Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction

Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network

Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement. This study is dedicated to investigating a new CAD scheme for predicting the malignant likelihood of lung nodules in computed tomography (CT) images in light of a deep learning strategy. Conceived from the residual learning and selective kernel, we investigated an efficient residual selective kernel (RSK) block to handle the diversity of lung nodules with various shapes and obscure structures. Founded on this RSK block, we established a multiview RSK network (MRSKNet), to which three anatomical planes in the axial, coronal, and sagittal directions were fed. To reinforce the classification efficiency, seven handcrafted texture features with a filter-like computation strategy were explored, among which the homogeneity (HOM) feature maps are combined with the corresponding intensity CT images for concatenation input, leading to an improved network architecture. Evaluated on the public benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) challenge database with ten-fold cross validation of binary classification, our experimental results indicated high area under receiver operating characteristic (AUC) and accuracy scores. A better compromise between recall and specificity was struck using the suggested concatenation strategy comparing to many state-of-the-art approaches. The proposed pulmonary nodule classification framework exhibited great efficacy and achieved a higher AUC of 0.9711. The association of handcrafted texture features with deep learning models is promising in advancing the classification performance. The developed pulmonary nodule CAD network architecture is of potential in facilitating the diagnosis of lung cancer for further image processing applications.

3D SAACNet with GBM for the classification of benign and malignant lung nodules

In view of the low diagnostic accuracy of the current classification methods of benign and malignant pulmonary nodules, this paper proposes a 3D segmentation attention network integrating asymmetric convolution (SAACNet) classification model combined with a gradient boosting machine (GBM). This can make full use of the spatial information of pulmonary nodules. First, the asymmetric convolution (AC) designed in SAACNet can not only strengthen feature extraction but also improve the network's robustness to object flip and rotation detection and improve network performance. Second, the segmentation attention network integrating AC (SAAC) block can effectively extract more fine-grained multiscale spatial information while adaptively recalibrating multidimensional channel attention weights. The SAACNet also uses a dual-path connection for feature reuse, where the model makes full use of features. In addition, this article makes the loss function pay more attention to difficult and misclassified samples by adding adjustment factors. Third, the GBM is used to splice the nodule size, originally cropped nodule pixels, and the depth features learned by SAACNet to improve the prediction accuracy of the overall model. A comprehensive ablation experiment is carried out on the public dataset LUNA16 and compared with other lung nodule classification models. The classification accuracy (ACC) is 95.18%, and the area under the curve (AUC) is 0.977. The results show that this method effectively improves the classification performance of pulmonary nodules. The proposed method has advantages in the classification of benign and malignant pulmonary nodules, and it can effectively assist radiologists in pulmonary nodule classification.

Novel Random Forest Ensemble Modeling Strategy Combined with Quantitative Structure-Property Relationship for Density Prediction of Energetic Materials

With the further development of the concept of green chemistry, the new generation of energetic materials tends to exhibit detonation properties such as higher insensitivity, higher density, and higher energy. Therefore, the precise molecular design and green and efficient synthesis of energetic materials will be one of the serious challenges. For the purpose of accurate prediction of detonation performance of energetic materials, an ensemble modeling strategy based on the combination of Monte Carlo (MC) and variable importance measurement (VIM) improved random forest (RF) and quantitative structure-property relationship (QSPR) is proposed, which was successfully used for density prediction of energetic materials. First, the structure of 162 energetic compounds was optimized by Gaussian software, and the molecular descriptor data were calculated by CODESSA software based on the optimized molecular structure. Then, the MCVIMRF_Med ensemble model was constructed on the basis of the above molecular descriptor data and the corresponding energetic compound density index. The joint X-Y distance algorithm (SPXY) is used to partition the data set. And then, MC is used to further divide the calibration set data into multiple subsets for the construction of the ensemble model. The subset size and the number of iterations of the MCVIMRF_Med ensemble model were optimized through MC cross validation. The final output strategy of the ensemble model is optimized based on the optimized parameters, and an output optimization method based on median screening is proposed and successfully applied for the prediction performance optimization of the MCVIMRF_Med ensemble model. To further investigate the performance of the MCVIMRF_Med ensemble model, the performance of it was compared with partial least squares, RF, VIMRF, and MCVIMRF calibration models. It shows that the MCVIMRF_Med ensemble model can achieve a better prediction result for the density of energetic materials, with R ² _CV of 0.9596, RMSECV of 0.0437 g/cm³, R ² _P of 0.9768, RMSEP of 0.0578 g/cm³, and relative analysis deviation of prediction set of 3.951. Therefore, the MCVIMRF_Med ensemble modeling strategy combined with QSPR is an effective approach for the density prediction of energetic materials. This work is expected to provide new research ideas and technical support for accurate prediction of detonation performance of energetic materials.

The Value of Artificial Intelligence Film Reading System Based on Deep Learning in the Diagnosis of Non-Small-Cell Lung Cancer and the Significance of Efficacy Monitoring: A Retrospective, Clinical, Nonrandomized, Controlled Study

Objective

To explore the value of artificial intelligence (AI) film reading system based on deep learning in the diagnosis of non-small-cell lung cancer (NSCLC) and the significance of curative effect monitoring.

