EGFR-TKI-sensitive mutations in lung carcinomas: are they related to clinical features and CT findings?

The efficacy of almonertinib and anlotinib combination therapy for advanced non-small-cell lung cancer patients who continued to experience cancer progression during third-generation EGFR-TKI treatment: a retrospective study

BACKGROUND

Epidermal growth factor receptor (EGFR) mutations are key drivers in a significant portion of non-small-cell lung cancer (NSCLC) patients. While third-generation EGFR-tyrosine kinase inhibitors (TKIs) such as osimertinib have demonstrated efficacy, the management of patients who continue to experience disease progression during treatment remains challenging. The emergence of drug resistance, including the development of secondary mutations, necessitates exploration of alternative treatment strategies. This study aims to evaluate and observe the efficacy and safety of almonertinib combined with anlotinib in patients after cancer progression during third-generation EGFR-TKI therapy.

METHODS

In this retrospective analysis, we included EGFR-mutated NSCLC patients who were resistant to third-generation EGFR-TKIs. All patients were treated with almonertinib combined with anlotinib. The clinical characteristics, treatment history, clinical benefits, and adverse events of these patients were retrospectively collected.

RESULTS

A total of 16 eligible patients were included in the analysis. The results revealed that combination therapy with almonertinib and anlotinib was effective in this patient cohort. The overall response rate was 25% and the disease control rate was 93.75%. The 6 and 12 months of PFS rates were 92.9% (95% confidence interval [CI] 80.3%, 100.0%) and 84.4% (95% CI 66.6%, 100.0%), respectively. Moreover, this combination therapy was generally well-tolerated, with manageable adverse events.

CONCLUSION

Our retrospective analysis suggests that almonertinib and anlotinib combination therapy may represent a viable option for EGFR-mutated NSCLC patients who have progressed on third-generation EGFR-TKIs, especially for those with posterior lines and no standard treatment options. Further investigation and larger clinical trials are warranted to validate these observations and refine treatment guidelines.

Clinical and Radiological Characteristics to Differentiate Between EGFR Exon 21 and Exon 19 Mutations in Patients With Lung Adenocarcinoma: A Systematic Literature Review and Meta-Analysis

Epidermal Growth Factor Receptor (EGFR) mutations in lung adenocarcinoma have been previously associated with specific clinical characteristics and Computed Tomography (CT) patterns. However, associations among individual EGFR mutations have not been evaluated. We aim to differentiate if the most common EGFR mutations (exon 21 and 19) are related to specific clinical characteristics or CT patterns. A systematic review and meta-analysis of 5 databases were conducted with literature from January 2002 to July 2021. Eligible studies were of an experimental or observational design that included lung adenocarcinoma patients with confirmed EGFR exon mutations (21 and 19) and associated clinical characteristics and CT imaging patterns. Quality was assessed using the QUADAS-2 tool. The association between clinical and CT patterns and EGFR exon mutations 21 and 19 was evaluated using odds ratios (OR) and then pooled and analyzed with a fixed or random-effects model. This study follows the preferred reporting items for systematic review and meta-analysis (PRISMA) guidelines. A total of 12 retrospective diagnostic accuracy studies were included. Pooled analysis showed that characteristics such as absence of smoking status (OR 1.29 [95% CI 0.97 - 1.70]), and female sex (OR 1.23 [95% CI 0.83 - 1.82]); and CT patterns such as Ground Glass Opacities (GGO) (OR 1.03 [95% CI 0.78 -1.34]), air bronchogram (OR 0.78 [95% CI 0.44 -1.39]), pleural retraction (OR 0.83 [95% CI 0.53 - 1.28]), and spiculation (OR 0.80 [95% CI 0.48 - 1.31]) were not significantly associated to a specific mutation. Specific EGFR exon 21 and 19 mutations cannot be differentiated through characteristics (absence of smoking status and female sex) or radiological patterns (GGO, air bronchogram, pleural retraction, and speculation). There is limited data to assess if early disease stage or vascular convergence aids in differentiating exon 21 from 19 mutations in patients with lung adenocarcinoma.

Clinical and CT patterns to predict EGFR mutation in patients with non-small cell lung cancer: A systematic literature review and meta-analysis

Purpose

This study aims to determine if the presence of specific clinical and computed tomography (CT) patterns are associated with epidermal growth factor receptor (EGFR) mutation in patients with non-small cell lung cancer.

Methods

A systematic literature review and meta-analysis was carried out in 6 databases between January 2002 and July 2021. The relationship between clinical and CT patterns to detect EGFR mutation was measured and pooled using odds ratios (OR). These results were used to build several mathematical models to predict EGFR mutation.

