Evidence map›Paper›PMID 31982990›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2020

Panli Li, Xiuying Wang, Chongrui Xu, Cheng Liu, Chaojie Zheng, Michael J Fulham, Dagan Feng, Lisheng Wang, Shaoli Song, Gang Huang

Abstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 4 pooled it
6.6field-weighted citation impact, top 2% 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

63 citing papers in PubMed, 4 syntheses or guidelines pooled it, 102 citations in OpenAlex.

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  15. Relationship Between [European journal of breast health · 2025
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  17. Resistance to neoadjuvant chemotherapy in breast cancers: a metabolic perspective.Journal of experimental & clinical cancer research : CR · 2025
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3 more citing papers are in PubMed but not listed here.

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

10 authors at 5 institutions in 2 countries.

Panli LiShanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China.
Xiuying WangSJTU-USYD Joint Research Alliance for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Chongrui XuBiomedical and Multimedia Information Technology Research Group, School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.
Cheng LiuDepartment of Nuclear Medicine, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Chaojie ZhengBiomedical and Multimedia Information Technology Research Group, School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.
Michael J FulhamSJTU-USYD Joint Research Alliance for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Dagan FengSJTU-USYD Joint Research Alliance for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Lisheng WangSJTU-USYD Joint Research Alliance for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China.
Shaoli SongShanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China. shaoli-song@163.com.
Gang HuangShanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China. huangg@sumhs.edu.cn.
Shanghai Jiao Tong University · CNUniversity of Sydney · AUFudan University Shanghai Cancer Center · CNRoyal Prince Alfred Hospital · AUShanghai University of Medicine and Health Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposePathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) is commonly accepted as the gold standard to assess outcome after NAC in breast cancer patients.

methodsThis retrospective study included 100 breast cancer patients who received NAC; there were 2210 PET/CT radiomic features extracted. Unsupervised and supervised machine learning models were used to identify the prognostic radiomic predictors through the following: (1) selection of the significant (p < 0.05) imaging features from consensus clustering and the Wilcoxon signed-rank test; (2) selection of the most discriminative features via univariate random forest (Uni-RF) and the Pearson correlation matrix (PCM); and (3) determination of the most predictive features from a traversal feature selection (TFS) based on a multivariate random forest (RF). The prediction model was constructed with RF and then validated with 10-fold cross-validation for 30 times and then independently validated. The performance of the radiomic predictors was measured in terms of area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

resultsThe PET/CT radiomic predictors achieved a prediction accuracy of 0.857 (AUC = 0.844) on the training split set and 0.767 (AUC = 0.722) on the independent validation set. When age was incorporated, the accuracy for the split set increased to 0.857 (AUC = 0.958) and 0.8 (AUC = 0.73) for the independent validation set and both outperformed the clinical prediction model. We also found a close association between the radiomic features, receptor expression, and tumor T stage.

conclusionRadiomic predictors from pre-treatment PET/CT scans when combined with patient age were able to predict pCR after NAC. We suggest that these data will be valuable for patient management.

Indexed as

Breast NeoplasmsFluorodeoxyglucose F18HumansModels, StatisticalNeoadjuvant TherapyPositron Emission Tomography Computed TomographyPrognosisRadiopharmaceuticalsRetrospective StudiesFluorodeoxyglucose F18RadiopharmaceuticalsBreast cancerNACpCRPET/CTRadiomics

Identifiers

PMID31982990
OpenAlexW3002866765

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.