Scientists released groundbreaking research on blood plasma diagnostic techniques through Electric-Field Molecular Fingerprinting (EMF) technology which employs laser-based spectroscopy to detect disease-linked molecular changes within blood plasma. This innovative diagnostic approach, which shows great potential for cost-efficient large-scale use, in detecting lung cancer.
Blood plasma serves as a complex information carrier since it reveals molecular patterns that represent both health status and disease state changes. The current diagnostic tools detect biomarkers, but these methods are labor-intensive and examine only a restricted number of molecules. The molecular fingerprinting approach holds great potential as an all-inclusive pattern detection method over molecular profiles. EMF is a modern spectroscopy tool that uses ultrafast infrared laser pulses for precise spectral measurements to produce data with better sensitivity and processing speed than traditional Fourier transform infrared (FTIR) spectroscopy.
Scientists conducted this research because it included the first widespread clinical utilization of EMF technology for cancer detection to assess both diagnostic precision and operational utility. A total of 2533 participants joined the Lasers4Life clinical project as therapy-naïve patients with lung, breast, prostate, or bladder cancer diagnoses and healthy controls. The research team collected blood plasma and processed samples following standardized procedures and randomly measured the samples to minimize bias.
A 3.5-minute analytical process involved both EMF assessments and cuvette cleansing procedures for plasma organic samples. An automated system incorporated in the EMF apparatus recorded time-responsive infrared molecular vibrations that operated using dual oscillating laser devices. The data demonstrated strong reliability because researchers analyzed 1185 pooled quality control samples with consistent results during a 7-month period.
Data processing included signal filtering and standardization procedures. Outlier samples were eliminated through the Local Outlier Factor, while statistical methods generated cohorts matched by demographics. Trained machine learning models based on logistic regression classifiers performed sample differentiation between cancer types and control samples by using cross-validated Receiver Operating Characteristic (ROC) curves together with Area Under the Curve (AUC) values for evaluation purposes.
The EMF technique demonstrated remarkable effectiveness in identifying cancer signatures, although lung cancer exhibited the best diagnostic abilities among all types. The independent testing of lung cancer yielded modest results with an AUC value of 0.81, while training demonstrated an AUC value of 0.88 ± 0.04.
Prostate and bladder cancers showed moderate performance with AUCs around 0.68–0.69. Breast cancer showed poor diagnostic capability (AUC ≈ 0.5) in testing conditions, possibly because systemic signatures were weak.
The EMF signals demonstrated strong resistance across different populations because sex variations and BMI, along with age, did not significantly affect readings. The model demonstrated 48% accuracy for cancer distinction in females, followed by 53% accuracy in males, surpassing the 33% random baseline.
The detection of cancer-specific patterns in blood plasma by EMF technology establishes it as an important tool for noninvasive medical diagnosis. The method demonstrates wide potential clinical use because it performs reliably across various patient groups, including diabetic and COPD patients.
The superior lung cancer detection outcomes can be explained by the more dangerous characteristics of this cancer type, which results in extensive molecular alterations across the whole body. Breast cancer detection has lower sensitivity than other cancer types, so further technological adjustments combined with more spectral information are needed to enhance detection capabilities.
A major advantage of EMF over FTIR technology is its ability to deliver noise-free, coherent infrared measurements. High-throughput clinical applications benefit from laser-based excitation since it gives fast data collection abilities. The long-term signal stability potential of EMF demonstrates its functionality for continuous patient monitoring toward early detection applications.
The groundbreaking clinical research shows that EMF detects cancer biomarkers with accuracy in blood plasma samples. The current research shows promising results for detecting lung, prostate, and bladder cancers, although scientists need to improve breast cancer detection capability. Follow-up evaluation studies involving diverse patient groups and various stages of cancer progression need to be performed to validate and improve its diagnostic potential.
References: Kepesidis KV, Jacob P, Schweinberger W, et al. Electric-field molecular fingerprinting to probe cancer. ACS Cent Sci. Published online April 9, 2025. doi:10.1021/acscentsci.4c02164
