Quantitative nuclear grade (QNG): a new image analysis-based biomarker of clinically relevant nuclear structure alterations.

R. W. Veltri, Alan Wayne Partin, M. C. Miller

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


This review addresses the potential clinical value of using quantitative nuclear morphometry information derived from computer-assisted image analysis for cancer detection and predicting outcomes such as tumor stage, recurrence, and progression. Today's imaging technology uses sophisticated hardware platforms coupled with powerful and user-friendly software packages that are commercially available as complete image analysis systems. There are many different mathematically derived nuclear morphometric descriptors (NMD's) (i.e. texture features) that can be calculated by these image analysis systems, but for the most part, these NMD's quantify nuclear size, shape, DNA content (ploidy), and chromatin organization (i.e. texture, both Markovian and non-Markovian) parameters. We have utilized commercially available image analysis systems and the NMD's calculated by these systems to create a mathematical solution, termed quantitative nuclear grade (QNG), for making clinical, diagnostic, and prognostic outcome predictions in both prostate and bladder cancer. A separate computational model is calculated for each outcome of interest using well-characterized and robust training, testing, and validation patient sample sets that adequately represent the selected population and clinical dilemma. A specific QNG solution may be calculated either by non-parametric statistical methods or non-linear mathematics employed by artificial neural networks (ANNs). The QNG solution, a measure of genomic instability, provides a unique independent variable to be used alone or to be included in an algorithm to assess a specific clinical outcome. This approach of customization of the nuclear morphometric descriptor (NMD) information through the calculation of a QNG solution mathematically adjusts for redundancy of features and reduces the complexity of the inputs used to create decision support tools for patient disease management. J. Cell. Biochem. Suppl. 35:151-157, 2000.

Original languageEnglish (US)
Pages (from-to)151-157
Number of pages7
JournalJournal of cellular biochemistry. Supplement
VolumeSuppl 35
StatePublished - 2000
Externally publishedYes


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