The clinical value of genetic testing in lung squamous cell carcinoma: A retrospective study

The clinical value of genetic testing in lung squamous cell carcinoma: A retrospective study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The clinical value of genetic testing in lung squamous cell carcinoma: A retrospective study Jun Fang, Haiping Huang, Lin Xiong, Huiwen Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9260557/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives To assess the necessity and clinical value of genetic testing in lung squamous cell carcinoma (LSCC) diagnosis and treatment. Methods This retrospective study enrolled 1,515 LSCC patients from Chongqing University Cancer Hospital. Medical records were reviewed to analyze gene mutations, treatment and healthcare economic outcomes for clinical benefit evaluation. Results Of 1,515 patients, 292 received genetic testing (221 smokers, 71 non-smokers). Fifty-six had clear driver gene mutations, with a 19.2% detection rate, dominated by EGFR (n = 26) and MET (n = 10) mutations. In non-smokers, the incidence of EGFR mutations (66.7%, 20/30) was significantly higher than that of other mutations, while the mutation types were scattered in smokers. Patients aged ≤ 65 years, females and non-smokers showed higher mutation rates. Targeted therapy improved objective response rate, disease control rate and median progression-free survival, with fewer grade 3–4 adverse events (4.7% vs 40.0%). Cost-effectiveness analysis showed a negative incremental cost-effectiveness ratio, meaning targeted therapy delivered better outcomes at lower costs. Conclusions Genetic testing is highly recommended for LSCC patients, especially non-smokers, those aged ≤ 65 years and females. Its combination with targeted therapy offers significant clinical benefits and favorable cost-effectiveness, guiding treatment selection and prognosis improvement. lung squamous cell carcinoma genetic testing targeted therapy conventional therapy Figures Figure 1 Introduction Lung cancer, as the malignant tumor with the highest incidence and mortality rate in China, poses a major challenge to public health. It causes severe physical and psychological suffering in patients and their families and imposes a heavy burden on the public healthcare system (Zheng 2024). In the diagnosis and treatment of lung cancer, the relatively mature precision therapy for lung adenocarcinoma has been established. Genetic testing has become one of the routine clinical examinations for this subtype, with its driver gene mutation rate reaching around 60% (Cancer Genome Atlas Research Network 2014 ). Based on the result of genetic testing, clinicians can formulate targeted therapy regimens for patients. This approach not only markedly improves treatment efficacy but also has less impact on patients' quality of life due to mild adverse events (AEs). Moreover, it results in lower systemic damage compared with traditional chemoradiotherapy. However, genetic testing has not been established as a routine examination for lung squamous cell carcinoma (LSCC) domestically or internationally. Previous studies have suggested limited clinical value of genetic testing in LSCC because of its low overall mutation rate and the absence of definite driver gene mutations in patients with a long smoking history (Reuss 2025). In recent years, several studies indicate that non-smoking patients with LSCC exhibit a higher incidence of driver gene mutations, and the progression of carcinogenesis in this subgroup may be more likely associated with such mutations (Murphy 2025). In this study, we performed a retrospective analysis of 1,515 patients diagnosed with LSCC at Chongqing University Cancer Hospital to evaluate the clinical value of genetic testing in LSCC. Subgroup and cost-effectiveness analyses were also conducted, aiming to provide more comprehensive evidence-based medical data for the precision diagnosis and treatment of LSCC. Methods Study population In this retrospective study, 1,515 patients with histopathologically confirmed LSCC treated at Chongqing University Cancer Hospital from January 2018 to December 2024 were included. Clinical data were collected for all patients, including sex, age, smoking status (smoking was defined as smoking ≥ 1 cigarette per day for at least 6 months; non-smoking was defined as never smoking or a cumulative smoking duration of < 6 months), pathological stage, genetic testing results, treatment regimens, treatment outcomes, quality-of-life assessments, and AEs. This retrospective study was approved by the Ethics Committee of Chongqing University Cancer Hospital (Approval No. CZLL2025-226-002). Informed consent was waived due to the use of fully anonymized clinical data. Gene mutation analysis Genetic testing was conducted on tumor tissues or body fluids using the amplification refractory mutation system (ARMS). Treatment After evaluating the patient's condition and communicating fully with the patient and their family, patients with identified gene mutations were prescribed corresponding targeted drugs. For the remaining patients with gene mutations but without corresponding targeted drugs, as well as those with no detectable gene mutations, conventional chemoradiotherapy regimens were administered until disease progression or death. Outcomes assessment All patients were scheduled for follow-up once a month after diagnosis. They received re-examination if any symptoms of disease progression occurred during treatment. The last follow-up was conducted in December 2025. And the median follow-up time was 20.0 months (range: 1.0–77.0 months). Tumor response assessment was performed according to the Response Evaluation Criteria in Solid Tumors (RECIST 1.0) and categorized into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Objective response rate (ORR) was defined as the proportion of patients achieving CR or PR. Disease control rate (DCR) was defined as the proportion of patients achieving CR, PR, or SD. Progression-free survival (PFS) was defined as the period from diagnosis to disease progression, death, or the last follow-up. Overall survival (OS) was defined as the period from diagnosis to death, or the last follow-up. Meanwhile, quality-of-life scores, psychological scores, and recurrence/metastasis status of the patients were recorded before and after treatment. Safety assessment AEs of grade ≥ 1 occurring during treatment were documented and categorized into grades 1–4 according to the severity. The incidence of AEs and the proportion of treatment discontinuations due to AEs were calculated for each treatment subgroup. Cost-effectiveness analysis The costs of testing and treatment were calculated based on the medical charging standards of Chongqing City in 2024, including the cost of genetic testing, the average monthly cost of targeted drugs, and conventional chemotherapy. After insurance reimbursement, the self-payment was approximately 9000 RMB, 4931.5 RMB, and 15078.3 RMB, respectively. The quality-adjusted life year (QALY) and the incremental cost-effectiveness ratio (ICER) were calculated, using three times the per capita gross domestic product (GDP) of China as the cost-effectiveness threshold. Statistical analysis Statistical analyses were performed with the SPSS package (version 19.0, IBM, USA). Survival analysis was conducted with the Kaplan-Meier method. Chi-square test was used to compare mutation detection rates, as well as tumor response rates, AEs incidence between different treatment groups, and subgroup mutation rates. Statistical significance was set at P < 0.05. Results Gene mutation analysis A total of 1,515 patients with LSCC were included in this study, among whom 292 underwent genetic testing (221 smokers and 71 non-smokers; 268 males and 24 females). The mean age was 65.2 years (range, 39.0–92.0 years). Definite driver gene mutations were identified in 56 of the 292 patients, with an overall mutation detection rate of 19.2%. Stratified by smoking status, the mutation rate was 11.8% in smokers and 42.3% in non-smokers. Among patients with detected mutations, EGFR mutation was identified in 26 cases, MET mutation in 10 cases, ERBB2 mutation in 5 cases, ROS1 mutation in 5 cases, ALK mutation in 4 cases, NRAS mutation in 4 cases, and RET mutation in 2 cases (Table 1 ). In non-smokers, the incidence of EGFR mutation (66.7%, 20/30) was significantly higher than that of other gene mutations, whereas mutation types in smokers were more scattered. Subgroup analysis showed that the mutation detection rate was significantly higher in patients aged ≤ 65 years (20.6%, 29/141) than in those aged > 65 years (17.9%, 27/151). In terms of gender stratification, the mutation rate in female patients (70.8%, 17/24) was significantly higher than that in male patients (14.6%, 39/268; χ²=44.99, P < 0.001). And the mutation rate in non-smoking patients (42.3%, 30/71) was significantly higher than that in smoking patients (11.8%, 26/221; χ²=32.23, P < 0.001). Table 1 Statistics of baseline characteristics and mutation status in patients with positive genetic test results Number Genotype Sex Age Smoking Status 01 ALK Male 81 Smoking* 02 ALK Male 64 Smoking 03 ALK Male 66 Smoking 04 ALK Male 56 Smoking 05 EGFR Male 61 Non-smoking** 06 EGFR Male 85 Smoking 07 EGFR Male 72 Non-smoking 08 EGFR Male 74 Smoking 09 EGFR Female 71 Non-smoking 10 EGFR