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Introduction: With the implementation of low-dose computed tomography (LDCT) screening, multiple pulmonary tumor nodules (MPTN) are diagnosed with increasing frequency and the selection of surgical treatments versus systemic therapies has become challenging on a daily basis in clinical practice. In the presence of multiple carcinomas, especially adenocarcinomas, pathologically determined to be of pulmonary origin, the distinction between separate primary lung carcinomas (SPLCs) and intrapulmonary metastases (IPMs) is important for staging, management, and prognostication., Methods: We systemically reviewed various means that aid in the differentiation between SPLCs and IPMs explored by histopathologic evaluation and molecular profiling, the latter includes DNA microsatellite analysis, array comparative genomic hybridization, TP53 and oncogenic driver mutation testing and, more recently, with promising effectiveness, next-generation sequencing (NGS) comprising small- or large-scale multi-gene panels., Results: Comprehensive histologic evaluation may suffice to differentiate between SPLCs and IPMs. However, molecular profiling using larger-scale NGS typically provides superior discriminatory power, allowing for more accurate classification. Based on the literature review and expert opinions, we proposed a combined 4-step histologic and molecular classification algorithm for addressing MPTN of adenocarcinoma histology that encourages a multidisciplinary approach. It is also noteworthy that new technologies combining machine learning and digital pathology may develop into valuable diagnostic tools for distinguishing SPLCs from IPMs in the future., Conclusions: Although histopathologic evaluation is often adequate to differentiate SPLCs from IPMs, molecular profiling should be performed when possible, especially in cases with tumors exhibiting similar morphology. This manuscript summarized the previous efforts in resolving the current challenges and highlighted the recent progress in the differentiation methods and algorithms used in categorizing multiple lung adenocarcinomas into SPLCs or IPMs, which are becoming more and more critical in precision lung cancer management., (Copyright © 2024 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Background: In Taiwan, lung cancers occur predominantly in never-smokers, of whom nearly 60% have stage IV disease at diagnosis. We aimed to assess the efficacy of low-dose CT (LDCT) screening among never-smokers, who had other risk factors for lung cancer., Methods: The Taiwan Lung Cancer Screening in Never-Smoker Trial (TALENT) was a nationwide, multicentre, prospective cohort study done at 17 tertiary medical centres in Taiwan. Eligible individuals had negative chest radiography, were aged 55-75 years, had never smoked or had smoked fewer than 10 pack-years and stopped smoking for more than 15 years (self-report), and had one of the following risk factors: a family history of lung cancer; passive smoke exposure; a history of pulmonary tuberculosis or chronic obstructive pulmonary disorders; a cooking index of 110 or higher; or cooking without using ventilation. Eligible participants underwent LDCT at baseline, then annually for 2 years, and then every 2 years up to 6 years thereafter, with follow-up assessments at each LDCT scan (ie, total follow-up of 8 years). A positive scan was defined as a solid or part-solid nodule larger than 6 mm in mean diameter or a pure ground-glass nodule larger than 5 mm in mean diameter. Lung cancer was diagnosed through invasive procedures, such as image-guided aspiration or biopsy or surgery. Here, we report the results of 1-year follow-up after LDCT screening at baseline. The primary outcome was lung cancer detection rate. The p value for detection rates was estimated by the χ 2 test. Univariate and multivariable logistic regression analyses were used to assess the association between lung cancer incidence and each risk factor. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of LDCT screening were also assessed. This study is registered with ClinicalTrials.gov, NCT02611570, and is ongoing., Findings: Between Dec 1, 2015, and July 31, 2019, 12 011 participants (8868 females) were enrolled, of whom 6009 had a family history of lung cancer. Among 12 011 LDCT scans done at baseline, 2094 (17·4%) were positive. Lung cancer was diagnosed in 318 (2·6%) of 12 011 participants (257 [2·1%] participants had invasive lung cancer and 61 [0·5%] had adenocarcinomas in situ). 