Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical information

• URL: http://arxiv.org/abs/2402.18417v1
• Date: Wed, 28 Feb 2024 15:35:41 GMT
• Title: Prediction of recurrence free survival of head and neck cancer using PET/CT radiomics and clinical information
• Authors: Mona Furukawa, Daniel R. McGowan, Bart{\l}omiej W. Papie\.z
• Abstract summary: We built Cox proportional hazard (CoxPH) models that predict the recurrence free survival (RFS) of oropharyngeal HNC patients. Our models utilise both clinical information and multimodal radiomics features extracted from tumour regions in Computed Tomography (CT) and Positron Emission Tomography (PET) Our under- and over-segmentation study confirms that segmentation accuracy affects radiomics extraction, however, it affects PET and CT differently.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The 5-year survival rate of Head and Neck Cancer (HNC) has not improved over the past decade and one common cause of treatment failure is recurrence. In this paper, we built Cox proportional hazard (CoxPH) models that predict the recurrence free survival (RFS) of oropharyngeal HNC patients. Our models utilise both clinical information and multimodal radiomics features extracted from tumour regions in Computed Tomography (CT) and Positron Emission Tomography (PET). Furthermore, we were one of the first studies to explore the impact of segmentation accuracy on the predictive power of the extracted radiomics features, through under- and over-segmentation study. Our models were trained using the HEad and neCK TumOR (HECKTOR) challenge data, and the best performing model achieved a concordance index (C-index) of 0.74 for the model utilising clinical information and multimodal CT and PET radiomics features, which compares favourably with the model that only used clinical information (C-index of 0.67). Our under- and over-segmentation study confirms that segmentation accuracy affects radiomics extraction, however, it affects PET and CT differently.

Related papers

• Lung-CADex: Fully automatic Zero-Shot Detection and Classification of Lung Nodules in Thoracic CT Images [45.29301790646322]
  Computer-aided diagnosis can help with early lung nodul detection and facilitate subsequent nodule characterization. We propose CADe, for segmenting lung nodules in a zero-shot manner using a variant of the Segment Anything Model called MedSAM. We also propose, CADx, a method for the nodule characterization as benign/malignant by making a gallery of radiomic features and aligning image-feature pairs through contrastive learning.
  arXiv  Detail & Related papers  (2024-07-02T19:30:25Z)
• Deep Learning-Based Segmentation of Tumors in PET/CT Volumes: Benchmark of Different Architectures and Training Strategies [0.12301374769426145]
  This study examines various neural network architectures and training strategies for automatically segmentation of cancer lesions. V-Net and nnU-Net models were the most effective for their respective datasets. Eliminating cancer-free cases from the AutoPET dataset was found to improve the performance of most models.
  arXiv  Detail & Related papers  (2024-04-15T13:03:42Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• 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)
• Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patients [42.09584755334577]
  Lung cancer is a leading cause of cancer mortality globally, highlighting the importance of understanding its mortality risks to design effective therapies. The National Lung Screening Trial (NLST) employed computed tomography texture analysis to quantify the mortality risks of lung cancer patients. We propose a novel Penalized Deep Partially Linear Cox Model (Penalized DPLC), which incorporates the SCAD penalty to select important texture features and employs a deep neural network to estimate the nonparametric component of the model.
  arXiv  Detail & Related papers  (2023-03-09T15:38:16Z)
• Recurrence-free Survival Prediction under the Guidance of Automatic Gross Tumor Volume Segmentation for Head and Neck Cancers [8.598790229614071]
  We developed an automated primary tumor (GTVp) and lymph nodes (GTVn) segmentation method. We extracted radiomics features from the segmented tumor volume and constructed a multi-modality tumor recurrence-free survival (RFS) prediction model.
  arXiv  Detail & Related papers  (2022-09-22T18:44:57Z)
• Multimodal PET/CT Tumour Segmentation and Prediction of Progression-Free Survival using a Full-Scale UNet with Attention [0.8138288420049126]
  The MICCAI 2021 HEad and neCK TumOR (HECKTOR) segmentation and outcome prediction challenge creates a platform for comparing segmentation methods. We trained multiple neural networks for tumor volume segmentation, and these segmentations were ensembled achieving an average Dice Similarity Coefficient of 0.75 in cross-validation. For prediction of patient progression free survival task, we propose a Cox proportional hazard regression combining clinical, radiomic, and deep learning features.
  arXiv  Detail & Related papers  (2021-11-06T10:28:48Z)
• 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)
• Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study [3.040206021972938]
  Radiomics is an area of research focusing on high throughput feature extraction from medical images. Generative models can improve the performance of low dose CT-based radiomics in different tasks.
  arXiv  Detail & Related papers  (2021-09-06T06:01:21Z)
• Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy [12.05638699290782]
  We present a novel 3D sequence-to-sequence model based on Convolution Long Short Term Memory (ConvLSTM) It predicts future anatomical deformations and changes in gross tumor volume as well as critical OARs. We validated our model on two radiotherapy datasets.
  arXiv  Detail & Related papers  (2021-06-16T18:29:16Z)
• CoRSAI: A System for Robust Interpretation of CT Scans of COVID-19 Patients Using Deep Learning [133.87426554801252]
  We adopted an approach based on using an ensemble of deep convolutionalneural networks for segmentation of lung CT scans. Using our models we are able to segment the lesions, evaluatepatients dynamics, estimate relative volume of lungs affected by lesions and evaluate the lung damage stage.
  arXiv  Detail & Related papers  (2021-05-25T12:06:55Z)

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.