Objectives We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification. Methods The MRI features were extracted for molecular subgroups by a novel multi-parameter convolutional neural network (CNN) called Bi-ResNet-MB. Then, MR features were used to establish a prognosis model based on XGBoost. Finally, a novel risk stratification system to stratify the patients based on the M2R Score (Machine learning-based Medulloblastoma Risk Score) was proposed. Results A total of 139 MB patients (36 female, average age 7.27 ± 3.62 years) were treated at Beijing Tiantan Hospital. The Bi-ResNet-MB model excelled in molecular subgroup classification, achieving an average AUC of 0.946 (95% CI: 0.899–0.993). For prognostic prediction, our models achieved AUCs of 0.840 (95% CI: 0.792–0.888), 0.949 (95% CI: 0.899–0.999), and 0.960 (95% CI: 0.915–1.000) for OS, and 0.946 (95% CI: 0.905–0.987), 0.932 (95% CI: 0.875–0.989), and 0.964 (95% CI: 0.921–1.000) for PFS at 1, 3, and 5 years. In an independent validation dataset of 108 patients (33 female, average age 7.11 ± 2.92 years), the average AUC of molecular subgroup classification reached 0.894 (95% CI: 0.797–1.000). For PFS prediction at 1, 3, and 5 years, the AUCs were 0.832 (95% CI: 0.724–0.920), 0.875 (95% CI: 0.781–0.967), and 0.907 (95% CI: 0.760–1.000), respectively. Conclusions Based on machine learning and MRI data, models for MB molecular subgroups and prognosis prediction and the novel risk stratification system may significantly benefit patients. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Medulloblastoma exhibits significant heterogeneity, leading to considerable variations in patient prognosis and there is a lack of effective risk assessment strategies. <Emphasis Type="BoldItalic">Findings</Emphasis> We have constructed a comprehensive machine learning system that excels in subgrouping diagnosis, prognosis assessment, and risk stratification for medulloblastoma patients preoperatively. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The utilization of non-invasive preoperative diagnosis and assessment is advantageous for clinicians in creating personalized treatment plans, particularly for high-risk patients. Additionally, it lays a foundation for the subsequent implementation of neoadjuvant therapy for medulloblastoma.

Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma

期刊论文
发表日期：

2025年1月30日

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Ziyang Liu,
Sikang Ren,
Heng Zhang,
Zhiyi Liao,
Zhiming Liu,
Xu An,
Jian Cheng,
Chunde Li,
Jian Gong,
Haijun Niu,
Jing Jing,
Zixiao Li,
Tao Liu,
Yongji Tian

摘要

Objectives

We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.

Methods

The MRI features were extracted for molecular subgroups by a novel multi-parameter convolutional neural network (CNN) called Bi-ResNet-MB. Then, MR features were used to establish a prognosis model based on XGBoost. Finally, a novel risk stratification system to stratify the patients based on the M²R Score (Machine learning-based Medulloblastoma Risk Score) was proposed.

Results

A total of 139 MB patients (36 female, average age 7.27 ± 3.62 years) were treated at Beijing Tiantan Hospital. The Bi-ResNet-MB model excelled in molecular subgroup classification, achieving an average AUC of 0.946 (95% CI: 0.899–0.993). For prognostic prediction, our models achieved AUCs of 0.840 (95% CI: 0.792–0.888), 0.949 (95% CI: 0.899–0.999), and 0.960 (95% CI: 0.915–1.000) for OS, and 0.946 (95% CI: 0.905–0.987), 0.932 (95% CI: 0.875–0.989), and 0.964 (95% CI: 0.921–1.000) for PFS at 1, 3, and 5 years. In an independent validation dataset of 108 patients (33 female, average age 7.11 ± 2.92 years), the average AUC of molecular subgroup classification reached 0.894 (95% CI: 0.797–1.000). For PFS prediction at 1, 3, and 5 years, the AUCs were 0.832 (95% CI: 0.724–0.920), 0.875 (95% CI: 0.781–0.967), and 0.907 (95% CI: 0.760–1.000), respectively.

Conclusions

Based on machine learning and MRI data, models for MB molecular subgroups and prognosis prediction and the novel risk stratification system may significantly benefit patients.

Key Points

Question Medulloblastoma exhibits significant heterogeneity, leading to considerable variations in patient prognosis and there is a lack of effective risk assessment strategies.

Findings We have constructed a comprehensive machine learning system that excels in subgrouping diagnosis, prognosis assessment, and risk stratification for medulloblastoma patients preoperatively.

Clinical relevance The utilization of non-invasive preoperative diagnosis and assessment is advantageous for clinicians in creating personalized treatment plans, particularly for high-risk patients. Additionally, it lays a foundation for the subsequent implementation of neoadjuvant therapy for medulloblastoma.