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Contrast-Enhanced Mammography Radiomics Analysis for Preoperative Prediction of Breast Cancer Molecular Subtypes

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Machine learning model for predicting tertiary lymphoid structures and treatment response in triple-negative breast cancer

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Predicting axillary response to neoadjuvant chemotherapy using peritumoral and intratumoral ultrasound radiomics in breast cancer subtypes

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Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review

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Intratumoral and peritumoral CT-based radiomics for predicting the microsatellite instability in gastric cancer

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Systematic radiomics analysis based on multiparameter MRI to preoperatively predict the expression of Ki67 and histological grade in patients with bladder cancer

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Development of an interpretable machine learning model for Ki-67 prediction in breast cancer using intratumoral and peritumoral ultrasound radiomics features

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Development of an Intratumoral and Peritumoral Radiomics Nomogram Using Digital Breast Tomosynthesis for Preoperative Assessment of Lymphovascular Invasion in Invasive Breast Cancer

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Comparison of Intratumoral and Peritumoral Deep Learning, Radiomics, and Fusion Models for Predicting KRAS Gene Mutations in Rectal Cancer Based on Endorectal Ultrasound Imaging

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Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations

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Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer

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Evaluation of parametrial infiltration in patients with IB‐IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18F‐fluorodeoxy glucose positron emission tomography/MR images

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Enhancing Early Breast Cancer Diagnosis With Contrast-Enhanced Ultrasound Radiomics: Insights From Intratumoral and Peritumoral Analysis

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Gd-EOB-DTPA-enhanced MRI radiomics and deep learning models to predict microvascular invasion in hepatocellular carcinoma: a multicenter study

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The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer

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AI-Based Characterization of Breast Cancer in Mammography and Tomosynthesis: A Review of Radiomics and Deep Learning for Subtyping, Staging, and Prognosis

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Construction and validation of a personalized nomogram of ultrasound for pretreatment prediction of breast cancer patients sensitive to neoadjuvant chemotherapy

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ABVS-Based Radiomics for Early Predicting the Efficacy of Neoadjuvant Chemotherapy in Patients with Breast Cancers

Jiang, W., Deng, X., Zhu, T., Fang, J., & Li, J. (2023) · Breast Cancer Targets and Therapy · doi:10.2147/bctt.s418376 · Cited by 8 · see citing works

Intra- and peritumoral radiomics nomogram based on DCE-MRI for the early prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

Zhu, Y., Zhang, S., Wei, W., Li, Y., Wang, L., Wang, Y., Fan, Y., Sun, H., & Xie, Z. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1561599 · Cited by 8 · see citing works

Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

Zhang, K., Lin, J., Lin, F., Wang, Z., Zhang, H., Zhang, S., Mao, N., & Qiao, G. (2023) · Journal of X-Ray Science and Technology · doi:10.3233/xst-221349 · Cited by 8 · see citing works

A self-interpretable deep learning network for early prediction of pathologic complete response to neoadjuvant chemotherapy based on breast pre-treatment dynamic contrast-enhanced magnetic resonance imaging

Gao, Y., Ding, D., & Zeng, H. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109431 · Cited by 9 · see citing works

Prediction of HER-2 expression status in breast cancer based on multi-parameter MRI intratumoral and peritumoral radiomics

Cao, M., Liu, X., Yang, A., Xu, Y. H., Zhang, Q., & Cao, Y. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110434 · Cited by 7 · see citing works

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non–Small-Cell Lung Cancer: A Retrospective Study

Jiang, Z., Li, Q., Ruan, J., Li, Y., Zhang, D., Xu, Y., Liao, Y., Zhang, X., Gao, D., & Li, Z. (2024) · Clinical Lung Cancer · doi:10.1016/j.cllc.2024.04.006 · Cited by 7 · see citing works

Comparing Random Survival Forests and Cox Regression for Nonresponders to Neoadjuvant Chemotherapy Among Patients With Breast Cancer: Multicenter Retrospective Cohort Study

Jin, Y., Zhao, M., Su, T., Fan, Y., Ouyang, Z., & Lv, F. (2025) · Journal of Medical Internet Research · doi:10.2196/69864 · Cited by 6 · see citing works

Clinical study on the prediction of ALN metastasis based on intratumoral and peritumoral DCE-MRI radiomics and clinico-radiological characteristics in breast cancer

