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281 MRI radiomics captures early treatment response in patient-derived organoid endometrial cancer mouse models
  1. Kristine E. Fasmer1,
  2. Heidi Espedal1,
  3. Hege F. Berg2,
  4. Jenny M. Lyngstad1,
  5. Camilla Krakstad2 and
  6. Ingfrid Salvesen Haldorsen1
  1. 1Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway
  2. 2Department of Obstetrics and Gynecology, Haukeland University Hospital, Bergen, Norway


Introduction/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.

Methodology 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 by LASSO (least-absolute-shrinkage and -selection operator) at endpoint images in the orthotopic O-PDX (RS_O), and subsequently applied on 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).

Conclusion We have developed and validated a radiomic signature that was able to capture treatment response prior to a decrease in tumor volume.

Disclosures The authors have no conflicts of interest to disclose.

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