Dependence of the AUC of NTCP models on the observational dose-range highlights cautions in comparison of discriminative performance.


Journal

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
ISSN: 1724-191X
Titre abrégé: Phys Med
Pays: Italy
ID NLM: 9302888

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 13 03 2023
revised: 19 06 2023
accepted: 05 08 2023
medline: 18 9 2023
pubmed: 14 8 2023
entrez: 14 8 2023
Statut: ppublish

Résumé

Normal tissue complication probability (NTCP) models are probabilistic models that describe the risk of radio-induced toxicity in tissues or organs. In the field of radiotherapy, the area under the ROC curve (AUC) is widely used to estimate the performance in risk prediction of NTCP models. In this work, we derived an analytical expression of the AUC for the logistic NTCP model in the case of both symmetrical and asymmetrical dose (to the normal tissue) windows around D Our analytical expression of the AUC made explicit the dependence on both the steepness of the logistic curve (β) and the dose window width (w), showing that an increase of w pushes AUC towards higher values. Increasing values of the AUC with increasing values of w were consistently observed across simulated data sets with diverse clinical settings from published studies and real clinical data sets. Our results reveal that the AUC of NTCP models inherits intrinsic characteristics from the clinical setting of the data set on which the models are developed, and warn against the use of the AUC to compare the performance of models constructed upon data from trials in which substantially different dose ranges were administered or accounting for different risk factors beyond the dose.

Sections du résumé

BACKGROUND BACKGROUND
Normal tissue complication probability (NTCP) models are probabilistic models that describe the risk of radio-induced toxicity in tissues or organs. In the field of radiotherapy, the area under the ROC curve (AUC) is widely used to estimate the performance in risk prediction of NTCP models.
METHODS METHODS
In this work, we derived an analytical expression of the AUC for the logistic NTCP model in the case of both symmetrical and asymmetrical dose (to the normal tissue) windows around D
RESULTS RESULTS
Our analytical expression of the AUC made explicit the dependence on both the steepness of the logistic curve (β) and the dose window width (w), showing that an increase of w pushes AUC towards higher values. Increasing values of the AUC with increasing values of w were consistently observed across simulated data sets with diverse clinical settings from published studies and real clinical data sets.
CONCLUSION CONCLUSIONS
Our results reveal that the AUC of NTCP models inherits intrinsic characteristics from the clinical setting of the data set on which the models are developed, and warn against the use of the AUC to compare the performance of models constructed upon data from trials in which substantially different dose ranges were administered or accounting for different risk factors beyond the dose.

Identifiants

pubmed: 37579522
pii: S1120-1797(23)00131-X
doi: 10.1016/j.ejmp.2023.102654
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

102654

Informations de copyright

Copyright © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

J Iacovacci (J)

Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

F Palorini (F)

Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

A Cicchetti (A)

Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy.

C Fiorino (C)

Medical Physics Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.

T Rancati (T)

Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy. Electronic address: tiziana.rancati@istitutotumori.mi.it.

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Classifications MeSH