Wang, Y., Shang, Y., Guo, Y., Hai, M., Gao, Y., Wu, Q., Li, S., Liao, J., Sun, X., Wu, Y., et al. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1357145 · Cited by 6 · see citing works

Intratumoral and peritumoral habitat imaging based on multiparametric MRI to predict cervical stromal invasion in early-stage endometrial carcinoma

Wang, X., Deng, C. L., Kong, R., Gong, Z., Dai, H., Song, Y., Wu, Y., Bi, G., Ai, C., & Bi, Q. (2024) · Academic Radiology · doi:10.1016/j.acra.2024.09.039 · Cited by 5 · see citing works

Evaluating peritumoral and intratumoral radiomics signatures for predicting lymph node metastasis in surgically resectable non-small cell lung cancer

Xu, R., Wang, K., Peng, B., Zhou, X., Wang, C., Lu, T., Shi, J., Zhao, J., & Zhang, L. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1427743 · Cited by 5 · see citing works

Evaluation of EGFR-TKIs and ICIs treatment stratification in non-small cell lung cancer using an encrypted multidimensional radiomics approach

Zhang, X., Qiu, X., Zhang, Y., Lai, Q., Zhang, G., & Zhang, G. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00824-w · Cited by 5 · see citing works

EUS-based intratumoral and peritumoral machine learning radiomics analysis for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer

Mo, S., Nan, Y., Qin, F., Zhao, H., Wang, Y., Qin, H., Wei, H., Jiang, H., & Qin, S. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1442209 · Cited by 5 · see citing works

Contrast-enhanced mammography: better with AI?

Zhang, T., & Mann, R. M. (2023) · European Radiology · doi:10.1007/s00330-023-10190-5 · Cited by 5 · see citing works

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

Yao, J., Zhou, W. Z., Jia, X., Zhu, Y., Chen, X., Zhan, W., & Zhou, J. (2025) · Breast Cancer Research and Treatment · doi:10.1007/s10549-025-07727-1 · Cited by 4 · see citing works

Multi-modal radiomics model based on four imaging modalities for predicting pathological complete response to neoadjuvant treatment in breast cancer

Liang, Y., Xu, H., Lin, J., Tang, W., Liu, X., Gan, K., Wan, Q., & Du, X. (2025) · BMC Cancer · doi:10.1186/s12885-025-14407-2 · Cited by 4 · see citing works

Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models —— dual-center study

Liu, J., Leng, X., Yuan, Z., Li, Y., Luo, H., Chen, J., Li, R., Li, X., He, Z., Zou, Q., et al. (2025) · BMC Cancer · doi:10.1186/s12885-025-15148-y · Cited by 4 · see citing works

Optimizing the Size of Peritumoral Region for Assessing Non-Small Cell Lung Cancer Heterogeneity Using Radiomics

Zhang, X., Zhang, G., Qiu, X., Yin, J., Tan, W., Yin, X., Yang, H., Wang, K., & Zhang, Y. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-7108-4_26 · Cited by 4 · see citing works

Value of intralesional and perilesional radiomics for predicting the bioactivity of hepatic alveolar echinococcosis

Zhang, S., Hou, J., Xia, W., Zhao, Z., Xu, M., Li, S., Xu, C., Zhang, T., & Liu, W. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1389177 · Cited by 4 · see citing works

Intratumoral and peritumoral radiomics based on automated breast volume scanner for predicting human epidermal growth factor receptor 2 status

Zhang, H., Miao, Q., Fu, Y., Pan, R., Jin, Q., Gu, C., & Ni, X. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1556317 · Cited by 4 · see citing works

Prediction of pathological complete response of breast cancer patients who received neoadjuvant chemotherapy with a nomogram based on clinicopathologic variables, ultrasound, and MRI

Zhang, M., Liu, X., Du, Y. L., Zha, H., Zha, X., Wang, J., Liu, X., Wang, S.-J., Zou, Q., Zhang, J., et al. (2023) · British Journal of Radiology · doi:10.1093/bjr/tqad014 · Cited by 4 · see citing works

High-risk breast lesions: a combined intratumoral and peritumoral radiomics nomogram model to predict pathologic upgrade and reduce unnecessary surgical excision

