Electric-Field Molecular Fingerprinting to Probe Cancer.
PMID 40290141 | PMCID PMC12022918 | DOI 10.1021/acscentsci.4c02164 · ACS central science · 2025
Human biofluids serve as indicators of various physiological states, and recent advances in molecular profiling technologies hold great potential for enhancing clinical diagnostics. Leveraging recent developments in laser-based electric-field molecular fingerprinting, we assess its potential for in vitro diagnostics. In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer. Employing machine learning, we detected infrared signatures specific to therapy-naïve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities. In an independent held-out test data set, designed to reflect different experimental conditions from those used during model training, we achieved a lung cancer detection ROC AUC of 0.81. Our study demonstrates that electric-field molecular fingerprinting is a robust technological framework broadly applicable to disease phenotyping under real-world conditions.
Validated evidence
| Type | Entity | Source evidence | Confidence | Extractor |
|---|---|---|---|---|
| phenotype | lung cancer | “Employing machine learning, we detected infrared signatures specific to therapy-naïve cancer states, distinguishing them from matched control individuals with a cross-validation ROC AUC of 0.88 for lung cancer and values ranging from 0.68 to 0.69 for the other three cancer entities.” | 0.98 | phenotype_alias_lexicon_v2 |
| population | Population | “In a proof-of-concept clinical study involving 2533 participants, we conducted randomized measurement campaigns to spectroscopically profile bulk venous blood plasma across lung, prostate, breast, and bladder cancer.” | 0.80 | saudi_context_rules_v1 |