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References from Intratumoral and Peritumoral Ultrasoundomics Models for Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer. Local targets link to admitted publications; unresolved targets remain external evidence.
Prognostic value of depression and anxiety on breast cancer recurrence and mortality: a systematic review and meta-analysis of 282,203 patients
10.1038/s41380-020-00865-6 · 2020 · External reference
Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries
2024 · External reference
Cancer statistics, 2026
2026 · External reference
Cancer statistics in China and United States, 2022: profiles, trends, and determinants
10.1097/cm9.0000000000002108 · 2022 · External reference
Clinical diagnosis and management of breast cancer
10.2967/jnumed.115.157834 · 2016 · External reference
Neoadjuvant therapy in hormone receptor-positive/HER2-negative breast cancer
10.1016/j.ctrv.2023.102669 · 2024 · External reference
Circulating tumor DNA in neoadjuvant-treated breast cancer reflects response and survival
10.1016/j.annonc.2020.11.007 · 2021 · External reference
Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis
10.1016/s0140-6736(13)62422-8 · 2014 · External reference
The role of ultrasound in breast cancer screening: the case for and against ultrasound
10.1053/j.sult.2017.09.006 · 2018 · External reference
Breast ultrasonography: state of the art
10.1148/radiol.13121606 · 2013 · External reference
Combination of color Doppler ultrasound and CT for diagnosing breast cancer
2021 · External reference
Multiparametric ultrasound examination for response assessment in breast cancer patients undergoing neoadjuvant therapy
10.1038/s41598-021-82141-3 · 2021 · External reference
Radiomics: extracting more information from medical images using advanced feature analysis
10.1016/j.ejca.2011.11.036 · 2012 · External reference
Radiomics: images are more than pictures, they are data
10.1148/radiol.2015151169 · 2016 · External reference
Integrated conventional ultrasound and radiomics model for predicting major pathological response to neoadjuvant therapy in triple-negative breast cancer
10.1186/s12880-025-02017-0 · 2025 · External reference
Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer
10.1016/j.ejca.2021.01.028 · 2021 · External reference
Prediction of breast cancer histological outcome by radiomics and artificial intelligence analysis in contrast-enhanced mammography
10.3390/cancers14092132 · 2022 · External reference
Preoperative prediction of axillary lymph node metastasis in breast cancer based on intratumoral and peritumoral DCE-MRI radiomics nomogram
10.1155/2022/6729473 · 2022 · External reference
Machine learning advances in microbiology: a review of methods and applications
2022 · External reference
Session introduction: artificial intelligence in clinical medicine: generative and interactive systems at the human-machine interface
2024 · External reference
Global scientific research landscape on medical informatics from 2011 to 2020: bibliometric analysis
10.2196/33842 · 2022 · External reference
Artificial intelligence in cancer diagnosis: opportunities and challenges
10.1016/j.prp.2023.154996 · 2024 · External reference
Predicting axillary response to neoadjuvant chemotherapy using peritumoral and intratumoral ultrasound radiomics in breast cancer subtypes
10.1016/j.isci.2024.110716 · 2024 · External reference
Explainable artificial intelligence in breast cancer detection and risk prediction: a systematic scoping review
10.1002/cai2.136 · 2024 · External reference
Tumor glucose and fatty acid metabolism in the context of anthracycline and taxane-based (neo)adjuvant chemotherapy in breast carcinomas
10.3389/fonc.2022.850401 · 2022 · External reference
Construction and interpretation of prediction model of teicoplanin trough concentration via machine learning
2022 · External reference
Profiling breast tumor heterogeneity and identifying breast cancer subtypes through tumor-associated immune cell signatures and immuno nano sensors
10.1002/smll.202406475 · 2024 · External reference
Tumor biology correlates with rates of breast-conserving surgery and pathologic complete response after neoadjuvant chemotherapy for breast cancer: findings from the ACOSOG Z1071 (Alliance) Prospective Multicenter Clinical Trial
10.1097/sla.0000000000000924 · 2014 · External reference
Breast Cancer, Version 3.2024, NCCN Clinical Practice Guidelines in Oncology
10.6004/jnccn.2024.0035 · 2024 · External reference
Tumor heterogeneity: preclinical models, emerging technologies, and future applications
10.3389/fonc.2023.1164535 · 2023 · External reference
Reproducibility in radiomics: a comparison of feature extraction methods and two independent datasets
2024 · External reference
Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions
10.1007/s00330-021-08134-y · 2022 · External reference
Intratumoral and peritumoral radiomics for preoperative prediction of neoadjuvant chemotherapy effect in breast cancer based on contrast-enhanced spectral mammography
10.1007/s00330-021-08414-7 · 2022 · External reference
Ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer
2024 · External reference
Interpretation of radiomics features-a pictorial review
10.1016/j.cmpb.2021.106609 · 2022 · External reference
Radiomics predicts the prognosis of patients with locally advanced breast cancer by reflecting the heterogeneity of tumor cells and the tumor microenvironment
10.1186/s13058-022-01516-0 · 2022 · External reference
Application of wavelet techniques for cancer diagnosis using ultrasound images: a review
10.1016/j.compbiomed.2015.12.006 · 2016 · External reference
Potential of the non-contrast-enhanced chest CT radiomics to distinguish molecular subtypes of breast cancer: a retrospective study
2022 · External reference
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
10.1007/s10822-020-00314-0 · 2020 · External reference