Multiomics assessment of lung adenocarcinoma subtypes defined through tumor purity-adjusted DNA methylation.

Objective: To analyze the clinical features and prognostic factors of different histological subtypes of lung adenocarcinoma. Methods: Data from 370 lung adenocarcinoma patients who underwent surgical resection for pathologically supported adenocarcinoma in our hospital between 2000 and 2003 were retro- spectively reviewed. The Kaplan-Meier method was used to estimate patient survival, and Cox’s proportional hazards model was performed for multivariate analysis. Results: The 5-year overall survival rate was 25.26%, and the mean survival time was 3.89 years. In multivariate analysis, histological subtype, incised margin residual, TNM stage, tumor size, and adjuvant chemotherapy were identified as independent survival predictors. The 5-year survival rate in bronchioloalveolar adenocarcinoma (BAC) patients was 41.30%, higher than in patients with other subtypes of lung adenocarcinoma (P=0.002). No significant difference was found in the prognosis among patients with different subtypes of adenocarcinoma without a BAC component. Conclusion: Ade-nocarcinoma with a BAC component is an independent subtype of lung adenocarcinoma. Its prognosis lies between those of BAC and adenocarcinoma without BAC. Histological subtype, incised margin residual, TNM stage, tumor size, and adjuvant chemotherapy are independent survival predictors.

Background: Gene expression-based subtyping in lung adenocarcinoma (AD) and lung squamous cell carcinoma (SQ) classifies AD and SQ tumors into distinct subtypes with variable expression of underlying biology including DNA damage response genes. These subtypes are linked to differences in chemotherapy sensitivity, and may impact response to therapeutics like PARP inhibitors. Methods: Using The Cancer Genome Atlas (TCGA) lung cancer gene expression datasets (AD n=515 and SQ n=501), AD subtypes (Terminal Respiratory Unit (TRU), Proximal Proliferative (PP), and Proximal Inflammatory (PI)) and SQ subtypes (Primitive, Classical, Secretory, and Basal) were defined using gene expression based centroid predictors. Association between AD and SQ expression subtypes and 3 published BRCAness/PARP inhibitor response signatures developed in ovarian and/or breast cancer (Konstantinopoulos et al., PMID 20547991; Daemen et al., PMID 22875744; McGrail et al., PMID 28649435) was examined using linear regression. Association between subtypes and expression of 15 recognized homologous recombination (HR) related genes (ATM, ATR, BRCA1, BRCA2, BRIP1, CDK12, CHEK1, CHEK2, FANCA, FANCI, FANCD2, MRE11A, RAD51C, RAD51L1, PTEN) was also examined using linear regression, and association tests included adjustment for the 3 BRCAness/PARP inhibitor response signatures and proliferation score. Results: AD and SQ subtypes showed strong association with the 3 BRCAness/PARP inhibitor response signatures (F-test p-values 7.7e-05, 5.9e-13, 9.4e-33 in AD and 1.9e-05, 9.0e-13, 2.7e-19 in SQ). AD and SQ subtypes showed strong association with 15 HR genes (max and median F-test p-values were 8.5e-04 and 7.5e-25 in AD, and 7.3e-04 and 1.4e-12 in SQ). The TRU subtype in AD showed low expression relative to the other AD subtypes for a majority of the HR genes, including BRCA1. In SQ, the same was true for the basal and secretory subtypes. Simultaneous adjustment for the 3 BRCAness/PARP inhibitor response signatures, as well as for proliferation, reduced association strength between subtype and HR gene expression in AD and less so in SQ. In AD, association between subtype and gene expression remained significant for 4 HR genes (using Bonferroni correction for 15 tests), including CHECK2, FANCI, BRIP1, and RAD51L1. In SQ, association between subtype and gene expression remained significant for all HR genes except CHEK1 and FANCA, (median and min Bonferroni-adjusted p-value 2.9e-04 and 2.6e-21). Conclusions: Intrinsic biologic subtypes of lung AD and SQ are associated with published BRCAness/PARP inhibitor response signatures and reveal differential expression of several HR-related genes. Evaluation of these subtypes, particularly in SQ, as potential biomarkers of PARP inhibitor sensitivity should be investigated. Citation Format: Gregory Mayhew, Chuck Perou, D Neil Hayes, Myla Lai-Goldman, Hawazin Faruki. Differences in BRCAness/PARP inhibitor response signatures and homologous recombination gene expression across lung adenocarcinoma and squamous cell carcinoma gene expression subtypes [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2017 Oct 26-30; Philadelphia, PA. Philadelphia (PA): AACR; Mol Cancer Ther 2018;17(1 Suppl):Abstract nr A037.

