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Background: Lung cancer (LC) is a leading cause of cancer-related mortality, and immunotherapy (IO) has shown promise in treating advanced-stage LC. However, identifying patients likely to benefit from IO and monitoring treatment response remains challenging. This study aims to develop a predictive model for progression-free survival (PFS) in LC patients with IO based on clinical features and advanced imaging biomarkers., Materials and Methods: A retrospective analysis was conducted on a cohort of 206 LC patients receiving IO treatment. Pre-treatment computed tomography images were used to extract advanced imaging biomarkers, including intratumoral and peritumoral-vasculature radiomics. Clinical features, including age, gene status, hematology, and staging, were also collected. Key radiomic and clinical features for predicting IO outcomes were identified using a two-step feature selection process, including univariate Cox regression and chi-squared test, followed by sequential forward selection. The DeepSurv model was constructed to predict PFS based on clinical and radiomic features. Model performance was evaluated using the area under the time-dependent receiver operating characteristic curve (AUC) and concordance index (C-index)., Results: Combining radiomics of intratumoral heterogeneity and peritumoral-vasculature with clinical features demonstrated a significant enhancement (p < 0.001) in predicting IO response. The proposed DeepSurv model exhibited a prediction performance with AUCs ranging from 0.76 to 0.80 and a C-index of 0.83. Furthermore, the predicted personalized PFS curves revealed a significant difference (p < 0.05) between patients with favorable and unfavorable prognoses., Conclusions: Integrating intratumoral and peritumoral-vasculature radiomics with clinical features enabled the development of a predictive model for PFS in LC patients with IO. The proposed model's capability to estimate individualized PFS probability and differentiate the prognosis status held promise to facilitate personalized medicine and improve patient outcomes in LC., (© 2024. The Author(s).)

Background and Objective: Epidermal growth factor receptor (EGFR)-targeted tyrosine kinase inhibitors (TKIs) are the first-line therapy for EGFR-mutant non-small-cell lung cancer (NSCLC). Early prediction of treatment failure in patients with brain metastases treated with EGFR-TKIs may help in making decisions for systemic drug therapy or local brain tumor control. This study examined the predictive power of the radiomics of both brain metastasis tumors and primary lung tumors. We propose a deep learning based CoxCC model based on quantitative brain magnetic resonance imaging (MRI), a prognostic index and clinical data; the model can be used to predict progression-free survival (PFS) after EGFR-TKI therapy in advanced EGFR-mutant NSCLC., Methods: This retrospective single-center study included 271 patients receiving first-line EGFR-TKI targeted therapy in 2018-2019. Among them, 72 patients who had brain metastases before receiving first-line EGFR-TKI treatment. Three radiomic features were extracted from pretreatment brain MRI images. A CoxCC model for the progression risk stratification of EGFR-TKI treatment was proposed on the basis of MRI radiomics, clinical features, and a prognostic index. We performed time-dependent PFS predictions to evaluate the performance of the CoxCC model., Results: The CoxCC model based on a prognostic index, clinical features, and radiomic features of brain metastasis exhibited higher performance than clinical features combined with indexes previously proposed for determining the prognosis of brain metastasis, including recursive partitioning analysis, diagnostic-specific graded prognostic assessment, graded prognostic assessment for lung cancer using molecular markers (lung-molGPA), and modified lung-molGPA, with c-index values of 0.75, 0.67, 0.66, 0.65, and 0.65, respectively. The model achieved areas under the curve of 0.88, 0.73, 0.92, and 0.90 for predicting PFS at 3, 6, 9 and 12 months, respectively. PFS significantly differed between the high- and low-risk groups (p < 0.001)., Conclusions: For patients with advanced-stage NSCLC with brain metastasis, MRI radiomics of brain metastases may predict PFS. The CoxCC model integrating brain metastasis radiomics, clinical features, and a prognostic index provided reliable multi-time-point PFS predictions for patients with advanced NSCLC and brain metastases receiving EGFR-TKI treatment., Competing Interests: Declaration of Competing Interest All authors declare no potential conflicts of interest., (Copyright © 2023. Published by Elsevier Inc.)

