Evidence map›Paper›PMID 39966783›Full record

ArticleBMC cancer2025

Integrating ultrasound radiomics and clinicopathological features for machine learning-based survival prediction in patients with nonmetastatic triple-negative breast cancer.

Wenwen, Zekun Jiang, Jingyan Liu, Dingbang Liu, Yiyue Li, Yushuang He, Haina Zhao, Lin Ma, Yixin Zhu, Qiongxian Long and 6 more

Abstract readEvaluation StudyMulticenter StudyValidation Study
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

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

  1. Pooled it
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  3. Review
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  9. Immunohistochemical predictors of local recurrence in breast carcinoma: development and sensitivity validation of an IHC-based risk score.Romanian journal of morphology and embryology = Revue roumaine de morphologie et embryologie
    Article
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

16 authors.

Wenwen *Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Zekun Jiang *Department of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Jingyan LiuDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Dingbang LiuWest China School of Medicine, Sichuan University, Chengdu, Sichuan, 610000, China.
Yiyue LiDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Yushuang HeDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Haina ZhaoDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Lin MaDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Yixin ZhuDepartment of Ultrasonography, Peking University Shenzhen Hospital, Shenzhen, 515100, China.
Qiongxian LongDepartment of Pathology, The Affiliated Nanchong Central Hospital of North Sichuan Medical College, Nanchong, Sichuan, 637000, China.
Jun GaoWest China School of Medicine, Sichuan University, Chengdu, Sichuan, 610000, China.
Honghao LuoDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Heng JiangCollege of Medicine, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
Kang LiDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China.
Xiaorong ZhongBreast Disease Center, Cancer Center, West China Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. zhongxiaorong@126.com.
Yulan PengDepartment of Ultrasound and West China Biomedical Big Data Center, West China Hospital, Sichuan University, No. 37 Guoxue Alley, Wuhou District, Chengdu, Sichuan, 610000, China. yulanpeng520@126.com.

Funding

135 project for disciplines of excellence, West China Hospital, Sichuan University ZYYC21004National Key Research and Development Program of China 2020YFA0714002National Key Research and Development Program of China under Grant 2020YFB1711500training grant from the Achievement Conversion and Guidance Project of Chengdu Science and Technology Bureau No.2017-CY02-00027-GX
6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the predictive value of implementing machine learning models based on ultrasound radiomics and clinicopathological features in the survival analysis of triple-negative breast cancer (TNBC) patients. METHODS AND MATERIALS: All patients, including retrospective cohort (training cohort, n = 306; internal validation cohort, n = 77) and prospective external validation cohort (n = 82), were diagnosed as locoregional TNBC and underwent pre-intervention sonographic evaluation in this multi-center study. A thorough chart review was conducted for each patient to collect clinicopathological and sonographic features, and ultrasound radiomics features were obtained by PyRadiomics. Deep learning algorithms were utilized to delineate ROIs on ultrasound images. Radiomics analysis pipeline modules were developed for analyzing features. Radiomic scores, clinical scores, and combined nomograms were analyzed to predict 2-year, 3-year, and 5-year overall survival (OS) and disease-free survival (DFS). Receiver operating characteristic (ROC) curves, calibration curves, and decision curves were used to evaluate the prediction performance.

findingsBoth clinical and radiomic scores showed good performance for overall survival and disease-free survival prediction in internal (median AUC of 0.82 and 0.72 respectively) and external validation (median AUC of 0.70 and 0.74 respectively). The combined nomograms had AUCs of 0.80-0.93 and 0.73-0.89 in the internal and external validation, which had best predictive performance in all tasks (p < 0.05), especially for 5-year OS (p < 0.01). For the overall evaluation of six tasks, combined models obtained better performance than clinical and radiomic scores [AUCs of 0.83 (0.73,0.93), 0.81 (0.72,0.93), and 0.70 (0.61,0.85) respectively].

interpretationThe combined nomograms based on pre-intervention ultrasound radiomics and clinicopathological features demonstrated exemplary performance in survival analysis. The new models may allow us to non-invasively classify TNBC patients with various disease outcome.

Indexed as

Machine LearningTriple Negative Breast NeoplasmsUltrasonography, MammaryAdultAgedFemaleHumansMiddle AgedNomogramsPrognosisProspective StudiesRadiomicsRetrospective StudiesROC CurveMachine learningPrognosisRadiomicsTriple negative breast cancerUltrasound

Identifiers

PMID39966783
PMCPMC11837701

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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.