Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading.
Identification of areas of grading difficulties in prostate cancer and comparison with artificial intelligence assisted grading.
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DOI：
10.1007/s00428-020-02858-w
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发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Eklund M
中科院分区：
文献类型：
--
作者：
Egevad L;Swanberg D;Delahunt B;Ström P;Kartasalo K;Olsson H;Berney DM;Bostwick DG;Evans AJ;Humphrey PA;Iczkowski KA;Kench JG;Kristiansen G;Leite KRM;McKenney JK;Oxley J;Pan CC;Samaratunga H;Srigley JR;Takahashi H;Tsuzuki T;van der Kwast T;Varma M;Zhou M;Clements M;Eklund M
国际泌尿病理学会(ISUP)拥有一个由专家监督的参考图像数据库，目的是建立前列腺癌分级的国际标准。在这里，我们的目标是找出评分困难的领域，并将结果与经过评分训练的人工智能系统获得的结果进行比较。在一系列87例癌症的针吸活组织检查中，专家未能在41.4%(36/87)中达成三分之二的共识。在共识案例和非共识案例中，加权kappa分别为0.77(0.68~0.84)和0.50(0.40~0.57)。在未达成共识的病例中，确定了四个主要原因：Gleason评分3 + 3与Gleason评分3 + 4腺体形成不良或融合(13例)的区别，Gleason评分3 + 4与4 + 3(7例)，Gleason评分4 + 3与4 + 4(8例)，以及Gleason模式5的一小部分(6例)。人工智能系统在非共识案例中的加权kappa值为0.53，使其成为24个案例中重复性第六好的观察员。人工智能可以作为决策支持，并通过其做出一致决策的能力来减少观察者之间的可变性。对这些癌症模式进行分级，最好地预测结果并指导治疗，这需要进一步的临床和遗传学研究。这类调查的结果应用于改进人工智能系统的校准。
The International Society of Urological Pathology (ISUP) hosts a reference image database supervised by experts with the purpose of establishing an international standard in prostate cancer grading. Here, we aimed to identify areas of grading difficulties and compare the results with those obtained from an artificial intelligence system trained in grading. In a series of 87 needle biopsies of cancers selected to include problematic cases, experts failed to reach a 2/3 consensus in 41.4% (36/87). Among consensus and non-consensus cases, the weighted kappa was 0.77 (range 0.68–0.84) and 0.50 (range 0.40–0.57), respectively. Among the non-consensus cases, four main causes of disagreement were identified: the distinction between Gleason score 3 + 3 with tangential cutting artifacts vs. Gleason score 3 + 4 with poorly formed or fused glands (13 cases), Gleason score 3 + 4 vs. 4 + 3 (7 cases), Gleason score 4 + 3 vs. 4 + 4 (8 cases) and the identification of a small component of Gleason pattern 5 (6 cases). The AI system obtained a weighted kappa value of 0.53 among the non-consensus cases, placing it as the observer with the sixth best reproducibility out of a total of 24. AI may serve as a decision support and decrease inter-observer variability by its ability to make consistent decisions. The grading of these cancer patterns that best predicts outcome and guides treatment warrants further clinical and genetic studies. Results of such investigations should be used to improve calibration of AI systems.
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影响因子：
6.4
作者：
Egevad, Lars;Delahunt, Brett;Clements, Mark
通讯作者：
Clements, Mark
影响因子：
6.4
作者：
Egevad, Lars;Cheville, John;Delahunt, Brett
通讯作者：
Delahunt, Brett
影响因子：
1.9
作者：
OCONNELL, DL;DOBSON, AJ
通讯作者：
DOBSON, AJ
影响因子：
5.6
作者：
Zhou, Ming;Li, Jianbo;Shah, Rajal B.
通讯作者：
Shah, Rajal B.
影响因子：
4.5
作者：
Grogan, Judith;Gupta, Ruta;Kench, James G.
通讯作者：
Kench, James G.