Lung Nodule Manager App Review.
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ABSTRACT:
SUBMITTER: Elovic A
PROVIDER: S-EPMC6456644 | biostudies-literature | 2019 Apr
REPOSITORIES: biostudies-literature
Publications
Lung Nodule Manager App Review.
Elovic Andres A Pourmand Ali A
Journal of digital imaging 20190401 2
Intermediate lung nodules are frequently discovered in CT imaging as either incidental or part of cancer screening. The Lung Nodule Manager allows for a quick retrieval of guidelines by physicians and health care professionals to determine the proper management and follow-up for patients. ...[more]
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Project description:PurposeEarly detection of lung cancer is of importance since it can increase patients' chances of survival. To detect nodules accurately during screening, radiologists would commonly take the axial, coronal, and sagittal planes into account, rather than solely the axial plane in clinical evaluation. Inspired by clinical work, the paper aims to develop an accurate deep learning framework for nodule detection by a combination of multiple planes.MethodsThe nodule detection system is designed in two stages, multiplanar nodule candidate detection, multiscale false positive (FP) reduction. At the first stage, a deeply supervised encoder-decoder network is trained by axial, coronal, and sagittal slices for the candidate detection task. All possible nodule candidates from the three different planes are merged. To further refine results, a three-dimensional multiscale dense convolutional neural network that extracts multiscale contextual information is applied to remove non-nodules. In the public LIDC-IDRI dataset, 888 computed tomography scans with 1186 nodules accepted by at least three of four radiologists are selected to train and evaluate our proposed system via a tenfold cross-validation scheme. The free-response receiver operating characteristic curve is used for performance assessment.ResultsThe proposed system achieves a sensitivity of 94.2% with 1.0 FP/scan and a sensitivity of 96.0% with 2.0 FPs/scan. Although it is difficult to detect small nodules (i.e., <6 mm), our designed CAD system reaches a sensitivity of 93.4% (95.0%) of these small nodules at an overall FP rate of 1.0 (2.0) FPs/scan. At the nodule candidate detection stage, results show that the system with a multiplanar method is capable to detect more nodules compared to using a single plane.ConclusionOur approach achieves good performance not only for small nodules but also for large lesions on this dataset. This demonstrates the effectiveness of our developed CAD system for lung nodule detection.
| S-EPMC7986069 | biostudies-literature
Project description:BACKGROUND:Lung cancer is the leading cause of cancer mortality in Australia. Guidelines suggest that patients with suspected lung cancer on thoracic imaging be referred for urgent specialist review. However, the term "suspected" is broad and includes the common finding of lung nodules, which often require periodic surveillance rather than urgent invasive investigation. The British Thoracic Society recommends that a lung nodule with a PanCan risk > 10% be considered for invasive investigation. This study aimed to assess which factors influence general practitioners (GPs) to request urgent review for a lung nodule and if these factors concur with PanCan risk prediction model variables. METHODS:A discrete choice experiment was developed that produced 32 individual case vignettes. Each vignette contained eight variables, four of which form the parsimonious PanCan risk prediction model. Two additional vignettes were created that addressed haemoptysis with a normal chest computed tomography (CT) scan and isolated mediastinal lymphadenopathy. The survey was distributed to 4160 randomly selected Australian GPs and they were asked if the patients in the vignettes required urgent (less than two weeks) specialist review. Multivariate logistic regression identified factors associated with request for urgent review. RESULTS:Completed surveys were received from 3.7% of participants, providing 152 surveys (1216 case vignettes) for analysis. The factors associated with request for urgent review were nodule spiculation (adj-OR 5.57, 95% CI 3.88-7.99, p < 0.0001), larger nodule size, presentation with haemoptysis (adj-OR 4.79, 95% CI 3.05-7.52, p < 0.0001) or weight loss (adj-OR 4.87, 95% CI 3.13-7.59, p < 0.0001), recommendation for urgent review by the reporting radiologist (adj-OR 4.68, 95% CI 2.86-7.65, p < 0.0001) and female GP gender (adj-OR 1.87, 95% CI 1.36-2.56, p 0.0001). In low risk lung nodules (PanCan risk < 10%), there was significant variability in perceived sense of urgency. Most GPs (83%) felt that a patient with haemoptysis and a normal chest CT scan did not require urgent specialist review but that a patient with isolated mediastinal lymphadenopathy did (75%). CONCLUSION:Future lung cancer investigation pathways may benefit from the addition of a risk prediction model to reduce variations in referral behavior for low risk lung nodules.
| S-EPMC6993503 | biostudies-literature
Project description:A solitary pulmonary nodule (SPN) is a well-defined radiographic opacity up to 3 cm in diameter that is surrounded by unaltered aerated lung. Frequently, it is an incidental finding on chest radiographs and chest CT scans. Determining the probability of malignancy is the first step in the evaluation of SPN. This can be done by looking at specific risk factors and the rate of radiographic progression. Subsequent management is guided by the type of the nodule. Patients with solid nodules and low pretest probability can be followed radiographically; those with high probability, who are good surgical candidates, can be referred for surgical resection. When the pretest probability is in the intermediate range additional testing such as biopsy should be done. Various modalities are now available to obtain tissue diagnosis. These modalities differ in their yield and complication rate. Patients with SPN should be well informed of each approach's risks and benefits and should be able to make an informed decision regarding the different diagnostic and therapeutic modalities.
| S-EPMC6712257 | biostudies-literature
Project description:The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.
