Unleashing the Power of Artificial Intelligence and Fusion Magnetic Resonance Imaging–Targeted Biopsy: Transforming Prostate Cancer Diagnosis

Lenfant, Louis; Seisen, Thomas; Rouprêt, Morgan; Pinar, Ugo; Mozer, Pierre C.

doi:10.1016/j.euo.2023.06.013

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…At the time of writing, the specific learning curve for TRUS segmentation has never been assessed. Thus, our findings are essential to estimate the extent of learning related to the planning phase and to consider the potential benefits of automating TRUS segmentation through innovations in artificial intelligence algorithms [23][24][25]. Indeed, the automation of this phase could potentially result in a 30% reduction in the overall procedure time.…”

Section: Discussionmentioning

confidence: 98%

Learning curve for fusion magnetic resonance imaging targeted prostate biopsy and three‐dimensional transrectal ultrasonography segmentation

Lenfant,

Beitone,

Troccaz

et al. 2024

BJU International

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ObjectiveTo report the learning curve of multiple operators for fusion magnetic resonance imaging (MRI) targeted biopsy and to determine the number of cases needed to achieve proficiency.Materials and MethodsAll adult males who underwent fusion MRI targeted biopsy between February 2012 and July 2021 for clinically suspected prostate cancer (PCa) in a single centre were included. Fusion transrectal MRI targeted biopsy was performed under local anaesthesia using the Koelis platform. Learning curves for segmentation of transrectal ultrasonography (TRUS) images and the overall MRI targeted biopsy procedure were estimated with locally weighted scatterplot smoothing by computing each operator's timestamps for consecutive procedures. Non‐risk‐adjusted cumulative sum (CUSUM) methods were used to create learning curves for clinically significant (i.e., International Society of Urological Pathology grade ≥ 2) PCa detection.ResultsOverall, 1721 patients underwent MRI targeted biopsy in our centre during the study period. The median (interquartile range) times for TRUS segmentation and for the MRI targeted biopsy procedure were 4.5 (3.5, 6.0) min and 13.2 (10.6, 16.9) min, respectively. Among the 14 operators with experience of more than 50 cases, a plateau was reached after 40 cases for TRUS segmentation time and 50 cases for overall MRI targeted biopsy procedure time. CUSUM analysis showed that the learning curve for clinically significant PCa detection required 25 to 45 procedures to achieve clinical proficiency. Pain scores ranged between 0 and 1 for 84% of patients, and a plateau phase was reached after 20 to 100 cases.ConclusionsA minimum of 50 cases of MRI targeted biopsy are necessary to achieve clinical and technical proficiency and to reach reproducibility in terms of timing, clinically significant PCa detection, and pain.

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Section: Discussionmentioning

confidence: 98%

Learning curve for fusion magnetic resonance imaging targeted prostate biopsy and three‐dimensional transrectal ultrasonography segmentation

Lenfant,

Beitone,

Troccaz

et al. 2024

BJU International

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“…Fortunately, progress in artificial intelligence has opened new avenues for the diagnosis and management of prostate cancer. In particular, artificial intelligence technology can improve the performance and the precision of fusion biopsy [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Quantitative ADC: An Additional Tool in the Evaluation of Prostate Cancer?

Lucarelli,

Villanova,

Maggialetti

et al. 2023

JPM

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Prostate cancer is one of the most common tumors among the male population. Magnetic resonance imaging (MRI), standardized by the PI-RADS version 2.1 scoring system, has a fundamental role in detecting prostate cancer and evaluating its aggressiveness. Diffusion-weighted imaging sequences and apparent diffusion coefficient values, in particular, are considered fundamental for the detection and characterization of lesions. In 2016 the International Society of Urological Pathology introduced a new anatomopathological 5-grade scoring system for prostate cancer. The aim of this study is to evaluate the correlation between quantitative apparent diffusion coefficient values (ADC) derived from diffusion-weighted imaging (DWI) sequences and the International Society of Urological Pathology (ISUP) and PI-RADS groups. Our retrospective study included 143 patients with 154 suspicious lesions, observed on prostate magnetic resonance imaging and compared with the histological results of the biopsy. We observed that ADC values can aid in discriminating between not clinically significant (ISUP 1) and clinically significant (ISUP 2-5) prostate cancers. In fact, ADC values were lower in ISUP 5 lesions than in negative lesions. We also found a correlation between ADC values and PI-RADS groups; we noted lower ADC values in the PI-RADS 5 and PI-RADS 4 groups than in the PI-RADS 3 group. In conclusion, quantitative apparent diffusion coefficient values can be useful to assess the aggressiveness of prostate cancer.

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Impact of Relative Volume Difference Between Magnetic Resonance Imaging and Three-dimensional Transrectal Ultrasound Segmentation on Clinically Significant Prostate Cancer Detection in Fusion Magnetic Resonance Imaging–targeted Biopsy

Lenfant,

Beitone,

Troccaz

et al. 2024

European Urology Oncology

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Unleashing the Power of Artificial Intelligence and Fusion Magnetic Resonance Imaging–Targeted Biopsy: Transforming Prostate Cancer Diagnosis

Cited by 5 publications

References 8 publications

Learning curve for fusion magnetic resonance imaging targeted prostate biopsy and three‐dimensional transrectal ultrasonography segmentation

Learning curve for fusion magnetic resonance imaging targeted prostate biopsy and three‐dimensional transrectal ultrasonography segmentation

Quantitative ADC: An Additional Tool in the Evaluation of Prostate Cancer?

Impact of Relative Volume Difference Between Magnetic Resonance Imaging and Three-dimensional Transrectal Ultrasound Segmentation on Clinically Significant Prostate Cancer Detection in Fusion Magnetic Resonance Imaging–targeted Biopsy

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