combination of BF, BV, MTT, and FEP was 0.814 (95% CI 0.729–0.900). Low-dose dual-input perfusion CT was extremely effective in distinguishing between benign from malignant pulmonary GGOs, with FEP exhibiting the highest diagnostic capability.

An Automatic Random Walker Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules.

Automatic and accurate segmentation of ground glass opacity (GGO) nodules still remains challenging due to inhomogeneous interiors, irregular shapes, and blurred boundaries from different patients. Despite successful applications in the image processing domains, the random walk has some limitations for segmentation of GGO pulmonary nodules. In this paper, an improved random walker method is proposed for the segmentation of GGO nodules. To calculate a new affinity matrix, intensity, spatial, and texture features are incorporated. It strengthens discriminative power between two adjacent nodes on the graph. To address the problem of robustness in seed acquisition, the geodesic distance is introduced and a novel local search strategy is presented to automatically acquire reliable seeds. For segmentation, a label constraint term is introduced to the energy function of original random walker, which alleviates the accumulation of errors caused by the initial seeds acquisition. Massive experiments conducted on Lung Images Dataset Consortium (LIDC) demonstrate that the proposed method achieves visually satisfactory results without user interactions. Both qualitative and quantitative evaluations also demonstrate that the proposed method obtains better performance compared with conventional random walker method and state-of-the-art segmentation methods in terms of the overlap score and F-measure.

Predicting Ki-67 labeling index level in early-stage lung adenocarcinomas manifesting as ground-glass opacity nodules using intra-nodular and peri-nodular radiomic features.

To explore the diagnostic value of radiomics in differentiating between lung adenocarcinomas appearing as ground-glass opacity nodules (GGO) with high- and low Ki-67 expression levels. From January 2018 to January 2021, patients with pulmonary GGO who received lung resection were evaluated for potential enrollment. The included GGOs were then randomly divided into a training cohort and a validation cohort with a ratio of 7:3. Logistic regression (LR), decision tree (DT), support vector machines (SVM), and adaboost (AB) were applied for radiomic model construction. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the established models. Seven hundred and sixty-nine patients with 769 GGOs were included in this study. Two hundred and forty-five GGOs were confirmed to be of high Ki-67 labeling index (LI). In the training cohort, gender, age, spiculation sign, pleural indentation sign, bubble sign, and maximum 2D diameter of the nodule were found to be significantly different between high- and low Ki-67 LI groups (p < 0.05), and spiculation sign and maximum 2D diameter of the nodule were further confirmed to be risk factors for Ki-67 LI. The radiomic model established using SVM exhibited an AUC of 0.731 in the validation cohort, which was higher than that of the clinical-radiographic model (AUC=0.675). Moreover, radiomic model combining both intra- and peri-nodular features showed better diagnostic efficacy than using intra-nodular features alone (AUC=0.731 and 0.720, respectively). The established radiomic model exhibited good diagnostic efficacy in differentiating between lung adenocarcinoma GGOs with high and low Ki-67 LI, which was higher than the clinical-radiographic model. Peri-nodular radiomic features showed added benefits to the radiomic model. As a novel noninvasive method, radiomics have the potential to be applied in the preliminary classification of Ki-67 expression level in lung adenocarcinoma GGOs.

Automatic pulmonary ground-glass opacity nodules detection and classification based on 3D neural network.

Pulmonary ground-glass opacity (GGO) nodules are more likely to be malignant compared with solid solitary nodules. Due to indistinct boundaries of GGO nodules, the detection and diagnosis are challenging for doctors. Therefore, designing an automatic GGO nodule detection and classification scheme is significantlyessential. In this paper, we proposed a two-stage 3D GGO nodule detection and classification framework. First, we used a pretrained 3D U-Net to extract lung parenchyma. Second, we adapted the architecture of Mask region-based convolutional neural networks (RCNN) to handle 3D medical images. The 3D model was then applied to detect the locations of GGO nodules and classify lesions (benign or malignant). The class-balanced loss function was also used to balance the number of benign and malignant lesions. Finally, we employed a novel false positive elimination scheme called the feature-based weighted clustering (FWC) to promote the detection accuracyfurther. The experiments were conducted based on fivefold cross-validation with the imbalanced data set. Experimental results showed that the mean average precision could keep a high level (0.5182) in the phase of detection. Meanwhile, the false positive rate was effectively controlled, and the competition performance metric (CPM) reached 0.817 benefited from the FWC algorithm. The comparative statistical analyses with other deep learning methods also proved the effectiveness of our proposedmethod. We put forward an automatic pulmonary GGO nodules detection and classification framework based on deep learning. The proposed method locate and classify nodules accurately, which could be an effective tool to help doctors in clinical diagnoses.