| S-EPMC5166709 | biostudies-literature
Project description:In the last few years, minimally invasive surgery has become the standard routine practice to manage lung nodules. Particularly in the case of robotic thoracic surgery, the identification of the lung nodules that do not surface on the visceral pleura could be challenging. Therefore, together with the evolution of surgical instruments to provide the best option in terms of invasiveness, lung nodule localization techniques should be improved to achieve the best outcomes in terms of safety and sensibility. In this review, we aim to overview all principal techniques used to detect the lung nodules that do not present the visceral pleura retraction. We investigate the accuracy of fluorescence guided thoracic surgery in nodule detection and the differences among the most common tracers used.
| S-EPMC9339676 | biostudies-literature
Project description:Lung cancer is the leading cause of cancer mortality globally, responsible for an estimated 1.76 million deaths worldwide in 2018 alone. Screening adults at high risk of lung cancer using low-dose computed tomography (LDCT) significantly reduces lung cancer mortality by finding the disease at an early, treatable stage. Many countries are actively considering whether to implement screening for their high-risk populations in light of the recently published Dutch-Belgian trial 'NELSON'. In deciding whether to implement a national screening programme, policymakers must weigh up the evidence for the relative risks posed to the entire screened population, including the potential psychological burden. This narrative review aimed to critically summarise the evidence for both negative and positive psychological responses experienced throughout the LDCT screening pathway, to describe their magnitude, duration and clinical relevance, and to draw out different aspects of measurement design crucial to their interpretation. A further aim was to discuss the available evidence for individual differences in psychological response, as well as interventions designed to promote psychological well-being. In summary, there was no evidence that the LDCT screening process caused adverse psychological outcomes overall, although those receiving indeterminate and suspicious LDCT results did report clinically raised anxiety and lung cancer-specific distress in the short-term. There was early evidence that demographic factors, smoking status and screening-ineligibility could be associated with individual differences in propensity to experience distress. Qualitative data suggested health beliefs could be modifiable mediators of these individual differences, but their aetiology requires quantitative and prospective research. There was also some evidence of positive psychological responses that could be capitalised on, and of the potential for person-centred communication interventions to achieve this. Further research needs to be embedded in real-world LDCT lung cancer screening services and use condition-specific measures to monitor outcomes and test evidence-based communication interventions in promoting psychological well-being.
| S-EPMC8182717 | biostudies-literature
Project description:This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is subjective and depends on the radiologist's experiences. Owing to the variation in shapes and appearances of a lung nodule, it is very difficult to identify the proper location of the nodule from a huge number of slices generated by the CT scanner. Small nodules (< 10 mm in diameter) may be missed by this manual detection process. Therefore, computer-aided diagnosis (CAD) system acts as a "second opinion" for the radiologists, by making final decision quickly with higher accuracy and greater confidence. The goal of this survey work is to present the current state of the artworks and their progress towards lung nodule detection to the researchers and readers in this domain. This review paper has covered the published works from 2009 to April 2018. Different nodule detection approaches are described elaborately in this work. Recently, it is observed that deep learning (DL)-based approaches are applied extensively for nodule detection and characterization. Therefore, emphasis has been given to convolutional neural network (CNN)-based DL approaches by describing different CNN-based networks.
| S-EPMC7256172 | biostudies-literature
Project description:BackgroundRadiomics, defined as quantitative features extracted from images, provide a non-invasive means of assessing malignant versus benign pulmonary nodules. In this study, we evaluate the consistency with which perinodular radiomics extracted from low-dose computed tomography images serve to identify malignant pulmonary nodules.Materials and methodsUsing the National Lung Screening Trial (NLST), we selected individuals with pulmonary nodules between 4mm to 20mm in diameter. Nodules were segmented to generate four distinct datasets; 1) a Tumor dataset containing tumor-specific features, 2) a 10 mm Band dataset containing parenchymal features between the segmented nodule boundary and 10mm out from the boundary, 3) a 15mm Band dataset, and 4) a Tumor Size dataset containing the maximum nodule diameter. Models to predict malignancy were constructed using support-vector machine (SVM), random forest (RF), and least absolute shrinkage and selection operator (LASSO) approaches. Ten-fold cross validation with 10 repetitions per fold was used to evaluate the performance of each approach applied to each dataset.ResultsWith respect to the RF, the Tumor, 10mm Band, and 15mm Band datasets achieved areas under the receiver-operator curve (AUC) of 84.44%, 84.09%, and 81.57%, respectively. Significant differences in performance were observed between the Tumor and 15mm Band datasets (adj. p-value <0.001). However, when combining tumor-specific features with perinodular features, the 10mm Band + Tumor and 15mm Band + Tumor datasets (AUC 87.87% and 86.75%, respectively) performed significantly better than the Tumor Size dataset (66.76%) or the Tumor dataset. Similarly, the AUCs from the SVM and LASSO were 84.71% and 88.91%, respectively, for the 10mm Band + Tumor.ConclusionsThe combined 10mm Band + Tumor dataset improved the differentiation between benign and malignant lung nodules compared to the Tumor datasets across all methodologies. This demonstrates that parenchymal features capture novel diagnostic information beyond that present in the nodule itself. (data agreement: NLST-163).
| S-EPMC9895123 | biostudies-literature
Project description: Not available
| S-EPMC7667915 | biostudies-literature
Project description:BackgroundLow-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images.MethodsIn this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules.ResultsThe results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%.ConclusionA modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.
| S-EPMC10953193 | biostudies-literature