AUTHOR=Espedal Heidi , Fasmer Kristine E. , Berg Hege F. , Lyngstad Jenny M. , Schilling Tomke , Krakstad Camilla , Haldorsen Ingfrid S. 

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

JOURNAL=Frontiers in Oncology

VOLUME=14

YEAR=2024

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2024.1334541

DOI=10.3389/fonc.2024.1334541

ISSN=2234-943X

ABSTRACT=<sec><title>Background</title><p>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 (<italic>RS</italic>) predicting response to standard chemotherapy.</p></sec><sec><title>Methods</title><p>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 (<italic>RS_O</italic>), and subsequently applied on the earlier study timepoints (<italic>RS_O</italic> 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 (<italic>RS_S</italic>) 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).</p></sec><sec><title>Results</title><p>The <italic>RS_O</italic> 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, <italic>RS_S</italic> 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 <italic>RS_S</italic> nor vMRI predicted response at day 3 in the external validation set (AUC=0.56 for both).</p></sec><sec><title>Conclusions</title><p>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.</p></sec>