State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics & Center for Molecular Imaging and Translational Medicine, School of Public Health, Xiamen University, Xiamen, China.
Department of Oncology, Taicang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, China.
PLoS One. 2024 Aug 7;19(8):e0305468. doi: 10.1371/journal.pone.0305468. eCollection 2024.
The objective of this study was to identify the risk factors that influence metastasis and prognosis in patients with nodular melanoma (NM), as well as to develop and validate a prognostic model using artificial intelligence (AI) algorithms.
The Surveillance, Epidemiology, and End Results (SEER) database was queried for 4,727 patients with NM based on the inclusion/exclusion criteria. Their clinicopathological characteristics were retrospectively reviewed, and logistic regression analysis was utilized to identify risk factors for metastasis. This was followed by employing Multilayer Perceptron (MLP), Adaptive Boosting (AB), Bagging (BAG), logistic regression (LR), Gradient Boosting Machine (GBM), and eXtreme Gradient Boosting (XGB) algorithms to develop metastasis models. The performance of the six models was evaluated and compared, leading to the selection and visualization of the optimal model. Through integrating the prognostic factors of Cox regression analysis with the optimal models, the prognostic prediction model was constructed, validated, and assessed.
Logistic regression analyses identified that marital status, gender, primary site, surgery, radiation, chemotherapy, system management, and N stage were all independent risk factors for NM metastasis. MLP emerged as the optimal model among the six models (AUC = 0.932, F1 = 0.855, Accuracy = 0.856, Sensitivity = 0.878), and the corresponding network calculator (https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/) was developed. The following were examined as independent prognostic factors: MLP, age, marital status, sequence number, laterality, surgery, radiation, chemotherapy, system management, T stage, and N stage. System management and surgery emerged as protective factors (HR < 1). To predict 1-, 3-, and 5-year overall survival (OS), a nomogram was created. The validation results demonstrated that the model exhibited good discrimination and consistency, as well as high clinical usefulness.
The developed prediction model more effectively reflects the prognosis of patients with NM and differentiates between the risk level of patients, serving as a useful supplement to the classical American Joint Committee on Cancer (AJCC) staging system and offering a reference for clinically stratified individualized treatment and prognosis prediction. Furthermore, the model enables clinicians to quantify the risk of metastasis in NM patients, assess patient survival, and administer precise treatments.
本研究旨在确定影响结节性黑色素瘤（NM）患者转移和预后的风险因素，并利用人工智能（AI）算法开发和验证预后模型。
根据纳入/排除标准，从监测、流行病学和最终结果（SEER）数据库中查询了 4727 名 NM 患者。回顾性分析了他们的临床病理特征，并采用逻辑回归分析确定转移的风险因素。随后，采用多层感知机（MLP）、自适应增强（AB）、袋装（BAG）、逻辑回归（LR）、梯度提升机（GBM）和极端梯度提升（XGB）算法分别建立转移模型。评估和比较了六种模型的性能，最终选择和可视化了最优模型。通过将 Cox 回归分析的预后因素与最优模型相结合，构建、验证和评估了预后预测模型。
逻辑回归分析确定婚姻状况、性别、原发部位、手术、放疗、化疗、系统治疗和 N 期是 NM 转移的独立危险因素。MLP 是六种模型中最优的模型（AUC = 0.932、F1 = 0.855、准确度 = 0.856、敏感度 = 0.878），并开发了相应的网络计算器（https://shimunana-nm-distant-m-nm-m-distant-8z8k54.streamlit.app/）。以下是独立的预后因素：MLP、年龄、婚姻状况、序列号、侧别、手术、放疗、化疗、系统治疗、T 期和 N 期。系统治疗和手术是保护因素（HR < 1）。为了预测 1 年、3 年和 5 年的总生存率（OS），创建了一个列线图。验证结果表明，该模型具有良好的区分度和一致性，具有较高的临床实用性。
所开发的预测模型能更有效地反映 NM 患者的预后，并区分患者的风险水平，是对经典美国癌症联合委员会（AJCC）分期系统的有益补充，为临床分层个体化治疗和预后预测提供了参考。此外，该模型使临床医生能够量化 NM 患者的转移风险，评估患者的生存情况，并进行精确治疗。
