Evidence for identification of acute myeloid leukemia using peripheral blood's infrared spectrum and logistic regression

Abstract

Invasive damage, poor prognosis and survival are problems for the traditional diagnostic and therapeutic methods of acute myeloid leukemia (AML), especially for the middle-aged and elderly. High sensitivity, less pain and fast speed are demands of AML diagnosis or long-term regular monitoring. Infrared (IR) spectroscopy has the potential to compensate for existing deficiencies and is explored in our work. Spectral differences between AML patients and controls are observed on the IR spectra, which indicate the changes in lipids and proteins in patients. The peak shift at 1390 cm−1 occurs on both peripheral blood (PB) and bone marrow (BM) spectra of AML patients making it possible to use PB rather than BM as the diagnostic source, which can relieve the suffering of diagnosis and monitoring. The logistic regression-based classification model can perfectly identify AML patients and controls with an accuracy of 100 % and the area under curve is equal to 1.00. Besides, some markers for identifying AML patients and controls are found. Statistical analysis shows that the peak area ratio A1451/A1390 has a significant difference, and the decision tree-based classification model finds that on the first derivative spectrum, the position of the largest difference is at 1391 cm−1. Furthermore, the proposed criterion based on markers we found can distinguish AML patients and controls with high accuracy of 95 %. Results show that PB’s IR spectrum combined with classification models can monitor the molecular-level biochemical changes and accurately identify AML patients and controls, which is helpful for the effective and efficient diagnosis and prognosis of AML.