Methods

We retrospectively selected 104 suspected NSCLC cases from the self-built chest CT pulmonary nodule database in our hospital, and all of them were confirmed by pathological examination. The lung CT images of the selected patients were introduced into the AI reading system of pulmonary nodules, and the recording software automatically identified the nodules, and the results were compared with the results of the original image report. The nodules detected by the AI software and film readers were evaluated by two chest experts and recorded their size and characteristics. Comparison of calculation sensitivity, false positive rate evaluation of the NSCLC software, and physician's efficiency of nodule detection whether there was a significant difference between the two groups.

Results

The sensitivity, specificity, accuracy, positive predictive rate, and false positive rate of NSCLC diagnosed by radiologists were 72.94% (62/85), 92.06% (58/63), 81.08% (62+58/148), 92.53% (62/67), and 7.93% (5/63), respectively. The sensitivity, specificity, accuracy, positive prediction rate, and false positive rate of AI film reading system in the diagnosis of NSCLC were 94.12% (80/85), 77.77% (49/63), 87.161% (80 + 49/148), 85.11% (80/94), and 22.22% (14/63), respectively. Compared with radiologists, the sensitivity and false positive rate of artificial intelligence film reading system in the diagnosis of NSCLC were higher (P < 0.05). The sensitivity, specificity, accuracy, positive prediction rate, and negative prediction rate of artificial intelligence film reading system in evaluating the efficacy of patients with NSCLC were 87.50% (63/72), 69.23% (9/13), 84.70% (63 + 9)/85, 94.02% (63/67), and 50% (9/18), respectively.

Conclusion

The AI film reading system based on deep learning has higher sensitivity for the diagnosis of NSCLC than radiologists and can be used as an auxiliary detection tool for doctors to screen for NSCLC, but its false positive rate is relatively high. Attention should be paid to identification. Meanwhile, the AI film reading system based on deep learning also has a certain guiding significance for the diagnosis and treatment monitoring of NSCLC.

Attention-embedded complementary-stream CNN for false positive reduction in pulmonary nodule detection

False positive reduction plays a key role in computer-aided detection systems for pulmonary nodule detection in computed tomography (CT) scans. However, this remains a challenge owing to the heterogeneity and similarity of anisotropic pulmonary nodules. In this study, a novel attention-embedded complementary-stream convolutional neural network (AECS-CNN) is proposed to obtain more representative features of nodules for false positive reduction. The proposed network comprises three function blocks: 1) attention-guided multi-scale feature extraction, 2) complementary-stream block with an attention module for feature integration, and 3) classification block. The inputs of the network are multi-scale 3D CT volumes due to variations in nodule sizes. Subsequently, a gradual multi-scale feature extraction block with an attention module was applied to acquire more contextual information regarding the nodules. A subsequent complementary-stream integration block with an attention module was utilized to learn the significantly complementary features. Finally, the candidates were classified using a fully connected layer block. An exhaustive experiment on the LUNA16 challenge dataset was conducted to verify the effectiveness and performance of the proposed network. The AECS-CNN achieved a sensitivity of 0.92 with 4 false positives per scan. The results indicate that the attention mechanism can improve the network performance in false positive reduction, the proposed AECS-CNN can learn more representative features, and the attention module can guide the network to learn the discriminated feature channels and the crucial information embedded in the data, thereby effectively enhancing the performance of the detection system.

On the performance of lung nodule detection, segmentation and classification

Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.

Medical Image Classification Algorithm Based on Visual Attention Mechanism-MCNN

Due to the complexity of medical images, traditional medical image classification methods have been unable to meet the actual application needs. In recent years, the rapid development of deep learning theory has provided a technical approach for solving medical image classification. However, deep learning has the following problems in the application of medical image classification. First, it is impossible to construct a deep learning model with excellent performance according to the characteristics of medical images. Second, the current deep learning network structure and training strategies are less adaptable to medical images. Therefore, this paper first introduces the visual attention mechanism into the deep learning model so that the information can be extracted more effectively according to the problem of medical images, and the reasoning is realized at a finer granularity. It can increase the interpretability of the model. Additionally, to solve the problem of matching the deep learning network structure and training strategy to medical images, this paper will construct a novel multiscale convolutional neural network model that can automatically extract high-level discriminative appearance features from the original image, and the loss function uses the Mahalanobis distance optimization model to obtain a better training strategy, which can improve the robust performance of the network model. The medical image classification task is completed by the above method. Based on the above ideas, this paper proposes a medical classification algorithm based on a visual attention mechanism-multiscale convolutional neural network. The lung nodules and breast cancer images were classified by the method in this paper. The experimental results show that the accuracy of medical image classification in this paper is not only higher than that of traditional machine learning methods but also improved compared with other deep learning methods, and the method has good stability and robustness.

