| Title |
Multiparametric magnetic resonance imaging versus Partin tables and the Memorial Sloan-Kettering cancer center nomogram in risk stratification of patients with prostate cancer referred to external beam radiation therapy
|
|---|---|
| Published in |
La radiologia medica, May 2018
|
| DOI | 10.1007/s11547-018-0903-6 |
| Pubmed ID | |
| Authors |
Rossano Girometti, Martina Pancot, Marco Andrea Signor, Martina Urbani, Luca Balestreri, Chiara Zuiani |
| Abstract |
To evaluate the agreement between multiparametric Magnetic Resonance Imaging (mpMRI), Partin tables (PT) and the Memorial Sloan Kettering Cancer Center nomogram (MSKCCn) in assessing risk category in prostate cancer (PCa) patients referred to External Beam Radiotherapy (EBRT). In this bicentric study, we prospectively enrolled 80 PCa patients who underwent pre-EBRT mpMRI on a 3.0T magnet with a multiparametric protocol including high-resolution, multiplanar T2-weighted sequences, diffusion-weighted imaging and dynamic contrast-enhanced imaging. National comprehensive cancer network risk categories were assessed using prostate-specific-antigen level, Gleason score and the T-stage as defined by mpMRI or nomograms. Cohen's kappa statistic was used to calculate the agreement between mpMRI and nomograms in assessing the T-stage (organ-confined (OC) vs. non-organ-confined (nOC) disease) and risk category (≤ low risk vs. intermediate risk vs. ≥ high risk). mpMRI showed poor agreement with PT and MSKCCn in assessing nOC versus OC (k = 0.16 for both), translating into an mpMRI-induced reclassification of PT- and MSKCCn-related risk category in 36.3% (k = 0.43) and 41.3% (k = 0.31) of cases, respectively, with most changes occurring towards intermediate risk category. mpMRI showed low agreement with nomograms as a tool to stratify PCa risk, leading to significant risk reclassification. Assuming that mpMRI is a more reliable surrogate standard of reference for pathology, this technique should refine or replace nomograms in risk classification before EBRT. |
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| Country | Count | As % |
|---|---|---|
| United States | 2 | 67% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
| Scientists | 1 | 33% |
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| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 4 | 21% |
| Other | 3 | 16% |
| Student > Doctoral Student | 2 | 11% |
| Lecturer | 2 | 11% |
| Student > Master | 2 | 11% |
| Other | 1 | 5% |
| Unknown | 5 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 37% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 11% |
| Neuroscience | 1 | 5% |
| Computer Science | 1 | 5% |
| Unknown | 8 | 42% |