Evidence map›Paper›PMID 39054441›Full record

ArticleBMC medical imaging2024

Biparametric MRI of the prostate radiomics model for prediction of pelvic lymph node metastasis in prostate cancers : a two-centre study.

Chunxing Li, Jisu Hu, Zhiyuan Zhang, Chaogang Wei, Tong Chen, Ximing Wang, Yakang Dai, Junkang Shen

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

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4 · The record

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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

8 authors.

Chunxing Li *Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Jisu Hu *Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Zhiyuan ZhangSchool of Medical Imaging, Biomedical Engineering, Xuzhou Medical University, Xuzhou, China.
Chaogang WeiDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Tong ChenDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Ximing WangDepartment of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Yakang DaiSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.
Junkang ShenDepartment of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China. shenjunkang@suda.edu.cn.

Funding

Key Research and Development Program of Suzhou SKY2022003Suzhou Science and Technology Bureau Development Plan SYS2020147
6 · The paper itself

Abstract

objectivesExploring the value of adding correlation analysis (radiomic features (RFs) of pelvic metastatic lymph nodes and primary lesions) to screen RFs of primary lesions in the feature selection process of establishing prediction model.

methodsA total of 394 prostate cancer (PCa) patients (263 in the training group, 74 in the internal validation group and 57 in the external validation group) from two tertiary hospitals were included in the study. The cases with pelvic lymph node metastasis (PLNM) positive in the training group were diagnosed by biopsy or MRI with a short-axis diameter ≥ 1.5 cm, PLNM-negative cases in the training group and all cases in validation group were underwent both radical prostatectomy (RP) and extended pelvic lymph node dissection (ePLND). The RFs of PLNM-negative lesion and PLNM-positive tissues including primary lesions and their metastatic lymph nodes (MLNs) in the training group were extracted from T2WI and apparent diffusion coefficient (ADC) map to build the following two models by fivefold cross-validation: the lesion model, established according to the primary lesion RFs selected by t tests and absolute shrinkage and selection operator (LASSO); the lesion-correlation model, established according to the primary lesion RFs selected by Pearson correlation analysis (RFs of primary lesions and their MLNs, correlation coefficient > 0.9), t test and LASSO. Finally, we compared the performance of these two models in predicting PLNM.

resultsThe AUC and the DeLong test of AUC in the lesion model and lesion-correlation model were as follows: training groups (0.8053, 0.8466, p = 0.0002), internal validation group (0.7321, 0.8268, p = 0.0429), and external validation group (0.6445, 0.7874, p = 0.0431), respectively.

conclusionThe lesion-correlation model established by features of primary tumors correlated with MLNs has more advantages than the lesion model in predicting PLNM.

Indexed as

Lymphatic MetastasisPelvisProstatic NeoplasmsAgedHumansLymph Node ExcisionLymph NodesMagnetic Resonance ImagingMaleMiddle AgedMultiparametric Magnetic Resonance ImagingProstatectomyRadiomicsRetrospective StudiesMagnetic resonance imagingPelvic metastatic lymph nodesProstate cancerRadiomics

Identifiers

PMID39054441
PMCPMC11271060

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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.