Evidence map›Paper›PMID 37186214›Full record

ArticleEuropean radiology2023

Construction and evaluation of a gated high-resolution neural network for automatic brain metastasis detection and segmentation.

Jiao Qu, Wenjing Zhang, Xin Shu, Ying Wang, Lituan Wang, Mengyuan Xu, Li Yao, Na Hu, Biqiu Tang, Lei Zhang and 1 more

Abstract read
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In one paragraph

Article in European radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.6field-weighted citation impact, top 16% of its field
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

5 citing papers in PubMed, 6 citations in OpenAlex.

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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

11 authors at 1 institution in 1 country.

Jiao QuDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
Wenjing ZhangDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
Xin ShuMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
Ying WangDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
Lituan WangMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
Mengyuan XuDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
Li YaoDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
Na HuDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
Biqiu TangDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China.
Lei ZhangMachine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, China.
Su LuiDepartment of Radiology, West China Hospital, Sichuan University, No. 37 Guoxue Xiang, Chengdu, 610041, China. lusuwcums@tom.com.ORCID http://orcid.org/0000-0003-3541-1769
Sichuan University · CN

Funding

National Natural Science Foundation of China Grant Nos. 82071908National Natural Science Foundation of China Grant Nos.82101998National Natural Science Foundation of China Grant Nos. 82120108014
6 · The paper itself

Abstract

objectivesTo construct and evaluate a gated high-resolution convolutional neural network for detecting and segmenting brain metastasis (BM).

methodsThis retrospective study included craniocerebral MRI scans of 1392 patients with 14,542 BMs and 200 patients with no BM between January 2012 and April 2022. A primary dataset including 1000 cases with 11,686 BMs was employed to construct the model, while an independent dataset including 100 cases with 1069 BMs from other hospitals was used to examine the generalizability. The potential of the model for clinical use was also evaluated by comparing its performance in BM detection and segmentation to that of radiologists, and comparing radiologists' lesion detecting performances with and without model assistance.

resultsOur model yielded a recall of 0.88, a dice similarity coefficient (DSC) of 0.90, a positive predictive value (PPV) of 0.93 and a false positives per patient (FP) of 1.01 in the test set, and a recall of 0.85, a DSC of 0.89, a PPV of 0.93, and a FP of 1.07 in dataset from other hospitals. With the model's assistance, the BM detection rates of 4 radiologists improved significantly, ranging from 5.2 to 15.1% (all p < 0.001), and also for detecting small BMs with diameter ≤ 5 mm (ranging from 7.2 to 27.0%, all p < 0.001).

conclusionsThe proposed model enables accurate BM detection and segmentation with higher sensitivity and less time consumption, showing the potential to augment radiologists' performance in detecting BM. CLINICAL RELEVANCE STATEMENT: This study offers a promising computer-aided tool to assist the brain metastasis detection and segmentation in routine clinical practice for cancer patients. KEY POINTS: • The GHR-CNN could accurately detect and segment BM on contrast-enhanced 3D-T1W images. • The GHR-CNN improved the BM detection rate of radiologists, including the detection of small lesions. • The GHR-CNN enabled automated segmentation of BM in a very short time.

Indexed as

Brain NeoplasmsNeural Networks, ComputerHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMagnetic Resonance ImagingRetrospective StudiesBrain neoplasmsDeep learningMachine learningMagnetic resonance imagingNeural networks, Computer

Identifiers

PMID37186214
OpenAlexW4366983270

What OpenQuestion holds

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Registered trials

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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.