Treatment Of Lung Cancer Patients Research Articles

Age differences in the treatment of lung cancer-a cohort study among 42,000 patients from Germany.

We aimed to describe treatment of lung cancer patients in Germany based on health claims data, focusing particularly on differences by age. Using the German Pharmacoepidemiological Research Database (GePaRD, ~ 20% of the German population) we identified lung cancer patients diagnosed in 2015-2018 based on a previously developed algorithm and followed them until death, end of continuous insurance or end of 2020. We described initial treatment patterns after diagnosis and survival, stratified among others by age. We included 42,629 incident lung cancer patients (58% male). Surgery within three months after diagnosis was performed in 36%, 31%, 29% and 18% of patients aged < 50, 50-69, 70-79 and ≥ 80, respectively. Among patients without surgery, systemic therapy was administered in 77%, 72%, 54% and 25% of patients aged < 50, 50-69, 70-79 and ≥ 80, respectively. Monoclonal antibodies were administered in 15-30% of patients across age groups, and 4% to 15% received protein kinase inhibitors. Overall, 21% of patients remained untreated. In the age groups < 50, 50-69, 70-79 and ≥ 80, this proportions was 9%, 12%, 22% and 48%, respectively. In conclusion, our study provides a comprehensive overview of the therapy of lung cancer patients in Germany and quantitatively demonstrates the considerable differences between age groups. In terms of clinical cancer registration, the results are useful to estimate the completeness of data for the different types of treatment.

Integrative transcriptomic analysis identifies emetine as a promising candidate for overcoming acquired resistance to ALK inhibitors in lung cancer.

Anaplastic lymphoma kinase (ALK; also known as ALK tyrosine kinase receptor) inhibitors (ALKi) are effective in treating lung cancer patients with chromosomal rearrangement of ALK. However, continuous treatment with ALKis invariably leads to acquired resistance in cancer cells. In this study, we propose an efficient strategy to suppress ALKi resistance through a meta-analysis of transcriptome data from various cell models of acquired resistance to ALKis. We systematically identified gene signatures that consistently showed altered expression during the development of resistance and conducted computational drug screening using these signatures. We identified emetine as a promising candidate compound to inhibit the growth of ALKi-resistant cells. We demonstrated that emetine exhibited effectiveness in inhibiting the growth of ALKi-resistant cells, and further interpreted its impact on the resistant signatures through drug-induced RNA-sequencing data. Our transcriptome-guided systematic approach paves the way for efficient drug discovery to overcome acquired resistance to cancer therapy.

AI bias in lung cancer radiotherapy

Aim: In lung cancer research, AI has been trained to read chest radiographs, which has led to improved health outcomes. However, the use of AI in healthcare settings is not without its own set of drawbacks, with bias being primary among them. This study seeks to investigate AI bias in diagnosing and treating lung cancer patients. The research objectives of this study are threefold: 1) To determine which features of patient datasets are most susceptible to AI bias; 2) to then measure the extent of such bias; and 3) from the findings generated, offer recommendations for overcoming the pitfalls of AI in lung cancer therapy for the delivery of more accurate and equitable healthcare. Methods: We created a synthetic database consisting of 50 lung cancer patients using a large language model (LLM). We then used a logistic regression model to detect bias in AI-informed treatment plans. Results: The empirical results from our synthetic patient data illustrate AI bias along the lines of (1) patient demographics (specifically, age) and (2) disease classification/histology. As it concerns patient age, the model exhibited an accuracy rate of 82.7% for patients &lt; 60 years compared to 85.7% for patients ≥ 60 years. Regarding disease type, the model was less adept in identifying treatment categories for adenocarcinoma (accuracy rate: 83.7%) than it was in predicting treatment categories for squamous cell carcinoma (accuracy rate: 92.3%). Conclusions: We address the implications of such results in terms of how they may exacerbate existing health disparities for certain patient populations. We conclude by outlining several strategies for addressing AI bias, including generating a more robust training dataset, developing software tools to detect bias, making the model’s code open access and soliciting user feedback, inviting oversight from an ethics review board, and augmenting patient datasets by synthesizing the underrepresented data.

