研究动态
Articles below are published ahead of final publication in an issue. Please cite articles in the following format: authors, (year), title, journal, DOI.

根据多机构数据集对 OPSCC 的淋巴转移进展途径进行建模。

Modelling the lymphatic metastatic progression pathways of OPSCC from multi-institutional datasets.

发表日期:2024 Jul 08
作者: Roman Ludwig, Adrian Daniel Schubert, Dorothea Barbatei, Lauence Bauwens, Jean-Marc Hoffmann, Sandrine Werlen, Olgun Elicin, Matthias Dettmer, Philippe Zrounba, Bertrand Pouymayou, Panagiotis Balermpas, Vincent Grégoire, Roland Giger, Jan Unkelbach
来源: Disease Models & Mechanisms

摘要:

口咽鳞状细胞癌(OPSCC)的选择性临床靶区(CTV-N)目前主要基于给定原发肿瘤位置不同淋巴结水平(LNL)中淋巴结转移的发生率。我们提出了同侧淋巴扩散的概率模型,该模型可以根据个体患者的 T 类别和诊断时 LNL 的临床受累情况来量化微观淋巴结受累风险。我们扩展了之前发布的隐马尔可夫模型 (HMM),它将 LNL(I、II、III、IV、V 和 VII)建模为隐藏二元随机变量 (RV)。每一项都代表患者淋巴受累的真实状态。诊断时的临床参与代表了观察到的二元 RV 通过敏感性和特异性与真实状态相关。原发肿瘤和隐藏的 RV 在图中相连。每条边代表在给定边的起始节点处的疾病的情况下,每个抽象时间步转移扩散的条件概率。为了了解这些概率,我们从来自三个机构的数据集(686 名 OPSCC 患者)的可能性中抽取马尔可夫链蒙特卡罗样本。我们使用不同图表的热力学积分来计算模型证据,以确定哪一个最能描述数据。最大化模型证据的图表将肿瘤与每个 LNL 以及 LNL I 到 V 按顺序连接起来。如果 III 级临床阴性,则预测 IV 级隐匿性疾病的风险低于 5%,并且除临床受累的晚期 T 类(T3 和 T4)患者外,V 级隐匿性疾病的风险低于 5%级别 II、III 和 IV。所提供的接受多机构数据培训的 OPSCC 患者淋巴结受累统计模型可以指导 OPSCC 容量降级治疗的临床试验设计,并有助于制定关于选择性淋巴结治疗的更多个人指南。© 2024。作者。
The elective clinical target volume (CTV-N) in oropharyngeal squamous cell carcinoma (OPSCC) is currently based mostly on the prevalence of lymph node metastases in different lymph node levels (LNLs) for a given primary tumor location. We present a probabilistic model for ipsilateral lymphatic spread that can quantify the microscopic nodal involvement risk based on an individual patient's T-category and clinical involvement of LNLs at diagnosis. We extend a previously published hidden Markov model (HMM), which models the LNLs (I, II, III, IV, V, and VII) as hidden binary random variables (RVs). Each represents a patient's true state of lymphatic involvement. Clinical involvement at diagnosis represents the observed binary RVs linked to the true state via sensitivity and specificity. The primary tumor and the hidden RVs are connected in a graph. Each edge represents the conditional probability of metastatic spread per abstract time-step, given disease at the edge's starting node. To learn these probabilities, we draw Markov chain Monte Carlo samples from the likelihood of a dataset (686 OPSCC patients) from three institutions. We compute the model evidence using thermodynamic integration for different graphs to determine which describes the data best.The graph maximizing the model evidence connects the tumor to each LNL and the LNLs I through V in order. It predicts the risk of occult disease in level IV is below 5% if level III is clinically negative, and that the risk of occult disease in level V is below 5% except for advanced T-category (T3 and T4) patients with clinical involvement of levels II, III, and IV. The provided statistical model of nodal involvement in OPSCC patients trained on multi-institutional data may guide the design of clinical trials on volume-deescalated treatment of OPSCC and contribute to more personal guidelines on elective nodal treatment.© 2024. The Author(s).