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Introduction: The clinical outcomes of sequential treatment of advanced epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) patients with first-line bevacizumab combined with 1st/2nd-generation EGFR-TKIs are unclear. Thus, we aimed to analyze the outcomes of these patients. Methods: Between January 2015 and December 2020, data for 102 advanced EGFR-mutated lung adenocarcinoma patients receiving first-line bevacizumab combined with erlotinib or afatinib followed by treatments at multiple institutions were retrospectively analyzed. All patients with progressive disease (PD) after first-line therapy underwent secondary T790M mutation detection. Results: The secondary T790M mutation positive rate of all study patients was 57.9%. First-line erlotinib use and progression-free survival (PFS) after first-line therapy > 12 months were positively associated with the T790M mutation (P <0.05). The response rates (RRs) to second-line treatments were 51.7% and 22.7% for the osimertinib and nonosimertinib groups, respectively (P = 0.001). The median PFS associated with second-line osimertinib and nonosimertinib therapy was 13.7 and 7.1 months, respectively (hazard ratio (HR) = 0.38; 95% confidence interval (CI), 0.23-0.63; P< 0.001). Patients with a secondary T790M mutation receiving second-line osimertinib treatment had a median overall survival (OS) of 54.3 months, and the median OS was 31.9 months for non-T790M-mutated patients receiving second-line nonosimertinib treatments (HR = 0.36; CI: 0.21-0.62, P < 0.001). Conclusion: The majority of acquired resistance to first-line bevacizumab combined with 1st/2nd-generation EGFR-TKIs is associated with the T790M mutation. Sequential osimertinib treatment in patients with positive secondary T790M mutation is associated with better outcomes among these patients. [ABSTRACT FROM AUTHOR]

Objective: This study mainly observes the efficacy and safety of almonertinib plus chemotherapy compared with almonertinib alone in the second-line treatment of advanced non-small cell lung cancer (NSCLC) with mutated epidermal growth factor receptor (EGFR). Methods: In this study, clinical data of 68 patients with advanced NSCLC who were treated in Jiangsu Provincial People's Hospital and Nanjing Chest Hospital between April 2020 and December 2022 were collected. Among them, the study group (n=30) received second-line almonertinib combined with platinum-based chemotherapy, while the control group (n=38) received almonertinib alone. The near-term and long-term effects and adverse events of the two groups were compared respectively. Results: The median follow-up time until 31 December 2022 was 16.3 months (95% CI: 11.32-21.34). Results of chi-square analysis showed no statistically significant difference in objective response rate (ORR) and disease control rate (DCR) between the study group and the control group (56.73% vs. 55.3%, P>0.05; 100% vs. 86.8%, P>0.05). Log-rank test comparing the two groups revealed that the median progression-free survival (mPFS) of the study group was significantly longer than that of the control group by 3.1 months (12.7 vs. 9.6 months, P=0.01). Multivariate COX proportional risk model showed a statistically significant effect of treatment method and PS score on PFS (HR=0.43, P=0.023; HR=3.82, P=0.001). In terms of safety, most of the adverse events (AEs) were mild, with no grade 4-5 in the two groups, and the overall tolerance of patients was good. Conclusion: For advanced NSCLC patients with EGFR mutations, second-line treatment with almonertinib plus chemotherapy significantly improved PFS compared with almonertinib alone without a significant increase in adverse events, providing efficacy and safety. [ABSTRACT FROM AUTHOR]

IntroductionThe clinical outcomes of sequential treatment of advanced epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) patients with first-line bevacizumab combined with 1st/2nd-generation EGFR-TKIs are unclear. Thus, we aimed to analyze the outcomes of these patients.Methods Between January 2015 and December 2020, data for 102 advanced EGFR-mutated lung adenocarcinoma patients receiving first-line bevacizumab combined with erlotinib or afatinib followed by treatments at multiple institutions were retrospectively analyzed. All patients with progressive disease (PD) after first-line therapy underwent secondary T790M mutation detection.Results The secondary T790M mutation positive rate of all study patients was 57.9%. First-line erlotinib use and progression-free survival (PFS) after first-line therapy > 12 months were positively associated with the T790M mutation (P

