Building a high-resolution T2-weighted MR-based probabilistic model of tumor occurrence in the prostate

• Published in: Abdominal imaging Vol. 43; no. 9; pp. 2487 - 2496
• Main Authors: Nagarajan, Mahesh B., Raman, Steven S., Lo, Pechin, Lin, Wei-Chan, Khoshnoodi, Pooria, Sayre, James W., Ramakrishna, Bharath, Ahuja, Preeti, Huang, Jiaoti, Margolis, Daniel J. A., Lu, David S. K., Reiter, Robert E., Goldin, Jonathan G., Brown, Matthew S., Enzmann, Dieter R.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.09.2018; Springer Nature B.V
• Subjects: Adult; Aged; Algorithms; Biopsy; Cancer surgery; Clinical decision making; Decision making; Diagnostic software; Diagnostic systems; Gastroenterology; Ground truth; Hepatology; Humans; Image Interpretation, Computer-Assisted; Imaging; Lesions; Magnetic Resonance Imaging - methods; Male; Medicine; Medicine & Public Health; Middle Aged; Neoplasm Grading; Probabilistic methods; Probabilistic models; Probability; Prostate cancer; Prostatectomy; Prostatic Neoplasms - pathology; Prostatic Neoplasms - surgery; Radiology; Retrospective Studies; Tumors; Urological surgery; Multi-parametric MRI; Prostate registration; Prostate cancer; Tumor occurrence probability map
• ISSN: 2366-004X; 2366-0058; 2366-0058
• DOI: 10.1007/s00261-018-1495-2

Purpose We present a method for generating a T2 MR-based probabilistic model of tumor occurrence in the prostate to guide the selection of anatomical sites for targeted biopsies and serve as a diagnostic tool to aid radiological evaluation of prostate cancer. Materials and methods In our study, the prostate and any radiological findings within were segmented retrospectively on 3D T2-weighted MR images of 266 subjects who underwent radical prostatectomy. Subsequent histopathological analysis determined both the ground truth and the Gleason grade of the tumors. A randomly chosen subset of 19 subjects was used to generate a multi-subject-derived prostate template. Subsequently, a cascading registration algorithm involving both affine and non-rigid B-spline transforms was used to register the prostate of every subject to the template. Corresponding transformation of radiological findings yielded a population-based probabilistic model of tumor occurrence. The quality of our probabilistic model building approach was statistically evaluated by measuring the proportion of correct placements of tumors in the prostate template, i.e., the number of tumors that maintained their anatomical location within the prostate after their transformation into the prostate template space. Results Probabilistic model built with tumors deemed clinically significant demonstrated a heterogeneous distribution of tumors, with higher likelihood of tumor occurrence at the mid-gland anterior transition zone and the base-to-mid-gland posterior peripheral zones. Of 250 MR lesions analyzed, 248 maintained their original anatomical location with respect to the prostate zones after transformation to the prostate. Conclusion We present a robust method for generating a probabilistic model of tumor occurrence in the prostate that could aid clinical decision making, such as selection of anatomical sites for MR-guided prostate biopsies.