From 2000 to 2004, there was only one study on radiomics in PCa, highlighting the lack of research foundation and limited analysis of radiomics association with PCa [27]. From 2005 to 2015, the era was considered the initial stage of the research field with an average of 3.73 articles published per year, indicating that radiomics research was just starting [28]. Only a few studies were focused on mp-MRI technology and its application in computer-aided detection of PCa [29]. Between 2016 and 2024, a marked increase was observed in the number of publications, averaging approximately 63.88 papers annually. The remarkable growth witnessed over the past eight years is considered the explosive phase in PCa radiomics research, which is becoming a research hotspot. In a recent publication, a predictive model incorporating clinical and MRI parameters was enhanced by integrating dual-parameter MRI radiomics analysis. This integration revealed that PSA density and PI-RADS exhibit significant efficacy as predictors for PCa. Therefore, the field of PCa radiomics research holds great potential and will emerge as a prominent focus of future research [30].

China and the USA ranked first and second in terms of the greatest number of publications and collaborations with other countries. Furthermore, it was observed that approximately 30% of the top 10 research institutions were in China. Regarding citation counts, eight universities from the United States were in the top ten list.

Recently, there was a marked increase in the numbers of citations and publications from China. Chinese academic institutions like Soochow University, Chinese Academy of Sciences, and Beihang University have had the highest paper publications and ranked among the top 10 institutes worldwide. Moreover, although there were numerous publications and citations from American institutions, many were not eligible for the top 10 for publication quantity. This observation indicates fragmented research patterns and limited involvement from institutions within this field. This investigation also revealed that esteemed academic institutions in China, including the Beijing University of Aeronautics and Astronautics and the University of Chinese Academy of Sciences, have collaborated with the United States. Regarding publication quantity, the USA secures the second position, while the Memorial Sloan Kettering Cancer Center institution ranks third in publications and first in citations. Among the top 10 authors with the highest publication quantity, 3 were affiliated with American institutions, 2 with Chinese institutions, and 3 with Italian institutions. This suggests that research efforts within this field are primarily concentrated in the United States, China, and Italy. Furthermore, Italian author Imbriaco Massimo was ranked 5th in the list of highest publication quantity, followed by British author Sala Evis; however, both these authors ranked 1st as the authors with the highest citations received, emphasizing their exceptional recognition and importance deserving attention.

Cancer (IF = 6.72, Q1) and Frontiers in Oncology (IF = 4, Q2) were the main journals for publishing PCa imagomics research indicating their popularity in the field. Furthermore, CA: A Cancer Journal for Clinicians (IF = 297, Q1) had the highest IF among the analyzed journals.

The authors, Madabhushi Anant from Emory University Woodruff Health Sciences Center and Stoyanova Radka from the University of Miami School of Medicine had the most publications, and their research was mainly focused on the use of machine learning methods in assisting PCa diagnosis and monitoring [17, 31], as well as investigating the invasive signal of PCa t2-weighted imaging in new magnetic resonance technology [32]. Radiomics or computer-extracted MRI texture features are helpful in the quantitative characterization of PCa [2]. A multi-institution study found that the radiological features of metastatic and peripheral MRI in PCa are different, with different radiological features of 3 Tesla multi-parameter MRI in the transition area than in the peripheral area [3]. In addition, there are related articles on radiomics role in radiotherapy for PCa, which employ high-throughput methods to identify and analyze multiple advanced quantitative radiological characteristics from medical images, which are well suited for the efficient screening of multiple series of prostate images before, during, and after radiation therapy [33]. Moreover, some institutions have also studied the independent influencing factors and model establishment of PCa radiomics. For example, biomarkers and models based on MRI radiomics [34], such as a deep learning model based on multi-center two-parameter MRI [35], have the potential to assess prostate radiological features and identify Gleason scores [36]. These studies facilitate the anticipation or reduction of unnecessary biopsies and offer guidance for clinical surgery and treatment. In addition, extensive collaboration was observed between authors, often across different research teams in this field. For significant progress and wide practical application of research findings in healthcare settings, researchers from diverse nations must strengthen cooperation, establish mutually advantageous partnerships, and exchange technical innovations and expertise in all aspects of imaging informatics.

