AI Insight
Researchers developed PLASMA (Predicting Lung Adenocarcinoma recurrence via Selective Multimodal Attention), a deep learning model designed to predict early tumor recurrence in lung adenocarcinoma (LUAD) patients following surgical resection. The model integrates three data types — clinical variables, mRNA expression profiles, and mutation data — using a multimodal attention mechanism. Trained on TCGA data, PLASMA achieved AUROC scores of 85.0% on the internal test set and 76.5% on an independent external validation cohort (TRACERx Lung), outperforming conventional machine learning approaches.
Why it matters
Up to 39% of LUAD patients experience recurrence after complete resection, and early identification of high-risk patients could enable more targeted surveillance and adjuvant treatment strategies. A validated recurrence prediction tool could meaningfully support clinical decision-making and improve post-operative patient management.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Lung adenocarcinoma (LUAD), a subtype of non-small cell lung cancer (NSCLC), is the most common primary lung cancer worldwide. Despite advancements in early detection and treatment, up to 39% of patients develop recurrent tumors following complete resection. Currently, no widely available models exist for reliably predicting early recurrence of LUAD, which is a significant prognostic factor of post-recurrence survival. Models leveraging deep learning (DL) techniques have demonstrated notable utility in cancer recurrence prediction, particularly when used in combination with both clinical and genomic data. We developed a DL-based model, Predicting Lung Adenocarcinoma recurrence via Selective Multimodal Attention (PLASMA), to predict early recurrence using clinical, mRNA expression, and mutation data from patients with primary stage I-III LUAD. Trained on The Cancer Genome Atlas (TCGA) dataset, PLASMA outperformed traditional machine learning models in predicting early recurrence in both the TCGA test set and an external validation set (TRACERx Lung), achieving area under the receiver operating characteristic curve (AUROC) scores of 85.0% and 76.5%, respectively. Our results support the potential of multimodal DL for early LUAD recurrence prediction and risk stratification.
Source: A Multimodal Neural Network Model for Early Recurrence Prediction in Lung Adenocarcinoma