Male 69 Non-smoking 11 EGFR Female 65 Non-smoking 12 EGFR Female 54 Non-smoking 13 EGFR Male 56 Smoking 14 EGFR Male 67 Non-smoking 15 EGFR Male 69 Non-smoking 16 EGFR Female 65 Non-smoking 17 EGFR Female 69 Non-smoking 18 EGFR Male 52 Smoking 19 EGFR Female 55 Non-smoking 20 EGFR Male 63 Non-smoking 21 EGFR Male 58 Smoking 22 EGFR Female 72 Non-smoking 23 EGFR Male 53 Non-smoking 24 EGFR Female 64 Non-smoking 25 EGFR Female 71 Non-smoking 26 EGFR Female 74 Non-smoking 27 EGFR Female 58 Non-smoking 28 EGFR Male 91 Non-smoking 29 EGFR Male 64 Smoking 30 EGFR Female 59 Non-smoking 31 ERBB2 Male 72 Non-smoking 32 ERBB2 Male 83 Smoking 33 ERBB2 Male 60 Smoking 34 ERBB2 Female 76 Non-smoking 35 ERBB2 Male 61 Smoking 36 MET Male 62 Non-smoking 37 MET Male 55 Smoking 38 MET Female 70 Non-smoking 39 MET Female 59 Non-smoking 40 MET Male 63 Smoking 41 MET Male 71 Smoking 42 MET Male 73 Non-smoking 43 MET Female 92 Non-smoking 44 MET Male 71 Smoking 45 MET Male 65 Smoking 46 NRAS Male 66 Smoking 47 NRAS Male 68 Smoking 48 NRAS Male 62 Non-smoking 49 NRAS Male 69 Smoking 50 RET Female 74 Non-smoking 51 RET Male 60 Smoking 52 ROS1 Male 69 Smoking 53 ROS1 Male 64 Smoking 54 ROS1 Male 76 Smoking 55 ROS1 Male 55 Smoking 56 ROS1 Male 58 Non-smoking * Smoking was defined as smoking ≥ 1 cigarette per day for at least 6 months ** Non-smoking was defined as never smoking or a cumulative smoking duration of < 6 months Efficacy analysis As of the last follow-up date (December 2025), among the 21 patients receiving targeted therapy, 4 achieved CR, 4 PR, 6 SD, and 7 PD, corresponding to an ORR of 38.1% and a DCR of 66.7%. The median PFS was 28.0 months (Fig. 1 ). Among the 35 patients receiving conventional therapy, 3 achieved CR, 4 PR, 4 SD, and 24 PD. ORR and DCR were 20.0% and 31.4%, respectively. Of these 35 patients, 11 remained progression-free, with a median PFS of 14.0 months (Fig. 1 ). The efficacy of targeted therapy corresponding to different gene mutation subtypes is shown in Table 2 . Among patients with EGFR mutations, targeted therapy was administered in 13 cases, achieving a DCR of 69.2% (9/13) and a mean OS of 34.8 months. In contrast, conventional therapy was given to 13 cases, with a DCR of 38.5% (5/13) and a mean OS of 33.2 months. Long-term survival analysis demonstrated a mean OS of 30.0 months and a median OS of 28.0 months in the targeted therapy group. In contrast, the conventional therapy group had a mean OS of 23.4 months and a median OS of 14.0 months. The differences in OS between the two groups were statistically significant (P < 0.05). Analysis of recurrence and metastasis revealed that the recurrence rate was 33.3% (7/21) in the targeted therapy group, including 2 cases of local recurrence and 5 cases of distant metastasis (predominantly brain metastasis). In the conventional therapy group, the recurrence rate was 68.6% (24/35), including 9 cases of local recurrence and 15 cases of distant metastasis (predominantly bone and intrapulmonary metastasis). The risk of distant metastasis was significantly lower in the targeted therapy group (HR = 0.31, 95% CI: 0.13–0.72, P = 0.007). Differences in quality-of-life and AEs between treatment groups Quality-of-life and psychological scores were assessed for all patients before and after treatment during regular follow-up. The results indicated that the clinical benefits of patients receiving targeted therapy were significantly superior to those receiving conventional therapy (Table 2 ). In the targeted therapy group (n = 21), the most common AEs were rash (38.1%) and diarrhea (28.6%), with the incidence of grade 3–4 AEs being only 4.7% (1/21, grade 3 rash [n = 1]). In the conventional therapy group (n = 35), the most common AEs were myelosuppression (65.7%) and gastrointestinal reactions (60.0%), with the incidence of grade 3–4 AEs being 34.3% (12/35, grade 3 myelosuppression [n = 8] and grade 3 nausea and vomiting [n = 4]). The proportion of treatment discontinuations due to AEs was significantly lower in the targeted therapy group (4.8%) than that in the conventional therapy group (34.3%; χ²=6.42, P = 0.011; Table 2 ). Table 2 Therapeutic efficacy under different treatment options Serial No. Treatment group Dead status Tumor response assessment Overall survival (Months) Progression-free survival (Months) Adverse reaction grade Quality of life assessment* C01 Conventional therapy Alive Complete response 54 54 1 1-1-1-1-1 C02 Conventional therapy Alive Complete response 77 77 1 1-1-1-2-1 C03 Conventional therapy Alive Complete response 47 47 1 1-1-1-1-1 C04 Conventional therapy Alive Partial response 16 16 1 1-1-1-1-2 C05 Conventional therapy Alive Partial response 14 14 2 1-1-1-2-2 C06 Conventional therapy Dead Partial response 62 62 1 1-1-1-2-1 C07 Conventional therapy Dead Partial response 9 9 1 1-1-1-2-1 C08 Conventional therapy Dead Stable disease 13 13 2 1-1-1-2-2 C09 Conventional therapy Alive Stable disease 28 28 1 1-1-1-2-1 C10 Conventional therapy Dead Stable disease 66 66 1 1-1-1-2-1 C11 Conventional therapy Alive Stable disease 20 20 2 1-1-2-2-2 C12 Conventional therapy Alive Progressive disease 26 3 3 1-1-1-2-3 C13 Conventional therapy Dead Progressive disease 9 5 3 1-1-2-3-2 C14 Conventional therapy Dead Progressive disease 8 1 3 2-2-3-2-2 C15 Conventional therapy Dead Progressive disease 2 2 3 1-2-2-1-3 C16 Conventional therapy Dead Progressive disease 6 4 4 1-2-3-2-3 C17 Conventional therapy Dead Progressive disease 30 13 2 1-1-1-2-2 C18 Conventional therapy Dead Progressive disease 5 1 3 1-2-3-2-2 C19 Conventional therapy Dead Progressive disease 3 2 3 2-2-3-2-2 C20 Conventional therapy Dead Progressive disease 7 4 2 1-2-2-2-1 C21 Conventional therapy Dead Progressive disease 14 10 3 1-2-2-3-1 C22 Conventional therapy Dead Progressive disease 60 13 1 1-1-2-2-1 C23 Conventional therapy Dead Progressive disease 11 7 1 1-1-2-1-1 C24 Conventional therapy Dead Progressive disease 2 2 4 2-2-3-3-2 C25 Conventional therapy Dead Progressive disease 20 16 2 1-1-1-2-2 C26 Conventional therapy Dead Progressive disease 8 2 2 1-1-2-2-2 C27 Conventional therapy Dead Progressive disease 19 3 4 2-2-3-4-3 C28 Conventional therapy Alive Progressive disease 44 2 4 2-2-3-3-2 C29 Conventional therapy Dead Progressive disease 55 23 1 1-1-2-2-1 C30 Conventional therapy Dead Progressive disease 3 1 4 2-2-3-4-3 C31 Conventional therapy Dead Progressive disease 2 1 4 2-2-3-4-3 C32 Conventional therapy Dead Progressive disease 8 4 2 1-1-2-2-2 C33 Conventional therapy Dead Progressive disease 1 1 2 1-1-2-2-2 C34 Conventional therapy Dead Progressive disease 8 4 3 1-1-2-3-3 C35 Conventional therapy Alive Progressive disease 61 4 1 1-1-1-1-1 T01 Targeted therapy Alive Complete response 20 20 1 1-1-1-1-2 T02 Targeted therapy Alive Complete response 68 68 1 1-1-1-1-1 T03 Targeted therapy Dead Complete response 34 34 1 1-1-1-1-1 T04 Targeted therapy Alive Complete response 15 15 1 1-1-1-1-1 T05 Targeted therapy Dead Partial response 35 35 2 1-1-2-1-2 T06 Targeted therapy Alive Partial response 19 19 1 1-1-1-1-1 T07 Targeted therapy Alive Partial response 56 56 2 1-1-1-2-2 T08 Targeted therapy Alive Partial response 30 30 1 1-1-1-1-1 T09 Targeted therapy Dead Stable disease 28 28 1 1-1-2-1-1 T10 Targeted therapy Dead Stable disease 16 16 1 1-1-1-1-1 T11 Targeted therapy Dead Stable disease 33 33 1 1-1-2-1-1 T12 Targeted therapy Dead Stable disease 28 28 1 1-1-1-1-1 T13 Targeted therapy Dead Stable disease 33 33 1 1-1-1-1-1 T14 Targeted therapy Dead Stable disease 11 11 1 1-1-2-1-1 T15 Targeted therapy Dead Progressive disease 17 14 1 1-1-1-1-2 T16 Targeted therapy Dead Progressive disease 3 3 3 1-1-2-3-3 T17 Targeted therapy Alive Progressive disease 26 2 2 1-1-2-2-1 T18 Targeted therapy Dead Progressive disease 27 16 2 1-1-2-2-1 T19 Targeted therapy Alive Progressive disease 61 24 1 1-1-2-1-1 T20 Targeted therapy Alive Progressive disease 43 31 1 1-1-2-1-1 T21 Targeted therapy Alive Progressive disease 28 17 1 1-1-1-2-1 * Health-related quality of life (HRQoL) was evaluated in this study using the EQ-5D-5L questionnaire, with corresponding scores obtained across five ordered dimensions: Mobility(MO), Self-care(SC), Usual activities(UA), Pain/discomfort(PD), and Anxiety/depression(AD). Cost-effectiveness analysis The QALY was 2.287 in the targeted therapy group and 1.534 in the conventional therapy group. The ICER exhibited a negative value, indicating that targeted therapy yielded more favorable health outcomes with lower treatment costs. As an absolutely dominant strategy, it possessed a significant cost-effectiveness advantage. Collectively, these findings indicate that genetic testing combined with targeted therapy confers a favorable cost-effectiveness profile in non-smoking patients with LSCC. Discussion As one of the major subtypes of lung cancer, LSCC is a consistent challenge in clinical diagnosis and treatment due to its high invasiveness and low response rate (Bray 2018; Zheng 2024). Currently, the standard treatments for LSCC primarily include chemotherapy and radiotherapy. Although these conventional approaches can control tumor progression to some extent, they also compromise patients’ quality of life (Garon 2015; Fernandes 2023). Their drug toxicity and radiation-induced damage harm normal tissues, leading to severe AEs such as myelosuppression and gastrointestinal reactions. Moreover, conventional therapy is associated with