Malignant Pulmonary Nodules Research Articles

Optical coherence tomography for identification of malignant pulmonary nodules based on random forest machine learning algorithm.

ObjectiveTo explore the feasibility of using random forest (RF) machine learning algorithm in assessing normal and malignant peripheral pulmonary nodules based on in vivo endobronchial optical coherence tomography (EB-OCT).MethodsA total of 31 patients with pulmonary nodules were admitted to Department of Respiratory Medicine, Zhongda Hospital, Southeast University, and underwent chest CT, EB-OCT and biopsy. Attenuation coefficient and up to 56 different image features were extracted from A-line and B-scan of 1703 EB-OCT images. Attenuation coefficient and 29 image features with significant p-values were used to analyze the differences between normal and malignant samples. A RF classifier was trained using 70% images as training set, while 30% images were included in the testing set. The accuracy of the automated classification was validated by clinically proven pathological results.ResultsAttenuation coefficient and 29 image features were found to present different properties with significant p-values between normal and malignant EB-OCT images. The RF algorithm successfully classified the malignant pulmonary nodules with sensitivity, specificity, and accuracy of 90.41%, 77.87% and 83.51% respectively.ConclusionIt is clinically practical to distinguish the nature of pulmonary nodules by integrating EB-OCT imaging with automated machine learning algorithm. Diagnosis of malignant pulmonary nodules by analyzing quantitative features from EB-OCT images could be a potentially powerful way for early detection of lung cancer.

Development and validation of a prediction model for malignant pulmonary nodules: A cohort study.

This study is to develop and validate a preoperative prediction model for malignancy of solitary pulmonary nodules. Data from 409 patients who underwent solitary pulmonary nodule resection at the First Affiliated Hospital of Nanjing Medical University, China between June 2018 and December 2020 were retrospectively collected. Then, the patients were nonrandomly split into a training cohort and a validation cohort. Clinical features, imaging parameters and laboratory data were then collected. Logistic regression analysis was used to develop a prediction model to identify variables significantly associated with malignant pulmonary nodules (MPNs) that were then included in the nomogram. We evaluated the discrimination and calibration ability of the nomogram by concordance index and calibration plot, respectively. MPNs were confirmed in 215 (52.6%) patients by a pathological examination. Multivariate logistic regression analysis identified 6 risk factors independently associated with MPN: gender (female, odds ratio [OR] = 2.487; 95% confidence interval [CI]: 1.313–4.711; P = .005), location of nodule (upper lobe of lung, OR = 1.126; 95%CI: 1.054–1.204; P < .001), density of nodule (pure ground glass, OR = 4.899; 95%CI: 2.572–9.716; P < .001; part-solid nodules, OR = 6.096; 95%CI: 3.153–14.186; P < .001), nodule size (OR = 1.193; 95%CI: 1.107–1.290; P < .001), GAGE7 (OR = 1.954; 95%CI: 1.054–3.624; P = .033), and GBU4–5 (OR = 2.576; 95%CI: 1.380–4.806; P = .003). The concordance index was 0.86 (95%CI: 0.83–0.91) and 0.88 (95%CI: 0.84–0.94) in the training and validation cohorts, respectively. The calibration curves showed good agreement between the predicted risk by the nomogram and real outcomes. We have developed and validated a preoperative prediction model for MPNs. The model could aid physicians in clinical treatment decision making.

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

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

A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images.

