Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study
Deep learning approach of diffusion-weighted imaging as an outcome predictor in laryngeal and hypopharyngeal cancer patients with radiotherapy-related curative treatment: a preliminary study
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DOI：
10.1007/s00330-022-08630-9
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发表时间：
2022-02
影响因子：
5.9
通讯作者：
Hayato Tomita;Tatsuaki Kobayashi;E. Takaya;Sono Mishiro;Daisuke Hirahara;Atsuko Fujikawa;Y. Kurihara;H. Mimura;Yasuyuki Kobayashi
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文献类型：
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作者：
Hayato Tomita;Tatsuaki Kobayashi;E. Takaya;Sono Mishiro;Daisuke Hirahara;Atsuko Fujikawa;Y. Kurihara;H. Mimura;Yasuyuki Kobayashi
本初步研究旨在利用弥散加权成像（DWI）和表观弥散系数（ADC）图建立深度学习（DL）模型，预测接受各种形式放疗相关治疗的喉癌和下咽癌患者的局部复发和2年无进展生存期（PFS）。方法选取70例接受放疗、放化疗或诱导（化疗）放疗的喉下咽癌患者，根据就诊时间分为训练组（N= 49）和试验组（N= 21）。所有患者放疗前及放疗后4周均行MR检查。DL模型提取了治疗前和治疗中DWI和ADC图的成像特征，并进行了训练，以预测2年随访期间的局部复发。实验组对各DL模型进行复发预测分析。此外，通过Kaplan-Meier和多变量Cox回归分析来评估DL模型和临床变量的预后意义。结果采用治疗内DWI （DWIintra）预测DL模型局部复发的最高面积和准确度分别为0.767和81.0%。log-rank检验显示，dwiintra与PFS显著相关（p= 0.013）。在多因素分析中，dwinintra是PFS的独立预后因素（p= 0.023）。结论应用dwinintramay建立的dl模型对治疗性放疗的喉、下咽癌患者具有预测预后的价值。模型相关的发现可能有助于确定治疗早期的治疗策略。•使用治疗内扩散加权成像的深度学习模型对接受根治性放疗的喉癌和下咽癌患者具有预后价值。•这些模型的发现可能有助于确定治疗早期的治疗策略。
ObjectivesThis 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.MethodsSeventy 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.ResultsThe 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 (DWIintra). The log-rank test showed that DWIintrawas significantly associated with PFS (p= 0.013). DWIintrawas an independent prognostic factor for PFS in multivariate analysis (p= 0.023).ConclusionDL models using DWIintramay 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.Key Points•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.