Virtual biopsy in abdominal pathology: where do we stand?

Defeudis, Arianna; Panić, Jovana; Nicoletti, Giulia; Mazzetti, Simone; Giannini, Valentina; Regge, Daniele

doi:10.1259/bjro.20220055

Cited by 3 publications

(1 citation statement)

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“…Virtual biopsy refers to the application of radiomics for the extraction of quantitative information not accessible through visual inspection from radiological images for tissue characterisation [ 66 , 67 ].…”

Section: Virtual Biopsymentioning

confidence: 99%

Artificial intelligence in interventional radiology: state of the art

Glielmo,

Fusco,

Gitto

et al. 2024

Eur Radiol Exp

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Artificial intelligence (AI) has demonstrated great potential in a wide variety of applications in interventional radiology (IR). Support for decision-making and outcome prediction, new functions and improvements in fluoroscopy, ultrasound, computed tomography, and magnetic resonance imaging, specifically in the field of IR, have all been investigated. Furthermore, AI represents a significant boost for fusion imaging and simulated reality, robotics, touchless software interactions, and virtual biopsy. The procedural nature, heterogeneity, and lack of standardisation slow down the process of adoption of AI in IR. Research in AI is in its early stages as current literature is based on pilot or proof of concept studies. The full range of possibilities is yet to be explored.Relevance statement Exploring AI’s transformative potential, this article assesses its current applications and challenges in IR, offering insights into decision support and outcome prediction, imaging enhancements, robotics, and touchless interactions, shaping the future of patient care.Key points• AI adoption in IR is more complex compared to diagnostic radiology.• Current literature about AI in IR is in its early stages.• AI has the potential to revolutionise every aspect of IR. Graphical Abstract

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Section: Virtual Biopsymentioning

confidence: 99%

Artificial intelligence in interventional radiology: state of the art

Glielmo,

Fusco,

Gitto

et al. 2024

Eur Radiol Exp

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Development and validation of a clinical decision support system based on PSA, microRNAs, and MRI for the detection of prostate cancer

Mazzetti,

Defeudis,

Nicoletti

et al. 2024

Eur Radiol

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Objectives The aims of this study are to develop and validate a clinical decision support system based on demographics, prostate-specific antigen (PSA), microRNA (miRNA), and MRI for the detection of prostate cancer (PCa) and clinical significant (cs) PCa, and to assess if this system performs better compared to MRI alone. Methods This retrospective, multicenter, observational study included 222 patients (mean age 66, range 46-75 years) who underwent prostate MRI, miRNA (let-7a-5p and miR-103a-3p) assessment, and biopsy. Monoparametric and multiparametric models including age, PSA, miRNA, and MRI outcome were trained on 65% of the data and then validated on the remaining 35% to predict both PCa (any Gleason grade [GG]) and csPCa (GG ≥ 2 vs GG = 1/negative). Accuracy, sensitivity, specificity, positive and negative predictive value (NPV), and area under the receiver operating characteristic curve were calculated. Results MRI outcome was the best predictor in the monoparametric model for both detection of PCa, with sensitivity of 90% (95%CI 73–98%) and NPV of 93% (95%CI 82–98%), and for csPCa identification, with sensitivity of 91% (95%CI 72–99%) and NPV of 95% (95%CI 84–99%). Sensitivity and NPV of PSA + miRNA for the detection of csPCa were not statistically different from the other models including MRI alone. Conclusion MRI stand-alone yielded the best prediction models for both PCa and csPCa detection in biopsy-naïve patients. The use of miRNAs let-7a-5p and miR-103a-3p did not improve classification performances compared to MRI stand-alone results. Clinical relevance statement The use of miRNA (let-7a-5p and miR-103a-3p), PSA, and MRI in a clinical decision support system (CDSS) does not improve MRI stand-alone performance in the detection of PCa and csPCa. Key Points • Clinical decision support systems including MRI improve the detection of both prostate cancer and clinically significant prostate cancer with respect to PSA test and/or microRNA. • The use of miRNAs let-7a-5p and miR-103a-3p did not significantly improve MRI stand-alone performance. • Results of this study were in line with previous works on MRI and microRNA.

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