Purpose: The aims of this study were to combine CT images with Ki-67 expression to distinguish various subtypes of lung adenocarcinoma and to pre-operatively predict the Ki-67 expression level based on CT radiomic features.Methods: Data from 215 patients with 237 pathologically proven lung adenocarcinoma lesions who underwent CT and immunohistochemical Ki-67 from January 2019 to April 2021 were retrospectively analyzed. The receiver operating curve (ROC) identified the Ki-67 cut-off value for differentiating subtypes of lung adenocarcinoma. A chi-square test or t-test analyzed the differences in the CT images between the negative expression group (n = 132) and the positive expression group (n = 105), and then the risk factors affecting the expression level of Ki-67 were evaluated. Patients were randomly divided into a training dataset (n = 165) and a validation dataset (n = 72) in a ratio of 7:3. A total of 1,316 quantitative radiomic features were extracted from the Analysis Kinetics (A.K.) software. Radiomic feature selection and radiomic classifier were generated through a least absolute shrinkage and selection operator (LASSO) regression and logistic regression analysis model. The predictive capacity of the radiomic classifiers for the Ki-67 levels was investigated through the ROC curves in the training and testing groups.Results: The cut-off value of the Ki-67 to distinguish subtypes of lung adenocarcinoma was 5%. A comparison of clinical data and imaging features between the two groups showed that histopathological subtypes and air bronchograms could be used as risk factors to evaluate the expression of Ki-67 in lung adenocarcinoma (p = 0.005, p = 0.045, respectively). Through radiomic feature selection, eight top-class features constructed the radiomic model to pre-operatively predict the expression of Ki-67, and the area under the ROC curves of the training group and the testing group were 0.871 and 0.8, respectively.Conclusion: Ki-67 expression level with a cut-off value of 5% could be used to differentiate non-invasive lung adenocarcinomas from invasive lung adenocarcinomas. It is feasible and reliable to pre-operatively predict the expression level of Ki-67 in lung adenocarcinomas based on CT radiomic features, as a non-invasive biomarker to predict the degree of malignant invasion of lung adenocarcinoma, and to evaluate the prognosis of the tumor.

背景与目的肺癌是国内外致死率最高的恶性肿瘤，肺结节的精确检测是降低肺癌死亡率的关键。人工智能辅助诊断系统在肺结节检测、良恶性鉴别和浸润亚型诊断等领域发展迅速，对其效能进行验证是促进其应用于临床的前提。本研究旨在评估人工智能辅助诊断系统预测肺结节早期肺腺癌浸润亚型的效能。方法回顾性分析2016年1月1日-2021年12月31日期间兰州大学第二医院收治的223例肺结节早期肺腺癌患者的临床资料，将早期肺腺癌分为浸润性腺癌组(n=170)和非浸润性腺癌组(n=53)，其中非浸润性腺癌组又分为微浸润性腺癌组(n=31)和浸润前病变组(n=22)。比较各组的恶性概率和影像特征等信息，分析其对早期肺腺癌浸润亚型的预测能力，并对人工智能辅助诊断早期肺腺癌浸润亚型定性诊断的结果与术后病理进行一致性分析。结果早期肺腺癌不同浸润亚型肺结节的平均CT值(P < 0.001)、直径(P < 0.001)、体积(P < 0.001)、恶性概率(P < 0.001)、胸膜凹陷征(P < 0.001)、分叶征(P < 0.001)、毛刺征(P < 0.001)差异均有统计学意义; 随着早期肺腺癌不同浸润亚型浸润性增加，各组参数显性征象比例也逐渐升高; 在二分类问题上，人工智能辅助诊断系统定性诊断早期肺腺癌浸润亚型的敏感性、特异性及曲线下面积(area under the curve, AUC)分别为81.76%、92.45%和0.871; 在三分类问题上，人工智能辅助诊断系统定性诊断早期肺腺癌浸润亚型的准确率、召回率、F1分数及AUC分别为83.86%、85.03%、76.46%和0.879。结论该人工智能辅助诊断系统对肺结节早期肺腺癌浸润亚型具有一定的预测价值，随着算法的优化和数据的完善或可为患者个体化治疗提供指导。

