Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging

• URL: http://arxiv.org/abs/2504.01692v1
• Date: Wed, 02 Apr 2025 12:48:01 GMT
• Title: Segmentation variability and radiomics stability for predicting Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging
• Authors: Isabella Cama, Alejandro Guzmán, Cristina Campi, Michele Piana, Karim Lekadir, Sara Garbarino, Oliver Díaz,
• Abstract summary: This study investigates the impact of segmentation variability on feature stability and predictive performance in radiomics-based prediction of Triple-Negative Breast Cancer subtype using Magnetic Resonance Imaging.<n>While incorporating peritumoral information may reduce feature stability, it does not diminish feature predictive capability.
• Score: 36.402807672379296
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most papers caution against using predictive models for disease stratification based on unselected radiomic features, as these features are affected by contouring variability. Instead, they advocate for the use of the Intraclass Correlation Coefficient (ICC) as a measure of stability for feature selection. However, the direct effect of segmentation variability on the predictive models is rarely studied. This study investigates the impact of segmentation variability on feature stability and predictive performance in radiomics-based prediction of Triple-Negative Breast Cancer (TNBC) subtype using Magnetic Resonance Imaging. A total of 244 images from the Duke dataset were used, with segmentation variability introduced through modifications of manual segmentations. For each mask, explainable radiomic features were selected using the Shapley Additive exPlanations method and used to train logistic regression models. Feature stability across segmentations was assessed via ICC, Pearson's correlation, and reliability scores quantifying the relationship between feature stability and segmentation variability. Results indicate that segmentation accuracy does not significantly impact predictive performance. While incorporating peritumoral information may reduce feature reproducibility, it does not diminish feature predictive capability. Moreover, feature selection in predictive models is not inherently tied to feature stability with respect to segmentation, suggesting that an overreliance on ICC or reliability scores for feature selection might exclude valuable predictive features.

Related papers

• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Model-based causal feature selection for general response types [8.228587135343071]
  Invariant causal prediction (ICP) is a method for causal feature selection which requires data from heterogeneous settings. We develop transformation-model (TRAM) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses. We provide an open-source R package 'tramicp' and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients.
  arXiv  Detail & Related papers  (2023-09-22T12:42:48Z)
• Nonlinear Permuted Granger Causality [0.6526824510982799]
  Granger causal inference is a contentious but widespread method used in fields ranging from economics to neuroscience. To allow for out-of-sample comparison, a measure of functional connectivity is explicitly defined using permutations of the covariate set. Performance of the permutation method is compared to penalized variable selection, naive replacement, and omission techniques via simulation.
  arXiv  Detail & Related papers  (2023-08-11T16:44:16Z)
• Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated Uncertainty [52.03490691733464]
  We introduce DEviS, an easily implementable foundational model that seamlessly integrates into various medical image segmentation networks. By leveraging subjective logic theory, we explicitly model probability and uncertainty for the problem of medical image segmentation. DeviS incorporates an uncertainty-aware filtering module, which utilizes the metric of uncertainty-calibrated error to filter reliable data.
  arXiv  Detail & Related papers  (2023-01-01T05:02:46Z)
• Loss-guided Stability Selection [0.0]
  It is well-known that model selection procedures like the Lasso or Boosting tend to overfit on real data. Standard Stability Selection is based on a global criterion, namely the per-family error rate. We propose a Stability Selection variant which respects the chosen loss function via an additional validation step.
  arXiv  Detail & Related papers  (2022-02-10T11:20:25Z)
• Employing an Adjusted Stability Measure for Multi-Criteria Model Fitting on Data Sets with Similar Features [0.1127980896956825]
  We show that our approach achieves the same or better predictive performance compared to the two established approaches. Our approach succeeds at selecting the relevant features while avoiding irrelevant or redundant features. For data sets with many similar features, the feature selection stability must be evaluated with an adjusted stability measure.
  arXiv  Detail & Related papers  (2021-06-15T12:48:07Z)
• Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately [83.68135652247496]
  A natural remedy is to remove spurious features from the model. We show that removal of spurious features can decrease accuracy due to inductive biases. We also show that robust self-training can remove spurious features without affecting the overall accuracy.
  arXiv  Detail & Related papers  (2020-12-07T23:08:59Z)
• Unlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [100.52588638477862]
  We develop an approximate Bayesian inference scheme based on posterior regularisation. We demonstrate the utility of our method in the context of transferring prognostic models of prostate cancer across globally diverse populations.
  arXiv  Detail & Related papers  (2020-06-26T13:50:19Z)
• Calibration of Neural Networks using Splines [51.42640515410253]
  Measuring calibration error amounts to comparing two empirical distributions. We introduce a binning-free calibration measure inspired by the classical Kolmogorov-Smirnov (KS) statistical test. Our method consistently outperforms existing methods on KS error as well as other commonly used calibration measures.
  arXiv  Detail & Related papers  (2020-06-23T07:18:05Z)

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.