ObjectivesBoth radiomics and deep learning methods have shown great promise in predicting lesion malignancy in various image-based oncology studies. However, it is still unclear which method to choose for a specific clinical problem given the access to the same amount of training data. In this study, we try to compare the performance of a series of carefully selected conventional radiomics methods, end-to-end deep learning models, and deep-feature based radiomics pipelines for pulmonary nodule malignancy prediction on an open database that consists of 1297 manually delineated lung nodules.MethodsConventional radiomics analysis was conducted by extracting standard handcrafted features from target nodule images. Several end-to-end deep classifier networks, including VGG, ResNet, DenseNet, and EfficientNet were employed to identify lung nodule malignancy as well. In addition to the baseline implementations, we also investigated the importance of feature selection and class balancing, as well as separating the features learned in the nodule target region and the background/context region. By pooling the radiomics and deep features together in a hybrid feature set, we investigated the compatibility of these two sets with respect to malignancy prediction.ResultsThe best baseline conventional radiomics model, deep learning model, and deep-feature based radiomics model achieved AUROC values (mean ± standard deviations) of 0.792 ± 0.025, 0.801 ± 0.018, and 0.817 ± 0.032, respectively through 5-fold cross-validation analyses. However, after trying out several optimization techniques, such as feature selection and data balancing, as well as adding context features, the corresponding best radiomics, end-to-end deep learning, and deep-feature based models achieved AUROC values of 0.921 ± 0.010, 0.824 ± 0.021, and 0.936 ± 0.011, respectively. We achieved the best prediction accuracy from the hybrid feature set (AUROC: 0.938 ± 0.010).ConclusionThe end-to-end deep-learning model outperforms conventional radiomics out of the box without much fine-tuning. On the other hand, fine-tuning the models lead to significant improvements in the prediction performance where the conventional and deep-feature based radiomics models achieved comparable results. The hybrid radiomics method seems to be the most promising model for lung nodule malignancy prediction in this comparative study.

A Cost-Effective and Non-Invasive pfeRNA-Based Test Differentiates Benign and Suspicious Pulmonary Nodules from Malignant Ones.

The ability to differentiate between benign, suspicious, and malignant pulmonary nodules is imperative for definitive intervention in patients with early stage lung cancers. Here, we report that plasma protein functional effector sncRNAs (pfeRNAs) serve as non-invasive biomarkers for determining both the existence and the nature of pulmonary nodules in a three-stage study that included the healthy group, patients with benign pulmonary nodules, patients with suspicious nodules, and patients with malignant nodules. Following the standards required for a clinical laboratory improvement amendments (CLIA)-compliant laboratory-developed test (LDT), we identified a pfeRNA classifier containing 8 pfeRNAs in 108 biospecimens from 60 patients by sncRNA deep sequencing, deduced prediction rules using a separate training cohort of 198 plasma specimens, and then applied the prediction rules to another 230 plasma specimens in an independent validation cohort. The pfeRNA classifier could (1) differentiate patients with or without pulmonary nodules with an average sensitivity and specificity of 96.2% and 97.35% and (2) differentiate malignant versus benign pulmonary nodules with an average sensitivity and specificity of 77.1% and 74.25%. Our biomarkers are cost-effective, non-invasive, sensitive, and specific, and the qPCR-based method provides the possibility for automatic testing of robotic applications.

Solitary pulmonary nodule malignancy predictive models applicable to routine clinical practice: a systematic review

BackgroundSolitary pulmonary nodule (SPN) is a common finding in routine clinical practice when performing chest imaging tests. The vast majority of these nodules are benign, and only a small proportion are malignant. The application of predictive models of nodule malignancy in routine clinical practice would help to achieve better diagnostic management of SPN. The present systematic review was carried out with the purpose of critically assessing studies aimed at developing predictive models of solitary pulmonary nodule (SPN) malignancy from SPN incidentally detected in routine clinical practice.MethodsWe performed a search of available scientific literature until October 2020 in Pubmed, SCOPUS and Cochrane Central databases. The inclusion criteria were observational studies carried out in low-risk population from 35 years old onwards aimed at constructing predictive models of malignancy of pulmonary solitary nodule detected incidentally in routine clinical practice. Studies had to be published in peer-reviewed journals, either in Spanish, Portuguese or English. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches. Exclusion criteria were non-human studies, or predictive models based in high-risk populations, or models based on computational approaches (such as radiomics). We used The Transparent Reporting of a multivariable Prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, to describe the type of predictive model included in each study, and The Prediction model Risk Of Bias ASsessment Tool (PROBAST) to evaluate the quality of the selected articles.ResultsA total of 186 references were retrieved, and after applying the exclusion/inclusion criteria, 15 articles remained for the final review. All studies analysed clinical and radiological variables. The most frequent independent predictors of SPN malignancy were, in order of frequency, age, diameter, spiculated edge, calcification and smoking history. Variables such as race, SPN growth rate, emphysema, fibrosis, apical scarring and exposure to asbestos, uranium and radon were not analysed by the majority of the studies. All studies were classified as high risk of bias due to inadequate study designs, selection bias, insufficient population follow-up and lack of external validation, compromising their applicability for clinical practice.ConclusionsThe studies included have been shown to have methodological weaknesses compromising the clinical applicability of the evaluated SPN malignancy predictive models and their potential influence on clinical decision-making for the SPN diagnostic management.Systematic review registrationPROSPERO CRD42020161559

