Second Clinical Medicine College, Binzhou Medical University, Yantai, Shandong 264003, People's Republic of China (L.D., F.S.); Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.).
Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., X.S.); Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai, Shandong 264000, People's Republic of China (L.D., X.H., P.Y., W.Z., C.W., Q.S., F.S., N.M., X.S.).
Acad Radiol. 2023 Dec;30(12):3032-3046. doi: 10.1016/j.acra.2023.03.039. Epub 2023 May 18.
This study is based on multicenter cohorts and aims to utilize computed tomography (CT) images to construct a radiomics nomogram for predicting the lateral neck lymph node (LNLN) metastasis in the papillary thyroid carcinoma (PTC) and further explore the biological basis under its prediction.
In the multicenter study, 1213 lymph nodes from 409 patients with PTC who underwent CT examinations and received open surgery and lateral neck dissection were included. A prospective test cohort was used in validating the model. Radiomics features were extracted from the CT images of each patient's LNLNs. Selectkbest, maximum relevance and minimum redundancy and the least absolute shrinkage and selection operator (LASSO) algorithm were used in reducing the dimensionality of radiomics features in the training cohort. Then, a radiomics signature (Rad-score) was calculated as the sum of each feature multiplied by the nonzero coefficient from LASSO. A nomogram was generated using the clinical risk factors of the patients and Rad-score. The nomograms' performance was analyzed in terms of accuracy, sensitivity, specificity, confusion matrix, receiver operating characteristic curves, and areas under the receiver operating characteristic curve (AUCs). The clinical usefulness of the nomogram was evaluated by decision curve analysis. Moreover, three radiologists with different working experiences and nomogram were compared to one another. Whole transcriptomics sequencing was performed in 14 tumor samples; the correlation of biological functions and high and low LNLN samples predicted by the nomogram was further investigated.
A total of 29 radiomics features were used in constructing the Rad-score. Rad-score and clinical risk factors (age, tumor diameter, location and number of suspected tumors) compose the nomogram. The nomogram exhibited good discrimination performance of the nomogram for predicting LNLN metastasis in the training cohort (AUC, 0.866), internal test cohort (0.845), external test cohort (0.725), and prospective test cohort (0.808) and showed diagnostic capability comparable to senior radiologists, significantly outperforming junior radiologists (p < 0.05). Functional enrichment analysis suggested that the nomogram can reflect the ribosome-related structures of cytoplasmic translation in patients with PTC.
Our radiomics nomogram provides a noninvasive method that incorporates radiomics features and clinical risk factors for predicting LNLN metastasis in patients with PTC.
本研究基于多中心队列，旨在利用计算机断层扫描（CT）图像构建预测甲状腺乳头状癌（PTC）患者侧颈部淋巴结（LNLN）转移的放射组学列线图，并进一步探讨其预测的生物学基础。
在这项多中心研究中，纳入了 409 例接受 CT 检查、行开放性手术和侧颈部清扫术的 PTC 患者的 1213 个 LNLN。前瞻性测试队列用于验证模型。从每位患者的 LNLN 的 CT 图像中提取放射组学特征。选择最佳特征、最大相关性和最小冗余以及最小绝对值收缩和选择算子（LASSO）算法用于减少训练队列中放射组学特征的维度。然后，将每个特征乘以 LASSO 中非零系数的总和计算为放射组学特征（Rad-score）。使用患者的临床危险因素和 Rad-score 生成列线图。根据准确性、敏感性、特异性、混淆矩阵、受试者工作特征曲线和受试者工作特征曲线下面积（AUC）来分析列线图的性能。通过决策曲线分析评估列线图的临床实用性。此外，还比较了三位具有不同工作经验的放射科医生与列线图的预测结果。对 14 个肿瘤样本进行全转录组测序，进一步研究了列线图预测的高和低 LNLN 样本的生物学功能相关性。
共构建了 29 个放射组学特征来构建 Rad-score。Rad-score 和临床危险因素（年龄、肿瘤直径、位置和可疑肿瘤数量）构成了列线图。该列线图在训练队列（AUC 为 0.866）、内部测试队列（0.845）、外部测试队列（0.725）和前瞻性测试队列（0.808）中对 LNLN 转移的预测具有良好的区分性能，且诊断能力与资深放射科医生相当，明显优于初级放射科医生（p<0.05）。功能富集分析表明，列线图可以反映 PTC 患者细胞质翻译的核糖体相关结构。
我们的放射组学列线图为预测 PTC 患者 LNLN 转移提供了一种结合放射组学特征和临床危险因素的非侵入性方法。
