Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021

• URL: http://arxiv.org/abs/2104.08072v1
• Date: Thu, 15 Apr 2021 10:49:34 GMT
• Title: Machine Learning and Glioblastoma: Treatment Response Monitoring Biomarkers in 2021
• Authors: Thomas Booth, Bernice Akpinar, Andrei Roman, Haris Shuaib, Aysha Luis, Alysha Chelliah, Ayisha Al Busaidi, Ayesha Mirchandani, Burcu Alparslan, Nina Mansoor, Keyoumars Ashkan, Sebastien Ourselin, Marc Modat
• Abstract summary: The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults. There is likely good diagnostic performance of machine learning models that use MRI features to distinguish between progression and mimics. The diagnostic performance of ML using implicit features did not appear to be superior to ML using explicit features.
• Score: 0.3266995794795542
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The aim of the systematic review was to assess recently published studies on diagnostic test accuracy of glioblastoma treatment response monitoring biomarkers in adults, developed through machine learning (ML). Articles were searched for using MEDLINE, EMBASE, and the Cochrane Register. Included study participants were adult patients with high grade glioma who had undergone standard treatment (maximal resection, radiotherapy with concomitant and adjuvant temozolomide) and subsequently underwent follow-up imaging to determine treatment response status. Risk of bias and applicability was assessed with QUADAS 2 methodology. Contingency tables were created for hold-out test sets and recall, specificity, precision, F1-score, balanced accuracy calculated. Fifteen studies were included with 1038 patients in training sets and 233 in test sets. To determine whether there was progression or a mimic, the reference standard combination of follow-up imaging and histopathology at re-operation was applied in 67% of studies. The small numbers of patient included in studies, the high risk of bias and concerns of applicability in the study designs (particularly in relation to the reference standard and patient selection due to confounding), and the low level of evidence, suggest that limited conclusions can be drawn from the data. There is likely good diagnostic performance of machine learning models that use MRI features to distinguish between progression and mimics. The diagnostic performance of ML using implicit features did not appear to be superior to ML using explicit features. There are a range of ML-based solutions poised to become treatment response monitoring biomarkers for glioblastoma. To achieve this, the development and validation of ML models require large, well-annotated datasets where the potential for confounding in the study design has been carefully considered.

Related papers

• Enhanced Cascade Prostate Cancer Classifier in mp-MRI Utilizing Recall Feedback Adaptive Loss and Prior Knowledge-Based Feature Extraction [4.00189087655119]
  We propose a solution that incorporates prior knowledge, addresses the issue of uneven medical sample distribution, and maintains high interpretability in mpMRI. Firstly, we introduce Prior Knowledge-Based Feature Extraction, which mathematically models the PI-RADS criteria for prostate cancer as diagnostic information into model training. Secondly, we propose Adaptive Recall Feedback Loss to address the extremely imbalanced data problem. Thirdly, we design an Enhanced Cascade Prostate Cancer that classifies prostate cancer into different levels in an interpretable way.
  arXiv  Detail & Related papers  (2024-08-19T07:18:06Z)
• SemioLLM: Assessing Large Language Models for Semiological Analysis in Epilepsy Research [45.2233252981348]
  Large Language Models have shown promising results in their ability to encode general medical knowledge. We test the ability of state-of-the-art LLMs to leverage their internal knowledge and reasoning for epilepsy diagnosis.
  arXiv  Detail & Related papers  (2024-07-03T11:02:12Z)
• Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques [39.97900702763419]
  This study focuses on analyzing dermoscopy images to determine the depth of melanomas. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. Results indicated that the models achieved significant improvements over previous methods.
  arXiv  Detail & Related papers  (2024-06-19T11:07:55Z)
• Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer: A systematic review [0.5792122879054292]
  externally validated machine learning models for diagnosis, classification, prognosis, or treatment outcome prediction in female breast cancer. Three studies externally validated ML models for diagnosis, 4 for classification, 2 for prognosis, and 1 for both classification and prognosis. Most studies used Convolutional Neural Networks and one used logistic regression algorithms.
  arXiv  Detail & Related papers  (2023-12-09T18:27:56Z)
• Radiomics Boosts Deep Learning Model for IPMN Classification [3.4659499358648675]
  Intraductal Papillary Mucinous Neoplasm (IPMN) cysts are pre-malignant pancreas lesions, and they can progress into pancreatic cancer. In this study, we propose a novel computer-aided diagnosis pipeline for IPMN risk classification from MRI scans.
  arXiv  Detail & Related papers  (2023-09-11T22:41:52Z)
• Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification [40.366659911178964]
  Endoscopy plays a major role in identifying any underlying abnormalities within the gastrointestinal (GI) tract. Deep learning, specifically Convolution Neural Networks (CNNs) which are designed to perform automatic feature learning without any prior feature engineering, has recently reported great benefits for GI endoscopy image analysis. We investigate three KD-based learning frameworks, response-based, feature-based, and relation-based mechanisms, and introduce a novel multi-head attention-based feature fusion mechanism to support relation-based learning.
  arXiv  Detail & Related papers  (2023-08-17T02:02:11Z)
• MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models [0.0]
  We employ deep learning models to predict the methylation status of the MGMT promoter in glioblastoma tumors using MRI scans. Our results show no correlation between these models' performance to assure the accuracy and reliability of deep learning systems in cancer diagnosis.
  arXiv  Detail & Related papers  (2023-04-03T07:55:42Z)
• The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients [31.567542945171834]
  We describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge. BraTS-Reg is the first public benchmark environment for deformable registration algorithms. The aim of BraTS-Reg is to continue to serve as an active resource for research.
  arXiv  Detail & Related papers  (2021-12-13T19:25:16Z)
• 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)
• Hemogram Data as a Tool for Decision-making in COVID-19 Management: Applications to Resource Scarcity Scenarios [62.997667081978825]
  COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure. This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients. Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
  arXiv  Detail & Related papers  (2020-05-10T01:45:03Z)
• Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort [40.24757332810004]
  Automated volumetry software (AVS) has recently become widely available to neuroradiologists. Machine learning techniques have emerged as promising approaches to assist diagnosis.
  arXiv  Detail & Related papers  (2020-03-19T08:44:46Z)

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.