이전|다음

mpMRI 기반 자동 전립선 생검 의사결정을 위한 GPT-4의 성능: 다기관 증거 연구

  • role： Contribute equally, First author同等贡献者, 第一作者
  • Affiliation：

    Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Institute of Urology, Beijing Municipal Health Commission, Beijing 101313, China

Ming-Jun Shi12,  
  • role： Contribute equally同等贡献者
  • Affiliation：

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Zhi-Xiang Wang34,  
  • role： Contribute equally同等贡献者
  • Affiliation：

    Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China

Shuang-Kun Wang5,  
  • Affiliation：

    Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Xuan-Hao Li1,  
  • Affiliation：

    Department of Pathology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Yan-Lin Zhang6,  
  • Affiliation：

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Ying Yan3,  
  • Affiliation：

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Ran An3,  
  • Affiliation：

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Li-Ning Dong3,  
  • Affiliation：

    Department of Urology, Peking University Third Hospital, Beijing 100083, China

Lei Qiu7,  
  • Affiliation：

    Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China

Tian Tian5,  
  • Affiliation：

    Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Department of Urology, Beijing Fuxing Hospital, Capital Medical University, Beijing 100039, China

Jia-Xin Liu18,  
  • Affiliation：

    Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Hong-Chen Song1,  
  • Affiliation：

    Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China

Ya-Fan Wang5,  
  • Affiliation：

    Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University, Beijing 100020, China

Che Deng5,  
  • Affiliation：

    Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Department of Urology, Beijing Miyun District Traditional Chinese Medicine Hospital, Beijing 101500, China

Zi-Bing Cao19,  
  • Affiliation：

    Department of Urology, Peking University Third Hospital, Beijing 100083, China

Hong-Yin Wang7,  
  • Affiliation：

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Zheng Wang3,  
  • Affiliation：

    Division of Science and Technology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

Wei Wei10,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Urology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

    Institute of Urology, Beijing Municipal Health Commission, Beijing 101313, China

  • Email：songjian1974@aliyun.com
  • Introduction：* Jian Song songjian1974@aliyun.com
Jian Song12*,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Urology, Peking University Third Hospital, Beijing 100083, China

  • Email：lujian@bjmu.edu.cn
  • Introduction：Jian Lu lujian@bjmu.edu.cn
Jian Lu7*,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

  • Email：weixuan315@163.com
  • Introduction：Xuan Wei weixuan315@163.com
Xuan Wei3*,  
  • role： Corresponding author通信作者
  • Affiliation：

    Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing 100050, China

  • Email：cjr.wzhch@vip.163.com
  • Introduction：Zhen-Chang Wang cjr.wzhch@vip.163.com
Zhen-Chang Wang3*

추상적인

다중 매개변수 자기공명영상(mpMRI)은 전립선암(PCa) 검출을 크게 발전시켰지만, 중간 수준의 전립선 영상 보고 및 데이터 시스템(PI-RADS) 점수에서의 침습적 생검 결정은 여전히 모호합니다. 자동 전립선 생검 권고를 위한 생성 사전학습 변환기-4(GPT-4)의 의사결정 능력을 탐구하기 위해, 2018년부터 2023년까지 3개의 대형 의료기관에서 전립선 생검을 받은 2299명의 환자를 포함하였으며, 생검 전 mpMRI와 문서화된 임상-조직병리 기록을 확보하였습니다. GPT-4는 주어진 프롬프트로 구조화된 보고서를 생성하였습니다. GPT-4의 성능은 오차 행렬을 사용하여 정량화되었으며, 민감도, 특이도 및 곡선 아래 면적이 계산되었습니다. 다양한 인위적 평가 절차가 수행되었습니다. 비교를 위해 윌콕슨 순위합 검정, 피셔 정확 검정 및 크루스칼-월리스 검정을 사용하였습니다. 중국 인구에서 가장 큰 샘플 크기를 활용하여, 중간 PI-RADS 점수(3점 및 4점)를 가진 환자가 39.7% (912/2299)를 차지하였으며, 이는 관심 하위 집합(SOI)으로 정의되었습니다. PI-RADS 점수 2~5에 해당하는 임상적으로 중요한 전립선암의 검출률은 각각 9.4%, 27.3%, 49.2%, 80.1%였습니다. SOI 환자의 약 47.5% (433/912)가 불필요한 전립선 생검을 받은 것으로 조직병리학적으로 증명되었습니다. GPT-4의 도움으로 SOI 인구의 20.8% (190/912)는 불필요한 생검을 피할 수 있었으며, 가장 이질적인 PI-RADS 3 점수 하위 그룹에서 더 좋은 성능[28.8% (118/410)]을 보였습니다. GPT-4가 생성한 보고서의 90.0% 이상이 포괄적이고 이해하기 쉬웠지만 정확도에 대해서는 덜 만족스러웠습니다(82.8%). GPT-4는 복잡한 문제를 처리하는 인지 잠재력도 보여주었습니다. 또한 사고의 연쇄 방법은 GPT-4의 의사결정 논리를 더 잘 이해하는 데 도움을 주었습니다. 결국, 우리는 의사와 환자 모두의 접근성을 촉진하기 위해 ProstAIGuide 플랫폼을 개발했습니다. 이 다기관 연구는 전립선 생검 의사결정에 대한 GPT-4의 임상적 유용성을 강조하며, 다양한 의료 시나리오에서 최신 인공지능 구현에 대한 이해를 발전시킵니다.

