Evaluating pulmonary nodules to detect lung cancer: Does Fleischner criteria really work?

Azharuddin, Muhammad; Adamo, Noor; Malik, Aamir Saeed; Livornese, Douglas

doi:10.1016/j.jcrpr.2017.05.003

Cited by 5 publications

(7 citation statements)

References 39 publications

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“…Since nodule size is an important risk factor and the definition of IPNs is not very well defined (6) with changing guidelines, we examined the performance when comparing malignant and benign pulmonary nodules that were similar in size ranges. We calculated the performance of the 41-probe classifier across the various nodule size ranges using baseline positive thresholds of 4 mm from the NLST study (24) 6 mm and 8 mm as discussed in the recent reports from the Fleischner Society (8) and the Lung Rads (7) as well as a baseline threshold of 10 mm. The ROC-AUCs and the specificities when the sensitivity is held at a performance of 90% were calculated for all possible ranges for the selected thresholds for training and independent validation sets (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…The National Lung Screening Trial (NLST) detected lung nodules ≥4 mm in diameter in 40% of patients screened, with 96.4% being false positives over the 3 rounds of screening (6). To reduce this high FPR the recent Lung-RADs classification (7) and new guidelines from the Fleischner group (8) set the detection of nodules ≥6mm as the positive threshold. However, positive CT scans remain particularly problematic for that class of indeterminate pulmonary nodules (IPNs), which range in size from 6 to 20 mm for which the best course of clinical action is not well specified (6).…”

Section: Introductionmentioning

confidence: 99%

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A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT

et al. 2019

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Low dose computed tomography (LDCT) is widely accepted as the preferred method for detecting pulmonary nodules. However, the determination of whether a nodule is benign (BN) or malignant (MN) involves either repeated scans or invasive procedures that sample the lung tissue. Non-invasive methods to assess these nodules are needed to reduce unnecessary invasive tests. In this study, we have developed a pulmonary nodule classifier (PNC) using RNA from whole blood collected in RNA-stabilizing PAXgene tubes that addresses this need. Samples were prospectively collected from high risk and incidental subjects with a positive lung CT scan. A total of 821 samples from 5 clinical sites were analyzed. MN samples were predominantly Stage 1 by pathologic diagnosis and 97% of BN were confirmed by four years of follow-up. A panel of diagnostic biomarkers was selected from a subset of the samples assayed on Illumina microarrays that achieved a ROC-AUC of 0.847 on independent validation. The microarray data was then used to design a biomarker panel of 559 gene probes to be validated on the clinically tested NanoString nCounter platform. RNA from 583 patients was used to assess and refine the NanoString PNC (nPNC) which was then validated on 158 independent samples (ROC-AUC = 0.825). The nPNC outperformed 3 clinical algorithms in discriminating malignant from benign pulmonary nodules ranging from 6-20mm using just 41 diagnostic biomarkers. Overall, this platform provides an accurate, non-invasive method for the diagnosis of pulmonary nodules in non-small cell lung cancer patients.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT

et al. 2019

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“…1,2 It is reported that the nodules with the diameter <4 mm account for 59.5% of uncalcified pulmonary nodules. 3 Existence of silicosis is well known since ancient times; but it has in present times grown to a global public health problem, reporting thousands of cases every year. Although, the prevalence has decreased compared with past decades, it is still an obscured and underreported disease.…”

Section: Introductionmentioning

confidence: 99%

“…In current radiological practices, silicosis is typically characterized as widespread, well‐defined small pulmonary nodules <4 mm . It is reported that the nodules with the diameter <4 mm account for 59.5% of uncalcified pulmonary nodules . Existence of silicosis is well known since ancient times; but it has in present times grown to a global public health problem, reporting thousands of cases every year.…”

Section: Introductionmentioning

confidence: 99%

Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks

Gupta

Saar

Märtens

et al. 2019

Int J Imaging Syst Tech

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Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error-prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer-assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidatescreening module and a false positive (FP) reduction module. The candidatescreening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features-based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro-nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well-suited FP reduction module of a CAD scheme.

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“…It is noted that previous studies are limited to small nodules with a diameter larger than 3 mm, no study on micro-nodules (diameter < 3 mm) has been done. It is reported that the pulmonary nodules with the diameter < 4 mm account for 59.5% in a total of 210 uncalcified pulmonary nodules [ 17 ]. Moreover, many recommendations have been given for the management of micro-nodules by different institutes.…”

Section: Introductionmentioning

confidence: 99%

CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images

Monkam

et al. 2018

BioMed Eng OnLine

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BackgroundEarly and automatic detection of pulmonary nodules from CT lung screening is the prerequisite for precise management of lung cancer. However, a large number of false positives appear in order to increase the sensitivity, especially for detecting micro-nodules (diameter < 3 mm), which increases the radiologists’ workload and causes unnecessary anxiety for the patients. To decrease the false positive rate, we propose to use CNN models to discriminate between pulmonary micro-nodules and non-nodules from CT image patches.MethodsA total of 13,179 micro-nodules and 21,315 non-nodules marked by radiologists are extracted with three different patch sizes (16 × 16, 32 × 32 and 64 × 64) from LIDC/IDRI database and used in the experiments. Three CNN models with different depths (1, 2 or 4 convolutional layers) are designed; their performances are evaluated by the fivefold cross-validation in term of the accuracy, area under the curve (AUC), F-score and sensitivity. The network parameters are also optimized.ResultsIt is found that the performance of the CNN models is greatly dependent on the patches size and the number of convolutional layers. The CNN model with two convolutional layers presented the best performance in case of 32 × 32 patches size, achieving an accuracy of 88.28%, an AUC of 0.87, a F-score of 83.45% and a sensitivity of 83.82%.ConclusionsThe CNN models with appropriate depth and size of image patches can effectively discriminate between pulmonary micro-nodules and non-nodules, and reduce the false positives and help manage lung cancer precisely.

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Evaluating pulmonary nodules to detect lung cancer: Does Fleischner criteria really work?

Cited by 5 publications

References 39 publications

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT

A Gene Expression Classifier from Whole Blood Distinguishes Benign from Malignant Lung Nodules Detected by Low-Dose CT

Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks

CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images

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