Chaddad Ahmad, Tan Guina, Liang Xiaojuan, Hassan Lama, Rathore Saima, Desrosiers Christian, Katib Yousef, Niazi Tamim
School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China.
The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada.
Cancers (Basel). 2023 Jul 28;15(15):3839. doi: 10.3390/cancers15153839.
The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.
多参数磁共振成像(mpMRI)的应用已成为指导前列腺病变活检和制定治疗方案的常用技术。虽然该技术很有效,但诸如放射组学等非侵入性方法已因提取成像特征以开发临床任务预测模型而受到欢迎。目的是尽量减少侵入性操作,以改善前列腺癌(PCa)的管理。本研究综述了基于MRI的PCa放射组学的最新研究进展,包括放射组学流程以及影响个性化诊断的潜在因素。还讨论了人工智能(AI)与医学成像的整合,这与放射基因组学和多组学的发展趋势一致。该调查强调需要来自多个机构的更多数据,以避免偏差并推广预测模型。基于AI的放射组学模型被认为是一种有前景的临床工具,具有良好的应用前景。