키워드

전립선 생검; 생성 사전학습 변환기-4 (GPT-4); 의사결정; 전립선암; 다중 매개변수 자기공명영상 (mpMRI)

References

  1. 1..
    Bergengren O,Pekala KR,Matsoukas K,Fainberg J,Mungovan SF,Bratt O, et al. 2022 update on prostate cancer epidemiology and risk factors—a systematic review. Eur Urol. 2023;84(2):191-206.
  2. 2..
    Culp MB,Soerjomataram I,Efstathiou JA,Bray F,Jemal A. Recent global patterns in prostate cancer incidence and mortality rates. Eur Urol.2020;77(1):38-52.
  3. 3..
    Xu H,Li YF,Yi XY,Zheng XN,Yang Y,Wang Y, et al. ADP-dependent glucokinase controls metabolic fitness in prostate cancer progression. Mil Med Res. 2023;10(1):64.
  4. 4..
    Constancio V,Lobo J,Sequeira JP,Henrique R,Jeronimo C. Prostate cancer epigenetics - from pathophysiology to clinical application. Nat Rev Urol.2025. .
  5. 5..
    Massanova M,Vere R,Robertson S,Crocetto F,Barone B,Dutto L, et al. Clinical and prostate multiparametric magnetic resonance imaging findings as predictors of general and clinically significant prostate cancer risk:a retrospective single-center study. Curr Urol. 2023;17(3):147-52.
  6. 6..
    Cornford P,Van Den Bergh RCN,Briers E,Van Den Broeck T,Brunckhorst O,Darraugh J, et al. EAU-EANM-ESTRO-ESUR-ISUP-SIOG guidelines on prostate cancer-2024 update. Part I: screening, diagnosis, and local treatment with curative intent. Eur Urol. 2024;86(2):148-63.
  7. 7..
    Ginsburg K,Cole AI,Silverman ME,Livingstone J,Smith DW,Heilbrun LK, et al. Should all prostate needle biopsy gleason score 4 + 4 = 8 prostate cancers be high risk? Implications for shared decision-making and patient counselling. Urol Oncol. 2020;38(3):78.e1-e6.
  8. 8..
    Lowenstein LM,Basourakos SP,Williams MD,Troncoso P,Gregg JR,Thompson TC, et al. Active surveillance for prostate and thyroid cancers:evolution in clinical paradigms and lessons learned. Nat Rev Clin Oncol.2019;16(3):168-84.
  9. 9..
    Borghesi M,Ahmed H,Nam R,Schaeffer E,Schiavina R,Taneja S, et al. Complications after systematic, random, and image-guided prostate biopsy. Eur Urol. 2017;71(3):353-65.
  10. 10..
    Devetzis K,Kum F,Popert R. Recent advances in systematic and targeted prostate biopsies. Res Rep Urol. 2021;13:799-809.
  11. 11..
    Van Poppel H,Hogenhout R,Albers P,Van Den Bergh RCN,Barentsz JO,Roobol MJ. Early detection of prostate cancer in 2020 and beyond: facts and recommendations for the European Union and the European Commission. Eur Urol. 2021;79(3):327-9.
  12. 12..
    Fütterer JJ,Briganti A,De Visschere P,Emberton M,Giannarini G,Kirkham A, et al. Can clinically significant prostate cancer be detected with mul-tiparametric magnetic resonance imaging? A systematic review of the literature. Eur Urol. 2015;68(6):1045-53.
  13. 13..
    Padhani AR,Weinreb J,Rosenkrantz AB,Villeirs G,Turkbey B,Barentsz J. Prostate imaging-reporting and data system steering committee: PI-RADS v2 status update and future directions. Eur Urol. 2019;75(3):385-96.
  14. 14..
    Barentsz JO,Richenberg J,Clements R,Choyke P,Verma S,Villeirs G, et al. ESUR prostate MR guidelines 2012. Eur Radiol. 2012;22(4):746-57.
  15. 15..
    Turkbey B,Rosenkrantz AB,Haider MA,Padhani AR,Villeirs G,Macura KJ, et al. Prostate Imaging Reporting and Data System Version 2.1: 2019 update of prostate imaging reporting and data system version 2. Eur Urol. 2019;76(3):340-51.
  16. 16..
    Weinreb JC,Barentsz JO,Choyke PL,Cornud F,Haider MA,Macura KJ, et al. PI-RADS prostate imaging - reporting and data system: 2015, version 2. Eur Urol. 2016;69(1):16-40.
