A novel system and method for early-stage detection of multiple cancers

A system for simultaneous detection of multiple cancers at early stages in a single analysis, the system comprising:

• at least one Liquid Chromatography (LC) device with a mass spectrometer (MS) (abbreviated, herein after, as LC-MS) for analysing and measuring masses for metabolite ions in one or more resultant reconstituted metabolites, using the LC-MS technique, the one or more resultant reconstituted metabolites being obtained after reconstituting of one or more dried metabolite extracts extracted from one or more biological fluid samples;
• at least one processor/computing device to align the masses obtained from metabolome profile of the metabolite ions in the one or more resultant reconstituted metabolites, and to minimize errors that may be generated in measurement of the masses for the metabolite ions;
• at least one processor/computing device applying one or more quality control processes on the aligned and normalized dataset to identify any errors that may have occurred in the detection of multiple cancers in the biological fluid samples, the at least one processor/computing device executing quality control processes is configured to execute at least one or more of the following steps (a), (b) and (c):
• (a) build a sequential neural network model to detect the errors based on variations in chromatogram profiles of faulty sample extraction, or due to an error in the mass spectrometry;
• (b) monitor for the presence of at least 6 out of 9 critical ions with m/z values ranging from 100 to 800, wherein the presence of 6 or more of the 9 critical ions in the chromatogram of a sample is taken as the threshold criteria for passing a quality control step;
• and
• (c) assess a matrix occupancy that includes calculating a threshold for minimum matrix occupancy with multiple runs of poor-quality samples, or with improper mass spectrometry run conditions;
• and
• at least one processor executing one or more AI/ML process on the measured metabolite ions:
• to create a first AI Model (Cancer Detection AI (CDAI) Model) for identifying and differentiating diseased cancerous samples from non-diseased normal samples, where the measured metabolite ions is randomly divided into a training dataset and a test dataset,
• to create a second AI Model (a Tissue Of Origin Identification (TOOAI Model)) to further identify and differentiate individual diseased cancerous sample from other diseased cancerous samples and the non-diseased normal samples, and to apply the TOOAI Model over cancer-positive samples determined by the CDAI Model to identify and differentiate individual diseased cancerous sample from the other diseased cancerous samples and the non-diseased normal samples by obtaining scores assigned to each of the individual diseased cancerous sample, where one score for each cancer type is defined by its tissue of origin, and denotes the probability of the sample belonging to the respective cancer type.