Modeling of solitary pulmonary nodules in PET/CT images using Monte Carlo methods

The classification of pulmonary nodules using computed tomography (CT) and positron emission tomography (PET)/CT is often a hard task for physicians. To this end, in our previous study, we developed an automated classification method using PET/CT images. In actual clinical practice, in addition to images, patient information (e.g., laboratory test results) is available and may be useful for automated classification. Here, we developed a hybrid scheme for automated classification of pulmonary nodules using these images and patient information. We collected 36 conventional CT images and PET/CT images of patients who underwent lung biopsy following bronchoscopy. Patient information was also collected. For classification, 25 shape and functional features were first extracted from the images. Benign and malignant nodules were identified using machine learning algorithms along with the images’ features and 17 patient-information-related features. In the leave-one-out cross-validation of our hybrid scheme, 94.4% of malignant nodules were identified correctly, and 77.7% of benign nodules were diagnosed correctly. The hybrid scheme performed better than that of our previous method that used only image features. These results indicate that the proposed hybrid scheme may improve the accuracy of malignancy analysis.

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Impact of solitary pulmonary nodule size on qualitative and quantitative assessment using 18F-fluorodeoxyglucose PET/CT: the SPUTNIK trial

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-020-05089-y ◽

2020 ◽

Cited By ~ 1

Author(s):

J. R. Weir-McCall ◽

◽

S. Harris ◽

K. A. Miles ◽

N. R. Qureshi ◽

...

Keyword(s):

Multicentre Trial ◽

Pulmonary Nodules ◽

Blood Pool ◽

Qualitative Assessment ◽

Solitary Pulmonary Nodules ◽

Nodule Size ◽

Diagnostic Threshold ◽

Pet Ct ◽

The Impact

Abstract Purpose To compare qualitative and semi-quantitative PET/CT criteria, and the impact of nodule size on the diagnosis of solitary pulmonary nodules in a prospective multicentre trial. Methods Patients with an SPN on CT ≥ 8 and ≤ 30 mm were recruited to the SPUTNIK trial at 16 sites accredited by the UK PET Core Lab. Qualitative assessment used a five-point ordinal PET-grade compared to the mediastinal blood pool, and a combined PET/CT grade using the CT features. Semi-quantitative measures included SUVmax of the nodule, and as an uptake ratio to the mediastinal blood pool (SURBLOOD) or liver (SURLIVER). The endpoints were diagnosis of lung cancer via biopsy/histology or completion of 2-year follow-up. Impact of nodule size was analysed by comparison between nodule size tertiles. Results Three hundred fifty-five participants completed PET/CT and 2-year follow-up, with 59% (209/355) malignant nodules. The AUCs of the three techniques were SUVmax 0.87 (95% CI 0.83;0.91); SURBLOOD 0.87 (95% CI 0.83; 0.91, p = 0.30 versus SUVmax); and SURLIVER 0.87 (95% CI 0.83; 0.91, p = 0.09 vs. SUVmax). The AUCs for all techniques remained stable across size tertiles (p > 0.1 for difference), although the optimal diagnostic threshold varied by size. For nodules < 12 mm, an SUVmax of 1.75 or visual uptake equal to the mediastinum yielded the highest accuracy. For nodules > 16 mm, an SUVmax ≥ 3.6 or visual PET uptake greater than the mediastinum was the most accurate. Conclusion In this multicentre trial, SUVmax was the most accurate technique for the diagnosis of solitary pulmonary nodules. Diagnostic thresholds should be altered according to nodule size. Trial registration ISRCTN - ISRCTN30784948. ClinicalTrials.gov - NCT02013063

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A COMPREHENSIVE FRAMEWORK FOR AUTOMATIC DETECTION OF PULMONARY NODULES IN LUNG CT IMAGES

Image Analysis & Stereology ◽

10.5566/ias.v33.p13-27 ◽

2014 ◽

Vol 33 (1) ◽

pp. 13 ◽

Cited By ~ 25

Author(s):

Mehdi Alilou ◽

Vassili Kovalev ◽

Eduard Snezhko ◽

Vahid Taimouri

Keyword(s):

Early Stage ◽

Pulmonary Nodules ◽

Maximum Intensity Projection ◽

Region Growing ◽

Ct Images ◽

Support Vector ◽

Solitary Pulmonary Nodules ◽

Computed Tomography Images ◽

Lung Structure ◽

Multiple Thresholds

Solitary pulmonary nodules may indicate an early stage of lung cancer. Hence, the early detection of nodules is the most efficient way for saving the lives of patients. The aim of this paper is to present a comprehensive Computer Aided Diagnosis (CADx) framework for detection of the lung nodules in computed tomography images. The four major components of the developed framework are lung segmentation, identification of candidate nodules, classification and visualization. The process starts with segmentation of lung regions from the thorax. Then, inside the segmented lung regions, candidate nodules are identified using an approach based on multiple thresholds followed by morphological opening and 3D region growing algorithm. Finally, a combination of a rule-based procedure and support vector machine classifier (SVM) is utilized to classify the candidate nodules. The proposed CADx method was validated on CT images of 60 patients, containing the total of 211 nodules, selected from the publicly available Lung Image Database Consortium (LIDC) image dataset. Comparing to the other state of the art methods, the proposed framework demonstrated acceptable detection performance (Sensitivity: 0.80; Fp/Scan: 3.9). Furthermore, we visualize a range of anatomical structures including the 3D lung structure and the segmented nodules along with the Maximum Intensity Projection (MIP) volume rendering method that will enable the radiologists to accurately and easily estimate the distance between the lung structures and the nodules which are frequently difficult at best to recognize from CT images.

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