BackgroundFor stage IV patients harboring EGFR mutations, there is a differential response to the first-line TKI treatment. We constructed three-dimensional convolutional neural networks (CNN) with deep transfer learning to stratify patients into subgroups with different response and progression risks.Materials and MethodsFrom 2013 to 2017, 339 patients with EGFR mutation receiving first-line TKI treatment were included. Progression-free survival (PFS) time and progression patterns were confirmed by routine follow-up and restaging examinations. Patients were divided into two subgroups according to the median PFS ( 9 months). We developed a PFS prediction model and a progression pattern classification model using transfer learning from a pre-trained EGFR mutation classification 3D CNN. Clinical features were fused with the 3D CNN to build the final hybrid prediction model. The performance was quantified using area under receiver operating characteristic curve (AUC), and model performance was compared by AUCs with Delong test.ResultsThe PFS prediction CNN showed an AUC of 0.744 (95% CI, 0.645–0.843) in the independent validation set and the hybrid model of CNNs and clinical features showed an AUC of 0.771 (95% CI, 0.676–0.866), which are significantly better than clinical features-based model (AUC, 0.624, P

The treatment sequence of immunotherapy (IO) and epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is of great importance for the survival of non-small cell lung cancer (NSCLC) patients with EGFR sensitive mutation. Here, we reported an advanced lung adenocarcinoma case concurrent with EGFR sensitive mutation and high PD-L1 expression (>50%) that was administrated with gefitinib firstly, and then became resistant to EGFR-TKI. He received the strategy of immunity-combined chemo-radiotherapy and responded significantly. However, the disease re-progressed after 10 months. Surprisingly, the tumor re-sensitized to gefitinib for 13 months. At final, following the treatment pressure of TKI-IO combination therapy-TKI strategy, tumor clone eventually transformed into small cell lung carcinoma (SCLC). For one thing, our study provided novel approach and extended the treatment spectra of overcoming immunotherapy resistance after EGFR resistance in driver oncogene-mutated NSCLC. For another thing, our case is the first time to report that SCLC transformation can be achieved after gefitinib–pembrolizumab–gefitinib resistance in EGFR sensitive mutation NSCLC, providing a new condition for SCLC transformation.

Background: For stage IV patients harboring EGFR mutations, there is a differential response to the first-line TKI treatment. We constructed three-dimensional convolutional neural networks (CNN) with deep transfer learning to stratify patients into subgroups with different response and progression risks. Materials and Methods: From 2013 to 2017, 339 patients with EGFR mutation receiving first-line TKI treatment were included. Progression-free survival (PFS) time and progression patterns were confirmed by routine follow-up and restaging examinations. Patients were divided into two subgroups according to the median PFS (<=9 months, > 9 months). We developed a PFS prediction model and a progression pattern classification model using transfer learning from a pre-trained EGFR mutation classification 3D CNN. Clinical features were fused with the 3D CNN to build the final hybrid prediction model. The performance was quantified using area under receiver operating characteristic curve (AUC), and model performance was compared by AUCs with Delong test. Results: The PFS prediction CNN showed an AUC of 0.744 (95% CI, 0.645–0.843) in the independent validation set and the hybrid model of CNNs and clinical features showed an AUC of 0.771 (95% CI, 0.676–0.866), which are significantly better than clinical features-based model (AUC, 0.624, P<0.01). The progression pattern prediction model showed an AUC of 0.762(95% CI, 0.643–0.882) and the hybrid model with clinical features showed an AUC of 0.794 (95% CI, 0.681–0.908), which can provide compensate information for clinical features-based model (AUC, 0.710; 95% CI, 0.582–0.839). Conclusion: The CNN exhibits potential ability to stratify progression status in patients with EGFR mutation treated with first-line TKI, which might help make clinical decisions. [ABSTRACT FROM AUTHOR]

