Vita-Salute San Raffaele University, Milan, Italy.
Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy.
Eur J Nucl Med Mol Imaging. 2024 Jul;51(9):2774-2783. doi: 10.1007/s00259-024-06730-w. Epub 2024 May 2.
Accurate identification of lymph node (LN) metastases is pivotal for surgical planning of pancreatic neuroendocrine tumours (PanNETs); however, current imaging techniques have sub-optimal diagnostic sensitivity. Aim of this study is to investigate whether [Ga]Ga-DOTATOC PET radiomics might improve the identification of LN metastases in patients with non-functioning PanNET (NF-PanNET) referred to surgical intervention.
Seventy-two patients who performed preoperative [Ga]Ga-DOTATOC PET between December 2017 and March 2022 for NF-PanNET. [Ga]Ga-DOTATOC PET qualitative assessment of LN metastases was measured using diagnostic balanced accuracy (bACC), sensitivity (SN), specificity (SP), positive and negative predictive values (PPV, NPV). SUVmax, SUVmean, Somatostatin receptor density (SRD), total lesion SRD (TLSRD) and IBSI-compliant radiomic features (RFs) were obtained from the primary tumours. To predict LN involvement, these parameters were engineered, selected and used to train different machine learning models. Models were validated using tenfold repeated cross-validation and control models were developed. Models' bACC, SN, SP, PPV and NPV were collected and compared (Kruskal-Wallis, Mann-Whitney).
LN metastases were detected in 29/72 patients at histology. [Ga]Ga-DOTATOC PET qualitative examination of LN involvement provided bACC = 60%, SN = 24%, SP = 95%, PPV = 78% and NPV = 65%. The best-performing radiomic model provided a bACC = 70%, SN = 77%, SP = 61%, PPV = 60% and NPV = 83% (outperforming the control model, p < 0.05*).
In this study, [Ga]Ga-DOTATOC PET radiomics allowed to increase diagnostic sensitivity in detecting LN metastases from 24 to 77% in NF-PanNET patients candidate to surgery. Especially in case of micrometastatic involvement, this approach might assist clinicians in a better patients' stratification.
准确识别淋巴结(LN)转移对于胰腺神经内分泌肿瘤(PanNETs)的手术规划至关重要;然而,目前的成像技术诊断灵敏度不理想。本研究旨在探讨[^18]Ga-DOTATOC PET 放射组学是否能提高无功能性胰腺神经内分泌肿瘤(NF-PanNET)患者行手术干预前识别 LN 转移的能力。
回顾性分析 2017 年 12 月至 2022 年 3 月期间因 NF-PanNET 行术前[^18]Ga-DOTATOC PET 的 72 例患者。使用诊断平衡准确性(bACC)、敏感度(SN)、特异度(SP)、阳性预测值(PPV)、阴性预测值(NPV)来评估 LN 转移的 [^18]Ga-DOTATOC PET 定性评估。从原发肿瘤中获得最大标准化摄取值(SUVmax)、平均标准化摄取值(SUVmean)、生长抑素受体密度(SRD)、肿瘤总体 SRD(TLSRD)和符合互信息标准的放射组学特征(RFs)。为了预测 LN 受累,对这些参数进行了工程化、选择,并用于训练不同的机器学习模型。使用十折交叉验证验证模型,并开发对照模型。收集并比较模型的 bACC、SN、SP、PPV 和 NPV(Kruskal-Wallis、Mann-Whitney)。
在组织学上,29/72 例患者的 LN 转移被检测到。[^18]Ga-DOTATOC PET 对 LN 受累的定性检查提供了 bACC=60%、SN=24%、SP=95%、PPV=78%和 NPV=65%。表现最佳的放射组学模型提供了 bACC=70%、SN=77%、SP=61%、PPV=60%和 NPV=83%(优于对照模型,p<0.05*)。
在这项研究中,[^18]Ga-DOTATOC PET 放射组学可以将 NF-PanNET 患者手术中检测 LN 转移的诊断灵敏度从 24%提高到 77%。特别是在微转移受累的情况下,该方法可能有助于临床医生对患者进行更好的分层。