Dataset Information

[Advances in Diagnosis and Management of Subcentimeter Pulmonary Nodules].

ABSTRACT: With the widespread use of high-resolution multislice spiral computed tomography and the popularization of regular physical examinations, the prevalence of clinically diagnosed subcentimeter pulmonary nodules is increasing. Subcentimeter pulmonary nodules have low malignant probability, however, the diagnosis and management are of high difficulty and it is likely to misdiagnose and miss malignant nodules. Therefore, the evaluation and management of subcentimeter pulmonary nodules have always been the key points of clinical work. This article reviews and summarizes the progress in the evaluation and management of subcentimeter pulmonary nodules.

SUBMITTER: Lv Y

PROVIDER: S-EPMC7260380 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Publications

[Advances in Diagnosis and Management of Subcentimeter Pulmonary Nodules].

Lv Yilv Y Ye Bo B

Zhongguo fei ai za zhi = Chinese journal of lung cancer 20200501 5

With the widespread use of high-resolution multislice spiral computed tomography and the popularization of regular physical examinations, the prevalence of clinically diagnosed subcentimeter pulmonary nodules is increasing. Subcentimeter pulmonary nodules have low malignant probability, however, the diagnosis and management are of high difficulty and it is likely to misdiagnose and miss malignant nodules. Therefore, the evaluation and management of subcentimeter pulmonary nodules have always bee ...[more]

PMID: 32429638

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Project description:This study assessed the diagnostic performance of a deep learning (DL)-based model for differentiating malignant subcentimeter (≤10 mm) solid pulmonary nodules (SSPNs) from benign ones in computed tomography (CT) images compared against radiologists with 10 and 15 years of experience in thoracic imaging (medium-senior seniority). Overall, 200 SSPNs (100 benign and 100 malignant) were retrospectively collected. Malignancy was confirmed by pathology, and benignity was confirmed by follow-up or pathology. CT images were fed into the DL model to obtain the probability of malignancy (range, 0-100%) for each nodule. According to the diagnostic results, enrolled nodules were classified into benign, malignant, or indeterminate. The accuracy and diagnostic composition of the model were compared with those of the radiologists using the McNemar-Bowker test. Enrolled nodules were divided into 3-6-, 6-8-, and 8-10-mm subgroups. For each subgroup, the diagnostic results of the model were compared with those of the radiologists. The accuracy of the DL model, in differentiating malignant and benign SSPNs, was significantly higher than that of the radiologists (71.5% vs. 38.5%, P<0.001). The DL model reported more benign or malignant deterministic results and fewer indeterminate results. In subgroup analysis of nodule size, the DL model also yielded higher performance in comparison with that of the radiologists, providing fewer indeterminate results. The accuracy of the two methods in the 3-6-, 6-8-, and 8-10-mm subgroups was 75.5% vs. 28.3% (P<0.001), 62.0% vs. 28.2% (P<0.001), and 77.6% vs. 55.3% (P=0.001), respectively, and the indeterminate results were 3.8% vs. 66.0%, 8.5% vs. 66.2%, and 2.6% vs. 35.5% (all P<0.001), respectively. The DL-based method yielded higher performance in comparison with that of the radiologists in differentiating malignant and benign SSPNs. This DL model may reduce uncertainty in diagnosis and improve diagnostic accuracy, especially for SSPNs smaller than 8 mm.

Dataset Information

[Advances in Diagnosis and Management of Subcentimeter Pulmonary Nodules].

Publications

[Advances in Diagnosis and Management of Subcentimeter Pulmonary Nodules].

Similar Datasets

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