Our study aimed to harness the power of CT scans, observed over time, in predicting how lung adenocarcinoma patients might respond to a treatment known as EGFR-TKI. Analyzing scans from 322 advanced stage lung cancer patients, we identified distinct image-based patterns. By integrating these patterns with comprehensive clinical information, such as gene mutations and treatment regimens, our predictive capabilities were significantly enhanced. Interestingly, the precision of these predictions, particularly related to radiomics features, diminished when data from various centers were combined, suggesting that the approach requires standardization across facilities. This novel method offers a potential pathway to anticipate disease progression in lung adenocarcinoma patients treated with EGFR-TKI, laying the groundwork for more personalized treatments. To further validate this approach, extensive studies involving a larger cohort are pivotal.

Background: Stereotactic ablative radiotherapy (SABR) is now the standard of care for patients with inoperable early-stage lung cancer. Many of these patients are elderly. EGFR (epidermal growth factor receptor) mutation is also common in the Asian population., Methods: To evaluate the effects of old age and EGFR mutation on treatment outcomes and toxicity, we reviewed the medical records of 71 consecutive patients with inoperable early-stage non-small cell lung cancer (NSCLC) who received SABR at Taipei Veterans General Hospital between 2015 and 2021., Results: The study revealed that median age, follow-up, Charlson comorbidity index, and ECOG score were 80 years, 2.48 years, 3, and 1, respectively. Of these patients, 37 (52.1%) were 80 years or older, and 50 (70.4%) and 21 (29.6%) had T1 and T2 diseases, respectively. EGFR mutation status was available for 33 (46.5%) patients, of whom 16 (51.5%) had a mutation. The overall survival rates at 1, 3, and 5 years were 97.2, 74.9, and 58.3%, respectively. The local control rate at 1, 3, and 5 years was 97.1, 92.5, and 92.5%, respectively. Using Cox proportional hazards regression we found that male sex was a risk factor for overall survival (p = 0.036, 95% CI: 1.118-26.188). Two patients had grade 2 pneumonitis, but no other grade 2 or higher toxicity was observed. We did not find any significant differences in treatment outcomes or toxicity between patients aged 80 or older and those with EGFR mutations in this cohort., Conclusion: These findings indicate that age and EGFR mutation status do not significantly affect the effectiveness or toxicity of SABR for patients with inoperable early-stage NSCLC., (© 2023 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.)

Background: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSCLC) with EGFR mutations. Approximately half of the patients with EGFR-mutated NSCLC are treated with EGFR-TKIs and develop disease progression within 1 year. Therefore, the early prediction of tumor progression in patients who receive EGFR-TKIs can facilitate patient management and development of treatment strategies. We proposed a deep learning approach based on both quantitative computed tomography (CT) characteristics and clinical data to predict progression-free survival (PFS) in patients with advanced NSCLC after EGFR-TKI treatment., Methods: A total of 593 radiomic features were extracted from pretreatment chest CT images. The DeepSurv models for the progression risk stratification of EGFR-TKI treatment were proposed based on CT radiomic and clinical features from 270 stage IIIB-IV EGFR-mutant NSCLC patients. Time-dependent PFS predictions at 3, 12, 18, and 24 months and estimated personalized PFS curves were calculated using the DeepSurv models., Results: The model combining clinical and radiomic features demonstrated better prediction performance than the clinical model. The model achieving areas under the curve of 0.76, 0.77, 0.76, and 0.86 can predict PFS at 3, 12, 18, and 24 months, respectively. The personalized PFS curves showed significant differences (p < 0.003) between groups with good (PFS > median) and poor (PFS < median) tumor control., Conclusions: The DeepSurv models provided reliable multi-time-point PFS predictions for EGFR-TKI treatment. The personalized PFS curves can help make accurate and individualized predictions of tumor progression. The proposed deep learning approach holds promise for improving the pre-TKI personalized management of patients with EGFR-mutated NSCLC., (© 2023. The Author(s).)