  17. 17..
    Zhao LT,Liu ZY,Xie WF,Shao LZ,Lu J,Tian J, et al. What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?. Mil Med Res. 2023;10(1):29.
  18. 18..
    Padhani AR,Barentsz J,Villeirs G,Rosenkrantz AB,Margolis DJ,Turkbey B, et al. PI-RADS Steering Committee: the PI-RADS multiparametric MRI and MRI-directed biopsy pathway. Radiology. 2019;292(2):464-74.
  19. 19..
    Renard-Penna R,Mozer P,Cornud F,Barry-Delongchamps N,Bruguière E,Portalez D, et al. Prostate imaging reporting and data system and likert scoring system: multiparametric MR imaging validation study to screen patients for initial biopsy. Radiology. 2015;275(2):458-68.
  20. 20..
    Barkovich EJ,Shankar PR,Westphalen AC. A systematic review of the existing prostate imaging reporting and data system version 2 (PI-RADSv2) literature and subset meta-analysis of PI-RADSv2 categories stratified by Gleason scores. AJR Am J Roentgenol. 2019;212(4):847-54.
  21. 21..
    Morash C. What do you do with PI-RADS-3?. Can Urol Assoc J.2021;15(4):122.
  22. 22..
    Nicola R,Bittencourt LK. PI-RADS 3 lesions: a critical review and discussion of how to improve management. Abdom Radiol (NY).2023;48(7):2401-5.
  23. 23..
    Boeken T,Feydy J,Lecler A,Soyer P,Feydy A,Barat M, et al. Artificial intelligence in diagnostic and interventional radiology: where are we now? Diagn Interv Imaging. 2023;104(1):1-5.
  24. 24..
    Bhayana R,Bleakney RR,Krishna S. GPT-4 in radiology: improvements in advanced reasoning. Radiology. 2023;307(5):e230987.
  25. 25..
    Cheng K,Li Z,Li C,Xie R,Guo Q,He Y, et al. The potential of GPT-4 as an AI-powered virtual assistant for surgeons specialized in joint arthro-plasty. Ann Biomed Eng. 2023;51(7):1366-70.
  26. 26..
    Kanjee Z,Crowe B,Rodman A. Accuracy of a generative artificial intelligence model in a complex diagnostic challenge. JAMA.2023;330(1):78-80.
  27. 27..
    Lee P,Bubeck S,Petro J. Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine. N Engl J Med. 2023;388(13):1233-9.
  28. 28..
    Epstein JI. Prostate cancer grading: a decade after the 2005 modified system. Mod Pathol. 2018;31(S1):S47-63.
  29. 29..
    Pierorazio PM,Walsh PC,Partin AW,Epstein JI. Prognostic Gleason grade grouping: data based on the modified Gleason scoring system. BJU Int. 2013;111(5):753-60.
  30. 30..
    Patel HD,Koehne EL,Shea SM,Fang AM,Gerena M,Gorbonos A, et al. A prostate biopsy risk calculator based on MRI: development and comparison of the prospective loyola university multiparametric MRI(PLUM) and prostate biopsy collaborative group (PBCG) risk calculators. BJU Int. 2023;131(2):227-35.
  31. 31..
    Wang NN,Zhou SR,Chen L,Tibshirani R,Fan RE,Ghanouni P, et al. The stanford prostate cancer calculator: development and external validation of online nomograms incorporating PI-RADS scores to predict clinically significant prostate cancer. Urol Oncol. 2021;39(12):831.e19-e27.
  32. 32..
    Wei J,Wang X,Schuurmans D,Bosma M,Ichter B,Xia F, et al. Chain-of-thought prompting elicits reasoning in large language models. arXiv.2022.
  33. 33..
    Elkhoury FF,Felker ER,Kwan L,Sisk AE,Delfin M,Natarajan S, et al. Com-parison of targeted vs systematic prostate biopsy in men who are biopsy naive: the prospective assessment of image registration in the diagnosis of prostate cancer (PAIREDCAP) study. JAMA Surg. 2019;154(9):811-8.
  34. 34..
    Pepe P,Pennisi M. Targeted biopsy in men high risk for prostate cancer: 68Ga-PSMA PET/CT versus mpMRI. Clin Genitourin Canc.2023;21(6):639-42.
