Editor's Note: For patients with an initial negative prostate biopsy, whether and when to perform a repeat biopsy remains an important clinical challenge in the early diagnosis of prostate cancer. On the one hand, unnecessary repeat biopsies may increase patients' pain, bleeding, infection, and psychological burden; on the other hand, inadequate risk assessment may delay the diagnosis of clinically significant prostate cancer. How to use more precise risk-stratification tools to reduce unnecessary invasive procedures while maintaining a low risk of missing clinically significant cancer has therefore become an important area of clinical interest. In this issue of Oncology Frontier · UroStream, Professor Shouzhen Chen of Qilu Hospital of Shandong University discusses a risk-stratification and prediction model combining the Prostate Imaging Reporting and Data System (PI-RADS) score with the Prostate Health Index (PHI) and PHI density (PHID). He provides an in-depth interpretation of the model's key findings, clinical value, and current limitations in patients undergoing repeat biopsy, while also discussing future directions involving multimodal biomarkers and emerging molecular imaging technologies.Combining PI-RADS and PHID to Improve Risk Prediction for Repeat Biopsy
Oncology Frontier · UroStream: First, could you introduce the main findings of this combined prediction-model study? Compared with using the PI-RADS score or PHI-related parameters alone, what improvements did the model demonstrate in predictive performance?
Professor Shouzhen Chen: There are currently a number of tools available for pre-biopsy risk assessment of prostate cancer. Traditional assessment mainly relies on parameters such as prostate-specific antigen (PSA), PSA density, and multiparametric magnetic resonance imaging. However, the predictive performance of individual indicators or conventional combination models remains subject to certain limitations.
Building on existing risk-assessment approaches, our study incorporated the Prostate Health Index (PHI) and PHI density (PHID) and combined them with the PI-RADS score to establish an integrated prediction model. The results demonstrated a significant improvement in the model’s overall predictive performance. We also performed stratified analyses addressing some of the most important clinical questions.
For patients with a negative initial prostate biopsy, whether and when to perform a repeat biopsy has long been a challenging issue for both clinicians and patients. Clinical decision-making must balance two goals: minimizing the risk of missing clinically significant prostate cancer while reducing unnecessary repeat biopsies and their associated risks.
Therefore, our study placed particular emphasis on improving the model’s negative predictive performance and established corresponding PHID thresholds for different PI-RADS scores. When a patient’s PHID was below the corresponding threshold, the model achieved a negative predictive value of 100%, indicating an extremely low probability of clinically significant prostate cancer.
For example, among patients with a PI-RADS score of 3, when PHID was <0.8, the risk of clinically significant prostate cancer was close to zero. Based on these findings, some patients may be able to defer repeat biopsy under close surveillance, thereby reducing unnecessary invasive procedures.
Advancing Multicenter Prospective Validation and Optimizing Multimodal Prediction Models
Oncology Frontier · UroStream: What limitations remain in the current prediction model combining PHI density with the PI-RADS score? What directions should be pursued for further validation and optimization?
Professor Shouzhen Chen: First, this was a retrospective study with a relatively limited sample size, and the data were derived from a single center in China. Therefore, the model’s stability, reproducibility, and applicability to different patient populations still need to be validated through larger, multicenter, prospective studies.
Second, PHI is essentially a serum biomarker, while the PI-RADS score depends on magnetic resonance imaging and image interpretation. Although combining the two can improve predictive performance, each retains its inherent limitations.
For example, serum biomarkers may be influenced by factors such as prostate volume and inflammation, while imaging assessment can be affected by differences in equipment and radiologist experience.
In the future, additional novel biomarkers could be incorporated, including DNA methylation markers, genomic alterations, RNA biomarkers, and other molecular testing parameters.
We could also explore emerging molecular imaging technologies such as PSMA PET/MR. By integrating clinical characteristics, serum biomarkers, imaging features, and molecular information, we may be able to develop more comprehensive risk-prediction models.
Through external validation, prospective validation, and multimodal data integration, future models may further improve their ability to identify clinically significant prostate cancer and enhance their generalizability in real-world clinical settings.
Identifying Very-Low-Risk Patients and Optimizing the Timing of Repeat Biopsy
Oncology Frontier · UroStream: Finally, how could this stratification model help clinicians determine the optimal timing for repeat biopsy more precisely, while balancing the risks of unnecessary testing and missed cancer?
Professor Shouzhen Chen: This is precisely where the study has important clinical value. We established corresponding PHID thresholds according to different PI-RADS risk categories, allowing patients with an initial negative biopsy to undergo more refined risk stratification.
For patients whose PHID is below the corresponding threshold and whose risk of clinically significant prostate cancer is extremely low, repeat biopsy may be deferred following adequate communication and standardized surveillance. These patients can undergo dynamic assessment through regular monitoring of PSA, PHI, PHID, and changes on MRI.
For patients whose PHID exceeds the threshold, or whose risk indicators continue to increase during follow-up, clinicians should remain vigilant and consider repeat biopsy or further diagnostic evaluation in a timely manner.
This risk-adapted management strategy can help concentrate limited medical resources on patients who genuinely require further diagnostic evaluation, while reducing unnecessary repeat biopsies among low-risk patients and consequently decreasing pain, bleeding, infection, and psychological burden.
More importantly, the purpose of this model is not simply to reduce the number of biopsies. Rather, it aims to improve risk identification so that unnecessary invasive procedures can be reduced while maintaining a strong safeguard against missing clinically significant prostate cancer.
Ultimately, this approach could provide patients with a more individualized and dynamic strategy for repeat biopsy and surveillance.
Professor Shouzhen Chen
