Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence

Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review

Ground-glass opacities (GGOs) are hazy opacities on chest computed tomography (CT) scans that can indicate various lung diseases, including early COVID-19, pneumonia, and lung cancer. Artificial intelligence (AI) is a promising tool for analyzing medical images, such as chest CT scans. The aim of this study was to evaluate AI models' performance in detecting GGO nodules using metrics like accuracy, sensitivity, specificity, F1 score, area under the curve (AUC) and precision. We designed a search strategy to include reports focusing on deep learning algorithms applied to high-resolution CT scans. The search was performed on PubMed, Google Scholar, Scopus, and ScienceDirect to identify studies published between 2016 and 2024. Quality appraisal of included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool, assessing the risk of bias and applicability concerns across four domains. Two reviewers independently screened studies reporting the diagnostic ability of AI-assisted CT scans in early GGO detection, where the review results were synthesized qualitatively. Out of 5,247 initially identified records, we found 18 studies matching the inclusion criteria of this study. Among evaluated models, DenseNet achieved the highest accuracy of 99.48%, though its sensitivity and specificity were not reported. WOANet showed an accuracy of 98.78%, with a sensitivity of 98.37% and high specificity of 99.19%, excelling particularly in specificity without compromising sensitivity. In conclusion, AI models can potentially detect GGO on chest CT scans. Future research should focus on developing hybrid models that integrate various AI approaches to improve clinical applicability.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT

BACKGROUND. Pure ground-glass nodules (pGGNs) on chest CT representing invasive adenocarcinoma (IAC) warrant lobectomy with lymph node resection. For pGGNs representing other entities, close follow-up or sublobar resection without node dissection may be appropriate. OBJECTIVE. The purpose of this study was to develop and validate an automated deep learning model for differentiation of pGGNs on chest CT representing IAC from those representing atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA). METHODS. This retrospective study included 402 patients (283 women, 119 men; mean age, 53.2 years) with a total of 448 pGGNs on noncontrast chest CT that were resected from January 2019 to June 2022 and were histologically diagnosed as AAH (n = 29), AIS (n = 83), MIA (n = 235), or IAC (n = 101). Lung-PNet, a 3D deep learning model, was developed for automatic segmentation and classification (probability of IAC vs other entities) of pGGNs on CT. Nodules resected from January 2019 to December 2021 were randomly allocated to training (n = 327) and internal test (n = 82) sets. Nodules resected from January 2022 to June 2022 formed a holdout test set (n = 39). Segmentation performance was assessed with Dice coefficients with radiologists' manual segmentations as reference. Classification performance was assessed by ROC AUC and precision-recall AUC (PR AUC) and compared with that of four readers (three radiologists, one surgeon). The code used is publicly available (https://github.com/XiaodongZhang-PKUFH/Lung-PNet.git). RESULTS. In the holdout test set, Dice coefficients for segmentation of IACs and of other lesions were 0.860 and 0.838, and ROC AUC and PR AUC for classification as IAC were 0.911 and 0.842. At threshold probability of 50.0% or greater for prediction of IAC, Lung-PNet had sensitivity, specificity, accuracy, and F1 score of 50.0%, 92.0%, 76.9%, and 60.9% in the holdout test set. In the holdout test set, accuracy and F1 score (p values vs Lung-PNet) for individual readers were as follows: reader 1, 51.3% (p = .02) and 48.6% (p = .008); reader 2, 79.5% (p = .75) and 75.0% (p = .10); reader 3, 66.7% (p = .35) and 68.3% (p < .001); reader 4, 71.8% (p = .48) and 42.1% (p = .18). CONCLUSION. Lung-PNet had robust performance for segmenting and classifying (IAC vs other entities) pGGNs on chest CT. CLINICAL IMPACT. This automated deep learning tool may help guide selection of surgical strategies for pGGN management.

PB-LNet: a model for predicting pathological subtypes of pulmonary nodules on CT images

OBJECTIVE

To investigate the correlation between CT imaging features and pathological subtypes of pulmonary nodules and construct a prediction model using deep learning.

