Feasibility Study of MRI and Multimodality CT/MRI Radiomics for Lung Nodule Classification

Lung cancer is the most common and lethal form of cancer, and its early diagnosis is key to the patient's survival. CT is the reference imaging scan for lung cancer screening; however, it presents the drawback of exposing the patient to ionizing radiation. Recent studies have shown the relevance of MRI in lung nodules diagnosis. In this work, we aimed to evaluate whether radiomics features from MRI are well-suited for lung nodules characterization and if the combination of CT and MRI features can yield better results than the features from the individual modalities. For such, we segmented paired CT and MRI nodules from 33 lung nodules patients, extracted 89 radiomics features from each modality, and combined it into a multimodality feature set. Those features were then used for classifying the nodules into benign and malignant by a set of machine learning algorithms, assessing the AUC across 30 trials. Our results show that MRI radiomics features are suitable for characterizing lung lesions, yielding AUC values up to 17% higher than their CT counterparts, and shedding light on MRI as a viable image modality for decision support systems. Conversely, our multimodality approach did not improve performance compared to the single-modality models, suggesting that the direct combination of multimodality features might not be an adequate strategy for dealing with multimodality medical images.

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Lung Cancer Classification Using Squeeze and Excitation Convolutional Neural Networks with Grad Cam++ Class Activation Function

Traitement du signal ◽

10.18280/ts.380421 ◽

2021 ◽

Vol 38 (4) ◽

pp. 1103-1112

Author(s):

Eali Stephen Neal Joshua ◽

Debnath Bhattacharyya ◽

Midhun Chakkravarthy ◽

Hye-Jin Kim

Keyword(s):

Lung Cancer ◽

Patient Survival ◽

Lung Nodule ◽

Activation Function ◽

Cancer Classification ◽

Systematic Evaluation ◽

Lung Nodules ◽

Nodule Detection ◽

Lung Cancer Therapy ◽

Nodule Classification

The leading cause of cancer-related death globally has been identified as lung cancer. Early lung nodule detection is critical for lung cancer therapy and patient survival. The Gard Cam++ Class Activation Function is used with a squeeze-and-excite network to provide a revolutionary method for differentiating malignant from benign lung nodules on CT scans. The new SENET (Squeeze-and-Excitation Networks) Grad Cam++ module, which combines the features calibration and discrimination benefits of SENET, has been shown to have a substantial potential for improving feature discriminability in lung cancer classification. According to the publicly available LUng Nodule Analysis 2016 (LUNA16) database, when assessed on 1230 nodules, the technique achieved an AUC of 0.9664 and an accuracy of 97.08% (600 malignant and 630 benign). The favorable results demonstrate the robustness of our technique to nodule classification, which we anticipate will be valuable in the future. The technology's objective is to aid radiologists in evaluating diagnostic data and differentiating benign from malignant lung nodules on CT images. To our knowledge, no systematic evaluation of SENET usefulness in classifying lung nodules has been done.

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Development and outcomes of a comprehensive multidisciplinary incidental lung nodule and lung cancer screening program

10.21203/rs.2.18446/v2 ◽

2020 ◽

Author(s):

