Early Lung Cancer Research Articles

Predicting radiation-induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy.

Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation. Our algorithm predicted post-treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5×109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC<0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC. Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.

AI-based approach to enable proactive identification of early lung cancer: A retrospective population health study and economic model.

10536 Background: Approximately 48,500 new cases of lung cancer are diagnosed each year in the UK with a five-year survival rate which lags behind other European countries. Most patients are diagnosed at stage III or IV disease, which has a significantly worse prognosis. The integration of real-world data and AI techniques presents a promising avenue for improvement, using novel solutions not achievable through traditional care models. This study explores proactive patient identification at scale, aligning with the "left shift" paradigm in population health. The approach uses innovative technologies and novel data sources to identify patients with early-stage disease, enhancing both clinical and economic outcomes. Importantly, these interventions should be feasible on a large scale without overburdening already stretched clinical services. Methods: A retrospective study of real-world data for an urban population with high socioeconomic disadvantage and health needs was undertaken. Two endpoints were determined: detection potential and health economic impact. For detection potential (DP), patients aged 40 and above who underwent chest X-rays (CXR) at a large academic medical centre between 2016-2022 were included. Natural Language Processing (NLP) analysed unstructured free-text data from Electronic Health Records, combined with ICD-10 codes, for predictive model (PM) development using Machine Learning (ML) classification techniques. The PM's performance was assessed using the area under the receiver operating characteristic curve (AUCROC). Health economic impact was evaluated through a theoretical extrapolation of DP. Assuming a 9% left shift from late (stage III or IV) to early (stage I or II) disease, a health economic model (HEM) compared the impact of the current care model to a potential future state facilitated by the digital medicine care model (DMCM). Results: 75,342 patients were included, with 755 lung cancer diagnoses occurring within 6 months of CXR. The PM achieved a peak AUROC of 0.75, exceeding the performance of currently used risk prediction models (Q Cancer Risk, Risk Assessment Tools). A 9% left shift equated to 4.5 years mean increase in survival. The DMCM was associated with a net healthcare cost benefit of £6k per patient diagnosed with lung cancer, improved quality-adjusted life years of £21M for the year 1 cohort over 10 years and increased economic productivity (from additional years in workforce) of £2.4M. Conclusions: The study demonstrates the practical utility of AI-supported early lung cancer detection at population scale. Real-world evidence is needed to prospectively validate the model as cost-effective, scalable, and efficient. Future work will seek to improve the model's performance against a broader at-risk patient population and explore its applicability to other late-presenting tumours.

Preliminary results of the Lung Cancer Mutation Consortium LCMC4 evaluation of actionable drivers in early stage lung cancer (LEADER) screening trial.

Preliminary results of the Lung Cancer Mutation Consortium LCMC4 evaluation of actionable drivers in early stage lung cancer (LEADER) screening trial.

Assessing quality of care in early stage resected stage non-small cell lung cancer (NSCLC): An evaluation of epidermal growth factor receptor (EGFR) testing and adjuvant (adj) therapy (tx).

