Lung Computed Tomography Images Research Articles

Machine learning for predicting accuracy of lung and liver tumor motion tracking using radiomic features.

Internal tumor motion is commonly predicted using external respiratory signals. However, the internal/external correlation is complex and patient-specific. The purpose of this study was to develop various models based on the radiomic features of computed tomography (CT) images to predict the accuracy of tumor motion tracking using external surrogates and to find accurate and reliable tracking algorithms. Images obtained from a total of 108 and 71 patients pathologically diagnosed with lung and liver cancers, respectively, were examined. Real-time position monitoring motion was fitted to tumor motion, and samples with fitting errors greater than 2 mm were considered positive. Radiomic features were extracted from internal target volumes of average intensity projections, and cross-validation least absolute shrinkage and selection operator (LassoCV) was used to conduct feature selection. Based on the radiomic features, a total of 26 separate models (13 for the lung and 13 for the liver) were trained and tested. Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to assess performance. Relative standard deviation was used to assess stability. Thirty-three and 22 radiomic features were selected for the lung and liver, respectively. For the lung, the AUC varied from 0.848 (decision tree) to 0.941 [support vector classifier (SVC), logistic regression]; sensitivity varied from 0.723 (extreme gradient boosting) to 0.848 [linear support vector classifier (linearSVC)]; specificity varied from 0.834 (gaussian naive bayes) to 0.936 [multilayer perceptron (MLP), wide and deep (W&D)]; and MLP and W&D had better performance and stability than the median. For the liver, the AUC varied from 0.677 [light gradient boosting machine (Light)] to 0.892 (logistic regression); sensitivity varied from 0.717 (W&D) to 0.862 (MLP); specificity varied from 0.566 (Light) to 0.829 (linearSVC); and logistic regression, MLP, and SVC had better performance and stability than the median. Respiratory-sensitive radiomic features extracted from CT images of lung and liver tumors were proved to contain sufficient information to establish an external/internal motion relationship. We developed a rapid and accurate method based on radiomics to classify the accuracy of monitoring a patient's external surface for lung and liver tumor tracking. Several machine learning algorithms-in particular, MLP-demonstrated excellent classification performance and stability.

Lung and Lung Tumor Segmentation of CT Images During MWA Therapy Using AI Algorithm

Microwave ablation (MWA) therapy as a thermal ablation procedure is an excellent alternative to open surgery for tumor treatment. The technique is considered advantageous for patients who are not candidates for open surgery due to factors such as age, anatomic limitations, resection, etc. Computed tomography (CT) is a commonly used interventional imaging modality during MWA therapy for localizing the tumor and finalizing the tumor treatment process. However, the CT scan of the body usually includes neighboring organs that are not relevant to lung tumor MWA therapy. Therefore, the segmentation of the lung and lung tumor in CT images provides valuable information about the tumor margin. This information can assist physicians in precisely and completely destroying the tumor during the MWA procedure. To solve the aforementioned problem, deep learning (DL), in particular, achieves a higher level of accuracy in segmentation than machine learning techniques due to its composition of multiple learning layers. The immediate goal is to distinguish among the different tissue structures of the tumor, healthy tissue, and the ablated area in lung CT images using the DL method to segment the organ and cancer area. Researchers have proposed various segmentation models. However, different segmentation tasks require different perception fields. In this study, we propose a new DL model that includes a residual block based on the U-Net model to accurately segment the lung organ and lung tumor tissue. The dataset consists of lung CT images acquired during MWA therapy using a CT scanner at the University Hospital Magdeburg. Manual tumor segmentation has been performed and confirmed by physicians. The results of our proposed method can be compared with those of the U-net model with a SSIM of 90%. Furthermore, accurately determining the margin area of the tumor tissue can decrease insufficient tumor ablation, which often leads to tumor recurrence. We anticipate that our proposed model can be generalized to perform tumor segmentation on CT images of different organs during MWA treatment. Finally, we hope that this method can achieve sufficient accuracy to decrease tumor recurrence and enable dose reduction for patients in interventional CT imaging. Doi: 10.28991/SciMedJ-2023-05-01-01 Full Text: PDF