Objective: Solid and micropapillary pattern are highly invasive histologic subtypes in lung adenocarcinoma and are associated with poor prognosis while the biopsy sample is not enough for the accurate histological diagnosis. This study aims to assess the correlation and predictive efficacy between metabolic parameters in (18)F-fluorodeoxy glucose positron emission tomography/computed tomography ((18)F-FDG PET-CT), including the maximum SUV (SUV(max)), metabolic tumor volume (MTV), total lesion glycolysis (TLG) and solid and micropapillary histological subtypes in lung adenocarcinoma. Methods: A total of 145 resected lung adenocarcinomas were included. The clinical data and preoperative (18)F-FDG PET-CT data were retrospectively analyzed. Mann-Whitney U test was used for the comparison of the metabolic parameters between solid and micropapillary subtype group and other subtypes group. Receiver operating characteristic (ROC) curve and areas under curve (AUC) were used for evaluating the prediction efficacy of metabolic parameters for solid or micropapillary patterns. Univariate and multivariate analyses were conducted to determine the prediction factors of the presence of solid or micropapillary subtypes. Results: Median SUV(max) and TLG in solid and papillary predominant subtypes group (15.07 and 34.98, respectively) were significantly higher than those in other subtypes predominant group (6.03 and 10.16, respectively, P<0.05). ROC curve revealed that SUV(max) and TLG had good efficacy for prediction of solid and micropapillary predominant subtypes [AUC=0.811(95% CI: 0.715~0.907) and 0.725(95% CI: 0.610~0.840), P<0.05]. Median SUV(max) and TLG in lung adenocarcinoma with the solid or micropapillary patterns (11.58 and 22.81, respectively) were significantly higher than those in tumors without solid and micropapillary patterns (4.27 and 6.33, respectively, P<0.05). ROC curve revealed that SUV(max) and TLG had good efficacy for predicting the presence of solid or micropapillary patterns [AUC=0.757(95% CI: 0.679~0.834) and 0.681(95% CI: 0.595~0.768), P<0.005]. Multivariate logistic analysis showed that the clinical stage (Stage Ⅲ-Ⅳ), SUV(max) ≥10.27 and TLG≥7.12 were the independent predictive factors of the presence of solid or micropapillary patterns (P<0.05). Conclusions: Preoperative SUV(max) and TLG of lung adenocarcinoma have good prediction efficacy for the presence of solid or micropapillary patterns, especially for the solid and micropapillary predominant subtypes and are independent factors of the presence of solid or micropapillary patterns.

This study aims to develop a CT-based hybrid deep learning network to predict pathological subtypes of early-stage lung adenocarcinoma by integrating residual network (ResNet) with Vision Transformer (ViT).A total of 1411 pathologically confirmed ground-glass nodules (GGNs) retrospectively collected from two centers were used as internal and external validation sets for model development. 3D ResNet and ViT were applied to investigate two deep learning frameworks to classify three subtypes of lung adenocarcinoma namely invasive adenocarcinoma (IAC), minimally invasive adenocarcinoma and adenocarcinoma in situ, respectively. To further improve the model performance, four Res-TransNet based models were proposed by integrating ResNet and ViT with different ensemble learning strategies. Two classification tasks involving predicting IAC from Non-IAC (Task1) and classifying three subtypes (Task2) were designed and conducted in this study.For Task 1, the optimal Res-TransNet model yielded area under the receiver operating characteristic curve (AUC) values of 0.986 and 0.933 on internal and external validation sets, which were significantly higher than that of ResNet and ViT models (p < 0.05). For Task 2, the optimal fusion model generated the accuracy and weighted F1 score of 68.3% and 66.1% on the external validation set.The experimental results demonstrate that Res-TransNet can significantly increase the classification performance compared with the two basic models and have the potential to assist radiologists in precision diagnosis.

