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中华腔镜泌尿外科杂志(电子版) ›› 2024, Vol. 18 ›› Issue (06) : 535 -540. doi: 10.3877/cma.j.issn.1674-3253.2024.06.001

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肾癌自动化病理诊断:AI离临床还有多远?
熊鹰1, 林敬莱2, 白奇1, 郭剑明1, 王烁3,()   
  1. 1.200032 上海,复旦大学附属中山医院泌尿外科
    2.361015 厦门,复旦大学附属中山医院厦门医院
    3.200032 上海,复旦大学基础医学院数字医学研究中心
  • 收稿日期:2024-08-22 出版日期:2024-12-01
  • 通信作者: 王烁
  • 基金资助:
    国家自然科学基金青年科学基金项目(81902563)

How far is AI from clinical application in automated renal cell carcinoma pathological diagnosis?

Ying Xiong1, Jinglai Lin2, Qi Bai1, Jianming Guo1, Shuo Wang3,()   

  1. 1.Department of Urology,Zhongshan Hospital,Fudan University,Shanghai 200032,China
    2.Department of Urology,Zhongshan Hospital (Xiamen),Fudan University,Xiamen 361015,China
    3.Digital Medical Research Center,School of Basic Medical Sciences,Fudan University,Shanghai 200032,China
  • Received:2024-08-22 Published:2024-12-01
  • Corresponding author: Shuo Wang
引用本文:

熊鹰, 林敬莱, 白奇, 郭剑明, 王烁. 肾癌自动化病理诊断:AI离临床还有多远?[J]. 中华腔镜泌尿外科杂志(电子版), 2024, 18(06): 535-540.

Ying Xiong, Jinglai Lin, Qi Bai, Jianming Guo, Shuo Wang. How far is AI from clinical application in automated renal cell carcinoma pathological diagnosis?[J]. Chinese Journal of Endourology(Electronic Edition), 2024, 18(06): 535-540.

肾细胞癌(RCC)作为最常见的泌尿系统恶性肿瘤之一,其病理诊断在疾病管理及治疗策略制定中占据关键地位。随着人工智能技术的蓬勃发展,特别是深度学习在医学图像处理领域的显著突破,肾癌智能病理诊断已成为当前研究的前沿热点。本文系统回顾了肾癌智能病理诊断的最新进展,详细阐述了人工智能技术在RCC组织类型鉴别、病理类型区分、核分级判定、预后评估及基因突变预测等方面的应用现状。尽管取得了一定成果,但肾癌智能病理诊断的临床应用仍面临诸多挑战,包括病理类型覆盖不全、数据集规模有限、数据标准化程度低、算法泛化能力不足以及缺乏前瞻性外部临床验证等。展望未来,研究应着重于解决现有问题,同时聚焦于提升模型可解释性、构建基于病理特征的大模型及多模态肾癌专用大模型,以期推动肾癌智能病理诊断技术的成熟与广泛应用,为RCC患者提供更加个性化、精准及高效的诊疗方案。

Renal cell carcinoma (RCC),one of the most prevalent malignancies in the urinary system,underscores the critical role of pathological diagnosis in disease management and treatment strategy formulation. With the vigorous development of artificial intelligence,particularly the notable breakthroughs of deep learning in medical image processing,intelligent pathological diagnosis for RCC has emerged as a forefront research area. This article systematically reviews the latest advancements in RCC intelligent pathological diagnosis,detailing the current applications of artificial intelligence in histological subtype discrimination,pathological type differentiation,nuclear grade determination,prognosis evaluation,and gene mutation prediction. Despite the achievements made,clinical adoption of RCC intelligent pathological diagnosis faces several challenges,including incomplete coverage of pathological types,limited dataset sizes,low data standardization,insufficient algorithm generalizability,and the absence of prospective external clinical validations. Moving forward,research should prioritize addressing these existing issues while focusing on enhancing model interpretability,building RCC pathological foundation models,and developing multimodal RCC-specific foundation models. These efforts aim to advance the maturity and extensive application of RCC intelligent pathological diagnosis technologies,ultimately providing more personalized,precise,and efficient diagnostic and treatment options for RCC patients.

