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Imaging and Markers as Novel Diagnostic Tools in Detecting Insignificant Prostate Cancer: A Critical Overview.

ABSTRACT: Recent therapeutic advances for managing low-risk prostate cancer include the active surveillance and focal treatment. However, locating a tumor and detecting its volume by adequate sampling is still problematic. Development of predictive biomarkers guiding individual therapeutic choices remains an ongoing challenge. At the same time, prostate cancer magnetic resonance imaging is gaining increasing importance for prostate diagnostics. The high morphological resolution of T2-weighted imaging and functional MRI methods may increase the specificity and sensitivity of diagnostics. Also, recent studies founded an ability of novel biomarkers to identify clinically insignificant prostate cancer, risk of progression, and association with poor differentiation and, therefore, with clinical significance. Probably, the above mentioned methods would improve tumor characterization in terms of its volume, aggressiveness, and focality. In this review, we attempted to evaluate the applications of novel imaging techniques and biomarkers in assessing the significance of the prostate cancer.

SUBMITTER: Reva S

PROVIDER: S-EPMC4897503 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Imaging and Markers as Novel Diagnostic Tools in Detecting Insignificant Prostate Cancer: A Critical Overview.

Reva Sergey S Nosov Alexander A Novikov Roman R Petrov Sergey S

International scholarly research notices 20140715

Recent therapeutic advances for managing low-risk prostate cancer include the active surveillance and focal treatment. However, locating a tumor and detecting its volume by adequate sampling is still problematic. Development of predictive biomarkers guiding individual therapeutic choices remains an ongoing challenge. At the same time, prostate cancer magnetic resonance imaging is gaining increasing importance for prostate diagnostics. The high morphological resolution of T2-weighted imaging and ...[more]

PMID: 27351008

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Project description:BACKGROUND:Strains of the endosymbiotic bacterium Wolbachia pipientis are extremely diverse both genotypically and in terms of their induced phenotypes in invertebrate hosts. Despite extensive molecular characterisation of Wolbachia diversity, little is known about the actual genomic diversity within or between closely related strains that group tightly on the basis of existing gene marker systems, including Multiple Locus Sequence Typing (MLST). There is an urgent need for higher resolution fingerprinting markers of Wolbachia for studies of population genetics, horizontal transmission and experimental evolution. RESULTS:The genome of the wMel Wolbachia strain that infects Drosophila melanogaster contains inter- and intragenic tandem repeats that may evolve through expansion or contraction. We identified hypervariable regions in wMel, including intergenic Variable Number Tandem Repeats (VNTRs), and genes encoding ankyrin (ANK) repeat domains. We amplified these markers from 14 related Wolbachia strains belonging to supergroup A and were successful in differentiating size polymorphic alleles. Because of their tandemly repeated structure and length polymorphism, the markers can be used in a PCR-diagnostic multilocus typing approach, analogous to the Multiple Locus VNTR Analysis (MLVA) established for many other bacteria and organisms. The isolated markers are highly specific for supergroup A and not informative for other supergroups. However, in silico analysis of completed genomes from other supergroups revealed the presence of tandem repeats that are variable and could therefore be useful for typing target strains. CONCLUSIONS:Wolbachia genomes contain inter- and intragenic tandem repeats that evolve through expansion or contraction. A selection of polymorphic tandem repeats is a novel and useful PCR diagnostic extension to the existing MLST typing system of Wolbachia, as it allows rapid and inexpensive high-throughput fingerprinting of closely related strains for which polymorphic markers were previously lacking.

Project description:Background and purposeIn comparison to conventional medical imaging diagnostic modalities, the aim of this overview article is to analyze the accuracy of the application of Artificial Intelligence (AI) techniques in the identification and diagnosis of malignant tumors in adult patients.Data sourcesThe acronym PIRDs was used and a comprehensive literature search was conducted on PubMed, Cochrane, Scopus, Web of Science, LILACS, Embase, Scielo, EBSCOhost, and grey literature through Proquest, Google Scholar, and JSTOR for systematic reviews of AI as a diagnostic model and/or detection tool for any cancer type in adult patients, compared to the traditional diagnostic radiographic imaging model. There were no limits on publishing status, publication time, or language. For study selection and risk of bias evaluation, pairs of reviewers worked separately.ResultsIn total, 382 records were retrieved in the databases, 364 after removing duplicates, 32 satisfied the full-text reading criterion, and 09 papers were considered for qualitative synthesis. Although there was heterogeneity in terms of methodological aspects, patient differences, and techniques used, the studies found that several AI approaches are promising in terms of specificity, sensitivity, and diagnostic accuracy in the detection and diagnosis of malignant tumors. When compared to other machine learning algorithms, the Super Vector Machine method performed better in cancer detection and diagnosis. Computer-assisted detection (CAD) has shown promising in terms of aiding cancer detection, when compared to the traditional method of diagnosis.ConclusionsThe detection and diagnosis of malignant tumors with the help of AI seems to be feasible and accurate with the use of different technologies, such as CAD systems, deep and machine learning algorithms and radiomic analysis when compared with the traditional model, although these technologies are not capable of to replace the professional radiologist in the analysis of medical images. Although there are limitations regarding the generalization for all types of cancer, these AI tools might aid professionals, serving as an auxiliary and teaching tool, especially for less trained professionals. Therefore, further longitudinal studies with a longer follow-up duration are required for a better understanding of the clinical application of these artificial intelligence systems.Trial registrationSystematic review registration. Prospero registration number: CRD42022307403.

Project description:Prostate cancer and prostatic calcifications have a high incidence in elderly men. We aimed to investigate the diagnostic capabilities of susceptibility-weighted imaging in detecting prostate cancer and prostatic calcifications. A total number of 156 men, including 34 with prostate cancer and 122 with benign prostate were enrolled in this study. Computed tomography, conventional magnetic resonance imaging, diffusion-weighted imaging, and susceptibility-weighted imaging were performed on all the patients. One hundred and twelve prostatic calcifications were detected in 87 patients. The sensitivities and specificities of the conventional magnetic resonance imaging, apparent diffusion coefficient, and susceptibility-filtered phase images in detecting prostate cancer and prostatic calcifications were calculated. McNemar's Chi-square test was used to compare the differences in sensitivities and specificities between the techniques. The results showed that the sensitivity and specificity of susceptibility-filtered phase images in detecting prostatic cancer were greater than that of conventional magnetic resonance imaging and apparent diffusion coefficient (P < 0.05). In addition, the sensitivity and specificity of susceptibility-filtered phase images in detecting prostatic calcifications were comparable to that of computed tomography and greater than that of conventional magnetic resonance imaging and apparent diffusion coefficient (P < 0.05). Given the high incidence of susceptibility-weighted imaging (SWI) abnormality in prostate cancer, we conclude that susceptibility-weighted imaging is more sensitive and specific than conventional magnetic resonance imaging, diffusion-weighted imaging, and computed tomography in detecting prostate cancer. Furthermore, susceptibility-weighted imaging can identify prostatic calcifications similar to computed tomography, and it is much better than conventional magnetic resonance imaging and diffusion-weighted imaging.

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Imaging and Markers as Novel Diagnostic Tools in Detecting Insignificant Prostate Cancer: A Critical Overview.

Publications

Imaging and Markers as Novel Diagnostic Tools in Detecting Insignificant Prostate Cancer: A Critical Overview.

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