Evaluate the performance of four artificial intelligence-aided diagnostic systems in identifying and measuring four types of pulmonary nodules

Purpose

This study aims to evaluate the performance of four artificial intelligence‐aided diagnostic systems in identifying and measuring four types of pulmonary nodules.

Methods

Four types of nodules were implanted in a commercial lung phantom. The phantom was scanned with multislice spiral computed tomography, after which four systems (A, B, C, D) were used to identify the nodules and measure their volumes.

Results

The relative volume error (RVE) of system A was the lowest for all nodules, except for small ground glass nodules (SGGNs). System C had the smallest RVE for SGGNs, −0.13 (−0.56, 0.00). In the Bland–Altman test, only systems A and C passed the consistency test, P = 0.40. In terms of precision, the miss rate (MR) of system C was 0.00% for small solid nodules (SSNs), ground glass nodules (GGNs), and solid nodules (SNs) but 4.17% for SGGNs. The comparable system D MRs for SGGNs, SSNs, and GGNs were 71.30%, 25.93%, and 47.22%, respectively, the highest among all the systems. Receiver operating characteristic curve analysis indicated that system A had the best performance in recognizing SSNs and GGNs, with areas under the curve of 0.91 and 0.68. System C had the best performance for SGGNs (AUC = 0.91).

Conclusion

Among four types nodules, SGGNs are the most difficult to recognize, indicating the need to improve higher accuracy and precision of artificial systems. System A most accurately measured nodule volume. System C was most precise in recognizing all four types of nodules, especially SGGN.

Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification

Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results.

Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches

Risk stratification (characterization) of tumors from radiology images can be more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor characterization through such tools can also enable non-invasive cancer staging, prognosis, and foster personalized treatment planning as a part of precision medicine. In this papet, we propose both supervised and unsupervised machine learning strategies to improve tumor characterization. Our first approach is based on supervised learning for which we demonstrate significant gains with deep learning algorithms, particularly by utilizing a 3D convolutional neural network and transfer learning. Motivated by the radiologists' interpretations of the scans, we then show how to incorporate task-dependent feature representations into a CAD system via a graph-regularized sparse multi-task learning framework. In the second approach, we explore an unsupervised learning algorithm to address the limited availability of labeled training data, a common problem in medical imaging applications. Inspired by learning from label proportion approaches in computer vision, we propose to use proportion-support vector machine for characterizing tumors. We also seek the answer to the fundamental question about the goodness of "deep features" for unsupervised tumor classification. We evaluate our proposed supervised and unsupervised learning algorithms on two different tumor diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans, respectively, and obtain the state-of-the-art sensitivity and specificity results in both problems.

Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT

PURPOSE

Computed tomography (CT) is an effective method for detecting and characterizing lung nodules in vivo. With the growing use of chest CT, the detection frequency of lung nodules is increasing. Noninvasive methods to distinguish malignant from benign nodules have the potential to decrease the clinical burden, risk, and cost involved in follow-up procedures on the large number of false-positive lesions detected. This study examined the benefit of including perinodular parenchymal features in machine learning (ML) tools for pulmonary nodule assessment.

METHODS

Lung nodule cases with pathology confirmed diagnosis (74 malignant, 289 benign) were used to extract quantitative imaging characteristics from computed tomography scans of the nodule and perinodular parenchyma tissue. A ML tool development pipeline was employed using k-medoids clustering and information theory to determine efficient predictor sets for different amounts of parenchyma inclusion and build an artificial neural network classifier. The resulting ML tool was validated using an independent cohort (50 malignant, 50 benign).

RESULTS

The inclusion of parenchymal imaging features improved the performance of the ML tool over exclusively nodular features (P < 0.01). The best performing ML tool included features derived from nodule diameter-based surrounding parenchyma tissue quartile bands. We demonstrate similar high-performance values on the independent validation cohort (AUC-ROC = 0.965). A comparison using the independent validation cohort with the Fleischner pulmonary nodule follow-up guidelines demonstrated a theoretical reduction in recommended follow-up imaging and procedures.

CONCLUSIONS

Radiomic features extracted from the parenchyma surrounding lung nodules contain valid signals with spatial relevance for the task of lung cancer risk classification. Through standardization of feature extraction regions from the parenchyma, ML tool validation performance of 100% sensitivity and 96% specificity was achieved.

Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks

Lung cancer is a leading cause of death worldwide. Although computed tomography (CT) examinations are frequently used for lung cancer diagnosis, it can be difficult to distinguish between benign and malignant pulmonary nodules on the basis of CT images alone. Therefore, a bronchoscopic biopsy may be conducted if malignancy is suspected following CT examinations. However, biopsies are highly invasive, and patients with benign nodules may undergo many unnecessary biopsies. To prevent this, an imaging diagnosis with high classification accuracy is essential. In this study, we investigate the automated classification of pulmonary nodules in CT images using a deep convolutional neural network (DCNN). We use generative adversarial networks (GANs) to generate additional images when only small amounts of data are available, which is a common problem in medical research, and evaluate whether the classification accuracy is improved by generating a large amount of new pulmonary nodule images using the GAN. Using the proposed method, CT images of 60 cases with confirmed pathological diagnosis by biopsy are analyzed. The benign nodules assessed in this study are difficult for radiologists to differentiate because they cannot be rejected as being malignant. A volume of interest centered on the pulmonary nodule is extracted from the CT images, and further images are created using axial sections and augmented data. The DCNN is trained using nodule images generated by the GAN and then fine-tuned using the actual nodule images to allow the DCNN to distinguish between benign and malignant nodules. This pretraining and fine-tuning process makes it possible to distinguish 66.7% of benign nodules and 93.9% of malignant nodules. These results indicate that the proposed method improves the classification accuracy by approximately 20% in comparison with training using only the original images.