Electrochemical Biosensors in Diagnostic Medicine: Detecting Lung Cancer Biomarkers – A Comprehensive Review

AbstractElectrochemical biosensors use biomolecules, such as proteins, enzymes, and antibodies, to translate the analytical signals detected in a sample. They have diverse applications including pesticide detection in agriculture, water analysis in various sectors, and biomedical and forensic diagnostics. With the estimated number of cancer cases in the US in 2024 being over two million, particularly lung cancer, which is notoriously difficult to diagnose early, the integration of biosensors into the Point‐of‐care Testing (PoCT) strategy can significantly improve the detection of cancer biomarkers, contributing to early diagnosis and successful treatment. Three‐dimensional (3D) printing is a promising alternative for reducing production costs and customizing devices in various ways. This review highlights recent trends and research on the development of electrochemical biosensors for early detection of lung cancer. These biosensors are expected to be more sensitive and selective for a variety of real samples and are precise, accurate, and stable during their commercialization. Significant progress has been made in the development of electrochemical devices for the early diagnosis of lung cancer, with various biomarker anchoring and detection strategies addressed throughout the study. Overcoming these challenges is key to advancing the use of these biosensors, thus improving diagnostic accuracy and enabling the successful treatment of lung cancer patients.

Artificial intelligence-assisted delineation for postoperative radiotherapy in patients with lung cancer: a prospective, multi-center, cohort study

BackgroundPostoperative radiotherapy (PORT) is an important treatment for lung cancer patients with poor prognostic features, but accurate delineation of the clinical target volume (CTV) and organs at risk (OARs) is challenging and time-consuming. Recently, deep learning-based artificial intelligent (AI) algorithms have shown promise in automating this process.ObjectiveTo evaluate the clinical utility of a deep learning-based auto-segmentation model for AI-assisted delineating CTV and OARs in patients undergoing PORT, and to compare its accuracy and efficiency with manual delineation by radiation oncology residents from different levels of medical institutions.MethodsWe previously developed an AI auto-segmentation model in 664 patients and validated its contouring performance in 149 patients. In this multi-center, validation trial, we prospectively involved 55 patients and compared the accuracy and efficiency of 3 contouring methods: (i) unmodified AI auto-segmentation, (ii) fully manual delineation by junior radiation oncology residents from different medical centers, and (iii) manual modifications based on AI segmentation model (AI-assisted delineation). The ground truth of CTV and OARs was delineated by 3 senior radiation oncologists. Contouring accuracy was evaluated by Dice similarity coefficient (DSC), Hausdorff distance (HD), and mean distance of agreement (MDA). Inter-observer consistency was assessed by volume and coefficient of variation (CV).ResultsAI-assisted delineation achieved significantly higher accuracy compared to unmodified AI auto-contouring and fully manual delineation by radiation oncologists, with median HD, MDA, and DCS values of 20.03 vs. 21.55 mm, 2.57 vs. 3.06 mm, 0.745 vs. 0.703 (all P&amp;lt;0.05) for CTV, respectively. The results of OARs contours were similar. CV for OARs was reduced by approximately 50%. In addition to better contouring accuracy, the AI-assisted delineation significantly decreased the consuming time and improved the efficiency.ConclusionAI-assisted CTV and OARs delineation for PORT significantly improves the accuracy and efficiency in the real-world setting, compared with pure AI auto-segmentation or fully manual delineation by junior oncologists. AI-assisted approach has promising clinical potential to enhance the quality of radiotherapy planning and further improve treatment outcomes of patients with lung cancer.

Lung cancer cell-derived exosomes: progress on pivotal role and its application in diagnostic and therapeutic potential.

Lung cancer is frequently detected in an advanced stage and has an unfavourable prognosis. Conventional therapies are ineffective for the treatment of metastatic lung cancer. While certain molecular targets have been identified as having a positive response, the absence of appropriate drug carriers prevents their effective utilization. Lung cancer cell-derived exosomes (LCCDEs) have gained attention for their involvement in the development of cancer, as well as their potential for use in diagnosing, treating, and predicting the outcome of lung cancer. This is due to their biological roles and their inherent ability to transport biomolecules from the donor cells. Lung cancer-associated cell-derived extracellular vesicles (LCCDEVs) have the ability to enhance cell proliferation and metastasis, influence angiogenesis, regulate immune responses against tumours during the development of lung cancer, control drug resistance in lung cancer treatment, and are increasingly recognised as a crucial element in liquid biopsy evaluations for the detection of lung cancer. Therapeutic exosomes, which possess inherent intercellular communication capabilities, are increasingly recognised as effective vehicles for targeted drug delivery in precision medicine for tumours. This is due to their exceptional biocompatibility, minimal immunogenicity, low toxicity, prolonged circulation in the bloodstream, biodegradability, and ability to traverse different biological barriers. Currently, multiple studies are being conducted to create new means of diagnosing and predicting outcomes using LCCDEs, as well as to develop techniques for utilizing exosomes as effective carriers for medication delivery. This paper provides an overview of the current state of lung cancer and the wide range of applications of LCCDEs. The encouraging findings and technologies suggest that the utilization of LCCDEs holds promise for the clinical treatment of lung cancer patients.