317 of 318 participants had adenocarcinoma and 246 (77·4%) of 318 had stage I disease. The prevalence of invasive lung cancer was higher among participants with a family history of lung cancer (161 [2·7%] of 6009 participants) than in those without (96 [1·6%] of 6002 participants). In participants with a family history of lung cancer, the detection rate of invasive lung cancer increased significantly with age, whereas the detection rate of adenocarcinoma in situ remained stable. In multivariable analysis, female sex, a family history of lung cancer, and age older than 60 years were associated with an increased risk of lung cancer and invasive lung cancer; passive smoke exposure, cumulative exposure to cooking, cooking without ventilation, and a previous history of chronic lung diseases were not associated with lung cancer, even after stratification by family history of lung cancer. In participants with a family history of lung cancer, the higher the number of first-degree relatives affected, the higher the risk of lung cancer; participants whose mother or sibling had lung cancer were also at an increased risk. A positive LDCT scan had 92·1% sensitivity, 84·6% specificity, a PPV of 14·0%, and a NPV of 99·7% for lung cancer diagnosis., Interpretation: TALENT had a high invasive lung cancer detection rate at 1 year after baseline LDCT scan. Overdiagnosis could have occurred, especially in participants diagnosed with adenocarcinoma in situ. In individuals who do not smoke, our findings suggest that a family history of lung cancer among first-degree relatives significantly increases the risk of lung cancer as well as the rate of invasive lung cancer with increasing age. Further research on risk factors for lung cancer in this population is needed, particularly for those without a family history of lung cancer., Funding: Ministry of Health and Welfare of Taiwan., Competing Interests: Declaration of interests We declare no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: The incidence and mortality of lung cancer are highest in Asia compared with Europe and USA, with the incidence and mortality rates being 34.4 and 28.1 per 100,000 respectively in East Asia. Diagnosing lung cancer at early stages makes the disease amenable to curative treatment and reduces mortality. In some areas in Asia, limited availability of robust diagnostic tools and treatment modalities, along with variations in specific health care investment and policies, make it necessary to have a more specific approach for screening, early detection, diagnosis, and treatment of patients with lung cancer in Asia compared with the West., Method: A group of 19 advisors across different specialties from 11 Asian countries, met on a virtual Steering Committee meeting, to discuss and recommend the most affordable and accessible lung cancer screening modalities and their implementation, for the Asian population., Results: Significant risk factors identified for lung cancer in smokers in Asia include age 50 to 75 years and smoking history of more than or equal to 20 pack-years. Family history is the most common risk factor for nonsmokers. Low-dose computed tomography screening is recommended once a year for patients with screening-detected abnormality and persistent exposure to risk factors. However, for high-risk heavy smokers and nonsmokers with risk factors, reassessment scans are recommended at an initial interval of 6 to 12 months with subsequent lengthening of reassessment intervals, and it should be stopped in patients more than 80 years of age or are unable or unwilling to undergo curative treatment., Conclusions: Asian countries face several challenges in implementing low-dose computed tomography screening, such as economic limitations, lack of efforts for early detection, and lack of specific government programs. Various strategies are suggested to overcome these challenges in Asia., (Copyright © 2023 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)

Background: We aimed to establish a radiomic prediction model for tumor spread through air spaces (STAS) in lung adenocarcinoma using radiomic values from high-grade subtypes (solid and micropapillary)., Methods: We retrospectively reviewed 327 patients with lung adenocarcinoma from 2 institutions (cohort 1: 227 patients; cohort 2: 100 patients) between March 2017 and March 2019. STAS was identified in 113 (34.6%) patients. A high-grade likelihood prediction model was constructed based on a historical cohort of 82 patients with "near-pure" pathologic subtype. The STAS prediction model based on the patch-wise mechanism identified the high-grade likelihood area for each voxel within the internal border of the tumor. STAS presence was indirectly predicted by a volume percentage threshold of the high-grade likelihood area. Performance was evaluated by receiver operating curve analysis with 10-repetition, 3-fold cross-validation in cohort 1, and was individually tested in cohort 2., Results: Overall, 227 patients (STAS-positive: 77 [33.9%]) were enrolled for cross-validation (cohort 1) while 100 (STAS-positive: 36 [36.0%]) underwent individual testing (cohort 2). The gray level cooccurrence matrix (variance) and histogram (75th percentile) features were selected to construct the high-grade likelihood prediction model, which was used as the STAS prediction model. The proposed model achieved good performance in cohort 1 with an area under the curve, sensitivity, and specificity, of 81.44%, 86.75%, and 62.60%, respectively, and correspondingly, in cohort 2, they were 83.16%, 83.33%, and 63.90%, respectively., Conclusions: The proposed computed tomography-based radiomic prediction model could help guide preoperative prediction of STAS in early-stage lung adenocarcinoma and relevant surgeries., (Copyright © 2022 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.)