Low-dose computed tomography (LDCT) has emerged as a standard method for detecting early-stage lung cancer. However, the tedious computer tomography (CT) slide reading, patient-by-patient check, and lack of standard criteria to determine the vague but possible nodule leads to variable outcomes of CT slide interpretation. To determine the artificial intelligence (AI)-assisted CT examination, AI algorithm-assisted CT screening was embedded in the hospital picture archiving and communication system, and a 200 person-scaled clinical trial was conducted at two medical centers. With AI algorithm-assisted CT screening, the sensitivity of detecting nodules sized 4−5 mm, 6~10 mm, 11~20 mm, and >20 mm increased by 41%, 11.2%, 10.3%, and 18.7%, respectively. Remarkably, the overall sensitivity of detecting varied nodules increased by 20.7% from 67.7% to 88.4%. Furthermore, the sensitivity increased by 18.5% from 72.5% to 91% for detecting ground glass nodules (GGN), which is challenging for radiologists and physicians. The free-response operating characteristic (FROC) AI score was ≥0.4, and the AI algorithm standalone CT screening sensitivity reached >95% with an area under the localization receiver operating characteristic curve (LROC-AUC) of >0.88. Our study demonstrates that AI algorithm-embedded CT screening significantly ameliorates tedious LDCT practices for doctors.

This study aimed to investigate the proportion of young OSA adults with sleep-related complaints in a sleep center, affiliated with a tertiary medical center for over a decade. This study presents a chronicle change in the numbers of young adults receiving polysomnography (PSG) and young patients with OSA from 2000 to 2017. We further analyzed 371 young patients with OSA among 2378 patients receiving PSG in our sleep center from 2016 to 2017 to capture their characteristics. Young adults constituted a substantial and relatively steady portion of examinees of PSG (25.1% ± 2.8%) and confirmed OSA cases (19.8 ± 2.4%) even though the total numbers increased with the years. Young adults with OSA tend to be sleepier, have a greater body mass index, and have a higher percentage of cigarette smoking and alcohol consumption. They also complained more about snoring and daytime sleepiness. They had a higher apnea-hypopnea index on average and experienced more hypoxemia during their sleep, both in terms of duration and the extent of desaturation. Even though the prevalence of comorbidities increased with age, hypertension in young male adults carried higher risks for OSA. Young adults with OSA have constituted a relatively constant portion of all confirmed OSA cases across time. The young adults with OSA were heavier, more symptomatic, and with more severe severity.Clinical trial: The Institutional Review Board of Taipei Veterans General Hospital approved the study (VGHIRB No. 2018-10-002CC). The study is registered with ClinicalTrials.gov, number NCT03885440., (© 2022. The Author(s).)

Early detection increases overall survival among patients with lung cancer. This study formulated a machine learning method that processes chest X-rays (CXRs) to detect lung cancer early. After we preprocessed our dataset using monochrome and brightness correction, we used different kinds of preprocessing methods to enhance image contrast and then used U-net to perform lung segmentation. We used 559 CXRs with a single lung nodule labeled by experts to train a You Only Look Once version 4 (YOLOv4) deep-learning architecture to detect lung nodules. In a testing dataset of 100 CXRs from patients at Taipei Veterans General Hospital and 154 CXRs from the Japanese Society of Radiological Technology dataset, the sensitivity of the AI model using a combination of different preprocessing methods performed the best at 79%, with 3.04 false positives per image. We then tested the AI by using 383 sets of CXRs obtained in the past 5 years prior to lung cancer diagnoses. The median time from detection to diagnosis for radiologists assisted with AI was 46 (3-523) days, longer than that for radiologists (8 (0-263) days). The AI model can assist radiologists in the early detection of lung nodules.

