Department of Radiology, 1228The Netherlands Cancer Institute, Amsterdam, The Netherlands.
GROW School for Oncology and Developmental Biology, 5211Maastricht University Medical Centre, Maastricht, The Netherlands.
Acta Radiol. 2023 Mar;64(3):1062-1070. doi: 10.1177/02841851221106598. Epub 2022 Jun 14.
Accurate response evaluation in patients with neuroendocrine liver metastases (NELM) remains a challenge. Radiomics has shown promising results regarding response assessment.
To differentiate progressive (PD) from stable disease (SD) with radiomics in patients with NELM undergoing somatostatin analogue (SSA) treatment.
A total of 46 patients with histologically confirmed gastroenteropancreatic neuroendocrine tumors (GEP-NET) with ≥1 NELM and ≥2 computed tomography (CT) scans were included. Response was assessed with Response Evaluation Criteria in Solid Tumors (RECIST1.1). Hepatic target lesions were manually delineated and analyzed with radiomics. Radiomics features were extracted from each NELM on both arterial-phase (AP) and portal-venous-phase (PVP) CT. Multiple instance learning with regularized logistic regression via LASSO penalization (with threefold cross-validation) was used to classify response. Three models were computed: (i) AP model; (ii) PVP model; and (iii) AP + PVP model for a lesion-based and patient-based outcome. Next, clinical features were added to each model.
In total, 19 (40%) patients had PD. Median follow-up was 13 months (range 1-50 months). Radiomics models could not accurately classify response (area under the curve 0.44-0.60). Adding clinical variables to the radiomics models did not significantly improve the performance of any model.
Radiomics features were not able to accurately classify response of NELM on surveillance CT scans during SSA treatment.
神经内分泌肝脏转移瘤（NELM）患者的准确疗效评估仍然是一个挑战。放射组学在评估疗效方面显示出了有前景的结果。
利用放射组学区分接受生长抑素类似物（SSA）治疗的 NELM 患者的疾病进展（PD）与稳定疾病（SD）。
共纳入 46 例经组织学证实的胃肠胰神经内分泌肿瘤（GEP-NET）患者，这些患者均有≥1 个 NELM 和≥2 次计算机断层扫描（CT）检查。采用实体瘤反应评估标准（RECIST1.1）评估疗效。手动勾画肝脏靶病灶并进行放射组学分析。从每个 NELM 的动脉期（AP）和门静脉期（PVP）CT 上提取放射组学特征。采用正则化逻辑回归的多实例学习（通过 LASSO 惩罚进行三折交叉验证）对疗效进行分类。计算了三种模型：（i）AP 模型；（ii）PVP 模型；（iii）基于病变和基于患者的 AP+PVP 模型。然后，将临床特征添加到每个模型中。
共有 19 例（40%）患者出现 PD。中位随访时间为 13 个月（范围 1-50 个月）。放射组学模型不能准确分类疗效（曲线下面积 0.44-0.60）。将临床变量添加到放射组学模型中并不能显著提高任何模型的性能。
在 SSA 治疗期间，对 NELM 进行监测 CT 扫描时，放射组学特征无法准确分类疗效。