Lymph node metastasis also called nodal metastasis (Nmet), is a clinically primary task for physicians. The survival and recurrence of lung cancer are related to the Nmet staging from Tumor-Node-Metastasis (TNM) reports. Furthermore, preoperative Nmet prediction is still a challenge for the patient in managing the surgical plan and making treatment decisions. We proposed a multi-energy level fusion model with a principal feature enhancement (PFE) block incorporating radiologist and computer science knowledge for Nmet prediction. The proposed model is custom-designed by gemstone spectral imaging (GSI) with different energy levels on dual-energy computer tomography (CT) from a primary tumor of lung cancer. In the experiment, we take three different energy level fusion datasets: lower energy level fusion (40, 50, 60, 70 keV), higher energy level fusion (110, 120, 130, 140 keV), and average energy level fusion (40, 70, 100, 140 keV). The proposed model is trained by lower energy level fusion that is 93% accurate and the value of Kappa is 86%. When we used the lower energy level images to train the fusion model, there has been a significant difference to other energy level fusion models. Hence, we apply 5-fold cross-validation, which is used to validate the performance result of the multi-keV model with different fusion datasets of energy level images in the pathology report. The cross-validation result also demonstrates that the model with the lower energy level dataset is more robust and suitable in predicting the Nmet of the primary tumor. The lower energy level shows more information of tumor angiogenesis or heterogeneity provided the proposed fusion model with a PFE block and channel attention blocks to predict Nmet from primary tumors., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Purpose: Recognizing molecular markers is helpful for guiding treatment plans for breast cancer. This study correlated estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2), and triple-negative breast cancer (TNBC) statuses to the degree of heterogeneity on breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)., Materials and Methods: A total of 102 biopsy-proven cancers from 102 patients between October 2010 and December 2012 were used in this study, including ER (59 positive, 43 negative), HER2 (47 positive, 55 negative), and TNBC (22 TNBC, 80 non-TNBC). At first, the tumor region was segmented by using a region growing method. Then, the region-based features were extracted by the proposed regionalization method to quantify intra-tumoral heterogeneity on breast DCE-MRI. The three-dimensional morphological features (texture features and shape feature) and the pharmacokinetic model were also extracted from the segmented tumor region. After feature extraction, a logistic regression was used to classify ER, HER2, and TNBC statuses respectively. The performances were evaluated by using receiver operating characteristic (ROC) curve analysis., Results: The proposed region-based features achieved the accuracy of 73.53%, 82.35%, and 77.45% for ER, HER2, and TNBC classifications. The corresponding area under the ROC curves (Az) achieves 0.7320, 0.8458, and 0.8328 that were better than those of texture features, shape features, and Tofts pharmacokinetic model., Conclusion: The intra-tumoral heterogeneity quantified by the region-based features can be used to reflect the vasculature complexity of different molecular markers and to provide prediction information of cell surface receptors on clinical examination., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Background: Elastography is a new sonographic imaging technique to acquire the strain information of tissues and transform the information into images. Radiologists have to observe the gray-scale distribution of tissues on the elastographic image interpreted as the reciprocal of Young's modulus to evaluate the pathological changes such as scirrhous carcinoma. In this study, a computer-aided diagnosis (CAD) system was developed to extract quantitative strain features from elastographic images to reduce operator-dependence and provide an automatic procedure for breast mass classification., Method: The collected image database was composed of 45 malignant and 45 benign breast masses. For each case, tumor segmentation was performed on the B-mode image to obtain tumor contour which was then mapped to the elastographic images to define the corresponding tumor area. The gray-scale pixels around tumor area were classified into white, gray, and black by fuzzy c-means clustering