Deep radiomic signature with immune cell markers predicts the survival
of glioma patients
- URL: http://arxiv.org/abs/2206.04349v1
- Date: Thu, 9 Jun 2022 08:52:15 GMT
- Title: Deep radiomic signature with immune cell markers predicts the survival
of glioma patients
- Authors: Ahmad Chaddad, Paul Daniel Mingli Zhang, Saima Rathore, Paul Sargos,
Christian Desrosiers, Tamim Niazi
- Abstract summary: 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.
- Score: 8.386631203775533
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Imaging biomarkers offer a non-invasive way to predict the response of
immunotherapy prior to treatment. In this work, we propose a novel type of deep
radiomic features (DRFs) computed from a convolutional neural network (CNN),
which capture tumor characteristics related to immune cell markers and overall
survival. Our study uses four MRI sequences (T1-weighted, T1-weighted
post-contrast, T2-weighted and FLAIR) with corresponding immune cell markers of
151 patients with brain tumor. 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. These features offer a compact, yet powerful
representation of regional texture encoding tissue heterogeneity. A
comprehensive set of experiments is performed to assess the relationship
between the proposed DRFs and immune cell markers, and measure their
association with overall survival. Results show a high correlation between DRFs
and various markers, as well as significant differences between patients
grouped based on these markers. Moreover, combining DRFs, clinical features and
immune cell markers as input to a random forest classifier helps discriminate
between short and long survival outcomes, with AUC of 72\% and
p=2.36$\times$10$^{-5}$. These results demonstrate the usefulness of proposed
DRFs as non-invasive biomarker for predicting treatment response in patients
with brain tumors.
Related papers
- Using Multiparametric MRI with Optimized Synthetic Correlated Diffusion Imaging to Enhance Breast Cancer Pathologic Complete Response Prediction [71.91773485443125]
Neoadjuvant chemotherapy has recently gained popularity as a promising treatment strategy for breast cancer.
The current process to recommend neoadjuvant chemotherapy relies on the subjective evaluation of medical experts.
This research investigates the application of optimized CDI$s$ to enhance breast cancer pathologic complete response prediction.
arXiv Detail & Related papers (2024-05-13T15:40:56Z) - Brain Tumor Recurrence vs. Radiation Necrosis Classification and Patient
Survivability Prediction [0.0]
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.
arXiv Detail & Related papers (2023-06-05T21:39:11Z) - Cancer-Net BCa-S: Breast Cancer Grade Prediction using Volumetric Deep
Radiomic Features from Synthetic Correlated Diffusion Imaging [82.74877848011798]
The prevalence of breast cancer continues to grow, affecting about 300,000 females in the United States in 2023.
The gold-standard Scarff-Bloom-Richardson (SBR) grade has been shown to consistently indicate a patient's response to chemotherapy.
In this paper, we study the efficacy of deep learning for breast cancer grading based on synthetic correlated diffusion (CDI$s$) imaging.
arXiv Detail & Related papers (2023-04-12T15:08:34Z) - Exploiting segmentation labels and representation learning to forecast
therapy response of PDAC patients [60.78505216352878]
We propose a hybrid deep neural network pipeline to predict tumour response to initial chemotherapy.
We leverage a combination of representation transfer from segmentation to classification, as well as localisation and representation learning.
Our approach yields a remarkably data-efficient method able to predict treatment response with a ROC-AUC of 63.7% using only 477 datasets in total.
arXiv Detail & Related papers (2022-11-08T11:50:31Z) - 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) - Severity classification in cases of Collagen VI-related myopathy with
Convolutional Neural Networks and handcrafted texture features [0.34998703934432684]
Three methods are proposed to classify target muscles in Collagen VI-related myopathy cases.
The best results were obtained with the hybrid model, resulting in a global accuracy of 93.8%.
arXiv Detail & Related papers (2022-02-28T15:09:42Z) - A New Deep Hybrid Boosted and Ensemble Learning-based Brain Tumor
Analysis using MRI [0.28675177318965034]
Two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs)
In the first phase, a novel deep boosted features and ensemble classifiers (DBF-EC) scheme is proposed to detect tumor MRI images from healthy individuals effectively.
In the second phase, a new hybrid features fusion-based brain tumor classification approach is proposed, comprised of dynamic-static feature and ML classifier to categorize different tumor types.
arXiv Detail & Related papers (2022-01-14T10:24:47Z) - EMT-NET: Efficient multitask network for computer-aided diagnosis of
breast cancer [58.720142291102135]
We propose an efficient and light-weighted learning architecture to classify and segment breast tumors simultaneously.
We incorporate a segmentation task into a tumor classification network, which makes the backbone network learn representations focused on tumor regions.
The accuracy, sensitivity, and specificity of tumor classification is 88.6%, 94.1%, and 85.3%, respectively.
arXiv Detail & Related papers (2022-01-13T05:24:40Z) - 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) - A digital score of tumour-associated stroma infiltrating lymphocytes
predicts survival in head and neck squamous cell carcinoma [1.116655705522709]
infiltration of T-lymphocytes in the stroma and tumour is an indication of an effective immune response against the tumour, resulting in better survival.
A deep learning based automated method was employed to segment tumour, stroma and lymphocytes.
The spatial patterns of lymphocytes and tumour-associated stroma were digitally quantified to compute the TASIL-score.
arXiv Detail & Related papers (2021-04-16T19:45:00Z) - Segmentation for Classification of Screening Pancreatic Neuroendocrine
Tumors [72.65802386845002]
This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs) in abdominal CT scans.
To the best of our knowledge, this task has not been studied before as a computational task.
Our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of $89.47%$ at a specificity of $81.08%$.
arXiv Detail & Related papers (2020-04-04T21:21:44Z)
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.