Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma

Hepatocellular Carcinoma Intra-arterial Treatment Response Prediction for Improved Therapeutic Decision-Making

This work proposes a pipeline to predict treatment response to intra-arterial therapy of patients with Hepatocellular Carcinoma (HCC) for improved therapeutic decision-making. Our graph neural network model seamlessly combines heterogeneous inputs of baseline MR scans, pre-treatment clinical information, and planned treatment characteristics and has been validated on patients with HCC treated by transarterial chemoembolization (TACE). It achieves Accuracy of $0.713 \pm 0.075$, F1 of $0.702 \pm 0.082$ and AUC of $0.710 \pm 0.108$. In addition, the pipeline incorporates uncertainty estimation to…

基于混合深度学习策略的H&E染色肝脏组织病理图像肝细胞癌分级分类

Hybrid deep learning-based strategy for the hepatocellular carcinoma cancer grade classification of H&E stained liver histopathology images

针对HCC早期诊断挑战，提出混合深度学习架构，利用迁移学习提取CNN特征并分类，在TCGA和KMC数据库上分别达到100%和96.71%的准确率，较预训练模型提升2%-4%。

SChISM研究：细胞游离DNA片段大小谱作为接受免疫检查点抑制剂治疗的晚期癌症进展的预测因子

The SChISM study: Cell-free DNA size profiles as predictors of progression in advanced carcinoma treated with immune-checkpoint inhibitors

SChISM研究通过分析126例晚期癌症患者的治疗前cfDNA大小谱，发现长片段占比等指标可有效预测ICI治疗应答与复发，效果优于常规PD-L1免疫组化检测。

学习有限多期CT：双分支原型引导框架用于肝细胞癌早期复发预测

Learning from Limited Multi-Phase CT: Dual-Branch Prototype-Guided Framework for Early Recurrence Prediction in HCC

提出双分支原型引导框架DuoProto，利用有限多期CT数据增强单期CT的肝细胞癌早期复发预测，通过跨域原型对齐和排序监督提升性能，优于现有方法。

多尺度跨模态映射分子、病理和放射表型在脂质缺乏型清细胞癌肾癌中的应用

Multiscale Cross-Modal Mapping of Molecular, Pathologic, and Radiologic Phenotypes in Lipid-Deficient Clear Cell Renal CellCarcinoma

本研究提出多尺度跨模态框架，用于术前识别脂质缺乏型清细胞肾癌的分子、病理和放射表型，实现非侵入性分子表型分析。