Qiao Zeng

No. 519, Beijing East Road
Nanchang, 330029, China

Last Updated: 09/26/2026

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Found 14 publications

Early identification of neoadjuvant therapy non-response via multimodal immune-imaging biomarkers in breast cancer.

Early identification of neoadjuvant therapy non-response via multimodal immune-imaging biomarkers in breast cancer.

Journal: Frontiers in immunology

Published: May 10, 2026

Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data.

Predicting response to neoadjuvant therapy in breast cancer using longitudinal DCE-MRI deep learning integrated with tumor microenvironment data.

Journal: Frontiers in immunology

Published: November 19, 2025

Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study.

Interpretable machine learning model using CT body composition combined with inflammatory and nutritional indicators to predict pathological complete response after neoadjuvant therapy in breast cancer: a retrospective study.

Journal: PeerJ

Published: September 11, 2025

GPER in metabolic homeostasis and disease: molecular mechanisms, nutritional regulation, and therapeutic potential.

GPER in metabolic homeostasis and disease: molecular mechanisms, nutritional regulation, and therapeutic potential.

Journal: Journal of translational medicine

Published: May 31, 2025

Delta dual‑region DCE-MRI radiomics from breast masses predicts axillary lymph node response after neoadjuvant therapy for breast cancer.

Delta dual‑region DCE-MRI radiomics from breast masses predicts axillary lymph node response after neoadjuvant therapy for breast cancer.

Journal: BMC cancer

Published: October 23, 2024

CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.

CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.

Journal: Insights into imaging

Published: September 17, 2024

Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.

Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.

Journal: Academic radiology

Published: September 13, 2024

Interpretable machine learning models based on body composition and inflammatory nutritional index (BCINI) to predict early postoperative recurrence of colorectal cancer: Multi-center study.

Interpretable machine learning models based on body composition and inflammatory nutritional index (BCINI) to predict early postoperative recurrence of colorectal cancer: Multi-center study.

Journal: Computer methods and programs in biomedicine

Published: July 25, 2024

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.

Model Based on Ultrasound Radiomics and Machine Learning to Preoperative Differentiation of Follicular Thyroid Neoplasm.

Journal: Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine

Published: April 29, 2024

CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.

CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study.

Journal: Academic radiology

Published: December 07, 2023

Radiomics Based on DCE-MRI for Predicting Response to Neoadjuvant Therapy in Breast Cancer.

Radiomics Based on DCE-MRI for Predicting Response to Neoadjuvant Therapy in Breast Cancer.

Journal: Academic radiology

Published: March 04, 2023

Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy.

Radiomics Based on Dynamic Contrast-Enhanced MRI to Early Predict Pathologic Complete Response in Breast Cancer Patients Treated with Neoadjuvant Therapy.

Journal: Academic radiology

Published: September 28, 2022

Showing 1-12 of 14

Last Updated: 09/26/2026