Lung adenocarcinoma is the most prevalent type of lung cancer, with invasive lung adenocarcinoma being the most common subtype. Screening and early treatment of high-risk individuals have improved survival; however, significant differences in prognosis still exist among patients at the same stage, especially in the early stages. Invasive lung adenocarcinoma has different histological morphologies and biological characteristics that can distinguish its prognosis. Notably, several studies have found that the pathological subtypes of invasive lung adenocarcinoma are closely associated with clinical treatment. This review summarised the distribution of various pathological subtypes of invasive lung adenocarcinoma in the population and their relationship with sex, smoking, imaging features, and other histological characteristics. We comprehensively analysed the genetic characteristics and biomarkers of the different pathological subtypes of invasive lung adenocarcinoma. Understanding the interaction between the pathological subtypes of invasive lung adenocarcinoma and the tumour microenvironment helps to reveal new therapeutic targets for lung adenocarcinoma. We also extensively reviewed the prognosis of various pathological subtypes and their effects on selecting surgical methods and adjuvant therapy and explored future treatment strategies.

1552 Background: The frequencies of known driver mutation in lung adenocarcinoma from patients in the United States have been reported by the NCI’s Lung Cancer Mutation Consortium (LCMC), indicating driver mutations were detected in 54% (280/516) of tumors. In this report, mutations found: EGFR 17%, KRAS 22%, HER2 0.6%, PIK3CA 1.2%, BRAF 2%, MET amplification 0.6%, MAP2K1 0.4%, NRAS 0.4%, AKT 0%, ALK rearrangements 7%. However little is known about ethnic difference of driver mutation frequencies and correlations between driver mutations and histological subtypes in lung adenocarcinoma. Methods: Known driver mutations in tumors from 97 Japanese patients with lung adenocarcinoma who underwent surgical resection between 1999 and 2003 in National Cancer Center Hospital East were analyzed by next-generation sequencing and confirmed by Sanger sequencing. Correlations between driver mutations and histological subtypes were also assessed. Results: Driver mutations were detected in 72% of tumors. Mutations found: EGFR 57%, KRAS9%, HER2 2%, PIK3CA 2%, BRAF 1%, MET amplification 1%, MAP2K1 0%, NRAS 0%, AKT 0%. Due to the limitation of rearrangement detection by exon-sequencing, ALK rearrangements were not analyzed. Compared with the report by LCMC, the frequency of EGFR mutations was high and that of KRAS mutations was low in the present study. All mutations were mutually exclusive. The number of predominant histological subtypes of tumors harbored EGFR mutations were papillary 28, acinar 3, solid 5, lepidic 19. That with KRAS mutations showed papillary 2, acinar 2, solid 2, lepidic 3, and HER2 mutations showed papillary 1 and acinar 1. Two tumors harbored PIK3CA mutations showed both histological acinar pattern. Each of BRAF mutation and MET amplification showed lepidic and papillary pattern, respectively. Conclusions: It was suggested that there should be ethnic difference of driver mutation frequencies in lung adenocarcinoma between Asian and non-Asian patients, although the details of ethnic distribution included in LCMC study has not been opened. In addition, each driver mutations did not correspond to specific histological subtypes of lung adenocarcinoma.

Lung adenocarcinoma is classified morphologically into five histological subtypes according to the WHO classification. While each histological subtype correlates with a distinct prognosis, the molecular basis has not been fully elucidated. Here we conducted DNA methylation analysis of 30 lung adenocarcinoma cases annotated with the predominant histological subtypes and three normal lung cases using the Infinium BeadChip. Unsupervised hierarchical clustering analysis revealed three subgroups with different methylation levels: high-, intermediate-, and low-methylation epigenotypes (HME, IME, and LME). Micropapillary pattern (MPP)-predominant cases and those with MPP components were significantly enriched in HME (p = 0.02 and p = 0.03, respectively). HME cases showed a significantly poor prognosis for recurrence-free survival (p < 0.001) and overall survival (p = 0.006). We identified 365 HME marker genes specifically hypermethylated in HME cases with enrichment of "cell morphogenesis" related genes; 305 IME marker genes hypermethylated in HME and IME, but not in LME, with enrichment "embryonic organ morphogenesis"-related genes; 257 Common marker genes hypermethylated commonly in all cancer cases, with enrichment of "regionalization"-related genes. We extracted surrogate markers for each epigenotype and designed pyrosequencing primers for five HME markers (TCERG1L, CXCL12, FAM181B, HOXA11, GAD2), three IME markers (TBX18, ZNF154, NWD2) and three Common markers (SCT, GJD2, BARHL2). DNA methylation profiling using Infinium data was validated by pyrosequencing, and HME cases defined by pyrosequencing results also showed the worse recurrence-free survival. In conclusion, lung adenocarcinomas are stratified into subtypes with distinct DNA methylation levels, and the high-methylation subtype correlated with MPP-predominant cases and those with MPP components and showed a poor prognosis.