to highlight stiff tissues with darker values. Quantitative strain features were then extracted from the black cluster and compared with the B-mode features in the classification of breast masses., Results: The performance of the proposed strain features achieved an accuracy of 80% (72/90), a sensitivity of 80% (36/45), a specificity of 80% (36/45), and a normalized area under the receiver operating characteristic curve, Az=0.84. Combining the strain features with the B-mode features obtained a significantly better Az=0.93, p-value<0.05., Conclusions: Summarily, the quantified strain features can be combined with the B-mode features to provide a promising suggestion in distinguishing malignant from benign tumors., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Introduction: Germline Bcl-2-like protein 11 (BIM) deletion polymorphism in Asian is a poor predictive factor for treatment outcomes to tyrosine kinase inhibitors (TKIs) in malignancies. We explored the impact of BIM deletion polymorphism on treatment outcome of advanced non-small-cell lung cancer (NSCLC)., Methods: We prospectively collected tissue samples, blood, and clinical data from two cohorts of advanced NSCLC patients. BIM deletion polymorphism was correlated with overall survival (OS) and progression-free survival (PFS) to epidermal growth factor receptor (EGFR) TKIs and chemotherapy treatment., Results: BIM deletion polymorphism was detected in blood of 16.2% (33 of 204) patients. The PFS to first-line EGFR-TKIs in 153 patients were 8.6 and 4.6 months for patients with wild-type BIM and BIM deletion polymorphism, respectively (p = 0.004). Among 120 patients who received chemotherapies, the PFS to chemotherapies were 5.6 and 3.5 months for patients with wild-type BIM and BIM deletion polymorphism, respectively (p = 0.050). The OS of all 204 patients was 24.8 and 16.8 months for patients with wild-type BIM and BIM deletion polymorphism, respectively (p = 0.005). Multivariate analyses suggested that BIM deletion polymorphism was an independent predictor for shorter PFS to EGFR-TKIs (hazard ratio [HR] 2.15, p = 0.002), PFS to chemotherapy (HR 2.40, p = 0.016), and OS (HR 1.65, p = 0.039)., Conclusions: BIM deletion polymorphism predicts shorter PFS to EGFR-TKIs and OS in advanced NSCLC.

To facilitate rapid and accurate assessment, this study proposed a novel fully automatic method to detect and identify focal tumor breast lesions using both kinetic and morphologic features from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). After motion registration of all phases of the DCE-MRI study, three automatically generated lines were used to segment the whole breast region of each slice. The kinetic features extracted from the pixel-based time-signal intensity curve (TIC) by a two-stage detection algorithm was first used, and then three-dimensional (3-D) morphologic characteristics of the detected regions were applied to differentiate between tumor and non-tumor regions. In this study, 95 biopsy-confirmed lesions (28 benign and 67 malignant lesions) in 54 women were used to evaluate the detection efficacy of the proposed system. The detection performance was analyzed using the free-response operating characteristics (FROC) curve and detection rate. The proposed computer-aided detection (CADe) system had a detection rate of 92.63% (88/95) of all tumor lesions, with 6.15 false positives per case. Based on the results, kinetic features extracted by TIC can be used to detect tumor lesions and 3-D morphology can effectively reduce the false positives., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Three-dimensional (3-D) dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) consists of a large number of images in different enhancement phases which are used to identify and characterize breast lesions. The purpose of this study was to develop a computer-assisted algorithm for tumor segmentation and characterization using both kinetic information and morphological features of 3-D breast DCE-MRI. An integrated color map created by intersecting kinetic and area under the curve (AUC) color maps was used to detect potential breast lesions, followed by the application of a region growing algorithm to segment the tumor. Modified fuzzy c-means clustering was used to identify the most representative kinetic curve of the whole segmented tumor, which was then characterized by using conventional curve analysis or pharmacokinetic model. The 3-D morphological features including shape features (compactness, margin, and ellipsoid fitting) and texture