Enhancing predictive imaging biomarker discovery through treatment effect analysis

• URL: http://arxiv.org/abs/2406.02534v1
• Date: Tue, 4 Jun 2024 17:54:44 GMT
• Title: Enhancing predictive imaging biomarker discovery through treatment effect analysis
• Authors: Shuhan Xiao, Lukas Klein, Jens Petersen, Philipp Vollmuth, Paul F. Jaeger, Klaus H. Maier-Hein,
• Abstract summary: This study focuses on the discovery of predictive imaging biomarkers, aiming to leverage pre-treatment images to unveil new causal relationships. Previous approaches relied on labor-intensive handcrafted or manually derived features, which may introduce biases. We propose an evaluation protocol for this task to assess a model's ability to identify predictive imaging biomarkers and differentiate them from prognostic ones.
• Score: 2.8300022117286523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identifying predictive biomarkers, which forecast individual treatment effectiveness, is crucial for personalized medicine and informs decision-making across diverse disciplines. These biomarkers are extracted from pre-treatment data, often within randomized controlled trials, and have to be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on the discovery of predictive imaging biomarkers, aiming to leverage pre-treatment images to unveil new causal relationships. Previous approaches relied on labor-intensive handcrafted or manually derived features, which may introduce biases. In response, we present a new task of discovering predictive imaging biomarkers directly from the pre-treatment images to learn relevant image features. We propose an evaluation protocol for this task to assess a model's ability to identify predictive imaging biomarkers and differentiate them from prognostic ones. It employs statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally designed for estimating the conditional average treatment effect (CATE) for this task, which previously have been primarily assessed for the precision of CATE estimation, overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates promising results in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets.

Related papers

• Label-free prediction of fluorescence markers in bovine satellite cells using deep learning [0.0]
  assessing the quality of bovine satellite cells (BSCs) is essential for the cultivated meat industry. This study aims to develop a label-free method for predicting fluorescence markers in isolated BSCs using deep learning.
  arXiv  Detail & Related papers  (2024-10-17T15:47:12Z)
• Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification [0.05277756703318046]
  This study models various types of treatment assignment biases using mutual information. By incorporating empirical biological mechanisms, we create a more realistic benchmark that reflects the complexities of real-world data.
  arXiv  Detail & Related papers  (2024-10-01T08:47:29Z)
• A marker-less human motion analysis system for motion-based biomarker discovery in knee disorders [60.99112047564336]
  The NHS has been having increased difficulty seeing all low-risk patients, this includes but not limited to suspected osteoarthritis (OA) patients. We propose a novel method of automated biomarker identification for diagnosis of knee disorders and the monitoring of treatment progression.
  arXiv  Detail & Related papers  (2023-04-26T16:47:42Z)
• Stain-invariant self supervised learning for histopathology image analysis [74.98663573628743]
  We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin stained images of breast cancer. Our method achieves the state-of-the-art performance on several publicly available breast cancer datasets.
  arXiv  Detail & Related papers  (2022-11-14T18:16:36Z)
• Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers [0.293168019422713]
  We show how a deep conditional generative model can be used to perturb local imaging features in baseline images that are pertinent to subject-specific future disease evolution. Our model produces counterfactuals with changes in imaging features that reflect established clinical markers predictive of future MRI lesional activity at the population level.
  arXiv  Detail & Related papers  (2022-08-03T18:58:45Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• The Future will be Different than Today: Model Evaluation Considerations when Developing Translational Clinical Biomarker [4.549866091318765]
  We present one evaluation strategy by using leave-one-study-out (LOSO) in place of conventional cross-validation (cv) methods. To demonstrate the performance of K-fold vs LOSO cv in estimating the effect size of biomarkers, we leveraged data from clinical trials and simulation studies.
  arXiv  Detail & Related papers  (2021-07-13T19:36:25Z)
• Malignancy Prediction and Lesion Identification from Clinical Dermatological Images [65.1629311281062]
  We consider machine-learning-based malignancy prediction and lesion identification from clinical dermatological images. We first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy.
  arXiv  Detail & Related papers  (2021-04-02T20:52:05Z)
• Deep Low-Shot Learning for Biological Image Classification and Visualization from Limited Training Samples [52.549928980694695]
  In situ hybridization (ISH) gene expression pattern images from the same developmental stage are compared. labeling training data with precise stages is very time-consuming even for biologists. We propose a deep two-step low-shot learning framework to accurately classify ISH images using limited training images.
  arXiv  Detail & Related papers  (2020-10-20T06:06:06Z)
• Bayesian Neural Networks for Uncertainty Estimation of Imaging Biomarkers [0.0]
  We propose to propagate segmentation uncertainty to the statistical analysis to account for variations in segmentation confidence. Our results for segmenting the liver in patients with diabetes mellitus clearly demonstrate the improvement of integrating biomarker uncertainty in the statistical inference.
  arXiv  Detail & Related papers  (2020-08-28T14:34:12Z)
• Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model [71.80319052891817]
  We present a relation-driven semi-supervised framework for medical image classification. It exploits the unlabeled data by encouraging the prediction consistency of given input under perturbations. Our method outperforms many state-of-the-art semi-supervised learning methods on both single-label and multi-label image classification scenarios.
  arXiv  Detail & Related papers  (2020-05-15T06:57:54Z)

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.