Solitary pulmonary nodule and 18F-FDG PET/CT. Part 1: epidemiology, morphological evaluation and cancer probability

Mosmann, Marcos Pretto; Borba, Marcelle Alves; Macedo, Francisco Pires Negromonte de; Liguori, Adriano de Araújo Lima; Neto, Arthur Villarim; Lima, Kênio Costa de

doi:10.1590/0100-3984.2014.0012

Cited by 25 publications

(24 citation statements)

References 31 publications

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“…CT often has led to a significant overlap between benign and malignant nodules resulting in useless invasive procedures, such as surgical biopsies (6). Integrated fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG-PET/CT) has been widely investigated in SPNs management (7,8) and it is reported as an accurate noninvasive test (9). However, 18 F-FDG-PET/CT presents a significant discordance between sensitivity (Se) and specificity (Spe) due to the presence of different conditions causing false positive (FP) or false negative (FN) results (10).…”

Section: Original Articlementioning

confidence: 99%

Diagnostic performance of fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a meta-analysis

et al. 2018

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Section: Original Articlementioning

confidence: 99%

Diagnostic performance of fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a meta-analysis

et al. 2018

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“…To use 18 F-FDG-PET/CT as an auxiliary tool in clinical decision-making, one should be aware of its limitations in the context of SPN. Due to the limited spatial resolution of 18 F-FDG-PET/CT, its use is not recommended for SPNs smaller than 8 mm (1,6,25) . Although the vast majority of pulmonary neoplasms have high 18 F-FDG avidity, some tumors do not, including mucinous carcinomas (26) or pulmonary metastases, in which the primary tumor also has a low glycolytic activity (24) .…”

Section: D a Bmentioning

confidence: 99%

“…Cases with low probability of malignancy (< 5%) are usually managed by active surveillance, whereas those with high probability (> 65%) biopsy or surgery is indicated (1,5) . Cases with intermediate probability of malignancy are usually biopsied for diagnostic clarification (6) . However, biopsy is an invasive procedure that can lead to a significant number of false-negative results (7) .…”

Section: Introductionmentioning

confidence: 99%

Use of PET/CT to aid clinical decision-making in cases of solitary pulmonary nodule: a probabilistic approach

et al. 2020

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Objective: To determine the frequency with which 18F-FDG-PET/CT findings change the probability of malignancy classification of solitary pulmonary nodules. Materials and Methods: This was a retrospective analysis of all 18F-FDG-PET/CT examinations performed for the investigation of a solitary pulmonary nodule between May 2016 and May 2017. We reviewed medical records and PET/CT images to collect the data necessary to calculate the pre-test probability of malignancy using the Swensen model and the Herder model. The probability of malignancy was classified as low if < 5%, intermediate if 5-65%, and high if > 65%. Cases classified as intermediate in the Swensen model were reclassified by the Herder model. Results: We reviewed the records for 33 patients, of whom 17 (51.5%) were male. The mean age was 68.63 ± 12.20 years. According to the Swensen model, the probability of malignancy was intermediate in 23 cases (69.7%). Among those, the application of the Herder model resulted in the probability of malignancy being reclassified as low in 6 (26.1%) and as high in 8 (34.8%). Conclusion: 18F-FDG-PET/CT was able to modify the probability of malignancy classification of a solitary pulmonary nodule in more than 50% of the cases evaluated.

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“…An SPN which might represent the early stages of lung cancer is a well marginated rounded abnormality in the lung with a diameter less than 3 cm. Early detection of SPNs at an appropriate time contributes to the enhancement of the survival rate [2].…”

Section: Introductionmentioning

confidence: 99%

Novel Techniques for Classification of Lung Nodules using Deep Learning Approach

Bhavanishankar¹,

Sudhamani²

2019

TOBEJ

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Objective: Lung cancer is proving to be one of the deadliest diseases that is haunting mankind in recent years. Timely detection of the lung nodules would surely enhance the survival rate. This paper focusses on the classification of candidate lung nodules into nodules/non-nodules in a CT scan of the patient. A deep learning approach –autoencoder is used for the classification. Investigation/Methodology: Candidate lung nodule patches obtained as the results of the lung segmentation are considered as input to the autoencoder model. The ground truth data from the LIDC repository is prepared and is submitted to the autoencoder training module. After a series of experiments, it is decided to use 4-stacked autoencoder. The model is trained for over 600 LIDC cases and the trained module is tested for remaining data sets. Results: The results of the classification are evaluated with respect to performance measures such as sensitivity, specificity, and accuracy. The results obtained are also compared with other related works and the proposed approach was found to be better by 6.2% with respect to accuracy. Conclusion: In this paper, a deep learning approach –autoencoder has been used for the classification of candidate lung nodules into nodules/non-nodules. The performance of the proposed approach was evaluated with respect to sensitivity, specificity, and accuracy and the obtained values are 82.6%, 91.3%, and 87.0%, respectively. This result is then compared with existing related works and an improvement of 6.2% with respect to accuracy has been observed.

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Solitary pulmonary nodule and 18F-FDG PET/CT. Part 1: epidemiology, morphological evaluation and cancer probability

Cited by 25 publications

References 31 publications

Diagnostic performance of fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a meta-analysis

Diagnostic performance of fluorine-18 fluorodeoxyglucose positron emission tomography in the management of solitary pulmonary nodule: a meta-analysis

Use of PET/CT to aid clinical decision-making in cases of solitary pulmonary nodule: a probabilistic approach

Novel Techniques for Classification of Lung Nodules using Deep Learning Approach

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