The chaos of choice in large B-cell lymphoma: a call to harmonize first-line trials.

Novel agents, including antibody-drug conjugates, bispecific antibodies, targeted therapies, and chimeric antigen receptor T-cell therapies, have demonstrated significant activity, raising the potential for improved outcomes from first-line treatment.

新型药物，包括抗体药物偶联物、双特异性抗体、靶向治疗和嵌合抗原受体T细胞疗法，已经显示出显著的活性，提高了从一线治疗开始改善结果的潜力。

However, this progress has introduced substantial complexity without a clear framework for clinical decision-making.

然而，这种进步引入了巨大的复杂性，没有一个明确的临床决策框架。

Ongoing phase 3 trials use heterogeneous designs, including differing control arms, eligibility criteria, and end points, yet share a common limitation: the inconsistent incorporation of biomarker-driven patient selection.

正在进行的第三阶段试验使用了异质性设计，包括不同的对照组、资格标准和终点，但共同存在一个局限性：生物标志物驱动的患者选择的一致性不足。

As a result, cross-trial comparisons are challenging, and identifying the optimal regimen for an individual patient remains difficult.

因此，跨试验比较具有挑战性，为个别患者确定最佳方案仍然困难。

Although the POLARIX trial established polatuzumab-based therapy (R-CHP-pola) as superior to rituximab plus CHOP (R-CHOP), many ongoing studies continue to use R-CHOP as the comparator, further complicating interpretation.

尽管POLARIX试验确定了基于波替珠单抗的疗法（R-CHP-pola）优于利妥昔单抗加CHOP（R-CHOP），但许多正在进行的研究继续使用R-CHOP作为对照，进一步复杂化了解释。

Moreover, "R-CHOP + X" strategies evaluated in clinically defined populations without biologic stratification may yield modest benefits across heterogeneous groups, limiting their applicability to individual patients.

此外，"R-CHOP + X"策略在没有生物分层的临床定义人群中进行评估，可能在异质性群体中产生适度的益处，限制了它们对个别患者的适用性。

These trends risk producing a fragmented landscape of effective yet noncomparable regimens without a principled framework for patient selection.

这些趋势可能会产生一个有效但不可比较的方案碎片化格局，而没有一个有原则的框架来选择患者。

We thus propose a harmonized, data-driven strategy to shape the next era of LBCL therapy: (1) pooled patient-level analyses, (2) standardized biomarker and minimal residual disease platforms, (3) artificial intelligence-enabled predictive modeling integrating trial and real-world data, and (4) regulatory approaches promoting collaborative trial design and shared biomarker analyses.

我们因此提出一个协调一致、数据驱动的策略来塑造弥漫大B细胞淋巴瘤（LBCL）治疗的下一个时代：（1）患者级数据的汇总分析，（2）标准化的生物标志物和微小残留病平台，（3）利用人工智能的预测建模，整合试验数据和真实世界数据，以及（4）促进合作试验设计和共享生物标志物分析的监管方法。

Ultimately, progress in LBCL will depend on aligning data, biology, and clinical decision-making to deliver precise, equitable, and effective care.

最终，LBCL的进步将取决于将数据、生物学和临床决策对齐，以提供精确、公平和有效的护理。