Integrating multimodal datasets in clinical oncology is frequently hindered by high dimensionality and blockwise missingness, where entire data sources are unavailable for specific patient subsets. Standard survival models often struggle with these gaps, leading to biased results or patient exclusion. We introduce Multimodality Stacking with Blockwise missing values (MSB), a late-fusion framework for survival analysis that independently models modality-specific features before aggregating predictions via a cross-validated stacking meta-learner. MSB was validated on the PIONeeR study (n=443 patients, 378 biomarkers across eight heterogeneous sources) to predict progression-free survival in advanced non-small cell lung cancer patients receiving immunotherapy. MSB yielded higher predictive performance (C-index) than baseline algorithms. Improvements varied by baseline strength: linear models showed a 15.9% increase (p<0.001 for the Wilcoxon signed-rank test), random survival forests gained 5.4% (p=0.002), and gradient boosting methods improved by 2.1% (p=0.030). Beyond discrimination, MSB reduced the generalization gap (train-test difference in 5 folds cross-validation repeated 3 times: 0.055 vs 0.380 for linear models). Permutation importance analysis identified routine laboratory markers, clinical features, and PD-L1 expression as primary predictive drivers. Missing block indicators showed negligible importance, suggesting the model learned from biomarker values rather than data availability patterns. MSB provides a statistically validated framework for multimodal survival prediction with blockwise missingness. By enabling systematic biomarker evaluation without requiring complete data, MSB offers a practical tool for predictive modeling in biomedical research, pending external validation. Implementation is available at https://github.com/MohamedBoussena/MSB under Inria license.

翻译：摘要：整合临床肿瘤学中的多模态数据集常因高维度和分块缺失（即部分患者子集的完整数据源缺失）而受阻。标准生存模型难以处理这些数据空缺，导致结果偏倚或患者排除。我们提出具有分块缺失处理能力的多模态堆叠框架（MSB），这是一种用于生存分析的晚期融合方法，可独立建模各模态特异性特征，并通过交叉验证的堆叠元学习器聚合预测结果。MSB在PIONeeR研究（纳入443例患者，涵盖378个生物标志物及八类异质性数据源）中验证了其预测接受免疫治疗的晚期非小细胞肺癌患者无进展生存期的能力。结果显示，MSB的预测性能（C指数）显著优于基线算法。改进幅度因基线强度而异：线性模型提升15.9%（Wilcoxon符号秩检验p<0.001），随机生存森林提升5.4%（p=0.002），梯度提升方法提升2.1%（p=0.030）。除区分能力外，MSB还缩小了泛化差距（五折交叉验证重复三次的训练-测试差值：0.055 vs 线性模型的0.380）。基于置换重要性分析识别出常规实验室标志物、临床特征及PD-L1表达为主要预测驱动因素。缺失块指示因子的重要性可忽略，表明模型从生物标志物数值中学习而非依赖数据可用性模式。MSB为处理分块缺失的多模态生存预测提供了经统计验证的框架。通过在不要求完整数据的前提下实现系统性生物标志物评估，MSB为生物医学研究中的预测建模提供了实用工具（尚需外部验证）。工具代码已按Inria许可证发布于https://github.com/MohamedBoussena/MSB。