Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI

• URL: http://arxiv.org/abs/2402.06982v1
• Date: Sat, 10 Feb 2024 16:13:09 GMT
• Title: Treatment-wise Glioblastoma Survival Inference with Multi-parametric Preoperative MRI
• Authors: Xiaofeng Liu, Nadya Shusharina, Helen A Shih, C.-C. Jay Kuo, Georges El Fakhri, Jonghye Woo
• Abstract summary: We propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments.
• Score: 34.830878479276286
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represented by the MR scans) and the choice of treatment are the cause of ST. While previous related MR-based glioblastoma ST studies have focused only on the direct mapping of MR scans to ST, they have not included the underlying causal relationship between treatments and ST. To address this limitation, we propose a treatment-conditioned regression model for glioblastoma ST that incorporates treatment information in addition to MR scans. Our approach allows us to effectively utilize the data from all of the treatments in a unified manner, rather than having to train separate models for each of the treatments. Furthermore, treatment can be effectively injected into each convolutional layer through the adaptive instance normalization we employ. We evaluate our framework on the BraTS20 ST prediction task. Three treatment options are considered: Gross Total Resection (GTR), Subtotal Resection (STR), and no resection. The evaluation results demonstrate the effectiveness of injecting the treatment for estimating GBM survival.

Related papers

• Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation [47.119513326344126]
  The BraTS-MEN-RT challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs. Each case includes a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space. Target volume annotations adhere to established radiotherapy planning protocols.
  arXiv  Detail & Related papers  (2024-05-28T17:25:43Z)
• Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods [55.07297021627281]
  The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. Diffusion MRI tractography is the only method that enables the study of the anatomy and variability of the CST pathway in human health.
  arXiv  Detail & Related papers  (2023-06-09T02:05:40Z)
• Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks [33.51490233427579]
  Two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The best performance achieved was a 61% Dice score, and the best classification performance was about 80% balanced accuracy. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.
  arXiv  Detail & Related papers  (2023-04-18T10:14:45Z)
• 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)
• TranSOP: Transformer-based Multimodal Classification for Stroke Treatment Outcome Prediction [2.358784542343728]
  We propose a transformer-based multimodal network (TranSOP) for a classification approach that employs clinical metadata and imaging information. This includes a fusion module to efficiently combine 3D non-contrast computed tomography (NCCT) features and clinical information. In comparative experiments using unimodal and multimodal data, we achieve a state-of-the-art AUC score of 0.85.
  arXiv  Detail & Related papers  (2023-01-25T21:05:10Z)
• Domain Adaptation of Automated Treatment Planning from Computed Tomography to Magnetic Resonance [0.5599792629509229]
  We created highly acceptable Magnetic resonance only treatment plans using a CT-trained machine learning model. clinically significant dose deviations from the CT based plans were observed.
  arXiv  Detail & Related papers  (2022-03-07T18:18:00Z)
• 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)
• 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)
• DTR Bandit: Learning to Make Response-Adaptive Decisions With Low Regret [59.81290762273153]
  Dynamic treatment regimes (DTRs) are personalized, adaptive, multi-stage treatment plans that adapt treatment decisions to an individual's initial features and to intermediate outcomes and features at each subsequent stage. We propose a novel algorithm that, by carefully balancing exploration and exploitation, is guaranteed to achieve rate-optimal regret when the transition and reward models are linear.
  arXiv  Detail & Related papers  (2020-05-06T13:03:42Z)
• Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations [114.16762407465427]
  We introduce the Counterfactual Recurrent Network (CRN) to estimate treatment effects over time. CRN uses domain adversarial training to build balancing representations of the patient history. We show how our model achieves lower error in estimating counterfactuals and in choosing the correct treatment and timing of treatment.
  arXiv  Detail & Related papers  (2020-02-10T20:47:36Z)

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.