PILN: A posterior information learning network for blind reconstruction of lung CT images

Background and objective:Computer tomography (CT) imaging technology has played significant roles in the diagnosis and treatment of various lung diseases, but the degradations in CT images usually cause the loss of detailed structural information and interrupt the judgement from clinicians. Therefore, reconstructing noise-free, high-resolution CT images with sharp details from degraded ones is of great importance for the computer-assisted diagnosis (CAD) system. However, current image reconstruction methods suffer from unknown parameters of multiple degradations in actual clinical images.Methods:To solve these problems, we propose a unified framework, so called Posterior Information Learning Network (PILN), for blind reconstruction of lung CT images. The framework consists of two stages: Firstly, a noise level learning (NLL) network is proposed to quantify the Gaussian and artifact noise degradations into different levels. Inception-residual modules are designed to extract multi-scale deep features from the noisy image, and residual self-attention structures are proposed to refine deep features to essential representations of noise. Secondly, by taking the estimated noise levels as prior information, a cyclic collaborative super-resolution (CyCoSR) network is proposed to iteratively reconstruct the high-resolution CT image and estimate the blur kernel. Two convolutional modules are designed based on cross-attention transformer structure, named as Reconstructor and Parser. The high-resolution image is restored from the degraded image by the Reconstructor under the guidance of the predicted blur kernel, while the blur kernel is estimated by the Parser according to the reconstructed image and the degraded one. The NLL and CyCoSR networks are formulated as an end-to-end framework to handle multiple degradations simultaneously.Results:The proposed PILN is applied to the Cancer Imaging Archive (TCIA) dataset and the Lung Nodule Analysis 2016 Challenge (LUNA16) dataset to evaluate its ability in reconstructing lung CT images. Compared with the state-of-the-art image reconstruction algorithms, it can provide high-resolution images with less noise and sharper details with respect to quantitative benchmarks.Conclusions:Extensive experimental results demonstrate that our proposed PILN can achieve better performance on blind reconstruction of lung CT images, providing noise-free, detail-sharp and high-resolution images without knowing the parameters of multiple degradation sources.

Automated diagnosis of COVID stages using texture‐based Gabor features in variational mode decomposition from CT images

AbstractCOVID‐19 is a deadly and fast‐spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time‐inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed; however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two‐dimensional variational mode decomposition in this work. This proposed approach decomposes pre‐processed CT scan pictures into sub‐bands. The texture‐based Gabor filter bank extracts the relevant features, and the student's t‐value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square‐ support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non‐COVID CT lung images. The results of the trial showed that our model outperformed cutting‐edge methods for COVID classification. Using tenfold cross‐validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning‐based models like random forest, K‐nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily.

Smart IoMT-based segmentation of coronavirus infections using lung CT scans

Computed Tomography (CT) is one of the biomedical imaging modalities which are used to confirm COVID-19 cases and/or to identify infected areas in the lung. Therefore, this article aims at assisting this crucial radiological task by proposing squeeze-and-excitation networks (SENets) within the Internet of medical things (IoMT) framework for automated segmentation of COVID-19 infections in lung CT images. The proposed SE block has been directly integrated with deep residual networks to form Seresnets based on U-Net and LinkNet models. Extensive tests were conducted on a public COVID-19 CT dataset including 20 cases and 1800 + annotated slices to evaluate the segmentation results of our proposed method. The proposed Seresnet models showed a good performance with a Dice score of 0.73, structure similarity index of 0.98, enhanced alignment measure of 0.98, and mean absolute error of 0.06. This study demonstrated a new advanced tool for radiologists to achieve automatic segmentation of the COVID-19 infected areas using CT scans. The main prospect of this research work is deploying our proposed IoMT segmentation framework in the medical diagnosis routine of positive COVID-19 patients.

