Department of Lymphatic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, 310022, China.
Binjiang Institute of Zhejiang University, Hangzhou, Zhejiang, 310053, China.
BMC Cancer. 2024 May 22;24(1):621. doi: 10.1186/s12885-024-12337-z.
Diffuse large B-cell lymphomas (DLBCLs) display high molecular heterogeneity, but the International Prognostic Index (IPI) considers only clinical indicators and has not been updated to include molecular data. Therefore, we developed a widely applicable novel scoring system with molecular indicators screened by artificial intelligence (AI) that achieves accurate prognostic stratification and promotes individualized treatments.
We retrospectively enrolled a cohort of 401 patients with DLBCL from our hospital, covering the period from January 2015 to January 2019. We included 22 variables in our analysis and assigned them weights using the random survival forest method to establish a new predictive model combining bidirectional long-short term memory (Bi-LSTM) and logistic hazard techniques. We compared the predictive performance of our "molecular-contained prognostic model" (McPM) and the IPI. In addition, we developed a simplified version of the McPM (sMcPM) to enhance its practical applicability in clinical settings. We also demonstrated the improved risk stratification capabilities of the sMcPM.
Our McPM showed superior predictive accuracy, as indicated by its high C-index and low integrated Brier score (IBS), for both overall survival (OS) and progression-free survival (PFS). The overall performance of the McPM was also better than that of the IPI based on receiver operating characteristic (ROC) curve fitting. We selected five key indicators, including extranodal involvement sites, lactate dehydrogenase (LDH), MYC gene status, absolute monocyte count (AMC), and platelet count (PLT) to establish the sMcPM, which is more suitable for clinical applications. The sMcPM showed similar OS results (P < 0.0001 for both) to the IPI and significantly better PFS stratification results (P < 0.0001 for sMcPM vs. P = 0.44 for IPI).
Our new McPM, including both clinical and molecular variables, showed superior overall stratification performance to the IPI, rendering it more suitable for the molecular era. Moreover, our sMcPM may become a widely used and effective stratification tool to guide individual precision treatments and drive new drug development.
弥漫性大 B 细胞淋巴瘤（DLBCL）表现出高度的分子异质性，但国际预后指数（IPI）仅考虑临床指标，尚未更新以纳入分子数据。因此，我们开发了一种广泛适用的新型评分系统，该系统使用人工智能（AI）筛选分子指标，可实现准确的预后分层，并促进个体化治疗。
我们回顾性地纳入了来自我院的 401 例 DLBCL 患者队列，涵盖了 2015 年 1 月至 2019 年 1 月期间的数据。我们在分析中纳入了 22 个变量，并使用随机生存森林方法为每个变量分配权重，以建立一种结合双向长短期记忆（Bi-LSTM）和逻辑风险技术的新预测模型。我们比较了我们的“包含分子的预后模型”（McPM）和 IPI 的预测性能。此外，我们开发了 McPM 的简化版本（sMcPM），以增强其在临床环境中的实际适用性。我们还展示了 sMcPM 改善风险分层能力。
我们的 McPM 显示出更高的预测准确性，其总体生存率（OS）和无进展生存率（PFS）的 C 指数和低综合 Brier 评分（IBS）均较高。根据接受者操作特征（ROC）曲线拟合，McPM 的整体性能也优于 IPI。我们选择了五个关键指标，包括结外侵犯部位、乳酸脱氢酶（LDH）、MYC 基因状态、绝对单核细胞计数（AMC）和血小板计数（PLT），建立了更适合临床应用的 sMcPM。sMcPM 的 OS 结果与 IPI 相似（两者均为 P<0.0001），并且 PFS 分层结果显著更好（sMcPM 为 P<0.0001，而 IPI 为 P=0.44）。
我们的新 McPM 包含临床和分子变量，其总体分层性能优于 IPI，更适合分子时代。此外，我们的 sMcPM 可能成为一种广泛使用且有效的分层工具，用于指导个体精准治疗和推动新药开发。