背景与目的前期研究表明核因子E2相关因子2（nuclear factor erythroid-2-related factor 2, Nrf2）和Kelch样环氧氯丙烷相关蛋白1（Kelch-like ECH-associated protein 1, Keap1）的表达在肺癌患者中存在个体差异，其与化疗或表皮生长因子受体酪氨酸激酶抑制剂（epidermal growth factor receptor tyrosine kinase inhibitors, EGFR-TKIs）的疗效相关，但Nrf2及Keap1在不同驱动基因肺腺癌患者中的表达情况仍不清楚。本研究旨在探讨Nrf2、Keap1在肺腺癌患者中的表达与EGFR基因突变状态的关系及其对EGFR-TKIs疗效的影响。方法应用免疫组化方法检测104例EGFR结果明确的肺腺癌患者，确定Nrf2、Keap1的表达情况，并分析其临床病理特征。结果104例患者中Nrf2阳性率为71.2%，Keap1高表达率为34.6%；Nrf2阳性率与性别、分期和EGFR突变状态显著相关（P < 0.05），而与年龄、吸烟、分化程度、病理亚型无关（P > 0.05）；Keap1表达水平与年龄、性别、吸烟、病理亚型、肿瘤分化、EGFR突变状态等均无关（P > 0.05）；EGFR-TKIs治疗的患者无进展生存期（progression free survival, PFS）和总生存期（overall survival, OS）与Nrf2表达水平显著相关（P > 0.05），但与Keap1表达水平无关（P < 0.05）。Nrf2高表达组的中位PFS、OS显著低于低表达/阴性组（P < 0.05）。多因素分析表明Nrf2表达水平是EGFR-TKIs PFS和OS的独立预测因素。结论Nrf2阳性率与EGFR基因突变状态显著相关，Nrf2在EGFR突变肺腺癌患者中的表达水平与EGFR TKIs疗效显著相关，因此，Nrf2是预测EGFR TKIs疗效的理想指标和潜在的干预靶点。

The histologic heterogeneity of lung adenocarcinoma is well known. Many histologic subtypes have been described, and recently their prognostic and predictive value has emerged. Laser capture microdissection may aid in the isolation of cancer cells from distinct subtypes of lung adenocarcinoma, thus enabling the description of their specific molecular features. Characterization of epidermal growth factor receptor (EGFR) mutations in histologic subtypes of lung adenocarcinoma has become an important issue. The purpose of this study was to analyze EGFR mutations in exons 18-21 in single histologic subtypes of lung adenocarcinoma after laser capture microdissection. A revision and reclassification of a series of 208 non-small cell lung cancers was conducted, and 62 adenocarcinomas with a total of 119 histologic component subtypes were identified. Laser capture microdissection of each subtype was performed. EGFR mutations in exons 18-21 were detected using polymerase chain reaction single-strand conformation polymorphism and direct DNA sequencing. EGFR mutations were detected only in 3 out of the 62 adenocarcinomas analyzed. Two adenocarcinomas harbored EGFR mutations in exon 19 (the E746-T751 deletion VA insertion and the LREAT deletion) and one adenocarcinoma the EGFR exon 21 L858R missense point mutation. EGFR mutations were observed in all component subtypes. This suggests that, in a patient with lung adenocarcinoma, EGFR mutations are not associated with particular component histologic subtypes and probably occur at an early stage of tumorigenesis. Notably, 2 out of the 3 mutated adenocarcinomas had a bronchioloalveolar component, whereas the third mutated adenocarcinoma had a papillary subtype. Although we detected EGFR mutations only in 3 out of 62 adenocarcinomas and EGFR mutations were present in every subtype of each mutated adenocarcinoma, our research might represent a basis for further studies in characterizing molecular profiles of different component subtypes of lung adenocarcinoma.