Scoping review of anticancer drug utilization in lung cancer patients at the end of life.

This scoping review aims to deepen the understanding of end-of-life anticancer drug use in lung cancer patients, a disease marked by high mortality and symptom burden. Insight into unique end-of-life treatment patterns is crucial for improving the appropriateness of cancer care for these patients. Comprehensive searches were carried out in Medline and Embase to find articles on the utilization of anticancer drugs in the end of life of lung cancer patients. We identified 68 publications, highlighting the methodological characteristics of studies including the timing of the research, disease condition, treatment regimen, type of treatment, and features of the treatment. We outlined the frequency of anticancer drug use throughout different end-of-life periods. This review provides a comprehensive overview of primary studies exploring end-of-life treatments in lung cancer patients. Methodological inconsistencies pose many challenges, revealing a notable proportion of patients experiencing potential overtreatment, warranting more standardized research methods for robust evaluations.

Construction of a lung cancer 3D culture model based on alginate/gelatin micro-beads for drug evaluation.

Lung cancer is one of the most common malignant tumors worldwide. Despite advances in lung cancer treatment, patients still face challenges related to drug resistance and recurrence. Current methods for evaluating anti-cancer drug activity are insufficient, as they rely on two-dimensional (2D) cell culture and animal models. Therefore, the development of an in vitro drug evaluation model capable of predicting individual sensitivity to anti-cancer drugs would greatly enhance the success rate of drug treatments for lung cancer patients. The purpose of this research is to utilise conditional reprogramming technology to cultivate patient-derived lung cancer cells and to construct an in vitro 3D culture model using sodium alginate (SA) and gelatin. The aim is to study the biological characteristics of cells in the 3D culture model and to further investigate the sensitivity of anti-cancer drugs based on the alginate-gelatin 3D culture model. This approach provides new means and insights for personalized precision anti-cancer therapy and the development of new anti-cancer drugs. Conditional reprogramming technology was used to generate conditionally reprogrammed lung adenocarcinoma cells (CRLCs). Alginate-gelatin hydrogel micro-beads were created to explore their potential use in the assessment of anti-cancer drugs. Cell proliferation was also examined using the MTS assay method. Live/dead staining was performed to estimate cell distribution and viability using calcein acetoxymethyl ester/propidium iodide (calcein-AM/PI) double staining. Protein expression was assessed by Western blot. The cells grown in the three-dimensional (3D) culture were in a state of continuous proliferation, and there was an obvious phenomenon of cell mass growth. The drug sensitivity assay results demonstrated that compared with the 2D-grown cells, the CRLCs grown in the alginate-gelatin hydrogel micro-beads exhibited more resistance to anti-cancer drugs. The results also showed that the 3D-cultured CRLCs showed greater protein expression levels of stem cell hallmarks, such as Nanog Homeobox (NANOG), SRY-Box Transcription Factor 2 (SOX-2), and aldehyde dehydrogenase 1 family member A1 (ALDH1A1), than the 2D-grown cells. These findings suggest that the 3D hydrogel cell culture models more closely mimicked the in vivo biological and clinical behavior of cells, and demonstrated higher innate resistance to anti-cancer drugs than the 2D cell culture models, and thus could serve as valuable tools for diagnosis, drug screening, and personalized medicine.

Evaluating the Time Interval Between Symptoms Onset, Diagnosis, and Therapeutic Intervention in Lung Cancer: A Cross-Sectional Study in Southern Iran.