Liao, T., Yang, Y., Lin, X. S., Ouyang, R., Deng, Y., & Ma, J. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1479565 · Cited by 4 · see citing works

Multi-center strain elastography radiomics for breast cancer diagnosis: integrating intratumoral and peritumoral regions

Li, G., Li, Q., Wu, H., Huang, Z., Tian, H., Yang, K., Chen, J., Xu, J., Yuan, L., & Dong, F. (2025) · Discover Oncology · doi:10.1007/s12672-025-02406-5 · Cited by 4 · see citing works

PMSG-Net: A priori-guided multilevel graph transformer fusion network for immunotherapy efficacy prediction

Yang, W., Wu, W., Wang, L., Zhang, S., Zhao, J.-J., & Qiang, Y. (2023) · Comput. Biol. Medicine · doi:10.1016/j.compbiomed.2023.107371 · Cited by 4 · see citing works

Enhancing the Opportunistic Bone Status Assessment Using Radiomics Based on Dual-Energy Spectral CT Material Decomposition Images

Cheng, Q., Zhang, J., Hu, M., Wang, S., Liu, Y., Li, J., & Wei, W. (2024) · Bioengineering · doi:10.3390/bioengineering11121257 · Cited by 3 · see citing works

Establishment of a predictive nomogram for breast cancer lympho-vascular invasion based on radiomics obtained from digital breast tomography and clinical imaging features

Liang, G., Zhang, S., Zheng, Y., Chen, W., Liang, Y., Dong, Y., Li, L., Li, J., Yang, C., Jiang, Z., et al. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01607-2 · Cited by 3 · see citing works

The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis

Singh, S., & Healy, N. A. (2024) · Insights into Imaging · doi:10.1186/s13244-024-01869-4 · Cited by 3 · see citing works

The value of multi-phase CT based intratumor and peritumoral radiomics models for evaluating capsular characteristics of parotid pleomorphic adenoma

Shen, Q., Xiang, C., Han, Y., Li, Y., & Huang, K. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1566555 · Cited by 3 · see citing works

Comparative analysis of multi-zone peritumoral radiomics in breast cancer for predicting NAC response using ABVS-based deep learning models

Wang, M., Chen, W., Ren, R., Lin, Y., Tang, J., & Wu, M. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1586715 · Cited by 3 · see citing works

Comparative Evaluation of Contrast-Enhanced Spectral Mammography and Digital Breast Tomosynthesis for Diagnosing and Treating Breast Cancer

Yang, D., & Gong, F. (2023) · Medical Science Monitor · doi:10.12659/msm.941880 · Cited by 3 · see citing works

Radiomics based on machine learning algorithms could predict prognosis and postoperative chemotherapy benefits of patients with gastric cancer: a retrospective cohort study

Xiang, Y., Hu, Y., Chen, C., Zhi, H., Zhang, Z., Lu, M., Chen, X., Luo, Z., Chen, S., Dias‐Neto, E., et al. (2023) · Journal of Gastrointestinal Oncology · doi:10.21037/jgo-23-627 · Cited by 3 · see citing works

Preoperative Prediction of Breast Cancer Histological Grade Using Intratumoral and Peritumoral Radiomics Features from T2WI and DWI MR Sequences

Guo, Y., Liao, J., Li, S., Shang, Y., Wang, Y., Wu, Q., Wu, Y., Wang, M., Yan, F., & Tan, H. (2024) · Breast Cancer Targets and Therapy · doi:10.2147/bctt.s487988 · Cited by 3 · see citing works

Development of radiomics-based models on mammograms with mass lesions to predict prognostically relevant characteristics of invasive breast cancer in a screening cohort

Peters, J., van Leeuwen, M. M., Moriakov, N., van Dijck, J. A. A. M., Mann, R. M., Teuwen, J., Lips, E. H., van den Belt‐Dusebout, A. W., Wesseling, J., de Vries, B. B. L. P., et al. (2025) · British Journal of Cancer · doi:10.1038/s41416-025-02995-6 · Cited by 3 · see citing works

Preoperative magnetic resonance imaging identify feasibility of breast-conserving surgery for breast cancer patients

Liu, L., He, S., Niu, Z., Yin, R., Guo, Y., Dou, Z., Ma, W., Ye, Z., & Lu, H. S. (2024) · Gland Surgery · doi:10.21037/gs-23-509 · Cited by 2 · see citing works