METHODS

We collected information of patients with pulmonary nodules treated by surgery and the reference standard for diagnosis was post-operative pathology. After using elastic distortion for data augmentation, the CT images were divided into a training set, a validation set and a test set in a ratio of 6:2:2. We used PB-LNet to analyze the nodules in pre-operative CT and predict their pathological subtypes. Accuracy was used as the model evaluation index and Class Activation Map was applied to interpreting the results. Comparative experiments with other models were carried out to achieve the best results. Finally, images from the test set without data augmentation were analyzed to judge the clinical utility.

RESULTS

Four hundred seventy-seven patients were included and the nodules were divided into six groups: benign lesions, precursor glandular lesions, minimally invasive adenocarcinoma, invasive adenocarcinoma Grade 1, Grade 2 and Grade 3. The accuracy of the test set was 0.84. Class Activation Map confirmed that PB-LNet classified the nodules mainly based on the lungs in CT images, which is in line with the actual situation in clinical practice. In comparative experiments, PB-LNet obtained the highest accuracy. Finally, 96 images from the test set without data augmentation were analyzed and the accuracy was 0.89.

CONCLUSIONS

In classifying CT images of lung nodules into six categories based on pathological subtypes, PB-LNet demonstrates satisfactory accuracy without the need of delineating nodules, while the results are interpretable. A high level of accuracy was also obtained when validating on real data, therefore demonstrates its usefulness in clinical practice.

Development and evaluation of an integrated model based on a deep segmentation network and demography-added radiomics algorithm for segmentation and diagnosis of early lung adenocarcinoma

Non-invasive early detection and differentiation grading of lung adenocarcinoma using computed tomography (CT) images are clinically important for both clinicians and patients, including determining the extent of lung resection. However, these are difficult to accomplish using preoperative images, with CT-based diagnoses often being different from postoperative pathologic diagnoses. In this study, we proposed an integrated detection and classification algorithm (IDCal) for diagnosing ground-glass opacity nodules (GGN) using CT images and other patient informatics, and compared its performance with that of other diagnostic modalities. All labeling was confirmed by a thoracic surgeon by referring to the patient's CT image and biopsy report. The detection phase was implemented via a modified FC-DenseNet to contour the lesions as elaborately as possible and secure the reliability of the classification phase for subsequent applications. Then, by integrating radiomics features and other patients' general information, the lesions were dichotomously reclassified into "non-invasive" (atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma) and "invasive" (invasive adenocarcinoma). Data from 168 GGN cases were used to develop the IDCal, which was then validated in 31 independent CT scans. IDCal showed a high accuracy of GGN detection (sensitivity, 0.970; false discovery rate, 0.697) and classification (accuracy, 0.97; f1-score, 0.98; ROAUC, 0.96). In conclusion, the proposed IDCal detects and classifies GGN with excellent performance. Thus, it can be suggested that our multimodal prediction model has high potential as an auxiliary diagnostic tool of GGN to help clinicians.

Comparison between single and serial computed tomography images in classification of acute appendicitis, acute right-sided diverticulitis, and normal appendix using EfficientNet

This study aimed to develop a convolutional neural network (CNN) using the EfficientNet algorithm for the automated classification of acute appendicitis, acute diverticulitis, and normal appendix and to evaluate its diagnostic performance. We retrospectively enrolled 715 patients who underwent contrast-enhanced abdominopelvic computed tomography (CT). Of these, 246 patients had acute appendicitis, 254 had acute diverticulitis, and 215 had normal appendix. Training, validation, and test data were obtained from 4,078 CT images (1,959 acute appendicitis, 823 acute diverticulitis, and 1,296 normal appendix cases) using both single and serial (RGB [red, green, blue]) image methods. We augmented the training dataset to avoid training disturbances caused by unbalanced CT datasets. For classification of the normal appendix, the RGB serial image method showed a slightly higher sensitivity (89.66 vs. 87.89%; p = 0.244), accuracy (93.62% vs. 92.35%), and specificity (95.47% vs. 94.43%) than did the single image method. For the classification of acute diverticulitis, the RGB serial image method also yielded a slightly higher sensitivity (83.35 vs. 80.44%; p = 0.019), accuracy (93.48% vs. 92.15%), and specificity (96.04% vs. 95.12%) than the single image method. Moreover, the mean areas under the receiver operating characteristic curve (AUCs) were significantly higher for acute appendicitis (0.951 vs. 0.937; p < 0.0001), acute diverticulitis (0.972 vs. 0.963; p = 0.0025), and normal appendix (0.979 vs. 0.972; p = 0.0101) with the RGB serial image method than those obtained by the single method for each condition. Thus, acute appendicitis, acute diverticulitis, and normal appendix could be accurately distinguished on CT images by our model, particularly when using the RGB serial image method.