SummaryLung cancer is an extremely heterogeneous disease, and its treatment remains one of the most challenging tasks in medicine. Few existing laboratory lung cancer models can faithfully recapitulate the diversity of the disease and predict therapy response. Here, we establish 12 patient-derived organoids from the most common lung cancer subtype, lung adenocarcinoma (LADC). Extensive gene and histopathology profiling show that the tumor organoids retain the histological architectures, genomic landscapes, and gene expression profiles of their parental tumors. Patient-derived lung cancer organoids are amenable for biomarker identification and high-throughput drug screening in vitro. This study should enable the generation of patient-derived lung cancer organoid lines, which can be used to further the understanding of lung cancer pathophysiology and to assess drug response in personalized medicine.

P1.01-38 Clinical Significance According to EGFR Mutation Subtypes in Lung Adenocarcinoma: Korean Multicenter Experience

Background The volume doubling time (VDT) is a key parameter in the differentiation of aggressive tumors from slow-growing tumors. How different histologic subtypes of primary lung adenocarcinomas vary in their VDT and the prognostic value of this measurement is unknown. Purpose To investigate differences in VDT between the predominant histologic subtypes of primary lung adenocarcinomas and to assess the correlation between VDT and prognosis. Materials and Methods This retrospective study included patients who underwent at least two serial CT examinations before undergoing operation between July 2010 and December 2018. Three-dimensional tumor segmentation was performed on two CT images and VDTs were calculated. VDTs were compared between predominant histologic subtypes and lesion types by using Kruskal-Wallis tests. Disease-free survival (DFS) was obtained in patients undergoing surgical procedures before July 2017. Univariable and multivariable Cox proportional hazards regression analyses were performed to determine predictors of DFS. Results Among 268 patients (mean age, 64 years ± 8 [standard deviation]; 143 men), there were 30 lepidic, 87 acinar, 109 papillary, and 42 solid or micropapillary predominant subtypes. The median VDT was 529 days (interquartile range, 278-872 days) for lung adenocarcinomas. VDTs differed across subtypes (P < .001) and were shortest in solid or micropapillary subtypes (229 days; interquartile range, 77-530 days). Solid lesions (VDT, 248 days) had shorter VDTs than subsolid lesions (part-solid lesions, 665 days; nonsolid lesions, 648 days) (P < .001). In the 148 patients (mean age, 64 years ± 8; 89 men) included in the survival analysis, 35 patients had disease recurrence and 17 patients died. VDT (<400 days) was an independent risk factor for poor DFS (hazard ratio, 2.6; P = .01) and higher TNM stage. Adding VDT to TNM stage improved model performance (C-index, 0.69 for TNM stage vs 0.77 for combined VDT class and TNM stage; P = .002). Conclusion Volume doubling times varied significantly according to the predominant histologic subtypes of lung adenocarcinoma and had additional prognostic value for disease-free survival. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Ko in this issue.

To investigate the value of computed tomography (CT)-based radiomics signatures in combination with clinical and CT morphological features to identify epidermal growth factor receptor (EGFR)-mutation subtypes in lung adenocarcinoma (LADC). From February 2012 to October 2019, 608 patients were confirmed with LADC and underwent chest CT scans. Among them, 307 (50.5%) patients had a positive EGFR-mutation and 301 (49.5%) had a negative EGFR-mutation. Of the EGFR-mutant patients, 114 (37.1%) had a 19del -mutation, 155 (50.5%) had a L858R-mutation, and 38 (12.4%) had other rare mutations. Three combined models were generated by incorporating radiomics signatures, clinical, and CT morphological features to predict EGFR-mutation status. Patients were randomly split into training and testing cohorts, 80% and 20%, respectively. Model 1 was used to predict positive and negative EGFR-mutation, model 2 was used to predict 19del and non-19del mutations, and model 3 was used to predict L858R and non-L858R mutations. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate their performance. For the three models, model 1 had AUC values of 0.969 and 0.886 in the training and validation cohorts, respectively. Model 2 had AUC values of 0.999 and 0.847 in the training and validation cohorts, respectively. Model 3 had AUC values of 0.984 and 0.806 in the training and validation cohorts, respectively. Combined models that incorporate radiomics signature, clinical, and CT morphological features may serve as an auxiliary tool to predict EGFR-mutation subtypes and contribute to individualized treatment for patients with LADC.