MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models.

MRI radiomics endometrial cancer patient-derived model patient-derived organoids preclinical imaging

Journal

Frontiers in oncology
ISSN: 2234-943X
Titre abrégé: Front Oncol
Pays: Switzerland
ID NLM: 101568867

Informations de publication

Date de publication:
2024
Historique:
received: 07 11 2023
accepted: 23 04 2024
medline: 22 5 2024
pubmed: 22 5 2024
entrez: 22 5 2024
Statut: epublish

Résumé

Radiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature ( Mice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX ( The We have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.

Sections du résumé

Background UNASSIGNED
Radiomics can capture microscale information in medical images beyond what is visible to the naked human eye. Using a clinically relevant mouse model for endometrial cancer, the objective of this study was to develop and validate a radiomic signature (
Methods UNASSIGNED
Mice orthotopically implanted with a patient-derived grade 3 endometrioid endometrial cancer organoid model (O-PDX) were allocated to chemotherapy (combined paclitaxel/carboplatin, n=11) or saline/control (n=13). During tumor progression, the mice underwent weekly T2-weighted (T2w) magnetic resonance imaging (MRI). Segmentation of primary tumor volume (vMRI) allowed extraction of radiomic features from whole-volume tumor masks. A radiomic model for predicting treatment response was derived employing least absolute shrinkage and selection operator (LASSO) statistics at endpoint images in the orthotopic O-PDX (
Results UNASSIGNED
The
Conclusions UNASSIGNED
We have developed and validated a radiomic signature that was able to capture chemotherapeutic treatment response both in an O-PDX and in a subcutaneous endometrial cancer mouse model. This study supports the promising role of preclinical imaging including radiomic tumor profiling to assess early treatment response in endometrial cancer models.

Identifiants

pubmed: 38774411
doi: 10.3389/fonc.2024.1334541
pmc: PMC11106402
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1334541

Informations de copyright

Copyright © 2024 Espedal, Fasmer, Berg, Lyngstad, Schilling, Krakstad and Haldorsen.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Heidi Espedal (H)

Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.
Western Australia National Imaging Facility, Centre for Microscopy, Characterization and Analysis, University of Western Australia, Perth, WA, Australia.

Kristine E Fasmer (KE)

Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.

Hege F Berg (HF)

Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway.

Jenny M Lyngstad (JM)

Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.

Tomke Schilling (T)

Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.

Camilla Krakstad (C)

Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Bergen, Norway.
Department of Gynecology and Obstetrics, Haukeland University Hospital, Bergen, Norway.

Ingfrid S Haldorsen (IS)

Department of Clinical Medicine, University of Bergen, Bergen, Norway.
Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway.

Classifications MeSH