Mehrpouya Zahra, Khatibi Toktam, Sedighipashaki Abdolazim
Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran.
Assistant Professor, Department of Radiooncology, School of Medicine Cancer Research Center, Hamedan university of medical sciences, Hamedan, Iran.
PLoS One. 2025 Aug 11;20(8):e0327782. doi: 10.1371/journal.pone.0327782. eCollection 2025.
Accurate classification of meningioma brain tumors is crucial for determining the appropriate treatment plan and improving patient outcomes. However, this task is challenging due to the slow-growing nature of these tumors and the potential for misdiagnosis. Additionally, deep learning models for tumor classification often require large amounts of labeled data, which can be costly and time-consuming to obtain, especially in the medical domain.
Our main aim is to enhance Meningioma Tumor Classification Accuracy.
This study proposes a multi-task learning (MTL) approach to enhance the accuracy of meningioma tumor classification while mitigating the need for excessive labeled data. The primary task involves classifying meningioma tumors based on MRI imaging data, while auxiliary tasks leverage patient demographic information, such as age and gender. By incorporating these additional data sources into the learning process, the proposed MTL framework leverages the interdependencies among multiple tasks to improve overall prediction accuracy. The study evaluates the performance of the MTL approach using a dataset of 2218 brain MRI images from 34 patients diagnosed with meningioma, obtained from the Mahdia Imaging Center in Hamadan, Iran.
Results demonstrate that the MTL model significantly outperforms single-task learning baselines, achieving 99.6% ± 0.2 accuracy on the test data in 95% confidence interval.
This highlights the efficacy of the proposed approach in enhancing meningioma tumor classification and its potential for aiding clinical decision-making and personalized treatment planning.
Our proposed method can be used in computer-aided diagnosis systems.
准确分类脑膜瘤脑肿瘤对于确定合适的治疗方案和改善患者预后至关重要。然而，由于这些肿瘤生长缓慢且存在误诊的可能性，这项任务具有挑战性。此外，用于肿瘤分类的深度学习模型通常需要大量的标记数据，获取这些数据可能成本高昂且耗时，尤其是在医学领域。
我们的主要目标是提高脑膜瘤肿瘤分类的准确性。
本研究提出一种多任务学习（MTL）方法，以提高脑膜瘤肿瘤分类的准确性，同时减少对大量标记数据的需求。主要任务是基于MRI成像数据对脑膜瘤肿瘤进行分类，而辅助任务则利用患者的人口统计学信息，如年龄和性别。通过将这些额外的数据源纳入学习过程，所提出的MTL框架利用多个任务之间的相互依赖关系来提高整体预测准确性。该研究使用从伊朗哈马丹的马赫迪耶成像中心获得的34例诊断为脑膜瘤的患者的2218张脑部MRI图像数据集，评估了MTL方法的性能。
结果表明，MTL模型显著优于单任务学习基线，在95%置信区间内的测试数据上实现了99.6%±0.2的准确率。
这突出了所提出方法在增强脑膜瘤肿瘤分类方面的有效性及其在辅助临床决策和个性化治疗规划方面的潜力。
我们提出的方法可用于计算机辅助诊断系统。