Delay in diagnosis and treatment of lung cancer is thought to be a major cause of its poor outcomes. We evaluated the delays within the presentation to the initiation of diagnostic and therapeutic interventions amongst lung cancer patients in Southern Iran. This cross-sectional study was conducted from March 2019 to March 2021. The data collected through interview included socio-demographic, medical and clinical findings, and the time intervals needed to visit physician, refer to specialist, request diagnostic procedures, reach diagnosis of lung cancer, and hospitalization. Eighty-nine patients (58 males and 31 females) with a mean age of 61.01 ± 12.25 years were included. The median time of symptom presentation and first physician visit interval was 25 days. Sixty-five days were spent for requesting, performing, and evaluating the diagnostic procedures. The median interval between diagnosis and initiation of treatment was 16 days. Totally, it took an average of 122 days from the presentation to the definite diagnosis of lung cancer. Patient-, diagnosis-, and treatment-related delays were not significantly correlated with any of the demographic, socioeconomic, and clinical (disease stage, symptom) variables, as well as the diagnosis tool and the first physician who visited the patient (p > 0.05). There was a significant delay but relatively similar to other countries in the diagnosis and treatment of lung cancer patients in Southern Iran. The largest portion of delay could be attributed to the raising clinical suspicion in the physicians, referral for diagnostic assessments, and the diagnosis process.

Frequency of Depression and Anxiety in Patients With Lung Cancer Undergoing Chemotherapy: A Single-Center, Cross-Sectional Study From Vietnam.

Background Chemotherapy is a crucial component of multimodality treatment for lung cancer patients. Although considered a vital and effective approach, chemotherapy can induce adverse effects, negatively impacting patients' quality of life. These adverse effects can also lead to psychological issues, particularly depression and anxiety. This study aimed to determine the frequency of depression, anxiety, and their associated factors in patients with lung cancer undergoing chemotherapy. Methodology A cross-sectional study was conducted on 92 patients with lung cancer receiving chemotherapy at the University Medical Center Ho Chi Minh City, Vietnam, from February to May 2023. The patients' depression and anxiety were assessed using the Hospital Anxiety and Depression Scale. Results Among 92 patients with a mean age of 60.4 ± 9.6 years and 58 males (63%), the prevalence of depression was 44.5% (41 patients), with significant associations between depression and gender, economic status, smoking status, and performance status. The prevalence of anxiety was 38% (35 patients), with significant associations between anxiety and gender, chemotherapy cycle, cancer stage, and performance status. Conclusions Our study highlights the importance of a multidisciplinary approach to cancer care, ensuring that lung cancer patients receive the necessary support to manage both physical and mental health challenges throughout chemotherapy.

A non-enhanced CT-based deep learning diagnostic system for COVID-19 infection at high risk among lung cancer patients.

Pneumonia and lung cancer have a mutually reinforcing relationship. Lung cancer patients are prone to contracting COVID-19, with poorer prognoses. Additionally, COVID-19 infection can impact anticancer treatments for lung cancer patients. Developing an early diagnostic system for COVID-19 pneumonia can help improve the prognosis of lung cancer patients with COVID-19 infection. This study proposes a neural network for COVID-19 diagnosis based on non-enhanced CT scans, consisting of two 3D convolutional neural networks (CNN) connected in series to form two diagnostic modules. The first diagnostic module classifies COVID-19 pneumonia patients from other pneumonia patients, while the second diagnostic module distinguishes severe COVID-19 patients from ordinary COVID-19 patients. We also analyzed the correlation between the deep learning features of the two diagnostic modules and various laboratory parameters, including KL-6. The first diagnostic module achieved an accuracy of 0.9669 on the training set and 0.8884 on the test set, while the second diagnostic module achieved an accuracy of 0.9722 on the training set and 0.9184 on the test set. Strong correlation was observed between the deep learning parameters of the second diagnostic module and KL-6. Our neural network can differentiate between COVID-19 pneumonia and other pneumonias on CT images, while also distinguishing between ordinary COVID-19 patients and those with white lung. Patients with white lung in COVID-19 have greater alveolar damage compared to ordinary COVID-19 patients, and our deep learning features can serve as an imaging biomarker.

Identification of a prognostic gene signature in patients with cisplatin resistant squamous cell lung cancer.