Delta radiomic features improve prediction for lung cancer incidence: A nested case-control analysis of the National Lung Screening Trial

Background

Current guidelines for lung cancer screening increased a positive scan threshold to a 6 mm longest diameter. We extracted radiomic features from baseline and follow‐up screens and performed size‐specific analyses to predict lung cancer incidence using three nodule size classes (<6 mm [small], 6‐16 mm [intermediate], and ≥16 mm [large]).

Methods

We extracted 219 features from baseline (T0) nodules and 219 delta features which are the change from T0 to first follow‐up (T1). Nodules were identified for 160 incidence cases diagnosed with lung cancer at T1 or second follow‐up screen (T2) and for 307 nodule‐positive controls that had three consecutive positive screens not diagnosed as lung cancer. The cases and controls were split into training and test cohorts; classifier models were used to identify the most predictive features.

Results

The final models revealed modest improvements for baseline and delta features when compared to only baseline features. The AUROCs for small‐ and intermediate‐sized nodules were 0.83 (95% CI 0.76‐0.90) and 0.76 (95% CI 0.71‐0.81) for baseline‐only radiomic features, respectively, and 0.84 (95% CI 0.77‐0.90) and 0.84 (95% CI 0.80‐0.88) for baseline and delta features, respectively. When intermediate and large nodules were combined, the AUROC for baseline‐only features was 0.80 (95% CI 0.76‐0.84) compared with 0.86 (95% CI 0.83‐0.89) for baseline and delta features.

Conclusions

We found modest improvements in predicting lung cancer incidence by combining baseline and delta radiomics. Radiomics could be used to improve current size‐based screening guidelines.

Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics

BACKGROUND AND OBJECTIVES

Detection and classification of pulmonary nodules are critical tasks in medical image analysis. The Lung Image Database Consortium (LIDC) database is a widely used resource for small pulmonary nodule classification research. This dataset is comprised of nodule characteristic evaluations and CT scans of patients. Although these characteristics are utilized in several studies, they can be used to improve classification performance.

METHODS

Numerous methods have been proposed to classify malignancy, but there are not many studies that facilitate nodule characteristics in classification steps. In this study, we use information on nodule characteristics and propose cascaded classification schemes. A group of hand-crafted features and deep features are used to define the nodules. In the first step of the classifier, the nodule characteristics are classified based on individual base classifiers. In the second step, the results of the first level classifier are combined for use in malignancy classification. In addition, stacking methods are applied to improve the performance of the cascaded classifiers.

RESULTS

The results confirmed that combining deep and hand-crafted features contribute to classification performance with an 8% improvement in average classification accuracy, 9% improvement in sensitivity, and 3% in specificity. Deep features from a nodule bounding area are more descriptive than the exact nodule region. The best performing cascaded classifier featured a classification accuracy of 84.70%, sensitivity of 67.37%, and specificity of 95.46%. First level stacking demonstrated similar results on classification accuracy and specificity but sensitivity was measured at 75.59%. Stacking on both levels provided the best classification accuracy and specificity with scores of 86.98% and 96.06%, respectively. When the malignancy ratings were grouped, stacking on both levels demonstrated better performance than other methods with a classification accuracy of 88.80%, sensitivity of 88.41%, and specificity of 94.12%.

CONCLUSIONS

Information on cascading characteristics with image features is beneficial for the classification of the malignancy ratings. Stacking approaches on both levels demonstrate better classification accuracy, but in the context of sensitivity, first level stacking performs better. Grouping the malignancy ratings results in better classification outcomes as in the case of similar studies in the literature.

Absolute cosine-based SVM-RFE feature selection method for prostate histopathological grading

OBJECTIVE

Feature selection (FS) methods are widely used in grading and diagnosing prostate histopathological images. In this context, FS is based on the texture features obtained from the lumen, nuclei, cytoplasm and stroma, all of which are important tissue components. However, it is difficult to represent the high-dimensional textures of these tissue components. To solve this problem, we propose a new FS method that enables the selection of features with minimal redundancy in the tissue components.

METHODOLOGY

We categorise tissue images based on the texture of individual tissue components via the construction of a single classifier and also construct an ensemble learning model by merging the values obtained by each classifier. Another issue that arises is overfitting due to the high-dimensional texture of individual tissue components. We propose a new FS method, SVM-RFE(AC), that integrates a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) embedded procedure with an absolute cosine (AC) filter method to prevent redundancy in the selected features of the SV-RFE and an unoptimised classifier in the AC.

