A Multi-Modal Heterogeneous Graph Forest to Predict Lymph Node Metastasis of Non-Small Cell Lung Cancer

SAMA: A Self-and-Mutual Attention Network for Accurate Recurrence Prediction of Non-Small Cell Lung Cancer Using Genetic and CT Data

Accurate preoperative recurrence prediction for non-small cell lung cancer (NSCLC) is a challenging issue in the medical field. Existing studies primarily conduct image and molecular analyses independently or directly fuse multimodal information through radiomics and genomics, which fail to fully exploit and effectively utilize the highly heterogeneous cross-modal information at different levels and model the complex relationships between modalities, resulting in poor fusion performance and becoming the bottleneck of precise recurrence prediction. To address these limitations, we propose a novel unified framework, the Self-and-Mutual Attention (SAMA) Network, designed to efficiently fuse and utilize macroscopic CT images and microscopic gene data for precise NSCLC recurrence prediction, integrating handcrafted features, deep features, and gene features. Specifically, we design a Self-and-Mutual Attention Module that performs three-stage fusion: the self-enhancement stage enhances modality-specific features; the gene-guided and CT-guided cross-modality fusion stages perform bidirectional cross-guidance on the self-enhanced features, complementing and refining each modality, enhancing heterogeneous feature expression; and the optimized feature aggregation stage ensures the refined interactive features for precise prediction. Extensive experiments on both publicly available datasets from The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) demonstrate that our method achieves state-of-the-art performance and exhibits broad applicability to various cancers.

Multimodal deep learning approaches for precision oncology: a comprehensive review

The burgeoning accumulation of large-scale biomedical data in oncology, alongside significant strides in deep learning (DL) technologies, has established multimodal DL (MDL) as a cornerstone of precision oncology. This review provides an overview of MDL applications in this field, based on an extensive literature survey. In total, 651 articles published before September 2024 are included. We first outline publicly available multimodal datasets that support cancer research. Then, we discuss key DL training methods, data representation techniques, and fusion strategies for integrating multimodal data. The review also examines MDL applications in tumor segmentation, detection, diagnosis, prognosis, treatment selection, and therapy response monitoring. Finally, we critically assess the limitations of current approaches and propose directions for future research. By synthesizing current progress and identifying challenges, this review aims to guide future efforts in leveraging MDL to advance precision oncology.

Predicting Lymph Node Metastasis of Lung Cancer: A Two-stage Multimodal Data Fusion Approach

Lung cancer is the leading cause of cancer death worldwide. Lymph node metastasis (LNM) status plays a vital role in determining the initial treatment for lung cancer patients, but it is difficult to diagnose accurately before surgery. Developing an LNM prediction model using multimodal data is the mainstream solution for this clinical problem. However, the current multimodal fusion methods may suffer from performance degradation when one type of modal data has poor predictive performance. In this study, we presented a two-stage multimodal data fusion approach to alleviate this problem. We first constructed unimodal prediction models using unimodal data separately and then used the encoders of the unimodal with frozen parameters as feature extractors and re-trained a new decoder to achieve the multimodal data fusion. We conducted experiments on real clinical multimodal data of 681 lung cancer patients collected from Peking University Cancer Hospital. Experimental results show that the proposed approach outperformed the state-of-the-art LNM prediction models and different multimodal fusion strategies. We conclude that the proposed method is a good option for multimodal data fusion when image data has poor discriminative performance.

Zero-shot information extraction from radiological reports using ChatGPT

INTRODUCTION

Electronic health records contain an enormous amount of valuable information recorded in free text. Information extraction is the strategy to transform free text into structured data, but some of its components require annotated data to tune, which has become a bottleneck. Large language models achieve good performances on various downstream NLP tasks without parameter tuning, becoming a possible way to extract information in a zero-shot manner.

METHODS

In this study, we aim to explore whether the most popular large language model, ChatGPT, can extract information from the radiological reports. We first design the prompt template for the interested information in the CT reports. Then, we generate the prompts by combining the prompt template with the CT reports as the inputs of ChatGPT to obtain the responses. A post-processing module is developed to transform the responses into structured extraction results. Besides, we add prior medical knowledge to the prompt template to reduce wrong extraction results. We also explore the consistency of the extraction results.

RESULTS

We conducted the experiments with 847 real CT reports. The experimental results indicate that ChatGPT can achieve competitive performances for some extraction tasks like tumor location, tumor long and short diameters compared with the baseline information extraction system. By adding some prior medical knowledge to the prompt template, extraction tasks about tumor spiculations and lobulations obtain significant improvements but tasks about tumor density and lymph node status do not achieve better performances.

CONCLUSION

ChatGPT can achieve competitive information extraction for radiological reports in a zero-shot manner. Adding prior medical knowledge as instructions can further improve performances for some extraction tasks but may lead to worse performances for some complex extraction tasks.

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.