Pathologic Diagnosis and Genetic Analysis of Sequential Biopsy Following Coaxial Low-Power Microwave Thermal Coagulation For Pulmonary Ground-Glass Opacity Nodules.

To evaluate the feasibility, safety, and diagnostic performance of sequential core-needle biopsy (CNB) technique following coaxial low-power microwave thermal coagulation (MTC) for ground-glass opacity (GGO) nodules. From December 2017 to July 2019, a total of 32 GGOs (with diameter of 12 ± 4mm) in 31 patients received two times of CNBs, both prior to and immediately after MTC at a power of 20 watts. The frequency and type of complications associated with CNBs were examined. The pathologic diagnosis and genetic analysis were performed for specimens obtained from the two types of biopsy. The technical success rates of pre- and post-MTC CNBs were 94% and 100%, respectively. The complication rate was significantly lower with post-MTC CNB as compared to pre-MTC CNB (42% versus 97%, p < 0.001). Larger amount of specimens could be obtained by post-MTC CNB. The pathological diagnosis rate of post-MTC CNB was significantly higher than that of pre-MTC CNB (100% versus 75%, p = 0.008), whereas the success rates of genetic analysis were comparable between the two groups (100% versus 84%, p = 0.063). Regular ablation could be further performed after post-MTC CNB to achieve local tumor control. Sequential biopsy following coaxial low-power MTC can reduce the risk of complications and provide high-quality specimens for pulmonary GGOs. Combining this technique with standard ablation allows for simultaneous diagnosis and treatment within a single procedure.

Technical safety and efficacy of a blunt-tip microwave ablation electrode for CT-guided ablation of pulmonary ground-glass opacity nodules.

To evaluate the safety and technical efficacy of a customized blunt-tip microwave ablation (MWA) electrode for CT-guided ablation of pulmonary ground-glass opacity nodules (GGOs). This was a retrospective before-after study. All consented patients with GGOs who underwent MWA treatment using conventional sharp-tip electrodes (group A) between January 2018 and December 2018 or new blunt-tip electrodes (group B) between January 2019 and December 2019 in our institution were included. The individual features of each patient and lesion, as well as technical and clinical information, were collected and analyzed. Sixteen (7 males, 9 females; mean age, 64.9 ± 12.3 years) and twenty-six (11 males, 15 females; mean age, 66.5 ± 10.7 years) patients were enrolled in groups A and B, respectively. The technique was successfully performed in all patients and a follow-up CT scan at 24 h after MWA showed that the technical efficacy rate was 100% in both groups. Twelve (75.0%) grade I complications were noted in group A, whereas 11 (42.3%) were noted in group B (p = 0.039, chi-square test). No bleeding occurred within the lesions in group B. The blunt-tip MWA electrode is a safe and technically effective tool for ablating GGO lesions. • A new blunt-tip MWA electrode was used for CT-guided ablation of GGO lesions. • The blunt-tip MWA electrode could improve the safety of GGO ablation. • The technical efficacy of ablation was maintained by using the blunt-tip MWA electrode.

Preoperative consolidation-to-tumor ratio is effective in the prediction of lymph node metastasis in patients with pulmonary ground-glass component nodules.

