Simplifying Disease Staging Models into a Single Anatomical Axis - A Case Study of Aortic Coarctation In-utero

Uxio Hermida*, Milou P.M.van Poppel, David Stojanovski, David F.A. Lloyd, Johannes K. Steinweg, Trisha V. Vigneswaran, John M. Simpson, Reza Razavi, Adelaide De Vecchi, Kuberan Pushparajah, Pablo Lamata

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

1 Citation (Scopus)

Abstract

Statistical shape modelling and classification methods are used to study characteristic disease phenotypes, to derive novel shape biomarkers, and to extract insights into disease mechanisms. Linear classification models are commonly chosen due to their ability to provide a single score, as well as easy-to-interpret characteristic shapes. In disease staging models, a multi-class problem is generally set. Then, one would expect that the set of linear models comparing any pair of classes will lead to the same unique anatomical axis capturing characteristic shapes for each group in the disease spectrum, and a unique mechanistic interpretation. In this work, we aim to explore the validity of this assumption and assess the confidence in simplifying both mechanistic interpretations and clinical classification performance into a single axis in disease staging models. To do so, we used a statistical shape model of fetal great arteries in cases with suspected coarctation of the aorta. Data included control, false positive and confirmed coarctation cases, representative of three categories of developmental impairment from the disease spectrum. Principal component analysis combined with a fisher linear discriminant analysis was used to explore phenotypes associated with each group and classification performance. A combination of classification overfitting, a co-linearity index between axes, and the three-dimensional extreme phenotypes provided useful information for simplification into a single anatomical axis. Careful consideration should be taken in disease progression studies where either overfitting or co-linearity are compromised, as the simplification with a single anatomical axis might lead to the inference of misleading mechanisms associated with disease.

Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart. Regular and CMRxMotion Challenge Papers - 13th International Workshop, STACOM 2022, Held in Conjunction with MICCAI 2022, Revised Selected Papers
EditorsOscar Camara, Esther Puyol-Antón, Avan Suinesiaputra, Alistair Young, Chen Qin, Maxime Sermesant, Shuo Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages269-279
Number of pages11
ISBN (Print)9783031234422
DOIs
Publication statusPublished - 2022
Event13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 18 Sept 202218 Sept 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13593 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference13th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2022, held in conjunction with the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period18/09/202218/09/2022

Keywords

  • Clinical biomarker
  • Computational anatomy
  • Discriminant analysis
  • Remodelling

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