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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Zong-Yu Xie

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Oct 2026

A deep learning radiomics model based on multiparametric MRI for predicting the efficacy of neoadjuvant chemotherapy in axillary lymph nodes of breast cancer.

OBJECTIVES In this study, we developed and validated a deep learning radiomics clinical (DLRC) model integrating multiparametric MRI (mpMRI) and clinical data to predict axillary pathological complete response (apCR) in cN+ breast cancer patients receiving neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS We retr...

Meng-Ling Liu, Ran Li, Ai-Ling Chen et al. · 0 citations
Sep 2026

Cross-phase attention-based multi-phase CT deep learning for preoperative risk stratification of gastric gastrointestinal stromal tumors in a multicenter study.

In retrospective external validation, MPFNet provided incremental discrimination over a combined clinical-radiomics model with acceptable calibration for preoperative risk stratification of 2-5 cm gastric GISTs and its use as a decision-support tool for planning the resection approach requires prospective evaluation.

Si-Hang Guo, Cheng-Ze Yu, Jian-Ying Xu et al. · 0 citations
Open access Oct 2026

Super‐Resolution MRI–Derived Tumor‐Stroma Ratio Phenotyping for Prognostic and Therapeutic Stratification in Pancreatic Ductal Adenocarcinoma

A super‐resolution MRI (srMRI)–based radiomics model is developed to enable noninvasive tumor‐stroma ratio (TSR) phenotyping in PDAC, enabling improved recurrence‐risk stratification and pre‐treatment clinical assessment.

Xing-Xing Wang, Zong-Yu Xie, Han Li et al. · 0 citations
Open access Sep 2026

Multiphasic enhanced CT-based radiomics signature for differentiating adrenal metastases from lipid-poor adrenal adenomas

To investigate the diagnostic value of machine learning models based on CT radiomics in differentiating adrenal metastases (AMs) from lipid-poor adenomas (LPAs). A total of 212 patients with AMs and 213 patients with LPAs were retrospectively analyzed based on CT images from the First Affiliated Hospital o...

F. Zhu, Hui Wang, Heng-Feng Shi et al. · 0 citations
Open access Sep 2026

An interpretable machine learning framework using multi-phase computed tomography for differentiation of adrenal lipid-poor adenomas and pheochromocytomas

The DT model strikes an optimal balance between high diagnostic accuracy and inherent interpretability, offering a practical tool that may enhance clinical confidence when managing indeterminate adrenal lesions.

Pan-Liang Zhao, F. Zhu, C. Yan et al. · 0 citations
Jul 2026

Topological imaging of single-cell CAF heterogeneity for predicting prognosis and adjuvant chemotherapy benefit in pancreatic cancer.

By translating adverse CAF subpopulation signatures into a CT-based topological biomarker, ra-CAPR enables individualized preoperative risk stratification and may facilitate risk-adapted postoperative management.

Ling-Ling Wang, Zong-Yu Xie, Qi-Ying Tang et al. · 0 citations

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