RESULTS

We conducted experiments on H&E histopathological prostate and colon cancer images with respect to three prostate classifications, namely benign vs. grade 3, benign vs. grade 4 and grade 3 vs. grade 4. The colon benchmark dataset requires a distinction between grades 1 and 2, which are the most difficult cases to distinguish in the colon domain. The results obtained by both the single and ensemble classification models (which uses the product rule as its merging method) confirm that the proposed SVM-RFE(AC) is superior to the other SVM and SVM-RFE-based methods.

CONCLUSION

We developed an FS method based on SVM-RFE and AC and successfully showed that its use enabled the identification of the most crucial texture feature of each tissue component. Thus, it makes possible the distinction between multiple Gleason grades (e.g. grade 3 vs. grade 4) and its performance is far superior to other reported FS methods.

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year survival rate ranges between 10%-16%. There has been a significant amount of research using machine learning to generate tools using patient data to improve outcomes.

METHODS

This narrative review is based on research material obtained from PubMed up to Nov 2017. The search terms include "artificial intelligence," "machine learning," "lung cancer," "Nonsmall Cell Lung Cancer (NSCLC)," "diagnosis" and "treatment."

RESULTS

Recent studies support the use of computer-aided systems and the use of radiomic features to help diagnose lung cancer earlier. Other studies have looked at machine learning (ML) methods that offer prognostic tools to doctors and help them in choosing personalized treatment options for their patients based on molecular, genetics and histological features. Combining artificial intelligence approaches into health care may serve as a beneficial tool for patients with NSCLC, and this review outlines these benefits and current shortcomings throughout the continuum of care.

CONCLUSION

We present a review of the various applications of ML methods in NSCLC as it relates to improving diagnosis, treatment and outcomes.

Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer

PURPOSE

To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer.

METHODS

We examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI). One hundred three CT radiomic features were extracted from each PN. Before the model building process, distinctive features were identified using a hierarchical clustering method. We then constructed a prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). A tenfold cross-validation (CV) was repeated ten times (10 × 10-fold CV) to evaluate the accuracy of the SVM-LASSO model. Finally, the best model from the 10 × 10-fold CV was further evaluated using 20 × 5- and 50 × 2-fold CVs.

RESULTS

The best SVM-LASSO model consisted of only two features: the bounding box anterior-posterior dimension (BB_AP) and the standard deviation of inverse difference moment (SD_IDM). The BB_AP measured the extension of a PN in the anterior-posterior direction and was highly correlated (r = 0.94) with the PN size. The SD_IDM was a texture feature that measured the directional variation of the local homogeneity feature IDM. Univariate analysis showed that both features were statistically significant and discriminative (P = 0.00013 and 0.000038, respectively). PNs with larger BB_AP or smaller SD_IDM were more likely malignant. The 10 × 10-fold CV of the best SVM model using the two features achieved an accuracy of 84.6% and 0.89 AUC. By comparison, Lung-RADS achieved an accuracy of 72.2% and 0.77 AUC using four features (size, type, calcification, and spiculation). The prediction improvement of SVM-LASSO comparing to Lung-RADS was statistically significant (McNemar's test P = 0.026). Lung-RADS misclassified 19 cases because it was mainly based on PN size, whereas the SVM-LASSO model correctly classified 10 of these cases by combining a size (BB_AP) feature and a texture (SD_IDM) feature. The performance of the SVM-LASSO model was stable when leaving more patients out with five- and twofold CVs (accuracy 84.1% and 81.6%, respectively).

CONCLUSION

We developed an SVM-LASSO model to predict malignancy of PNs with two CT radiomic features. We demonstrated that the model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS.

A novel adaptive ensemble classification framework for ADME prediction

AECF is a GA based ensemble method. It includes four components which are (1) data balancing, (2) generating individual models, (3) combining individual models, and (4) optimizing the ensemble.

Vasculature surrounding a nodule: A novel lung cancer biomarker

PURPOSE

To investigate whether the vessels surrounding a nodule depicted on non-contrast, low-dose computed tomography (LDCT) can discriminate benign and malignant screen detected nodules.

MATERIALS AND METHODS

We collected a dataset consisting of LDCT scans acquired on 100 subjects from the Pittsburgh Lung Screening study (PLuSS). Fifty subjects were diagnosed with lung cancer and 50 subjects had suspicious nodules later proven benign. For the lung cancer cases, the location of the malignant nodule in the LDCT scans was known; while for the benign cases, the largest nodule in the LDCT scan was used in the analysis. A computer algorithm was developed to identify surrounding vessels and quantify the number and volume of vessels that were connected or near the nodule. A nonparametric receiver operating characteristic (ROC) analysis was performed based on a single nodule per subject to assess the discriminability of the surrounding vessels to provide a lung cancer diagnosis. Odds ratio (OR) were computed to determine the probability of a nodule being lung cancer based on the vessel features.