Knowledge mapping visualization of the pulmonary ground-glass opacity published in the web of science

Objectives

With low-dose computed tomography(CT) lung cancer screening, many studies with an increasing number of patients with ground-glass opacity (GGO) are published. Hence, the present study aimed to analyze the published studies on GGO using bibliometric analysis. The findings could provide a basis for future research in GGO and for understanding past advances and trends in the field.

Methods

Published studies on GGO were obtained from the Web of Science Core Collection. A bibliometric analysis was conducted using the R package and VOSviewer for countries, institutions, journals, authors, keywords, and articles relevant to GGO. In addition, a bibliometric map was created to visualize the relationship.

Results

The number of publications on GGO has been increasing since 2011. China is ranked as the most prolific country; however, Japan has the highest number of citations for its published articles. Seoul National University and Professor Jin Mo Goo from Korea had the highest publications. Most top 10 journals specialized in the field of lung diseases. Radiology is a comprehensive journal with the greatest number of citations and highest H-index than other journals. Using bibliometric analysis, research topics on "prognosis and diagnosis," "artificial intelligence," "treatment," "preoperative positioning and minimally invasive surgery," and "pathology of GGO" were identified. Artificial intelligence diagnosis and minimally invasive treatment may be the future of GGO. In addition, most top 10 literatures in this field were guidelines for lung cancer and pulmonary nodules.

Conclusions

The publication volume of GGO has increased rapidly. The top three countries with the highest number of published articles were China, Japan, and the United States. Japan had the most significant number of citations for published articles. Most key journals specialized in the field of lung diseases. Artificial intelligence diagnosis and minimally invasive treatment may be the future of GGO.

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.

Artificial Intelligence Algorithm-Based Feature Extraction of Computed Tomography Images and Analysis of Benign and Malignant Pulmonary Nodules

This study was aimed to explore the effect of CT image feature extraction of pulmonary nodules based on an artificial intelligence algorithm and the image performance of benign and malignant pulmonary nodules. In this study, the CT images of pulmonary nodules were collected as the research object, and the lung nodule feature extraction model based on expectation maximization (EM) was used to extract the image features. The Dice similarity coefficient, accuracy, benign and malignant nodule edges, internal signs, and adjacent structures were compared and analyzed to obtain the extraction effect of this feature extraction model and the image performance of benign and malignant pulmonary nodules. The results showed that the detection sensitivity of pulmonary nodules in this model was 0.955, and the pulmonary nodules and blood vessels were well preserved in the image. The probability of burr sign detection in the malignant group was 73.09% and that in the benign group was 8.41%. The difference was statistically significant (P < 0.05). The probability of malignant component leaf sign (69.96%) was higher than that of a benign component leaf sign (0), and the difference was statistically significant (P < 0.05). The probability of cavitation signs in the malignant group (59.19%) was higher than that in the benign group (3.74%), and the probability of blood vessel collection signs in the malignant group (74.89%) was higher than that in the benign group (11.21%), with statistical significance (P < 0.05). The probability of the pleural traction sign in the malignant group was 17.49% higher than that in the benign group (4.67%), and the difference was statistically significant (P < 0.05). In summary, the feature extraction effect of CT images based on the EM algorithm was ideal. Imaging findings, such as the burr sign, lobulation sign, vacuole sign, vascular bundle sign, and pleural traction sign, can be used as indicators to distinguish benign and malignant nodules.

