Tailored Supramarginal Resection in Glioblastoma Guided by Artificial Intelligence-based Recurrence Probability Maps. A Non-randomized Pilot Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 20
- 试验地点
- 2
- 主要终点
- Feasibility using the proportion of consent
研究概览
简要总结
Glioblastomas are the most common and poorly prognostic primary brain neoplasms. Despite advances in surgical techniques and chemotherapy, the median survival time for these patients remains less than 15 months. This highlights the need for more effective treatments and improved prognostic tools. The globally accepted surgical strategy currently consists of achieving the maximum safe resection of the enhancing tumor volume. However, the non-enhancing peritumoral region contains viable cells that cause the inevitable recurrence that these patients face. Clinicians currently lack an imaging tool or modality to differentiate neoplastic infiltration in the peritumoral region from vasogenic edema. In addition, it is not always feasible to include all the T2-FLAIR signal alterations surrounding the enhancing tumor in the surgical planning due to the proximity of eloquent areas and the higher risk of postoperative deficits.
However, the investigators have developed a model to predict regions of recurrence based on machine learning and MRI radiomic features that have been trained and evaluated in a multi-institutional cohort.
The investigators aim to analyze whether an adjusted supramarginal resection guided by these new recurrence probability maps improves survival in selected patients with glioblastoma.
详细描述
The SupraGlio-AI study aims to test the feasibility of the proposed AI-guided tailored supratotal resection for glioblastomas. The study will provide preliminary data on the accuracy of the AI model in predicting recurrence and the impact of using this information in surgical planning. This information will be crucial in determining the potential for a larger, randomized controlled trial in the future. The pilot study will also allow for refinement of the study design, intervention, and data collection processes before a larger-scale study is conducted. In addition to testing the feasibility and efficacy of the AI-guided tailored supratotal resection, this pilot study also has two secondary objectives: 1) Survival Analysis: The survival analysis will provide insights into the impact of using the AI model on patient outcomes and help determine the potential benefits of this approach. 2) Histopathological and Transcriptomic Analysis: The study will also include a histopathological and transcriptomic analysis of the tissue samples obtained from the high-risk regions defined by the AI model. This analysis will provide information on the molecular and cellular changes occurring in these regions and may offer insights into the underlying biology of glioblastoma recurrence. These data will inform the development of future studies aimed at improving patient outcomes.
By incorporating these secondary objectives, this pilot study will contribute to a more comprehensive understanding of the potential benefits of using AI in guiding tailored supratotal resection for glioblastomas. The results will inform future research and potentially lead to the development of improved treatment approaches for patients with this type of brain tumor.
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Single Group
- 主要目的
- Treatment
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •A suspected diagnosis of supratentorial glioblastoma by MRI.
- •Tumor in non eloquent brain region according to the UCSF (University of California, San Francisco) classification, including the sensor motor areas (precentral and postcentral gyri), perisylvian language areas in the dominant hemisphere (superior temporal, inferior frontal, and inferior parietal gyri), basal ganglia, internal capsule, thalamus, and visual cortex around the calcarine sulcus
- •Indication for surgical treatment and where supramarginal resection is considered possible according to the preoperative imaging. This consideration needs to be verified by two specialists in neurosurgery. This criterion needs to be verified by two senior neurosurgeons.
- •Karnofsky Performance Score ≥ 70;
- •Written informed consent
排除标准
- •Tumors in eloquent areas.
- •Recurrent gliomas (except biopsy)
- •MR image data not usable due to artifacts during acquisition. Inability to give written informed consent
- •KPS < 70
- •Severe comorbidity.
结局指标
主要结局
Feasibility using the proportion of consent
时间窗: Screening/Enrollment
Among all screened patients, the proportion of patients consenting to participate
Feasibility using eligibility
时间窗: Screening/Enrollment
Among all screened patients, the proportion of patients who meet the eligibility criteria
次要结局
- Postoperative complication(30 days)
- Efficacy using overall survival(From date of surgery until the date of death from any cause, assessed up to 36 months)
- Safety using the neurological function(30 days)
- Extent of resection(< 72 hours after surgery)
- Efficacy using progression-free survival(From date of surgery until the date of first documented progression, assessed up to 36 months)
- Safety using global disability(30 days)
研究者
Santiago Cepeda
Principal Investigator
Hospital del Rio Hortega