Evidence map›Paper›PMID 34481459›Full record

ArticleBMC medical imaging2021

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

Yemei Liu, Pei Yang, Yong Pi, Lisha Jiang, Xiao Zhong, Junjun Cheng, Yongzhao Xiang, Jianan Wei, Lin Li, Zhang Yi and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  12. Artificial intelligence-aided lytic spinal bone metastasis classification on CT scans.International journal of computer assisted radiology and surgery · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yemei Liu *Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China.
Pei Yang *Laboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China.
Yong PiMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China.
Lisha JiangLaboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China.
Xiao ZhongLaboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China.
Junjun ChengLaboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China.
Yongzhao XiangLaboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China.
Jianan WeiMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China.
Lin LiLaboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China.
Zhang YiMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China.
Huawei CaiLaboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China. hw.cai@yahoo.com.
Zhen ZhaoLaboratory of Clinical Nuclear Medicine, Department of Nuclear Medicine, West China Hospital, Sichuan University, No. 37 Guo Xue Alley, Chengdu, 610041, China. zhaozhen1982@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

methodsWe retrospectively collected the

resultsIn the fivefold cross validation, our proposed network reached the best average accuracy (81.23%) in identifying suspicious bone lesions compared with InceptionV3 (80.61%), VGG16 (81.13%) and DenseNet169 (76.71%). Additionally, the CNN model's lesion-based average sensitivity and specificity were 81.30% and 81.14%, respectively. Based on the lesion burden numbers of each image, the area under the receiver operating characteristic curve (AUC) was 0.847 in the few group (lesion number n ≤ 3), 0.838 in the medium group (n = 4-6), and 0.862 in the extensive group (n > 6). For the three major primary tumor types, the CNN-based lesion identifying AUC value was 0.870 for lung cancer, 0.900 for prostate cancer, and 0.899 for breast cancer.

conclusionThe CNN model suggests potential in identifying suspicious benign and malignant bone lesions from whole-body bone scintigraphic images.

Indexed as

Diagnosis, Computer-AssistedNeural Networks, ComputerRadionuclide ImagingBone and BonesBone NeoplasmsFemaleHumansMaleMiddle AgedRetrospective StudiesSensitivity and SpecificityArtificial intelligenceBone metastasisBone scintigraphyConvolutional neural network

Identifiers

PMID34481459
PMCPMC8417997

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.