Boundary Aware Semantic Segmentation using Pyramid-dilated Dense U-Net for Lung Segmentation in Computed Tomography Images.

The main objective of this work is to propose an efficient segmentation model for accurate and robust lung segmentation from computed tomography (CT) images, even when the lung contains abnormalities such as juxtapleural nodules, cavities, and consolidation. A novel deep learning-based segmentation model, pyramid-dilated dense U-Net (PDD-U-Net), is proposed to directly segment lung regions from the whole CT image. The model is integrated with pyramid-dilated convolution blocks to capture and preserve multi-resolution spatial features effectively. In addition, shallow and deeper stream features are embedded in the nested U-Net structure at the decoder side to enhance the segmented output. The effect of three loss functions is investigated in this paper, as the medical image analysis method requires precise boundaries. The proposed PDD-U-Net model with shape-aware loss function is tested on the lung CT segmentation challenge (LCTSC) dataset with standard lung CT images and the lung image database consortium-image database resource initiative (LIDC-IDRI) dataset containing both typical and pathological lung CT images. The performance of the proposed method is evaluated using Intersection over Union, dice coefficient, precision, recall, and average Hausdorff distance metrics. Segmentation results showed that the proposed PDD-U-Net model outperformed other segmentation methods and achieved a 0.983 dice coefficient for the LIDC-IDRI dataset and a 0.994 dice coefficient for the LCTSC dataset. The proposed PDD-U-Net model with shape-aware loss function is an effective and accurate method for lung segmentation from CT images, even in the presence of abnormalities such as cavities, consolidation, and nodules. The model's integration of pyramid-dilated convolution blocks and nested U-Net structure at the decoder side, along with shape-aware loss function, contributed to its high segmentation accuracy. This method could have significant implications for the computer-aided diagnosis system, allowing for quick and accurate analysis of lung regions.

Automatic Diagnosis of COVID-19 Pneumonia using Artificial Intelligence Deep Learning Algorithm Based on Lung Computed Tomography Images.

The lung computed tomography (CT) scan contains valuable information and patterns that provide the possibility of early diagnosis of COVID-19 disease as a global pandemic by the image processing software. In this research, based on deep learning of artificial intelligence, the software has been designed that is used clinically to diagnose COVID-19 disease with high accuracy. Convolutional neural network architecture developed based on Inception-V3 for deep learning of lung image patterns, feature extraction, and image classification. The theory of transfer learning was utilized to increase the learning power of the system. Changes applied in the network layers to increase the detection power. The process of learning was repeated 30 times. All diagnostic statistical parameters of the diagnostic were analyzed to validate the software. Based on the data of Imam Khomeini Hospital in Sari, the validity, sensitivity, and accuracy of the software in diagnosing of affected to COVID-19 and nonaffected to it were obtained 98%, 98%, and 98%, respectively. Diagnostic statistical parameters on some data were 100%. The modified algorithm of Inception-V3 applied to heterogeneous data also had acceptable precision. The proposed basic architecture of Inception-v3 utilized for this research has an admissible speed and exactness in learning CT scan images of patients' lungs, and diagnosis of COVID-19 pneumonia, which can be utilized clinically as a powerful diagnostic tool.

An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier.

A critical step to ameliorate diagnosis and extend patient survival is Benign-malignant Pulmonary Nodule (PN) classification at earlier detection. On account of the noise of Computed Tomography (CT) images, the prevailing Lung Nodule (LN) detection techniques exhibit broad variation in accurate prediction. Thus, a novel Nodule Detection along with Classification algorithm for early diagnosis of Lung Cancer (LC) has been proposed. Initially, employing the Adaptive Mode Ostu Binarization (AMOB) technique, the Lung Volumes (LVs) isextortedas of the image together with the extracted lung regions is pre-processed. Then, detection of LNs takes place, and utilizing Geodesic Fuzzy C-Means Clustering (GFCM) Segmentation Algorithm, it is segmented.Next, the vital features are extracted, and the Nodules are classified by utilizing Logarithmic Layer Xception Neural Network (LLXcepNN) Classifier grounded on the extracted feature. The nodules are classified as Benign Nodules (BN) and Malignant Nodules (MN) by the proposed classifier. Lastly, the Lung CT images are scrutinized. Thus, when weighed against the prevailing techniques, the proposed systems' acquired outcomes exhibit that the rate of accuracy of classification is enhanced.