The treatment sequence of immunotherapy (IO) and epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is of great importance for the survival of non-small cell lung cancer (NSCLC) patients with EGFR sensitive mutation. Here, we reported an advanced lung adenocarcinoma case concurrent with EGFR sensitive mutation and high PD-L1 expression (>50%) that was administrated with gefitinib firstly, and then became resistant to EGFR-TKI. He received the strategy of immunity-combined chemo-radiotherapy and responded significantly. However, the disease re-progressed after 10 months. Surprisingly, the tumor re-sensitized to gefitinib for 13 months. At final, following the treatment pressure of TKI-IO combination therapy-TKI strategy, tumor clone eventually transformed into small cell lung carcinoma (SCLC). For one thing, our study provided novel approach and extended the treatment spectra of overcoming immunotherapy resistance after EGFR resistance in driver oncogene-mutated NSCLC. For another thing, our case is the first time to report that SCLC transformation can be achieved after gefitinib–pembrolizumab–gefitinib resistance in EGFR sensitive mutation NSCLC, providing a new condition for SCLC transformation. [ABSTRACT FROM AUTHOR]

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine.

To recognize the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (LADC) has become a prerequisite of deciding whether EGFR-tyrosine kinase inhibitor (EGFR-TKI) medicine can be used. Polymerase chain reaction assay or gene sequencing is for measuring EGFR status, however, the tissue samples by surgery or biopsy are required. We propose to develop deep learning models to recognize EGFR status by using radiomics features extracted from non-invasive CT images. Preoperative CT images, EGFR mutation status and clinical data have been collected in a cohort of 709 patients (the primary cohort) and an independent cohort of 205 patients. After 1,037 CT-based radiomics features are extracted from each lesion region, 784 discriminative features are selected for analysis and construct a feature mapping. One Squeeze-and-Excitation (SE) Convolutional Neural Network (SE-CNN) has been designed and trained to recognize EGFR status from the radiomics feature mapping. SE-CNN model is trained and validated by using 638 patients from the primary cohort, tested by using the rest 71 patients (the internal test cohort), and further tested by using the independent 205 patients (the external test cohort). Furthermore, SE-CNN model is compared with machine learning (ML) models using radiomics features, clinical features, and both features. EGFR(-) patients show the smaller age, higher odds of female, larger lesion volumes, and lower odds of subtype of acinar predominant adenocarcinoma (APA), compared with EGFR(+). The most discriminative features are for texture (614, 78.3%) and the features of first order of intensity (158, 20.1%) and the shape features (12, 1.5%) follow. SE-CNN model can recognize EGFR mutation status with an AUC of 0.910 and 0.841 for the internal and external test cohorts, respectively. It outperforms the CNN model without SE, the fine-tuned VGG16 and VGG19, three ML models, and the state-of-art models. Utilizing radiomics feature mapping extracted from non-invasive CT images, SE-CNN can precisely recognize EGFR mutation status of LADC patients. The proposed method combining radiomics features and deep leaning is superior to ML methods and can be expanded to other medical applications. The proposed SE-CNN model may help make decision on usage of EGFR-TKI medicine. [ABSTRACT FROM AUTHOR]

