Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics

Abstract

In prostate cancer (PCa), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance strategies, and introduced specific and personalized radioreceptor therapies. Nuclear medicine, therefore, holds great promise for improving the quality of life of PCa patients, through managing and processing a vast amount of molecular imaging data and beyond, using a multi-omics approach and improving patients’ risk-stratification for tailored medicine. Artificial intelligence (AI) and radiomics may allow clinicians to improve the overall efficiency and accuracy of using these “big data” in both the diagnostic and theragnostic field: from technical aspects (such as semi-automatization of tumor segmentation, image reconstruction, and interpretation) to clinical outcomes, improving a deeper understanding of the molecular environment of PCa, refining personalized treatment strategies, and increasing the ability to predict the outcome. This systematic review aims to describe the current literature on AI and radiomics applied to molecular imaging of prostate cancer.

Nuclear Medicine and Cancer Theragnostics: Basic Concepts

“…Even though the PET radiotracer choline labeled with either 18 F or 11 C may detect early recurrence of prostate carcinoma, it has been replaced by other, more specific radiotracers [46]. Other radiotracers considered include bombesin analogs that showed a positive diagnostic rate of 71.8% in patients that exhibited a negative conventional imaging [47]. Bombesin analogues labeled with 68 Ga can target the gastrin-releasing peptide receptors and can be used for PET imaging, with advantages in its availability, half-life, and relative low expenditure [48].…”

Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review

“…These applications include cancer screening and diagnosis [ 37 , 38 , 39 , 40 ], diagnosis and classification [ 41 , 42 , 43 , 44 ], predicting prognosis and treatment response [ 45 , 46 , 47 , 48 , 49 ], automated segmentation [ 50 , 51 , 52 , 53 , 54 ], and radiology-pathology correlation (radiogenomics) [ 55 , 56 , 57 , 58 ]. In particular, within the field of diagnosis and classification, the ability of AI models to classify benign vs. malignant tumours has been shown to achieve high accuracy, sensitivity, and specificity in various organs, such as in the case of breast [ 59 , 60 , 61 ], prostate [ 62 , 63 ], lung [ 38 , 64 , 65 , 66 ], and brain lesions [ 67 , 68 ]. This review article aims to provide an overview of the current evidence on the effectiveness of machine learning in differentiating bone lesions on various imaging modalities.…”

On the Use of Artificial Intelligence for Dosimetry of Radiopharmaceutical Therapies

“…The broad adoption of artificial intelligence (AI) in medicine in recent years also opens up the possibility of advancing the integration of routine patient-specific dosimetry into clinical workflows in multiple ways, e. g., by assisting in accelerating image acquisition, by offering a high level of automatization (e. g., regarding image segmentation and registration), or by improving accuracy and speed of the absorbed dose calculation process itself [14,15,16]. Therefore, the purpose of this review is to highlight recent advances in the application of AI to internal dosimetry for RPT.…”