Purpose: Obstructive sleep apnea (OSA) is characterized by intermittent hypoxemia and sleep fragmentation. While apnea is pronounced with severe desaturation during rapid eye movement (REM) sleep, REM-related OSA is a distinct phenotype of OSA associated with respiratory disturbances predominantly during REM sleep. In this study, we investigated the clinical features of REM-related OSA in Taiwan., Patients and Methods: All patients diagnosed with OSA in the Taipei Veterans General Hospital from 2015 to 2017 were analyzed retrospectively and classified into REM-related OSA (REM-OSA) group, non-REM related OSA (NREM-OSA) group, and non-stage specific-OSA group. The clinical demographics, OSA-related symptoms, polysomnography results, and medical comorbidities of the three groups were analyzed., Results: Among 1331 patients with OSA, 414 (31.1%) were classified as REM-OSA, 808 (60.7%) as NREM-OSA, and 109 (8.2%) as non-stage specific-OSA. After being adjusted for OSA severity, the REM-OSA group was associated with less portion of males, longer desaturation duration, and lower nadir oxygen saturation (SpO 2 ) compared with the NREM-OSA group in mild and moderate OSA. In moderate OSA, the non-stage specific-OSA group featured more OSA severity and more desaturation compared with the other groups. The Epworth Sleepiness Scale scores and the prevalence of comorbidities did not vary among the REM-OSA, NREM-OSA, and non-stage specific-OSA groups. High REM-AHI/NREM-AHI ratio was associated with young age, female gender, high BMI, and low AHI., Conclusion: OSA patients with high REM-AHI/NREM-AHI ratio are related to young age, female gender, high BMI, and low AHI. Patients with REM-related OSA presented with longer desaturation duration and lower nadir SpO2 after being adjusted for OSA severity., Competing Interests: The authors declare that they know of no conflicts of interest in relation to this paper., (© 2022 Chiu et al.)

Detection of driver gene mutations is important in advanced NSCLC. The cobas EGFR mutation test is a mutant allele-specific real-time PCR assay with limitation owing to its primer design. Next-generation sequencing-based assay has a higher mutation detection coverage; however, its clinical impact remains unclear. We retrospectively collected the records of stage IV NSCLC patients with wild-type EGFR tested by cobas test. FoundationOne CDx was used for comprehensive genomic profiles. We then evaluated the missed EGFR mutations by the cobas test. We studied 62 patients. The median age was 60 (range: 35-86 years). Most patients were male and 58.1% were smokers. 91.9% were adenocarcinomas. Of the 62 samples, 7 (11.3%) were detected with EGFR mutations by NGS. Among these overlooked EGFR mutations, five were exon 20 insertions, and two were exon 19 deletions. Two patients received EGFR TKIs and showed durable response with PFS 5.9 months and 10.1 months, respectively. Using NGS as the standard, the false-negative rate of the cobas EGFR mutation test was 11.3%-in a population with a high prevalence of EGFR mutations. The most overlooked mutations were exon 20 insertions. A comprehensive EGFR mutation assay can provide significant benefits to patients with NSCLC., (© 2022. The Author(s).)

Whether ICIs combined with chemotherapy can improve outcomes in EGFR-mutant non-small cell lung cancer (NSCLC) remains uncertain. Patients with EGFR-mutant NSCLC and who progressed on first-line EGFR-TKIs treatment were retrospectively collected. We reviewed the outcome of these patients treated with ICIs or ICIs combined chemotherapy (ICI + C). Total 30 patients were included. The ORR were 9.1% and 25.0% for the ICI and ICI + C groups. The ICI + C group showed the trend of longer progression-free survival and overall survival periods. Patients without the T790M mutation had a significantly longer PFS than did those without this mutation (4.23 [95% CI: 2.75-5.72] vs. 1.70 [95% CI: 0.00-3.51] months, HR:4.45, p = 0.019). ICIs combined with chemotherapy tended to be more effective than ICIs alone in pretreated EGFR-mutant NSCLC. The T790M mutation may be a potential biomarker., (© 2021. The Author(s).)

Background: Leptomeningeal metastasis (LM) is a fatal complication of advanced non-small-cell lung cancer (NSCLC)., Objective: The aim of this study was to evaluate the utility of cerebrospinal fluid (CSF) as a medium for epidermal growth factor receptor (EGFR) mutation testing in clinical practice., Patients and Methods: We prospectively enrolled patients with EGFR-mutant NSCLC who underwent CSF sampling for suspected LM. The supernatant of CSF after routine cytology examination was collected. The diagnosis of LM was established according to EANO-ESMO criteria. CSF and plasma cell-free DNA (cfDNA) were retrieved for EGFR mutation testing., Results: Fifty-one patients with a median age of 62.7 years were enrolled. The median duration from initial diagnosis to CSF sampling was 23.0 months and most patients (94.1%) had received at least one EGFR-tyrosine kinase inhibitor. Adenocarcinoma cells were found in 37 CSF samples (72.5%), and 48 (94.1%) patients had confirmed or probable LM. Thirty-five of these 48 patients (72.9%) had valid EGFR mutation-testing results using CSF cfDNA and tended to have higher white blood cell counts and positive cytology in their CSF compared to those with invalid mutation testing results. The overall detection rate of EGFR mutation in CSF cfDNA was 68.8%, and the T790M detection rate was 14.6%. In 37 patients with paired CSF and plasma samples, the concordance rate of the EGFR mutation results was 29.7%., Conclusions: For patients with EGFR-mutant NSCLC with LM, CSF supernatant is a valuable source for EGFR mutation testing and may provide important information.

