Benfares Anass, Mourabiti Abdelali Yahya, Alami Badreddine, Boukansa Sara, El Bouardi Nizar, Lamrani Moulay Youssef Alaoui, El Fatimi Hind, Amara Bouchra, Serraj Mounia, Mohammed Smahi, Abdeljabbar Cherkaoui, Anass El Affar, Qjidaa Mamoun, Maaroufi Mustapha, Mohammed Ouazzani Jamil, Hassan Qjidaa
Laboratory of Computer, Signals, Automation and Cognitivism, Dhar El Mehraz Faculty of Sciences, Sidi Mohammed Ben Abdellah University, Fez, Morocco.
Radiology Department of University Hospital Center Hassan II Fez, Sidi Mohammed Ben Abdellah University, Fez, Morocco.
Eur J Radiol Open. 2024 Sep 21;13:100601. doi: 10.1016/j.ejro.2024.100601. eCollection 2024 Dec.
To propose an intelligent, non-invasive, highly precise, and rapid method to predict the mutation status of the Epidermal Growth Factor Receptor (EGFR) to accelerate treatment with Tyrosine Kinase Inhibitor (TKI) for patients with untreated adenocarcinoma Non-Small Cell Lung Cancer.
Real-world data from 521 patients with adenocarcinoma NSCLC who performed a CT scan and underwent surgery or pathological biopsy to determine EGFR gene mutation between January 2021 and July 2022, is collected. Solutions to the problems that prevent the model from achieving very high precision, namely: human errors made during the annotation of the database and the low precision of the output decision of the model, are proposed. Thus, among the 521 analyzed cases, only 40 were selected as patients with EGFR gene mutation and 98 cases with wild-type EGFR.
The proposed model is trained, validated, and tested on 12,040 2D images extracted from the 138 CT scans images where patients were randomly partitioned into training (80 %) and test (20 %) sets. The performance obtained for EGFR gene mutation prediction was 95.22 % for accuracy, 960.2 for F1_score, 95.89 % for precision, 96.92 % for sensitivity, 94.01 % for Cohen kappa, and 98 % for AUC.
An EGFR gene mutation status prediction method, with high-performance thanks to an intelligent prediction model entrained by highly accurate annotated data is proposed. The outcome of this project will facilitate rapid decision-making when applying a TKI as an initial treatment.
提出一种智能、非侵入性、高精度且快速的方法来预测表皮生长因子受体（EGFR）的突变状态，以加速未治疗的非小细胞肺癌腺癌患者的酪氨酸激酶抑制剂（TKI）治疗。
收集了2021年1月至2022年7月期间521例非小细胞肺癌腺癌患者的真实世界数据，这些患者进行了CT扫描并接受了手术或病理活检以确定EGFR基因突变。针对阻碍模型实现非常高精度的问题，即数据库标注过程中人为错误以及模型输出决策精度低，提出了解决方案。因此，在521例分析病例中，仅选择40例作为EGFR基因突变患者和98例EGFR野生型患者。
所提出的模型在从138例CT扫描图像中提取的12,040张二维图像上进行训练、验证和测试，患者被随机分为训练集（80%）和测试集（20%）。EGFR基因突变预测的性能为：准确率95.22%，F1_score为960.2，精确率95.89%，灵敏度96.92%，Cohen kappa为94.01%，AUC为98%。
提出了一种EGFR基因突变状态预测方法，由于由高度准确标注数据训练的智能预测模型，该方法具有高性能。该项目的结果将有助于在将TKI作为初始治疗应用时快速决策。