Detecting COVID-19 From Lung Computed Tomography Images: A Swarm Optimized Artificial Neural Network Approach

COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images.

Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are pre-processed with the filters called adaptive median filter and average filter. The filtered images are enhanced with histogram equalization and the ROI(Regions of Interest) cancer tissues are segmented using Guaranteed Convergence Particle Swarm Optimization technique. For classification of images, Probabilistic Neural Networks (PNN) based classification is used. The experimentation is carried out by simulating the model in MATLAB, with the input CT lung images LIDC-IDRI (Lung Image Database Consortium-Image Database Resource Initiative) benchmark Dataset. The results ensure that the proposed model outperforms existing methods with accurate classification results with minimal processing time.

Coordinate-based fast lightweight path search algorithm for electromagnetic navigation bronchoscopy.

Electromagnetic navigation bronchoscopy (ENB) uses electromagnetic positioning technology to guide the bronchoscope to accurately and quickly reach the lesion along the planned path. However, enormous data in high-resolution lung computed tomography (CT) and the complex structure of multilevel branching bronchial tree make fast path search challenging for path planning. We propose a coordinate-based fast lightweight path search (CPS) algorithm for ENB. First, the centerline is extracted from the bronchial tree by applying topological thinning. Then, Euclidean-distance-based coordinate search is applied. The centerline points are represented by their coordinates, and adjacent points along the navigation path are selected considering the shortest Euclidean distance to the target on the centerline nearest the lesion. From the top of the trachea centerline, search is repeated until reaching the target. In 50 high-resolution lung CT images acquired from five scanners, the CPS algorithm achieves accuracy, average search time, and average memory consumption of 100%, 88.5ms, and 166.0MB, respectively, reducing search time by 74.3% and 73.1% and memory consumption by 83.3% and 83.0% compared with Dijkstra and A* algorithms, respectively. CPS algorithm is suitable for path search in multilevel branching bronchial tree navigation based on high-resolution lung CT images.

Unsupervised medical image feature learning by using de-melting reduction auto-encoder

Unsupervised feature learning is a fundamental and highly prioritized problem in medical image analysis. Although it has shown considerable improvements, it remains challenging because of its weak feature expression ability, low model-learning efficiency, and weak robustness. To address these limitations, a novel unsupervised feature learning method in the medical image classification task, named de-melting reduction auto-encoder (DMRAE), is proposed in this study. A joint fusion network structure is constructed; it not only improves the expression of target features but also reduces the loss of feature decoding and parameters. To obtain a robust solution, a newly designed decomposed-reconstructed loss function is used to strengthen the semantic context between adjacent feature extractor layers, successfully avoiding the insufficient model-learning ability from the single optimization objective and improving the quality of the extracted features. Finally, extensive experiments on datasets consisting of 400 breast ultrasonographic images and 6000 lung computed tomography images are conducted to demonstrate the effectiveness of the proposed method. Experimental results reveal that the DMRAE significantly reduces the annotation effort and outperforms existing methods by a significant margin.

Learning label diffusion maps for semi-automatic segmentation of lung CT images with COVID-19

