# Serum Fourier-Transform Infrared Spectroscopy with Machine Learning for Screening of Pediatric Acute Lymphoblastic Leukemia: A Proof-of-Concept Study

## TL;DR

This study explores using infrared spectroscopy and machine learning to detect pediatric leukemia from blood serum, offering a non-invasive alternative to traditional methods.

## Contribution

The novel use of serum FTIR spectroscopy combined with machine learning for early detection of pediatric ALL is demonstrated as a potential diagnostic tool.

## Key findings

- Serum FTIR spectroscopy detected biochemical differences between ALL patients and controls in protein, lipid, and nucleic acid regions.
- Machine learning models achieved moderate accuracy (AUC ≈ 0.80) in distinguishing ALL from controls using serum FTIR data.
- ALL samples showed higher amide I/II absorbance, lower lipid bands, and increased nucleic acid signals compared to controls.

## Abstract

Acute lymphoblastic leukemia is the most common pediatric malignancy and its diagnosis still relies on bone-marrow evaluation, an invasive and technically demanding procedure. There is ongoing interest in developing minimally invasive methods that could support earlier recognition of the disease. In this study, we assessed whether an infrared spectroscopy of blood serum can detect biochemical differences between children with leukemia and those without. Characteristic spectral shifts were observed in regions associated with proteins, lipids, and nucleic acids. Based on these patterns, machine-learning models achieved moderate accuracy in distinguishing leukemia from controls. Although the method is fast, cost-effective, and uses minimal serum material, the present results remain preliminary. Validation in larger, multi-center cohorts will be essential to establish reliability and clinical value before potential adoption in pediatric diagnostics.

Background: Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy, yet diagnosis still relies primarily on invasive bone-marrow procedures and advanced laboratory assays. Non-invasive, rapid, and cost-effective tools remain an unmet need. Fourier-transform infrared (FTIR) spectroscopy has shown promise for detecting cancer-associated biochemical changes in biofluids and cells. Methods: Serum from pediatric ALL patients and controls (n = 103; ALL = 45, controls = 58: healthy = 14, hematology controls = 44 with anemia, thrombocytopenia, leukopenia, and pancytopenia) was analyzed using FTIR. Spectra (800–1800, 2800–3500 cm−1) were preprocessed with baseline correction, derivative filtering, and normalization. Group differences were assessed statistically, and logistic regression with stratified 10-fold cross-validation was applied; Receiver operating characteristic (ROC)\precision–recall (PR) analyses were based on out-of-fold predictions. Results: Distinct spectral alterations were observed between ALL and controls. Leukemia samples showed higher amide I (~1640 cm−1) and amide II (~1545 cm−1) absorbance, lower lipid-related bands (~1450, ~2920 cm−1), and increased nucleic-acid–associated signals (~1080 cm−1). Differences were significant (q < 0.05) with moderate effect sizes. Logistic regression achieved area under the curve (AUC) ≈ 0.80 with sensitivity ~0.73–0.84 across practical decision thresholds (0.50 → 0.30) and higher recall attainable at the expense of specificity. Principal component analysis (PCA)\hierarchical cluster analysis (HCA) indicated partial but consistent group separation, aligning with supervised performance. Conclusions: Serum FTIR spectroscopy shows promise for distinguishing pediatric ALL from controls by reflecting disease-related metabolic changes. The technique is rapid, label-free, and requires only small serum volumes. Our findings represent proof-of-concept, and validation in larger, multi-center studies is needed before clinical implementation can be considered.