In the absence of targeted mutations and immune checkpoints, platinum-based chemotherapy remains a gold standard agent in the treatment of patients with lung squamous cell carcinoma (LUSC). However, cisplatin resistance greatly limits its therapeutic efficacy and presents challenges in the treatment of lung cancer patients. Therefore, the potential clinical needs for this research focus on identifying novel molecular signatures to further elucidate the underlying mechanisms of cisplatin resistance in LUSC. A growing body of evidence indicates that alternative splicing (AS) events significantly influence the tumor progression and survival of patients with LUSC. However, there are few systematic analyses of AS reported in LUSC. This study aims to explore the role of messenger RNA (mRNA), microRNA (miRNA), and AS in predicting prognosis in patients with cisplatin-resistant LUSC and provide potential therapeutic targets and drugs. Gene expression and miRNA expression, using RNA sequencing (RNA-seq), and SpliceSeq data were downloaded from The Cancer Genome Atlas (TCGA) database. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to construct predictive models. Kaplan-Meier survival analyses were used to evaluate patients' prognosis. Single-sample gene set enrichment analysis (ssGSEA) conducted via the R package "GSEAbase" was used to evaluate the immune-related characteristics. Immunohistochemistry was used to examine protein expression. The Connectivity Map (CMap) database was used to screen for potential drugs. The 3-(4,5)-dimethylthiahiazo (-z-y1)-3,5-di-phenytetrazoliumromide (MTT) assay was used to determine and calculate the half-maximal inhibitory concentration (IC50) of the drugs, sulforaphane and parthenolide. In this study, bioinformatics were used to identify mRNAs, miRNAs, and AS events related to response to cisplatin and to establish an integrated prognostic signature for 70 patients with LUSC and cisplatin resistance. The prognostic signature served as an independent prognostic factor with high accuracy [hazard ratio (HR) =2.346, 95% confidence interval (CI): 1.568-3.510; P<0.001], yielding an area under the curve (AUC) of 0.825, 0.829, and 0.877 for 1-, 3-, and 5-year survival, respectively. It also demonstrated high predictive performance in this cohort of patients with LUSC, with an AUC of 0.734, 0.767, and 0.776 for 1-, 3-, and 5-year survival, respectively. This integrated signature was also found to be an independent indicator among conventional clinical features (HR =2.288, 95% CI: 1.547-3.383; P<0.001). In addition, we analyzed the correlation of the signature with immune infiltration and identified several small-molecule drugs that had the potential to improve the survival of patients with LUSC. This study provides a framework for the mRNA-, miRNA-, and AS-based evaluation of cisplatin response and several potential therapeutic drugs for targeting cisplatin resistance in LUSC. These findings may serve as a theoretical basis for the clinical alleviation of cisplatin resistance and thus help to improve treatment responses to chemotherapy in patients with LUSC.

Identification of JUN gene and cellular microenvironment in response to PD-1 blockade treatment in lung cancer patients via single-cell RNA sequencing.

Exploring the molecular mechanisms of PD-1/PDL-1 blockade for non-small cell lung cancer (NSCLC) would facilitate understanding for tumor microenvironment (TME) and development of individualized medicine. To date, biomarkers of response to PD-1 blockade therapy were still limited. In this study, we hypothesize that cell type in the tumor microenvironment can influence the effect of PD-1 blockade immunotherapy through specific genes. Therefore, we re-analyze the single-cell RNA sequencing data and validation in tissue from lung adenocarcinoma patients. Dynamic changes of cellular subpopulation were observed after anti-PD-1 immunotherapy among TMEs between primary/metastasis or good/poor response patients. Non-exhausted CD8 T cells and dysregulated genes were observed in responsing patients from PD-1 blockade therapy. Among all changed genes, JUN, involved in PD-1 blockade immunotherapy pathway, and could be considered as a PD-1 responsing biomarker.

Unveiling the Effects of the COVID-19 Pandemic on Lung Cancer Surgery.

The aim of this study is to investigate the impact of the COVID-19 pandemic on the surgical treatment of lung cancer patients. Data from patients who underwent surgery during the pandemic were analyzed and compared to pre-pandemic and post-pandemic periods. Multiple parameters were examined, and their changes yielded significant results compared to other periods of the study. The statistical analysis revealed a significant decrease in the number of surgical interventions during the pandemic (p < 0.001), followed by a significant rebound thereafter. During this period, there was a significant increase in the T stage of cancer compared to both pre-pandemic and post-pandemic periods (p = 0.027). Additionally, the mean Charlson comorbidity index score was significantly higher during the pandemic compared to the pre-pandemic period (p = 0.042). In this crisis period, a significant decrease was recorded in both the total hospitalization duration (p = 0.015) and the pre-operative hospitalization duration (p = 0.006). These findings provide evidence of significant changes in clinical and therapeutic strategies applied to lung cancer surgery patients during the study period. The pandemic has had a substantial and complex impact, the full extent of which remains to be fully understood.

