Evidence map›Paper›PMID 38135946›Full record

ArticleBioengineering (Basel, Switzerland)2023

Radiomics Prediction of Muscle Invasion in Bladder Cancer Using Semi-Automatic Lesion Segmentation of MRI Compared with Manual Segmentation.

Yaojiang Ye, Zixin Luo, Zhengxuan Qiu, Kangyang Cao, Bingsheng Huang, Lei Deng, Weijing Zhang, Guoqing Liu, Yujian Zou, Jian Zhang and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 2 pooled it
2.9field-weighted citation impact, top 9% 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

11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 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 5 institutions in 1 country.

Yaojiang YeDepartment of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan 523059, China.
Zixin LuoMedical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Zhengxuan QiuMedical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Kangyang CaoMedical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Bingsheng HuangMedical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Lei DengDepartment of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan 523059, China.
Weijing ZhangImaging Department, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China.
Guoqing LiuCollege of Physics and Optoelectronic Engineering, Shenzhen University, Shenzhen 518060, China.
Yujian ZouDepartment of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan 523059, China.
Jian ZhangShenzhen University Medical School, Shenzhen University, Shenzhen 518060, China.
Jianpeng LiDepartment of Radiology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan People's Hospital), Dongguan 523059, China.
Shenzhen University · CNDongguan People’s Hospital · CNSouthern Medical University · CNHKUST Shenzhen Research Institute · CNState Key Laboratory of Oncology in South China

Funding

Dongguan Science and Technology of Social Development Program 20211800905212Guangdong Basic and Applied Basic Research Foundation 2020A1515010571Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions 2022SHIBS0003Shenzhen Science and Technology Innovation Commission Key Technical Project JSGG20210713091539014
6 · The paper itself

Abstract

Conventional radiomics analysis requires the manual segmentation of lesions, which is time-consuming and subjective. This study aimed to assess the feasibility of predicting muscle invasion in bladder cancer (BCa) with radiomics using a semi-automatic lesion segmentation method on T2-weighted images. Cases of non-muscle-invasive BCa (NMIBC) and muscle-invasive BCa (MIBC) were pathologically identified in a training cohort and in internal and external validation cohorts. For bladder tumor segmentation, a deep learning-based semi-automatic model was constructed, while manual segmentation was performed by a radiologist. Semi-automatic and manual segmentation results were respectively used in radiomics analyses to distinguish NMIBC from MIBC. An equivalence test was used to compare the models' performance. The mean Dice similarity coefficients of the semi-automatic segmentation method were 0.836 and 0.801 in the internal and external validation cohorts, respectively. The area under the receiver operating characteristic curve (AUC) were 1.00 (0.991) and 0.892 (0.894) for the semi-automated model (manual) on the internal and external validation cohort, respectively (both

Indexed as

magnetic resonance imagingmusclesradiomicssemi-automatic segmentationurinary bladder neoplasms

Identifiers

PMID38135946
PMCPMC10740947
OpenAlexW4389049482

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.