[1]
Bray F,Laversanne M,Sung H,et al. Global cancer statistics 2022:GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin,2024,74(3): 229-263.
[2]
Bukavina L,Bensalah K,Bray F,et al. Epidemiology of renal cell carcinoma: 2022 update[J]. Eur Urol,2022,82(5): 529-542.
[3]
Moch H,Amin MB,Berney DM,et al. The 2022 World Health Organization classification of tumours of the urinary system and male genital organs-part A: renal,penile,and testicular tumours[J]. Eur Urol,2022,82(5): 458-468.
[4]
Shuch B,Hofmann JN,Merino MJ,et al. Pathologic validation of renal cell carcinoma histology in the Surveillance,Epidemiology,and End Results program[J]. Urol Oncol,2014,32(1): 23.e9-23.13.
[5]
Warren AY,Harrison D. WHO/ISUP classification,grading and pathological staging of renal cell carcinoma: standards and controversies[J]. World J Urol,2018,36(12): 1913-1926.
[6]
van der Laak J,Litjens G,Ciompi F. Deep learning in histopathology:the path to the clinic[J]. Nat Med,2021,27(5): 775-784.
[7]
Echle A,Rindtorff NT,Brinker TJ,et al. Deep learning in cancer pathology: a new generation of clinical biomarkers[J]. Br J Cancer,2021,124(4): 686-696.
[8]
Coudray N,Tsirigos A. Deep learning links histology,molecular signatures and prognosis in cancer[J]. Nat Cancer,2020,1(8): 755-757.
[9]
Cruz-Roa A,Gilmore H,Basavanhally A,et al. Accurate and reproducible invasive breast cancer detection in whole-slide images:a deep learning approach for quantifying tumor extent[J]. Sci Rep,2017,7: 46450.
[10]
Wang X,Chen H,Gan C,et al. Weakly supervised deep learning for whole slide lung cancer image analysis[J]. IEEE Trans Cybern,2020,50(9): 3950-3962.
[11]
Kartasalo K,Bulten W,Delahunt B,et al. Artificial intelligence for diagnosis and gleason grading of prostate cancer in biopsies-current status and next steps[J]. Eur Urol Focus,2021,7(4): 687-691.
[12]
Courtiol P,Maussion C,Moarii M,et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome[J]. Nat Med,2019,25(10): 1519-1525.
[13]
Tabibu S,Vinod PK,Jawahar CV. Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning[J]. Sci Rep,2019,9(1): 10509.
[14]
Chen S,Zhang N,Jiang L,et al. Clinical use of a machine learning histopathological image signature in diagnosis and survival prediction of clear cell renal cell carcinoma[J]. Int J Cancer,2021,148(3): 780-790.
[15]
Marostica E,Barber R,Denize T,et al. Development of a histopathology informatics pipeline for classification and prediction of clinical outcomes in subtypes of renal cell carcinoma[J]. Clin Cancer Res,2021,27(10): 2868-2878.
[16]
Zhu M,Ren B,Richards R,et al. Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides[J]. Sci Rep,2021,11(1): 7080.
[17]
Cheng J,Han Z,Mehra R,et al. Computational analysis of pathological images enables a better diagnosis of TFE3 Xp11.2 translocation renal cell carcinoma[J]. Nat Commun,2020,11(1): 1778.
[18]
Browning L,Colling R,Verrill C. WHO/ISUP grading of clear cell renal cell carcinoma and papillary renal cell carcinoma; validation of grading on the digital pathology platform and perspectives on reproducibility of grade[J]. Diagn Pathol,2021,16(1): 75.
[19]
Holdbrook DA,Singh M,Choudhury Y,et al. Automated renal cancer grading using nuclear pleomorphic patterns[J]. JCO Clin Cancer Inform,2018,2: 1-12.
[20]
Chanchal AK,Lal S,Kumar R,et al. A novel dataset and efficient deep learning framework for automated grading of renal cell carcinoma from kidney histopathology images[J]. Sci Rep,2023,13(1): 5728.
[21]
Chen S,Jiang L,Gao F,et al. Machine learning-based pathomics signature could act as a novel prognostic marker for patients with clear cell renal cell carcinoma[J]. Br J Cancer,2022,126(5): 771-777.
[22]
Gui CP,Chen YH,Zhao HW,et al. Multimodal recurrence scoring system for prediction of clear cell renal cell carcinoma outcome: a discovery and validation study[J]. Lancet Digit Health,2023,5(8):e515-e524.
[23]
Huang KB,Gui CP,Xu YZ,et al. A multi-classifier system integrated by clinico-histology-genomic analysis for predicting recurrence of papillary renal cell carcinoma[J]. Nat Commun,2024,15(1): 6215.
[24]
Chen S,Gao F,Guo T,et al. Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study[J]. Int J Surg,2024,110(5): 2970-2977.
[25]
Acosta PH,Panwar V,Jarmale V,et al. Intratumoral resolution of driver gene mutation heterogeneity in renal cancer using deep learning[J]. Cancer Res,2022,82(15): 2792-2806.
[26]
Khor LY,Dhakal HP,Jia X,et al. Tumor necrosis adds prognostically significant information to grade in clear cell renal cell carcinoma: a study of 842 consecutive cases from a single institution[J]. Am J Surg Pathol,2016,40(9): 1224-1231.
[27]
Paner GP,Chumbalkar V,Montironi R,et al. Updates in grading of renal cell carcinomas beyond clear cell renal cell carcinoma and papillary renal cell carcinoma[J]. Adv Anat Pathol,2022,29(3): 117-130.
[28]
Amerikanos P,Maglogiannis I. Image analysis in digital pathology utilizing machine learning and deep neural networks[J]. J Pers Med,2022,12(9): 1444.
[29]
Jiang Y,Yang M,Wang S,et al. Emerging role of deep learningbased artificial intelligence in tumor pathology[J]. Cancer Commun,2020,40(4): 154-166.
[30]
Abels E,Pantanowitz L,Aeffner F,et al. Computational pathology definitions,best practices,and recommendations for regulatory guidance: a white paper from the Digital Pathology Association[J]. J Pathol,2019,249(3): 286-294.
[31]
Chen RJ,Ding T,Lu MY,et al. Towards a general-purpose foundation model for computational pathology[J]. Nat Med,2024,30(3): 850-862.
[32]
Lu MY,Chen B,Williamson DFK,et al. A visual-language foundation model for computational pathology[J]. Nat Med,2024,30(3): 863-874.
[33]
Xu H,Usuyama N,Bagga J,et al. A whole-slide foundation model for digital pathology from real-world data[J]. Nature,2024,630(8015): 181-188.
[34]
Acosta JN,Falcone GJ,Rajpurkar P,et al. Multimodal biomedical AI[J]. Nat Med,2022,28(9): 1773-1784.
[35]
Zhou HY,Yu Y,Wang C,et al. A transformer-based representationlearning model with unified processing of multimodal input for clinical diagnostics[J]. Nat Biomed Eng,2023,7(6): 743-755.
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