Evidence map›Paper›PMID 32190563›Full record

ArticleQuantitative imaging in medicine and surgery2020

Radiomics prediction model for the improved diagnosis of clinically significant prostate cancer on biparametric MRI.

Mengjuan Li, Tong Chen, Wenlu Zhao, Chaogang Wei, Xiaobo Li, Shaofeng Duan, Libiao Ji, Zhihua Lu, Junkang Shen

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

Article in Quantitative imaging in medicine and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed, 3 pooled it
5.4field-weighted citation impact, top 3% 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

51 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 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

9 authors at 2 institutions in 1 country.

Mengjuan LiDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Tong ChenDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Wenlu ZhaoDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Chaogang WeiDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Xiaobo LiGE Healthcare Life Science, Shanghai 200000, China.
Shaofeng DuanGE Healthcare Life Science, Shanghai 200000, China.
Libiao JiDepartment of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China.
Zhihua LuDepartment of Radiology, The Affiliated Changshu Hospital of Soochow University, Suzhou 215501, China.
Junkang ShenDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou 215000, China.
Soochow University · CNSecond Affiliated Hospital of Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo evaluate the potential of clinical-based model, a biparametric MRI-based radiomics model and a clinical-radiomics combined model for predicting clinically significant prostate cancer (PCa).

methodsIn total, 381 patients with clinically suspicious PCa were included in this retrospective study; of those, 199 patients did not have PCa upon biopsy, while 182 patients had PCa. All patients underwent 3.0-T MRI examinations with the same acquisition parameters, and clinical risk factors associated with PCa (age, prostate volume, serum PSA, etc.) were collected. We randomly stratified the training and test sets using a 6:4 ratio. The radiomic features included gradient-based histogram features, grey-level co-occurrence matrix (GLCM), run-length matrix (RLM), and grey-level size zone matrix (GLSZM). Three models were developed using multivariate logistic regression analysis to predict clinically significant PCa: a clinical model, a radiomics model and a clinical-radiomics combined model. The diagnostic performance and clinical net benefit of each model were compared via receiver operating characteristic (ROC) curve analysis and decision curves, respectively.

resultsBoth the radiomics model (AUC: 0.98) and the clinical-radiomics combined model (AUC: 0.98) achieved greater predictive efficacy than the clinical model (AUC: 0.79). The decision curve analysis also showed that the radiomics model and combined model had higher net benefits than the clinical model.

conclusionsCompared with the evaluation of clinical risk factors associated with PCa only, the radiomics-based machine learning model can improve the predictive accuracy for clinically significant PCa, in terms of both diagnostic performance and clinical net benefit.

Indexed as

classificationclinical risk factorsmachine learningProstate cancerradiomics

Identifiers

PMID32190563
PMCPMC7063275
OpenAlexW3007206189

What OpenQuestion holds

Textmetadata
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.