首页> 外文期刊>Quantitative Imaging in Medicine and Surgery >Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system
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Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system

机译:使用人工智能驱动的计算机辅助诊断系统评估肺癌,转移和良性病变的预测准确性

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Background: Artificial intelligence (AI) products have been widely used for the clinical detection of primary lung tumors. However, their performance and accuracy in risk prediction for metastases or benign lesions remain underexplored. This study evaluated the accuracy of an AI-driven commercial computer-aided detection (CAD) product (InferRead CT Lung Research, ICLR) in malignancy risk prediction using a real-world database. Methods: This retrospective study assessed 486 consecutive resected lung lesions, including 320 adenocarcinomas, 40 other malignancies, 55 metastases, and 71 benign lesions, from September 2015 to November 2018. The malignancy risk probability of each lesion was obtained using the ICLR software based on a 3D convolutional neural network (CNN) with DenseNet architecture as a backbone (without clinical data). Two resident doctors independently graded each lesion using patient clinical history. One doctor (R1) has 3 years of chest radiology experience, and the other doctor (R2) has 3 years of general radiology experience. Cochran’s Q test was used to assess the performances of the AI compared to the radiologists. Results: The accuracy of malignancy-risk prediction using the ICLR for adenocarcinomas, other malignancies, metastases, and benign lesions was 93.4% (299/320), 95.0% (38/40), 50.9% (28/55), and 40.8% (29/71), respectively. The accuracy was significantly higher in adenocarcinomas and other malignancies compared to metastases and benign lesions (all P0.05). The overall accuracy of risk prediction for R1 was 93.6% (455/486) and 87.4% for R2 (425/486), both of which were higher than the 81.1% accuracy obtained with the ICLR (394/486) (R1 vs. ICLR: P0.001; R2 vs. ICLR: P=0.001), especially in assessing the risk of metastases (P0.05). R1 performed better than R2 at risk prediction (P=0.001). Conclusions: The accuracy of the ICLR for risk prediction is very high for primary lung cancers but poor for metastases and benign lesions.
机译:背景:人工智能(AI)产品已广泛用于原发性肺肿瘤的临床检测。然而,它们对转移或良性病变的风险预测的性能和准确性仍然是缺乏缺陷的。本研究评估了使用现实世界数据库的恶性风险预测中AI驱动的商业计算机辅助检测(CAD)产品(CAD)产品(ICLRR)的准确性。方法:该回顾性研究评估了486个连续切除的肺病灶,包括320名腺癌,40个其他恶性肿瘤,55例转移率和71个良性病变,从2015年9月到2018年11月。使用基于ICLR软件获得每个病变的恶性风险概率具有DenSenet架构的3D卷积神经网络(CNN)作为骨干(没有临床数据)。两位居民医生使用患者的临床历史独立分级每个病变。一位医生(R1)有3年的胸部放射体验,其他医生(R2)有3年的一般放射体验。 Cochran的Q试验用于评估与放射科学家相比AI的性能。结果:利用ICLR进行腺癌,其他恶性肿瘤,转移和良性病变的恶性风险预测的准确性为93.4%(299/320),95.0%(38/40),50.9%(28/55)和40.8 %(29/71)分别。与转移和良性病变相比,腺癌和其他恶性肿瘤的准确性显着高得多(所有P <0.05)。 R1的风险预测的总体准确性为93.6%(455/486)和R2(425/486)的87.4%,其两者均高于ICLR(394/486)获得的81.1%的精度(R1与ICLR:P <0.001; R2与ICLR:P = 0.001),特别是在评估转移的风险(P <0.05)。 R1在风险预测下比R2更好(P = 0.001)。结论:对原发性肺癌的风险预测ICLR的准确性非常高,但转移和良性病变差。

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