An ISHAP-based interpretation-model-guided classification method for malignant pulmonary nodule

The classification of benign and malignant pulmonary nodules can provide an important aid to the diagnosis of lung cancer. However, high-performance classification models are still less accepted approaches in clinic applications due to their structural complexity and low interpretability. In this study, an ISHAP (Improved SHapley Additive exPlanations)-based interpretation-model-guided classification method is proposed for the classification of benign and malignant pulmonary nodules. First, semantic and radiomics features are extracted by using medical priori knowledge and image understanding in the lung images dataset. Then, optimal features, classifiers and their parameters are guided to select and set adaptively based on the proposed ISHAP explanation and recursive feature elimination algorithm. The proposed ISHAP-based interpretation-model-guided classification method on the Lung Image Database Consortium (LIDC) dataset achieves a sensitivity of 0.862, a specificity of 0.885, an accuracy of 0.873 and an area under the receiver-operating-characteristic curve of 0.941 on testing dataset. Meanwhile, our analysis with ISHAP-based interpretation model can reveal the essential factors of semantic and radiomics features in the classification of malignant pulmonary nodules. Experimental results demonstrate that the proposed method achieves satisfactory classification performance and makes the predictions more transparent and trustworthy by interpreting the model.

Role of magnetic resonance spectroscopy in differential diagnosis of solitary pulmonary lesions.

The aim of our study was to evaluate the availability of magnetic resonance spectroscopy (MRS) for the differentiation of benign or malignant pulmonary nodules and masses. A total of 59 patients (45 male, 14 female) with pulmonary nodules and masses were included in this prospective study. MRS was applied to the pulmonary lesions of the patients and choline levels were determined. Afterwards CT-guided percutaneous needle biopsy was performed. According to the biopsy results, pulmonary lesions were benign in 25 patients and malignant in 34 patients. Choline levels were significantly higher in malignant lesions compared with benign lesions (p < 0.001). When the other conditions were kept constant, the probability of malignancy significantly increased by 17.38-fold (95% CI, 3.78-79.93) in those with choline levels >1.65 µmol/g compared to those with choline levels ≤1.65 µmol/g (p < 0.001). MRS is a noninvasive method that can be used in the differential diagnosis of pulmonary nodules and masses.

Detection of circulating rare cells benefitted the diagnosis of malignant solitary pulmonary nodules.

Solitary pulmonary nodules (SPNs) are challenging in differentiating between benignancy and malignancy. Therefore, more effective non-invasive biomarkers are urgently needed. The purpose of this investigation was to examine whether circulating rare cells (CRCs) could facilitate the differentiation between benign and malignant SPNs as well as its sensitivity and specificity. 164 patients diagnosed with SPNs, 24 healthy volunteers, and 25 patients diagnosed with advanced-stage lung cancer were included. CT/PET-CT images, serum tumor markers, and biopsy results were collected. The CRCs were examined using subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH) and their relationship with malignant or benign SPNs was analyzed. The total CRC numbers from patients with malignant SPNs diagnosed by biopsy were significantly greater compared to those with benign SPNs (P < 0.0001), but not significantly different from patients with advanced lung cancer (P > 0.05). The total CRCs, with a cut-off value of 21.5 units, showed 67.6% sensitivity and 73.3% specificity [area under curve (AUC) 95% CI, 0.778 (0.666-0.889)] in discriminating benign and malignant SPNs and the triploid CRCs exhibited a high positive likelihood ratio of 8.4, which suggested that CRCs appeared to have a distinct advantage in discriminating benign and malignant SPNs compared to CT/PET-CT images and serum tumor markers and could be a potential screening indicator for lung cancer in the high-risk population. SE-iFISH could effectively detect CRCs including circulating tumor cells (CTCs) and circulating tumor-derived endothelial cells (CTECs) and the detection of CRCs could benefit the differentiation of patients with benign and malignant SPNs.

