title: Evaluating the cancer detection and grading potential of prostatic-zinc imaging: a simulation study.
abstract: The present work deals with the analysis of prostatic-zinc-concentration images. The goal is to evaluate potential clinically relevant information that can be extracted from such images. In the absence of experimental images, synthetic ones are produced from clinically measured zinc-concentration distributions in certified benign and cancerous tissue samples, classified by the lesion grade. We describe the method for producing the images and model the effect of counting statistics noise. We present in detail the image analysis, which is based on a combination of standard image processing and segmentation tools, optimized for this particular application. The information on lowest zinc value obtained from the image analysis is translated to clinical data such as tumour presence, location, size and grade. Their confidence is evaluated with the help of standard statistical tools such as receiver operating characteristic analysis. The present work predicts a potential for detecting small prostate-cancer lesions, of grade (4+3) and above, with very good specificity and sensitivity. The present analysis further provides data on the pixel size and image counting statistics requested from the trans-rectal probe that will record in vivo prostatic-zinc maps in patients.
authors.affiliation: Department of Particle Physics, Weizmann Institute of Science, 76100 Rehovot, Israel. marco.cortesi@weizmann.ac.il
authors.forename: M
authors.initials: M
authors.lastname: Cortesi
authors.forename: R
authors.initials: R
authors.lastname: Chechik
authors.forename: A
authors.initials: A
authors.lastname: Breskin
authors.forename: D
authors.initials: D
authors.lastname: Vartsky
authors.forename: J
authors.initials: J
authors.lastname: Ramon
authors.forename: G
authors.initials: G
authors.lastname: Raviv
authors.forename: A
authors.initials: A
authors.lastname: Volkov
authors.forename: E
authors.initials: E
authors.lastname: Fridman
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D011471:Prostatic Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014018:Tissue Distribution; D015032:Zinc / Q000032:analysis*
chemical_list: D014408:Biomarkers, Tumor; D015032:Zinc