Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS

• Published in: Medical image analysis 2013-02, Vol.17 (2), p.219-235
• Main Authors: Tiwari, Pallavi, Kurhanewicz, John, Madabhushi, Anant
• Format: Article
• Language: English
• Subjects: Algorithms; Biomarkers, Tumor - analysis; Data integration; Diagnosis, Computer-Assisted - methods; Grading; Graph embedding; Humans; Magnetic Resonance Imaging - methods; Magnetic Resonance Spectroscopy - methods; Male; Prostate cancer; Prostatic Neoplasms - diagnosis; Prostatic Neoplasms - metabolism; Reproducibility of Results; Semi-supervised; Sensitivity and Specificity
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2012.10.004

[Display omitted] ► A method to combine MRI/MRS for distinguishing high versus low-grade CaP regions. ► SeSMiK-GE leverages MKL, semi-supervised learning, and dimensionality reduction. ► MRI/MRS signature outperformed individual protocols, other integration strategies. ► May assist CaP patients for opting active surveillance instead of aggressive therapy. Even though 1 in 6 men in the US, in their lifetime are expected to be diagnosed with prostate cancer (CaP), only 1 in 37 is expected to die on account of it. Consequently, among many men diagnosed with CaP, there has been a recent trend to resort to active surveillance (wait and watch) if diagnosed with a lower Gleason score on biopsy, as opposed to seeking immediate treatment. Some researchers have recently identified imaging markers for low and high grade CaP on multi-parametric (MP) magnetic resonance (MR) imaging (such as T2 weighted MR imaging (T2w MRI) and MR spectroscopy (MRS)). In this paper, we present a novel computerized decision support system (DSS), called Semi Supervised Multi Kernel Graph Embedding (SeSMiK-GE), that quantitatively combines structural, and metabolic imaging data for distinguishing (a) benign versus cancerous, and (b) high- versus low-Gleason grade CaP regions from in vivo MP-MRI. A total of 29 1.5Tesla endorectal pre-operative in vivo MP MRI (T2w MRI, MRS) studies from patients undergoing radical prostatectomy were considered in this study. Ground truth for evaluation of the SeSMiK-GE classifier was obtained via annotation of disease extent on the pre-operative imaging by visually correlating the MRI to the ex vivo whole mount histologic specimens. The SeSMiK-GE framework comprises of three main modules: (1) multi-kernel learning, (2) semi-supervised learning, and (3) dimensionality reduction, which are leveraged for the construction of an integrated low dimensional representation of the different imaging and non-imaging MRI protocols. Hierarchical classifiers for diagnosis and Gleason grading of CaP are then constructed within this unified low dimensional representation. Step 1 of the hierarchical classifier employs a random forest classifier in conjunction with the SeSMiK-GE based data representation and a probabilistic pairwise Markov Random Field algorithm (which allows for imposition of local spatial constraints) to yield a voxel based classification of CaP presence. The CaP region of interest identified in Step 1 is then subsequently classified as either high or low G