Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.
Development and Validation of a Novel Radiomics-Based Nomogram With Machine Learning to Preoperatively Predict Histologic Grade in Pancreatic Neuroendocrine Tumors.
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DOI：
10.3389/fonc.2022.843376
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发表时间：
2022
影响因子：
4.7
通讯作者：
中科院分区：
文献类型：
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肿瘤分级是胰腺神经内分泌肿瘤（PNTR）生物学侵袭性的决定因素，也是目前帮助建立个体化治疗策略的最佳工具。一种非侵入性的方法，以准确地预测术前的PNIPs的组织学分级是迫切需要的，而且非常有限。模型训练和放射组学签名的构建分别在三期（平扫、动脉和静脉）CT中进行。应用Mann-Whitney U检验和最小绝对收缩和选择算子（LASSO）进行特征预选和放射组学签名构建。通过将放射组学特征与临床特征相结合来训练SVM线性模型。然后选择最佳模型来构建诺模图。本研究共纳入139例PNTs（包括训练集中的83例和独立验证集中的56例）。我们基于八特征放射组学特征（第1组）构建了一个模型，将PNET患者分为1级和2/3级组，训练组和验证组的AUC分别为0.911（95%置信区间（CI），0.908-0.914）和0.837（95% CI，0.827-0.847）。将平相CT的放射组学特征与T分期和扩张的主胰管（MPD）/胆管（BD）（第2组）相结合的列线图显示出最佳性能（训练集：AUC = 0.919，95%CI = 0.916-0.922;验证集：AUC = 0.875，95%CI = 0.867-0.883）。我们开发的诺模图将放射组学特征与临床特征相结合，可用于术前预测1级和2/3级PNM。
Tumor grade is the determinant of the biological aggressiveness of pancreatic neuroendocrine tumors (PNETs) and the best current tool to help establish individualized therapeutic strategies. A noninvasive way to accurately predict the histology grade of PNETs preoperatively is urgently needed and extremely limited. The models training and the construction of the radiomic signature were carried out separately in three-phase (plain, arterial, and venous) CT. Mann–Whitney U test and least absolute shrinkage and selection operator (LASSO) were applied for feature preselection and radiomic signature construction. SVM-linear models were trained by incorporating the radiomic signature with clinical characteristics. An optimal model was then chosen to build a nomogram. A total of 139 PNETs (including 83 in the training set and 56 in the independent validation set) were included in the present study. We build a model based on an eight-feature radiomic signature (group 1) to stratify PNET patients into grades 1 and 2/3 groups with an AUC of 0.911 (95% confidence intervals (CI), 0.908–0.914) and 0.837 (95% CI, 0.827–0.847) in the training and validation cohorts, respectively. The nomogram combining the radiomic signature of plain-phase CT with T stage and dilated main pancreatic duct (MPD)/bile duct (BD) (group 2) showed the best performance (training set: AUC = 0.919, 95% CI = 0.916–0.922; validation set: AUC = 0.875, 95% CI = 0.867–0.883). Our developed nomogram that integrates radiomic signature with clinical characteristics could be useful in predicting grades 1 and 2/3 PNETs preoperatively with powerful capability.
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影响因子：
5.9
作者：
Kim, Dong Wook;Kim, Hyoung Jung;Hong, Seung-Mo
通讯作者：
Hong, Seung-Mo
影响因子：
28.4
作者：
Dasari, Arvind;Shen, Chan;Yao, James C.
通讯作者：
Yao, James C.
胰腺神经内分泌肿瘤中经常改变DAXX/ATRX，MEN1和MTOR途径基因。
DOI：
10.1126/science.1200609
发表时间：
2011-03-04
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Jiao Y;Shi C;Edil BH;de Wilde RF;Klimstra DS;Maitra A;Schulick RD;Tang LH;Wolfgang CL;Choti MA;Velculescu VE;Diaz LA Jr;Vogelstein B;Kinzler KW;Hruban RH;Papadopoulos N
通讯作者：
Papadopoulos N
影响因子：
9
作者：
Pulvirenti A;Javed AA;Landoni L;Jamieson NB;Chou JF;Miotto M;He J;Gonen M;Pea A;Tang LH;Nessi C;Cingarlini S;D'Angelica MI;Gill AJ;Kingham TP;Scarpa A;Weiss MJ;Balachandran VP;Samra JS;Cameron JL;Jarnagin WR;Salvia R;Wolfgang CL;Allen PJ;Bassiy C
通讯作者：
Bassiy C
影响因子：
--
作者：
Fan JH;Zhang YQ;Shi SS;Chen YJ;Yuan XH;Jiang LM;Wang SM;Ma L;He YT;Feng CY;Sun XB;Liu Q;Deloso K;Chi Y;Qiao YL
通讯作者：
Qiao YL