The First College of Clinical Medical Science, China Three Gorges University, Yichang Central People's Hospital, Yichang, China.
Yichang Hospital of Traditional Chinese Medicine, Yichang, China.
PLoS One. 2024 Nov 15;19(11):e0313570. doi: 10.1371/journal.pone.0313570. eCollection 2024.
Lung cancer, a leading cause of death, sees variable outcomes with iodine-125 seed implantation. Predictive tools are lacking, complicating clinical decisions. This study integrates radiomics and clinical features to develop a predictive model, advancing personalized treatment.
To construct a nomogram model combining enhanced CT image features and general clinical characteristics to evaluate the efficacy of radioactive iodine-125 seed implantation in lung cancer treatment.
Patients who underwent lung iodine-125 seed implantation at the Nuclear Medicine Department of Xiling Campus, Yichang Central People's Hospital from January 1, 2018, to January 31, 2024, were randomly divided into a training set (73 cases) and a test set (31 cases). Radiomic features were extracted from the enhanced CT images, and optimal clinical factors were analyzed to construct clinical, radiomics, and combined models. The best model was selected and validated for its role in assessing the efficacy of iodine-125 seed implantation in lung cancer patients.
Three clinical features and five significant radiomic features were successfully selected, and a combined nomogram model was constructed to evaluate the efficacy of iodine-125 seed implantation in lung cancer patients. The AUC values of the model in the training and test sets were 0.95 (95% CI: 0.91-0.99) and 0.83 (95% CI: 0.69-0.98), respectively. The calibration curve demonstrated good agreement between predicted and observed values, and the decision curve indicated that the combined model outperformed the clinical or radiomics model across the majority of threshold ranges.
A combined nomogram model was successfully developed to assess the efficacy of iodine-125 seed implantation in lung cancer patients, demonstrating good clinical predictive performance and high clinical value.
肺癌是导致死亡的主要原因之一，其碘-125 种子植入的结果存在差异。缺乏预测工具，使临床决策复杂化。本研究整合放射组学和临床特征，开发预测模型，推进个体化治疗。
构建结合增强 CT 图像特征和一般临床特征的列线图模型，以评估放射性碘-125 种子植入治疗肺癌的疗效。
2018 年 1 月 1 日至 2024 年 1 月 31 日，在宜昌市中心人民医院西陵院区核医学科接受肺碘-125 种子植入的患者被随机分为训练集（73 例）和测试集（31 例）。从增强 CT 图像中提取放射组学特征，并分析最佳临床因素，构建临床、放射组学和联合模型。选择并验证最佳模型，以评估其在评估肺癌患者碘-125 种子植入疗效中的作用。
成功筛选出 3 个临床特征和 5 个显著的放射组学特征，构建了一个联合列线图模型，以评估肺癌患者碘-125 种子植入的疗效。该模型在训练集和测试集中的 AUC 值分别为 0.95（95%CI：0.91-0.99）和 0.83（95%CI：0.69-0.98）。校准曲线显示预测值与观察值之间具有良好的一致性，决策曲线表明，在大多数阈值范围内，联合模型优于临床或放射组学模型。
成功开发了一种联合列线图模型，用于评估肺癌患者碘-125 种子植入的疗效，具有良好的临床预测性能和高临床价值。