Objectives: Brain metastasis (BM) is common in patients with epidermal growth factor receptor (EGFR)-mutant lung cancer. However, the brain surveillance strategy during treatment in advanced lung cancer patients varies, and the impact on clinical outcome is unclear. Here we aimed to evaluate the effect of different brain surveillance strategies on the clinical characteristics and treatment outcome in patients with EGFR-mutant lung adenocarcinoma treated with first-line EGFR tyrosine kinase inhibitors (EGFR-TKIs)., Materials and Methods: This is a retrospective observational study conducted in a medical center in an area with high prevalence of EGFR mutation. Patients with initially diagnosed stage IV EGFR-mutant lung adenocarcinoma were included. Patients undergoing regular brain magnetic resonance imaging (MRI) every 3-6 months were categorized in the regular follow-up (RFU) group, and the rest were categorized in the liberal follow-up (LFU) group. Clinical outcomes were collected and analyzed., Results: A total of 310 patients were included, and 43.5% initially had brain metastases. Patients in the LFU group were significantly older than those in the RFU group (median age: 67 vs 62, p < 0.001). The overall survival and time-to-treatment failure of patients with initial EGFR-TKIs treatment showed no statistical difference between the two groups. However, the intracranial progression free survival was significantly shorter in the RFU group than in the LFU group (p = 0.009). The risk of mortality was similar in the LFU and RFU groups. There was no difference in the intracranial progression patterns and cause of death between the two groups., Conclusions: For EGFR-mutant lung adenocarcinoma patients who used EGFR-TKIs as the frontline therapy, regular or liberal brain MRI follow-up showed no significant impact on the outcome, irrespective of initial brain metastasis., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Background: It is importance to train the interprofessional collaboration (IPC) and team-efficiency (TE) of medical trainees. This prospective study evaluates whether implementation of scenario/video-created workshops in integrated IPC and TE (IIT) program provides additional benefits for IPC-TE skills of nursing trainees., Methods: Mock simulation with two IIT scenarios was held as preintervention IPC-TE assessment. Basic and advanced workshops were arranged for teams of intervention groups for creation of discipline-specific scenario and video. Thirty-six nursing trainees were randomized into teams of five members (three nursing students, one standardized medical student, and one standardized trainees of other profession) in either intervention (scenario plus video and scenario) or control groups. After intervention, all groups received the formal simulation-based assessment using another two IIT scenario. In addition to instructors-based assessment of team' performance in mock and formal IIT simulation using interprofessional team collaboration scale (AITCS), self-assessment of attitudes and program-value score were completed by each trainee, using attitudes toward interprofessional health care teams scale (ATIHCTS) at all stages., Results: Nursing trainees in intervention group gave high satisfaction score to this IIT intervention. In comparison with control group, greater increase in instructor-assessed team performance in the "partnership," "cooperation," and "shared decision making" domains of AITCS and the self-assessed "quality of care delivery" and TE domains of ATIHCTS were noted in the intervention groups. The overall improvement was greater in the scenario plus video group than those in the scenario group. Further, these improvements among nursing trainees persisted until follow-up stage at 4-weeks later., Conclusion: For nursing trainees, our study suggested that implementation of a scenario creation-based training resulted in additional improvement in trainee' IPC and TE behaviors and attitudes. Additionally, making video of newly created nurse-specific scenario enhances partnership and cooperation among nursing trainees and their interprofessional team members.

