Prediction of Distant Metastasis for Head and Neck Cancer Patients Using Multi-Modal Tumor and Peritumoral Feature Fusion Network

• URL: http://arxiv.org/abs/2508.20469v1
• Date: Thu, 28 Aug 2025 06:39:38 GMT
• Title: Prediction of Distant Metastasis for Head and Neck Cancer Patients Using Multi-Modal Tumor and Peritumoral Feature Fusion Network
• Authors: Zizhao Tang, Changhao Liu, Nuo Tong, Shuiping Gou, Mei Shi,
• Abstract summary: Metastasis remains the major challenge in the clinical management of head and neck squamous cell carcinoma (HN SCC)<n>This study develops a deep learning-based framework to predict metastasis risk in HN SCC patients by integrating computed tomography (CT) images, radiomics, and clinical data.
• Score: 6.753955554949766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metastasis remains the major challenge in the clinical management of head and neck squamous cell carcinoma (HNSCC). Reliable pre-treatment prediction of metastatic risk is crucial for optimizing treatment strategies and prognosis. This study develops a deep learning-based multimodal framework to predict metastasis risk in HNSCC patients by integrating computed tomography (CT) images, radiomics, and clinical data. 1497 HNSCC patients were included. Tumor and organ masks were derived from pretreatment CT images. A 3D Swin Transformer extracted deep features from tumor regions. Meanwhile, 1562 radiomics features were obtained using PyRadiomics, followed by correlation filtering and random forest selection, leaving 36 features. Clinical variables including age, sex, smoking, and alcohol status were encoded and fused with imaging-derived features. Multimodal features were fed into a fully connected network to predict metastasis risk. Performance was evaluated using five-fold cross-validation with area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). The proposed fusion model outperformed single-modality models. The 3D deep learning module alone achieved an AUC of 0.715, and when combined with radiomics and clinical features, predictive performance improved (AUC = 0.803, ACC = 0.752, SEN = 0.730, SPE = 0.758). Stratified analysis showed generalizability across tumor subtypes. Ablation studies indicated complementary information from different modalities. Evaluation showed the 3D Swin Transformer provided more robust representation learning than conventional networks. This multimodal fusion model demonstrated high accuracy and robustness in predicting metastasis risk in HNSCC, offering a comprehensive representation of tumor biology. The interpretable model has potential as a clinical decision-support tool for personalized treatment planning.

Related papers

• Multimodal Slice Interaction Network Enhanced by Transfer Learning for Precise Segmentation of Internal Gross Tumor Volume in Lung Cancer PET/CT Imaging [34.37798183254656]
  Internal gross tumor volume (IGTV) in PET/CT imaging is pivotal for optimal radiation therapy in lung cancer tumors.<n>We present a transfer learningbased methodology utilizing a multimodal interactive perception network with MAMBA.<n>We introduce a slice interaction module (SIM) within a 2.5D segmentation framework to effectively model inter-slice relationships.
  arXiv  Detail & Related papers  (2025-09-26T18:48:08Z)
• Live(r) Die: Predicting Survival in Colorectal Liver Metastasis [0.01268579273097071]
  Colorectal cancer frequently metastasizes to the liver, significantly reducing long-term survival.<n>Current prognostic models, often based on limited clinical or molecular features, lack sufficient predictive power.<n>We present a fully automated framework for surgical outcome prediction from pre- and post-contrast MRI.
  arXiv  Detail & Related papers  (2025-09-10T19:02:59Z)
• LGE-Guided Cross-Modality Contrastive Learning for Gadolinium-Free Cardiomyopathy Screening in Cine CMR [51.11296719862485]
  We propose a Contrastive Learning and Cross-Modal alignment framework for gadolinium-free cardiomyopathy screening using cine CMR sequences.<n>By aligning the latent spaces of cine CMR and Late Gadolinium Enhancement (LGE) sequences, our model encodes fibrosis-specific pathology into cine CMR embeddings.
  arXiv  Detail & Related papers  (2025-08-23T07:21:23Z)
• Improved Prognostic Prediction of Pancreatic Cancer Using Multi-Phase CT by Integrating Neural Distance and Texture-Aware Transformer [37.55853672333369]
  This paper proposes a novel learnable neural distance that describes the precise relationship between the tumor and vessels in CT images of different patients. The developed risk marker was the strongest predictor of overall survival among preoperative factors.
  arXiv  Detail & Related papers  (2023-08-01T12:46:02Z)
• Liver Tumor Screening and Diagnosis in CT with Pixel-Lesion-Patient Network [37.931408083443074]
  Pixel-Lesion-pAtient Network (PLAN) is proposed to jointly segment and classify each lesion with improved anchor queries and a foreground-enhanced sampling loss. PLAN achieves 95% and 96% in patient-level sensitivity and specificity. On contrast-enhanced CT, our lesion-level detection precision, recall, and classification accuracy are 92%, 89%, and 86%, outperforming widely used CNN and transformers for lesion segmentation.
  arXiv  Detail & Related papers  (2023-07-17T06:21:45Z)
• Multimodal Deep Learning for Personalized Renal Cell Carcinoma Prognosis: Integrating CT Imaging and Clinical Data [3.790959613880792]
  Renal cell carcinoma represents a significant global health challenge with a low survival rate. This research aimed to devise a comprehensive deep-learning model capable of predicting survival probabilities in patients with renal cell carcinoma. The proposed framework comprises three modules: a 3D image feature extractor, clinical variable selection, and survival prediction.
  arXiv  Detail & Related papers  (2023-07-07T13:09:07Z)
• Integrative Imaging Informatics for Cancer Research: Workflow Automation for Neuro-oncology (I3CR-WANO) [0.12175619840081271]
  We propose an artificial intelligence-based solution for the aggregation and processing of multisequence neuro-Oncology MRI data. Our end-to-end framework i) classifies MRI sequences using an ensemble classifier, ii) preprocesses the data in a reproducible manner, and iv) delineates tumor tissue subtypes. It is robust to missing sequences and adopts an expert-in-the-loop approach, where the segmentation results may be manually refined by radiologists.
  arXiv  Detail & Related papers  (2022-10-06T18:23:42Z)
• CAE-Transformer: Transformer-based Model to Predict Invasiveness of Lung Adenocarcinoma Subsolid Nodules from Non-thin Section 3D CT Scans [36.093580055848186]
  Lung Adenocarcinoma (LAUC) has recently been the most prevalent. Timely and accurate knowledge of the invasiveness of lung nodules leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. The primary imaging modality to assess and predict the invasiveness of LAUCs is the chest CT. In this paper, a predictive transformer-based framework, referred to as the "CAE-Transformer", is developed to classify LAUCs.
  arXiv  Detail & Related papers  (2021-10-17T04:37:24Z)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• DeepPrognosis: Preoperative Prediction of Pancreatic Cancer Survival and Surgical Margin via Contrast-Enhanced CT Imaging [26.162788846435365]
  Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers and carries a dismal prognosis. We propose a novel deep neural network for the survival prediction of resectable PDAC patients, named as 3D Contrast-Enhanced Convolutional Long Short-Term Memory network(CE-ConvLSTM) We present a multi-task CNN to accomplish both tasks of outcome and margin prediction where the network benefits from learning the tumor resection margin related features to improve survival prediction.
  arXiv  Detail & Related papers  (2020-08-26T22:51:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.