Evidence map›Paper›PMID 34993146›Full record

ArticleFrontiers in oncology2021

Convolutional Neural Network-Based Diagnostic Model for a Solid, Indeterminate Solitary Pulmonary Nodule or Mass on Computed Tomography.

Ke Sun, Shouyu Chen, Jiabi Zhao, Bin Wang, Yang Yang, Yin Wang, Chunyan Wu, Xiwen Sun

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Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Ke SunDepartment of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
Shouyu ChenDepartment of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China.
Jiabi ZhaoDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Bin WangDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Yang YangDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Yin WangDepartment of Computer Science and Technology, College of Electronics and Information Engineering, Tongji University, Shanghai, China.
Chunyan WuDepartment of Pathology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Xiwen SunDepartment of Radiology, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo establish a non-invasive diagnostic model based on convolutional neural networks (CNNs) to distinguish benign from malignant lesions manifesting as a solid, indeterminate solitary pulmonary nodule (SPN) or mass (SPM) on computed tomography (CT).

methodA total of 459 patients with solid indeterminate SPNs/SPMs on CT were ultimately included in this retrospective study and assigned to the train (n=366), validation (n=46), and test (n=47) sets. Histopathologic analysis was available for each patient. An end-to-end CNN model was proposed to predict the natural history of solid indeterminate SPN/SPMs on CT. Receiver operating characteristic curves were plotted to evaluate the predictive performance of the proposed CNN model. The accuracy, sensitivity, and specificity of diagnoses by radiologists alone were compared with those of diagnoses by radiologists by using the CNN model to assess its clinical utility.

resultsFor the CNN model, the AUC was 91% (95% confidence interval [CI]: 0.83-0.99) in the test set. The diagnostic accuracy of radiologists with the CNN model was significantly higher than that without the model (89

conclusionThe CNN model could be a valuable tool in non-invasively differentiating benign from malignant lesions manifesting as solid, indeterminate SPNs/SPMs on CT.

Indexed as

computed tomographydifferential diagnosisindeterminate solitary pulmonary nodulelung adenocarcinomaneural network modelsolid

Identifiers

PMID34993146
PMCPMC8724915

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