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: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: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: Not available
| S-EPMC7667915 | biostudies-literature
Project description:We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.
| S-EPMC6063408 | biostudies-other
Project description:Background Lung cancer screening revealed that people with small pulmonary nodules are mostly asymptomatic and that some of these people are at risk of developing lung cancer, so we intended to explore the repeatability of small lung nodule measurement in low-dose lung screening. Methods We scanned eight ground-glass nodules (GGNs) and solid nodules, with diameters of 3, 5, 8, and 10?mm. They were divided according to the different combination schemes of tube voltage (KV) and tube current (mA) as 70, 80, 100, and 120 KV, and currents of nine tubes were divided as 20, 30, 40, 50, 60, 70, 80, 90, and 100 mAs. Results Compared with the conventional dose group (120 kVp, 100 mAs), the nodule diameter and solid nodule volume measured by all scanning combinations were more consistent (P?>?0.05), the volumes of 10?mm GGNs combinations were consistent (P?>?0.05), the volumes of 8?mm GGNs were consistent (P?>?0.05), the volumes of 5?mm GGNs combinations were consistent (P?>?0.05), and the volumes of 3?mm were consistent (P?>?0.05). Conclusion In lung cancer screening, CT parameters should be as follows: tube voltage is more than 80 kVp, and tube current is 80 mAs in order to meet the requirements for the accurate measurement of the diameter and volume of pulmonary nodules.
| S-EPMC7532632 | biostudies-literature
Project description:Pulmonary nodules are increasingly identified on imaging exams performed for a number of clinical presentations and can pose a diagnostic problem for clinicians. Guideline-directed management algorithms are structured on nodule pre-test probability of malignancy. The risk of malignancy can be clinician-assigned or calculated utilizing validated risk prediction calculators. Once pre-test probability of cancer is estimated, nodule management options range from a conservative approach with serial imaging to more invasive measures including biopsy procedures or surgical resection. Here we review pulmonary nodule management with a focus on methods for assigning malignancy risk and highlight novel ways currently under active research to improve nodule risk assessment and management.
| S-EPMC7330782 | biostudies-literature
Project description:Radiomics, which extract large amount of quantification image features from diagnostic medical images had been widely used for prognostication, treatment response prediction and cancer detection. The treatment options for lung nodules depend on their diagnosis, benign or malignant. Conventionally, lung nodule diagnosis is based on invasive biopsy. Recently, radiomics features, a non-invasive method based on clinical images, have shown high potential in lesion classification, treatment outcome prediction.Lung nodule classification using radiomics based on Computed Tomography (CT) image data was investigated and a 4-feature signature was introduced for lung nodule classification. Retrospectively, 72 patients with 75 pulmonary nodules were collected. Radiomics feature extraction was performed on non-enhanced CT images with contours which were delineated by an experienced radiation oncologist.Among the 750 image features in each case, 76 features were found to have significant differences between benign and malignant lesions. A radiomics signature was composed of the best 4 features which included Laws_LSL_min, Laws_SLL_energy, Laws_SSL_skewness and Laws_EEL_uniformity. The accuracy using the signature in benign or malignant classification was 84% with the sensitivity of 92.85% and the specificity of 72.73%.The classification signature based on radiomics features demonstrated very good accuracy and high potential in clinical application.
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Project description:Our previous study indicated that multiprojection chest radiography could significantly improve radiologists' performance for lung nodule detection in clinical practice. In this study, the authors further verify that multiprojection chest radiography can greatly improve the performance of a computer-aided diagnostic (CAD) scheme.Our database consisted of 59 subjects, including 43 subjects with 45 nodules and 16 subjects without nodules. The 45 nodules included 7 real and 38 simulated ones. The authors developed a conventional CAD scheme and a new fusion CAD scheme to detect lung nodules. The conventional CAD scheme consisted of four steps for (1) identification of initial nodule candidates inside lungs, (2) nodule candidate segmentation based on dynamic programming, (3) extraction of 33 features from nodule candidates, and (4) false positive reduction using a piecewise linear classifier. The conventional CAD scheme processed each of the three projection images of a subject independently and discarded the correlation information between the three images. The fusion CAD scheme included the four steps in the conventional CAD scheme and two additional steps for (5) registration of all candidates in the three images of a subject, and (6) integration of correlation information between the registered candidates in the three images. The integration step retained all candidates detected at least twice in the three images of a subject and removed those detected only once in the three images as false positives. A leave-one-subject-out testing method was used for evaluation of the performance levels of the two CAD schemes.At the sensitivities of 70%, 65%, and 60%, our conventional CAD scheme reported 14.7, 11.3, and 8.6 false positives per image, respectively, whereas our fusion CAD scheme reported 3.9, 1.9, and 1.2 false positives per image, and 5.5, 2.8, and 1.7 false positives per patient, respectively. The low performance of the conventional CAD scheme may be attributed to the high noise level in chest radiography, and the small size and low contrast of most nodules.This study indicated that the fusion of correlation information in multiprojection chest radiography can markedly improve the performance of CAD scheme for lung nodule detection.
| S-EPMC3321052 | biostudies-other