Octopus arms: A phenomenological study on radiotherapy experiences of patients with lung cancer.

Incidental pulmonary nodules (IPN) are nodules identified on imaging performed for a reason unrelated to the diagnosis of cancer. This study analyzes improvement in time-to-diagnosis for patients first presenting with an IPN that was later diagnosed as lung cancer through a formal IPN program. This is a 5-year retrospective, single-center analysis of outcomes for IPN patients diagnosed with lung cancer who presented through a formal IPN program (FIP) pathway compared with a matched group of patients diagnosed with lung cancer presenting through traditional IPN referral (TIR) pathway. The primary endpoint was time-to-diagnosis of lung cancer. A chart review was performed to determine the date of the initial CT scan identifying the IPN to the date of the procedure to diagnose lung cancer in the FIP group and the TIR group. The number of days from identification of the IPN to diagnosis was compared between the 2 groups. Between May 2019 and May 2024, a total of 9303 patients were identified with IPNs through the formal IPN program. Ninety-six of these patients (1%) were diagnosed with cancer following nodule biopsy. Eighty-one of the patients (84%), making up the FIP group, were diagnosed with primary lung cancer. Twenty-one of the 81 primary lung cancer patients in the FIP group (27%) were diagnosed with early-stage disease and underwent curative resection. The average time from identification of an IPN to diagnosis of lung cancer in the FIP cohort was 43 days. The average time from identification of the IPN to diagnosis of lung cancer in the TIR cohort was 177 days. There was a 134-day difference between the 2 groups from identification of an IPN to diagnosis of lung cancer. Implementation of a formal IPN program can reduce time-to-diagnosis of lung cancer in patients presenting with an IPN by 134 days compared with traditional referral patterns. Formal IPN programs can result in a shift towards an earlier stage of diagnosis for lung cancer.

Surgical advocacy in action: The fight for coverage for lung cancer screening.

Xbp1-driven lipid metabolism promotes immunosuppression in lung cancer: Jianpi Chutan Jiedu formula reshapes metabolic-immune crosstalk.

Lung cancer remains a leading cause of global cancer mortality, with therapeutic efficacy significantly hampered by intrinsic and acquired therapy resistance. Solute carrier (SLC) and ATP-binding cassette (ABC) transporters have emerged as pivotal regulators orchestrating the transmembrane transport of diverse substrates to critically influence lung cancer progression and treatment response. This review systematically examines the pathophysiological roles of key transporters in lung cancer progression and therapeutic response, with a particular emphasis on their functions in mediating crosstalk within the tumor microenvironment (TME). It assesses the current knowledge in this field, elucidates the underlying molecular mechanisms, and highlights potential avenues for future research and clinical translation. SLC and ABC transporters critically contribute to the aggressiveness of lung cancer by orchestrating multiple facets of its malignant phenotype. This encompasses promoting cell proliferation, suppressing cell death, facilitating tumor invasion and metastasis, sustaining cancer stem cell (CSC) self-renewal, and modulating chemotherapy sensitivity and resistance. Furthermore, they mediate immunosuppression within the TME through dynamic crosstalk with resident stromal and immune cells. Thus, the substantial involvement of SLC and ABC transporters in lung cancer pathogenesis and therapeutic resistance definitively positions them as pivotal molecular vulnerabilities and actionable targets for developing novel therapeutic strategies to overcome drug resistance and improve clinical outcomes.