Deep Learning (DL) has become one of the key approaches for dealing with many challenges in medical imaging, which includes lung segmentation in Computed Tomography (CT). The use of seeded segmentation methods is another effective approach to get accurate partitions from complex CT images, as they give users autonomy, flexibility and easy usability when selecting specific targets for measurement purposes or pharmaceutical interventions. In this paper, we combine the accuracy of deep contour leaning with the versatility of seeded segmentation to yield a semi-automatic framework for segmenting lung CT images from patients affected by COVID-19. More specifically, we design a DL-driven approach that learns label diffusion maps from a contour detection network integrated with a label propagation model, used to diffuse the seeds over the CT images. Moreover, the trained model induces the diffusion of the seeds by only taking as input a marked CT-scan, segmenting hundreds of CT slices in an unsupervised and recursive way. Another important trait of our framework is that it is capable of segmenting lung structures even in the lack of well-defined boundaries and regardless of the level of COVID-19 infection. The accuracy and effectiveness of our learned diffusion model are attested to by both qualitative as well as quantitative comparisons involving several user-steered segmentations methods and eight CT data sets containing different types of lesions caused by COVID-19.

Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning

The world has been undergoing the most ever unprecedented circumstances caused by the coronavirus pandemic, which is having a devastating global effect in different aspects of life. Since there are not effective antiviral treatments for Covid-19 yet, it is crucial to early detect and monitor the progression of the disease, thereby helping to reduce mortality. While different measures are being used to combat the virus, medical imaging techniques have been examined to support doctors in diagnosing the disease. In this paper, we present a practical solution for the detection of Covid-19 from chest X-ray (CXR) and lung computed tomography (LCT) images, exploiting cutting-edge Machine Learning techniques. As the main classification engine, we make use of EfficientNet and MixNet, two recently developed families of deep neural networks. Furthermore, to make the training more effective and efficient, we apply three transfer learning algorithms. The ultimate aim is to build a reliable expert system to detect Covid-19 from different sources of images, making it be a multi-purpose AI diagnosing system. We validated our proposed approach using four real-world datasets. The first two are CXR datasets consist of 15,000 and 17,905 images, respectively. The other two are LCT datasets with 2,482 and 411,528 images, respectively. The five-fold cross-validation methodology was used to evaluate the approach, where the dataset is split into five parts, and accordingly the evaluation is conducted in five rounds. By each evaluation, four parts are combined to form the training data, and the remaining one is used for testing. We obtained an encouraging prediction performance for all the considered datasets. In all the configurations, the obtained accuracy is always larger than 95.0%. Compared to various existing studies, our approach yields a substantial performance gain. Moreover, such an improvement is statistically significant.

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.

Validation of an Image Based Deep Neural Network Model for Individualized Radiotherapy in Lung Malignancies

<h3>Purpose/Objective(s)</h3> The current standard of thoracic stereotactic body radiotherapy (SBRT) relies on varied dose prescriptions and is not currently guided by individual tumor features. A deep neural network (DNN) was previously developed that queries thoracic computer tomography (CT)-derived feature space and identifies radiation sensitivity parameters. The DNN can predict treatment failure and provide predictive information for individualization of radiotherapy (RT) dose within a single institution. In this study, the internally tested DNN was applied to an external validation set across distinct systems with the hypothesis that deep learning (DL) scores would continue to predict local failure with a higher C-index than standard radiomic or clinical features. <h3>Materials/Methods</h3> Two distinct cohort-based registries from disparate health systems were used to identify 849 internal and 271 external patients with cancer in the thorax and who were treated with high dose RT using SBRT. Pre-therapy lung CT images from the internal cohort were input to a multi-task DNN to generate an image fingerprint with the primary objective of predicting time to event local treatment outcomes. These findings were subsequently tested in the external cohort. DL scores from each patient were combined with clinical variables to provide an individualized dose for RT that projects a treatment failure probability of <5% at 24 months. <h3>Results</h3> External and internal study populations were significantly different in baseline characteristics, permitting an assessment of model transportability. These included patient demographics, use of motion control, CT scanner model, stage of disease, histology, tumor axial diameter, and dose received (p<0.001). In the external study cohort, radiation treatments in patients with high deep learning scores failed at a significantly higher rate than in those with low scores. The 3-year cumulative incidences of local failure were 25.1% (95% CI: 17.6-33.2) and 10.2% (95% CI: 5.5-16.6), respectively. DL scores independently predicted local failure (hazard ratio 2.16, 95% CI: 1.05-4.44, p = 0.04). A multivariable model that included DL score and cancer types predicted treatment failures with a C-index of 0.71 (95% CI: 0.64-0.76), a significant improvement (<i>p</i> = <0.001) compared to 3D volume and cancer type (C-index: 0.69 [95% CI: 0.63-0.75]). External study patients with recommended RT doses less than or equal to the actual delivered dose had 3-year cumulative incidence of local failure of 0%. Greater discordance between the recommended dose and delivered dose (differences >0 Gy) was associated with higher rates of local failure (<i>p</i> = <0.0001). <h3>Conclusion</h3> The DNN accurately predicted treatment failures from pretreatment CT-images across two distinct clinical settings. Results indicate that our image based DNN can be successfully implemented across other appropriately related populations and provide a robust dose guidance framework for individualized RT in prospective clinical studies.