RESULTS

The areas under the ROC curves (AUCs) for vessel count and vessel volume were 0.722 (95% CI=0.616-0.811, p<0.01) and 0.676 (95% CI=0.565-0.772), respectively. The number of vessels attached to a nodule was significantly higher in the lung cancer group 9.7 (±9.6) compared to the non-lung cancer group 4.0 (±4.3) CONCLUSION: Our preliminary results showed that malignant nodules are often surrounded by more vessels compared to benign nodules, suggesting that the surrounding vessel characteristics could serve as lung cancer biomarker for indeterminate nodules detected during LDCT lung cancer screening using only the information collected during the initial visit.

3D multi-view convolutional neural networks for lung nodule classification

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy.

Quantitative Computed Tomography Classification of Lung Nodules: Initial Comparison of 2- and 3-Dimensional Analysis

OBJECTIVE

The aim of this study was to compare the performance of 2- (2D) and 3-dimensional (3D) quantitative computed tomography (CT) methods for classifying lung nodules as lung cancer, metastases, or benign.

METHODS

Using semiautomated software and computerized analysis, we analyzed more than 50 quantitative CT features of 96 solid nodules in 94 patients, in 2D from a single slice and in 3D from the entire nodule volume. Multivariable logistic regression was used to classify nodule types. Model performance was assessed by the area under the receiver operating characteristic curve (AUC) using leave-one-out cross-validation.

RESULTS

The AUC for distinguishing 53 primary lung cancers from 18 benign nodules and 25 metastases ranged from 0.79 to 0.83 and was not significantly different for 2D and 3D analyses (P = 0.29-0.78). Models distinguishing metastases from benign nodules were statistically significant only by 3D analysis (AUC = 0.84).

CONCLUSIONS

Three-dimensional CT methods did not improve discrimination of lung cancer, but may help distinguish benign nodules from metastases.

Learning with distribution of optimized features for recognizing common CT imaging signs of lung diseases

Common CT imaging signs of lung diseases (CISLs) are defined as the imaging signs that frequently appear in lung CT images from patients. CISLs play important roles in the diagnosis of lung diseases. This paper proposes a novel learning method, namely learning with distribution of optimized feature (DOF), to effectively recognize the characteristics of CISLs. We improve the classification performance by learning the optimized features under different distributions. Specifically, we adopt the minimum spanning tree algorithm to capture the relationship between features and discriminant ability of features for selecting the most important features. To overcome the problem of various distributions in one CISL, we propose a hierarchical learning method. First, we use an unsupervised learning method to cluster samples into groups based on their distribution. Second, in each group, we use a supervised learning method to train a model based on their categories of CISLs. Finally, we obtain multiple classification decisions from multiple trained models and use majority voting to achieve the final decision. The proposed approach has been implemented on a set of 511 samples captured from human lung CT images and achieves a classification accuracy of 91.96%. The proposed DOF method is effective and can provide a useful tool for computer-aided diagnosis of lung diseases on CT images.

Improving malignancy prediction through feature selection informed by nodule size ranges in NLST

Computed tomography (CT) is widely used during diagnosis and treatment of Non-Small Cell Lung Cancer (NSCLC). Current computer-aided diagnosis (CAD) models, designed for the classification of malignant and benign nodules, use image features, selected by feature selectors, for making a decision. In this paper, we investigate automated selection of different image features informed by different nodule size ranges to increase the overall accuracy of the classification. The NLST dataset is one of the largest available datasets on CT screening for NSCLC. We used 261 cases as a training dataset and 237 cases as a test dataset. The nodule size, which may indicate biological variability, can vary substantially. For example, in the training set, there are nodules with a diameter of a couple millimeters up to a couple dozen millimeters. The premise is that benign and malignant nodules have different radiomic quantitative descriptors related to size. After splitting training and testing datasets into three subsets based on the longest nodule diameter (LD) parameter accuracy was improved from 74.68% to 81.01% and the AUC improved from 0.69 to 0.79. We show that if AUC is the main factor in choosing parameters then accuracy improved from 72.57% to 77.5% and AUC improved from 0.78 to 0.82. Additionally, we show the impact of an oversampling technique for the minority cancer class. In some particular cases from 0.82 to 0.87.

Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy

Background

CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings.

Methods

The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives.

Results

The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant.

Conclusions

From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.

An Official American Thoracic Society Research Statement: A Research Framework for Pulmonary Nodule Evaluation and Management

BACKGROUND

Pulmonary nodules are frequently detected during diagnostic chest imaging and as a result of lung cancer screening. Current guidelines for their evaluation are largely based on low-quality evidence, and patients and clinicians could benefit from more research in this area.

METHODS

In this research statement from the American Thoracic Society, a multidisciplinary group of clinicians, researchers, and patient advocates reviewed available evidence for pulmonary nodule evaluation, characterized six focus areas to direct future research efforts, and identified fundamental gaps in knowledge and strategies to address them. We did not use formal mechanisms to prioritize one research area over another or to achieve consensus.

