Lin Jinyu, Yang Jian, Qian Yinling, Tang Xuanshuang, Zhu Minheng, Luo Wang, Lin Wenjun, Chen Mengjing, Zheng Xianqing, Yuan Xiangdong, Tao Haisu
Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
The Department of Hepatobiliary Surgery I, Zhujiang Hospital of Southern Medical University, Guangzhou, China.
Quant Imaging Med Surg. 2025 Jun 6;15(6):5160-5176. doi: 10.21037/qims-2024-2954. Epub 2025 Jun 3.
Splenomegaly serves as a crucial indicator for various diseases, particularly in hepatosplenomegaly and hematological disorders. Accurate assessment of splenomegaly is essential for improving diagnostic accuracy and treatment decisions, yet individualized diagnosis necessitates a standard reference for splenic volume. This study aimed to develop an interpretable machine learning (ML) model to evaluate standard splenic volume (SSV), enhancing personalized clinical decision-making.
We conducted a retrospective analysis of 1,186 volunteers from a multicenter cohort and evaluated 11 ML algorithms. SHapley Additive exPlanations (SHAP) were employed for feature selection and interpretation. Model performance was rigorously evaluated through key metrics such as root mean squared error (RMSE), coefficient of determination (R), and additional validation parameters, further validated through comparisons with prior published formulas. We also developed free, open-access web-based calculators for the predictive model.
Model development and internal validation involved 511 eligible volunteers, with external validation from an additional 111 volunteers. The random forest (RF) model (ML_SSV) integrating features such as age, body weight (BW), body height, body mass index (BMI), body surface area (BSA), red blood cell count, platelet count, total bilirubin, fibrinogen, and D-dimer, demonstrated exceptional predictive accuracy. In external validation, the model achieved an RMSE of 22.6 mL (R=0.80), with residual analysis confirming normally distributed errors (range: -58.32 to 67.01 mL; P=0.201). Notably, a simplified RF model (ML_SSVa) utilizing only four non-invasive parameters (age, BW, BMI, BSA) retained robust performance, with an RMSE of 36.0 mL (R=0.70) in external validation. Furthermore, both models outperformed all existing formulas in cross-validation analyses. The models were deployed as open-access calculators at https://mlssv.vip.cpolar.cn (ML_SSV) and https://mlssva.vip.cpolar.cn (ML_SSVa), enabling real-time estimation with SHAP-based interpretability.
This study establishes a novel interpretable ML model rigorously validated through statistical and clinical benchmarks. These models enable the assessment of SSV, providing a reference baseline for the individualized diagnosis of splenomegaly to enhance diagnostic accuracy and support data-driven clinical decision-making.
脾肿大是多种疾病的关键指标，尤其是在肝脾肿大和血液系统疾病中。准确评估脾肿大对于提高诊断准确性和治疗决策至关重要，然而个体化诊断需要脾体积的标准参考值。本研究旨在开发一种可解释的机器学习（ML）模型来评估标准脾体积（SSV），以加强个性化临床决策。
我们对来自多中心队列的1186名志愿者进行了回顾性分析，并评估了11种ML算法。采用SHapley加性解释（SHAP）进行特征选择和解释。通过均方根误差（RMSE）、决定系数（R）等关键指标以及其他验证参数对模型性能进行了严格评估，并与先前发表的公式进行比较进一步验证。我们还为预测模型开发了基于网络的免费开放访问计算器。
模型开发和内部验证涉及511名符合条件的志愿者，另有111名志愿者进行外部验证。整合年龄、体重（BW）、身高、体重指数（BMI）、体表面积（BSA）、红细胞计数、血小板计数、总胆红素、纤维蛋白原和D-二聚体等特征的随机森林（RF）模型（ML_SSV）显示出卓越的预测准确性。在外部验证中，该模型的RMSE为22.6 mL（R = 0.80），残差分析证实误差呈正态分布（范围：-58.32至67.01 mL；P = 0.201）。值得注意的是，仅使用四个非侵入性参数（年龄、BW、BMI、BSA）的简化RF模型（ML_SSVa）保持了稳健的性能，在外部验证中的RMSE为36.0 mL（R = 0.70）。此外，在交叉验证分析中，这两个模型均优于所有现有公式。这些模型已作为开放访问计算器部署在https://mlssv.vip.cpolar.cn（ML_SSV）和https://mlssva.vip.cpolar.cn（ML_SSVa），可实现基于SHAP可解释性的实时估计。
本研究建立了一种通过统计和临床基准严格验证的新型可解释ML模型。这些模型能够评估SSV，为脾肿大的个体化诊断提供参考基线，以提高诊断准确性并支持数据驱动的临床决策。