Preoperative stratification of Ki-67 proliferation index in intrahepatic mass-forming cholangiocarcinoma via tumor-halo radiomic phenotyping on biparametric MRI: a multicenter diagnostic model

Li, X., Xing, L., Zhuo, L., Xue, L., Duan, J., Dong, J., Zhang, Y., Wang, X., Kang, B., Yin, X., et al. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-02062-9 · Cited by 2 · see citing works

CT-based subregional and peritumoral radiomics for predicting pathological T stage of clear cell renal cell carcinoma: an exploratory study of biological mechanisms

Huang, J.-L., Liu, Q., Wang, C., Lang, X., Zeng, Y.-X., & Zhou, D.-Q. (2026) · Insights into Imaging · doi:10.1186/s13244-026-02226-3 · Cited by 2 · see citing works

The Effectiveness of an Adaptive Method to Analyse the Transition between Tumour and Peritumour for Answering Two Clinical Questions in Cancer Imaging

Mottola, M., Golfieri, R., & Bevilacqua, A. (2024) · Sensors · doi:10.3390/s24041156 · Cited by 2 · see citing works

Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI

Fan, W., Sun, W., Xu, M. Z., Pan, J., & Man, F. Y. (2024) · Frontiers in Oncology · doi:10.3389/fonc.2024.1307907 · Cited by 2 · see citing works

Early treatment monitoring of multidrug-resistant tuberculosis based on CT radiomics of cavity and cavity periphery

Lv, X., Li, Y., Ding, C., Qin, L., Xu, X., Zheng, Z., & Hou, D. (2025) · European Radiology Experimental · doi:10.1186/s41747-025-00581-2 · Cited by 2 · see citing works

Predicting recurrence risk in endometrial cancer: a multisequence MRI intratumoral and peritumoral radiomics nomogram approach

Li, J., Ma, D., Chen, X., Wei, J., Xu, J., Zhao, Y., & Gao, Z. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1569729 · Cited by 1 · see citing works

Enhancing parametrial invasion assessment in cervical squamous cell carcinoma: the collaborative impact of diffusion kurtosis imaging and T2-weighted imaging, exploring tumour core and 5-mm peritumoural tissue

Chen, X., Huang, Y., Cai, M., Tang, S., Wang, Z., Li, S., Shen, F., & Xiang, Z. (2025) · British Journal of Radiology · doi:10.1093/bjr/tqaf100 · Cited by 1 · see citing works

Intratumoral and peritumoral radiomics for preoperative prediction of the efficacy of HIFU ablation for uterine fibroids based on multiparametric MRI: a multicenter study

Li, C., Liao, H., Jian, L. I. U., & Gao, J. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2025.1699632 · Cited by 1 · see citing works

Radiomics and artificial intelligence for predicting response to neoadjuvant drug therapy in patients with breast cancer: a review

Suleymanova, M. M., Karmazanovsky, G. G., Kondratyev, E. V., Popov, A. Y., Nechaev, V. A., Ermoshchenkova, M. S., & Kuzmina, E. S. (2025) · Digital Diagnostics · doi:10.17816/dd634972 · Cited by 1 · see citing works

Enhancing Pathological Complete Response Prediction in Breast Cancer: The Added Value of Pretherapeutic Contrast-Enhanced Cone Beam Breast CT Semantic Features

Wang, Y., Ma, Y., Wang, F., Liu, A., Zhao, M., Bian, K., Zhu, Y., Yin, L., & Ye, Z. (2025) · Academic Radiology · doi:10.1016/j.acra.2025.01.016 · Cited by 1 · see citing works

Mammographic and Ultrasonographic Imaging Analysis for Neoadjuvant Chemotherapy Evaluation: Volume Reduction Indexes That Correlate With Pathological Complete Response

de Mello, J. M. D. R. B., Sarvacinski, F., Schaefer, F. C., Ercolani, D. S., Lobato, N. R., Martins, Y. C., Zwetsch, G., Bittelbrunn, F. P., Ferreira, C. F., & Damin, A. P. S. (2022) · Cureus · doi:10.7759/cureus.29960 · Cited by 1 · see citing works