the risk of tumor recurrence and metastasis, which further poses a challenge to patients’ quality of life. In addition, while the use of immune checkpoint inhibitors has shown efficacy in some patients, the ORR remains below 20%, which is insufficient to meet the clinical needs (Shim 2017). Targeted therapy guided by genetic testing is an intervention method that targets specific molecular sites of tumor cells (Bouchard 2025 ; Jeon 2025). It specifically inhibits tumor cell proliferation and reduces damage to normal tissues (Jaiyesimi 2024; Su 2025). Compared with conventional therapy, it has the advantages of milder side effects and better tolerability, which exert a more positive impact on patients’ quality of life (Attili 2023). The results of this study demonstrated that patients with LSCC receiving targeted therapy experienced early relief of symptoms such as cough, hemoptysis, and dyspnea caused by tumor compression or invasion, which significantly improved their physical condition and quality of life scores. These benefits may be attributed to the relatively milder adverse reactions and the precision of targeted therapy. In this study, the incidence of grade 3–4 AEs in the targeted therapy group was only 4.7%, significantly lower than that in the conventional therapy group (34.3%), further confirming the safety advantage of targeted therapy. Previous clinical studies on the efficacy of targeted therapy for lung adenocarcinoma have shown that the ORR can be increased by 2–3 fold, and the median OS can be extended by 6–12 months in treated patients relative to the control group (Mok 2017). There is a gap in the efficacy of targeted therapy for LSCC compared with lung adenocarcinoma, largely due to its underutilization in the former. Promoting genetic testing, particularly among non-smoking patients with LSCC, is expected to narrow this gap (Niu 2022). In this study, the overall mutation detection rate in patients with LSCC reached 19.2%, with a significant difference between the smoking (11.8%) and non-smoking (42.3%) groups. However, the current rate of genetic testing in patients with LSCC (19.3%, 292/1515) remains at a low level in clinical practice. This can be attributed to three reasons. First, the previous notion holds that LSCC is a non-mutation-driver tumor with a lower mutation rate compared to lung adenocarcinoma. Second, the mutation spectrum of LSCC related to smoking is complex and lacks clear and highly frequent mutations that can serve as targets (Heist 2012). Third, there are debates regarding the cost-effectiveness ratio between testing expenses and clinical benefits, which has also limited the routine implementation of genetic testing in clinical practice to some extent. The findings of this study indicated that although the overall mutation rate in LSCC was relatively low, group analysis revealed that the gene mutation rate in non-smoking patients was significantly higher than that in smoking patients, with more prominent mutation characteristics observed in non-smoking females aged ≤ 65 years with advanced-stage disease. The underlying mechanisms for this phenomenon may be directly related to the differences in carcinogenic factors, DNA repair capacity, and disease-driving pathways (Lu 2024). First, the primary carcinogenic drivers in smoking patients with LSCC are a small subset of highly recurrent smoking signature mutations (such as TP53 ) induced by polycyclic aromatic hydrocarbons (such as benzopyrene) in tobacco (Denissenko 1996). Their tumor progression relies more on non-mutation-driven factors like chronic inflammation and oxidative stress, resulting in a lower dependence on gene mutations (Forder 2023). In contrast, the pathogenic factors for non-smoking patients involve multiple elements, such as secondhand smoke, air pollution, occupational exposures, and genetic susceptibility, with their driver mutations covering a variety of genes, such as EGFR , ALK , and ROS1 (Murphy 2025). Non-smoking patients with LSCC often exhibit a greater dependence on genetic mutations as the core driving events, with more diverse mutation types and a higher overall mutation proportion (Huang 2016). Second, defects in DNA repair-related genes such as ERCC1 and BRCA are more common in non-smoking patients, which impairs the capacity for DNA damage repair and results in the failure to timely eliminate abnormal mutations (Azuma 2007). This leads to the accumulation of abnormal mutations and increases the detectable mutation rate. This discovery provides a theoretical foundation for the implementation of precision therapy in non-smoking, younger, and advanced-stage patients with LSCC. Conventional detection methods only target a few genes, such as EGFR . With the development of high-throughput sequencing technology, next-generation techniques like multi-gene panel testing can simultaneously cover multiple potential targets, including ALK , ROS1 , and MET , significantly enhancing the detection capability for gene mutations in LSCC (Lindquist 2017). Related studies have shown that multi-gene panel testing can increase the comprehensive mutation detection rate by over 30%, creating opportunities for more patients to access targeted therapy (Illei 2017). At the same time, the accelerated development of targeted drugs for rare mutations has further expanded the clinical value of genetic testing. The healthcare economic analysis revealed that the ICER of the targeted therapy group was negative, which was below the threshold of three times the per capita GDP in China obviously. The finding addresses cost-related concerns in clinical adoption and demonstrates that genetic testing combined with targeted therapy offers favorable health economic value for non-smoking patients with LSCC. It provides economic justification for the routine clinical implementation of this approach. A comparison of the 2024 edition of the NCCN Clinical Practice Guidelines in Oncology for Lung Cancer and the CSCO Guidelines for the Diagnosis and Treatment of Non-Small Cell Lung Cancer reveals that both currently recommend genetic testing for EGFR , ALK , and ROS1 in patients with LSCC who are non-smokers or light smokers (Category Ⅱ recommendation)(Riely 2024). However, potential targets such as MET and RET are not included. Based on the findings of this study, we suggest that the scope of genetic testing for non-smoking patients with LSCC should be expanded to cover multiple genes, including EGFR , ALK , ROS1 , MET , RET , ERBB2 , and NRAS . For smoking patients with advanced LSCC who are intolerant to conventional chemotherapy, next-generation sequencing (NGS)-based multi-gene testing may still be considered to explore additional treatment opportunities (Zhao 2022). This study is limited by its single-center retrospective design. Future multicenter, prospective studies are therefore warranted to dynamically monitor the evolution of gene mutations during treatment. Additionally, collecting data on patients' health insurance payment types and treatment costs could further validate the long-term cost-effectiveness of genetic testing, thereby providing higher-level evidence for optimizing precision diagnosis and treatment guidelines for LSCC. With the advancement of detection technologies and the development of targeted therapies, the era of precision therapy for LSCC has gradually arrived. Expanding the application of genetic testing in clinical practice, particularly for patients characterized by non-smoking status, younger age, and advanced disease stages, holds significant guiding value for treatment strategy selection and outcomes. Therefore, it is necessary to gradually enhance the awareness of the value of genetic testing among both clinicians and patients, and promote the transition of LSCC diagnosis and treatment from empirical medicine to precision medicine. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jun Fang, Haiping Huang, Lin Xiong and Huiwen Ma. The first draft of the manuscript was written by Jun Fang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability The datasets generated and analysed during the current study are available from the corresponding author on reasonable request. Ethics approv al This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Chongqing University Cancer Hospital (Date 2025.12.02/No. CZLL2025-226-002). Consent to participate Informed consent was waived by the Ethics Committee of Chongqing University Cancer Hospital in view of the retrospective nature of the study and all the procedures being performed were part of the routine care. References Attili I, Corvaja C, Spitaleri G, Del Signore E, Trillo Aliaga P, Passaro A, de Marinis F (2023) New Generations of Tyrosine Kinase Inhibitors in Treating NSCLC with Oncogene Addiction: Strengths and Limitations. Cancers (Basel) 15(20):5079. https://doi.org/10.3390/cancers15205079 Azuma K, Komohara Y, Sasada T, Terazaki Y, Ikeda J, Hoshino T, Itoh K, Yamada A, Aizawa H (2007) Excision repair cross-complementation group 1 predicts progression-free and overall survival in non-small cell lung cancer patients treated with platinum-based chemotherapy. Cancer Sci 98(9):1336–1343. https://doi.org/10.1111/j.1349-7006.2007.00557.x Bouchard N, Daaboul N (2025) Lung Cancer: Targeted Therapy in 2025. Curr Oncol 32(3):146. https://doi.org/10.3390/curroncol32030146 Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424. https://doi.org/10.3322/caac.21492 Cancer Genome Atlas Research Network (2014) Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511):543–550. https://doi.org/10.1038/nature13385 Denissenko MF, Pao A, Tang MS, Pfeifer GP (1996) Preferential Formation of Benzo[a]pyrene Adducts at Lung Cancer Mutational Hotspots in P53. Science 274:430–432. https://doi.org/10.1126/science.274.5286.430 Fernandes SS, Saini JK (2023 Jul-Sep) The impact of chemotherapy on quality of life in advanced-stage lung cancer patients. Indian J Cancer 60(3):310–315. https://doi.org/10.4103/ijc.IJC_1246_20 Forder A, Zhuang R, Souza VGP, Brockley LJ, Pewarchuk ME, Telkar N, Stewart GL, Benard K, Marshall EA, Reis PP, Lam WL (2023) Mechanisms Contributing to the Comorbidity of COPD and Lung Cancer. Int J Mol Sci 24(3):2859. https://doi.org/10.3390/ijms24032859 Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, Patnaik A, Aggarwal C, Gubens M, Horn L, Carcereny E, Ahn MJ, Felip E, Lee JS, Hellmann MD, Hamid O, Goldman JW, Soria JC, Dolled-Filhart M, Rutledge RZ, Zhang J, Lunceford JK, Rangwala R, Lubiniecki GM, Roach C, Emancipator K, Gandhi L (2015) Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med 372(21):2018–2028. https://doi.org/10.1056/NEJMoa1501824 Heist RS, Sequist LV, Engelman JA (2012) Genetic Changes in Squamous Cell Lung Cancer: A Review. J Thorac Oncol 7(5):924–933. https://doi.org/10.1097/JTO.0b013e31824cc334 Huang Y, Wang R, Pan Y, Zhang Y, Li H, Cheng C, Zheng D, Zheng S, Li Y, Shen X, Hu H, Cai D, Wang S, Zhang Y, Xiang J, Sun Y, Zhang J, Chen H (2016) Clinical and genetic features of lung squamous cell cancer in never-smokers. Oncotarget 7(24):35979–35988. https://doi.org/10.18632/oncotarget.8745 Illei PB, Belchis D, Tseng LH, Nguyen D, De Marchi F, Haley L, Riel S, Beierl K, Zheng G, Brahmer JR, Askin FB, Gocke CD, Eshleman JR, Forde PM, Lin MT (2017) Clinical mutational profiling of 1006 lung cancers by next generation sequencing. Oncotarget 8(57):96684–96696. https://doi.org/10.18632/oncotarget.18042 Jaiyesimi IA, Leighl NB, Ismaila N, Alluri K, Florez N, Gadgeel S, Masters G, Schenk EL, Schneider BJ, Sequist L, Singh N, Bazhenova L, Blanchard E, Freeman-Daily J, Furuya N, Halmos B, Azar IH, Kuruvilla S, Mullane M, Naidoo J, Reuss JE, Spigel DR, Owen DH, Patel JD (2024) Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, Version 2023.3. J Clin Oncol 42(11):e1–e22. https://doi.org/10.1200/JCO.23.02744 Jeon H, Wang S, Song J, Gill H, Cheng H, Update (2025) : Management of Non–Small-Cell Lung Cancer. Lung. 2025;203(1):53. https://doi.org/10.1007/s00408-025-00801-x Lu W, Aarsand R, Schotte K, Han J, Lebedeva E, Tsoy E, Maglakelidze N, Soriano JB, Bill W, Halpin DMG, Rivera MP, Fong KM, Kathuria H, Yorgancıoğlu A, Gappa M, Lam DC, Rylance S, Sohal SS (2024) Tobacco and COPD: presenting the World Health Organization (WHO) Tobacco Knowledge Summary. Respir Res 25(1):338. https://doi.org/10.1186/s12931-024-02961-5 Lindquist KE, Karlsson A, Levéen P, Brunnström H, Reuterswärd C, Holm K, Jönsson M, Annersten K, Rosengren F, Jirström K, Kosieradzki J, Ek L, Borg Å, Planck M, Jönsson G, Staaf J (2017) Clinical framework for next generation sequencing based analysis of treatment predictive mutations and multiplexed gene fusion detection in non-small cell lung cancer. Oncotarget 8(21):34796–34810. https://doi.org/10.18632/oncotarget.16276 Murphy C, Pandya T, Swanton C, Solomon BJ (2025) Lung Cancer in Nonsmoking Individuals: A Review. JAMA 334(20):1836–1845. https://doi.org/10.1001/jama.2025.17695 Mok TS, Wu Y-L, Ahn M-J, Garassino MC, Kim HR, Ramalingam SS, Shepherd FA, He Y, Akamatsu H, Theelen WS, Lee CK, Sebastian M, Templeton A, Mann H, Marotti M, Ghiorghiu S, Papadimitrakopoulou VA (2017) AURA3 Investigators. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive Lung Cancer. N Engl J Med 376(7):629–640. https://doi.org/10.1056/NEJMoa1612674 Niu Z, Jin R, Zhang Y, Li H (2022) Signaling pathways and targeted therapies in lung squamous cell carcinoma: mechanisms and clinical trials. Signal Transduct Target Ther 7(1):353. https://doi.org/10.1038/s41392-022-01200-x Riely GJ, Wood DE, Ettinger DS, Aisner DL, Akerley W, Bauman JR, Bharat A, Bruno DS, Chang JY, Chirieac LR, DeCamp M, Desai AP, Dilling TJ, Dowell J, Durm GA, Gettinger S, Grotz TE, Gubens MA, Juloori A, Lackner RP, Lanuti M, Lin J, Loo BW, Lovly CM, Maldonado F, Massarelli E, Morgensztern D, Mullikin TC, Ng T, Owen D, Owen DH, Patel SP, Patil T, Polanco PM, Riess J, Shapiro TA, Singh AP, Stevenson J, Tam A, Tanvetyanon T, Yanagawa J, Yang SC, Yau E, Gregory KM, Hang L (2024) Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 22(4):249–274. https://doi.org/10.6004/jnccn.2204.0023 Reuss JE, Zaemes J, Gandhi N, Walker P, Patel SP, Xiu J, Aggarwal C, Vanderwalde A, Ramalingam SS, Halmos B, Liu SV (2025) Comprehensive molecular profiling of squamous non-small cell lung cancer reveals high incidence of actionable genomic alterations among patients with no history of smoking. Lung Cancer 108101. https://doi.org/10.1016/j.lungcan.2025.108101 Shim HS, Choi YL, Kim L, Chang S, Kim WS, Roh MS, Kim TJ, Ha SY, Chung JH, Jang SJ, Lee GK, Korean Cardiopulmonary Pathology Study Group; Korean Molecular Pathology Study Group (2017) Korean Molecular Pathology Study Group. Molecular Testing of Lung Cancers. J Pathol Transl Med 51(3):242–254. https://doi.org/10.4132/jptm.2017.04.10 Su PL, Furuya N, Asrar A, Rolfo C, Li Z, Carbone DP, He K (2025) Recent advances in therapeutic strategies for non-small cell lung cancer. J Hematol Oncol 18(1):35. https://doi.org/10.1186/s13045-025-01679-1 Zheng RS, Chen R, Han BF, Wang SM, Li L, Sun KX, Zeng HM, Wei WW, He J (2024) Cancer incidence and mortality in China, 2022. Zhonghua Zhong Liu Za Zhi 46(3):221–231. https://doi.org/10.3760/cma.j.cn112152-20240119-00035 Zhao J, Wu Y, Chen MJ, Xu Y, Zhong W, Wang MZ (2022) Characterization of driver mutations in Chinese non-small cell lung cancer patients using a novel targeted sequencing panel. J Thorac Dis 14(12):4669–4684. https://doi.org/10.21037/jtd-22-909 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9260557","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":615230107,"identity":"dbe0b3f5-81c4-4e75-9d65-abf2f156d67a","order_by":0,"name":"Jun Fang","email":"","orcid":"","institution":"Chongqing Kanghuazhonglian Cardiovascular Disease Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Fang","suffix":""},{"id":615230108,"identity":"039cff03-b8ae-49e9-8ac4-6e746271d37b","order_by":1,"name":"Haiping Huang","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haiping","middleName":"","lastName":"Huang","suffix":""},{"id":615230117,"identity":"e8b0f569-89bc-473f-a1ee-612909c81305","order_by":2,"name":"Lin Xiong","email":"","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Xiong","suffix":""},{"id":615230118,"identity":"515a9f40-9f55-4ebd-baf7-74e7cdd58313","order_by":3,"name":"Huiwen Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYDACCQaGAx8MJOTY2JsPEK2F8eGMCgtjPp5jCURrYTbmOFOROE8iR4E4Hbqze8ykGdsk0tsYchgYflRsI6zF7M4ZM+nCNoncNoazBxh7ztwmQsuN3G3SM0FaGPsSmBnbiNXCC3QYGzOPAdFaNhvznJFIYGMjXkv+R2AgSxi28bAlHCTSL2kJwKisk5ef//jggx8VRGhBAQdIVD8KRsEoGAWjABcAAPilPIhSL/htAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing University Cancer Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huiwen","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2026-03-29 18:39:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9260557/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9260557/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106189912,"identity":"e7f1c57c-da20-4ede-a512-8e83ae0c512d","added_by":"auto","created_at":"2026-04-05 17:11:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":68715,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival curves for different treatment modalities\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9260557/v1/cd1291866b4d4ecd0589f55d.png"},{"id":106612436,"identity":"f9873eaf-1423-4a31-9d3b-3b5f52762502","added_by":"auto","created_at":"2026-04-10 12:28:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1265994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9260557/v1/d35501ce-3965-4774-bf4a-1e69d2733bf4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The clinical value of genetic testing in lung squamous cell carcinoma: A retrospective study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer, as the malignant tumor with the highest incidence and mortality rate in China, poses a major challenge to public health. It causes severe physical and psychological suffering in patients and their families and imposes a heavy burden on the public healthcare system (Zheng 2024). In the diagnosis and treatment of lung cancer, the relatively mature precision therapy for lung adenocarcinoma has been established. Genetic testing has become one of the routine clinical examinations for this subtype, with its driver gene mutation rate reaching around 60% (Cancer Genome Atlas Research Network \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Based on the result of genetic testing, clinicians can formulate targeted therapy regimens for patients. This approach not only markedly improves treatment efficacy but also has less impact on patients' quality of life due to mild adverse events (AEs). Moreover, it results in lower systemic damage compared with traditional chemoradiotherapy.