Computer-aided detection of pulmonary nodules based on convolutional neural networks: a review

Computer-aided detection (CADe) technology has been proven to increase the detection rate of pulmonary nodules that has important clinical significance for the early diagnosis of lung cancer. In this study, we systematically review the latest techniques in pulmonary nodule CADe based on deep learning models with convolutional neural networks in computed tomography images. First, the brief descriptions and popular architecture of convolutional neural networks are introduced. Second, several common public databases and evaluation metrics are briefly described. Third, state-of-the-art approaches with excellent performances are selected. Subsequently, we combine the clinical diagnostic process and the traditional four steps of pulmonary nodule CADe into two stages, namely, data preprocessing and image analysis. Further, the major optimizations of deep learning models and algorithms are highlighted according to the progressive evaluation effect of each method, and some clinical evidence is added. Finally, various methods are summarized and compared. The innovative or valuable contributions of each method are expected to guide future research directions. The analyzed results show that deep learning-based methods significantly transformed the detection of pulmonary nodules, and the design of these methods can be inspired by clinical imaging diagnostic procedures. Moreover, focusing on the image analysis stage will result in improved returns. In particular, optimal results can be achieved by optimizing the steps of candidate nodule generation and false positive reduction. End-to-end methods, with greater operating speeds and lower computational consumptions, are superior to other methods in CADe of pulmonary nodules.

A novel data augmentation based on Gabor filter and convolutional deep learning for improving the classification of COVID-19 chest X-Ray images

A dangerous infectious disease of the current century, the COVID-19 has apparently originated in a city in China and turned into a widespread pandemic within a short time. In this paper, a novel method has been presented for improving the screening and classification of COVID-19 patients based on their chest X-Ray (CXR) images. This method eliminates the severe dependence of the deep learning models on large datasets and the deep features extracted from them. In this approach, we have not only resolved the data limitation problem by combining the traditional data augmentation techniques with the generative adversarial networks (GANs), but also have enabled a deeper extraction of features by applying different filter banks such as the Sobel, Laplacian of Gaussian (LoG) and the Gabor filters. To verify the satisfactory performance of the proposed approach, it was applied on several deep transfer models and the results in each step were compared with each other. For training the entire models, we used 4560 CXR images of various patients with the viral, bacterial, fungal, and other diseases; 360 of these images are in the COVID-19 category and the rest belong to the non-COVID-19 diseases. According to the results, the Gabor filter bank achieves the highest growth in the values of the defined evaluation criteria and in just 45 epochs, it is able to elevate the accuracy by up to 32%. We then applied the proposed model on the DenseNet-201 model and compared its performance in terms of the detection accuracy with the performances of 10 existing COVID-19 detection techniques. Our approach was able to achieve an accuracy of 98.5% in the two-class classification procedure; which makes it a state-of-the-art method for detecting the COVID-19.

Propofol Anesthesia Depth Monitoring Based on Self-Attention and Residual Structure Convolutional Neural Network

Methods

We compare nine index values, select CNN+EEG, which has good correlation with BIS index, as an anesthesia state observation index to identify the parameters of the model, and establish a model based on self-attention and dual resistructure convolutional neural network. The data of 93 groups of patients were selected and randomly grouped into three parts: training set, validation set, and test set, and compared the best and worst results predicted by BIS.

Result

The best result is that the model's accuracy of predicting BLS on the test set has an overall upward trend, eventually reaching more than 90%. The overall error shows a gradual decrease and eventually approaches zero. The worst result is that the model's accuracy of predicting BIS on the test set has an overall upward trend. The accuracy rate is relatively stable without major fluctuations, but the final accuracy rate is above 70%.

Conclusion

The prediction of BIS indicators by the deep learning method CNN algorithm shows good results in statistics.

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

PURPOSE

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 significantly essential.

METHODS

In this paper, we proposed a two-stage 3D GGO nodule detection and classification framework. First, we used a pre-trained 3D U-Net to extract lung parenchyma. Second, we adapted the architecture of Mask 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 accuracy further.