ObjectivesLarge-scale, population-based real-world studies on the treatment outcomes of first-line tyrosine kinase inhibitors (TKIs) and subsequent systemic chemotherapy agents for lung adenocarcinoma (with activating epidermal growth factor receptor [EGFR] mutations) remain limited.Materials and MethodsFrom March 2014 to December 2016, patients with advanced lung adenocarcinoma, identified from the Taiwan Cancer Registry were included in this study if they received any of the three TKIs as first-line treatment. The primary outcome was overall survival (OS). The secondary outcome was time-to-treatment discontinuation (TTD).ResultsA total of 4,889 patients (median age: 67 years and two-thirds with distant metastasis) were recruited (1,778 gefitinib, 1,599 erlotinib, and 1,512 afatinib users). A 1:1 propensity score (PS)-matched cohorts of 1,228 afatinib/erlotinib and 1054 afatinib/gefitinib was created. After PS matching, it was found that afatinib was not associated with better OS (afatinib vs. erlotinib, HR: 0.96, 95% CI: 0.86–1.07; afatinib vs. gefitinib, HR: 0.91, 95% CI: 0.81–1.02). In the subgroup analysis, afatinib demonstrated a survival benefit in patients with active smoking (afatinib vs. erlotinib, HR: 0.69, 95% CI: 0.51–0.93; afatinib vs. gefitinib, HR: 0.67, 95% CI: 0.48–0.94) and ECOG > 1 (afatinib vs. erlotinib, HR: 0.79, 95% CI: 0.63–0.99; afatinib vs. gefitinib, HR: 0.78, 95% CI: 0.62–0.98). A total of 41.1% (n = 1992) of first-line TKI users received subsequent chemotherapy. Among the three TKI groups, pemetrexed usage was associated with better OS compared with other chemotherapy agents, with the exception of gemcitabine in the afatinib and gefitinib groups. Pemetrexed and gemcitabine had the longest TTD of 3–4 months.ConclusionsAmong patients with EGFR mutant lung adenocarcinoma, afatinib use may not provide longer OS compared with first-generation TKIs. Afatinib may be preferably considered among patients with active smoking and should not be withheld among those with worse performance status. With 40% of patients receiving subsequent chemotherapy, pemetrexed may be the preferred agent, while gemcitabine can be a reasonable alternative.

Objectives: Large-scale, population-based real-world studies on the treatment outcomes of first-line tyrosine kinase inhibitors (TKIs) and subsequent systemic chemotherapy agents for lung adenocarcinoma (with activating epidermal growth factor receptor [EGFR] mutations) remain limited. Materials and Methods: From March 2014 to December 2016, patients with advanced lung adenocarcinoma, identified from the Taiwan Cancer Registry were included in this study if they received any of the three TKIs as first-line treatment. The primary outcome was overall survival (OS). The secondary outcome was time-to-treatment discontinuation (TTD). Results: A total of 4,889 patients (median age: 67 years and two-thirds with distant metastasis) were recruited (1,778 gefitinib, 1,599 erlotinib, and 1,512 afatinib users). A 1:1 propensity score (PS)-matched cohorts of 1,228 afatinib/erlotinib and 1054 afatinib/gefitinib was created. After PS matching, it was found that afatinib was not associated with better OS (afatinib vs. erlotinib, HR: 0.96, 95% CI: 0.86–1.07; afatinib vs. gefitinib, HR: 0.91, 95% CI: 0.81–1.02). In the subgroup analysis, afatinib demonstrated a survival benefit in patients with active smoking (afatinib vs. erlotinib, HR: 0.69, 95% CI: 0.51–0.93; afatinib vs. gefitinib, HR: 0.67, 95% CI: 0.48–0.94) and ECOG > 1 (afatinib vs. erlotinib, HR: 0.79, 95% CI: 0.63–0.99; afatinib vs. gefitinib, HR: 0.78, 95% CI: 0.62–0.98). A total of 41.1% (n = 1992) of first-line TKI users received subsequent chemotherapy. Among the three TKI groups, pemetrexed usage was associated with better OS compared with other chemotherapy agents, with the exception of gemcitabine in the afatinib and gefitinib groups. Pemetrexed and gemcitabine had the longest TTD of 3–4 months. Conclusions: Among patients with EGFR mutant lung adenocarcinoma, afatinib use may not provide longer OS compared with first-generation TKIs. Afatinib may be preferably considered among patients with active smoking and should not be withheld among those with worse performance status. With 40% of patients receiving subsequent chemotherapy, pemetrexed may be the preferred agent, while gemcitabine can be a reasonable alternative. [ABSTRACT FROM AUTHOR]

