Evidence map›Paper›PMID 40377810›Full record

ArticleBreast cancer research and treatment2025

Machine learning prediction of pathological complete response to neoadjuvant chemotherapy with peritumoral breast tumor ultrasound radiomics: compare with intratumoral radiomics and clinicopathologic predictors.

Jiejie Yao, Wei Zhou, Xiaohong Jia, Ying Zhu, Xiaosong Chen, Weiwei Zhan, Jianqiao Zhou

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

Article in Breast cancer research and treatment, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jiejie Yao *Department of Ultrasound, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, 2 Nd Ruijin Road 197, Shanghai, 200025, China.
Wei Zhou *Department of Ultrasound, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, 2 Nd Ruijin Road 197, Shanghai, 200025, China.
Xiaohong JiaDepartment of Ultrasound, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, 2 Nd Ruijin Road 197, Shanghai, 200025, China.
Ying ZhuDepartment of Ultrasound, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, 2 Nd Ruijin Road 197, Shanghai, 200025, China.
Xiaosong ChenDepartment of Comprehensive Breast Health Center, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, 200025, China.
Weiwei ZhanDepartment of Ultrasound, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, 2 Nd Ruijin Road 197, Shanghai, 200025, China.
Jianqiao ZhouDepartment of Ultrasound, Ruijin Hospital, Shanghai JiaoTong University School of Medicine, 2 Nd Ruijin Road 197, Shanghai, 200025, China. zhousu30@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeNoninvasive, accurate and novel approaches to predict patients who will achieve pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) could assist treatment strategies. The aim of this study was to explore the application of machine learning (ML) based peritumoral ultrasound radiomics signature (PURS), compared with intratumoral radiomics (IURS) and clinicopathologic factors, for early prediction of pCR.

methodsWe analyzed 358 locally advanced breast cancer patients (250 in the training set and 108 in the test set), who accepted NAC and post NAC surgery at our institution. The clinical and pathological data were analyzed using the independent t test and the Chi-square test to determine the factors associated with pCR. The PURS and IURS of baseline breast tumors were extracted by using 3D-slicer and PyRadiomics software. Five ML classifiers including linear discriminant analysis (LDA), support vector machine (SVM), random forest (RF), logistic regression (LR), and adaptive boosting (AdaBoost) were applied to construct radiomics predictive models. The performance of PURS, IURS models and clinicopathologic predictors were assessed with respect to sensitivity, specificity, accuracy and the areas under the curve (AUCs).

resultsNinety-seven patients achieved pCR. The clinicopathologic predictors obtained an AUC of 0.759. Among PURS models, the RF classifier achieved better efficacy (AUC of 0.889) than LR (0.849), AdaBoost (0.823), SVM (0.746) and LDA (0.732). The RF classifier also obtained a maximum AUC of 0.931 than 0.920 (AdaBoost), 0.875 (LR), 0.825 (SVM), and 0.798 (LDA) in IURS models in the test set. The RF based PURS yielded higher predictive ability (AUC 0.889; 95% CI 0.814, 0.947) than clinicopathologic factors (AUC 0.759; 95% CI 0.657, 0.861; p < 0.05), but lower efficacy compared with IURS (AUC 0.931; 95% CI 0.865, 0.980; p < 0.05).

conclusionThe peritumoral US radiomics, as a novel potential biomarker, can assist clinical therapy decisions.

Indexed as

Breast NeoplasmsMachine LearningUltrasonography, MammaryAdultAgedAntineoplastic Combined Chemotherapy ProtocolsChemotherapy, AdjuvantFemaleHumansMiddle AgedNeoadjuvant TherapyPrognosisRadiomicsROC CurveTreatment OutcomeMachine learningNeoadjuvant chemotherapyPathological complete responsePeritumoral and intratumoral ultrasound radiomics

Identifiers

PMID40377810
PMCPMC12133900

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