Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review

Rouvière, Olivier; Jaouen, Tristan; Baseilhac, Pierre; Benomar, Mohammed Lamine; Escande, Raphael; Crouzet, Sébastien; Souchon, Rémi

doi:10.1016/j.diii.2022.11.005

Cited by 15 publications

(4 citation statements)

References 69 publications

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“…Using quantitative thresholds for apparent diffusion coefficient or DCE-derived parameters may also improve prostate MRI accuracy and inter-reader agreement [16,[32][33][34], but there is still progress to be made on the reproducibility of MRI biomarkers [35][36][37][38]. Finally, assistance by Artificial Intelligence algorithms may facilitate prostate MRI reading in the future; however, conflicting results have been recently published on this matter [39][40][41][42][43][44][45].…”

Section: Discussionmentioning

confidence: 99%

Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study)

et al. 2023

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Objective To assess PI-RADSv2.1 and PI-RADSv2 descriptors across readers with varying experience. Methods Twenty-one radiologists (7 experienced (≥ 5 years) seniors, 7 less experienced seniors and 7 juniors) assessed 240 ‘predefined’ lesions from 159 pre-biopsy multiparametric prostate MRIs. They specified their location (peripheral, transition or central zone) and size, and scored them using PI-RADSv2.1 and PI-RADSv2 descriptors. They also described and scored ‘additional’ lesions if needed. Per-lesion analysis assessed the ‘predefined’ lesions, using targeted biopsy as reference; per-lobe analysis included ‘predefined’ and ‘additional’ lesions, using combined systematic and targeted biopsy as reference. Areas under the curve (AUCs) quantified the performance in diagnosing clinically significant cancer (csPCa; ISUP ≥ 2 cancer). Kappa coefficients (κ) or concordance correlation coefficients (CCC) assessed inter-reader agreement. Results At per-lesion analysis, inter-reader agreement on location and size was moderate-to-good (κ = 0.60–0.73) and excellent (CCC ≥ 0.80), respectively. Agreement on PI-RADSv2.1 scoring was moderate (κ = 0.43–0.47) for seniors and fair (κ = 0.39) for juniors. Using PI-RADSv2.1, juniors obtained a significantly lower AUC (0.74; 95% confidence interval [95%CI]: 0.70–0.79) than experienced seniors (0.80; 95%CI 0.76–0.84; p = 0.008) but not than less experienced seniors (0.74; 95%CI 0.70–0.78; p = 0.75). As compared to PI-RADSv2, PI-RADSv2.1 downgraded 17 lesions/reader (interquartile range [IQR]: 6–29), of which 2 (IQR: 1–3) were csPCa; it upgraded 4 lesions/reader (IQR: 2–7), of which 1 (IQR: 0–2) was csPCa. Per-lobe analysis, which included 60 (IQR: 25–73) ‘additional’ lesions/reader, yielded similar results. Conclusions Experience significantly impacted lesion characterization using PI-RADSv2.1 descriptors. As compared to PI-RADSv2, PI-RADSv2.1 tended to downgrade non-csPCa lesions, but this effect was small and variable across readers.

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Section: Discussionmentioning

confidence: 99%

Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study)

et al. 2023

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“…Although MRI-based radiomics studies have shown promising results, most of them suffer from several limitations mainly due to lack of external validation and prospectively designed studies, sine qua non conditions for translation into clinical practice. Limitations include also sensitivity to image processing from different MRI scanners, underlying the need for radiomic techniques robust across institutions [41]. Finally, a lot of radiomics studies do not integrate clinical data limiting the assessment at imaging features while clinical data may bring relevant information.…”

Section: Current Limations Challenges Conclusionmentioning

confidence: 99%

Research on texture images and radiomics in urology: a review of urological MR imaging applications

Valeri,

Nguyen

2023

Current Opinion in Urology

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Purpose of review Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives. Recent findings Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization. Summary Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.

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“…Still, prostate mpMRI grapples with variability among experts, ambiguities in addressing of PI-RADS 3 lesions, and instances of elusive csPCa [11,13]. This highlights the need for diagnostic refinement through advanced technology [3,4].…”

Section: Prostatementioning

confidence: 99%

“…Analyzing subtle changes in prostate, kidney, and bladder images can enhance early detection of urologic cancers, identification of the extent and stage of malignant tissues, and even predict tumor aggressiveness and responsiveness to treatment. The quantitative nature of radiomics allows for more consistency in radiographic assessment, minimizing inter-observer variability and enhancing diagnostic capabilities [3][4][5]. This review provides an overview of the most recent advancements in the domain of AI-powered radiomics applied to the field of urologic oncology, focusing on prostate, kidney, and bladder malignancies.…”

Section: Introductionmentioning

confidence: 99%

AI-powered radiomics: revolutionizing detection of urologic malignancies

Gelikman,

Rais-Bahrami,

Pinto

et al. 2023

Current Opinion in Urology

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Purpose of Review This review aims to highlight the integration of artificial intelligence-powered radiomics in urologic oncology, focusing on the diagnostic and prognostic advancements in the realm of managing prostate, kidney, and bladder cancers. Recent Findings As artificial intelligence continues to shape the medical imaging landscape, its integration into the field of urologic oncology has led to impressive results. For prostate cancer diagnostics, machine learning has shown promise in refining clinically-significant lesion detection, with some success in deciphering ambiguous lesions on multiparametric MRI. For kidney cancer, radiomics has emerged as a valuable tool for better distinguishing between benign and malignant renal masses and predicting tumor behavior from CT or MRI scans. Meanwhile, in the arena of bladder cancer, there is a burgeoning emphasis on prediction of muscle invasive cancer and forecasting disease trajectory. However, many studies showing promise in these areas face challenges due to limited sample sizes and the need for broader external validation. Summary Radiomics integrated with artificial intelligence offers a pioneering approach to urologic oncology, ushering in an era of enhanced diagnostic precision and reduced invasiveness, guiding patient-tailored treatment plans. Researchers must embrace broader, multicentered endeavors to harness the full potential of this field.

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Artificial intelligence algorithms aimed at characterizing or detecting prostate cancer on MRI: How accurate are they when tested on independent cohorts? – A systematic review

Cited by 15 publications

References 69 publications

Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study)

Characterization of high-grade prostate cancer at multiparametric MRI: assessment of PI-RADS version 2.1 and version 2 descriptors across 21 readers with varying experience (MULTI study)

Research on texture images and radiomics in urology: a review of urological MR imaging applications

AI-powered radiomics: revolutionizing detection of urologic malignancies

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