\u003c/p\u003e \u003cp\u003eHowever, genetic testing has not been established as a routine examination for lung squamous cell carcinoma (LSCC) domestically or internationally. Previous studies have suggested limited clinical value of genetic testing in LSCC because of its low overall mutation rate and the absence of definite driver gene mutations in patients with a long smoking history (Reuss 2025). In recent years, several studies indicate that non-smoking patients with LSCC exhibit a higher incidence of driver gene mutations, and the progression of carcinogenesis in this subgroup may be more likely associated with such mutations (Murphy 2025).\u003c/p\u003e \u003cp\u003eIn this study, we performed a retrospective analysis of 1,515 patients diagnosed with LSCC at Chongqing University Cancer Hospital to evaluate the clinical value of genetic testing in LSCC. Subgroup and cost-effectiveness analyses were also conducted, aiming to provide more comprehensive evidence-based medical data for the precision diagnosis and treatment of LSCC.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eIn this retrospective study, 1,515 patients with histopathologically confirmed LSCC treated at Chongqing University Cancer Hospital from January 2018 to December 2024 were included. Clinical data were collected for all patients, including sex, age, smoking status (smoking was defined as smoking\u0026thinsp;\u0026ge;\u0026thinsp;1 cigarette per day for at least 6 months; non-smoking was defined as never smoking or a cumulative smoking duration of \u0026lt;\u0026thinsp;6 months), pathological stage, genetic testing results, treatment regimens, treatment outcomes, quality-of-life assessments, and AEs. This retrospective study was approved by the Ethics Committee of Chongqing University Cancer Hospital (Approval No. CZLL2025-226-002). Informed consent was waived due to the use of fully anonymized clinical data.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGene mutation analysis\u003c/h3\u003e\n\u003cp\u003eGenetic testing was conducted on tumor tissues or body fluids using the amplification refractory mutation system (ARMS).\u003c/p\u003e\n\u003ch3\u003eTreatment\u003c/h3\u003e\n\u003cp\u003eAfter evaluating the patient's condition and communicating fully with the patient and their family, patients with identified gene mutations were prescribed corresponding targeted drugs. For the remaining patients with gene mutations but without corresponding targeted drugs, as well as those with no detectable gene mutations, conventional chemoradiotherapy regimens were administered until disease progression or death.\u003c/p\u003e\n\u003ch3\u003eOutcomes assessment\u003c/h3\u003e\n\u003cp\u003eAll patients were scheduled for follow-up once a month after diagnosis. They received re-examination if any symptoms of disease progression occurred during treatment. The last follow-up was conducted in December 2025. And the median follow-up time was 20.0 months (range: 1.0\u0026ndash;77.0 months). Tumor response assessment was performed according to the Response Evaluation Criteria in Solid Tumors (RECIST 1.0) and categorized into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Objective response rate (ORR) was defined as the proportion of patients achieving CR or PR. Disease control rate (DCR) was defined as the proportion of patients achieving CR, PR, or SD. Progression-free survival (PFS) was defined as the period from diagnosis to disease progression, death, or the last follow-up. Overall survival (OS) was defined as the period from diagnosis to death, or the last follow-up. Meanwhile, quality-of-life scores, psychological scores, and recurrence/metastasis status of the patients were recorded before and after treatment.\u003c/p\u003e\n\u003ch3\u003eSafety assessment\u003c/h3\u003e\n\u003cp\u003eAEs of grade\u0026thinsp;\u0026ge;\u0026thinsp;1 occurring during treatment were documented and categorized into grades 1\u0026ndash;4 according to the severity. The incidence of AEs and the proportion of treatment discontinuations due to AEs were calculated for each treatment subgroup.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCost-effectiveness analysis\u003c/h2\u003e \u003cp\u003eThe costs of testing and treatment were calculated based on the medical charging standards of Chongqing City in 2024, including the cost of genetic testing, the average monthly cost of targeted drugs, and conventional chemotherapy. After insurance reimbursement, the self-payment was approximately 9000 RMB, 4931.5 RMB, and 15078.3 RMB, respectively. The quality-adjusted life year (QALY) and the incremental cost-effectiveness ratio (ICER) were calculated, using three times the per capita gross domestic product (GDP) of China as the cost-effectiveness threshold.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed with the SPSS package (version 19.0, IBM, USA). Survival analysis was conducted with the Kaplan-Meier method. Chi-square test was used to compare mutation detection rates, as well as tumor response rates, AEs incidence between different treatment groups, and subgroup mutation rates. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGene mutation analysis\u003c/h2\u003e \u003cp\u003eA total of 1,515 patients with LSCC were included in this study, among whom 292 underwent genetic testing (221 smokers and 71 non-smokers; 268 males and 24 females). The mean age was 65.2 years (range, 39.0\u0026ndash;92.0 years). Definite driver gene mutations were identified in 56 of the 292 patients, with an overall mutation detection rate of 19.2%. Stratified by smoking status, the mutation rate was 11.8% in smokers and 42.3% in non-smokers.\u003c/p\u003e \u003cp\u003eAmong patients with detected mutations, \u003cem\u003eEGFR\u003c/em\u003e mutation was identified in 26 cases, \u003cem\u003eMET\u003c/em\u003e mutation in 10 cases, \u003cem\u003eERBB2\u003c/em\u003e mutation in 5 cases, \u003cem\u003eROS1\u003c/em\u003e mutation in 5 cases, \u003cem\u003eALK\u003c/em\u003e mutation in 4 cases, \u003cem\u003eNRAS\u003c/em\u003e mutation in 4 cases, and \u003cem\u003eRET\u003c/em\u003e mutation in 2 cases (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In non-smokers, the incidence of \u003cem\u003eEGFR\u003c/em\u003e mutation (66.7%, 20/30) was significantly higher than that of other gene mutations, whereas mutation types in smokers were more scattered.\u003c/p\u003e \u003cp\u003eSubgroup analysis showed that the mutation detection rate was significantly higher in patients aged\u0026thinsp;\u0026le;\u0026thinsp;65 years (20.6%, 29/141) than in those aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years (17.9%, 27/151). In terms of gender stratification, the mutation rate in female patients (70.8%, 17/24) was significantly higher than that in male patients (14.6%, 39/268; χ\u0026sup2;=44.99, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). And the mutation rate in non-smoking patients (42.3%, 30/71) was significantly higher than that in smoking patients (11.8%, 26/221; χ\u0026sup2;=32.23, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of baseline characteristics and mutation status in patients with positive genetic test results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eALK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEGFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERBB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERBB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERBB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERBB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eERBB2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNRAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNon-smoking\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cp\u003e* Smoking was defined as smoking\u0026thinsp;\u0026ge;\u0026thinsp;1 cigarette per day for at least 6 months\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003cp\u003e** Non-smoking was defined as never smoking or a cumulative smoking duration of \u0026lt;\u0026thinsp;6 months\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003ch2\u003eEfficacy analysis\u003c/h2\u003e \u003cp\u003eAs of the last follow-up date (December 2025), among the 21 patients receiving targeted therapy, 4 achieved CR, 4 PR, 6 SD, and 7 PD, corresponding to an ORR of 38.1% and a DCR of 66.7%. The median PFS was 28.0 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 35 patients receiving conventional therapy, 3 achieved CR, 4 PR, 4 SD, and 24 PD. ORR and DCR were 20.0% and 31.4%, respectively. Of these 35 patients, 11 remained progression-free, with a median PFS of 14.0 months (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe efficacy of targeted therapy corresponding to different gene mutation subtypes is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Among patients with EGFR mutations, targeted therapy was administered in 13 cases, achieving a DCR of 69.2% (9/13) and a mean OS of 34.8 months. In contrast, conventional therapy was given to 13 cases, with a DCR of 38.5% (5/13) and a mean OS of 33.2 months.