Synthetic FDG-Positron Emission Tomography Images for Patients with Non-Small Cell Lung Cancer: A Deep Learning-Based Approach Using Computed Tomography Images

<h3>Purpose/Objective(s)</h3> Deep learning-based, medical image-to-image transformation has been limited so far to computed tomography (CT) / magnetic resonance (MR) translation, and few studies have been reported on Positron Emission Tomography (PET) synthesis. Synthetic ¹⁸F-FDG-PET (sFDG-PET) image from CT provides the advantage of lowering exposure, cost, and labor, and facilitating longitudinal follow-up, particularly for patients with limited access to a PET scanner. Furthermore, CT can be performed on demand, whereas FDG-PET entails advanced preparation. Nevertheless, sFDG-PET generation faces the impediment of determining a valid anatomy-to-function transformation. In this work, we present a novel, deep learning-based sFDG-PET generation method from lung CT images. <h3>Materials/Methods</h3> An IRB-approved retrospective study was conducted at our institution for patients undergoing stereotactic body radiotherapy for early-stage non-small cell lung cancer. For each patient, CT, measured PET images, and planning target (PTV) volumes were extracted using COMKAT Image Tool. A workflow to generate sFDG-PET images as outputs from CT inputs, referred to as 2C-UcGAN, was developed based on conditional generative adversarial network design, and consisted of a U-Net generator accepting a 2channel-input: axial CT slices as input in channel 1, and mediastinal window-adjusted CT slices in channel 2. Training patches (size = 128 × 128 × 128) were centered around the PTV, and training was performed on an Intel workstation, 128G of RAM, NVIDIA TITAN XP GPU, Epoch = 50, using a programming environment. Standardized uptake values (SUVs) were calculated. A classic pix2pix network was used as a benchmark for comparison. Mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) of SUVs normalized to body weight were compared, in addition to sensitivity (Sen), specificity (Spe), positive and negative predictive values (PPV and NPV) for PTV uptake. <h3>Results</h3> A total of 165 patients were included and randomly divided into training (n=100), validation (n=19), and testing (n=46). Among the testing patients, 15 nodules were not ¹⁸F-FDG-PET avid. Results are listed in Table 1. Prediction time was in the range of a second. 2C-UcGAN outperformed pix2pix with a MAE of 1.21 g/mL and yielded higher PTV Sen and PPV than pix2pix, with acceptable Spe and NPV. <h3>Conclusion</h3> Using an intelligent combination of windowed CT radiological features, promising results are attained for metabolic characteristic prediction. The achieved MAE presents a considerable improvement in accuracy compared to the previously reported algorithm. Given its potential for implementing CT-only follow-up, further studies are warranted across different patient populations.

