Multiparametric MRI-Based Radiomics Signature with Machine Learning for Preoperative Prediction of Prognosis Stratification in Pediatric Medulloblastoma

Artificial Intelligence for Neuroimaging in Pediatric Cancer

BACKGROUND/OBJECTIVES

Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population.

METHODS

A comprehensive literature review was conducted, focusing on AI's impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed.

RESULTS

AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations.

CONCLUSIONS

To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer.

Machine learning-based models for prediction of survival in medulloblastoma: a systematic review and meta-analysis

BACKGROUND

Medulloblastoma (MB) is the pediatric population's most frequent malignant intracranial lesions. Prognostication plays a crucial role in optimizing treatment strategy in the MB setting. Several studies have developed ML-based models to predict survival outcomes in individuals with MB. In this systematic review and meta-analysis study, we aimed to evaluate the role of ML-based models in predicting survival in MB patients.

METHOD

Literature records were retrieved on May 14th, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The quality assessment was performed using the QUADAS-2 tool. The meta-analysis and sensitivity analysis were conducted using R software.

RESULTS

Six studies were included, with 2771 patients ranging from 46 to 1759 individuals. A total of 23 ML and DL models were developed, 20 of which were ML and three DL. Random forest (RF) was the most frequent classifier, as it was utilized in nine models, followed by support vector machine (SVM). Eight models were included in the meta-analysis. Our meta-analysis revealed a pooled AUC of 0.77 (95% CI: 0.75-0.80). In addition, the radionics-based and genomics-based models had a pooled AUC of 0.77 (95% CI: 0.76-079) and 0.76 (0.63-0.88), respectively (P = 0.77).

CONCLUSION

Our results suggested that ML-based models, especially ML algorithms, could play a vital and efficient role in the prediction of survival of patients based on radiomics and genomics.

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

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.

Development of a neoadjuvant chemotherapy efficacy prediction model for nasopharyngeal carcinoma integrating magnetic resonance radiomics and pathomics: a multi-center retrospective study

OBJECTIVE

This study aimed to develop and validate a predictive model for assessing the efficacy of neoadjuvant chemotherapy (NACT) in nasopharyngeal carcinoma (NPC) by integrating radiomics and pathomics features using a particle swarm optimization-supported support vector machine (PSO-SVM).

METHODS

A retrospective multi-center study was conducted, which included 389 NPC patients who received NACT from three institutions. Radiomics features were extracted from magnetic resonance imaging scans, while pathomics features were derived from histopathological images. A total of 2,667 radiomics features and 254 pathomics features were initially extracted. Feature selection involved intra-class correlation coefficient evaluation, Mann-Whitney U test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. The PSO-SVM model was constructed and validated using 10-fold cross-validation on the training set and further evaluated using an external validation set. Model performance was assessed using the area under the curve (AUC) of the receiver operating characteristic curve, calibration curves, and decision curve analysis.

RESULTS

Eight significant predictive features (five radiomics and three pathomics) were identified. The PSO-SVM radiopathomics model achieved superior performance compared to models based solely on radiomics or pathomics features. The AUCs for the PSO-SVM radiopathomics model were 0.917 (95% CI: 0.887-0.948) in internal validation and 0.814 (95% CI: 0.742-0.887) in external validation. Calibration curves demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis showed that the PSO-SVM radiopathomics model provided higher clinical net benefit over a wider range of risk thresholds compared to other models.

CONCLUSION

The PSO-SVM radiopathomics model effectively integrates radiomics and pathomics features, offering enhanced predictive accuracy and clinical utility for assessing NACT efficacy in NPC. The multi-center approach and robust validation underscore its potential for personalized treatment planning, supporting improved clinical decision-making for NPC patients.

Multiparametric MRI-Based Interpretable Radiomics Machine Learning Model Differentiates Medulloblastoma and Ependymoma in Children: A Two-Center Study

RATIONALE AND OBJECTIVES

Medulloblastoma (MB) and Ependymoma (EM) in children, share similarities in age group, tumor location, and clinical presentation. Distinguishing between them through clinical diagnosis is challenging. This study aims to explore the effectiveness of using radiomics and machine learning on multiparametric magnetic resonance imaging (MRI) to differentiate between MB and EM and validate its diagnostic ability with an external set.

MATERIALS AND METHODS

Axial T2 weighted image (T2WI) and contrast-enhanced T1weighted image (CE-T1WI) MRI sequences of 135 patients from two centers were collected as train/test sets. Volume of interest (VOI) was manually delineated by an experienced neuroradiologist, supervised by a senior. Feature selection analysis and the least absolute shrinkage and selection operator (LASSO) algorithm identified valuable features, and Shapley additive explanations (SHAP) evaluated their significance. Five machine-learning classifiers-extreme gradient boosting (XGBoost), Bernoulli naive Bayes (Bernoulli NB), Logistic Regression (LR), support vector machine (SVM), linear support vector machine (Linear SVC) classifiers were built based on T2WI (T2 model), CE-T1WI (T1 model), and T1 + T2WI (T1 + T2 model). A human expert diagnosis was developed and corrected by senior radiologists. External validation was performed at Sun Yat-Sen University Cancer Center.

RESULTS

31 valuable features were extracted from T2WI and CE-T1WI. XGBoost demonstrated the highest performance with an area under the curve (AUC) of 0.92 on the test set and maintained an AUC of 0.80 during external validation. For the T1 model, XGBoost achieved the highest AUC of 0.85 on the test set and the highest accuracy of 0.71 on the external validation set. In the T2 model, XGBoost achieved the highest AUC of 0.86 on the test set and the highest accuracy of 0.82 on the external validation set. The human expert diagnosis had an AUC of 0.66 on the test set and 0.69 on the external validation set. The integrated T1 + T2 model achieved an AUC of 0.92 on the test set, 0.80 on the external validation set, achieved the best performance. Overall, XGBoost consistently outperformed in different classification models.

CONCLUSION

The combination of radiomics and machine learning on multiparametric MRI effectively distinguishes between MB and EM in childhood, surpassing human expert diagnosis in training and testing sets.

A Radiomic Approach for Evaluating Intra-Subgroup Heterogeneity in SHH and Group 4 Pediatric Medulloblastoma: A Preliminary Multi-Institutional Study

Medulloblastoma (MB) is the most frequent malignant brain tumor in children with extensive heterogeneity that results in varied clinical outcomes. Recently, MB was categorized into four molecular subgroups, WNT, SHH, Group 3, and Group 4. While SHH and Group 4 are known for their intermediate prognosis, studies have reported wide disparities in patient outcomes within these subgroups. This study aims to create a radiomic prognostic signature, medulloblastoma radiomics risk (mRRisk), to identify the risk levels within the SHH and Group 4 subgroups, individually, for reliable risk stratification. Our hypothesis is that this signature can comprehensively capture tumor characteristics that enable the accurate identification of the risk level. In total, 70 MB studies (48 Group 4, and 22 SHH) were retrospectively curated from three institutions. For each subgroup, 232 hand-crafted features that capture the entropy, surface changes, and contour characteristics of the tumor were extracted. Features were concatenated and fed into regression models for risk stratification. Contrasted with Chang stratification that did not yield any significant differences within subgroups, significant differences were observed between two risk groups in Group 4 (p = 0.04, Concordance Index (CI) = 0.82) on the cystic core and non-enhancing tumor, and SHH (p = 0.03, CI = 0.74) on the enhancing tumor. Our results indicate that radiomics may serve as a prognostic tool for refining MB risk stratification, towards improved patient care.