Gregory LeMense ◽

Ernest A. Waller ◽

Cheryl Campbell ◽

Tyler Bowen

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Screening Program ◽

Lung Nodule ◽

Lung Cancer Screening ◽

Treatment Modalities ◽

Lung Nodules ◽

Hospital System ◽

Number Of Patients

Abstract Background: Appropriate management of lung nodules detected incidentally or through lung cancer screening can increase the rate of early-stage diagnoses and potentially improve treatment outcomes. However, the implementation and management of comprehensive lung nodule programs is challenging. Methods: This single-center, retrospective report describes the development and outcomes of a comprehensive lung nodule program at a community practice in Tennessee. Computed tomography (CT) scans potentially revealing incidental lung nodules were identified by a computerized search. Incidental or screening-identified lung nodules that were enlarging or not seen in prior scans were entered into a nodule database and guideline-based review determined whether to conduct a diagnostic intervention or radiologic follow-up. Referral rates, diagnosis methods, stage distribution, treatment modalities, and days to treatment are reported. Results: The number of patients with lung nodules referred to the program increased over 2 years, from 665 patients in Year 1 to 745 patients in Year 2. Most nodules were incidental (62%-65%). Nodules identified with symptoms (15.2% in Year 1) or through screening (12.6% in Year 1) were less common. In Year 1, 27% (182/665) of nodules required a diagnostic intervention and 18% (121/665) were malignant. Most diagnostic interventions were image-guided bronchoscopy (88%) or percutaneous biopsy (9%). The proportion of Stage I-II cancer diagnoses increased from 23% prior to program implementation to 36% in Year 1 and 38% in Year 2. In screening cases, 71% of patients completed follow-up scans within 18 months. Only 2% of Year 1 patients under watchful waiting required a diagnostic intervention, of which 1% received a cancer diagnosis. Conclusions: The current study reports outcomes over the first two years of a lung cancer screening and incidental nodule program. The results show that the program was successful, given the appropriate level of data management and oversight. Comprehensive lung nodule programs have the potential to benefit the patient, physician, and hospital system.

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Multi-Level Cross Residual Network for Lung Nodule Classification

Sensors ◽

10.3390/s20102837 ◽

2020 ◽

Vol 20 (10) ◽

pp. 2837 ◽

Cited By ~ 2

Author(s):

Juan Lyu ◽

Xiaojun Bi ◽

Sai Ho Ling

Keyword(s):

Lung Cancer ◽

Early Stage ◽

Binary Classification ◽

Lung Nodule ◽

Disease Diagnosis ◽

Lung Nodules ◽

Lung Cancer Patients ◽

Multi Scale ◽

Multi Level ◽

Nodule Classification

Computer-aided algorithm plays an important role in disease diagnosis through medical images. As one of the major cancers, lung cancer is commonly detected by computer tomography. To increase the survival rate of lung cancer patients, an early-stage diagnosis is necessary. In this paper, we propose a new structure, multi-level cross residual convolutional neural network (ML-xResNet), to classify the different types of lung nodule malignancies. ML-xResNet is constructed by three-level parallel ResNets with different convolution kernel sizes to extract multi-scale features of the inputs. Moreover, the residuals are connected not only with the current level but also with other levels in a crossover manner. To illustrate the performance of ML-xResNet, we apply the model to process ternary classification (benign, indeterminate, and malignant lung nodules) and binary classification (benign and malignant lung nodules) of lung nodules, respectively. Based on the experiment results, the proposed ML-xResNet achieves the best results of 85.88% accuracy for ternary classification and 92.19% accuracy for binary classification, without any additional handcrafted preprocessing algorithm.

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The impact of lung parenchyma attenuation on nodule volumetry in lung cancer screening

Insights into Imaging ◽

10.1186/s13244-021-01027-0 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Diana Penha ◽

Erique Pinto ◽

Bruno Hochhegger ◽

Colin Monaghan ◽

Edson Marchiori ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Screening Program ◽

Intraclass Correlation ◽

Lung Nodule ◽

Lung Cancer Screening ◽

Lung Parenchyma ◽

Lung Nodules ◽

Measurement Variability ◽

The Impact

Abstract Background Recent recommendations for lung nodule management include volumetric analysis using tools that present intrinsic measurement variability, with possible impacts on clinical decisions and patient safety. This study was conducted to evaluate whether changes in the attenuation of the lung parenchyma adjacent to a nodule affect the performance of nodule segmentation using computed tomography (CT) studies and volumetric tools. Methods Two radiologists retrospectively applied two commercially available volumetric tools for the assessment of lung nodules with diameters of 5–8 mm detected by low-dose chest CT during a lung cancer screening program. The radiologists recorded the success and adequacy of nodule segmentation, nodule volume, manually and automatically (or semi-automatically) obtained long- and short-axis measurements, mean attenuation of adjacent lung parenchyma, and presence of interstitial lung abnormalities or disease, emphysema, pleural plaques, and linear atelectasis. Regression analysis was performed to identify predictors of good nodule segmentation using the volumetric tools. Interobserver and intersoftware agreement on good nodule segmentation was assessed using the intraclass correlation coefficient. Results In total, data on 1265 nodules (mean patient age, 68.3 ± 5.1 years; 70.2% male) were included in the study. In the regression model, attenuation of the adjacent lung parenchyma was highly significant (odds ratio 0.987, p < 0.001), with a large effect size. Interobserver and intersoftware agreement on good segmentation was good, although one software package performed better and measurements differed consistently between software packages. Conclusion For lung nodules with diameters of 5–8 mm, the likelihood of good segmentation declines with increasing attenuation of the adjacent parenchyma.