BackgroundPreoperative positron emission tomography/computed tomography (PET/CT) is recommended as a guideline for staging of lung cancer. However, for patients with pulmonary ground‐glass opacity (GGO) nodules who are supposed to have a relatively low risk of incidence of lymphatic metastasis, it remains uncertain whether PET/CT is more effective than consolidation‐to‐tumor ratio (CTR) in the prediction of regional lymphatic metastasis.MethodsThe data on patients who underwent surgery for lung cancer from 2011 to 2016 were collected retrospectively, which included CTR, results of PET/CT, and pathological characteristics. The patients who had undergone preoperative PET/CT were identified to find the risk factors for lymphatic metastasis. A receiver operating characteristic (ROC) curve and multiple logistic regression was utilized to clarify the predictive value of CTR and main tumor maximal standardized uptake value (SUVmax).ResultsAmong 217 patients who had PET/CT before lobectomy, chest computed tomography revealed that 75 patients had CTR greater than 62%. The patients with lymphatic metastasis were shown to have higher CTR and higher main tumor SUVmax. Multiple logistic regression showed that younger age (<60 years), higher main tumor SUVmax on PET/CT, and greater CTR were independent predictive factors for lymphatic metastasis. The area under the ROC curve was comparable, 0.817 for CTR, and 0.816 for main tumor SUVmax.ConclusionsThe present study revealed that CTR was not inferior to main tumor SUVmax considering the predictive power for lymphatic metastasis preoperatively in lung cancer patients with a GGO component. PET/CT might not be necessary preoperatively in selected patients.

3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation.

Classifying ground-glass lung nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) on diagnostic CT images is important to evaluate the therapy options for lung cancer patients. In this paper, we propose a joint deep learning model where the segmentation can better facilitate the classification of pulmonary GGNs. Based on our observation that masking the nodule to train the model results in better lesion classification, we propose to build a cascade architecture with both segmentation and classification networks. The segmentation model works as a trainable preprocessing module to provide the classification-guided 'attention' weight map to the raw CT data to achieve better diagnosis performance. We evaluate our proposed model and compare with other baseline models for 4 clinically significant nodule classification tasks, defined by a combination of pathology types, using 4 classification metrics: Accuracy, Average F1 Score, Matthews Correlation Coefficient (MCC), and Area Under the Receiver Operating Characteristic Curve (AUC). Experimental results show that the proposed method outperforms other baseline models on all the diagnostic classification tasks.

Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference

BackgroundImage segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper.MethodsFirst, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed.ResultsTo verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively.ConclusionsThe experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging.

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

BackgroundRecent 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.MethodsFrom 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.ResultsOf 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.ConclusionsENB is a feasible and highly accurate localization method for minimally invasive lung resection of small GGNs.

Radiation dose and image quality of noncontrast chest CT in domestic and imported main stream manufacturers

Objective: To compare the radiation dose and image quality of noncontrast chest CT and detection of ground-glass opacity pulmonary nodules (GGN) in domestic 128-slice spiral CT with the other CT scanners from three main stream manufacturers. Methods: From May 8, 2018 to October 31, 2018, noncontrast chest CT images from Neusoft 128-slice CT (75 males, 25 females, (42±16) years), dual-source 64-slice CT (53 males, 47 females, (50±16) years) and dual-source 128-slice CT scanners(69 males, 31 females, (62±17) years), Toshiba 128-slice CT (51 males, 49 females, (58±13) years) and GE 128-slice CT scanner (55 males, 45 females, (60±10) years) were collected in Eastern Theater Command and Tianjin People's Hospital. Radiation dose and image quality were evaluated.GGN detected both in Neusoft CT and dual-source CT scanners were used to analyze the displaying ability of lesions. Results: The noise in lung window of Neusoft CT ((37.8±4.9) HU) was higher than that of other mainstream CT scanners, and the noise in mediastinal window ((8.4±1.9) HU) was lower than that of GE 128-slice CT ((9.8±3.2) HU), but higher than that of dual-source CT and Toshiba 128-slice CT ((6.9±3.5)HU) (P<0.05). The absolute value of lung SNR in Neusoft CT was lower than that of other mainstream CT scanners, and the SNR in aorta (4.6±1.3) was lower than those of dual-source CT and Toshiba 128-slice CT(6.8±2.2) (P<0.05), but was not statistically significant compared with GE 128-slice CT (5.0±1.7). The mean CT value of upper lung ((-863±31) HU) at Neusoft CT was higher than 128-row dual-source CT ((-869±35) HU), and the mean CT value of aorta ((37±7) HU) was lower than that of Toshiba 128-slice CT((42±7) HU) and GE 128-slice CT ((45±9) HU) (P<0.05), while the mean CT values of the remaining lung and aorta were not statistically significant (P>0.05). The two readers had good to excellent consistency for image quality in five scanners (the highest kappa value=0.984). The delineation ability of Neusoft CT for GGN boundary was lower than that of dual-source CT (P<0.05), but had similar abilities to display the solid components, lobulation, burring, vacuoles, vascular bundle sign and pleural depression sign of GGN (all P>0.05). Radiation dose of Neusoft CT was lower than Toshiba 128-slice CT, but higher than dual-source 64-sliceCT and GE 128-slice CT scanners (P<0.05). Conclusions: With lower radiation dose than Toshiba 128-slice CT, Neusoft CT chest examination can meet the requirements of clinical diagnosis, but higher radiation dose and the lower image quality than dual-source CT and GE 128-slice CT shown in this study indicate further improvement is needed in terms of software and hardware.

