Personalising Prostate Radiotherapy in the Era of Precision Medicine: A Review

Emerging trends and patterns in healthcare-seeking behavior: A systematic review

OBJECTIVES

The study of healthcare-seeking behavior is essential for optimizing resource allocation and improving healthcare services. Its complexity and diversity have made it a prominent research area. Understanding factors influencing healthcare-seeking decisions allows targeted interventions and policy development to address barriers and ensure equitable access to quality healthcare for diverse populations. Such research plays a vital role in enhancing healthcare outcomes and overall population health.

METHODS

The study utilized a systematic quantitative literature review approach, employing the Web of Science (WOS) Core Collection and PubMed databases as data sources. Additionally, bibliometric tools such as CiteSpace and VOSviewer were employed for analysis and visualization of the literature.

RESULTS

A comprehensive statistical analysis and visualization were performed on the annual publication volume, publication countries, journals, keywords, and keyword co-occurrence patterns up until 2023. Through this analysis, a framework was established, identifying the determinants and fundamental elements of healthcare-seeking behavior. These findings contribute to the advancement of research in this field and inform future studies and interventions aimed at improving healthcare-seeking behavior.

CONCLUSIONS

Based on the aforementioned literature review and framework, several conclusions were drawn. The determinants that facilitate healthcare-seeking behavior include improving health education awareness, enhancing healthcare resources, reducing costs, and ensuring system soundness. Additionally, providing social environment support was found to be crucial. Furthermore, the fundamental elements of healthcare-seeking behavior were identified as healthcare demand, healthcare choices, and the process of diagnosis and treatment. These findings provide valuable insights for developing interventions and policies to promote optimal healthcare-seeking behavior.

Radiomic-based machine learning model for the accurate prediction of prostate cancer risk stratification

OBJECTIVES

To precisely predict prostate cancer (PCa) risk stratification, we constructed a machine learning (ML) model based on magnetic resonance imaging (MRI) radiomic features.

METHODS

Between August 2016 and May 2021, patients with histologically proven PCa who underwent pre-operative MRI and prostate-specific antigen screening were included. The patients were grouped into different risk categories as defined by the European Association of Urology-European Association of Nuclear Medicine-European Society for Radiotherapy and Oncology-European Society of Urogenital Radiology-International Society of Geriatric Oncology guidelines. Using Artificial Intelligence Kit software, PCa regions of interest were delineated and radiomic features were extracted. Subsequently, predictable models were built by utilising five traditional ML approaches: support vector machine, logistic regression, gradient boosting decision tree, k-nearest neighbour and random forest (RF) classifiers. The classification capacity of the developed models was assessed by area under the receiver operating characteristic curve (AUC) analysis.

RESULTS

A total of 213 patients were enrolled, including 16 low-risk, 65 intermediate-risk, and 132 high-risk PCa patients. The risk stratification of PCa could be revealed by MRI radiomic features, and second-order features accounted for most of the selected features. Among the five established ML models, the RF model showed the best overall predictive performance (AUC = 0.87). After further analysis of the subgroups based on the RF model, the prediction of the high-risk group was the best (AUC = 0.89).

CONCLUSION

This study demonstrated that the MR radiomics-based ML method could be a promising tool for predicting PCa risk stratification precisely.

ADVANCES IN KNOWLEDGE

The ML models have valuable prospect for accurate PCa risk assessment, which might contribute to customize treatment and surveillance strategies.

Should We Reconsider the Necessity of a Refinement of Prostate Cancer Risk Classification and Radiotherapy Treatment Strategy? Experiences from a Retrospective Analysis of Data from a Single Institution

Background: Radiation therapy has undergone significant technical development in the past decade. However, the complex therapy of intermediate-risk patients with organ-confined prostate carcinoma still poses many questions. Our retrospective study investigated the impact of selected components of the treatment process including radiotherapy, hormone deprivation, risk classification, and patients’ response to therapy. Methods: The impact of delivered dose, planning accuracy, duration of hormone deprivation, risk classification, and the time to reach prostate-specific antigen (PSA) nadir state were analyzed among ninety-nine individuals afflicted with organ-confined disease. Progression was defined as a radiological or biochemical relapse within five years from radiotherapy treatment. Results: We found that 58.3% of the progressive population consisted of intermediate-risk patients. The progression rate in the intermediate group was higher (21.9%) than in the high-risk population (12.1%). Dividing the intermediate group, according to the International Society of Urological Pathology (ISUP) recommendations, resulted in the non-favorable subgroup having the highest rate of progression (33.3%) and depicting the lowest percentage of progression-free survival (66.7%). Conclusion: Extended pelvic irradiation on the regional lymph nodes may be necessary for the ISUP Grade 3 subgroup, similarly to the high-risk treatment. Therapy optimization regarding the intermediate-risk population based on the ISUP subgrouping suggestions is highly recommended in the treatment of organ-confined prostate cancer.