An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.
An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.
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DOI：
10.1016/j.compmedimag.2021.101894
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发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Wong STC
中科院分区：
文献类型：
--
作者：
He T;Fong JN;Moore LW;Ezeana CF;Victor D;Divatia M;Vasquez M;Ghobrial RM;Wong STC
肝移植（LT）是治疗肝细胞癌（HCC）的有效方法，HCC是最常见的原发性肝癌类型。由于基于肿瘤大小的临床分配政策，小肝癌（<5 cm）患者优先于其他患者进行移植。试图从仅在小HCC患者中实现成功移植和较长无病生存期的流行范式转变为将移植选择扩展到最高肿瘤负荷（> 5cm）的HCC患者，我们开发了一种融合的人工智能（AI），一种将短暂临床数据与定量组织学和放射学特征相结合的模型，用于更客观地评估肝移植的风险对于肝癌患者来说。在2008-2019年期间接受肝细胞癌LT的患者有资格纳入分析。所有LT后复发的患者都被纳入，没有复发的患者被随机选择纳入深度学习模型。移植前和移植后的磁共振成像（MRI）扫描和报告分别使用CapsNet网络和自然语言处理进行压缩，作为多特征径向基函数网络的输入。我们应用组织学图像分析算法从复发患者的外植体组织中检测感兴趣的病理区域。多层感知器被设计为前馈，监督神经网络拓扑结构，与复发风险的最终评估。我们使用曲线下面积（AUC）和F-1评分来评估不同网络组合的可预测性。共有109名患者被纳入（训练组87名，测试组22名），其中20名癌症复发呈阳性。七种模型（AUC; F-1评分），包括仅临床特征（0.55; 0.52）、仅磁共振成像（MRI）（0.64; 0.61）、仅病理图像（0.64; 0.61）、MRI+病理（0.68; 0.65）、MRI+临床（0.78; 0.75）、病理+临床（0.77;临床、MRI和病理学特征的组合（0.87; 0.84）。最终的组合模型显示了80%的召回率和89%的准确率。所实现的模型的总准确率为82%。我们验证了结合临床特征和多尺度组织病理学和放射学图像特征的深度学习模型可用于发现肿瘤大小和生物标志物分析之外的复发风险因素。这种预测性的收敛AI模型有可能改变HCC患者的LT分配系统，并将移植治疗选择扩展到肿瘤负荷最高的HCC患者。
Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical allocation policies based on tumor size. Attempting to shift from the prevalent paradigm that successful transplantation and longer disease-free survival can only be achieved in patients with small HCC to expanding the transplantation option to patients with HCC of the highest tumor burden (>5 cm), we developed a convergent artificial intelligence (AI) model that combines transient clinical data with quantitative histologic and radiomic features for more objective risk assessment of liver transplantation for HCC patients. Patients who received a LT for HCC between 2008-2019 were eligible for inclusion in the analysis. All patients with post-LT recurrence were included, and those without recurrence were randomly selected for inclusion in the deep learning model. Pre- and post-transplant magnetic resonance imaging (MRI) scans and reports were compressed using CapsNet networks and natural language processing, respectively, as input for a multiple feature radial basis function network. We applied a histological image analysis algorithm to detect pathologic areas of interest from explant tissue of patients who recurred. The multilayer perceptron was designed as a feed-forward, supervised neural network topology, with the final assessment of recurrence risk. We used area under the curve (AUC) and F-1 score to assess the predictability of different network combinations. A total of 109 patients were included (87 in the training group, 22 in the testing group), of which 20 were positive for cancer recurrence. Seven models (AUC; F-1 score) were generated, including clinical features only (0.55; 0.52), magnetic resonance imaging (MRI) only (0.64; 0.61), pathological images only (0.64; 0.61), MRI plus pathology (0.68; 0.65), MRI plus clinical (0.78, 0.75), pathology plus clinical (0.77; 0.73), and a combination of clinical, MRI, and pathology features (0.87; 0.84). The final combined model showed 80% recall and 89% precision. The total accuracy of the implemented model was 82%. We validated that the deep learning model combining clinical features and multi-scale histopathologic and radiomic image features can be used to discover risk factors for recurrence beyond tumor size and biomarker analysis. Such a predictive, convergent AI model has the potential to alter the LT allocation system for HCC patients and expand the transplantation treatment option to patients with HCC of the highest tumor burden.
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5.2
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Zhao, Haitao
影响因子：
4.6
作者：
Piotrowska Z;Hazar-Rethinam M;Rizzo C;Nadres B;Van Seventer EE;Shahzade HA;Lennes IT;Iafrate AJ;Dias-Santagata D;Leshchiner I;Jessop NA;Hu H;Digumarthy SR;Nagy RJ;Lanman RB;Moody S;Niederst MJ;Engelman JA;Hata AN;Corcoran RB;Sequist LV
通讯作者：
Sequist LV