features (based on the grey level co-occurrence matrix) of the segmented tumor were obtained to characterize the lesion. One hundred and thirty-two biopsy-proven lesions (63 benign and 69 malignant) were used to evaluate the performance of the proposed computer-aided system for breast MRI. Five combined features including rate constant (kep), volume of plasma (vp), energy (G1), entropy (G2), and compactness (C1), had the best performance with an accuracy of 91.67% (121/132), sensitivity of 91.30% (63/69), specificity of 92.06% (58/63), and Az value of 0.9427. Combining the kinetic and morphological features of 3-D breast MRI is a potentially useful and robust algorithm when attempting to differentiate benign and malignant lesions., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The purpose of this study is to evaluate the diagnostic efficacy of the representative characteristic kinetic curve of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) extracted by fuzzy c-means (FCM) clustering for the discrimination of benign and malignant breast tumors using a novel computer-aided diagnosis (CAD) system. About the research data set, DCE-MRIs of 132 solid breast masses with definite histopathologic diagnosis (63 benign and 69 malignant) were used in this study. At first, the tumor region was automatically segmented using the region growing method based on the integrated color map formed by the combination of kinetic and area under curve color map. Then, the FCM clustering was used to identify the time-signal curve with the larger initial enhancement inside the segmented region as the representative kinetic curve, and then the parameters of the Tofts pharmacokinetic model for the representative kinetic curve were compared with conventional curve analysis (maximal enhancement, time to peak, uptake rate and washout rate) for each mass. The results were analyzed with a receiver operating characteristic curve and Student's t test to evaluate the classification performance. Accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the combined model-based parameters of the extracted kinetic curve from FCM clustering were 86.36% (114/132), 85.51% (59/69), 87.30% (55/63), 88.06% (59/67) and 84.62% (55/65), better than those from a conventional curve analysis. The A(Z) value was 0.9154 for Tofts model-based parametric features, better than that for conventional curve analysis (0.8673), for discriminating malignant and benign lesions. In conclusion, model-based analysis of the characteristic kinetic curve of breast mass derived from FCM clustering provides effective lesion classification. This approach has potential in the development of a CAD system for DCE breast MRI., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Background: Thyroid transcription factor 1 (TTF-1) positivity correlates with a higher prevalence of epidermal growth factor receptor (EGFR) mutation in lung adenocarcinoma. It is unknown whether TTF-1 expression affects the clinical outcome of patients with advanced lung adenocarcinoma, who have received EGFR tyrosine kinase inhibitors (TKIs) during the treatment course., Methods: This study enrolled patients with advanced lung adenocarcinoma who had results of EGFR mutation analysis and TTF-1 immunostaining. The impact of TTF-1 expression on overall survival (OS) and progression-free survival (PFS) under EGFR TKI treatment was evaluated. Multivariate analyses were done to examine the independent predictors of OS and PFS., Results: Of 496 patients with advanced lung adenocarcinoma, 443 had TTF-1-positive adenocarcinoma. Patients with TTF-1-positive lung adenocarcinoma had longer OS than did those with TTF-1-negative lung adenocarcinoma (median survival, 27.4 vs 11.8 months, P = .001). In patients with EGFR TKI treatment, those with TTF-1-positive lung adenocarcinoma and mutant EGFR had longer OS. In patients with EGFR mutation, those with TTF-1-positive lung adenocarcinoma had longer PFS than did those with TTF-1-negative lung adenocarcinoma (median survival, 8.7 vs 5.7 months, P = .043). Multivariate analysis showed that negative TTF-1 expression is a predictor for shorter OS, and a predictor for shorter PFS under EGFR TKI treatment., Conclusions: TTF-1 shows independent prognostic significance in advanced lung adenocarcinoma. Patients with TTF-1-negative lung adenocarcinoma have not only shorter OS, but also shorter PFS under EGFR TKI treatment, despite the existence of mutant EGFR. Further studies are needed to investigate the optimal treatment of patients with TTF-1-negative lung adenocarcinoma.

