Prostate cancer is the second leading cause of cancer-related death in men. Its early and correct diagnosis is of particular importance to controlling and preventing the disease from spreading to other tissues. Artificial intelligence and machine learning have effectively detected and graded several cancers, in particular prostate cancer. The purpose of this review is to show the diagnostic performance (accuracy and area under the curve) of supervised machine learning algorithms in detecting prostate cancer using multiparametric MRI. A comparison was made between the performances of different supervised machine-learning methods. This review study was performed on the recent literature sourced from scientific citation websites such as Google Scholar, PubMed, Scopus, and Web of Science up to the end of January 2023. The findings of this review reveal that supervised machine learning techniques have good performance with high accuracy and area under the curve for prostate cancer diagnosis and prediction using multiparametric MR imaging. Among supervised machine learning methods, deep learning, random forest, and logistic regression algorithms appear to have the best performance.
Nanotheranostics has attracted much attention due to its widespread application in molecular imaging and cancer therapy. Molecular imaging using nanoparticles has attracted special attention in the diagnosis of cancer at early stages. With the progress made in nanotheranostics, studying drug release, accumulation in the target tissue, biodistribution, and treatment effectiveness are other important factors. However, according to the studies conducted in this regard, each nanoparticle has some advantages and limitations that should be examined and then used in clinical applications. The main goal of this review is to explore the recent advancements in nanotheranostics for cancer therapy and diagnosis. Then, it is attempted to present recent studies on nanotheranostics used as a contrast agent in various imaging modalities and a platform for cancer therapy.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Breast cancer is the most prevalent cancer among women and the leading cause of death. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced magnetic resonance imaging (MRI) procedures that are widely used in the diagnostic and treatment evaluation of breast cancer. This review article describes the characteristics of new MRI methods and reviews recent findings on breast cancer diagnosis. This review study was performed on the literature sourced from scientific citation websites such as Google Scholar, PubMed, and Web of Science until July 2021. All relevant works published on the mentioned scientific citation websites were investigated. Because of the propensity of malignancies to limit diffusion, DWI can improve MRI diagnostic specificity. Diffusion tensor imaging gives additional information about diffusion directionality and anisotropy over traditional DWI. Recent findings showed that DWI and DTI and their characteristics may facilitate earlier and more accurate diagnosis, followed by better treatment. Overall, with the development of instruments and novel MRI modalities, it may be possible to diagnose breast cancer more effectively in the early stages.
Polyoxometalates (POMs) are mineral nanoclusters with many advantages in various diagnostic fields, in particular cancer detection. This study aimed to synthesize and evaluate the performance of gadolinium–manganese–molybdenum polyoxometalate (Gd-Mn-Mo; POM) nanoparticles coated with chitosan–imidazolium (POM@CSIm NPs) for detecting 4T1 breast cancer cells by magnetic resonance imaging in vitro and in vivo. The POM@Cs-Im NPs were fabricated and characterized by FTIR, ICP-OES, CHNS, UV–visible, XRD, VSM, DLS, Zeta potential, and SEM. Cytotoxicity, cellular uptake, and MR imaging in vivo and in vitro of L929 and 4T1 cells were also assessed. The efficacy of nanoclusters was demonstrated using MR images of BALB/C mice bearing a 4T1 tumor in vivo. The evaluation of the in vitro cytotoxicity of the designed NPs showed their high biocompatibility. In fluorescence imaging and flow cytometry, NPs had a higher uptake rate by 4T1 than L929 (p < 0.05). Furthermore, NPs significantly increased the signal strength of MR images, and its relaxivity (r1) was calculated as 4.71 mM−1 s−1. MR imaging also confirmed the attachment of nanoclusters to cancer cells and their selective accumulation in the tumor region. Overall, the results showed that fabricated POM@CSIm NPs have considerable potential as an MR imaging nano-agent for early 4T1 cancer detection.
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