Scholarlink

<p>Early identification of acute hematologic toxicity (HT) in locally advanced rectal cancer (LARC) patients undergoing radiotherapy is crucial for optimizing clinical outcomes. Here, we retrospectively collected multi-center LARC patients (<i>n</i> = 464, <i>n</i> = 56, and <i>n</i> = 79) with complete CT images, dose maps, hematologic biomarkers, and demographic information. A Transformer-based multimodal fusion model was constructed to combine the visual and non-visual representation features for HT prediction, and the study also testified to the modality-specific and region-specific contributions to HT. The multimodal fusion model achieved a state-of-the-art HT prediction performance in LARC patients: with an area under the curve (AUC) of 0.828 (95% confidence interval [CI]: 0.820–0.835), 0.757 (95% CI: 0.750–0.766), and 0.756 (95% CI: 0.752–0.762) in internal and two external testing datasets. The initial hematologic biomarkers were the best unimodal risk indicator, while the planning target volume served as the most sensitive region. The study confirms the sole and combined contributions of each modality to the radiotherapy-induced HT in LARC patients, and the multimodal fusion model shows promising interpretability and generalization for HT occurrence, which offers valuable insights to optimize personalized treatment plans for high-risk patients.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multimodal prediction for radiotherapy-induced hematologic toxicity in rectal cancer patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月14日

查看全文
  • Bin Jing,
  • Xiao Liang,
  • Huidong Wang,
  • Quan Wang,
  • Jiacheng Liu,
  • Xianggao Zhu,
  • Yongheng Li,
  • Hao Wu,
  • Haodi Zhong,
  • Han Liu,
  • Haizhen Yue,
  • Chunlin Li,
  • Harisson Bai,
  • Zhicheng Jiao,
  • Di Wang

摘要

Early identification of acute hematologic toxicity (HT) in locally advanced rectal cancer (LARC) patients undergoing radiotherapy is crucial for optimizing clinical outcomes. Here, we retrospectively collected multi-center LARC patients (n = 464, n = 56, and n = 79) with complete CT images, dose maps, hematologic biomarkers, and demographic information. A Transformer-based multimodal fusion model was constructed to combine the visual and non-visual representation features for HT prediction, and the study also testified to the modality-specific and region-specific contributions to HT. The multimodal fusion model achieved a state-of-the-art HT prediction performance in LARC patients: with an area under the curve (AUC) of 0.828 (95% confidence interval [CI]: 0.820–0.835), 0.757 (95% CI: 0.750–0.766), and 0.756 (95% CI: 0.752–0.762) in internal and two external testing datasets. The initial hematologic biomarkers were the best unimodal risk indicator, while the planning target volume served as the most sensitive region. The study confirms the sole and combined contributions of each modality to the radiotherapy-induced HT in LARC patients, and the multimodal fusion model shows promising interpretability and generalization for HT occurrence, which offers valuable insights to optimize personalized treatment plans for high-risk patients.