Results

34 retrospective diagnostic accuracy studies met the inclusion and exclusion criteria. The results showed that ground-glass opacities (GGO) have an OR of 1.86 (95%CI 1.34 −2.57), air bronchogram OR 1.60 (95%CI 1.38 – 1.85), vascular convergence OR 1.39 (95%CI 1.12 – 1.74), pleural retraction OR 1.99 (95%CI 1.72 – 2.31), spiculation OR 1.42 (95%CI 1.19 – 1.70), cavitation OR 0.70 (95%CI 0.57 – 0.86), early disease stage OR 1.58 (95%CI 1.14 – 2.18), non-smoker status OR 2.79 (95%CI 2.34 – 3.31), female gender OR 2.33 (95%CI 1.97 – 2.75). A mathematical model was built, including all clinical and CT patterns assessed, showing an area under the curve (AUC) of 0.81.

Conclusions

GGO, air bronchogram, vascular convergence, pleural retraction, spiculated margins, early disease stage, female gender, and non-smoking status are significant risk factors for EGFR mutation. At the same time, cavitation is a protective factor for EGFR mutation. The mathematical model built acts as a good predictor for EGFR mutation in patients with lung adenocarcinoma.

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GGO, air bronchogram, vascular convergence, pleural retraction, and spiculated margins, are risk factors for EGFR mutation.

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Early disease stage, female gender and non-smoking status are risk factors for EGFR mutation.

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Cavitation is a protective factor for EGFR mutation.

Development and Validation of a Concise Prediction Scoring System for Asian Lung Cancer Patients with EGFR Mutation Before Treatment

Purpose We aimed to determine the epidermal growth factor receptor (EGFR) genetic profile of lung cancer in Asians, and develop and validate a non-invasive prediction scoring system for EGFR mutation before treatment. Methods This was a single-center retrospective cohort study using data of patients with lung cancer who underwent EGFR detection (n = 1450) from December 2014 to October 2020. Independent predictors were filtered using univariate and multivariate logistic regression analyses. According to the weight of each factor, a prediction scoring system for EGFR mutation was constructed. The model was internally validated using bootstrapping techniques and temporally validated using prospectively collected data (n = 210) between November 2020 and June 2021.Results In 1450 patients with lung cancer, 723 single mutations and 51 compound mutations were observed in EGFR. Thirty-nine cases had two or more synchronous gene mutations. We developed a scoring system according to the independent clinical predictors and stratified patients into risk groups according to their scores: low-risk (score <4), moderate-risk (score 4-8), and high-risk (score >8) groups. The C-statistics of the scoring system model was 0.754 (95% CI 0.729-0.778). The factors in the validation group were introduced into the prediction model to test the predictive power of the model. The results showed that the C-statistics was 0.710 (95% CI 0.638-0.782). The Hosmer–Lemeshow goodness-of-fit showed that χ² = 6.733, P = 0.566. Conclusions The scoring system constructed in our study may be a non-invasive tool to initially predict the EGFR mutation status for those who are not available for gene detection in clinical practice.

Machine learning-based radiomics strategy for prediction of cell proliferation in non-small cell lung cancer

PURPOSE

To explore the feasibility and performance of machine learning-based radiomics classifier to predict the cell proliferation(Ki-67)in non-small cell lung cancer (NSCLC).

METHODS

245 histopathological confirmed NSCLC patients who underwent CT scans were retrospectively included. The Ki-67 proliferation index (Ki-67 PI) were measured within 2 weeks after CT scans. A lesion volume of interest (VOI) was manually delineated and radiomics features were extracted by MaZda software from CT images. A random forest feature selection algorithm (RFFS) was used to reduce features. Six kinds of machine learning methods were used to establish radiomics classifiers, subjective imaging feature classifiers and combined classifiers, respectively. The performance of these classifiers was evaluated by the receiver operating characteristic curve (ROC) and compared with Delong test.

RESULTS

103 radiomics features were extracted and 20 optimal features were selected using RFFS. Among the radiomics classifiers established by six machine learning methods, random forest-based radiomics classifier achieved the best performance (AUC = 0.776) in predicting the Ki-67 expression level with sensitivity and specificity of 0.726 and 0.661, which was better than that of subjective imaging classifiers (AUC = 0.625, P < 0.05). However, the combined classifiers did not improve the predictive performance (AUC = 0.780, P > 0.05), with sensitivity and specificity of 0.752 and 0.633.

CONCLUSIONS

The machine learning-based CT radiomics classifier in NSCLC can facilitate the prediction of the expression level of Ki-67 and provide a novel non-invasive strategy for assessing the cell proliferation.