ИИ в онкологии

Machine learning-driven national analysis for predicting adverse outcomes in intramedullary spinal cord tumor surgery.

Our nationwide analysis underscores the evolution in IMSCT management and demonstrates the efficacy of machine learning in predicting mortality and eLOS, providing valuable insights for improved patient care.

DOI: 10.1007/s00586-025-09029-y
Ghanem M, Ghaith AK, Tsai SHL, Yeh YC, Akinduro OO, Michaelides L, El-Hajj VG, Saad H, Tfaily A, Nieves AB, Quiñones-Hinojosa A, Abode-Iyamah K, Bydon M
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2025-06-27

From BERT to generative AI - Comparing encoder-only vs. large language models in a cohort of lung cancer patients for named entity recognition in unstructured medical reports.

LLMs in their current form are unsuitable for comprehensive entity extraction tasks in clinical domains, particularly when faced with a high number of entity types per document, though instructing them to return more entities in subsequent refinements may improve recall. Additionally, their computational overhead does not provide proportional performance gains. Encoder-based NER models, particularly those pre-trained on biomedical data, remain the preferred choice for extracting information from unstructured medical documents.

DOI: 10.1016/j.compbiomed.2025.110665
Arzideh K, Schäfer H, Allende-Cid H, Baldini G, Hilser T, Idrissi-Yaghir A, Laue K, Chakraborty N, Doll N, Antweiler D, Klug K, Beck N, Giesselbach S, Friedrich CM, Nensa F, Schuler M, Hosch R
Computers in biology and medicine, 2025-06-26

Machine learning-based construction and validation of an radiomics model for predicting ISUP grading in prostate cancer: a multicenter radiomics study based on [68Ga]Ga-PSMA PET/CT.

This study established and validated a radiomics model based on 68Ga-PSMA PET/CT, offering an accurate, non-invasive method for predicting ISUP grades in prostate cancer. A multicenter design with external validation ensured the model's robustness and broad applicability. This is the largest study to date on PSMA radiomics for predicting ISUP grades. Notably, integrating SUVmax and PSA metrics with radiomic features significantly improved prediction accuracy, providing new insights and tools for personalized diagnosis and treatment.

DOI: 10.1007/s00259-025-07412-x
Zhang H, Jiang X, Yang G, Tang Y, Qi L, Chen M, Hu S, Gao X, Zhang M, Chen S, Cai Y
European journal of nuclear medicine and molecular imaging, 2025-06-26

Multimodal deep learning for predicting neoadjuvant treatment outcomes in breast cancer: a systematic review.

DL models demonstrate promising accuracy for pCR prediction, especially when integrating multiple modalities and longitudinal imaging. However, significant methodological heterogeneity, reliance on retrospective data, and limited external validation hinder clinical translation. Future research should prioritize prospective validation, integration underutilized data (multi-omics, clinical), and explainable AI to advance DL predictors to the clinical setting.

DOI: 10.1186/s13062-025-00661-8
Krasniqi E, Filomeno L, Arcuri T, Ferretti G, Gasparro S, Fulvi A, Roselli A, D'Onofrio L, Pizzuti L, Barba M, Maugeri-Saccà M, Botti C, Graziano F, Puccica I, Cappelli S, Pelle F, Cavicchi F, Villanucci A, Paris I, Calabrò F, Rea S, Costantini M, Pe
Biology direct, 2025-06-25

Serum-Based Detection of Pancreatic and Ovarian Cancer via a Nanoparticle-Enhanced Fluorescence Array and Machine Learning.

The proposed assay array methodology enables the systematic evaluation of NPs' diagnostic potential using fluorescence spectroscopy. The differential interactions between NPs and serum proteins specific to PDAC and OV highlight the method's capability to discern pathological states. These findings suggest a path forward for developing NP-assisted fluorescence spectroscopy as a viable tool for cancer diagnostics, potentially leading to earlier detection and improved patient outcomes.

DOI: 10.1021/acs.analchem.5c00974
Morcuende-Ventura V, Sánchez-Gracia O, Abian-Franco N, Jiménez-Pardo I, Herrer L, Castillo-Vallés M, Lancelot A, Falcó-Martí FJ, Hermoso-Durán S, Pazo-Cid R, Lanas Á, Velazquez-Campoy A, Sierra T, Abian O
Analytical chemistry, 2025-06-25