Ripoll, LauraGisbert Mullor, HéctorRubio, IvánGuill Berbegal, DavidCanals, AntonioJover, RodrigoVidal, Lorena2025-10-142025-10-142025Ripoll, L., Gisbert, H., Rubio, I., Guill-Berbegal, D., Canals, A., Jover, R., & Vidal, L. (2025). New algorithm to predict colorectal cancer based on fecal volatile organic compounds profile. Computers in Biology and Medicine, 197, 111093. https://doi.org/10.1016/j.compbiomed.2025.1110930010-48251879-0534https://hdl.handle.net/11268/16394In this study, an algorithm designed for the analysis of fecal samples for colorectal cancer diagnostics, utilizing the data from the advanced technique of thermal-desorption-gas chromatography-mass spectrometry (TD-GCMS), is constructed. The algorithm performs a comprehensive analysis across the entire spectral range to identify compound patterns for differentiating among three distinct health states: colorectal cancer, colorectal adenomas and controls with normal colonoscopy. The algorithm underwent a rigorous optimization process, resulting in a sensitivity and specificity of 100 %, effectively eliminating both false positives and false negatives. During the validation phase, the algorithm demonstrated remarkable performance, with sensitivity ranging from 74 % to 68 %, specificity ranging from 58 % to 52 %, and accuracy 66 %–62 % (range across twenty randomized train-test splits). Notably, in the context of polyp samples, the algorithm obtained a sensitivity range from 54 % to 50 %, even when trained with data from only healthy individuals (i.e., controls) and cancer patients. Moreover, a detailed table of compounds and their probabilities of occurrence in cancer, adenomas, and healthy samples is provided, offering insight into the interpretability of the algorithm. This qualitative approach signals a significant advancement in diagnostic precision and promises to enhance early detection of colorectal cancer, marking a substantial contribution to the field.engAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/journal article10.1016/j.compbiomed.2025.111093open accessCiencias médicasProgramación informáticaBiología molecularGoal 3: Ensure healthy lives and promote well-being for all at all agesGoal 4: Quality educationGoal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation