Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT

Lecture notes in computer science, Год журнала: 2023, Номер unknown, С. 1 - 30

Опубликована: Янв. 1, 2023

Язык: Английский

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PET/CT based transformer model for multi-outcome prediction in oropharyngeal cancer

Radiotherapy and Oncology, Год журнала: 2024, Номер 197, С. 110368 - 110368

Опубликована: Июнь 2, 2024

Язык: Английский

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From Head and Neck Tumour and Lymph Node Segmentation to Survival Prediction on PET/CT: An End-to-End Framework Featuring Uncertainty, Fairness, and Multi-Region Multi-Modal Radiomics

Cancers, Год журнала: 2023, Номер 15(7), С. 1932 - 1932

Опубликована: Март 23, 2023

Automatic delineation and detection of the primary tumour (GTVp) lymph nodes (GTVn) using PET CT in head neck cancer recurrence-free survival prediction can be useful for diagnosis patient risk stratification. We used data from nine different centres, with 524 359 cases training testing, respectively. utilised posterior sampling weight space proposed segmentation model to estimate uncertainty false positive reduction. explored prognostic potential radiomics features extracted predicted GTVp GTVn SHAP analysis explainability. evaluated bias models respect age, gender, chemotherapy, HPV status, lesion size. achieved an aggregate Dice score 0.774 0.760 on test set GTVn, observed a per image reduction 19.5% 7.14% threshold Radiomics both are most prognostic, our achieves C-index 0.672 set. Our framework incorporates estimation, fairness, explainability, demonstrating accurate

Язык: Английский

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Weighted Fusion Transformer for Dual PET/CT Head and Neck Tumor Segmentation

IEEE Access, Год журнала: 2024, Номер 12, С. 110905 - 110919

Опубликована: Янв. 1, 2024

Accurate tumor segmentation in PET/CT imaging is essential for the diagnosis and treatment of cancer, impacting therapeutic outcomes patient management. Our study introduces a new approach integrating Weighted Fusion Transformer Network to enhance volumes. This method synergizes PET CT modalities through FormerU-Net architecture that employs convolutional neural networks alongside transformer blocks, aiming leverage unique advantages each modality. We evaluated proposed using multi-institutional dataset, applying key performance metrics such as Dice Similarity Coefficient aggregate, Jaccard Index, Volume Correlation, Average Surface Distance assess precision. The results indicate CT/PET/Fusion strategy significantly improves delineation, outperforming traditional methods. main findings suggest this integrative could potentially redefine standard clinical practice. Lastly, offers promising direction enhancing accuracy oncological imaging, with implications improvement patient-specific strategies.

Язык: Английский

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PET and CT based DenseNet outperforms advanced deep learning models for outcome prediction of oropharyngeal cancer

Radiotherapy and Oncology, Год журнала: 2025, Номер unknown, С. 110852 - 110852

Опубликована: Март 1, 2025

In the HECKTOR 2022 challenge set [1], several state-of-the-art (SOTA, achieving best performance) deep learning models were introduced for predicting recurrence-free period (RFP) in head and neck cancer patients using PET CT images. This study investigates whether a conventional DenseNet architecture, with optimized numbers of layers image-fusion strategies, could achieve comparable performance as SOTA models. The dataset comprises 489 oropharyngeal (OPC) from seven distinct centers. It was randomly divided into training (n = 369) an independent test 120). Furthermore, additional 400 OPC patients, who underwent chemo(radiotherapy) at our center, employed external testing. Each patients' data included pre-treatment CT- PET-scans, manually generated GTV (Gross tumour volume) contours primary tumors lymph nodes, RFP information. present compared against three developed on dataset. When inputting CT, early fusion (considering them different channels input) approach, DenseNet81 (with 81 layers) obtained internal C-index 0.69, metric Notably, removal input yielded same 0.69 while improving 0.59 to 0.63. PET-only models, when utilizing late (concatenation extracted features) PET, demonstrated superior values 0.68 0.66 both sets, better only set. basic architecture predictive par featuring more intricate architectures set, test. imaging

Язык: Английский

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ASF-LKUNet: Adjacent-scale fusion U-Net with large kernel for multi-organ segmentation

Computers in Biology and Medicine, Год журнала: 2024, Номер 181, С. 109050 - 109050

Опубликована: Авг. 27, 2024

Язык: Английский

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Prediction of Recurrence Free Survival of Head and Neck Cancer Using PET/CT Radiomics and Clinical Information

Опубликована: Май 27, 2024

Язык: Английский

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Enhancing Predictive Accuracy for Recurrence-Free Survival in Head and Neck Tumor: A Comparative Study of Weighted Fusion Radiomic Analysis

Diagnostics, Год журнала: 2024, Номер 14(18), С. 2038 - 2038

Опубликована: Сен. 14, 2024

Despite advancements in oncology, predicting recurrence-free survival (RFS) head and neck (H&N) cancer remains challenging due to the heterogeneity of tumor biology treatment responses. This study aims address research gap prognostic efficacy traditional clinical predictors versus advanced radiomics features explore potential weighted fusion techniques for enhancing RFS prediction. We utilized data, radiomic from CT PET scans, various algorithms stratify patients into low- high-risk groups RFS. The predictive performance each model was evaluated using Kaplan–Meier analysis, significance differences rates assessed confidence interval (CI) tests. with a 90% emphasis on significantly outperformed individual modalities, yielding highest C-index. Additionally, incorporation contextual information by varying peritumoral radii did not substantially improve prediction accuracy. While model, individually, achieve statistical differentiation, combined feature set showed improved performance. integration data through enhances accuracy outcomes cancer. Our findings suggest that utilization multi-modal helps developing more reliable models underscore imaging refining assessments. propels discussion forward, indicating pivotal step toward adoption precision medicine care.

Язык: Английский

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Automated tumor localization and segmentation through hybrid neural network in head and neck cancer

Medical dosimetry, Год журнала: 2024, Номер unknown

Опубликована: Окт. 1, 2024

Язык: Английский

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Automatic Delineation and Prognostic Assessment of Head and Neck Tumor Lesion in Multi-Modality Positron Emission Tomography / Computed Tomography Images Based on Deep Learning: A Survey

Neurocomputing, Год журнала: 2024, Номер 610, С. 128531 - 128531

Опубликована: Сен. 10, 2024

Язык: Английский

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