  Gastric cancer (GC) is a prevalent malignancy worldwide, ranking as the fifth
most common cancer with over 1 million new cases and 700 thousand deaths in
2020. Locally advanced gastric cancer (LAGC) accounts for approximately
two-thirds of GC diagnoses, and neoadjuvant chemotherapy (NACT) has emerged as
the standard treatment for LAGC. However, the effectiveness of NACT varies
significantly among patients, with a considerable subset displaying treatment
resistance. Ineffective NACT not only leads to adverse effects but also misses
the optimal therapeutic window, resulting in lower survival rate. However,
existing multimodal learning methods assume the availability of all modalities
for each patient, which does not align with the reality of clinical practice.
The limited availability of modalities for each patient would cause information
loss, adversely affecting predictive accuracy. In this study, we propose an
incomplete multimodal data integration framework for GC (iMD4GC) to address the
challenges posed by incomplete multimodal data, enabling precise response
prediction and survival analysis. Specifically, iMD4GC incorporates unimodal
attention layers for each modality to capture intra-modal information.
Subsequently, the cross-modal interaction layers explore potential inter-modal
interactions and capture complementary information across modalities, thereby
enabling information compensation for missing modalities. To evaluate iMD4GC,
we collected three multimodal datasets for GC study: GastricRes (698 cases) for
response prediction, GastricSur (801 cases) for survival analysis, and
TCGA-STAD (400 cases) for survival analysis. The scale of our datasets is
significantly larger than previous studies. The iMD4GC achieved impressive
performance with an 80.2% AUC on GastricRes, 71.4% C-index on GastricSur, and
66.1% C-index on TCGA-STAD, significantly surpassing other compared methods.
