基于机器学习的宫颈癌患者术后风险因素评估模型。
Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer.
机构信息
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230000, China (Z.Y., Q.Z., L.L., W.Q.); Department of Radiology, West Branch of the First Affiliated Hospital of the University of Science and Technology of China, Hefei, Anhui 230001, China (Z.Y., L.L.).
Department of Radiology, The First Affiliated Hospital of Anhui Medical University, 81 Meishan Road, Hefei, Anhui 230000, China (Z.Y., Q.Z., L.L., W.Q.).
出版信息
Acad Radiol. 2024 Apr;31(4):1410-1418. doi: 10.1016/j.acra.2023.09.031. Epub 2023 Oct 25.
RATIONALE AND OBJECTIVES
To investigate the value of machine learning-based radiomics, intravoxel incoherent motion (IVIM) diffusion-weighted imaging and its combined model in predicting the postoperative risk factors of parametrial infiltration (PI), lymph node metastasis (LNM), deep muscle invasion (DMI), lymph-vascular space invasion (LVSI), pathological type (PT), differentiation degree (DD), and Ki-67 expression level in patients with cervical cancer.
MATERIALS AND METHODS
The data of 180 patients with cervical cancer were retrospectively analyzed and randomized 2:1 into a training and validation group. The IVIM-DWI and radiomics parameters of primary lesions were measured in all patients. Seven machine learning methods were used to calculate the optimal radiomics score (Rad-score), which was combined with IVIM-DWI and clinical parameters to construct nomograms for predicting the risk factors of cervical cancer, with internal and external validation.
RESULTS
The diagnostic efficacy of the nomograms based on clinical and imaging parameters was significantly better than MRI assessment alone. The area under the curve (AUC) of nomograms and MRI for the assessment of PI, LNM, and DMI were 0.981 vs 0.868, 0.848 vs 0.639, and 0.896 vs 0.780, respectively. Nomograms also performed well in the assessment of LVSI, PT, DD, and Ki-67 expression levels, with AUC of 0.796, 0.854, 0.806, 0.839 and 0.840, 0.856, 0.810, 0.832 in the training and validation groups.
CONCLUSION
Machine learning-based nomograms can serve as a useful tool for assessing postoperative risk factors in patients with cervical cancer.
背景与目的
探讨基于机器学习的放射组学、体素内不相干运动（IVIM）扩散加权成像及其联合模型在预测宫颈癌患者宫旁浸润（PI）、淋巴结转移（LNM）、深肌层浸润（DMI）、脉管间隙浸润（LVSI）、病理类型（PT）、分化程度（DD）和 Ki-67 表达水平等术后危险因素中的价值。
材料与方法
回顾性分析 180 例宫颈癌患者的资料，按照 2:1 的比例随机分为训练组和验证组。所有患者均测量原发灶 IVIM-DWI 及放射组学参数，采用 7 种机器学习方法计算最优放射组学评分（Rad-score），联合 IVIM-DWI 及临床参数构建预测宫颈癌术后危险因素的列线图，进行内部和外部验证。
结果
基于临床和影像参数的列线图诊断效能明显优于单独 MRI 评估。列线图和 MRI 评估 PI、LNM 和 DMI 的曲线下面积（AUC）分别为 0.981 与 0.868、0.848 与 0.639、0.896 与 0.780。列线图在评估 LVSI、PT、DD 和 Ki-67 表达水平方面也表现良好，在训练组和验证组中的 AUC 分别为 0.796、0.854、0.806、0.839 和 0.840、0.856、0.810、0.832。
结论
基于机器学习的列线图可作为评估宫颈癌患者术后危险因素的有用工具。
Zotero 插件