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RESEARCH PRODUCT
Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI.
Jamie MarkoStefano SioleticAli Riza KuralErcan KaraarslanAndrei S PursykoChris KnaussRossano GiromettiJennifer B. GordetskyJennifer B. GordetskyClayton P. SmithLeonardo Kayat BittencourtLeonardo Kayat BittencourtVictor Martins TonsoYan Mee LawSoroush Rais-bahramiKarabekir ErcanRicardo Silvestre E Silva MacarencoDerya YakarBaris TurkbeyBradford J. WoodYesim SaglicanBerrak GumuskayaMehmet CoşkunBurak ArgunJoanna H. ShihPeter A. PintoSherif MehralivandSherif MehralivandNathan LayCristina Magi-galluzziRonaldo Hueb BaroniPeter L. ChoykeAnne Y. WarrenMaria J. MerinoAbdullah Erdem CandaStephanie HarmonRonald M. SummersTristan BarrettDaniel MargolisSandra BednarovaAshkan A. Malayerisubject
MaleUrologyAdenocarcinomaSensitivity and SpecificityArticle030218 nuclear medicine & medical imaging03 medical and health sciencesProstate cancerRandom Allocation0302 clinical medicineLaparoscopicProstateArtificial IntelligenceStatistical significanceMedicineHumansAttentionRadiology Nuclear Medicine and imagingDiagnosis Computer-AssistedMultiparametric Magnetic Resonance ImagingAgedRetrospective StudiesObserver VariationProstate cancerbusiness.industryartificial intelligence; laparoscopic; MRI; multiparametric; prostate cancer; radical prostatectomy; robot-assistedSignificant differenceCancerMultiparametric MRIProstatic NeoplasmsGeneral MedicineRobot-assistedMiddle Agedmedicine.diseaseRadical prostatectomymedicine.anatomical_structure030220 oncology & carcinogenesisMapping systemMultiparametricArtificial intelligencebusinessAlgorithmsMridescription
OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-10-01 | AJR. American journal of roentgenology |