  35. 35..
    Mian BM,Tikkinen KaO,Kibel AS. Transrectal versus transperineal prostate biopsy: weighing the trade-offs. Lancet Oncol. 2025;26(5):533-5.
  36. 36..
    Pepe P,Pennisi M. Morbidity following transperineal prostate biopsy: our experience in 8.500 men. Arch Ital Urol Andro. 2022;94(2):155-9.
  37. 37..
    Ankerst DP,Straubinger J,Selig K,Guerrios L,De Hoedt A,Hernandez J, et al. A contemporary prostate biopsy risk calculator based on multiple heterogeneous cohorts. Eur Urol. 2018;74(2):197-203.
  38. 38..
    Ankerst DP,Hoefler J,Bock S,Goodman PJ,Vickers A,Hernandez J, et al. Prostate cancer prevention trial risk calculator 2.0 for the prediction of low- vs high-grade prostate cancer. Urology. 2014;83(6):1362-7.
  39. 39..
    Roobol MJ,Steyerberg EW,Kranse R,Wolters T,Van Den Bergh RCN,Bangma CH, et al. A risk-based strategy improves prostate-specific antigen-driven detection of prostate cancer. Eur Urol. 2010;57(1):79-85.
  40. 40..
    Orbe Villota PM,Leiva Centeno JA,Lugones J,Minuzzi PG,Varea SM. Comparison between the european randomized study for screening of prostate cancer (ERSPC) and prostate biopsy collaborative group (PBCG)risk calculators: prediction of clinically significant prostate cancer risk in a cohort of patients from argentina. Actas Urol Esp. 2024;48(3):210-7.
  41. 41..
    Shen J,Zhang CJP,Jiang B,Chen J,Song J,Liu Z, et al. Artificial intelligence versus clinicians in disease diagnosis: systematic review. JMIR Med Inf. 2019;7(3):e10010-e.
  42. 42..
    Wadden JJ. Defining the undefinable: the black box problem in health-care artificial intelligence. J Med Ethics. 2021;48(10):764-8.
  43. 43..
    Gugliandolo SG,Pepa M,Isaksson LJ,Marvaso G,Raimondi S,Botta F, et al. MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218). Eur Radiol. 2021;31(2):716-28.
  44. 44..
    Ponsiglione A,Gambardella M,Stanzione A,Green R,Cantoni V,Nappi C, et al. Radiomics for the identification of extraprostatic extension with prostate MRI: a systematic review and meta-analysis. Eur Radiol.2024;34(6):3981-91.
  45. 45..
    Woźnicki P,Westhoff N,Huber T,Riffel P,Froelich MF,Gresser E, et al. Multiparametric MRI for prostate cancer characterization: combined use of radiomics model with PI-RADS and clinical parameters. Cancers.2020;12(7):1767.
  46. 46..
    Vietri MT,D’elia G,Caliendo G,Resse M,Casamassimi A,Passariello L, et al. Hereditary prostate cancer: genes related, target therapy and prevention. Int J Mol Sci. 2021;22(7):3753.
  47. 47..
    Giri VN,Knudsen KE,Kelly WK,Abida W,Andriole GL,Bangma CH, et al. Role of genetic testing for inherited prostate cancer risk: phila-delphia prostate cancer consensus conference 2017. J Clin Oncol.2018;36(4):414-24.
  48. 48..
    Tuffaha H,Edmunds K,Fairbairn D,Roberts MJ,Chambers S,Smith DP, et al. Guidelines for genetic testing in prostate cancer: a scoping review. Prostate Cancer Prostatic Dis. 2024;27(4):594-603.
  49. 49..
    Casanova-Salas I,Athie A,Boutros PC,Del Re M,Miyamoto DT,Pienta KJ, et al. Quantitative and qualitative analysis of blood-based liquid biopsies to inform clinical decision-making in prostate cancer. Eur Urol.2021;79(6):762-71.
  50. 50..
    Hoffmann TJ,Graff RE,Madduri RK,Rodriguez AA,Cario CL,Feng K, et al. Genome-wide association study of prostate-specific antigen levels in 392, 522 men identifies new loci and improves prediction across ancestry groups. Nat Genet. 2025;57(2):334-44.

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