POLR1D silencing suppresses lung cancer cells proliferation and migration via inhibition of PI3K-Akt pathway

AimThe most common type of cancer that leads to death worldwide is lung cancer. Despite significant surgery and chemotherapy improvements, lung cancer patient’s survival rate is still poor. The RNA polymerase I subunit D (POLR1D) gene can induce various cancers. A current study reported that POLR1D plays a vital role in cancer prognosis. However, its biological function in the development of lung cancer remains unclear.MethodsReverse transcription PCR (RT-PCR) measured the relative POLR1D protein expression level in lung cancer cell lines. Lung cancer cell proliferation, migration, and invasion were analyzed by performing cell counting kit-8 (CCK-8), and transwell. The phosphatidylinositol 3-kinase/serine-threonine kinase (PI3K/AKT) signaling pathway-related protein expressions were examined by Western blotting assay.ResultsPOLR1D protein expression was elevated in lung cancer. Lung cancer cell loss-of-function tests showed that POLR1D silencing could attenuate cell viability both in SK-MES-1 and in H2170 cells. Furthermore, silencing POLR1D inhibited SK-MES-1 and H2170 cells proliferation, migration, and invasion. Moreover, SK-MES-1 and H2170 cells’ migration and invasion capacity were potentially suppressed by the knockdown of POLR1D. The progression of multiple cancers has been implicated in the PI3K/AKT pathway. Here, we observed that POLR1D silencing suppressed lung cancer progression by inhibition of the PI3K-Akt pathway.ConclusionsThe study speculated that POLR1D might provide a new potential therapeutic possibility for treating lung cancer patients via targeting PI3K/AKT.

Combination of betulinic acid and EGFR-TKIs exerts synergistic anti-tumor effects against wild-type EGFR NSCLC by inducing autophagy-related cell death via EGFR signaling pathway

BackgroundEpidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) have revolutionized the treatment of lung cancer patients with mutated EGFR. However, the efficacy of EGFR-TKIs in wild-type EGFR tumors has been shown to be marginal. Methods that can sensitize EGFR-TKIs to EGFR wild-type NSCLC remain rare. Hence, we determined whether combination treatment can maximize the therapeutic efficacy of EGFR-TKIs.MethodsWe established a focused drug screening system to investigate candidates for overcoming the intrinsic resistance of wild-type EGFR NSCLC to EGFR-TKIs. Molecular docking assays and western blotting were used to identify the binding mode and blocking effect of the candidate compounds. Proliferation assays, analyses of drug interactions, colony formation assays, flow cytometry and nude mice xenograft models were used to determine the effects and investigate the molecular mechanism of the combination treatment.ResultsBetulinic acid (BA) is effective at targeting EGFR and synergizes with EGFR-TKIs (gefitinib and osimertinib) preferentially against wild-type EGFR. BA showed inhibitory activity due to its interaction with the ATP-binding pocket of EGFR and dramatically enhanced the suppressive effects of EGFR-TKIs by blocking EGFR and modulating the EGFR-ATK-mTOR axis. Mechanistic studies revealed that the combination strategy activated EGFR-induced autophagic cell death and that the EGFR-AKT-mTOR signaling pathway was essential for completing autophagy and cell cycle arrest. Activation of the mTOR pathway or blockade of autophagy by specific chemical agents markedly attenuated the effect of cell cycle arrest. In vivo administration of the combination treatment caused marked tumor regression in the A549 xenografts.ConclusionsBA is a potential wild-type EGFR inhibitor that plays a critical role in sensitizing EGFR-TKI activity. BA combined with an EGFR-TKI effectively suppressed the proliferation and survival of intrinsically resistant lung cancer cells via the inhibition of EGFR as well as the induction of autophagy-related cell death, indicating that BA combined with an EGFR-TKI may be a potential therapeutic strategy for overcoming the primary resistance of wild-type EGFR-positive lung cancers.Graphical abstract

Novel therapeutic strategies for rare mutations in non-small cell lung cancer

Lung cancer is still the leading cause of cancer-related mortality. Over the past two decades, the management of non-small cell lung cancer (NSCLC) has undergone a significant revolution. Since the first identification of activating mutations in the epidermal growth factor receptor (EGFR) gene in 2004, several genetic aberrations, such as anaplastic lymphoma kinase rearrangements (ALK), neurotrophic tropomyosin receptor kinase (NTRK) and hepatocyte growth factor receptor (MET), have been found. With the development of gene sequencing technology, the development of targeted drugs for rare mutations, such as multikinase inhibitors, has provided new strategies for treating lung cancer patients with rare mutations. Patients who harbor this type of oncologic driver might acquire a greater survival benefit from the use of targeted therapy than from the use of chemotherapy and immunotherapy. To date, more new agents and regimens can achieve satisfactory results in patients with NSCLC. In this review, we focus on recent advances and highlight the new approval of molecular targeted therapy for NSCLC patients with rare oncologic drivers.