Lymphadenectomy is Unnecessary for Pure Ground-Glass Opacity Pulmonary Nodules.

Background: Lobectomy plus lymph node dissection is the standard treatment of early-stage lung cancer, but the low lymph node metastasis rate with ground-glass opacity (GGO) makes surgeons not perform lymphadenectomy. This study aimed to re-evaluate the lymph node metastasis rate of GGO to help make a clinical judgment. Methods: We performed this retrospective study to enroll patients who received lung cancer surgery from 2011 to 2016. Patient characteristics collected included tumor size, solid part size and lymph node metastasis rate. These patients were categorized into pure GGO and part solid GGO groups to undergo analysis. Results: Lymph node metastasis rates were 0%, 3.8% and 6.9% in order of the pure GGO group, the GGO predominant group and the solid predominant group. In the lobectomy patients, the solid predominant group still showed to have the highest lymph node metastasis rate and recurrence rate (8.3% and 10.1%). Conclusion: It is unnecessary to perform lymphadenectomy for patients with pure GGO in view of the 0% lymph node metastasis rate. The higher lymph node metastasis rate in the patients with the solid predominant group, 6.9%, suggested that surgeons should choose a rational lymphadenectomy method according to their GGO property and clinical judgment.

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

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

Delayed cut-end recurrence after wedge resection for pulmonary ground-glass opacity adenocarcinoma despite negative surgical margin

Most pulmonary ground-glass opacity (GGO) nodules are pathologically well differentiated adenocarcinomas. We performed a limited resection trial of GGO lesions 2cm or smaller from 2003 to 2009, in which 95 patients were accumulated. We confirmed negative surgical cut-end during surgery by margin lavage cytology. In the trial, a 51-year-old man underwent right lower lobe wedge resection for a 1.7cm mixed GGO lesion. The tumor was papillary predominant adenocarcinoma, pT1NxM0. The resection scar became thicker and was diagnosed as adenocarcinoma by needle biopsy 10years after the initial surgery. We performed a right lower lobectomy and lymph node dissection. Pathologically, the second tumor was adenocarcinoma similar to the initial one, papillary predominant, and was diagnosed as cut-end recurrence. Small papillary predominant adenocarcinoma might develop delayed cut-end recurrence more than 5years after limited resection. Careful follow-up with special attention to the cut-end is necessary ideally for 10years.

Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis

This study aims to develop a CT-based radiomic features analysis approach for diagnosis of ground-glass opacity (GGO) pulmonary nodules, and also assess whether computer-aided diagnosis (CADx) performance changes in classifying between benign and malignant nodules associated with histopathological subtypes namely, adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC), respectively.The study involves 182 histopathology-confirmed GGO nodules collected from two cancer centers. Among them, 59 are benign, 50 are AIS, 32 are MIA, and 41 are IAC nodules. Four training/testing data sets—(1) all nodules, (2) benign and AIS nodules, (3) benign and MIA nodules, (4) benign and IAC nodules—are assembled based on their histopathological subtypes. We first segment pulmonary nodules depicted in CT images by using a 3D region growing and geodesic active contour level set algorithm. Then, we computed and extracted 1117 quantitative imaging features based on the 3D segmented nodules. After conducting radiomic features normalization process, we apply a leave-one-out cross-validation (LOOCV) method to build models by embedding with a Relief feature selection, synthetic minority oversampling technique (SMOTE) and three machine-learning classifiers namely, support vector machine classifier, logistic regression classifier and Gaussian Naïve Bayes classifier.When separately using four data sets to train and test three classifiers, the average areas under receiver operating characteristic curves (AUC) are 0.75, 0.55, 0.77 and 0.93, respectively. When testing on an independent data set, our scheme yields higher accuracy than two radiologists (61.3% versus radiologist 1: 53.1% and radiologist 2: 56.3%).This study demonstrates that: (1) the feasibility of using CT-based radiomic features analysis approach to distinguish between benign and malignant GGO nodules, (2) higher performance of CADx scheme in diagnosing GGO nodules comparing with radiologist, and (3) a consistently positive trend between classification performance and invasive grade of GGO nodules. Thus, to improve the CADx performance in diagnosing of GGO nodules, one should assemble an optimal training data set dominated with more nodules associated with non-invasive lung adenocarcinoma (i.e. AIS and MIA).