RESULTS

The experiments were conducted based on five-fold cross-validation with the imbalanced dataset. 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 proposed method.

CONCLUSIONS

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. This article is protected by copyright. All rights reserved.

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients With Underlying Health Conditions: A Multicenter Study

OBJECTIVE

Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed.

METHODS

Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis.

RESULTS

The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001).

CONCLUSION

The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

[Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]

"The Expert Group on Tumor Ablation Therapy of Chinese Medical Doctor Association, The Tumor Ablation Committee of Chinese College of Interventionalists, The Society of Tumor Ablation Therapy of Chinese Anti-Cancer Association and The Ablation Expert Committee of the Chinese Society of Clinical Oncology" have organized multidisciplinary experts to formulate the consensus for thermal ablation of pulmonary subsolid nodules or ground-glass nodule (GGN). The expert consensus reviews current literatures and provides clinical practices for thermal ablation of GGN. The main contents include: (1) clinical evaluation of GGN, (2) procedures, indications, contraindications, outcomes evaluation and related complications of thermal ablation for GGN and (3) future development directions.
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[Expert Consensus for Thermal Ablation of Pulmonary Subsolid Nodules (2021 Edition)]

"The Expert Group on Tumor Ablation Therapy of Chinese Medical Doctor Association, The Tumor Ablation Committee of Chinese College of Interventionalists, The Society of Tumor Ablation Therapy of Chinese Anti-Cancer Association and The Ablation Expert Committee of the Chinese Society of Clinical Oncology" have organized multidisciplinary experts to formulate the consensus for thermal ablation of pulmonary subsolid nodules or ground-glass nodule (GGN). The expert consensus reviews current literatures and provides clinical practices for thermal ablation of GGN. The main contents include: (1) clinical evaluation of GGN, (2) procedures, indications, contraindications, outcomes evaluation and related complications of thermal ablation for GGN and (3) future development directions.
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High precision localization of pulmonary nodules on chest CT utilizing axial slice number labels

BACKGROUND

Reidentification of prior nodules for temporal comparison is an important but time-consuming step in lung cancer screening. We develop and evaluate an automated nodule detector that utilizes the axial-slice number of nodules found in radiology reports to generate high precision nodule predictions.

METHODS

888 CTs from Lung Nodule Analysis were used to train a 2-dimensional (2D) object detection neural network. A pipeline of 2D object detection, 3D unsupervised clustering, false positive reduction, and axial-slice numbers were used to generate nodule candidates. 47 CTs from the National Lung Cancer Screening Trial (NLST) were used for model evaluation.

RESULTS

Our nodule detector achieved a precision of 0.962 at a recall of 0.573 on the NLST test set for any nodule. When adjusting for unintended nodule predictions, we achieved a precision of 0.931 at a recall 0.561, which corresponds to 0.06 false positives per CT. Error analysis revealed better detection of nodules with soft tissue attenuation compared to ground glass and undeterminable attenuation. Nodule margins, size, location, and patient demographics did not differ between correct and incorrect predictions.

CONCLUSIONS

Utilization of axial-slice numbers from radiology reports allowed for development of a lung nodule detector with a low false positive rate compared to prior feature-engineering and machine learning approaches. This high precision nodule detector can reduce time spent on reidentification of prior nodules during lung cancer screening and can rapidly develop new institutional datasets to explore novel applications of computer vision in lung cancer imaging.

The application of artificial intelligence and radiomics in lung cancer

Lung cancer is one of the most leading causes of death throughout the world, and there is an urgent requirement for the precision medical management of it. Artificial intelligence (AI) consisting of numerous advanced techniques has been widely applied in the field of medical care. Meanwhile, radiomics based on traditional machine learning also does a great job in mining information through medical images. With the integration of AI and radiomics, great progress has been made in the early diagnosis, specific characterization, and prognosis of lung cancer, which has aroused attention all over the world. In this study, we give a brief review of the current application of AI and radiomics for precision medical management in lung cancer.

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

BACKGROUND

Image 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.

METHODS

First, 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.

RESULTS

To 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.

CONCLUSIONS

The 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.