Adaptive morphology aided 2-pathway convolutional neural network for lung nodule classification

Early-stage detection and identification of malignant pulmonary nodules can allow proper medication and increase the survival rate of lung cancer patients. High-Resolution Computed Tomography (HRCT) image slices are in use for the screening of lung cancer. However, appropriate identification of lung nodules at the early stage of the disease is challenging owing to similar morphological properties of benign and malignant nodules. Introduction of computer vision and advanced image analysis techniques for the development of Computer-aided diagnosis (CADx) systems have significantly improved the classification performance and increase the speed the interpreting lung CT images for the identification of lung cancer. Deep learning-based techniques have recently emerged as an efficient tool for the improved characterization of lung nodules. In this research work, a deep learning (DL) based framework has been introduced using the concept of adaptive morphology-based operations combined with Gabor filter (GF) for accurate lung nodule classification. The new framework, 2-Pathway Morphology-based Convolutional Neural Network (2PMorphCNN) with its two trainable paths can capture both textural and morphological features of the lung nodules that results in better classification accuracy. The proposed system has been trained and evaluated on LIDC-IDRI dataset and achieved sensitivity, specificity, accuracy of 96.85%, 95.17%, and 96.10% with an Area under the ROC Curve (AUC) of 0.9936 for lung nodule characterization. It has been observed that the reported automatic lung nodule classification framework outperforms other state-of-the-art nodule classification methodologies by capturing and combining textural and morphological features from the HRCT lung nodule image.

HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification

Accurate segmentation and classification of pulmonary nodules are of great significance to early detection and diagnosis of lung diseases, which can reduce the risk of developing lung cancer and improve patient survival rate. In this paper, we propose an effective network for pulmonary nodule segmentation and classification at one time based on adversarial training scheme. The segmentation network consists of a High-Resolution network with Multi-scale Progressive Fusion (HR-MPF) and a proposed Progressive Decoding Module (PDM) recovering final pixel-wise prediction results. Specifically, the proposed HR-MPF firstly incorporates boosted module to High-Resolution Network (HRNet) in a progressive feature fusion manner. In this case, feature communication is augmented among all levels in this high-resolution network. Then, downstream classification module would identify benign and malignant pulmonary nodules based on feature map from PDM. In the adversarial training scheme, a discriminator is set to optimize HR-MPF and PDM through back propagation. Meanwhile, a reasonably designed multi-task loss function optimizes performance of segmentation and classification overall. To improve the accuracy of boundary prediction crucial to nodule segmentation, a boundary consistency constraint is designed and incorporated in the segmentation loss function. Experiments on publicly available LUNA16 dataset show that the framework outperforms relevant advanced methods in quantitative evaluation and visual perception.

Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.

Under the background of urgent need for computer-aided technology to provide physicians with objective decision support, aiming at reducing the false positive rate of nodule CT detection in pulmonary nodules detection and improving the accuracy of lung nodule recognition, this paper puts forward a method based on ensemble learning to distinguish between malignant and benign pulmonary nodules. Firstly, trained on a public data set, a multi-layer feature fusion YOLOv3 network is used to detect lung nodules. Secondly, a CNN was trained to differentiate benign from malignant pulmonary nodules. Then, based on the idea of ensemble learning, the confidence probability of the above two models and the label of the training set are taken as data features to build a Logistic regression model. Finally, two test sets (public data set and private data set) were tested, and the confidence probability output by the two models was fused into the established logistic regression model to determine benign and malignant pulmonary nodules. The YOLOv3 network was trained to detect chest CT images of the test set. The number of pulmonary nodules detected in the public and private test sets was 356 and 314, respectively. The accuracy, sensitivity and specificity of the two test sets were 80.97%, 81.63%, 78.75% and 79.69%, 86.59%, 72.16%, respectively. With CNN training pulmonary nodules benign and malignant discriminant model analysis of two kinds of test set, the result of accuracy, sensitivity and specificity were 90.12%, 90.66%, 89.47% and 88.57%, 85.62%, 90.87%, respectively. Fused model based on YOLOv3 network and CNN is tested on two test sets, and the result of accuracy, sensitivity and specificity were 93.82%, 94.85%, 92.59% and 92.31%, 92.68%, 91.89%, respectively. The ensemble learning model is more effective than YOLOv3 network and CNN in removing false positives, and the accuracy of the ensemble. Learning model is higher than the other two networks in identifying pulmonary nodules.

3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT.

