[18F]-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) is a well-established modality in the work-up of patients with suspected or confirmed diagnosis of lung cancer. Recent research efforts have focused on extracting theragnostic and textural information from manually indicated lung lesions. Both semi-automatic and fully automatic use of artificial intelligence (AI) to localise and classify FDG-avid foci has been demonstrated. To fully harness AI’s usefulness, we have developed a method which both automatically detects abnormal lung lesions and calculates the total lesion glycolysis (TLG) on FDG PET-CT. One hundred twelve patients (59 females and 53 males) who underwent FDG PET-CT due to suspected or for the management of known lung cancer were studied retrospectively. These patients were divided into a training group (59%; n = 66), a validation group (20.5%; n = 23) and a test group (20.5%; n = 23). A nuclear medicine physician manually segmented abnormal lung lesions with increased FDG-uptake in all PET-CT studies. The AI-based method was trained to segment the lesions based on the manual segmentations. TLG was then calculated from manual and AI-based measurements, respectively and analysed with Bland-Altman plots. The AI-tool’s performance in detecting lesions had a sensitivity of 90%. One small lesion was missed in two patients, respectively, where both had a larger lesion which was correctly detected. The positive and negative predictive values were 88% and 100%, respectively. The correlation between manual and AI TLG measurements was strong (R2 = 0.74). Bias was 42 g and 95% limits of agreement ranged from − 736 to 819 g. Agreement was particularly high in smaller lesions. The AI-based method is suitable for the detection of lung lesions and automatic calculation of TLG in small- to medium-sized tumours. In a clinical setting, it will have an added value due to its capability to sort out negative examinations resulting in prioritised and focused care on patients with potentially malignant lesions.

中文翻译：

---

基于AI的肺癌患者[ ¹⁸ F] FDG PET-CT肺部病变检测

[18F]-氟脱氧葡萄糖（FDG）正电子发射断层扫描与计算机断层扫描（PET-CT）是一种在对疑似或确诊为肺癌的患者进行检查时公认的方法。最近的研究工作集中在从人工指示的肺部病变中提取鼻窦和纹理信息。半自动和全自动使用人工智能（AI）对FDG-avid病灶进行定位和分类都得到了证明。为了充分利用AI的有用性，我们开发了一种方法，该方法可以自动检测异常肺部病变并计算FDG PET-CT上的总病变糖酵解（TLG）。回顾性研究了112例因怀疑或已知肺癌的治疗而接受FDG PET-CT检查的患者（59例女性和53例男性）。这些患者分为训练组（59％； n = 66），验证组（20.5％； n = 23）和测试组（20.5％； n = 23）。在所有PET-CT研究中，一名核医学医生手动分割了异常肺部病变，增加了FDG的摄取。训练了基于AI的方法，以基于手动分割对病变进行分割。然后分别从手动和基于AI的测量中计算出TLG，并使用Bland-Altman图进行分析。AI工具在检测病变方面的性能具有90％的灵敏度。分别有两名患者错过了一个小病灶，两个病灶都被正确地发现了较大的病灶。阳性和阴性预测值分别为88％和100％。手动和AI TLG测量之间的相关性很强（R2 = 0.74）。偏差为42 g，一致度的95％范围为-736至819 g。在较小的病灶中一致性特别高。基于AI的方法适用于中小型肿瘤的肺部病变检测和TLG自动计算。在临床环境中，由于其具有对阴性检查进行分类的能力，从而可以对具有潜在恶性病变的患者进行优先且集中的护理，因此具有附加价值。