Machine learning based multi‐label classification of single or mixed‐composition urinary stones in in vivo spectral CT

摘要

Abstract Background Urinary stones comprise both single and mixed compositions. Knowledge of the stone composition helps the urologists choose appropriate medical interventions for patients. The parameters from the spectral computerized tomography (CT) analysis have potential values for identification of the urinary stone compositions. Purpose The present study aims to identify the compositions of urinary stones in vivo using parameters from spectral CT and machine learning, based on multi‐label classification modeling. Methods This retrospective study collected 252 urinary stone samples with single/mixed compositions (including carbapatite CP, calcium oxalate monohydrate COM, calcium oxalate dehydrate COD, uric acid UA, and struvite STR), which were confirmed by ex vivo infrared spectroscopy. Parameters were extracted from an energy spectrum analysis (ESA) of the spectral CT, including the effective atomic number (Zeff), Zeff histogram, CT values at a given x‐ray energy level, and material densities. These ESA parameters were utilized for composition analysis via a multi‐label classification fusion framework, where 250 multi‐label models were built and the classification decisions from the top performance models were integrated by a multi‐criterion weighted fusion (MCWF) approach in order to reach a consensus prediction. An example‐based metric Accexam$Ac{c_{exam}}$ and label‐based metric Acclabel$Ac{c_{label}}$ were used for global and label‐wise accuracy evaluations, respectively. The top‐ranked parameters associated with discriminating the stone composition were also identified. Results The multi‐label classification fusion framework achieved an overall Accexam$Ac{c_{exam}}$ of 81.2, with Acclabel$Ac{c_{label}}$ of 86.7 (CP), 90.6 (COM), 80.6 (COD), 95.0 (UA), and 94.4 (STR) for each composition on the independent testing cohort 1, and Accexam$Ac{c_{exam}}$ of 76.4 with Acclabel$Ac{c_{label}}$ of 80.5 (CP), 88.7 (COM), 74.9 (COD), 94.4 (UA), and 98.5 (STR) on the independent testing cohort 2. Conclusion The parameters extracted from the ESA on spectral CT can be utilized to characterize single or mixed stone compositions via multi‐label classification modeling. The generalization capability of the proposed methodology still requires further verification.