Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides.
Development and evaluation of a deep neural network for histologic classification of renal cell carcinoma on biopsy and surgical resection slides.
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DOI：
10.1038/s41598-021-86540-4
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发表时间：
2021-03-29
影响因子：
4.6
通讯作者：
Hassanpour S
中科院分区：
文献类型：
--
作者：
Zhu M;Ren B;Richards R;Suriawinata M;Tomita N;Hassanpour S
肾细胞癌(RCC)是成人最常见的肾癌。肾细胞癌的组织病理学分类对患者的诊断、预后和治疗具有重要意义。对于病理学家来说，在显微镜下对活检和手术切除切片上复杂的肾癌组织模式进行重组和分类仍然是一项高度专业化、容易出错和耗时的任务。在这项研究中，我们开发了一个深度神经网络模型，它可以准确地将数字化手术切除切片和活检切片分为五个相关类别：透明细胞RCC、乳头状RCC、嫌色RCC、肾嗜酸细胞瘤和正常。除了整个幻灯片分类流水线外，我们还通过重新处理补丁级别的分类结果来可视化识别出的幻灯片上的指示区域和特征，以确保我们的诊断模型的可解释性。我们在独立的测试集上对我们的模型进行了评估，这些测试集包括来自我们的第三医疗机构的78张手术切除完整切片和79张活检切片，以及来自癌症基因组图谱(TCGA)数据库的917张手术切除切片。内切片、内活检片和外TCGA片的平均曲线下面积(AUC)分别为0.98(95%CI：0.97~1.00)、0.98(95%CI：0.96~1.00)和0.97(95%CI：0.96~0.98)。我们的结果表明，我们的方法在不同的数据源和样本类型上具有很高的泛化能力。更重要的是，我们的模型有可能通过以下方式帮助病理学家：(1)自动预筛选幻灯片以减少假阴性病例，(2)突出数字化幻灯片上的重要区域以加速诊断，以及(3)提供客观和准确的诊断作为第二意见。
Renal cell carcinoma (RCC) is the most common renal cancer in adults. The histopathologic classification of RCC is essential for diagnosis, prognosis, and management of patients. Reorganization and classification of complex histologic patterns of RCC on biopsy and surgical resection slides under a microscope remains a heavily specialized, error-prone, and time-consuming task for pathologists. In this study, we developed a deep neural network model that can accurately classify digitized surgical resection slides and biopsy slides into five related classes: clear cell RCC, papillary RCC, chromophobe RCC, renal oncocytoma, and normal. In addition to the whole-slide classification pipeline, we visualized the identified indicative regions and features on slides for classification by reprocessing patch-level classification results to ensure the explainability of our diagnostic model. We evaluated our model on independent test sets of 78 surgical resection whole slides and 79 biopsy slides from our tertiary medical institution, and 917 surgical resection slides from The Cancer Genome Atlas (TCGA) database. The average area under the curve (AUC) of our classifier on the internal resection slides, internal biopsy slides, and external TCGA slides is 0.98 (95% confidence interval (CI): 0.97–1.00), 0.98 (95% CI: 0.96–1.00) and 0.97 (95% CI: 0.96–0.98), respectively. Our results suggest that the high generalizability of our approach across different data sources and specimen types. More importantly, our model has the potential to assist pathologists by (1) automatically pre-screening slides to reduce false-negative cases, (2) highlighting regions of importance on digitized slides to accelerate diagnosis, and (3) providing objective and accurate diagnosis as the second opinion.
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影响因子：
3.7
作者：
Araújo T;Aresta G;Castro E;Rouco J;Aguiar P;Eloy C;Polónia A;Campilho A
通讯作者：
Campilho A
影响因子：
--
作者：
Muglia VF;Prando A
通讯作者：
Prando A
DOI：
10.1016/j.patol.2019.02.004
发表时间：
2019-10-01
期刊：
Revista espanola de patologia : publicacion oficial de la Sociedad Espanola de Anatomia Patologica y de la Sociedad Espanola de Citologia
影响因子：
--
作者：
Gutierrez Olivares, Victor Manuel;Gonzalez Torres, Luz Mery;Niebles De la Hoz, Maria Camila
通讯作者：
Niebles De la Hoz, Maria Camila
影响因子：
4
作者：
Hua KL;Hsu CH;Hidayati SC;Cheng WH;Chen YJ
通讯作者：
Chen YJ
DOI：
10.1038/nrdp.2017.9
发表时间：
2017-03-09
期刊：
Nature reviews. Disease primers
影响因子：
--
作者：
Hsieh JJ;Purdue MP;Signoretti S;Swanton C;Albiges L;Schmidinger M;Heng DY;Larkin J;Ficarra V
通讯作者：
Ficarra V