Scholarlink

<p>Brain MRI is the primary imaging modality for pediatric brain tumors, yet incomplete acquisitions are common, hindering the clinical utility of existing deep learning models for tumor segmentation and prognosis. These models are typically trained on complete MRI sequences and exhibit performance degradation when MRI sequences are missing at test time. In this retrospective study of 715 patients from the Children’s Brain Tumor Network and BraTS-PEDs, and 43 patients with 157 longitudinal MRIs from PNOC003/007 clinical trials, we developed strategies for handling missing sequences. Methods included a dropout-trained segmentation model that randomly omitted FLAIR and/or T1w inputs during training, a generative model for image synthesis, copy-substitution heuristics, and zeroed inputs. The dropout model achieved robust segmentation under missing MRI, with ≤0.04 Dice drop relative to complete-input and stable prognostic accuracy in survival analysis using model-derived tumor volumes and clinical covariates. Generative synthesis achieved high image quality (SSIM &gt; 0.90) and removed artifacts, benefiting visual interpretability. Together, these approaches can facilitate broader deployment of AI tools in real-world pediatric neuro-oncology settings.</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

AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月13日

查看全文
  • Dimosthenis Chrysochoou,
  • Deep B. Gandhi,
  • Sahand Adib,
  • Ariana M. Familiar,
  • Bhavyasri Vunnava,
  • Sanaz Varshochi,
  • Neda Khalili,
  • Nastaran Khalili,
  • Jeffrey B. Ware,
  • Wenxin Tu,
  • Paarth Jain,
  • Hannah Anderson,
  • Shuvanjan Haldar,
  • Phillip B. Storm,
  • Andrea Franson,
  • Michael Prados,
  • Cassie Kline,
  • Sabine Mueller,
  • Adam Resnick,
  • Arastoo Vossough,
  • Christos Davatzikos,
  • Ali Nabavizadeh,
  • Anahita Fathi Kazerooni

摘要

Brain MRI is the primary imaging modality for pediatric brain tumors, yet incomplete acquisitions are common, hindering the clinical utility of existing deep learning models for tumor segmentation and prognosis. These models are typically trained on complete MRI sequences and exhibit performance degradation when MRI sequences are missing at test time. In this retrospective study of 715 patients from the Children’s Brain Tumor Network and BraTS-PEDs, and 43 patients with 157 longitudinal MRIs from PNOC003/007 clinical trials, we developed strategies for handling missing sequences. Methods included a dropout-trained segmentation model that randomly omitted FLAIR and/or T1w inputs during training, a generative model for image synthesis, copy-substitution heuristics, and zeroed inputs. The dropout model achieved robust segmentation under missing MRI, with ≤0.04 Dice drop relative to complete-input and stable prognostic accuracy in survival analysis using model-derived tumor volumes and clinical covariates. Generative synthesis achieved high image quality (SSIM > 0.90) and removed artifacts, benefiting visual interpretability. Together, these approaches can facilitate broader deployment of AI tools in real-world pediatric neuro-oncology settings.