ArticleCancer imaging : the official publication of the International Cancer Imaging Society2024
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Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- The Role of Radiomics and Artificial Intelligence Applied to Staging PSMA PET in Assessing Prostate Cancer Aggressiveness.Journal of clinical medicine · 2025Review
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
purposeTo develop a radiomics-based model using [
methodsA total of 75 men with biopsy GGG 1-2 PCa who underwent radical prostatectomy (RP) were enrolled. The patients were randomly divided into a training group (70%) and a testing group (30%). Radiomics features of entire prostate were extracted from the [
resultsAmong the 75 patients, 30 had AP confirmed by RP. The clinical model showed an area under the curve (AUC) of 0.821 (0.695-0.947) in the training set and 0.795 (0.603-0.987) in the testing set. The radiomics model achieved AUC values of 0.830 (0.720-0.941) in the training set and 0.829 (0.624-1.000) in the testing set. The combined model, which incorporated the Radiomics score (Radscore) and free prostate-specific antigen (FPSA)/total prostate-specific antigen (TPSA), demonstrated higher diagnostic efficacy than both the clinical and radiomics models, with AUC values of 0.875 (0.780-0.970) in the training set and 0.872 (0.678-1.000) in the testing set. DCA showed that the net benefits of the combined model and radiomics model exceeded those of the clinical model.
conclusionThe combined model shows potential in stratifying men with biopsy GGG 1-2 PCa based on the presence of AP at final pathology and outperforms models based solely on clinical or radiomics features. It may be expected to aid urologists in better selecting suitable patients for AS.
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