目的:尽管肺癌筛查试验表明,与胸部X线摄影相比,计算机断层扫描可降低死亡率,两者被广泛视为不同类型的临床实践。人工智能可以通过在胸片中检测肺部肿瘤来改善预后。目前,人工智能被用来帮助医生解释放射图,但是随着未来人工智能的发展,它可能会成为一种替代医生的方式。因此,在这项研究中,我们调查了人工智能诊断肺癌的现状。
方法:总共,我们招募了174例连续的恶性肺肿瘤患者,这些患者在手术前接受了人工智能检查的胸部X线摄影术后接受了手术.使用医学图像分析软件EIRLX射线肺结节1.12版(LPIXELInc.,东京,日本)。
结果:人工智能确定了90例肺部肿瘤(所有患者为51.7%,不包括18例原位腺癌患者为57.7%)。在组织学类型之间,人工智能的检出率没有显着差异。所有18例原位腺癌均未被人工智能或医生检测到。在单变量分析中,人工智能可以检测具有较大组织病理学肿瘤大小的病例(p<0.0001),较大的组织病理学侵袭大小(p<0.0001),正电子发射断层扫描-计算机断层扫描的最大标准化摄取值更高(p<0.0001)。在多变量分析中,在具有较大组织病理学侵袭性大小的病例中,AI检测显著较高(p=0.006).在156例不包括原位腺癌的病例中,我们检查了基于肿瘤部位的人工智能检测率。与上肺野区域的肿瘤相比,下肺野区域的肿瘤检测频率较低(p=0.019),而中肺野区域的肿瘤检测频率更高(p=0.014)。
结论:我们的研究表明,使用人工智能,肿瘤相关发现的诊断和与解剖结构重叠的区域的诊断并不令人满意.虽然目前人工智能诊断的地位是帮助医生做出诊断,未来人工智能有可能替代人类。然而,人工智能应该在未来作为一种增强,帮助医生在工作流程中扮演放射科医生的角色。
OBJECTIVE: Although lung cancer screening trials have showed the efficacy of computed tomography to decrease mortality compared with chest radiography, the two are widely taken as different kinds of clinical practices. Artificial intelligence can improve outcomes by detecting lung tumors in chest radiographs. Currently, artificial intelligence is used as an aid for physicians to interpret radiograms, but with the future evolution of artificial intelligence, it may become a modality that replaces physicians. Therefore, in this study, we investigated the current situation of lung cancer diagnosis by artificial intelligence.
METHODS: In total, we recruited 174 consecutive patients with malignant pulmonary tumors who underwent surgery after chest radiography that was checked by artificial intelligence before surgery. Artificial intelligence diagnoses were performed using the medical image analysis software EIRL X-ray Lung Nodule version 1.12, (LPIXEL Inc., Tokyo, Japan).
RESULTS: The artificial intelligence determined pulmonary tumors in 90 cases (51.7% for all patients and 57.7% excluding 18 patients with adenocarcinoma in situ). There was no significant difference in the detection rate by the artificial intelligence among histological types. All eighteen cases of adenocarcinoma in situ were not detected by either the artificial intelligence or the physicians. In a univariate analysis, the artificial intelligence could detect cases with larger histopathological tumor size (p < 0.0001), larger histopathological invasion size (p < 0.0001), and higher maximum standardized uptake values of positron emission tomography-computed tomography (p < 0.0001). In a multivariate analysis, detection by AI was significantly higher in cases with a large histopathological invasive size (p = 0.006). In 156 cases excluding adenocarcinoma in situ, we examined the rate of artificial intelligence detection based on the tumor site. Tumors in the lower lung field area were less frequently detected (p = 0.019) and tumors in the middle lung field area were more frequently detected (p = 0.014) compared with tumors in the upper lung field area.
CONCLUSIONS: Our study showed that using artificial intelligence, the diagnosis of tumor-associated findings and the diagnosis of areas that overlap with anatomical structures is not satisfactory. While the current standing of artificial intelligence diagnostics is to assist physicians in making diagnoses, there is the possibility that artificial intelligence can substitute for humans in the future. However, artificial intelligence should be used in the future as an enhancement, to aid physicians in the role of a radiologist in the workflow.