Comprehensive assessment of the role of spectral data pre-processing in spectroscopy-based liquid biopsy

• Published in: Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Vol. 339; p. 126261
• Main Authors: Vrtělka, Ondřej, Králová, Kateřina, Fousková, Markéta, Setnička, Vladimír
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 15.10.2025
• Subjects: Algorithms; Carcinoma, Hepatocellular - diagnosis; Carcinoma, Hepatocellular - pathology; Chiroptical spectroscopy; Classification; Data pre-processing; Diagnostics; Humans; Least-Squares Analysis; Liquid biopsy; Liquid Biopsy - methods; Liver Cirrhosis - diagnosis; Liver Cirrhosis - pathology; Liver Neoplasms - diagnosis; Liver Neoplasms - pathology; Machine learning; Spectrophotometry, Infrared - methods; Spectrum Analysis, Raman - methods; Vibrational spectroscopy; Chiroptical spectroscopy; Diagnostics; Machine learning; Classification; Liquid biopsy; Data pre-processing; Vibrational spectroscopy
• DOI: 10.1016/j.saa.2025.126261
• ISSN: 1386-1425

[Display omitted] •The choice of data pre-processing and classifier greatly affects classification.•The suitability of each pre-processing step depends on the data type and quality.•Certain pre-processing steps have a greater impact on classification than others.•Top pre-processing pipelines reached an AUROC of up to 0.82 for HCC diagnostics. Spectroscopic data often contain artifacts or noise related to the sample characteristics, instrumental variations, or experimental design flaws. Therefore, classifying the raw data is not recommended and might lead to biased results. Nevertheless, most issues may be addressed through appropriate data pre-processing. Effective pre-processing is particularly crucial in critical applications like liquid biopsy for disease detection, where even minor performance improvements may impact patient outcomes. Unfortunately, there is no consensus regarding optimal pre-processing, complicating cross-study comparisons. This study presents a comprehensive evaluation of various pre-processing methods and their combinations to assess their influence on classification results. The goal was to identify whether some pre-processing methods are associated with higher classification outcomes and find an optimal strategy for the given data. Data from Raman optical activity and infrared and Raman spectroscopy were processed, applying tens of thousands of possible pre-processing pipelines. The resulting data were classified using three algorithms to distinguish between subjects with liver cirrhosis and those who had developed hepatocellular carcinoma. Results highlighted that some specific pre-processing methods often ranked among the best classification results, such as the Rolling Ball for correcting the baseline of Raman spectra or the Doubly Reweighted Penalized Least Squares and Mixture model in the case of Raman optical activity. On the other hand, the selection of filtering and/or normalization approach usually did not have a significant impact. Nonetheless, the pre-processing of top-scoring pipelines also depended on the classifier utilized. The best pipelines yielded an AUROC of 0.775–0.823, varying with the evaluated spectroscopic data and classifier.