External validation of the computerized analysis of TRUS of the prostate with the ANNA/C-TRUS system: a potential role of artificial intelligence for improving prostate cancer detection

被引:11
|
作者
Lorusso, Vito [1 ,2 ,3 ]
Kabre, Boukary [4 ]
Pignot, Geraldine [1 ]
Branger, Nicolas [1 ]
Pacchetti, Andrea [1 ]
Thomassin-Piana, Jeanne [5 ]
Brunelle, Serge [6 ]
Nicolai, Nicola [2 ]
Musi, Gennaro [3 ,7 ]
Salem, Naji [8 ]
Montanari, Emanuele [3 ,9 ]
de Cobelli, Ottavio [3 ,7 ]
Gravis, Gwenaelle [10 ]
Walz, Jochen [1 ]
机构
[1] Inst Paoli Calmettes Canc Ctr, Dept Urol, Marseille, France
[2] Fdn IRCCS Ist Nazl Tumori, Urol Unit, Milan, Italy
[3] Univ Milan, Milan, Italy
[4] CHU Yalgado Ouedraogo, Dept Urol, Ouagadougou, Burkina Faso
[5] Inst Paoli Calmettes Canc Ctr, Dept Pathol, Marseille, France
[6] Inst Paoli Calmettes Canc Ctr, Dept Radiol, Marseille, France
[7] IRCCS, Dept Urol, IEO, European Inst Oncol, Milan, Italy
[8] Inst Paoli Calmettes Canc Ctr, Dept Radiotherapy, Marseille, France
[9] Fdn IRCCS Ca Granda Osped Maggiore Policlin, Dept Urol, Milan, Italy
[10] Inst Paoli Calmettes Canc Ctr, Dept Oncol, Marseille, France
关键词
Prostate cancer; Imaging; Ultrasound; TRUS; Transrectal; Artificial intelligence; Diagnosis; ANNA; C-TRUS; Biopsy; NETWORK ANALYSIS ANNA; MULTIPARAMETRIC MRI; DIAGNOSIS; ULTRASOUND; BIOPSY;
D O I
10.1007/s00345-022-03965-w
中图分类号
R5 [内科学]; R69 [泌尿科学(泌尿生殖系疾病)];
学科分类号
1002 ; 100201 ;
摘要
Purpose Prostate cancer (PCa) imaging has been revolutionized by the introduction of multi-parametric Magnetic Resonance Imaging (mpMRI). Transrectal ultrasound (TRUS) has always been considered a low-performance modality. To overcome this, a computerized artificial neural network analysis (ANNA/C-TRUS) of the TRUS based on an artificial intelligence (AI) analysis has been proposed. Our aim was to evaluate the diagnostic performance of the ANNA/C-TRUS system and its ability to improve conventional TRUS in PCa diagnosis. Methods We retrospectively analyzed data from 64 patients with PCa and scheduled for radical prostatectomy who underwent TRUS followed by ANNA/C-TRUS analysis before the procedure. The results of ANNA/C-TRUS analysis with whole mount sections from final pathology. Results On a per-sectors analysis, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV) and accuracy were 62%, 81%, 80%, 64% and 78% respectively. The values for the detection of clinically significant prostate cancer were 69%, 77%, 88%, 50% and 75%. The diagnostic values for high grade tumours were 70%, 74%, 91%, 41% and 74%, respectively. Cancer volume (<= 0.5 or greater) did not influence the diagnostic performance of the ANNA/C-TRUS system. Conclusions ANNA/C-TRUS represents a promising diagnostic tool and application of AI for PCa diagnosis. It improves the ability of conventional TRUS to diagnose prostate cancer, preserving its simplicity and availability. Since it is an AI system, it does not hold the inter-observer variability nor a learning curve. Multicenter biopsy-based studies with the inclusion of an adequate number of patients are needed to confirm these results.
引用
收藏
页码:619 / 625
页数:7
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