Computed tomography-guided percutaneous hook wire localization of pulmonary nodular lesions before video-assisted thoracoscopic surgery: Highlighting technical aspects.

OBJECTIVE:Confirming the histologic diagnosis of small pulmonary nodules or Ground-glass opacity nodules (GGNs) of unknown origin is difficult. These nodules are not always appropriate for percutaneous transthoracic needle biopsy. Preoperative localization of pulmonary lesions provides more precise target points to ensure complete surgical excision. The goal of the present study was to evaluate the validity and effectiveness of computed tomography-guided preoperative hook wire localization with our technique for video-assisted thoracoscopic surgery (VATS).METHODS:We retrospectively investigated 113 patients who had undergone preoperative hook wire localization before VATS resection for newly present or growing pulmonary nodular lesions between May 2007 and December 2016. Procedural and perioperative outcomes were assessed to evaluate the safety and efficacy of preoperative localization technique.RESULTS:A total of 113 pulmonary nodules were localized and successfully resected in all 113 patients. The mean diameter of nodules was 10.8 ± 6.1 mm (range, 3–28). The mean distance from the pleural surface was 20.2 ± 12.4 mm (range, 5–55). The mean procedure time of localization was 23.7 ± 6.3 min. Asymptomatic minimal pneumothorax and mild parenchymal hemorrhage occurred in 26 (23.0%) and 8 (7.1%) patients, respectively. There were 32 (28.3%) deep lung nodules, in which the distance to pleural surface was more than 25 mm. Wire dislodgement occurred in 4 (3.5%) patients. Complete resection of all lung lesions was achieved, and definite histological diagnosis was obtained in all patients. Pathologic examination revealed 42 (37.2%) primary lung cancers, 2 (1.8%) lymphomas, 53 (46.9%) metastases, 16 (14.1%) benign lesions.CONCLUSIONS:Preoperative percutaneous hook wire localization is a dependable and useful technique to facilitate positioning small and deep pulmonary nodules for thoracoscopic complete excision and accurate diagnosis.

Identification of preoperative prediction factors of tumor subtypes for patients with solitary ground-glass opacity pulmonary nodules

BackgroundRecent wide spread use of low-dose helical computed tomography for the screening of lung cancer have led to an increase in the detection rate of very faint and smaller lesions known as ground-glass opacity nodules. The purpose of this study was to investigate the clinical factors of lung cancer patients with solitary ground-glass opacity pulmonary nodules on computed tomography.MethodsA total of 423 resected solitary ground-glass opacity nodules were retrospectively evaluated. We analyzed the clinical, imaging and pathological data and investigated the clinical differences in patient with adenocarcinoma in situ / minimally invasive adenocarcinoma and those with invasive adenocarcinoma.ResultsThree hundred and ninety-three adenocarcinomas (92.9%) and 30 benign nodules were diagnosed. Age, the history of family cancer, serum carcinoembryonic antigen level, tumor size, ground-glass opacity types, and bubble-like sign in chest CT differed significantly between adenocarcinoma in situ / minimally invasive adenocarcinoma and invasive adenocarcinoma (p:0.008, 0.046, 0.000, 0.000, 0.000 and 0.001). Receiver operating characteristic curves and univariate analysis revealed that patients with more than 58.5 years, a serum carcinoembryonic antigen level > 1.970 μg/L, a tumor size> 13.50 mm, mixed ground-glass opacity nodules and a bubble-like sign were more likely to be diagnosed as invasive adenocarcinoma. The combination of five factors above had an area under the curve of 0.91, with a sensitivity of 82% and a specificity of 87%.ConclusionThe five-factor combination helps us to distinguish adenocarcinoma in situ / minimally invasive adenocarcinoma from invasive adenocarcinoma and to perform appropriate surgery for solitory ground-glass opacity nodules.