Early detection is significant to reduce lung cancer-related death. Computer-aided detection system (CADs) can help radiologists to make an early diagnosis. In this paper, we propose a novel 3D gray density coding feature (3D GDC) and fuse it with extracted geometric features. The fusion feature and random forest are used for benign-malignant pulmonary nodule classification on Chest CT. First, a dictionary model is created to acquire codebook. It is used to obtain feature descriptors and includes 3D block database (BD) and distance matrix clustering centers. 3D BD is balanced and randomly selecting from benign and malignant pulmonary nodules of training data. Clustering centers is got by clustering the distance matrix, which is the distance between every two blocks in 3D BD. Then, feature descriptor is obtained by coding the pulmonary nodule with codebook, and 3D GDC feature is the result of histogram statistics on feature descriptor. Second, geometric features are extracted for fusion feature. Finally, random forest is performed for benign-malignant pulmonary nodule classification with fusion feature of the 3D gray density coding feature and the geometric features. We verify the effectiveness of our method on the public LIDC-IDRI dataset and the private ZSHD dataset. For LIDC-IDRI dataset, compared with other state-of-the-art methods, we achieve more satisfactory results with 93.17 ± 1.94% for accuracy and 97.53 ± 1.62% for AUC. As for private ZSHD dataset, it contains a total of 238 lung nodules from 203 patients. The accuracy and AUC achieved by our method are 90.0% and 93.15%. The results show that our method can provide doctors with more accurate results of benign-malignant pulmonary nodule classification for auxiliary diagnosis, and our method is more interpretable than 3D CNN methods, which can provide doctors with more auxiliary information.

Computed Tomography Image under Convolutional Neural Network Deep Learning Algorithm in Pulmonary Nodule Detection and Lung Function Examination.

The objective of this study was to perform segmentation and extraction of CT images of pulmonary nodules based on convolutional neural networks (CNNs). The Mask-RCNN algorithm model is a typical end-to-end image segmentation model, which uses the R-FCN structure for nodule detection. The effect of applying the two algorithm models to the computed tomography (CT) diagnosis of pulmonary nodules was analyzed, and different indexes of pulmonary nodule CT images in lung function examination after algorithm optimization were compared. A total of 56 patients diagnosed with pulmonary nodules by surgery or puncture were taken as the research objects. Based on the Mask-RCNN algorithm, a model for CT image segmentation processing of pulmonary nodules was proposed. Subsequently, the 3D Faster-RCNN model was used to label the nodules in the pulmonary nodules. The experimental results showed that the trained Mask-RCNN algorithm model can effectively complete the segmentation task of lung CT images, but there was a little jitter at the boundary. The speed of R-FCN algorithm for nodular detection was 0.172 seconds/picture, and the accuracy was 88.9%. CT scans were performed on the 56 patients based on a deep learning algorithm. The results showed that 30 cases of malignant pulmonary nodules were confirmed, and the diagnostic accuracy was 93.75%. There were 22 benign lesions, the diagnostic accuracy was 91.67%, and the overall diagnostic accuracy was 92.85%. This study effectively improved the diagnostic efficiency of CT images of pulmonary nodules, and the accuracy of CT images in the diagnosis of pulmonary nodules was analyzed and evaluated. It provided theoretical support for the follow-up diagnosis of pulmonary nodules and the treatment of lung cancer. It also significantly improved the diagnostic effect and detection efficiency of pulmonary nodules.