RESULTS

There was widespread agreement that novel tests (including novel imaging tests and biopsy techniques, biomarkers, and prognostic models) may improve diagnostic accuracy for identifying cancerous nodules. Before they are used in clinical practice, however, better evidence is needed to show that they improve more distal outcomes of importance to patients. In addition, the pace of research and the quality of clinical care would be improved by the development of registries that link demographic and nodule characteristics with patient-level outcomes. Methods to share data from registries are also necessary.

CONCLUSIONS

This statement may help researchers to develop impactful and innovative research projects and enable funders to better judge research proposals. We hope that it will accelerate the pace and increase the efficiency of discovery to improve the quality of care for patients with pulmonary nodules.

Test-retest reproducibility analysis of lung CT image features

Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test-retest, the biological range and a feature independence measure. There were 66 (30.14%) features with concordance correlation coefficient ≥ 0.90 across test-retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R (2) Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91% for a size-based feature and 92% for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test-retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

Background

Lung cancer is a leading cause of death worldwide; it refers to the uncontrolled growth of abnormal cells in the lung. A computed tomography (CT) scan of the thorax is the most sensitive method for detecting cancerous lung nodules. A lung nodule is a round lesion which can be either non-cancerous or cancerous. In the CT, the lung cancer is observed as round white shadow nodules. The possibility to obtain a manually accurate interpretation from CT scans demands a big effort by the radiologist and might be a fatiguing process. Therefore, the design of a computer-aided diagnosis (CADx) system would be helpful as a second opinion tool.

Methods

The stages of the proposed CADx are: a supervised extraction of the region of interest to eliminate the shape differences among CT images. The Daubechies db1, db2, and db4 wavelet transforms are computed with one and two levels of decomposition. After that, 19 features are computed from each wavelet sub-band. Then, the sub-band and attribute selection is performed. As a result, 11 features are selected and combined in pairs as inputs to the support vector machine (SVM), which is used to distinguish CT images containing cancerous nodules from those not containing nodules.

Results

The clinical data set used for experiments consists of 45 CT scans from ELCAP and LIDC. For the training stage 61 CT images were used (36 with cancerous lung nodules and 25 without lung nodules). The system performance was tested with 45 CT scans (23 CT scans with lung nodules and 22 without nodules), different from that used for training. The results obtained show that the methodology successfully classifies cancerous nodules with a diameter from 2 mm to 30 mm. The total preciseness obtained was 82%; the sensitivity was 90.90%, whereas the specificity was 73.91%.

Conclusions

The CADx system presented is competitive with other literature systems in terms of sensitivity. The system reduces the complexity of classification by not performing the typical segmentation stage of most CADx systems. Additionally, the novelty of the algorithm is the use of a wavelet feature descriptor.

Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography

The objective of this study was to investigate the method of the combination of radiological and textural features for the differentiation of malignant from benign solitary pulmonary nodules by computed tomography. Features including 13 gray level co-occurrence matrix textural features and 12 radiological features were extracted from 2,117 CT slices, which came from 202 (116 malignant and 86 benign) patients. Lasso-type regularization to a nonlinear regression model was applied to select predictive features and a BP artificial neural network was used to build the diagnostic model. Eight radiological and two textural features were obtained after the Lasso-type regularization procedure. Twelve radiological features alone could reach an area under the ROC curve (AUC) of 0.84 in differentiating between malignant and benign lesions. The 10 selected characters improved the AUC to 0.91. The evaluation results showed that the method of selecting radiological and textural features appears to yield more effective in the distinction of malignant from benign solitary pulmonary nodules by computed tomography.

A novel approach to malignant-benign classification of pulmonary nodules by using ensemble learning classifiers

Computer-aided detection systems can help radiologists to detect pulmonary nodules at an early stage. In this paper, a novel Computer-Aided Diagnosis system (CAD) is proposed for the classification of pulmonary nodules as malignant and benign. The proposed CAD system using ensemble learning classifiers, provides an important support to radiologists at the diagnosis process of the disease, achieves high classification performance. The proposed approach with bagging classifier results in 94.7 %, 90.0 % and 77.8 % classification sensitivities for benign, malignant and undetermined classes (89.5 % accuracy), respectively.

A new method for pulmonary nodule detection using decision trees

A computer-aided detection (CAD) can help radiologists in diagnosing of lung diseases at an early level. In this study, a new CAD system for pulmonary nodule detection from CT imagery is presented by using morphological features and patient information properties. Decision trees are utilized for classification and overall detection performance is evaluated. Results are compared to similar techniques in the literature by using standard measures. Proposed CAD system with random forest classifier result in 90.5 % sensitivity and 87.6 % specificity of detection performance.

Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set

Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. In this paper, the usage of support vector machine (SVM) classification for lung cancer is investigated, presenting a systematic quantitative evaluation against Boosting, Decision trees, k-nearest neighbor, LASSO regressions, neural networks and random forests. A large database of 5984 regions of interest (ROIs) and 488 input features (including textural features, patient characteristics, and morphological features) were used to train the classifiers and evaluate for their performance. The evaluation for classifiers' performance was based on a tenfold cross validation framework, receiver operating characteristic curve (ROC), and Matthews correlation coefficient. Area under curve (AUC) of SVM, Boosting, Decision trees, k-nearest neighbor, LASSO, neural networks, random forests were 0.94, 0.86, 0.73, 0.72, 0.91, 0.92, and 0.85, respectively. It was proved that SVM classification offered significantly increased classification performance compared to the reference methods. This scheme may be used as an auxiliary tool to differentiate between benign and malignant SPNs of CT images in future.

