# Routine laboratory trajectories encode the onset of organ-level complications in cancer

arXiv: https://arxiv.org/abs/2606.08538 · HTML: https://mlsanity.com/paper/2606.08538/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 55 references that are in the map.
- First posted on arXiv 7 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data](https://mlsanity.com/paper/2502.07836/) (2025)

## Abstract (arXiv)

Routine laboratory panels drawn during cancer treatment constitute longitudinal physiological recordings of organ function, yet their temporal structure is discarded by single-timepoint prognostic tools. A transformer trained on 2,777,595 laboratory measurements from 3,905 patients with multiple myeloma or ovarian cancer predicted the two-year onset of 162 treatment-associated complications, including therapy-related myelodysplastic syndromes, spanning eight clinical categories, achieving 1.5- to 6.1-fold enrichment above prevalence at the group level. It matched or outperformed non-sequential baselines across grouped endpoints (AUROC gains up to +0.11), demonstrating that longitudinal laboratory trajectories capture evolving complication-specific physiology inaccessible from isolated measurements. Predictions generalised across both cancers, divergence concentrating in disease-specific complications, and biomarker masking recovered signatures consistent with established pathophysiology. External validation on MIMIC-IV and MMRF CoMMpass confirmed transferability across independent healthcare systems (AUROC up to 0.85). Routine oncological laboratory data encode organ deterioration weeks to months before clinical onset, enabling complication-specific surveillance without additional testing infrastructure.