Radiomics nomogram analysis of T2-fBLADE-TSE in pulmonary nodules evaluation

ObjectivesTo develop and validate a radiomics nomogram for differentiating between malignant pulmonary nodules and benign nodules. Methods56 benign and 51 malignant nodules from 96 patients were analyzed using manual segmentation of the T2-fBLADE-TSE, while the nodules signal intensity (SIlesion), lesion muscle ratio (LMR) and nodule size were all measured and recorded. The maximum relevance and minimum redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) were used to select nonzero coefficients and develop the model in pulmonary nodules diagnosis. The radiomics nomogram was also developed. The clinical prediction value was determined by the decision curve analysis (DCA). ResultsThe nodule size, SIlesion and LMR of the benign group were 1.78 ± 0.57 cm, 227.50 ± 81.39 and 2.40 ± 1.27 respectively, in contrast to the 2.00 ± 0.64 cm, 232.87 ± 82.21 and 2.17 ± 0.91, respectively, in the malignant group (P = 0.09, 0.60 and 0.579). A total of 13 radiomics features were retained. The Rad-score of the benign nodules group was lower than that of the malignant nodules group (P < 0.001 & 0.049, training & test set). The AUC of radiomics signature for nodules diagnosis was 0.82 (95% CI, 0.73–0.91) in the training set and 0.71 (95% CI, 0.51–0.90) in the test set. A nomogram, consisting of 13 radiomics features and nodule size, produced good prediction in the training set (AUC, 0.82; 95% CI, 0.73–0.91), which was significantly better than that of T2-based quantitative parameters (AUC, 0.62; 95% CI, 0.50–0.75, P = 0.003). In the test set, the performance of radiomics nomogram (AUC, 0.70; 95% CI, 0.51–0.90) was also better than that of T2-based quantitative parameters (AUC, 0.46; 95% CI, 0.25–0.67) (P = 0.145). The DCA showed that radiomics nomogram and T2-based quantitative parameter had overall net benefits, while the performance of nomogram was better. ConclusionWe constructed and validated a T2-fBLADE-TSE-based radiomics nomogram that can help to differentiate between malignant pulmonary nodules and benign nodules.

Role of chest MRI for the diagnosis of malignant pulmonary nodules: a systematic review and a meta-analysis

AIM: To evaluate the ability of magnetic resonance imaging (MRI) of the chest to detect malignant pulmonary nodules compared to compute tomography (CT).&#x0D; MATERIALS AND METHODS: We searched the following databases with the final date of search on April 7th, 2021: PubMed, Google Scholar. We selected studies according to the inclusion and exclusion criteria that assessed the detection of malignant lung nodules by MRI and CT and included information about sensitivity and specificity. Method of the analysis and data grouping was chosen with regard to statistical heterogeneity of the studies included in the analysis. We used the 2 test and I2 statistic to evaluate the heterogeneity.&#x0D; RESULTS: We selected 168 articles for the systematic review from the PubMed and Google Scholar databases. We included 21 studies on 1,188 patients in the meta-analysis and revealed statistically significant heterogeneity (р0,00001 for 2 test; I2=99%) for sensitivity and specificity. Hence, we used a random-effect model for further analysis. As a result, values of sensitivity for detection of pulmonary nodules with MRI of 70.4%100%, specificity ― from 60.6% to 100%.&#x0D; CONCLUSIONS: Thus, MRI has sufficient sensitivity and specificity for detecting malignant pulmonary nodules primarily discovered with CT.

Chest Computerized Tomography Images under Iterative Model Reconstruction Algorithm in Patients with Lung Cancer

To explore the effect of the full iterative model reconstruction algorithm (IMR) on chest CT image processing and its adoption value in the clinical diagnosis of lung cancer patients, multislice spiral CT (MSCT) scans were performed on 96 patients with pulmonary nodules. Reconstruction was performed by hybrid iterative reconstruction (iDose4) and IMR2 algorithms. Then, the image contrast, spatial resolution, density resolution, image uniformity, and noise of the CT reconstructed image were recorded. The benign and malignant pulmonary nodules of patients were collected and classified into malignant pulmonary nodule group and benign pulmonary nodule group, and the differences in chest CT imaging characteristics between the two groups were compared. The subject’s receiver operating characteristic (ROC) curve was used to analyze the diagnostic sensitivity, specificity, and area under the curve (AUC) of CT for benign and malignant pulmonary nodules. It was found that the spatial resolution, density resolution, image uniformity, and contrast of the CT image reconstructed by the IMR2 algorithm were remarkably greater than those of the iDose4 algorithm, and the noise was considerably less than that of the iDose4 algorithm ( P &lt; 0.05 ). Among 96 patients with pulmonary nodules, 65 were malignant nodules, including 15 squamous cell carcinoma, 31 adenocarcinoma, and 19 small cell carcinomas. There were 31 cases of benign nodules, including 14 cases of hamartoma, 10 cases of tuberculous granuloma, 2 cases of sclerosing hemangioma, and 5 cases of diffuse lymphocyte proliferation. The pulmonary nodule malignant group and the pulmonary nodule benign group had statistical differences in pulmonary nodule size, nodule morphology, burr sign, lobular sign, vascular sign, bronchial sign, and pleural depression sign ( P &lt; 0.05 ). The sensitivity, specificity, and area under the curve (AUC) of IMR2 algorithm processing chest CT images for liver cancer diagnosis were 85.7%, 82.3%, and 0.815, respectively, which were significantly higher than the original CT images ( P &lt; 0.05 ). In short, chest MSCT based on the IMR2 algorithm can greatly improve the diagnosis efficiency of lung cancer and had practical significance for the timely detection of early lung cancer.

