A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.
A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.
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DOI：
10.1038/s41598-021-87644-7
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发表时间：
2021-04-14
影响因子：
4.6
通讯作者：
Tsuneki M
中科院分区：
文献类型：
--
作者：
Kanavati F;Toyokawa G;Momosaki S;Takeoka H;Okamoto M;Yamazaki K;Takeo S;Iizuka O;Tsuneki M
肺癌的主要组织学类型，如腺癌（ADC）、鳞状细胞癌（SCC）和小细胞肺癌（SCLC）之间的区分对于确定最佳的癌症治疗至关重要。苏木精和伊红（H&E）染色的小支气管肺活检（TBLB）切片是诊断的主要来源之一；然而，对于病理学家来说，仅从h&e染色的切片来诊断一小部分病例是一个挑战，这些病例要么需要进一步的免疫组织化学反应，要么推迟到手术切除才能确诊。我们训练了一个深度学习模型，使用579个wsi的训练集将h&e染色的TBLB标本全切片图像分为ADC、SCC、SCLC和非肿瘤性。训练后的模型能够对83个具有挑战性的不确定情况的独立测试集进行分类，接收算子曲线下面积（AUC）为0.99。我们在四个独立的测试集（一个TBLB和三个外科，总共2407个wsis）上进一步评估了该模型，显示出非常有希望的结果，auc范围为0.94至0.99。
The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLC) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis; however, a subset of cases present a challenge for pathologists to diagnose from H&E-stained slides alone, and these either require further immunohistochemistry or are deferred to surgical resection for definitive diagnosis. We trained a deep learning model to classify H&E-stained Whole Slide Images of TBLB specimens into ADC, SCC, SCLC, and non-neoplastic using a training set of 579 WSIs. The trained model was capable of classifying an independent test set of 83 challenging indeterminate cases with a receiver operator curve area under the curve (AUC) of 0.99. We further evaluated the model on four independent test sets—one TBLB and three surgical, with combined total of 2407 WSIs—demonstrating highly promising results with AUCs ranging from 0.94 to 0.99.
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实现癌症基因组数据的共同愿景。
DOI：
10.1056/nejmp1607591
发表时间：
2016-09-22
期刊：
The New England journal of medicine
影响因子：
--
作者：
Grossman RL;Heath AP;Ferretti V;Varmus HE;Lowy DR;Kibbe WA;Staudt LM
通讯作者：
Staudt LM
影响因子：
10.6
作者：
Gurda GT;Zhang L;Wang Y;Chen L;Geddes S;Cho WC;Askin F;Gabrielson E;Li QK
通讯作者：
Li QK
影响因子：
4.6
作者：
Kanavati, Fahdi;Toyokawa, Gouji;Tsuneki, Masayuki
通讯作者：
Tsuneki, Masayuki
影响因子：
2.5
作者：
Kyritsis, Ioannis;Krebs, Bettina;Welter, Stefan
通讯作者：
Welter, Stefan
影响因子：
2.9
作者：
Li, Daojuan;Xu, Xiaoli;He, Yutong
通讯作者：
He, Yutong