Introduction: The aim of this study was to investigate the diagnostic value of percutaneous computed tomography (CT)-guided coaxial transthoracic needle biopsy (TNB) for small pulmonary lesions (≤ 3 cm) with persistent ground-glass opacity (GGO)., Methods: From January 2004 to February 2010, consecutive patients with persistent small GGO lesions (≤ 3 cm) who underwent CT-guided TNB were analyzed. The pathologic results of CT-guided TNB were compared with final diagnoses, and the GGO percentage on CT was correlated with the stromal invasion in surgical pathology., Results: We performed CT-guided TNB on 1612 patients during the study period. Among them, 55 patients had persistent small GGO lesions (size range 0.5-3.0 cm; 1.72 ± 0.73 cm), 47 were diagnosed with lung adenocarcinomas, and 8 with benign nonspecific lesions. Minor procedure-related complications occurred in 26 patients (47.3%) with small pneumothorax and 11 patients (20.0%) with self-limited mild hemoptysis. The final diagnoses of the 43 patients receiving lobectomy were invasive adenocarcinoma (n = 23), pure bronchioloalveolar carcinoma (n = 19), and mucosa-associated lymphoid tissue lymphoma (n = 1). The diagnostic accuracy was 93.0% (40/43) using CT-guided TNB. Pure GGO lesions had a higher incidence of pure bronchioloalveolar carcinoma than GGO-dominant lesions (70.0% versus 21.7%; p = 0.004). Compared with surgical pathology, stromal invasion was underestimated in 43.5% (10/23) of the TNB specimens, especially in pure GGO lesions (83%, 5/6). In logistic regression analysis, the GGO percentage correlated inversely with stromal tumor invasion (p = 0.0028)., Conclusions: CT-guided coaxial TNB is a safe and useful method for diagnosing small (≤ 3 cm) persistent GGO lesions. Stromal invasion may be underestimated by TNB in GGO lesions.

This study analyzed the change in breast density in women receiving tamoxifen treatment using 3-D MRI. Sixteen women were studied. Each woman received breast MRI before and after tamoxifen. The breast and the fibroglandular tissue were segmented using a computer-assisted algorithm, based on T1-weighted images. The fibroglandular tissue volume (FV) and breast volume (BV) were measured and the ratio was calculated as the percent breast density (%BD). The changes in breast volume (ΔBV), fibroglandular tissue volume (ΔFV) and percent density (Δ%BD) between two MRI studies were analyzed and correlated with treatment duration and baseline breast density. The ΔFV showed a reduction in all 16 women. The Δ%BD showed a mean reduction of 5.8%. The reduction of FV was significantly correlated with baseline FV (P<.001) and treatment duration (P=.03). The percentage change in FV was correlated with duration (P=.049). The reduction in %BD was positively correlated with baseline %BD (P=.02). Women with higher baseline %BD showed more reduction of %BD. Three-dimensional MRI may be useful for the measurement of the small changes of ΔFV and Δ%BD after tamoxifen. These changes can potentially be used to correlate with the future reduction of cancer risk., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Introduction: Knowledge about the current status of the epidermal growth factor receptor (EGFR) has resulted in an improvement in the treatment of non-small cell lung cancer. In contrast, small cell lung cancer (SCLC) continues to frustrate clinicians with its tendency toward early metastasis and chemotherapy resistance. Recent studies have reported the EGFR mutation and its response to gefitinib treatment in SCLC. We would like to share our experience of EGFR studies on SCLC patients., Methods: Between 2004 and 2009, we prospectively collected 76 specimens from patients with SCLC at the National Taiwan University Hospital, Taiwan. These specimens included 10 computed tomography-guided biopsy specimens, 17 echo-guided aspiration specimens, 37 echo-guided biopsy specimens, 1 surgical lobectomy specimen, and 11 malignant pleural effusion specimens. Molecular genetic analysis of the specimens was conducted to detect the EGFR mutation., Results: Among the 76 SCLC specimens we examined, 2 (2.6%) tested positive for the EGFR mutation and both were deletions in exon 19. One patient was administered gefitinib after several lines of chemotherapy but showed no treatment response. To date, only 11 EGFR mutant-positive SCLC patients, including our 2 patients, have been reported. Most of these patients were never smokers. The SCLC harboring EGFR mutation were more likely to be combined with adenocarcinoma compared with the whole SCLC population., Conclusions: The EGFR mutation is rare in SCLC patients. Despite the presence of the EGFR mutation, gefitinib may not be effective in treating SCLC patients.