Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study

Li, Yuekai; Li, Fengcai; Han, Shaoli; Ning, Jing; Su, Peng; Liu, J M; Qu, Lili; Huang, Shih Ting; Wang, Shiwei; Li, Xin; Li, Xiang

doi:10.1007/s43657-023-00108-y

Cited by 3 publications

(1 citation statement)

References 27 publications

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“… 7 Various aspects of facial analyses are now using this innovative approach increasingly. 8 , 9 , 10 , 11 In addition to facial phenotyping, DDAs such as unsupervised machine learning can be used to discover novel phenotypes of various diseases, including diabetes, 12 sepsis, 13 dilated cardiomyopathy, 14 pulmonary arterial hypertension, 15 heart failure, 16 gliomas, 17 and primary prostate cancer, 18 that may help in elucidating the mechanisms of diseases and treatment effects.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive evaluation of the phenotype-first and data-driven approaches in analyzing facial morphological traits

Qiao,

Tan,

Yan

et al. 2024

iScience

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Section: Introductionmentioning

confidence: 99%

A comprehensive evaluation of the phenotype-first and data-driven approaches in analyzing facial morphological traits

Qiao,

Tan,

Yan

et al. 2024

iScience

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PET imaging of synaptic vesicle glycoprotein 2 subtype A for neurological recovery in ischemic stroke

Luo,

Jin,

Chen

et al. 2024

Eur J Nucl Med Mol Imaging

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A systematic review on artificial intelligence evaluating PSMA PET scan for intraprostatic cancer

Liu,

Cundy,

Woon

et al. 2024

BJU International

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ObjectivesTo assess artificial intelligence (AI) ability to evaluate intraprostatic prostate cancer (PCa) on prostate‐specific membrane antigen positron emission tomography (PSMA PET) scans prior to active treatment (radiotherapy or prostatectomy).Materials and MethodsThis systematic review was registered on the International Prospective Register of Systematic Reviews (PROSPERO identifier: CRD42023438706). A search was performed on Medline, Embase, Web of Science, and Engineering Village with the following terms: ‘artificial intelligence’, ‘prostate cancer’, and ‘PSMA PET’. All articles published up to February 2024 were considered. Studies were included if patients underwent PSMA PET scan to evaluate intraprostatic lesions prior to active treatment. The two authors independently evaluated titles, abstracts, and full text. The Prediction model Risk Of Bias Assessment Tool (PROBAST) was used.ResultsOur search yield 948 articles, of which 14 were eligible for inclusion. Eight studies met the primary endpoint of differentiating high‐grade PCa. Differentiating between International Society of Urological Pathology (ISUP) Grade Group (GG) ≥3 PCa had an accuracy between 0.671 to 0.992, sensitivity of 0.91, specificity of 0.35. Differentiating ISUP GG ≥4 PCa had an accuracy between 0.83 and 0.88, sensitivity was 0.89, specificity was 0.87. AI could identify non‐PSMA‐avid lesions with an accuracy of 0.87, specificity of 0.85, and specificity of 0.89. Three studies demonstrated ability of AI to detect extraprostatic extensions with an area under curve between 0.70 and 0.77. Lastly, AI can automate segmentation of intraprostatic lesion and measurement of gross tumour volume.ConclusionAlthough the current state of AI differentiating high‐grade PCa is promising, it remains experimental and not ready for routine clinical application. Benefits of using AI to assess intraprostatic lesions on PSMA PET scans include: local staging, identifying otherwise radiologically occult lesions, standardisation and expedite reporting of PSMA PET scans. Larger, prospective, multicentre studies are needed.

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Performance of 18F-DCFPyL PET/CT in Primary Prostate Cancer Diagnosis, Gleason Grading and D'Amico Classification: A Radiomics-Based Study

Cited by 3 publications

References 27 publications

A comprehensive evaluation of the phenotype-first and data-driven approaches in analyzing facial morphological traits

A comprehensive evaluation of the phenotype-first and data-driven approaches in analyzing facial morphological traits

PET imaging of synaptic vesicle glycoprotein 2 subtype A for neurological recovery in ischemic stroke

A systematic review on artificial intelligence evaluating PSMA PET scan for intraprostatic cancer

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