The impact of decision tools during oncological consultation with lung cancer patients: A systematic review within the I3LUNG project.

To date, lung cancer is one of the most lethal diagnoses worldwide. A variety of lung cancer treatments and modalities are available, which are generally presented during the patient and doctor consultation. The implementation of decision tools to facilitate patient's decision-making and the management of their healthcare process during medical consultation is fundamental. Studies have demonstrated that decision tools are helpful to promote health management and decision-making of lung cancer patients during consultations. The main aim of the present work within the I3LUNG project is to systematically review the implementation of decision tools to facilitate medical consultation about oncological treatments for lung cancer patients. In the present study, we conducted a systematic review following the PRISMA guidelines. We used an electronic computer-based search involving three databases, as follows: Embase, PubMed, and Scopus. 10 articles met the inclusion criteria and were included. They explicitly refer to decision tools in the oncological context, with lung cancer patients. The discussion highlights the most encouraging results about the positive role of decision aids during medical consultations about oncological treatments, especially regarding anxiety, decision-making, and patient knowledge. However, no one main decision aid tool emerged as essential. Opting for a more recent timeframe to select eligible articles might shed light on the current array of decision aid tools available. Future review efforts could utilize alternative search strategies to explore other lung cancer-specific outcomes during medical consultations for treatment decisions and the implementation of decision aid tools. Engaging with experts in the fields of oncology, patient decision-making, or health communication could provide valuable insights and recommendations for relevant literature or research directions that may not be readily accessible through traditional search methods. The development of guidelines for future research were provided with the aim to promote decision aids focused on patients' needs.

Unravelling the lung cancer diagnostic pathway: identifying gaps and opportunities for improvement.

A fast and well-organized complex diagnostic process is important for better success in the treatment of lung cancer patients. The aim of our study was to reveal the gaps and inefficiencies in the diagnostic process and to suggest improvement strategies in a single tertiary centre in Slovenia. We employed a comprehensive approach to carefully dissect all the steps in the diagnostic journey for individuals suspected of having lung cancer. We gathered and analysed information from employees and patients involved in the process by dedicated questionnaires. Further, we analysed the patients' data and calculated the diagnostic intervals for patients in two different periods. The major concerns among employees were stress and excessive administrative work. The important result of the visual journey and staff reports was the design of electronic diagnostic clinical pathway (eDCP), which could substantially increase safety and efficacy by diminishing the administrative burden of the employees. The patients were generally highly satisfied with diagnostic journey, but reported too long waiting times. By analysing two time periods, we revealed that diagnostic intervals exceeded the recommended timelines and got importantly shorter after two interventions - strengthening the diagnostic team and specially by purchase of additional PET-CT machine (the average time from general practitioner (GP) referral to the multidisciplinary treatment board (MDTB) decision was 50.8 [± 3.0] prior and 37.1 [± 2.3] days after the interventions). The study illuminated opportunities for refining the diagnostic journey for lung cancer patients, underscoring the importance of both administrative and capacity-related enhancements.

An Interventional Radiologist's Guide to Lung Cancer.

Lung cancer continues to be the third leading cause of cancer and the leading cause of cancer deaths. As the field of interventional oncology continues to grow, interventional radiologists are increasingly treating lung cancer patients. Involvement begins with tissue diagnosis for which biomarkers and immunohistochemistry are used to guide selective and advanced medical therapies. An interventional radiologist must be aware of the rationale behind tissue diagnosis and techniques to minimize biopsy complications. Staging is an important part of tumor board conversations and drives treatment pathways. Surgical therapy remains the gold standard for early-stage disease but with an aging population the need for less invasive treatments such as radiation therapy and ablation continue to grow. The interventionalist must be aware of the indications, techniques, and pre- and posttherapy managements for percutaneous ablation. Endovascular therapy is broadly divided into therapeutic treatment of lung cancer, which is gaining traction, and treatment of lung cancer complications such as hemoptysis. This review aims to provide a good basis for interventional radiologists treating lung cancer patients.