F18-FDG PET-CT analyses of small peripheral adenocarcinoma of the lung

Takahashi, Yoshiyuki; Takashima, S; Watanabe, Shinichiro; Tomita, Yoshihiro; Hakucho, Tomoaki; Miyake, Chie; Morimoto, Daisuke; Hosoki, Takuya; Nakanishi, Katsuyuki; Higashiyama, Masahiko

doi:10.1258/ar.2012.120138

Cited by 1 publication

(1 citation statement)

References 20 publications

(24 reference statements)

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“…Thus, detection methods based on PET/CT are proposed. Takahashi and colleagues focused on the commonly-used combined modality of PET/CT [ 14 ]. Because CT scanning generates attenuation-coefficient-based images of anatomy and PET depicts tissue metabolism based on uptake of radiolabeled tracers such as fluorodeoxyglucose, both of these methods together may improve detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

A New Method of Detecting Pulmonary Nodules with PET/CT Based on an Improved Watershed Algorithm

Zhao

Qiang

et al. 2015

PLoS ONE

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BackgroundIntegrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.MethodOur proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.ResultsOur proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).

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Section: Introductionmentioning

confidence: 99%