Giannini Valentina, Pusceddu Laura, Defeudis Arianna, Nicoletti Giulia, Cappello Giovanni, Mazzetti Simone, Sartore-Bianchi Andrea, Siena Salvatore, Vanzulli Angelo, Rizzetto Francesco, Fenocchio Elisabetta, Lazzari Luca, Bardelli Alberto, Marsoni Silvia, Regge Daniele
Department of Surgical Sciences, University of Turin, 10124 Turin, Italy.
Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy.
Cancers (Basel). 2022 Jan 4;14(1):241. doi: 10.3390/cancers14010241.
The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R-) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R- lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies.
本文的目的是开发并验证一种delta放射组学评分，以预测个体结直肠癌肝转移（lmCRC）对一线FOLFOX化疗的反应。对301例lmCRC在基线期和一线FOLFOX第一个周期后进行的CT图像上进行手动分割，并通过将基线期CT的纹理特征与时间点1（TP1）的纹理特征相减来计算107个放射组学特征。根据RECIST1.1标准，如果lmCRC在8个月前出现疾病进展（PD），则分类为无反应者（R-），否则为反应者（R+）。在特征选择后，我们开发了一个决策树统计模型，该模型使用来自一家医院的所有lmCRC进行训练。最终输出是一个delta放射组学特征，随后在外部数据集上进行验证。在两个数据集上评估了正确分类个体病变的敏感性、特异性、阳性（PPV）和阴性（NPV）预测值。在训练和验证数据集中，每个病变的敏感性、特异性、PPV和NPV分别为99%、94%、95%、99%、85%、92%、90%和87%。delta放射组学特征能够可靠地预测R- lmCRC，这些病变在TP1时被病变RECIST错误分类为R+（平均训练集和验证集为93%，而RECIST为67%）。本研究中开发的delta放射组学特征能够可靠地预测个体lmCRC对基于奥沙利铂化疗的反应。该特征预测为预后不良或无反应的病变可进一步研究，这可能为病变特异性治疗铺平道路。