SALES PREDICTING TELEMEDICINE SERVICES DURING COVID-19 USING MACHINE LEARNING TECHNIQUES

CUNHA, A. L. M. D., ZUCHETTI, M. F., Oliveira, S., CHIBA, V. I. Z., & Gonçalves, M. C. (2023) · Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção · doi:10.14488/enegep2023_tn_st_399_1956_46011

Construction and evaluation of a multifactorial clinical model for discriminating benign and malignant breast tumors using LASSO algorithm based on retrospective cohort study

Cui, W. (2024) · American Journal of Cancer Research · doi:10.62347/ilij7959

Research on predicting pathological complete response in breast cancer following neoadjuvant chemotherapy using a multiparametric MRI radiomics model

Liu, X., Yang, A., Cao, M., Zhang, Q., & Cao, Y. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.101769

Mammography Based Nomogram Integrating Radiomics and Clinical Features to Predict Benign or Malignant Regression of BI-RADS 4A Lesions at Follow-Up

Yao, G., Chen, L., Wu, T., Liu, Y., Wan, Y., Xia, Z., Liu, B., & Zheng, M. (2025) · International Journal of Women s Health · doi:10.2147/ijwh.s539131

Contrast-Enhanced Mammography: A Comprehensive Overview of Current Status and Future Directions

Renganathan, R., Palanisamy, S., Kini, D. V., Subramaniam, P., Krishnappa, L., & Chang, J. M. (2025) · Journal of the Korean Society of Radiology · doi:10.3348/jksr.2025.0053

Development and validation of a novel disulfidptosis-related gene signature for prediction of survival and immune microenvironment in osteosarcoma by WGCNA analysis

Zheng, Y., Cai, H., Huang, H., Wu, G., Ren, A., Su, J., Bao, J., & Wu, F. (2025) · Discover Oncology · doi:10.1007/s12672-025-04146-y

Achieving single-contrast administration for enhanced chest CT and contrast-enhanced spectral mammography without compromising diagnostic quality: a comparative study

Guo, Z., Li, Y., Hao, Y., Lv, Y., Shen, Y., Zhang, Y., Zhang, N., Zhao, T., Xu, X., & Guo, D. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00968-9

Additional file 1 of Development and validation of ultrasound-based radiomics model to predict germline BRCA mutations in patients with breast cancer

Deng, T., Liang, J., Yan, C., Ni, M., Xiang, H., Li, C., Ou, J., Lin, Q., Liu, L., Tang, G., et al. (2024) · Figshare · doi:10.6084/m9.figshare.25322821

Additional file 1 of Development and validation of a radiopathomic model for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer patients

Zhang, J., Wu, Q., Yin, W., Yang, L., Xiao, B., Wang, J.-Z., & Yao, X. (2023) · Figshare · doi:10.6084/m9.figshare.22814919

Additional file 1 of A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy

Zhou, J., Bai, Y., Zhang, Y., Wang, Z., Sun, S., Lin, L., Gu, Y., & You, C. (2024) · Figshare · doi:10.6084/m9.figshare.26409955

Dual-Region Ultrasound Radiomics for the Non-Invasive Prediction of HER2 Status in Breast Cancer Patients with Equivocal ImmunohistochemistryPreprint

Li, X., Huang, L., Sun, Y. Y., Yang, C., Li, W., Song, H., & Yan, M. (2026) · Research Square · doi:10.21203/rs.3.rs-8217137/v1

Preoperative prediction of tumor deposits in advanced gastric cancer using intratumoral and peritumoral CT radiomics: development and validation of an ensemble model

Yao, Y., Zhan, P., Yuan, M., Chen, Y., Fan, Y., & Gao, J. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1763646

Deep learning radiomics nomogram based on multi-regional features for predicting lymph node metastasis and prognosis in colorectal cancer

Lei, X., Qian, G., Sun, Z.-G., Tang, Z.-Q., Liu, Y.-C., Du, R., & Li, Y. (2026) · World Journal of Gastrointestinal Oncology · doi:10.4251/wjgo.v18.i4.115635

The study of nomogram model based on CT radiomics and clinical features for histological classification of parotid gland tumors

Shen, Q., Liu, Y., Xu, F., Han, Y., Liu, X. Q., & Huang, K. (2026) · Scientific Reports · doi:10.1038/s41598-026-46970-4

