磁共振成像放射组学在患者来源的类器官子宫内膜癌小鼠模型中捕捉早期治疗反应。

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

作者信息

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

机构信息

Department of Clinical Medicine, University of Bergen, Bergen, Norway.

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

出版信息

Front Oncol. 2024 May 7;14:1334541. doi: 10.3389/fonc.2024.1334541. eCollection 2024.

DOI:10.3389/fonc.2024.1334541

PMID:38774411

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11106402/

Abstract

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 () 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 (), and subsequently applied on the earlier study timepoints ( 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 () 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 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, 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 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.

摘要

背景

放射组学能够捕捉医学图像中的微观信息，这些信息是肉眼无法看到的。本研究旨在利用一种与临床相关的子宫内膜癌小鼠模型，开发并验证一种预测对标准化疗反应的放射组学特征。

方法

将原位植入患者来源的3级子宫内膜样子宫内膜癌类器官模型（O-PDX）的小鼠分为化疗组（紫杉醇/卡铂联合，n = 11）或生理盐水/对照组（n = 13）。在肿瘤进展过程中，小鼠每周接受一次T2加权（T2w）磁共振成像（MRI）。通过分割原发性肿瘤体积（vMRI），可以从全肿瘤体积掩码中提取放射组学特征。利用最小绝对收缩和选择算子（LASSO）统计方法，在原位O-PDX的终点图像上建立预测治疗反应的放射组学模型，并随后应用于早期研究时间点（基线、第1 - 3周）。为了进行外部验证，在一个单独的T2w - MRI数据集中，对来自同一O-PDX模型的分割全肿瘤体积皮下肿瘤（在化疗（n = 8个肿瘤）或生理盐水/对照（n = 8个肿瘤）开始后的三个时间点（基线、第3天和第10天/终点）成像）测试放射组学模型。

结果

该模型在预测从基线到终点的治疗反应时，受试者操作特征（ROC）曲线下面积（AUC）迅速增加；AUC = 0.38（基线）；0.80（第1周），0.85（第2周），0.96（第3周）和1.0（终点）。相比之下，vMRI的AUC分别为0.37（基线）；0.69（第1周）；0.83（第2周）；0.92（第3周）和0.97（终点）。在外部验证数据集中进行测试时，该模型在第10天/终点预测治疗反应具有较高的准确性（AUC = 0.85），并且其AUC倾向于高于vMRI（AUC = 0.78，p = 0.18）。在外部验证集中，该模型和vMRI在第3天都无法预测反应（两者AUC均为0.56）。