Lambin, P. (citations = 222) was the most cited author, followed by Gillies, R.J. (citations = 180) and Turkbey, B. (citations = 163). In 2012, Lambin, P. published an article titled "Radiomics: Extracting more information from medical images using advanced feature analysis," which indicated the temporal and spatial heterogeneity of solid tumors. This limitation hinders the use of invasive molecular analysis based on biopsies but offers great potential for medical imaging to capture tumor heterogeneity non-invasively. Furthermore, his paper had a large number of high-quality reviews [37]. Radiomics is a high-throughput mining technique that identifies quantitative image characteristics from standard medical imaging. Furthermore, it not only extracts the data but can also apply it in clinical decision support systems to improve prognosis, diagnosis, and prediction accuracies. Therefore, it is becoming a hotspot in cancer research. Moreover, radiomics analysis provides powerful tools for modern medicine by employing complex image analysis tools for the rapid development and validation of medical imaging data using image-based signatures for precise diagnosis and treatment. In 2016, Gillies, R.J. indicated that the field of medical image analysis has significantly progressed over the past decade with the increased number of pattern recognition tools and dataset size. This marks a departure from the traditional practice of solely relying on visual interpretation for medical images. Radiological data contains various statistical measures, including first-, second-, and higher-order statistics. These statistics are combined with patient information and analyzed using sophisticated bioinformatics tools to develop models that can enhance diagnostic accuracy, prognosis, and prediction [20]. In 2020, Turkbey, B. revealed that targeted biopsies via MRI reduced the misclassification of visible lesions associated with male PCa [38]. Overall, to summarize the past twenty years of developments in the field of radiomics research on Pca, new tools and standardized protocols have revolutionized medical imaging techniques enabling high-throughput extraction of numerous image features from radiographic images. However, further validation is necessary in multicenter environments and laboratories to ensure their reliability.

Co-cited literature is the basis of research in a specific field and represents the references that are jointly cited by multiple publications [39]. In 2016, Gillies, R.J. et al. published an article in Radiology (IF = 13.4) entitled "Radiomics: Images Are More than Pictures, They Are Data", which received the highest number of citations in PCa imaging studies [20]. The above study may indicate that there has been significant growth in the field of medical image analysis in the past decade, due to the elevating number of pattern recognition tools and dataset scales. These advancements have promoted the development of high-throughput selection of quantitative features, transforming images into mineable data, which can then be analyzed to support decision-making. Therefore, it can be inferred that in the near future, converting digital images into mineable data will become a routine practice [40]. A paper investigated the application value of prostate MRI Haralick texture analysis in PCa diagnosis and Gleason score discrimination. PyRadiomics is an open-source flexible platform that can extract various engineering features from medical images via standardized algorithms and image processing techniques [17, 40]. Influential papers in this field are published in both fundamental and clinical journals, indicating a strong connection between basic research and applied research in PCa radiomics. However, citation frequency is a relatively simple indicator, because some researchers overcite their own or their team's previous research results in order to increase the citation rate of their papers, regardless of whether these results are closely related to the current research. This behavior may distort the frequency of citations and cannot truly reflect the actual value of the paper and its contribution to the academic field. Moreover, the high citation frequency is often calculated over a period of time, but some papers may not receive enough attention at the initial stage of publication, but their value will gradually be discovered and recognized over time, and the citation rate will gradually increase. Conversely, there are some papers that may receive high citations in the short term because of factors such as hot topics, but their value proves limited over time.

Recently, the citation explosion of reference literature has indicated the emergence of new research topics, which have received marked attention from researchers [41]. By analyzing the research content of these highly cited papers (Table 6), the crucial role that radiomics plays in PCa diagnosis and treatment can be assessed. In addition to revealing the citation explosion of reference literature, keywords also allow quick identification of the distribution and evolution of research topics in the field of PCa radiomics. The keyword analysis (Fig. 7) revealed that the field of PCa radiomics research mainly focuses on the following aspects: PCa, radiomics, multiparametric MRI, machine learning, AI, texture analysis, and deep learning.

Bibliometrics has many advantages. (1) It represents the pioneering utilization of bibliometric techniques in a systematic examination of PCa imaging omics research, thereby providing comprehensive guidance to relevant scholars. (2) This research simultaneously employed three bibliometric tools including VOS viewer, Cite Space, and the R package 'bibliometrix', which enhanced the impartiality of the data analysis process. (3) Compared to traditional literature reviews, bibliometric analysis facilitates a more thorough understanding of popular areas and cutting-edge advancements.

However, this study still has certain limitations. (1) The literature was only acquired from WoSCC, and relevant research from other databases may have been overlooked, potentially limiting the representation of the entire field of PCa imaging informatics. (2) This research was only focused on English publications, which have underestimated non-English papers and introduce language bias, and potential loss of valuable literature in other languages. (3) The main data was searched and screened by one researcher and verified by another researcher, which may have different degrees of bias driven by personal subjective thinking, thus affecting the accuracy of the research results. (4) Limitations of indicators: Although the commonly used indicators in this study, such as the number of publications and the number of citations, can reflect the influence of research results to a certain extent, these indicators cannot fully represent the quality and value of the research, and there is still a lack of in-depth investigation of the research content. (5)There is also a chance of research bias due to potential disparities between research findings and real-world situations caused by recently published high-quality literature with low citation frequency due to its short publication time. (6) Lack of uniform standards and norms: In the study of bibliometrics, different researchers may adopt different statistical methods, indicator definitions and data processing methods. At present, the lack of unified standards and norms also makes the research results of bibliometrics have certain uncertainties in application. (7) This paper uses bibliometrics to analyze the application of radiomics in prostate cancer, which can only provide a preliminary overview of the application of radiomics in prostate cancer at the macro level. Therefore, up-to-date bibliometric data should be acquired and analyzed to further validate these data and elucidate the scientific trends and hotspots in PCa imaging informatics.