Classification of pulmonary nodules by using hybrid features

Early detection of pulmonary nodules is extremely important for the diagnosis and treatment of lung cancer. In this study, a new classification approach for pulmonary nodules from CT imagery is presented by using hybrid features. Four different methods are introduced for the proposed system. The overall detection performance is evaluated using various classifiers. The results are compared to similar techniques in the literature by using standard measures. The proposed approach with the hybrid features results in 90.7% classification accuracy (89.6% sensitivity and 87.5% specificity).

Computer-aided diagnosis for early-stage lung cancer based on longitudinal and balanced data

Background

Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. Typically, the problem can be approached by developing more discriminative diagnosis methods. In this paper, computer-aided diagnosis was used to facilitate the prediction of characteristics of solitary pulmonary nodules in CT of lungs to diagnose early-stage lung cancer.

Methods

The synthetic minority over-sampling technique (SMOTE) was used to account for raw data in order to balance the original training data set. Curvelet-transformation textural features, together with 3 patient demographic characteristics, and 9 morphological features were used to establish a support vector machine (SVM) prediction model. Longitudinal data as the test data set was used to evaluate the classification performance of predicting early-stage lung cancer.

Results

Using the SMOTE as a pre-processing procedure, the original training data was balanced with a ratio of malignant to benign cases of 1∶1. Accuracy based on cross-evaluation for the original unbalanced data and balanced data was 80% and 97%, respectively. Based on Curvelet-transformation textural features and other features, the SVM prediction model had good classification performance for early-stage lung cancer, with an area under the curve of the SVMs of 0.949 (P<0.001). Textural feature (standard deviation) showed benign cases had a higher change in the follow-up period than malignant cases.

Conclusions

With textural features extracted from a Curvelet transformation and other parameters, a sensitive support vector machine prediction model can increase the rate of diagnosis for early-stage lung cancer. This scheme can be used as an auxiliary tool to differentiate between benign and malignant early-stage lung cancers in CT images.

Multimodal sparse representation-based classification for lung needle biopsy images

Lung needle biopsy image classification is a critical task for computer-aided lung cancer diagnosis. In this study, a novel method, multimodal sparse representation-based classification (mSRC), is proposed for classifying lung needle biopsy images. In the data acquisition procedure of our method, the cell nuclei are automatically segmented from the images captured by needle biopsy specimens. Then, features of three modalities (shape, color, and texture) are extracted from the segmented cell nuclei. After this procedure, mSRC goes through a training phase and a testing phase. In the training phase, three discriminative subdictionaries corresponding to the shape, color, and texture information are jointly learned by a genetic algorithm guided multimodal dictionary learning approach. The dictionary learning aims to select the topmost discriminative samples and encourage large disagreement among different subdictionaries. In the testing phase, when a new image comes, a hierarchical fusion strategy is applied, which first predicts the labels of the cell nuclei by fusing three modalities, then predicts the label of the image by majority voting. Our method is evaluated on a real image set of 4372 cell nuclei regions segmented from 271 images. These cell nuclei regions can be divided into five classes: four cancerous classes (corresponding to four types of lung cancer) plus one normal class (no cancer). The results demonstrate that the multimodal information is important for lung needle biopsy image classification. Moreover, compared to several state-of-the-art methods (LapRLS, MCMI-AB, mcSVM, ESRC, KSRC), the proposed mSRC can achieve significant improvement (mean accuracy of 88.1%, precision of 85.2%, recall of 92.8%, etc.), especially for classifying different cancerous types.

Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey

Computer-aided detection (CADe) and diagnosis (CAD) has been a rapidly growing, active area of research in medical imaging. Machine leaning (ML) plays an essential role in CAD, because objects such as lesions and organs may not be represented accurately by a simple equation; thus, medical pattern recognition essentially require "learning from examples." One of the most popular uses of ML is the classification of objects such as lesion candidates into certain classes (e.g., abnormal or normal, and lesions or non-lesions) based on input features (e.g., contrast and area) obtained from segmented lesion candidates. The task of ML is to determine "optimal" boundaries for separating classes in the multidimensional feature space which is formed by the input features. ML algorithms for classification include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), multilayer perceptrons, and support vector machines (SVM). Recently, pixel/voxel-based ML (PML) emerged in medical image processing/analysis, which uses pixel/voxel values in images directly, instead of features calculated from segmented lesions, as input information; thus, feature calculation or segmentation is not required. In this paper, ML techniques used in CAD schemes for detection and diagnosis of lung nodules in thoracic CT and for detection of polyps in CT colonography (CTC) are surveyed and reviewed.