Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction

• URL: http://arxiv.org/abs/2306.03270v1
• Date: Mon, 5 Jun 2023 21:39:11 GMT
• Title: Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient Survivability Prediction
• Authors: M. S. Sadique, W. Farzana, A. Temtam, E. Lappinen, A. Vossough, K. M. Iftekharuddin
• Abstract summary: GBM is the most aggressive brain tumor in adults that has a short survival rate even after aggressive treatment with surgery and radiation therapy. The changes on magnetic resonance imaging (MRI) for patients with GBM after radiotherapy are indicative of radiation-induced necrosis (RN) or recurrent brain tumor (rBT) This study proposes computational modeling with statistically rigorous repeated random sub-sampling to balance the subset sample size for rBT and RN classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: GBM (Glioblastoma multiforme) is the most aggressive type of brain tumor in adults that has a short survival rate even after aggressive treatment with surgery and radiation therapy. The changes on magnetic resonance imaging (MRI) for patients with GBM after radiotherapy are indicative of either radiation-induced necrosis (RN) or recurrent brain tumor (rBT). Screening for rBT and RN at an early stage is crucial for facilitating faster treatment and better outcomes for the patients. Differentiating rBT from RN is challenging as both may present with similar radiological and clinical characteristics on MRI. Moreover, learning-based rBT versus RN classification using MRI may suffer from class imbalance due to lack of patient data. While synthetic data generation using generative models has shown promise to address class imbalance, the underlying data representation may be different in synthetic or augmented data. This study proposes computational modeling with statistically rigorous repeated random sub-sampling to balance the subset sample size for rBT and RN classification. The proposed pipeline includes multiresolution radiomic feature (MRF) extraction followed by feature selection with statistical significance testing (p<0.05). The five-fold cross validation results show the proposed model with MRF features classifies rBT from RN with an area under the curve (AUC) of 0.8920+-.055. Moreover, considering the dependence between survival time and censor time (where patients are not followed up until death), we demonstrate the feasibility of using MRF radiomic features as a non-invasive biomarker to identify patients who are at higher risk of recurrence or radiation necrosis. The cross-validated results show that the MRF model provides the best overall performance with an AUC of 0.770+-.032.

Related papers

• Risk Classification of Brain Metastases via Radiomics, Delta-Radiomics and Machine Learning [7.165205048529115]
  We hypothesized that using radiomics and machine learning (ML), metastases at high risk for subsequent progression could be identified during follow-up prior to the onset of significant tumor growth. The classification is realized via the maximum-relevance minimal-redundancy (MRMR) technique and support vector machines (SVM) The results indicate that risk stratification of BM based on radiomics and machine learning during post-SRT follow-up is possible with good accuracy and should be further pursued to personalize and improve post-SRT follow-up.
  arXiv  Detail & Related papers  (2023-02-17T10:55:18Z)
• Advancing Radiograph Representation Learning with Masked Record Modeling [52.04899592688968]
  We formulate the self- and report-completion as two complementary objectives and present a unified framework based on masked record modeling (MRM) MRM reconstructs masked image patches and masked report tokens following a multi-task scheme to learn knowledge-enhanced semantic representations. Specifically, we find that MRM offers superior performance in label-efficient fine-tuning.
  arXiv  Detail & Related papers  (2023-01-30T18:33:32Z)
• Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines [0.0]
  We introduce open-radiomics, a set of radiomics datasets and a comprehensive radiomics pipeline. Experiments are conducted on BraTS 2020 open-source Magnetic Resonance Imaging (MRI) dataset. Unlike binWidth and image normalization, tumor subregion and imaging sequence significantly affected performance of the models.
  arXiv  Detail & Related papers  (2022-07-29T16:37:46Z)
• Automated SSIM Regression for Detection and Quantification of Motion Artefacts in Brain MR Images [54.739076152240024]
  Motion artefacts in magnetic resonance brain images are a crucial issue. The assessment of MR image quality is fundamental before proceeding with the clinical diagnosis. An automated image quality assessment based on the structural similarity index (SSIM) regression has been proposed here.
  arXiv  Detail & Related papers  (2022-06-14T10:16:54Z)
• Deep radiomic signature with immune cell markers predicts the survival of glioma patients [8.386631203775533]
  We propose a novel type of deep radiomic features (DRFs) computed from a convolutional neural network (CNN) The proposed method extracts a total of 180 DRFs by aggregating the activation maps of a pre-trained 3D-CNN within labeled tumor regions of MRI scans. Results show a high correlation between DRFs and various markers, as well as significant differences between patients grouped based on these markers.
  arXiv  Detail & Related papers  (2022-06-09T08:52:15Z)
• Modeling of Textures to Predict Immune Cell Status and Survival of Brain Tumour Patients [4.542148087324063]
  Radiomics has shown a capability for different types of cancers such as glioma to predict the clinical outcome. We aim to predict the immune marker status (low versus high) and overall survival for glioma patients using deep radiomic features (DRFs) Our experiments are performed to assess the relationship between the proposed DRFs, three immune cell markers (Macrophage M1, Neutrophils and T Cells Follicular Helper) and measure their association with overall survival.
  arXiv  Detail & Related papers  (2022-06-04T03:52:12Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• A Deep Learning Approach to Predicting Collateral Flow in Stroke Patients Using Radiomic Features from Perfusion Images [58.17507437526425]
  Collateral circulation results from specialized anastomotic channels which provide oxygenated blood to regions with compromised blood flow. The actual grading is mostly done through manual inspection of the acquired images. We present a deep learning approach to predicting collateral flow grading in stroke patients based on radiomic features extracted from MR perfusion data.
  arXiv  Detail & Related papers  (2021-10-24T18:58:40Z)
• Controlling False Positive/Negative Rates for Deep-Learning-Based Prostate Cancer Detection on Multiparametric MR images [58.85481248101611]
  We propose a novel PCa detection network that incorporates a lesion-level cost-sensitive loss and an additional slice-level loss based on a lesion-to-slice mapping function. Our experiments based on 290 clinical patients concludes that 1) The lesion-level FNR was effectively reduced from 0.19 to 0.10 and the lesion-level FPR was reduced from 1.03 to 0.66 by changing the lesion-level cost.
  arXiv  Detail & Related papers  (2021-06-04T09:51:27Z)
• MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response [58.0291320452122]
  This paper aims at a unified deep learning approach to predict patient prognosis and therapy response. We formalize the prognosis modeling as a multi-modal asynchronous time series classification task. Our predictive model could further stratify low-risk and high-risk patients in terms of long-term survival.
  arXiv  Detail & Related papers  (2020-10-08T15:30:17Z)
• Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images [3.510687032586989]
  We present a model to take into account the heterogeneous nature of a tumor to predict survival. The tumor heterogeneity is measured in terms of its mass by combining information regarding the radiodensity obtained in images with the gross tumor volume (GTV) We propose a novel feature called Tumor Mass within a GTV (TMG), that improves the prediction of survivability, compared to existing models which use GTV.
  arXiv  Detail & Related papers  (2020-03-07T08:47:26Z)

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.