Author

Qifeng Wang

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Review Open access Aug 2026

Building an interpretable machine learning prognosis prediction model-based on baseline examinations of patients with esophageal cancer undergoing surgery

To create an interpretable machine learning model based on non-invasive biomarkers for the early diagnosis and improved prognostic value of esophageal cancer. We gathered a private dataset at Sichuan Cancer Hospital, comprising 3204 esophageal cancer patients who underwent surgery. Baseline markers and preoperative bio...

Rui Zhan, Qi-Feng Wang · Frontiers in Oncology · 0 citations

Aug 2026

Dynamic iterative coarse-to-fine prompt learning based on SAM for precise esophageal cancer gross target volume segmentation

Experimental results show that this segmentation method based on SAM achieves better segmentation performance than existing single-pass methods on both internal and external validation sets, and can provide a reliable efficient scheme for the formulation of precise radiotherapy plans for esophageal cancer.

Yuxuan Yao, Hong-Fei Sun, Cheng-Wei Chen et al. · Physics in Medicine and Biol... · 0 citations

#small language model Open access Aug 2026

MGTP-Seg: a mask guidance and text prompting network for gross tumor volume segmentation in esophageal cancer radiotherapy

This work demonstrates that MGTP-Seg not only provides an accurate, interpretable, and clinically relevant solution for automatic GTV delineation, but also offers a novel methodological framework to fuse spatial priors with semantic knowledge in medical image analysis.

Cheng-Wei Chen, Hong-Fei Sun, Yuxuan Yao et al. · Physics in Medicine and Biol... · 0 citations