Radiomic analysis of pulmonary ground-glass opacity nodules for distinction of preinvasive lesions, invasive pulmonary adenocarcinoma and minimally invasive adenocarcinoma based on quantitative texture analysis of CT.

ObjectiveTo identify the differences among preinvasive lesions, minimally invasive adenocarcinomas (MIAs) and invasive pulmonary adenocarcinomas (IPAs) based on radiomic feature analysis with computed tomography (CT).MethodsA total of 109 patients with ground-glass opacity lesions (GGOs) in the lungs determined by CT examinations were enrolled, all of whom had received a pathologic diagnosis. After the manual delineation and segmentation of the GGOs as regions of interest (ROIs), the patients were subdivided into three groups based on pathologic analyses: the preinvasive lesions (including atypical adenomatous hyperplasia and adenocarcinoma in situ) subgroup, the MIA subgroup and the IPA subgroup. Next, we obtained the texture features of the GGOs. The data analysis was aimed at finding both the differences between each pair of the groups and predictors to distinguish any two pathologic subtypes using logistic regression. Finally, a receiver operating characteristic (ROC) curve was applied to accurately evaluate the performances of the regression models. ResultsWe found that the voxel count feature (P<0.001) could be used as a predictor for distinguishing IPAs from preinvasive lesions. However, the surface area feature (P=0.040) and the extruded surface area feature (P=0.013) could be predictors of IPAs compared with MIAs. In addition, the correlation feature (P=0.046) could distinguish preinvasive lesions from MIAs better.ConclusionsPreinvasive lesions, MIAs and IPAs can be discriminated based on texture features within CT images, although the three diseases could all appear as GGOs on CT images. The diagnoses of these three diseases are very important for clinical surgery.

Segmentation of Ground Glass Opacity Pulmonary Nodules Based on a Modified Random Walker Approach

Segmentation of Ground Glass Opacity Pulmonary Nodules Based on a Modified Random Walker Approach

Factors associated with the growth of ground-glass opacity nodules on chest computed tomography.

e20051 Background: Chest computed tomography (CT) has become increasingly popular for screening and various reasons, and many cases of ground-glass opacity pulmonary nodules (GGN) that cannot be detected by simple chest radiography have been found incidentally. However, the natural history of GGN is not well understood and guidelines for the evaluation and follow-up of GGN have not yet been established yet. The purpose of this study was to investigate the clinical and CT scan characteristics associated with the growth of GGNs. Methods: We retrospectively reviewed GGN on chest CT performed between March 2011 and February 2014 at a tertiary referral hospital. Data were collected for the patient's age, sex, underlying lung disease, history of malignancy or comorbidities, size, border, number of GGN and presence of solid portion in GGN. For each GGN, an increase in size over time was investigated. We applied a Cox regression analysis to identify factors associated with the growth of GGN. Results: In a total of 504 patients, 916 GGN were found on chest CT. The mean age of the subjects was 59.6 years and 37% of men. The mean follow-up was 17.1 months. Cox regression analysis was performed on age, sex, history of malignancy, maximum diameter of GGN, number of GGN, margins of GGN, and presence of solid portion in GGN. History of malignancy (adjusted hazard ratio [HR] 1.770, 95% confidence intervals [CI] 1.049-2.986, p = 0.032), solitary nodule (adjusted HR 2.335, 95% CI 1.401-3.867, p = 0.001), and nodules with the longest diameter of 10 mm or more (adjusted HR 1.833, 95% CI 1.032-3.253, p = 0.039) were associated with the growth of GGN. Conclusions: A history of malignancy, solitary nodule, and nodules with the longest diameter of 10 mm or more may increase in size in the future. Therefore, the history of malignancy and the size and number of nodules should be considered to determine the duration of follow-up and appropriate biopsy timing.