This article was migrated. The article was marked as recommended. Background: Holistic nursing of intensive care unit (ICU) patients encompasses diverse challenges requiring regular in situ evaluation, training and assessment. Simulation has been adopted as a research and training tool in medicine; however, evidence for its use in enhancing holistic care at multi-sites is limited. Objective: This study aims to conduct a simulation-based research (SBR) at four ICU for standardized training of holistic nursing. Methods: There are stages of evaluating, training+in-training assessment and post-course assessment in this SBR. Specifically, the curriculum-mapped scenarios were developed according to the deficiency of each site after evaluating stage. At the training stage, the first simulation by team was defined as preparation step and the in-training assessment was undertaken at the second simulation. Results: From January 2017 to October 2018, sixty-four ICU nurses (16 teams, 4 teams in each site) at RCU, PICU, NICU and GYN ICU, attend 8 similar courses (2 courses at each site) over 20 months. In comparison with baseline performance, in-training assessments revealed the significant improvement of attendee's skills of holistic nursing. Attendees commented that simulation was a valuable training modality to enhance skills of holistic care including history taking, physical examination, communication and teamwork that are rarely taught among ICU nurses. Post-course workplace assessment by senior nurses revealed the high frequency of clinical application of holistic nursing by attendees. Additionally, post-course self assessment revealed a high attendee's confidence of holistic approaching in ICU. Conclusion: This pilot SBR demonstrated the feasibility of a standardized holistic care simulation program across four ICUs. In situ simulation and post-course workplace assessment affords situational learning without compromising patient safety and is an exciting and novel training of holistic nursing for ICU that could be integrated into regular intervention., (Copyright: © 2019 Shiao TH et al.)

Overnight polysomnography (PSG) is currently the standard diagnostic procedure for obstructive sleep apnea (OSA). It has been known that monitoring of head position in sleep is crucial not only for the diagnosis (positional sleep apnea) but also for the management of OSA (positional therapy). However, there are no sensor systems available clinically to hook up with PSG for accurate head position monitoring. In this paper, an accelerometer-based sensing system for accurate head position monitoring is developed and realized. The core CORDIC- (COordinate Rotation DIgital Computer-) based tilting sensing algorithm is realized in the system to quickly and accurately convert accelerometer raw data into the desired head position tilting angles. The system can hook up with PSG devices for diagnosis to have head position information integrated with other PSG-monitored signals. It has been applied in an IRB test in Taipei Veterans General Hospital and has been proved that it can meet the medical needs of accurate head position monitoring for PSG diagnosis.

Objective: Patients with sleep apnea sustain cessation of breath during sleep, leading to intermittent hypoxia, systemic inflammation, and sympathetic activation. These insults may contribute to initiation or progression of peptic ulcers. This retrospective matched-control cohort study explored the relationship of sleep apnea and subsequent development of peptic ulcer bleeding., Methods: From 2000 to 2009, patients with newly diagnosed sleep apnea were identified from the Taiwan National Health Insurance Research Database. A control group without sleep apnea, matched for age, gender, comorbidities, and medications, was selected for comparison. In both groups, subjects with history of peptic ulcer bleeding, nonspecific gastrointestinal bleeding, or malignancy were excluded. The 2 cohorts were followed up and observed for occurrence of peptic ulcer bleeding., Results: Of the 35,480 sampled patients (7096 patients with sleep apnea vs 28,384 controls), 84 (0.24%) experienced peptic ulcer bleeding during a follow-up period of 3.57±2.61 years, including 32 (0.45% of patients with sleep apnea) from the sleep apnea cohort and 52 (0.18% of control) from the control group (log-rank test, P<.0001). In comparison with subjects without development of peptic ulcer bleeding, those with peptic ulcer bleeding were older and had a higher percentage of sleep apnea, coronary artery disease, peptic ulcer, ischemic stroke, and medication for nonsteroidal anti-inflammatory drugs. By Cox regression analysis, sleep apnea, older age, and peptic ulcer history were independent predictors of peptic ulcer bleeding. Patients with sleep apnea experienced a 2.400-fold (95% confidence interval, 1.544-3.731; P<.001) higher risk for incident peptic ulcer bleeding after adjusting for other variables., Conclusions: Sleep apnea may be an independent risk factor for peptic ulcer bleeding., (Copyright © 2013 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.