Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Pathology Department, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, Rozzano, Milan, Italy.
J Med Internet Res. 2024 Oct 22;26:e50023. doi: 10.2196/50023.
Gastrointestinal stromal tumors (GISTs) present a complex clinical landscape, where precise preoperative risk assessment plays a pivotal role in guiding therapeutic decisions. Conventional methods for evaluating mitotic count, such as biopsy-based assessments, encounter challenges stemming from tumor heterogeneity and sampling biases, thereby underscoring the urgent need for innovative approaches to enhance prognostic accuracy.
The primary objective of this study was to develop a robust and reliable computational tool, PROMETheus (Preoperative Mitosis Estimator Tool), aimed at refining patient stratification through the precise estimation of mitotic count in GISTs.
Using advanced Bayesian network methodologies, we constructed a directed acyclic graph (DAG) integrating pertinent clinicopathological variables essential for accurate mitotic count prediction on the surgical specimen. Key parameters identified and incorporated into the model encompassed tumor size, location, mitotic count from biopsy specimens, surface area evaluated during biopsy, and tumor response to therapy, when applicable. Rigorous testing procedures, including prior predictive simulations, validation utilizing synthetic data sets were employed. Finally, the model was trained on a comprehensive cohort of real-world GIST cases (n=80), drawn from the repository of the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Humanitas Research Hospital, with a total of 160 cases analyzed.
Our computational model exhibited excellent diagnostic performance on synthetic data. Different model architecture were selected based on lower deviance and robust out-of-sample predictive capabilities. Posterior predictive checks (retrodiction) further corroborated the model's accuracy. Subsequently, PROMETheus was developed. This is an intuitive tool that dynamically computes predicted mitotic count and risk assessment on surgical specimens based on tumor-specific attributes, including size, location, surface area, and biopsy-derived mitotic count, using posterior probabilities derived from the model.
The deployment of PROMETheus represents a potential advancement in preoperative risk stratification for GISTs, offering clinicians a precise and reliable means to anticipate mitotic counts on surgical specimens and a solid base to stratify patients for clinical studies. By facilitating tailored therapeutic strategies, this innovative tool is poised to revolutionize clinical decision-making paradigms, ultimately translating into improved patient outcomes and enhanced prognostic precision in the management of GISTs.
胃肠道间质瘤（GIST）呈现出复杂的临床特征，精确的术前风险评估在指导治疗决策方面起着关键作用。传统的评估有丝分裂计数的方法，如基于活检的评估，由于肿瘤异质性和采样偏差而面临挑战，因此迫切需要创新方法来提高预测准确性。
本研究的主要目的是开发一种强大而可靠的计算工具，即 PROMETheus（术前有丝分裂计数估计工具），旨在通过精确估计 GIST 中的有丝分裂计数来细化患者分层。
我们使用先进的贝叶斯网络方法，构建了一个有向无环图（DAG），该图集成了对手术标本中准确有丝分裂计数预测至关重要的相关临床病理变量。模型中确定并纳入的关键参数包括肿瘤大小、位置、活检标本中的有丝分裂计数、活检时评估的表面积，以及肿瘤对治疗的反应（如适用）。我们采用了严格的测试程序，包括预先的预测模拟和利用合成数据集进行验证。最后，该模型在来自 Istituto di Ricovero e Cura a Carattere Scientifico（IRCCS）Humanitas 研究医院的真实 GIST 病例库（n=80）上进行了训练，总共分析了 160 个病例。
我们的计算模型在合成数据上表现出出色的诊断性能。根据较低的偏差和稳健的样本外预测能力选择了不同的模型架构。后验预测检查（回溯预测）进一步证实了模型的准确性。随后，开发了 PROMETheus。这是一个直观的工具，根据肿瘤的特定属性，包括大小、位置、表面积和活检有丝分裂计数，使用模型得出的后验概率，动态计算手术标本上预测的有丝分裂计数和风险评估。
PROMETheus 的应用代表了 GIST 术前风险分层的一个潜在进展，为临床医生提供了一种精确可靠的方法来预测手术标本上的有丝分裂计数，并为临床研究中患者分层提供了坚实的基础。通过促进量身定制的治疗策略，这个创新工具有望彻底改变临床决策模式，最终改善 GIST 患者的预后并提高预测准确性。
