Department of Radiology, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
Department of Advanced Biomedical Imaging Informatics, St. Marianna University School of Medicine, 2-16-1 Sugao, Miyamae-ku, Kawasaki, Kanagawa, 216-8511, Japan.
Eur Radiol. 2022 Aug;32(8):5353-5361. doi: 10.1007/s00330-022-08630-9. Epub 2022 Feb 24.
This preliminary study aimed to develop a deep learning (DL) model using diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps to predict local recurrence and 2-year progression-free survival (PFS) in laryngeal and hypopharyngeal cancer patients treated with various forms of radiotherapy-related curative therapy.
Seventy patients with laryngeal and hypopharyngeal cancers treated by radiotherapy, chemoradiotherapy, or induction-(chemo)radiotherapy were enrolled and divided into training (N = 49) and test (N = 21) groups based on presentation timeline. All patients underwent MR before and 4 weeks after the start of radiotherapy. The DL models that extracted imaging features on pre- and intra-treatment DWI and ADC maps were trained to predict the local recurrence within a 2-year follow-up. In the test group, each DL model was analyzed for recurrence prediction. Additionally, the Kaplan-Meier and multivariable Cox regression analyses were performed to evaluate the prognostic significance of the DL models and clinical variables.
The highest area under the receiver operating characteristics curve and accuracy for predicting the local recurrence in the DL model were 0.767 and 81.0%, respectively, using intra-treatment DWI (DWI). The log-rank test showed that DWI was significantly associated with PFS (p = 0.013). DWI was an independent prognostic factor for PFS in multivariate analysis (p = 0.023).
DL models using DWI may have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. The model-related findings may contribute to determining the therapeutic strategy in the early stage of the treatment.
• Deep learning models using intra-treatment diffusion-weighted imaging have prognostic value in patients with laryngeal and hypopharyngeal cancers treated by curative radiotherapy. • The findings from these models may contribute to determining the therapeutic strategy at the early stage of the treatment.
本初步研究旨在开发一种基于深度学习（DL）的模型，利用弥散加权成像（DWI）和表观弥散系数（ADC）图预测接受不同形式放疗相关根治性治疗的喉和下咽癌患者的局部复发和 2 年无进展生存（PFS）。
共纳入 70 例接受放疗、放化疗或诱导（放）化疗的喉和下咽癌患者，根据就诊时间分为训练组（N=49）和测试组（N=21）。所有患者在放疗前和放疗开始后 4 周进行磁共振成像（MRI）检查。DL 模型提取治疗前后 DWI 和 ADC 图的影像学特征，以预测 2 年随访内的局部复发。在测试组中，对每个 DL 模型进行复发预测分析。此外，进行 Kaplan-Meier 和多变量 Cox 回归分析，以评估 DL 模型和临床变量的预后意义。
DL 模型在治疗期间使用 DWI 预测局部复发的最高受试者工作特征曲线下面积和准确率分别为 0.767 和 81.0%。对数秩检验显示 DWI 与 PFS 显著相关（p=0.013）。多变量分析显示 DWI 是 PFS 的独立预后因素（p=0.023）。
使用 DWI 的 DL 模型可能对接受根治性放疗的喉和下咽癌患者具有预后价值。模型相关发现可能有助于在治疗早期确定治疗策略。
• 治疗期间使用弥散加权成像的深度学习模型对接受根治性放疗的喉和下咽癌患者具有预后价值。
• 这些模型的发现可能有助于在治疗早期确定治疗策略。
