Discovery of Urinary Proteomic Signature for Differential Diagnosis of Acute Appendicitis

Abstract

Acute appendicitis is one of the most common acute abdomens, but the confident preoperative diagnosis is still a challenge. In order to profile noninvasive urinary biomarkers that could discriminate acute appendicitis from other acute abdomens, we carried out mass spectrometric experiments on urine samples from patients with different acute abdomens and evaluated diagnostic potential of urinary proteins with various machine-learning models. Firstly, outlier protein pools of acute appendicitis and controls were constructed using the discovery dataset (32 acute appendicitis and 41 control acute abdomens) against a reference set of 495 normal urine samples. Ten outlier proteins were then selected by feature selection algorithm and were applied in construction of machine-learning models using naïve Bayes, support vector machine, and random forest algorithms. The models were assessed in the discovery dataset by leave-one-out cross validation and were verified in the validation dataset (16 acute appendicitis and 45 control acute abdomens). Among the three models, random forest model achieved the best performance: the accuracy was 84.9% in the leave-one-out cross validation of discovery dataset and 83.6% (sensitivity: 81.2%, specificity: 84.4%) in the validation dataset. In conclusion, we developed a 10-protein diagnostic panel by the random forest model that was able to distinguish acute appendicitis from confusable acute abdomens with high specificity, which indicated the clinical application potential of noninvasive urinary markers in disease diagnosis.

Artificial Intelligence and Acute Appendicitis: A Systematic Review of Diagnostic and Prognostic Models

“…LR followed in frequency, appearing in nine studies [ 3 , 24 – 26 , 28 , 30 , 32 , 42 , 43 ]. DT [ 25 , 30 , 35 , 36 , 40 , 41 , 43 ] and SVM [ 23 , 24 , 28 , 30 , 37 , 45 ] were each utilized in seven studies, while RF [ 23 , 24 , 29 , 31 , 45 ] was implemented in six studies.…”

“…Radiological assessments, particularly CT images, were the chosen input modality in three studies [ 12 , 20 , 22 ]. Laboratory data served as the exclusive input for four studies [ 19 , 23 , 32 , 36 ]. Additionally, three studies deployed a combination of clinical observations and laboratory data as their input features [ 38 , 40 , 41 ].…”

LC–MS peak assignment based on unanimous selection by six machine learning algorithms

“…We concluded that no suitable threshold was found in the dataset subjected to PCA and therefore applied six machine learning algorithms, which are used for and are familiar to researchers in the proteomics field 13 – 20 , to classify the peptide peaks from the dataset. A total of 737 peaks, including 380 peptide and 357 noise peaks, were divided into a training set (418 data peaks; 219 peptide and 199 noise peaks) and a test set (319 data peaks; 161 peptide and 158 noise peaks) and then independently analyzed with the six different supervised machine learning algorithms using the dataset.…”

“…Some recent investigations have used machine learning techniques to identify peptide peaks from large datasets and classify proteins in comparative analyses 13 – 20 . A mass precision algorithm was developed to extract the signal from the noise, thus improving quantitation using a random forest (RF) classifier and heuristic score 13 .…”

The Latest Developments in Using Proteomic Biomarkers from Urine and Serum for Non-Invasive Disease Diagnosis and Prognosis

“…This upregulation of APOC1 could potentially indicate the presence of a bacterial infection in Acute Appendicitis, distinguishing it from other conditions within the CON group, such as cholecystitis and pancreatitis. 128,129 A study conducted using LC-MS/MS by Berbee et al found that APOC1 (apolipoprotein C1) is upregulated in acute appendicitis. 129 Allister et al studied serum concentrations of C-reactive protein (CRP) and granulocyte colonystimulating factor (GCSF) and detected a substantial difference between patients with acute appendicitis and healthy controls.…”