Article in Radiology. Imaging cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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.
Xue-Yan Wang *Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.ORCID 0000-0002-3031-3548
Jia-Xin Huang *Department of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.ORCID 0000-0002-8038-0175
Feng-Tao LiuBreast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.ORCID 0000-0003-3336-7207
Hui-Ning HuangDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.ORCID 0009-0000-9751-7214
Jing-Si MeiBreast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Gui-Ling HuangDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.
Yu-Ting ZhangDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.
Mei-Qin XiaoDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.ORCID 0009-0004-4025-2832
Yan-Fen XuDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.
Ming-Jie WeiDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.ORCID 0000-0002-8111-1815
Xiao-Qing PeiDepartment of Medical Ultrasound, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, No. 651 Dongfeng Road East, Guangzhou 510060, China.ORCID 0000-0002-6832-1228
Funding
No grant is acknowledged in the PubMed record.
6 · The paper itself
Abstract
Purpose To determine whether bimodal US improves prediction of pathologic complete response following neoadjuvant chemotherapy (NAC) compared with MRI, as well as to assess the diagnostic value of a combined imaging model. Materials and Methods In this prospective two-center study, participants with primary breast cancer undergoing NAC between January 2020 and January 2024 were enrolled. Preoperative bimodal US (grayscale and shear wave) and MRI data were collected. Complete response on US (uCR) and MR images (mCR) were defined by radiologists. Diagnostic models based on uCR, mCR, and their combination were evaluated using postsurgical pathology as the reference standard. Pathologic complete response was defined as no residual tumor (ypT0) or no invasive cancer with possible ductal carcinoma in situ (ypT0/Tis). Model performance was evaluated by area under the receiver operating characteristic curve (AUC), with sensitivity, specificity, positive and negative likelihood ratios, and comparisons by the DeLong test. All tests were two-sided (
Indexed as
Breast NeoplasmsMagnetic Resonance ImagingNeoadjuvant TherapyUltrasonography, MammaryAdultBreastChemotherapy, AdjuvantFemaleHumansMiddle AgedPathologic Complete ResponsePredictive Value of TestsProspective StudiesSensitivity and SpecificityTreatment OutcomeBreastUltrasound
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.
Predicting Breast Cancer Pathologic Complete Response after Neoadjuvant Chemotherapy Using Bimodal US and MRI. · full record | OpenQuestion