\u003c/p\u003e \u003cp\u003eLong-term survival analysis demonstrated a mean OS of 30.0 months and a median OS of 28.0 months in the targeted therapy group. In contrast, the conventional therapy group had a mean OS of 23.4 months and a median OS of 14.0 months. The differences in OS between the two groups were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Analysis of recurrence and metastasis revealed that the recurrence rate was 33.3% (7/21) in the targeted therapy group, including 2 cases of local recurrence and 5 cases of distant metastasis (predominantly brain metastasis). In the conventional therapy group, the recurrence rate was 68.6% (24/35), including 9 cases of local recurrence and 15 cases of distant metastasis (predominantly bone and intrapulmonary metastasis). The risk of distant metastasis was significantly lower in the targeted therapy group (HR\u0026thinsp;=\u0026thinsp;0.31, 95% CI: 0.13\u0026ndash;0.72, P\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDifferences in quality-of-life and AEs between treatment groups\u003c/h2\u003e \u003cp\u003eQuality-of-life and psychological scores were assessed for all patients before and after treatment during regular follow-up. The results indicated that the clinical benefits of patients receiving targeted therapy were significantly superior to those receiving conventional therapy (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the targeted therapy group (n\u0026thinsp;=\u0026thinsp;21), the most common AEs were rash (38.1%) and diarrhea (28.6%), with the incidence of grade 3\u0026ndash;4 AEs being only 4.7% (1/21, grade 3 rash [n\u0026thinsp;=\u0026thinsp;1]). In the conventional therapy group (n\u0026thinsp;=\u0026thinsp;35), the most common AEs were myelosuppression (65.7%) and gastrointestinal reactions (60.0%), with the incidence of grade 3\u0026ndash;4 AEs being 34.3% (12/35, grade 3 myelosuppression [n\u0026thinsp;=\u0026thinsp;8] and grade 3 nausea and vomiting [n\u0026thinsp;=\u0026thinsp;4]). The proportion of treatment discontinuations due to AEs was significantly lower in the targeted therapy group (4.8%) than that in the conventional therapy group (34.3%; χ\u0026sup2;=6.42, P\u0026thinsp;=\u0026thinsp;0.011; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTherapeutic efficacy under different treatment options\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerial No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003cp\u003egroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTumor response assessment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOverall survival (Months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProgression-free survival (Months)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAdverse reaction grade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQuality of life assessment*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-3-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e2-2-3-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-2-2-1-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-2-3-2-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-2-3-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e2-2-3-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-2-2-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-2-2-3-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e2-2-3-3-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e2-2-3-4-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e2-2-3-3-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e2-2-3-4-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e2-2-3-4-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-3-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComplete response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-1-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePartial response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStable disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-1-2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-3-3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-2-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTargeted therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProgressive disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c8\"\u003e \u003cp\u003e1-1-1-2-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e* Health-related quality of life (HRQoL) was evaluated in this study using the EQ-5D-5L questionnaire, with corresponding scores obtained across five ordered dimensions: Mobility(MO), Self-care(SC), Usual activities(UA), Pain/discomfort(PD), and Anxiety/depression(AD).\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCost-effectiveness analysis\u003c/h2\u003e \u003cp\u003eThe QALY was 2.287 in the targeted therapy group and 1.534 in the conventional therapy group. The ICER exhibited a negative value, indicating that targeted therapy yielded more favorable health outcomes with lower treatment costs. As an absolutely dominant strategy, it possessed a significant cost-effectiveness advantage. Collectively, these findings indicate that genetic testing combined with targeted therapy confers a favorable cost-effectiveness profile in non-smoking patients with LSCC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs one of the major subtypes of lung cancer, LSCC is a consistent challenge in clinical diagnosis and treatment due to its high invasiveness and low response rate (Bray 2018; Zheng 2024). Currently, the standard treatments for LSCC primarily include chemotherapy and radiotherapy. Although these conventional approaches can control tumor progression to some extent, they also compromise patients\u0026rsquo; quality of life (Garon 2015; Fernandes 2023). Their drug toxicity and radiation-induced damage harm normal tissues, leading to severe AEs such as myelosuppression and gastrointestinal reactions. Moreover, conventional therapy is associated with the risk of tumor recurrence and metastasis, which further poses a challenge to patients\u0026rsquo; quality of life. In addition, while the use of immune checkpoint inhibitors has shown efficacy in some patients, the ORR remains below 20%, which is insufficient to meet the clinical needs (Shim 2017).\u003c/p\u003e \u003cp\u003eTargeted therapy guided by genetic testing is an intervention method that targets specific molecular sites of tumor cells (Bouchard \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jeon 2025). It specifically inhibits tumor cell proliferation and reduces damage to normal tissues (Jaiyesimi 2024; Su 2025). Compared with conventional therapy, it has the advantages of milder side effects and better tolerability, which exert a more positive impact on patients\u0026rsquo; quality of life (Attili 2023). The results of this study demonstrated that patients with LSCC receiving targeted therapy experienced early relief of symptoms such as cough, hemoptysis, and dyspnea caused by tumor compression or invasion, which significantly improved their physical condition and quality of life scores. These benefits may be attributed to the relatively milder adverse reactions and the precision of targeted therapy. In this study, the incidence of grade 3\u0026ndash;4 AEs in the targeted therapy group was only 4.7%, significantly lower than that in the conventional therapy group (34.3%), further confirming the safety advantage of targeted therapy.\u003c/p\u003e \u003cp\u003ePrevious clinical studies on the efficacy of targeted therapy for lung adenocarcinoma have shown that the ORR can be increased by 2\u0026ndash;3 fold, and the median OS can be extended by 6\u0026ndash;12 months in treated patients relative to the control group (Mok 2017). There is a gap in the efficacy of targeted therapy for LSCC compared with lung adenocarcinoma, largely due to its underutilization in the former. Promoting genetic testing, particularly among non-smoking patients with LSCC, is expected to narrow this gap (Niu 2022). In this study, the overall mutation detection rate in patients with LSCC reached 19.2%, with a significant difference between the smoking (11.8%) and non-smoking (42.3%) groups. However, the current rate of genetic testing in patients with LSCC (19.3%, 292/1515) remains at a low level in clinical practice. This can be attributed to three reasons. First, the previous notion holds that LSCC is a non-mutation-driver tumor with a lower mutation rate compared to lung adenocarcinoma. Second, the mutation spectrum of LSCC related to smoking is complex and lacks clear and highly frequent mutations that can serve as targets (Heist 2012). Third, there are debates regarding the cost-effectiveness ratio between testing expenses and clinical benefits, which has also limited the routine implementation of genetic testing in clinical practice to some extent.