Azuri Ido, Wattad Ameer, Peri-Hanania Keren, Kashti Tamar, Rosen Ronnie, Caspi Yaron, Istaiti Majdolen, Wattad Makram, Applbaum Yaakov, Zimran Ari, Revel-Vilk Shoshana, C Eldar Yonina
Bioinformatics Unit, Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot 7610001, Israel.
Department of Radiology, Shaare Zedek Medical Center, Jerusalem 9103102, Israel.
J Clin Med. 2023 Aug 18;12(16):5361. doi: 10.3390/jcm12165361.
The enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitoring of the response to treatment. A commonly used method in clinical practice to estimate the spleen volume is the employment of a formula that uses the measurements of the craniocaudal length, diameter, and thickness of the spleen in MRI. However, the inaccuracy of this formula is significant, which, in turn, emphasizes the need for a more precise and reliable alternative. To this end, we employed deep-learning techniques, to achieve a more accurate spleen segmentation and, subsequently, calculate the resulting spleen volume with higher accuracy on a testing set cohort of 20 patients with GD. Our results indicate that the mean error obtained using the deep-learning approach to spleen volume estimation is 3.6 ± 2.7%, which is significantly lower than the common formula approach, which resulted in a mean error of 13.9 ± 9.6%. These findings suggest that the integration of deep-learning methods into the clinical routine practice for spleen volume calculation could lead to improved diagnostic and monitoring outcomes.
肝脏和脾脏肿大（肝脾肿大）是戈谢病（GD）的常见表现。利用磁共振成像（MRI）等成像工具准确估算GD患者的肝脏和脾脏体积，对于基线评估和治疗反应监测至关重要。临床实践中常用的估算脾脏体积的方法是使用一个公式，该公式利用MRI中脾脏的头尾长度、直径和厚度测量值。然而，这个公式的不准确性很显著，这反过来强调了需要一种更精确、更可靠的替代方法。为此，我们采用深度学习技术，在20例GD患者的测试集队列中实现更准确的脾脏分割，并随后以更高的精度计算所得的脾脏体积。我们的结果表明，使用深度学习方法估算脾脏体积获得的平均误差为3.6±2.7%，显著低于常用公式方法，后者的平均误差为13.9±9.6%。这些发现表明，将深度学习方法整合到临床常规实践中进行脾脏体积计算，可能会改善诊断和监测结果。