Implementation of blockchain technology using extended CNN for lung cancer prediction

Early identification of lung cancer is essential since the disease progresses quickly. Early-stage lung cancer diagnosis will be the first usage of the Internet of Things (IoT). With a worldwide network of IoT devices and a high degree of trust in the model's accuracy, on-the-fly training for IoT devices is very essential. As many as a million lives are saved each year because to early detection of illness, which seals the airways and prevents infection. Image processing and machine learning techniques provided the first evidence of malignant growth. Symptoms of lung cancer generally don't show up until the disease has advanced very far. At this stage, getting medical help becomes quite difficult. A whistling sound, hoarseness, weight gain in the face and/or an increase in the size of the upper chest may appear first, followed by the curling or rising of your fingers or the experience of pain when swallowing. Sputum with a red or rust-colored hue is a sign of malignancy, as is shortness of breath and chronic chest pain. In addition to identifying and arranging lung knobs, a lung computed tomography image may also be utilised to estimate their risk level. Preparation does not have as much of an impact on ECNN's accuracy and temporal complexity as it did on previous frameworks. They are made up of abnormal cells that form a tumour. An uncontrolled development and destruction of the lungs. Various kinds of lung cancer begin to develop as a result of this process, which continues until a tumour forms. Lung cells are damaged when they come into contact with airborne contaminants. + The new approach offered is ECNN+.

Describing Pulmonary Nodules Using 3D Clustering

Introduction. Determining the tumor (nodule) characteristics in terms of the shape, location, and type is an essential step after nodule detection in medical images for selecting the appropriate clinical intervention by radiologists. Computer-aided detection (CAD) systems efficiently succeeded in the nodule detection by 2D processing of computed tomography (CT)-scan lung images; however, the nodule (tumor) description in more detail is still a big challenge that faces these systems.Materials and Methods. In this paper, the 3D clustering is carried out on volumetric CT-scan images containing the nodule and its structures to describe the nodule progress through the consecutive slices of the lung in CT images.Results. This paper combines algorithms to cluster and define nodule’s features in 3D visualization. Applying some 3D functions to the objects, clustered using the K-means technique of CT lung images, provides a 3D visual exploration of the nodule shape and location. This study mainly focuses on clustering in 3D to discover complex information for a case missed in the radiologist’s report. In addition, the 3D-Density-based spatial clustering of applications with noise (DBSCAN) method and another 3D application (plotly) have been applied to evaluate the proposed system in this work. The proposed method has discovered a complicated case in data and automatically provides information about the nodule types (spherical, juxta-pleural, and pleural-tail). The algorithm is validated on the standard data consisting of the lung computed tomography scans with nodules greater and less than 3mm in size.Discussion and Conclusions. Based on the proposed model, it is possible to cluster lung nodules in volumetric CT scan and determine a set of characteristics such as the shape, location and type.

Contrastive learning and subtyping of post-COVID-19 lung computed tomography images.

Patients who recovered from the novel coronavirus disease 2019 (COVID-19) may experience a range of long-term symptoms. Since the lung is the most common site of the infection, pulmonary sequelae may present persistently in COVID-19 survivors. To better understand the symptoms associated with impaired lung function in patients with post-COVID-19, we aimed to build a deep learning model which conducts two tasks: to differentiate post-COVID-19 from healthy subjects and to identify post-COVID-19 subtypes, based on the latent representations of lung computed tomography (CT) scans. CT scans of 140 post-COVID-19 subjects and 105 healthy controls were analyzed. A novel contrastive learning model was developed by introducing a lung volume transform to learn latent features of disease phenotypes from CT scans at inspiration and expiration of the same subjects. The model achieved 90% accuracy for the differentiation of the post-COVID-19 subjects from the healthy controls. Two clusters (C1 and C2) with distinct characteristics were identified among the post-COVID-19 subjects. C1 exhibited increased air-trapping caused by small airways disease (4.10%, p = 0.008) and diffusing capacity for carbon monoxide %predicted (DLCO %predicted, 101.95%, p < 0.001), while C2 had decreased lung volume (4.40L, p < 0.001) and increased ground glass opacity (GGO%, 15.85%, p < 0.001). The contrastive learning model is able to capture the latent features of two post-COVID-19 subtypes characterized by air-trapping due to small airways disease and airway-associated interstitial fibrotic-like patterns, respectively. The discovery of post-COVID-19 subtypes suggests the need for different managements and treatments of long-term sequelae of patients with post-COVID-19.