Multi-Regional Feature Learning in AI-Based Breast Cancer Analysis: Overview, Emerging Trends, and Future Perspectives

Monkam, P., Duan, X., Zhao, B., Zhao, D., Qi, S., & Qian, W. (2026) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-026-10577-9

Integration of longitudinal MRI and biopsy whole slide images for early prediction of neoadjuvant therapy response and personalized management in breast cancer: A multicenter retrospective and prospective study

Zhang, S., Shi, Z., Siyao, D. U., Zhu, T., Li, P., Zhao, R., Zhu, X., Wu, S., Tan, Y., Xu, W., et al. (2026) · Cancer Letters · doi:10.1016/j.canlet.2026.218508

Development and internal validation of mammography feature-based prognostic models for distant recurrence-free survival of invasive breast cancer in a screening cohort

Peters, J., de Vries, B. B. L. P., van Dijck, J. A. A. M., Mann, R. M., Moriakov, N., Teuwen, J., Karssemeijer, N., Lips, E. H., Wesseling, J., van Leeuwen, M. M., et al. (2026) · npj Breast Cancer · doi:10.1038/s41523-026-00946-9

Intra- and peritumoral radiomics for predicting equivocal HER2 status of breast cancer on contrast-enhanced mammography

Xu, C., Qiu, J., Zhang, S., Chen, Y., Wang, X., Zhang, H., Wang, Q., Chu, T., Li, Z. J., Lu, P., et al. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1807530

AI-assisted digital breast tomosynthesis as a decision-support tool for post-neoadjuvant surgical planning in breast cancer

Gündoğdu, A., Ozdemir, N. B. U., Wetherilt, C. S., Aktaş, M., Gentile, D., Çomçalı, B., & Yilmaz, O. C. (2026) · Langenbeck s Archives of Surgery · doi:10.1007/s00423-026-04116-0

Establishing a Multimodal Model for Predicting Lymphovascular Invasion in Breast Cancer Using Deep Transfer Learning Based on Ultrasound and Dynamic Contrast-Enhanced Magnetic Resonance Imaging

He, M., Zhu, F., Jiang, F., Liu, Y., Guo, Y., Yang, Y., Jiang, H., Xiong, F., Cheng, W., Xu, Z., et al. (2026) · Oncology Research and Treatment · doi:10.1159/000553438

Intratumoral and Peritumoral Ultrasoundomics Models for Preoperative Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer

Zhou, P., Yang, L., He, Y., Wang, L., Zhao, R., Gu, M., Zhu, P., Luo, X., Kong, X., & Du, P. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.09.038

Incremental Value and Cross-Center Validation of Pretreatment Tri-Modal Ultrasound Deep Learning for Predicting Breast Pathologic Complete Response to Neoadjuvant Chemotherapy: A Multicenter Study

Wei, C., Pan, T., Li, T., Zheng, C., Duan, Y., & Nie, F. (2026) · Cancers · doi:10.3390/cancers18203326

Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer

Zhang, K., Lin, J., Lin, F., Wang, Z., Zhang, H., Zhang, S., Mao, N., & Qiao, G. (2023) · Journal of X-Ray Science and Technology · doi:10.3233/xst-221349 · Cited by 8 · see citing works

Contrast-Enhanced Spectral Mammography-Based Radiomics Nomogram for the Prediction of Neoadjuvant Chemotherapy-Insensitive Breast Cancers

Wang, Z., Lin, F., Ma, H., Shi, Y., Dong, J., Yang, P., Zhang, K., Guo, N., Zhang, R., Cui, J., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.605230 · Cited by 39 · see citing works

Predictive value of tumoral and peritumoral radiomic features in neoadjuvant chemotherapy response for breast cancer: a retrospective study

Pesapane, F., Rotili, A., Scalco, E., Pupo, D., Carriero, S., Corso, F., Marco, P. D., Origgi, D., Nicosia, L., Ferrari, F., et al. (2025) · La radiologia medica · doi:10.1007/s11547-025-01969-1 · Cited by 8 · see citing works

Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI

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Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study

Liu, Z., Li, Z., Qu, J., Zhang, R., Zhou, X., Li, L., Sun, K., Tang, Z., Jiang, H., Li, H., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-3190 · Cited by 494 · see citing works