2021
DOI: 10.7150/thno.61207
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Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies

Abstract: Prostate cancer (PCa) is one of the most frequently diagnosed malignancies of men in the world. Due to a variety of treatment options in different risk groups, proper diagnostic and risk stratification is pivotal in treatment of PCa. The development of precise medical imaging procedures simultaneously to improvements in big data analysis has led to the establishment of radiomics -a computer-based method of extracting and analyzing image features quantitatively. This approach bears the potential to assess and i… Show more

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Cited by 46 publications
(46 citation statements)
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“…The accuracy of MRI-based prognostic prediction could be improved through dedicated Radiomics models. Although Radiomics are largely focused on diagnostics [ 38 ], some authors present an exceptionally high correlation of MRI features with the risk of bone metastases [ 39 , 40 ]. On the other hand, these results are based on rather modest study groups, prone to overfitting, and unlike PI-RADS require a measurable increase in resources necessary for each patient’s initial evaluation.…”
Section: Discussionmentioning
confidence: 99%
“…The accuracy of MRI-based prognostic prediction could be improved through dedicated Radiomics models. Although Radiomics are largely focused on diagnostics [ 38 ], some authors present an exceptionally high correlation of MRI features with the risk of bone metastases [ 39 , 40 ]. On the other hand, these results are based on rather modest study groups, prone to overfitting, and unlike PI-RADS require a measurable increase in resources necessary for each patient’s initial evaluation.…”
Section: Discussionmentioning
confidence: 99%
“…In our exploratory analysis, SUVmax values ≥ median extracted from lymph nodes were associated with unfavorable BRFS, potentially representing patients with biologically more aggressive disease. Radiomic features (RFs) allow extracting deeper information from medical images ( 22 ), enabling non-invasive tumor characterization and prediction of lymph node involvement ( 23 ). Thus, image analysis through RF bears the potential to identify additional prognosticators and should be analyzed in the future.…”
Section: Discussionmentioning
confidence: 99%
“… 33 36 Recent evidence are reported for detection of PCa lesions (especially PIRADS 3 lesions), 37 tumor delineation, 38 tumor localization, 39 and segmentation of prostate volume and lesions using PSMA-PET. 40 , 41 As current risk stratification models, which could predict oncological outcomes, are unable to accurately delineate the prognosis for each patient and for each stage of disease, there is an ongoing need for the detection of personalized and precise detections tools and treatment. 12 Radiomics features (RFs) analysis can give information for detection, risk stratification, and treatment.…”
Section: Introductionmentioning
confidence: 99%