\u003c/p\u003e \u003cp\u003eThe findings of this study indicated that although the overall mutation rate in LSCC was relatively low, group analysis revealed that the gene mutation rate in non-smoking patients was significantly higher than that in smoking patients, with more prominent mutation characteristics observed in non-smoking females aged\u0026thinsp;\u0026le;\u0026thinsp;65 years with advanced-stage disease. The underlying mechanisms for this phenomenon may be directly related to the differences in carcinogenic factors, DNA repair capacity, and disease-driving pathways (Lu 2024). First, the primary carcinogenic drivers in smoking patients with LSCC are a small subset of highly recurrent smoking signature mutations (such as \u003cem\u003eTP53\u003c/em\u003e) induced by polycyclic aromatic hydrocarbons (such as benzopyrene) in tobacco (Denissenko 1996). Their tumor progression relies more on non-mutation-driven factors like chronic inflammation and oxidative stress, resulting in a lower dependence on gene mutations (Forder 2023). In contrast, the pathogenic factors for non-smoking patients involve multiple elements, such as secondhand smoke, air pollution, occupational exposures, and genetic susceptibility, with their driver mutations covering a variety of genes, such as \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eALK\u003c/em\u003e, and \u003cem\u003eROS1\u003c/em\u003e (Murphy 2025). Non-smoking patients with LSCC often exhibit a greater dependence on genetic mutations as the core driving events, with more diverse mutation types and a higher overall mutation proportion (Huang 2016). Second, defects in DNA repair-related genes such as \u003cem\u003eERCC1\u003c/em\u003e and \u003cem\u003eBRCA\u003c/em\u003e are more common in non-smoking patients, which impairs the capacity for DNA damage repair and results in the failure to timely eliminate abnormal mutations (Azuma 2007). This leads to the accumulation of abnormal mutations and increases the detectable mutation rate. This discovery provides a theoretical foundation for the implementation of precision therapy in non-smoking, younger, and advanced-stage patients with LSCC.\u003c/p\u003e \u003cp\u003eConventional detection methods only target a few genes, such as \u003cem\u003eEGFR\u003c/em\u003e. With the development of high-throughput sequencing technology, next-generation techniques like multi-gene panel testing can simultaneously cover multiple potential targets, including \u003cem\u003eALK\u003c/em\u003e, \u003cem\u003eROS1\u003c/em\u003e, and \u003cem\u003eMET\u003c/em\u003e, significantly enhancing the detection capability for gene mutations in LSCC (Lindquist 2017). Related studies have shown that multi-gene panel testing can increase the comprehensive mutation detection rate by over 30%, creating opportunities for more patients to access targeted therapy (Illei 2017). At the same time, the accelerated development of targeted drugs for rare mutations has further expanded the clinical value of genetic testing.\u003c/p\u003e \u003cp\u003eThe healthcare economic analysis revealed that the ICER of the targeted therapy group was negative, which was below the threshold of three times the per capita GDP in China obviously. The finding addresses cost-related concerns in clinical adoption and demonstrates that genetic testing combined with targeted therapy offers favorable health economic value for non-smoking patients with LSCC. It provides economic justification for the routine clinical implementation of this approach.\u003c/p\u003e \u003cp\u003eA comparison of the 2024 edition of the NCCN Clinical Practice Guidelines in Oncology for Lung Cancer and the CSCO Guidelines for the Diagnosis and Treatment of Non-Small Cell Lung Cancer reveals that both currently recommend genetic testing for \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eALK\u003c/em\u003e, and \u003cem\u003eROS1\u003c/em\u003e in patients with LSCC who are non-smokers or light smokers (Category Ⅱ recommendation)(Riely 2024). However, potential targets such as \u003cem\u003eMET\u003c/em\u003e and \u003cem\u003eRET\u003c/em\u003e are not included. Based on the findings of this study, we suggest that the scope of genetic testing for non-smoking patients with LSCC should be expanded to cover multiple genes, including \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eALK\u003c/em\u003e, \u003cem\u003eROS1\u003c/em\u003e, \u003cem\u003eMET\u003c/em\u003e, \u003cem\u003eRET\u003c/em\u003e, \u003cem\u003eERBB2\u003c/em\u003e, and \u003cem\u003eNRAS\u003c/em\u003e. For smoking patients with advanced LSCC who are intolerant to conventional chemotherapy, next-generation sequencing (NGS)-based multi-gene testing may still be considered to explore additional treatment opportunities (Zhao 2022).\u003c/p\u003e \u003cp\u003eThis study is limited by its single-center retrospective design. Future multicenter, prospective studies are therefore warranted to dynamically monitor the evolution of gene mutations during treatment. Additionally, collecting data on patients' health insurance payment types and treatment costs could further validate the long-term cost-effectiveness of genetic testing, thereby providing higher-level evidence for optimizing precision diagnosis and treatment guidelines for LSCC.\u003c/p\u003e \u003cp\u003eWith the advancement of detection technologies and the development of targeted therapies, the era of precision therapy for LSCC has gradually arrived. Expanding the application of genetic testing in clinical practice, particularly for patients characterized by non-smoking status, younger age, and advanced disease stages, holds significant guiding value for treatment strategy selection and outcomes. Therefore, it is necessary to gradually enhance the awareness of the value of genetic testing among both clinicians and patients, and promote the transition of LSCC diagnosis and treatment from empirical medicine to precision medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Jun Fang, Haiping Huang, Lin Xiong and Huiwen Ma. The first draft of the manuscript was written by Jun Fang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approv\u003c/strong\u003e\u003cstrong\u003eal\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Chongqing University Cancer Hospital (Date 2025.12.02/No. CZLL2025-226-002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was waived by the Ethics Committee of Chongqing University Cancer Hospital in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAttili I, Corvaja C, Spitaleri G, Del Signore E, Trillo Aliaga P, Passaro A, de Marinis F (2023) New Generations of Tyrosine Kinase Inhibitors in Treating NSCLC with Oncogene Addiction: Strengths and Limitations. Cancers (Basel) 15(20):5079. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/cancers15205079\u003c/span\u003e\u003cspan address=\"10.3390/cancers15205079\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzuma K, Komohara Y, Sasada T, Terazaki Y, Ikeda J, Hoshino T, Itoh K, Yamada A, Aizawa H (2007) Excision repair cross-complementation group 1 predicts progression-free and overall survival in non-small cell lung cancer patients treated with platinum-based chemotherapy. Cancer Sci 98(9):1336\u0026ndash;1343. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1349-7006.2007.00557.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1349-7006.2007.00557.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouchard N, Daaboul N (2025) Lung Cancer: Targeted Therapy in 2025. Curr Oncol 32(3):146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/curroncol32030146\u003c/span\u003e\u003cspan address=\"10.3390/curroncol32030146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394\u0026ndash;424. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21492\u003c/span\u003e\u003cspan address=\"10.3322/caac.21492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCancer Genome Atlas Research Network (2014) Comprehensive molecular profiling of lung adenocarcinoma. Nature 511(7511):543\u0026ndash;550. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nature13385\u003c/span\u003e\u003cspan address=\"10.1038/nature13385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenissenko MF, Pao A, Tang MS, Pfeifer GP (1996) Preferential Formation of Benzo[a]pyrene Adducts at Lung Cancer Mutational Hotspots in P53. Science 274:430\u0026ndash;432. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/science.274.5286.430\u003c/span\u003e\u003cspan address=\"10.1126/science.274.5286.430\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes SS, Saini JK (2023 Jul-Sep) The impact of chemotherapy on quality of life in advanced-stage lung cancer patients. Indian J Cancer 60(3):310\u0026ndash;315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4103/ijc.IJC_1246_20\u003c/span\u003e\u003cspan address=\"10.4103/ijc.IJC_1246_20\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForder A, Zhuang R, Souza VGP, Brockley LJ, Pewarchuk ME, Telkar N, Stewart GL, Benard K, Marshall EA, Reis PP, Lam WL (2023) Mechanisms Contributing to the Comorbidity of COPD and Lung Cancer. Int J Mol Sci 24(3):2859. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms24032859\u003c/span\u003e\u003cspan address=\"10.3390/ijms24032859\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaron EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, Patnaik A, Aggarwal C, Gubens M, Horn L, Carcereny E, Ahn MJ, Felip E, Lee JS, Hellmann MD, Hamid O, Goldman JW, Soria JC, Dolled-Filhart M, Rutledge RZ, Zhang J, Lunceford JK, Rangwala R, Lubiniecki GM, Roach C, Emancipator K, Gandhi L (2015) Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med 372(21):2018\u0026ndash;2028. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa1501824\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1501824\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeist RS, Sequist LV, Engelman JA (2012) Genetic Changes in Squamous Cell Lung Cancer: A Review. J Thorac Oncol 7(5):924\u0026ndash;933. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1097/JTO.0b013e31824cc334\u003c/span\u003e\u003cspan address=\"10.1097/JTO.0b013e31824cc334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Wang R, Pan Y, Zhang Y, Li H, Cheng C, Zheng D, Zheng S, Li Y, Shen X, Hu H, Cai D, Wang S, Zhang Y, Xiang J, Sun Y, Zhang J, Chen H (2016) Clinical and genetic features of lung squamous cell cancer in never-smokers. Oncotarget 7(24):35979\u0026ndash;35988. