通过多阶段融合CT与PET影像，提升肺癌亚型分类准确率。
Multi-stage intermediate fusion for multimodal learning to classify non-small cell lung cancer subtypes from CT and PET
- 在特征提取不同阶段融合CT与PET图像，保留空间关联性。
- 准确率72.4%，AUC达68.1%，优于早期、晚期及现有中间融合方法。
- 适合需要非侵入性诊断的肺癌精准医疗场景。
在精准医疗时代，准确分类非小细胞肺癌（NSCLC）组织学亚型至关重要，但当前侵入性检测常不可行且可能引发临床并发症。本研究提出一种多阶段中间融合方法，基于CT和PET图像对NSCLC亚型进行分类。该方法在特征提取的不同阶段整合两种模态，采用体素级融合以挖掘不同抽象层级间的互补信息，同时保持空间相关性。相比仅使用CT或PET的单模态方法，以及早期和晚期融合技术，本方法在特征提取过程中实现中间融合的优势。此外，与现有唯一针对PET/CT图像进行组织学亚型分类的中间融合方法相比，本模型在关键指标上表现更优，准确率为0.724，AUC为0.681。该非侵入性方法有望显著提升诊断准确性，支持更精准的治疗决策，推动肺癌个性化管理。
原文摘要 · Abstract (English)
Accurate classification of histological subtypes of non-small cell lung cancer (NSCLC) is essential in the era of precision medicine, yet current invasive techniques are not always feasible and may lead to clinical complications. This study presents a multi-stage intermediate fusion approach to classify NSCLC subtypes from CT and PET images. Our method integrates the two modalities at different stages of feature extraction, using voxel-wise fusion to exploit complementary information across varying abstraction levels while preserving spatial correlations. We compare our method against unimodal approaches using only CT or PET images to demonstrate the benefits of modality fusion, and further benchmark it against early and late fusion techniques to highlight the advantages of intermediate fusion during feature extraction. Additionally, we compare our model with the only existing intermediate fusion method for histological subtype classification using PET/CT images. Our results demonstrate that the proposed method outperforms all alternatives across key metrics, with an accuracy and AUC equal to 0.724 and 0.681, respectively. This non-invasive approach has the potential to significantly improve diagnostic accuracy, facilitate more informed treatment decisions, and advance personalized care in lung cancer management.
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