Predicting bone metastases type in prostate cancer using MRI

In a recent study published in Scientific Reports, a group of researchers investigated the differences in prostate magnetic resonance imaging (MRI) findings and clinical characteristics between patients with prostate cancer exhibiting osteoblastic and non-osteoblastic bone metastases (BMs).

Study: MR imaging features to predict the type of bone metastasis in prostate cancer. Image Credit: Image Point Fr/


Prostate cancer is highly prevalent worldwide, with BMs being a significant complication. BMs can lead to skeletal problems and negatively impact patients' quality of life. While most BMs in prostate cancer are considered osteoblastic, some can be osteolytic or mixed.

Prostate MRI has proven to be a valuable tool in detecting clinically significant prostate cancer, and its use is widespread in clinical practice. The Prostate Imaging Reporting and Data System (PI-RADS) has been widely adopted as a standardized reporting system, aiding in risk stratification and facilitating consistent evaluation of MRI findings.

About the study

In the present study, data from 2,314 patients with confirmed prostate cancer between 2014 and 2021 were included. The study obtained approval from the ethics committee of the University of Tokyo and was conducted in accordance with relevant guidelines and regulations.

Computed tomography (CT) scans were performed to detect BMs, and MRI was carried out using 1.5 T or 3.0 T scanners. T2-weighted and diffusion-weighted images were acquired, and apparent diffusion coefficient (ADC) maps were calculated.

Clinical data, such as age, Gleason score (GS), and prostate-specific antigen (PSA) density, were collected from medical records. Imaging data, including PSA density, normalized ADC (nADCmean), and normalized T2 signal intensity (nT2SI), were analyzed.

Statistical analyses were conducted using Student's t-test, Mann-Whitney U test, and multivariate logistic regression to compare parameters between the osteoblastic and non-osteoblastic BM groups. The significance level was set at p < 0.05, and R software was also used for statistical analysis.

Study results

The results of the study revealed that out of the 2,314 patients analyzed, 101 patients were found to have BMs, and 60 patients underwent prostate MRI within six months before the pathological diagnosis. After applying inclusion and exclusion criteria, 32 patients were included in the final analysis.

The authors further reported that among the 32 patients included in the study, 25 were classified into the osteoblastic group, with a mean age of 73 ± 6.6 years, while the remaining 7 were placed in the non-osteoblastic group, with a mean age of 69 ± 13.1 years. Within the non-osteoblastic group, two patients had osteolytic BMs, and five had mixed BMs.

The results of all the clinical and radiological analyses showed that PSA density and nT2SI were much higher in the non-osteoblastic group compared to the osteoblastic group. Specifically, median PSA density was 23.1 ng/mL/cm3 (range: 0.69–44.7) in the non-osteoblastic group versus 1.3 (range: 0.076–401) in the osteoblastic group (p = 0.018). The nT2SI values were mean ± SD 3.3 ± 0.94 in the non-osteoblastic group and 2.6 ± 0.61 in the osteoblastic group (p = 0.027).

However, no significant differences were found in age at diagnosis, GS, nADCmean, or PI-RADS category between the two groups. Nevertheless, a multivariate logistic regression analysis was conducted using PSA density, GS, and nT2SI, and it showed that nT2SI was an independent predictor for the non-osteoblastic group (p = 0.039).


The authors found PSA density to be a useful predictor for various aspects of prostate cancer, including local invasion, lymph node metastasis, biochemical recurrence, and the presence of BMs. In this study, higher PSA density in the non-osteoblastic BM group likely reflected the aggressiveness of prostate cancer. However, the difference in GS between the two groups did not reach statistical significance, possibly due to some overlap between the groups.

The nT2SI, which reflects T2 signal intensity normalized to a reference structure, was significantly higher in the non-osteoblastic BM group than in osteoblastic group. Higher nT2SI, despite a high GS, may indicate a predisposition to non-osteoblastic BMs. The study suggested that nT2SI could be a potential independent predictor for the non-osteoblastic BM group.

Further, the authors observed no significant difference in nADCmean between the two groups. While ADC is an important factor in determining PI-RADS categories and has been associated with GS and cell density, its relationship with the type of BMs in prostate cancer needs further investigation with larger patient cohorts.


To summarize, the findings suggest that MRI-based nT2SI measurement may be a valuable tool in predicting the type of BMs in prostate cancer, specifically distinguishing between osteoblastic and non-osteoblastic metastases.

This could have implications for the management and treatment strategies for prostate cancer patients with different BM types. Additionally, the study emphasizes the importance of considering MRI imaging, particularly for patients with high nT2SI and PSA density, to detect osteolytic BMs that may not be visible in bone scintigraphy.

Journal reference:
  • Koyama H, Kurokawa R, Kato S et al. (2023). MR imaging features to predict the type of bone metastasis in prostate cancer. Scientific Reports. doi:

Posted in: Device / Technology News | Medical Research News | Medical Condition News | Disease/Infection News

Tags: Antigen, Bone, Cancer, Cell, Computed Tomography, CT, Gleason Score, Imaging, Lymph Node, Magnetic Resonance Imaging, Metastasis, Prostate, Prostate Cancer, Prostate-Specific Antigen, Software, Tomography

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Written by

Vijay Kumar Malesu

Vijay holds a Ph.D. in Biotechnology and possesses a deep passion for microbiology. His academic journey has allowed him to delve deeper into understanding the intricate world of microorganisms. Through his research and studies, he has gained expertise in various aspects of microbiology, which includes microbial genetics, microbial physiology, and microbial ecology. Vijay has six years of scientific research experience at renowned research institutes such as the Indian Council for Agricultural Research and KIIT University. He has worked on diverse projects in microbiology, biopolymers, and drug delivery. His contributions to these areas have provided him with a comprehensive understanding of the subject matter and the ability to tackle complex research challenges.

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