Peter Neidlinger

All Publications

• A deep learning framework for efficient pathology image analysis NATURE COMMUNICATIONS Neidlinger, P., Lenz, T., Foersch, S., Loeffler, C. M. L., Clusmann, J., Gustav, M., Shaktah, L. A., Langer, R., Dislich, B., Boardman, L. A., French, A. J., Goode, E. L., Gsur, A., Brezina, S., Gunter, M. J., Steinfelder, R., Behrens, H., Rocken, C., Harrison, T., Peters, U., Phipps, A. I., Curigliano, G., Fusco, N., Marra, A., Hoffmeister, M., Brenner, H., Kather, J. 2026; 17 (1)

  Abstract

  Artificial intelligence has transformed digital pathology by enabling biomarker prediction from high-resolution whole-slide images. However, current methods are computationally inefficient, processing thousands of redundant tiles per slide and requiring complex aggregation models. We introduce EAGLE (Efficient Approach for Guided Local Examination), a deep learning framework that emulates pathologists by selectively analyzing informative regions. EAGLE combines task-agnostic tile selection with detailed feature extraction and is benchmarked against leading slide- and tile-level foundation models across 43 tasks from nine cancer types spanning morphology, biomarker prediction, treatment response and prognosis. EAGLE outperforms patch aggregation methods by up to 23% and achieves the highest overall classification performance. It processes one slide in 2.27 s, reducing computational time by more than 99% compared with existing models. This efficiency supports rapid and auditable workflows by enabling review of the exact tiles used for each prediction and reducing dependence on high-performance computing. By reliably identifying informative regions and minimizing artifacts, EAGLE provides robust and auditable outputs, supported by systematic negative controls and attention concentration analyses. Its unified embedding enables rapid slide search, integration into multi-omics pipelines and emerging clinical foundation models.

  View details for DOI 10.1038/s41467-026-74918-9

  View details for Web of Science ID 001810432000004

  View details for PubMedID 42386722

  View details for PubMedCentralID PMC13324285

• Single-cell-level digital twins for preterm birth prevention strategies. bioRxiv : the preprint server for biology Einhaus, J., Neidlinger, P., Fondeur, O., Sato, M., Anronikov, A., Miyazaki, K., Amar, J. N., Ando, K., Badea, V., Gaudilliere, D. K., Sabayev, M., Feyaerts, D., Diop, M., Tsai, A. S., Cambriel, A., Ganio, E. A., Lagarde, R., O'Kelly, E., Stelzer, I. A., Hedou, J., Wong, R. J., Blumenfeld, Y. J., Lyell, D. J., Shaw, G. M., Oskotsky, T. T., Sirota, M., Giudice, L., Stevenson, D. K., Aghaeepour, N., Gaudilliere, B. 2025

  Abstract

  Digital twin models can accelerate therapeutic development by enabling low-risk testing of candidate interventions. In preterm labor (PTL), a major pregnancy complication where clinical trials face unique ethical and financial barriers, digital twins are especially valuable for evaluating new therapies targeting immune dysfunctions driving PTL. Yet, current models lack single-cell resolution, limiting detection of cell-type-specific mechanisms, off-target effects, and the design of personalized interventions. We present Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell-level digital twin framework that models immunomodulatory treatment effects on the timing of labor using immunome-wide, single-cell simulations. SIMCO's digital twins are trained and validated on a newly generated mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs preselected for PTL prevention. Applying SIMCO to an independent cohort of pregnant individuals, we simulate treatment effects on gestational length, screening for candidate drugs that delay labor timing and providing system-level mechanistic insight for each drug candidate. Tetrahydrofolate, maprotiline, and the combination of aspirin and lansoprazole emerged as top candidates for PTL prevention, delaying labor onset primarily through enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in naïve CD4+ T cells. The codebase is available at https://github.com/ofondeur/SIMCO/.

  View details for DOI 10.1101/2025.09.29.679252

  View details for PubMedID 41256687

  View details for PubMedCentralID PMC12621756