A method for detecting the recurrence of a gastroenteropancreatic neuroendocrine neoplasm (GEP-NEN) in a human subject following a first treatment, the method comprising: determining the expression levels of at least 22 biomarkers and a housekeeping gene, ALG9, from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 22 biomarkers and housekeeping gene, ALG9, wherein the at least 22 biomarkers comprise APLP2, ARAF, CD59, CTGF, FZD7, KRAS, MKI67/KI67, MORF4L2, NAP1L1, NOL3, PNMA2, RAF1, RSF1, SLC18A2/VMAT2, SPATA7, SSTR1, SSTR3, SSTR4, SSTR5, TPH1, TRMT112, and ZFHX3,; normalizing the expression levels of APLP2, ARAF, CD59, CTGF, FZD7, KRAS, MKI67/KI67, MORF4L2, NAP1L1, NOL3, PNMA2, RAF1, RSF1, SLC18A2/VMAT2, SPATA7, SSTR1, SSTR3, SSTR4, SSTR5, TPH1, TRMT112, and ZFHX3 to the expression level of ALG9 to obtain normalized expression levels; risk classifying the test sample with respect to the presence or development of a GEP-NEN using the normalized expression levels in a classification system, wherein the classification system is a machine learning system that comprises four different algorithms: Support Vector Machine, Linear Discrimination Analysis, K-Nearest Neighbor, and Naive Bayes; assigning a Multi-Analyte Risk Classification for NETs (MAARC-NET) score based on a result of each of the four different algorithms; comparing the MAARC-NET score with a predetermined cutoff value; and determining the recurrence of a GEP-NEN in the subject following the first treatment, when the MAARC-NET score is equal to or greater than the predetermined cutoff value or determining the nonrecurrence of a GEP-NEN in the subject when the MAARC-NET score is below the predetermined cutoff value, wherein the predetermined cutoff value is 2 on a MAARC-NET scoring system scale of 0-8, or 0% on a scale of 0-100% determining the expression levels of at least 22 biomarkers and a housekeeping gene, ALG9, from a test sample from the subject by contacting the test sample with a plurality of agents specific to detect the expression of the at least 22 biomarkers and housekeeping gene, ALG9, wherein the at least 22 biomarkers comprise APLP2, ARAF, CD59, CTGF, FZD7, KRAS, MKI67/KI67, MORF4L2, NAP1L1, NOL3, PNMA2, RAF1, RSF1, SLC18A2/VMAT2, SPATA7, SSTR1, SSTR3, SSTR4, SSTR5, TPH1, TRMT112, and ZFHX3,; normalizing the expression levels of APLP2, ARAF, CD59, CTGF, FZD7, KRAS, MKI67/KI67, MORF4L2, NAP1L1, NOL3, PNMA2, RAF1, RSF1, SLC18A2/VMAT2, SPATA7, SSTR1, SSTR3, SSTR4, SSTR5, TPH1, TRMT112, and ZFHX3 to the expression level of ALG9 to obtain normalized expression levels; risk classifying the test sample with respect to the presence or development of a GEP-NEN using the normalized expression levels in a classification system, wherein the classification system is a machine learning system that comprises four different algorithms: Support Vector Machine, Linear Discrimination Analysis, K-Nearest Neighbor, and Naive Bayes; assigning a Multi-Analyte Risk Classification for NETs (MAARC-NET) score based on a result of each of the four different algorithms; comparing the MAARC-NET score with a predetermined cutoff value; and