PURPOSE: Although Crohn's colitis (CC) and ulcerative colitis (UC) share several clinical features, they have different causes, mechanisms of tissue damage, and treatment options. Therefore, the accurate diagnosis is of paramount importance in terms of medical care. The distinction between CC/UC is made on the basis of clinical, radiologic, endoscopic, and pathologic interpretations but cannot be differentiated in up to 15% of inflammatory bowel disease patients. Correct management of this "indeterminate colitis" depends on the accuracy of future, and yet not known, destination diagnosis (CC/UC). EXPERIMENTAL DESIGN: We have developed a proteomic methodology that has the potential to discriminate between UC/CC. The histologic layers of 62 confirmed UC/CC tissues were analyzed using MALDI-MS for proteomic profiling. RESULTS: A Support Vector Machine algorithm consisting of 25 peaks was able to differentiate spectra from CC and UC with 76.9% spectral accuracy when using a leave-20%-out cross-validation. Application of the model to the entire dataset resulted in accurate classification of 19/26 CC patients and 36/36 UC patients when using a 2/3 correct cutoff. A total of 114 peaks were found to have Wilcoxin rank sum p-values of less than 0.05. CONCLUSION AND CLINICAL RELEVANCE: This information may provide new avenues for the development of novel personalized therapeutic targets.
PURPOSE: Although Crohn's colitis (CC) and ulcerative colitis (UC) share several clinical features, they have different causes, mechanisms of tissue damage, and treatment options. Therefore, the accurate diagnosis is of paramount importance in terms of medical care. The distinction between CC/UC is made on the basis of clinical, radiologic, endoscopic, and pathologic interpretations but cannot be differentiated in up to 15% of inflammatory bowel diseasepatients. Correct management of this "indeterminate colitis" depends on the accuracy of future, and yet not known, destination diagnosis (CC/UC). EXPERIMENTAL DESIGN: We have developed a proteomic methodology that has the potential to discriminate between UC/CC. The histologic layers of 62 confirmed UC/CC tissues were analyzed using MALDI-MS for proteomic profiling. RESULTS: A Support Vector Machine algorithm consisting of 25 peaks was able to differentiate spectra from CC and UC with 76.9% spectral accuracy when using a leave-20%-out cross-validation. Application of the model to the entire dataset resulted in accurate classification of 19/26 CC patients and 36/36 UC patients when using a 2/3 correct cutoff. A total of 114 peaks were found to have Wilcoxin rank sum p-values of less than 0.05. CONCLUSION AND CLINICAL RELEVANCE: This information may provide new avenues for the development of novel personalized therapeutic targets.
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