In Taiwan, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (EGFR-TKIs), gefitinib, erlotinib, and afatinib are served as first-line therapy for non-small lung cell cancer (NSCLC) patients with EGFR sensitizing mutations. However, the majority of patients who initially respond to EGFR-TKIs, progress through acquiring EGFR T790M mutations (T790M), which is the most common resistant mechanism. Patients with T790M gain the opportunity of subsequent treatment with third-generation EGFR-TKI, osimertinib. This study aimed to evaluate the association between prior EGFR-TKI therapy and incidence of acquired T790M resistance in lung adenocarcinoma patients who have progressed on first/second-generation EGFR-TKI therapy. This retrospective study included lung adenocarcinoma patients who had a radiographically-confirmed progressive disease under EGFR-TKI treatment and had re-biopsy samples for T790M testing from seven medical centers in Taiwan from June 2013 to December 2018. Patients harboring de novo T790M or using more than one EGFR-TKI were excluded. Of the 407 patients enrolled, the overall T790M acquisition rate was 52.8%. The patients treated with gefitinib, erlotinib or afatinib had a statistically significant difference in the T790M rates (59.9, 45.5, and 52.7%, respectively; p = 0.037) after disease progression. Patients with common baseline EGFR mutations (Del-19 and L858R) (p = 0.005) and longer treatment duration with EGFR-TKIs (p < 0.001) had higher chances of T790M acquisition. Multivariate logistic regression analysis further showed that patients with common baseline EGFR mutations, gefitinib (compared to erlotinib) administration, and longer treatment duration with EGFR-TKIs had higher T790M incidence. There was no significant difference in the incidence of acquired T790M between different re-biopsy tissue samples or complications. In conclusion, this study showed that patients who progressed from gefitinib treatment, bearing common EGFR mutations, and with longer EGFR-TKI treatment duration had increased incidence of T790M acquisition and, therefore, were suitable for subsequent osimertinib treatment.

In Taiwan, epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (EGFR-TKIs), gefitinib, erlotinib, and afatinib are served as first-line therapy for non-small lung cell cancer (NSCLC) patients with EGFR sensitizing mutations. However, the majority of patients who initially respond to EGFR-TKIs, progress through acquiring EGFR T790M mutations (T790M), which is the most common resistant mechanism. Patients with T790M gain the opportunity of subsequent treatment with third-generation EGFR-TKI, osimertinib. This study aimed to evaluate the association between prior EGFR-TKI therapy and incidence of acquired T790M resistance in lung adenocarcinoma patients who have progressed on first/second-generation EGFR-TKI therapy. This retrospective study included lung adenocarcinoma patients who had a radiographically-confirmed progressive disease under EGFR-TKI treatment and had re-biopsy samples for T790M testing from seven medical centers in Taiwan from June 2013 to December 2018. Patients harboring de novo T790M or using more than one EGFR-TKI were excluded. Of the 407 patients enrolled, the overall T790M acquisition rate was 52.8%. The patients treated with gefitinib, erlotinib or afatinib had a statistically significant difference in the T790M rates (59.9, 45.5, and 52.7%, respectively; p = 0.037) after disease progression. Patients with common baseline EGFR mutations (Del-19 and L858R) (p = 0.005) and longer treatment duration with EGFR-TKIs (p < 0.001) had higher chances of T790M acquisition. Multivariate logistic regression analysis further showed that patients with common baseline EGFR mutations, gefitinib (compared to erlotinib) administration, and longer treatment duration with EGFR-TKIs had higher T790M incidence. There was no significant difference in the incidence of acquired T790M between different re-biopsy tissue samples or complications. In conclusion, this study showed that patients who progressed from gefitinib treatment, bearing common EGFR mutations, and with longer EGFR-TKI treatment duration had increased incidence of T790M acquisition and, therefore, were suitable for subsequent osimertinib treatment. [ABSTRACT FROM AUTHOR]

Radiomics has become an area of interest for tumor characterization in 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images.