Accurate pathological classification of lung cancer is essential for informing treatment strategies. However, invasive biopsy procedures are not feasible for high-risk patients or those with inaccessible lesions. This study aimed to develop a machine learning model utilizing routine clinical and laboratory data for classification of non-invasive lung cancer. Data from patients admitted to Sichuan Provincial Cancer Hospital were retrospectively analyzed. Key features were determined using LASSO and Boruta algorithms. Four machine learning models, including logistic regression, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), and random forest (RandomForest), were trained and optimized through five-fold cross-validation. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, and F1 score. An online calculator was developed using R Shiny for clinical deployment. A total of 1122 patients with lung cancer were included and randomly assigned to the training and test sets. In the training set, 16 features were incorporated into the models. The RandomForest model demonstrated superior performance compared with the other models, achieving an AUC of 0.999, an accuracy of 0.984, and an F1 score of 1.000. Notably, sex and tumor markers were identified as significant predictors. In the test set, the RandomForest model attained a micro-averaged AUC of 0.969 and macro-averaged AUC of 0.940. Sensitivity and specificity varied from 0.667 to 0.995 across subtypes. A web-based tool was implemented to facilitate real-time clinical application ( https://nkuwangkai.shinyapps.io/lung-cancer-v1/ ). This study presented a robust, non-invasive machine learning model for lung cancer subtype classification, addressing critical gaps in clinical practice for biopsy-ineligible patients. A web-based calculator was developed to facilitate clinical application. Nonetheless, future multicenter validation is warranted to expand the generalizability of this model and promote adoption in diverse healthcare settings.

Mutational landscapes of brain metastases across various histological subtypes of lung cancer.

Lung cancer is the leading cause of cancer-related mortality in the United States. While smoking cigarettes remains the dominant risk factor, declining smoking rates have drawn attention to Radon. Despite the known risks from individual exposure to radon, how long-term lung cancer trends vary by county-level Radon risk classification remains unknown. Using SEER-8 cancer registry data from Connecticut, Iowa, New Mexico, and Utah, we compared lung cancer incidence between counties classified by the EPA as low-risk and high-risk (>4.0 pCi/L) for radon exposure. Constructing a random effects Generalized Least Squares regression, which adjusted for county-level smoking prevalence, we quantified the excess lung cancer rate attributable to radon risk across decade, sex, and histologic subtype. Compared to low-risk counties, counties at high risk of radon exposure had a significantly higher overall lung cancer incidence: 13.5+cases per 100,000 person-years (95% CI: 10.0, 17.1). This gap between low and high-risk counties was largest for Adenocarcinoma and small cell carcinoma, and larger in males than females. However, only in females did we observe the gap in lung cancer incidence between low- and high-risk counties grow decade after decade. This study illustrates how county-level radon data can be leveraged to enhance cancer surveillance and guide prevention and control strategies. Further research is needed to evaluate the impact and cost-effectiveness of radon prevention or mitigation policies, especially in females. We argue that public health systems, over the next fifty years, must prioritize eliminating the two main drivers of America's deadliest cancer.

TRAF6 promotes the ferroptosis defense through AKT/mitochondria damage in KRAS-driven lung cancer.

Decellularized extracellular matrices (dECMs) are promising biomaterials for generating tissue-specific in vitro models due to their organotypic extracellular matrix (ECM) protein profiles compared to natural and synthetic alternatives. However, most dECM-based hydrogels rely on collagen fibrillogenesis, resulting in limited mechanical tuneability and cell instructivity. Here, we developed LungMA, a photocrosslinkable, methacrylated lung dECM hydrogel engineered for precise stiffness modulation and tissue-specific lung cancer modelling. The decellularization process removed >99 % of native DNA, ensuring minimal cellular remnants while preserving key ECM components including laminin-332, collagen VI, and heparan sulfate proteoglycan 2 (HSPG2). Methacrylation and photoinitiation enabled formation of stable LungMA hydrogels with tunable stiffnesses ranging from 1 kPa (healthy lung) to 4 kPa (fibrotic lung). Using A549 non-small-cell lung cancer (NSCLC) cells, we demonstrated that matrix composition and stiffness influenced cell morphology, proliferation, and drug response. Soft LungMA (1 kPa) promoted motile, sheet-like cellular organization, whereas stiff LungMA (>4 kPa) induced compact spheroids associated with chemoresistance. Increasing matrix stiffness resulted in an increase in doxorubicin IC50 from 0.40 μM (soft LungMA) to 1.23 μM (stiff LungMA), and cisplatin IC50 from 0.03 μM to 8.34 μM, reflecting clinical observations where fibrosis correlates with poor prognosis. In contrast, gelatin methacryloyl (GelMA) and basement membrane extract (BME)-based hydrogels failed to induce these stiffness-dependent effects during cisplatin treatment underscoring the instructive role of lung-specific ECM components and matrix stiffness on chemotherapeutic outcomes. LungMA provides a physiologically relevant, mechanically tunable, lung-specific platform that replicates in vivo-like cancer phenotypes and drug responses. This work supports the application of LungMA for oncology research, disease modelling, and high-throughput drug screening as a clinically relevant, non-animal alternative for lung cancer studies.

