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

被引:0
作者
Espedal, Heidi [1 ,2 ,3 ]
Fasmer, Kristine E. [1 ,2 ]
Berg, Hege F. [4 ,5 ]
Lyngstad, Jenny M. [1 ,2 ]
Schilling, Tomke [1 ,2 ]
Krakstad, Camilla [4 ,5 ]
Haldorsen, Ingfrid S. [1 ,2 ]
机构
[1] Univ Bergen, Dept Clin Med, Bergen, Norway
[2] Haukeland Hosp, Mohn Med Imaging & Visualizat Ctr, Dept Radiol, Bergen, Norway
[3] Univ Western Australia, Ctr Microscopy Characterizat & Anal, Western Australia Natl Imaging Facil, Perth, WA, Australia
[4] Univ Bergen, Ctr Canc Biomarkers, Dept Clin Sci, Bergen, Norway
[5] Haukeland Hosp, Dept Gynecol & Obstet, Bergen, Norway
来源
FRONTIERS IN ONCOLOGY | 2024年 / 14卷
关键词
patient-derived organoids; MRI radiomics; endometrial cancer; preclinical imaging; patient-derived model;
D O I
10.3389/fonc.2024.1334541
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background 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 (RS) predicting response to standard chemotherapy.Methods 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 (RS_O), and subsequently applied on the earlier study timepoints (RS_O at baseline, and week 1-3). For external validation, the radiomic model was tested in a separate T2w-MRI dataset on segmented whole-volume subcutaneous tumors (RS_S) from the same O-PDX model, imaged at three timepoints (baseline, day 3 and day 10/endpoint) after start of chemotherapy (n=8 tumors) or saline/control (n=8 tumors).Results The RS_O yielded rapidly increasing area under the receiver operating characteristic (ROC) curves (AUCs) for predicting treatment response from baseline until endpoint; AUC=0.38 (baseline); 0.80 (week 1), 0.85 (week 2), 0.96 (week 3) and 1.0 (endpoint). In comparison, vMRI yielded AUCs of 0.37 (baseline); 0.69 (w1); 0.83 (week 2); 0.92 (week 3) and 0.97 (endpoint). When tested in the external validation dataset, RS_S yielded high accuracy for predicting treatment response at day10/endpoint (AUC=0.85) and tended to yield higher AUC than vMRI (AUC=0.78, p=0.18). Neither RS_S nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both).Conclusions 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.
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页数:11
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