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Development and outcomes of a comprehensive multidisciplinary incidental lung nodule and lung cancer screening program

10.21203/rs.2.18446/v1 ◽

2019 ◽

Author(s):

Gregory LeMense ◽

Ernest A. Waller ◽

Cheryl Campbell ◽

Tyler Bowen

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

Cancer Diagnosis ◽

Screening Program ◽

Early Stage ◽

Lung Nodule ◽

Lung Cancer Screening ◽

Lung Nodules ◽

Hospital System ◽

Number Of Patients

Abstract BackgroundAppropriate management of lung nodules detected incidentally or through lung cancer screening can increase the rate of early-stage diagnoses and potentially improve treatment outcomes. However, the implementation and management of comprehensive lung nodule programs is challenging. MethodsA single-center, retrospective study was conducted to describe the development and outcomes of a lung nodule program at a community practice in Tennessee.ResultsThe number of patients with lung nodules referred to the program increased over 2 years, with 665 patients in Year 1 and 745 patients in Year 2. Most nodules were incidental (60% Year 1, 65% Year 2). In Year 1, 17% of nodules were symptomatic and 12% were identified through screening. Of the 665 nodules in Year 1, 182 underwent a diagnostic intervention and 121 (18%) received a cancer diagnosis. Most diagnostic interventions were image-guided bronchoscopy (88%) or percutaneous biopsy (9%). The proportion of Stage I-II cancer diagnoses increased from 23% prior to program implementation to 36% in Year 1 and 38% in Year 2. Among screening cases, follow-up scans were conducted within 18 months in 71%. Only 2% of patients under watchful waiting required a diagnostic intervention, of which 1% received a cancer diagnosis.ConclusionsThe current study reports outcomes over the first two years of a lung cancer screening and incidental nodule program. The program was successful and manageable, given the appropriate level of data management and oversight. Comprehensive lung nodule programs have the potential to benefit the patient, physician, and hospital system.

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Ant Colony Optimization Approaches to Clustering of Lung Nodules from CT Images

Computational and Mathematical Methods in Medicine ◽

10.1155/2014/572494 ◽

2014 ◽

Vol 2014 ◽

pp. 1-16 ◽

Cited By ~ 4

Author(s):

Ravichandran C. Gopalakrishnan ◽

Veerakumar Kuppusamy

Keyword(s):

Lung Cancer ◽

Genetic Algorithm ◽

Lung Nodule ◽

Machine Learning Algorithms ◽

Lung Nodules ◽

Watershed Algorithm ◽

Lung Nodule Detection ◽

Novel Approach ◽

Nodule Detection ◽

Shape And Size

Lung cancer is becoming a threat to mankind. Applying machine learning algorithms for detection and segmentation of irregular shaped lung nodules remains a remarkable milestone in CT scan image analysis research. In this paper, we apply ACO algorithm for lung nodule detection. We have compared the performance against three other algorithms, namely, Otsu algorithm, watershed algorithm, and global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO, namely, refined ACO, logical ACO, and variant ACO. Variant ACO shows better reduction in false positives. In addition we propose black circular neighborhood approach to detect nodule centers from the edge detected image. Genetic algorithm based clustering is performed to cluster the nodules based on intensity, shape, and size. The performance of the overall approach is compared with hierarchical clustering to establish the improvisation in the proposed approach.