Diagnosis of pulmonary nodules by DNA methylation analysis in bronchoalveolar lavage fluids

BackgroundLung cancer is the leading cause of cancer-related mortality. The alteration of DNA methylation plays a major role in the development of lung cancer. Methylation biomarkers become a possible method for lung cancer diagnosis.ResultsWe identified eleven lung cancer-specific methylation markers (CDO1, GSHR, HOXA11, HOXB4-1, HOXB4-2, HOXB4-3, HOXB4-4, LHX9, MIR196A1, PTGER4-1, and PTGER4-2), which could differentiate benign and malignant pulmonary nodules. The methylation levels of these markers are significantly higher in malignant tissues. In bronchoalveolar lavage fluid (BALF) samples, the methylation signals maintain the same differential trend as in tissues. An optimal 5-marker model for pulmonary nodule diagnosis (malignant vs. benign) was developed from all possible combinations of the eleven markers. In the test set (57 tissue and 71 BALF samples), the area under curve (AUC) value achieves 0.93, and the overall sensitivity is 82% at the specificity of 91%. In an independent validation set (111 BALF samples), the AUC is 0.82 with a specificity of 82% and a sensitivity of 70%.ConclusionsThis model can differentiate pulmonary adenocarcinoma and squamous carcinoma from benign diseases, especially for infection, inflammation, and tuberculosis. The model’s performance is not affected by gender, age, smoking history, or the solid components of nodules.

Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.

To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size. We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0cm), T1b (1.0cm < diameter ≤ 2.0cm), and T1c (2.0cm < diameter ≤ 3.0cm). CT images were divided into four settings according to slice-thickness-convolution-kernels: setting 1 (slice thickness/reconstruction type: 1.25mm sharp), setting 2 (5mm sharp), setting 3 (5mm smooth), and random setting. We created twelve groups from two interacting conditions. Each PN was segmented and had 1160 radiomics features extracted. Non-redundant features with high predictive ability in training were selected to build a distinct model under each of the twelve subsets. The performance (AUCs) on predicting PN malignancy were as follows: T1a group: 0.84, 0.64, 0.68, and 0.68; T1b group: 0.68, 0.74, 0.76, and 0.70; T1c group: 0.66, 0.64, 0.63, and 0.70, for the setting 1, setting 2, setting 3, and random setting, respectively. In the T1a group, the AUC of radiomics model in setting 1 was statistically significantly higher than all others; In the T1b group, AUCs of radiomics models in setting 3 were statistically significantly higher than some; and in the T1c group, there were no statistically significant differences among models. For PNs less than 1cm, CT image acquisition parameters have a significant influence on diagnostic performance of radiomics in predicting malignancy, and a model created using images reconstructed with thin section and a sharp kernel algorithm achieved the best performance. For PNs larger than 1cm, CT reconstruction parameters did not affect diagnostic performance substantially. • CT image acquisition parameters have a significant influence on the diagnostic performance of radiomics in pulmonary nodules less than 1cm. • In pulmonary nodules less than 1cm, a radiomics model created by using images reconstructed with thin section and a sharp kernel algorithm achieved the best diagnostic performance. • For PNs larger than 1cm, CT image acquisition parameters do not affect diagnostic performance substantially.

Advances and Clinical Application of Malignant Probability Prediction Models for Solitary Pulmonary Nodule

随着计算机断层扫描（computed tomography, CT）的普及，孤立性肺结节（solitary pulmonary nodule, SPN）发病率明显上升。肺结节良恶性评估对肺结节诊治决策至关重要。许多肺结节恶性概率预测模型已被开发，这些模型基于患者临床和影像学特征来评估肺结节恶性概率。近年来，肺结节恶性概率预测模型在国内逐渐引起关注。本文基于肺结节恶性概率预测模型研究，重点关注在中国患者群体中模型的建立或验证情况，综述肺结节恶性概率预测模型的研究进展和临床应用情况，并为模型未来发展提出思路。