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/oncotarget.8745\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.8745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIllei PB, Belchis D, Tseng LH, Nguyen D, De Marchi F, Haley L, Riel S, Beierl K, Zheng G, Brahmer JR, Askin FB, Gocke CD, Eshleman JR, Forde PM, Lin MT (2017) Clinical mutational profiling of 1006 lung cancers by next generation sequencing. Oncotarget 8(57):96684\u0026ndash;96696. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/oncotarget.18042\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.18042\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaiyesimi IA, Leighl NB, Ismaila N, Alluri K, Florez N, Gadgeel S, Masters G, Schenk EL, Schneider BJ, Sequist L, Singh N, Bazhenova L, Blanchard E, Freeman-Daily J, Furuya N, Halmos B, Azar IH, Kuruvilla S, Mullane M, Naidoo J, Reuss JE, Spigel DR, Owen DH, Patel JD (2024) Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, Version 2023.3. J Clin Oncol 42(11):e1\u0026ndash;e22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1200/JCO.23.02744\u003c/span\u003e\u003cspan address=\"10.1200/JCO.23.02744\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJeon H, Wang S, Song J, Gill H, Cheng H, Update (2025) : Management of Non\u0026ndash;Small-Cell Lung Cancer. Lung. 2025;203(1):53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00408-025-00801-x\u003c/span\u003e\u003cspan address=\"10.1007/s00408-025-00801-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu W, Aarsand R, Schotte K, Han J, Lebedeva E, Tsoy E, Maglakelidze N, Soriano JB, Bill W, Halpin DMG, Rivera MP, Fong KM, Kathuria H, Yorgancıoğlu A, Gappa M, Lam DC, Rylance S, Sohal SS (2024) Tobacco and COPD: presenting the World Health Organization (WHO) Tobacco Knowledge Summary. Respir Res 25(1):338. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12931-024-02961-5\u003c/span\u003e\u003cspan address=\"10.1186/s12931-024-02961-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLindquist KE, Karlsson A, Lev\u0026eacute;en P, Brunnstr\u0026ouml;m H, Reutersw\u0026auml;rd C, Holm K, J\u0026ouml;nsson M, Annersten K, Rosengren F, Jirstr\u0026ouml;m K, Kosieradzki J, Ek L, Borg \u0026Aring;, Planck M, J\u0026ouml;nsson G, Staaf J (2017) Clinical framework for next generation sequencing based analysis of treatment predictive mutations and multiplexed gene fusion detection in non-small cell lung cancer. Oncotarget 8(21):34796\u0026ndash;34810. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/oncotarget.16276\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.16276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy C, Pandya T, Swanton C, Solomon BJ (2025) Lung Cancer in Nonsmoking Individuals: A Review. JAMA 334(20):1836\u0026ndash;1845. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2025.17695\u003c/span\u003e\u003cspan address=\"10.1001/jama.2025.17695\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMok TS, Wu Y-L, Ahn M-J, Garassino MC, Kim HR, Ramalingam SS, Shepherd FA, He Y, Akamatsu H, Theelen WS, Lee CK, Sebastian M, Templeton A, Mann H, Marotti M, Ghiorghiu S, Papadimitrakopoulou VA (2017) AURA3 Investigators. Osimertinib or Platinum-Pemetrexed in EGFR T790M-Positive Lung Cancer. N Engl J Med 376(7):629\u0026ndash;640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1056/NEJMoa1612674\u003c/span\u003e\u003cspan address=\"10.1056/NEJMoa1612674\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu Z, Jin R, Zhang Y, Li H (2022) Signaling pathways and targeted therapies in lung squamous cell carcinoma: mechanisms and clinical trials. Signal Transduct Target Ther 7(1):353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41392-022-01200-x\u003c/span\u003e\u003cspan address=\"10.1038/s41392-022-01200-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiely GJ, Wood DE, Ettinger DS, Aisner DL, Akerley W, Bauman JR, Bharat A, Bruno DS, Chang JY, Chirieac LR, DeCamp M, Desai AP, Dilling TJ, Dowell J, Durm GA, Gettinger S, Grotz TE, Gubens MA, Juloori A, Lackner RP, Lanuti M, Lin J, Loo BW, Lovly CM, Maldonado F, Massarelli E, Morgensztern D, Mullikin TC, Ng T, Owen D, Owen DH, Patel SP, Patil T, Polanco PM, Riess J, Shapiro TA, Singh AP, Stevenson J, Tam A, Tanvetyanon T, Yanagawa J, Yang SC, Yau E, Gregory KM, Hang L (2024) Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw 22(4):249\u0026ndash;274. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.6004/jnccn.2204.0023\u003c/span\u003e\u003cspan address=\"10.6004/jnccn.2204.0023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReuss JE, Zaemes J, Gandhi N, Walker P, Patel SP, Xiu J, Aggarwal C, Vanderwalde A, Ramalingam SS, Halmos B, Liu SV (2025) Comprehensive molecular profiling of squamous non-small cell lung cancer reveals high incidence of actionable genomic alterations among patients with no history of smoking. Lung Cancer 108101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.lungcan.2025.108101\u003c/span\u003e\u003cspan address=\"10.1016/j.lungcan.2025.108101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShim HS, Choi YL, Kim L, Chang S, Kim WS, Roh MS, Kim TJ, Ha SY, Chung JH, Jang SJ, Lee GK, Korean Cardiopulmonary Pathology Study Group; Korean Molecular Pathology Study Group (2017) Korean Molecular Pathology Study Group. Molecular Testing of Lung Cancers. J Pathol Transl Med 51(3):242\u0026ndash;254. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4132/jptm.2017.04.10\u003c/span\u003e\u003cspan address=\"10.4132/jptm.2017.04.10\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu PL, Furuya N, Asrar A, Rolfo C, Li Z, Carbone DP, He K (2025) Recent advances in therapeutic strategies for non-small cell lung cancer. J Hematol Oncol 18(1):35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s13045-025-01679-1\u003c/span\u003e\u003cspan address=\"10.1186/s13045-025-01679-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng RS, Chen R, Han BF, Wang SM, Li L, Sun KX, Zeng HM, Wei WW, He J (2024) Cancer incidence and mortality in China, 2022. Zhonghua Zhong Liu Za Zhi 46(3):221\u0026ndash;231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3760/cma.j.cn112152-20240119-00035\u003c/span\u003e\u003cspan address=\"10.3760/cma.j.cn112152-20240119-00035\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Wu Y, Chen MJ, Xu Y, Zhong W, Wang MZ (2022) Characterization of driver mutations in Chinese non-small cell lung cancer patients using a novel targeted sequencing panel. J Thorac Dis 14(12):4669\u0026ndash;4684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.21037/jtd-22-909\u003c/span\u003e\u003cspan address=\"10.21037/jtd-22-909\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"lung squamous cell carcinoma, genetic testing, targeted therapy, conventional therapy","lastPublishedDoi":"10.21203/rs.3.rs-9260557/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9260557/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eTo assess the necessity and clinical value of genetic testing in lung squamous cell carcinoma (LSCC) diagnosis and treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective study enrolled 1,515 LSCC patients from Chongqing University Cancer Hospital. Medical records were reviewed to analyze gene mutations, treatment and healthcare economic outcomes for clinical benefit evaluation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf 1,515 patients, 292 received genetic testing (221 smokers, 71 non-smokers). Fifty-six had clear driver gene mutations, with a 19.2% detection rate, dominated by EGFR (n\u0026thinsp;=\u0026thinsp;26) and MET (n\u0026thinsp;=\u0026thinsp;10) mutations. In non-smokers, the incidence of EGFR mutations (66.7%, 20/30) was significantly higher than that of other mutations, while the mutation types were scattered in smokers. Patients aged\u0026thinsp;\u0026le;\u0026thinsp;65 years, females and non-smokers showed higher mutation rates. Targeted therapy improved objective response rate, disease control rate and median progression-free survival, with fewer grade 3\u0026ndash;4 adverse events (4.7% vs 40.0%). Cost-effectiveness analysis showed a negative incremental cost-effectiveness ratio, meaning targeted therapy delivered better outcomes at lower costs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGenetic testing is highly recommended for LSCC patients, especially non-smokers, those aged\u0026thinsp;\u0026le;\u0026thinsp;65 years and females. Its combination with targeted therapy offers significant clinical benefits and favorable cost-effectiveness, guiding treatment selection and prognosis improvement.\u003c/p\u003e","manuscriptTitle":"The clinical value of genetic testing in lung squamous cell carcinoma: A retrospective study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:11:54","doi":"10.21203/rs.3.rs-9260557/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae1e9f19-e014-45f8-9e06-1064d22c089d","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-10T12:28:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:11:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9260557","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9260557","identity":"rs-9260557","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}