A qualitative study of patient perspectives for a mindfulness-based intervention in lung cancer.

Despite strong evidence supporting screening in reducing lung cancer mortality, uptake remains low globally. This study aimed to assess the public perceptions of the acceptability and feasibility of lung cancer screening among potentially high-risk adults in Hong Kong, and to identify associated factors. A cross-sectional survey was conducted among adults in Hong Kong identified as potentially high-risk for lung cancer. Perceptions of screening acceptability, and feasibility were measured alongside sociodemographic characteristics, health status, and psychosocial beliefs based on Health Belief Model(HBM) constructs. Multivariable logistic regressions were used to examine associations between participant characteristics and implementation outcomes. Among the 1,100 participants, 90.1% found lung cancer screening acceptable and 88.4% found it feasible. Financial factors, including health insurance coverage and household income≥HKD 50,000, were positively associated with perceived screening acceptability. Responsibility for household cooking was also associated with both acceptability and feasibility. Regarding HBM constructs, higher perceived benefits and self-efficacy were the strongest predictors of both acceptability and feasibility. Perceived higher threat of lung cancer were significantly more likely to perceive lung cancer screening as feasible compared to their counterparts. Perceived barriers were negatively associated with both outcomes (p<0.05). Acceptability and feasibility toward lung cancer screening in Hong Kong are influenced by psychosocial readiness and financial capacity. Enhancing public education, addressing affordability, and embedding screening in primary care with public-private partnerships may support future program success. Findings offer actionable insights for policymakers designing equitable and sustainable lung cancer screening strategies in Hong Kong and similar contexts.

Introduction: Lung cancer is a major cause of cancer-related deaths in India. The occurrence of adenocarcinoma is more common than squamous cell carcinoma in many areas, even among the non-smokers. Aim: To assess whether histological types of lung cancer vary with sociodemographic factors, smoking status, and the form of smoked tobacco. Materials and Methods: This retrospective record-based study reviewed 342 lung cancer cases from May 2022 to December 2024 at a tertiary centre with 310 cases being confirmed histological subtypes. Records with missing demographic or smoking details were excluded, along with 32 Fine-Needle Aspiration Cytology (FNAC) cases that did not have subtype information. Demographic details with smoking categories, and type of smoked tobacco were collected from patient files and cross-checked. Active, passive, and non-smokers were identified from the documentation, and tobacco forms were listed. The collected data and variables were analysed with the Chi-square test, and mean age across subtypes was calculated with Analysis of Variance (ANOVA). Results: Adenocarcinoma formed the largest group (37.4%), followed by squamous cell carcinoma (33.2%), large cell carcinoma (16.1%), and small cell carcinoma (13.2%). Men included approximately 80.6% of the sample, and the mean age was 62.68 years. More than half of the patients were active or passive smokers, while 23.2% had never smoked. The distribution of histological types was similar among the genders, age, and rural or urban areas. Smoking status did not change the histological pattern (p=0.246). The type of smoked tobacco also showed no difference in subtype distribution (p=0.976). Conclusion: Adenocarcinoma is the dominant lung cancer subtype and occurs in all sociodemographic and exposure groups. Routine histologic and molecular analysis for every patient is needed to guide the treatment protocol and improve diagnostic accuracy.

Advancing Rapid Acquisition in Lung PET-CT: Protocol Optimisation via Reduced 18F-FDG Activity and Scan Duration.