Radiomics has become an area of interest for tumor characterization in 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images. [ABSTRACT FROM AUTHOR]

Introduction: The clinical outcomes of sequential treatment of advanced epidermal growth factor receptor (EGFR)-mutated non-small cell lung cancer (NSCLC) patients with first-line bevacizumab combined with 1st/2nd-generation EGFR-TKIs are unclear. Thus, we aimed to analyze the outcomes of these patients., Methods: Between January 2015 and December 2020, data for 102 advanced EGFR-mutated lung adenocarcinoma patients receiving first-line bevacizumab combined with erlotinib or afatinib followed by treatments at multiple institutions were retrospectively analyzed. All patients with progressive disease (PD) after first-line therapy underwent secondary T790M mutation detection., Results: The secondary T790M mutation positive rate of all study patients was 57.9%. First-line erlotinib use and progression-free survival (PFS) after first-line therapy > 12 months were positively associated with the T790M mutation (P <0.05). The response rates (RRs) to second-line treatments were 51.7% and 22.7% for the osimertinib and nonosimertinib groups, respectively (P = 0.001). The median PFS associated with second-line osimertinib and nonosimertinib therapy was 13.7 and 7.1 months, respectively (hazard ratio (HR) = 0.38; 95% confidence interval (CI), 0.23-0.63; P< 0.001). Patients with a secondary T790M mutation receiving second-line osimertinib treatment had a median overall survival (OS) of 54.3 months, and the median OS was 31.9 months for non-T790M-mutated patients receiving second-line nonosimertinib treatments (HR = 0.36; CI: 0.21-0.62, P < 0.001)., Conclusion: The majority of acquired resistance to first-line bevacizumab combined with 1st/2nd-generation EGFR-TKIs is associated with the T790M mutation. Sequential osimertinib treatment in patients with positive secondary T790M mutation is associated with better outcomes among these patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Hsu, Huang, Lin, Lee, Chiu, Wu, Kuo, Ju, Huang, Ko, Wang and Yang.)

Objective: This study mainly observes the efficacy and safety of almonertinib plus chemotherapy compared with almonertinib alone in the second-line treatment of advanced non-small cell lung cancer (NSCLC) with mutated epidermal growth factor receptor (EGFR)., Methods: In this study, clinical data of 68 patients with advanced NSCLC who were treated in Jiangsu Provincial People's Hospital and Nanjing Chest Hospital between April 2020 and December 2022 were collected. Among them, the study group (n=30) received second-line almonertinib combined with platinum-based chemotherapy, while the control group (n=38) received almonertinib alone. The near-term and long-term effects and adverse events of the two groups were compared respectively., Results: The median follow-up time until 31 December 2022 was 16.3 months (95% CI: 11.32-21.34). Results of chi-square analysis showed no statistically significant difference in objective response rate (ORR) and disease control rate (DCR) between the study group and the control group (56.73% vs. 55.3%, P>0.05 ; 100% vs. 86.8%, P>0.05 ). Log-rank test comparing the two groups revealed that the median progression-free survival (mPFS) of the study group was significantly longer than that of the control group by 3.1 months (12.7 vs. 9.6 months, P=0.01 ). Multivariate COX proportional risk model showed a statistically significant effect of treatment method and PS score on PFS (HR=0.43, P=0.023 ; HR=3.82, P=0.001 ). In terms of safety, most of the adverse events (AEs) were mild, with no grade 4-5 in the two groups, and the overall tolerance of patients was good., Conclusion: For advanced NSCLC patients with EGFR mutations, second-line treatment with almonertinib plus chemotherapy significantly improved PFS compared with almonertinib alone without a significant increase in adverse events, providing efficacy and safety., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fang, Xiang and Lu.)

Radiomics has become an area of interest for tumor characterization in 18 F-Fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images., (Copyright © 2019 Li, Yin, Zhang, Dai, Liu, Chen, Zhu, Ma and Xu.)