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

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 (Z_eff ), Z_eff 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 $Ac{c}_{exam}$ and label-based metric $Ac{c}_{label}$ 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 $Ac{c}_{exam}$ of 81.2%, with $Ac{c}_{label}$ 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 $Ac{c}_{exam}$ of 76.4% with $Ac{c}_{label}$ 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.

Keywords: chemical compositions; machine learning; multi-label classification; spectral CT; urinary stone.