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An Efficient DA-Net Architecture for Lung Nodule Segmentation

Mathematics ◽

10.3390/math9131457 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1457

Author(s):

Muazzam Maqsood ◽

Sadaf Yasmin ◽

Irfan Mehmood ◽

Maryam Bukhari ◽

Mucheol Kim

Keyword(s):

Computed Tomography ◽

Lung Cancer ◽

Ct Scan ◽

Cancer Diagnosis ◽

Lung Nodule ◽

Search Space ◽

Lung Nodules ◽

Suggested Model ◽

Lung Cancer Diagnosis ◽

Segmentation Framework

A typical growth of cells inside tissue is normally known as a nodular entity. Lung nodule segmentation from computed tomography (CT) images becomes crucial for early lung cancer diagnosis. An issue that pertains to the segmentation of lung nodules is homogenous modular variants. The resemblance among nodules as well as among neighboring regions is very challenging to deal with. Here, we propose an end-to-end U-Net-based segmentation framework named DA-Net for efficient lung nodule segmentation. This method extracts rich features by integrating compactly and densely linked rich convolutional blocks merged with Atrous convolutions blocks to broaden the view of filters without dropping loss and coverage data. We first extract the lung’s ROI images from the whole CT scan slices using standard image processing operations and k-means clustering. This reduces the search space of the model to only lungs where the nodules are present instead of the whole CT scan slice. The evaluation of the suggested model was performed through utilizing the LIDC-IDRI dataset. According to the results, we found that DA-Net showed good performance, achieving an 81% Dice score value and 71.6% IOU score.

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Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals

European Radiology ◽

10.1007/s00330-013-2799-9 ◽

2013 ◽

Vol 23 (7) ◽

pp. 1836-1845 ◽

Cited By ~ 36

Author(s):

Marjolein A. Heuvelmans ◽

Matthijs Oudkerk ◽

Geertruida H. de Bock ◽

Harry J. de Koning ◽

Xueqian Xie ◽

...

Keyword(s):

Lung Cancer ◽

Cancer Screening ◽

False Positive ◽

Doubling Time ◽

Lung Cancer Screening ◽

Lung Nodules ◽

Fast Growing ◽

Volume Doubling Time ◽

Volume Doubling

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Application of Deep Learning in Lung Cancer Imaging Diagnosis

Journal of Healthcare Engineering ◽

10.1155/2022/6107940 ◽

2022 ◽

Vol 2022 ◽

pp. 1-12

Author(s):

Wenfa Jiang ◽

Ganhua Zeng ◽

Shuo Wang ◽

Xiaofeng Wu ◽

Chenyang Xu

Keyword(s):

Lung Cancer ◽

Computer Vision ◽

Deep Learning ◽

Malignant Tumors ◽

Lung Nodule ◽

Ct Images ◽

Lung Nodules ◽

X Rays ◽

Imaging Diagnosis ◽

Risk Of Cancer

Lung cancer is one of the malignant tumors with the highest fatality rate and nearest to our lives. It poses a great threat to human health and it mainly occurs in smokers. In our country, with the acceleration of industrialization, environmental pollution, and population aging, the cancer burden of lung cancer is increasing day by day. In the diagnosis of lung cancer, Computed Tomography (CT) images are a fairly common visualization tool. CT images visualize all tissues based on the absorption of X-rays. The diseased parts of the lung are collectively referred to as pulmonary nodules, the shape of nodules is different, and the risk of cancer will vary with the shape of nodules. Computer-aided diagnosis (CAD) is a very suitable method to solve this problem because the computer vision model can quickly scan every part of the CT image of the same quality for analysis and will not be affected by fatigue and emotion. The latest advances in deep learning enable computer vision models to help doctors diagnose various diseases, and in some cases, models have shown greater competitiveness than doctors. Based on the opportunity of technological development, the application of computer vision in medical imaging diagnosis of diseases has important research significance and value. In this paper, we have used a deep learning-based model on CT images of lung cancer and verified its effectiveness in the timely and accurate prediction of lungs disease. The proposed model has three parts: (i) detection of lung nodules, (ii) False Positive Reduction of the detected nodules to filter out “false nodules,” and (iii) classification of benign and malignant lung nodules. Furthermore, different network structures and loss functions were designed and realized at different stages. Additionally, to fine-tune the proposed deep learning-based mode and improve its accuracy in the detection Lung Nodule Detection, Noudule-Net, which is a detection network structure that combines U-Net and RPN, is proposed. Experimental observations have verified that the proposed scheme has exceptionally improved the expected accuracy and precision ratio of the underlined disease.

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