Sex-specific associations of creatinine and antioxidant biomarkers with lung cancer risk by drinking and smoking behavior: A prospective cohort study.

Participation in lung cancer biospecimen studies: an analysis of the ECOG-ACRIN phase 3 E1505 and E5508 clinical trials.

Cadmium-induced activation of inflammatory cancer-associated fibroblasts-like cells enhances malignant phenotypes of lung cancer cells.

This study aimed to identify prognostic factors associated with recurrence after anatomical segmentectomy in patients with clinical stage IA1 and IA2 non-small-cell lung cancer (NSCLC). All patients who underwent anatomical segmentectomy for clinical stage IA1 and IA2 NSCLC between January 2018 and January 2020 were included. Recurrence was histologically confirmed, with recurrent tumors presenting histopathological features identical to the primary lung cancer. Baseline clinical and pathological characteristics of patients with and without recurrence were compared using inferential statistical methods. Kaplan-Meier and Cox regression analyses evaluated significant predictors of recurrence over time. A total of 180 patients were retrospectively analyzed (mean age 70.4 ± 7.5 years; 63.9% males; mean follow-up 3.1 ± 0.9 years). Recurrence occurred in 18 patients (10%). Logistic regression analysis indicated that recurrence was 5.1 times more prevalent in patients with tumor size ≥ 1.5cm than in those with tumor size < 1.5cm (odds ratio [OR] 5.1; 95% confidence interval [CI] 1.4-18.4, p = 0.01) and 2.9 times more prevalent in patients with pathological upstaging than in those without (OR 2.9; 95% CI 1.0-8.2, p = 0.04). Kaplan-Meier analysis revealed significant differences in recurrence-free estimates between patients with solid tumors ≥ 1.5cm and those < 1.5cm (p < 0.01) and between those with and without pathological upstaging (p = 0.05). Recurrence after anatomical segmentectomy for stage IA1 and IA2 NSCLC is significantly influenced by tumor size and pathological upstaging, underscoring the importance of tailored surgical strategies and meticulous pathological assessment to optimize long-term outcomes and reduce recurrence risk.

This study evaluated the association between percutaneous transthoracic needle biopsy (PTNB) and recurrence in early-stage non-small-cell lung cancer (NSCLC). A retrospective study was conducted to analyze patients with cT1b-2aN0M0 NSCLC who underwent lobectomy between 2009 and 2021. The exclusion criteria ruled out multiple primary lung cancers and bronchoscopic biopsy. Patients were classified into a PTNB group and a non-biopsy group according to whether they underwent preoperative PTNB. Propensity score-matching was applied, and cumulative incidence of recurrence (CIR) was compared. Fine and Gray competing-risk regression was used to evaluate the association between PTNB and recurrence. Subgroup analyses were conducted according to pathologic stage and the presence of high-risk pathologic features. Of 2208 eligible patients, 674 (30.5 %) underwent PTNB, and 1534 (69.5 %) did not. After propensity score-matching, each group included 416 patients with balanced baseline characteristics. In the matched cohort, the 5-year CIR was significantly higher in the PTNB group (21.1 %; 95 % confidence interval [CI], 18.0-26.1 %) than in the non-biopsy group (13.7 %; 95 % CI, 10.2-17.3 %; P < 0.001). In multivariate competing-risk regression analysis, PTNB was independently associated with increased recurrence risk in models incorporating clinical variables alone (subdistribution hazard ratio [SHR], 1.892; 95 % CI, 1.407-2.545; P < 0.001) as well as in models incorporating both clinical and pathologic variables (SHR, 1.851; 95 % CI, 1.375-2.493; P < 0.001). Subgroup analyses demonstrated that the association between PTNB and recurrence persisted regardless of pathologic tumor-node-metastasis (pTNM) stage and the presence of high-risk pathologic features. Preoperative PTNB was associated with an increased risk of recurrence for patients with early-stage NSCLC.

Exploring synergistic therapeutic potential of Erlotinib and artemisinin in non-small cell lung Cancer (NSCLC) using pharmacological networking and mathematical modeling.