8072 Background: In December 2020, the FDA approved the use of osimertinib in the treatment of stage IB-IIIA (ES) NSCLC patients (pts) with EGFR exon 19 deletions and exon 21 L858R mutations (EGRFm) following tumor resection. Using real-world data, we sought to understand EGFR testing rates among ES pts and whether pts received osimertinib in the adjuvant setting based on EGFRm. Methods: We used the Integra Connect PrecisionQ real-world de-identified database of over 3 million cancer pts across 500 sites of care. From this database, we identified Stage IB – IIIA pts evaluated via medical chart curation who underwent lung cancer resection (R) between 12/1/2021 to 7/31/2023 (N = 803). We assessed adj treatment by stage, EGFR testing rates overall and by stage, EGFR testing rates by adjuvant treatment and non-adj treatment pts, timing of EGFR testing, and treatment rates with an EGFR TKI among EGFRm pts. Descriptive analyses were used and proportions were compared using a chi-squared test. Results: The mean age of the 803 ES R pts in the sample was 69 (SD = 8.3), 53.3% of the pts were female, , and 12% never smoked. We found adj treatment rates among ES R pts to be 56%; these were highest among stage IIIA (N = 245) pts at 70.2%, compared to stage IIB (N = 241) at 68.8%, stage IIA (N = 69) at 53.6% and stage IB (N = 240) at 31.7% (p &lt; 0.001). Among ES R pts, 70% (N = 563) were tested for EGFR; this rate was 78% (N = 455) among ES pts who were R and received adj treatment, compared to 59% (N = 348) among ES R pts who did not receive adj treatment (p &lt; 0.001). EGFR testing rates were highest among stage IIB pts at 76%, compared to stage IIIA pts at 73%, stage IIA pts at 72%, and stage IB pts at 60% (p &lt; .001). Among ES pts who were R and tested for EGFR (N = 563), 14% were tested before surgery, 38% were tested after surgery but before adj treatment, and 48% were tested after initiating adjuvant therapy. EGFRm was found in 10.4% of EGFR tested pts. Among EGFRm ES R pts (N = 59), 54% were treated with an EGFR TKI. EGFRm ES R pts treated with an adj EGFR TKI were highest among stage IIB pts (N = 14) at 93%, compared to stage IIA pts (N = 5) at 60%, stage IB pts (N = 20) at 45%, and stage IIIA pts (N = 20) at 35% (p &lt; 0.01). However, only 69% (N = 41) of EGFRm ES R pts received adj treatment (either chemotherapy or EGFR TKI) and among those pts, 78% (N = 32) received an EGFR TKI. Conclusions: Based on our findings using real-world data for EGFR testing and adjuvant osimertinib use, and in light of the published results from the ADAURA trial which showed disease free survival benefit (HR 0.2) and overall survival benefit (HR 0.49) of adjuvant osimertinib in ES NSCLC, we have identified an opportunity to improve patient outcomes following resection of ES NSCLC by increasing EGFR testing and encouraging treatment with EGFR TKI in exon 19 deletion or exon 21 L858R mutated pts.

Development of a serum miRNA panel for detection of early stage non-small cell lung cancer.

3058 Background: Lung cancer remains the leading cause of cancer deaths, largely due to late-stage diagnosis. Early detection significantly improves survival rates, but current methods like chest X-rays and low-dose CT (LDCT) scans face limitations in sensitivity and specificity. MicroRNAs (miRNAs) are small non-coding RNAs whose dysregulation has been widely observed in different stages of cancer. Evidence suggests that circulating miRNAs could be promising biomarkers for early diagnosis of lung cancer. In this study, we developed and validated a 15-miRNA panel as a blood-based minimally invasive test for early detection of non-small cell lung cancer (NSCLC) using the proprietary multiplex quantitative real-time PCR (qPCR). Methods: 332 serum samples (182 Stage I&amp;II NSCLC, 150 healthy controls) were divided into training (n=266) and validation (n=66) cohorts. Differentially expressed serum miRNAs were screened with Miseq sequencing followed by qPCR validation. The multi-miR panel was developed with a logistic regression model and validated using an independent cohort. Receiver operating characteristic curves were constructed to evaluate the diagnostic accuracy of the panel. A stem-loop quadruplex qPCR assay was invented for concurrent detection of 4 miRs in a single reaction. Results: 15 differentially expressed miRNAs were selected for logistic model construction based on their Ct values (Ct&lt;35) and high diagnostic accuracy of stage I&amp;II NSCLC (AUC&gt;0.7). The 15-miRNA panel model achieved 88.4% sensitivity, 81.7% specificity (AUC = 0.912) in the training cohort (Table), and was validated with 94.4% sensitivity, 73.3% specificity (AUC = 0.892) in the validation cohort. Quantification of 15 miRs and 1 internal control was performed in 4 reactions tubes via quadruplex qPCR assay. Conclusions: The serum 15-miR panel developed and validated in this retrospective study exhibited robust performance for detection of early stage NSCLC. The panel demonstrates potential diagnostic applications and clinical utility to be implemented together with LDCT in current lung cancer screening program as a blood biomarker to reduce false positive results. In addition, the proposed stem-loop quadruplex qPCR system offers an efficient and promising approach for miRNA profiling in future clinical applications. [Table: see text]

CtDNA-Lung-DETECT: ctDNA outcomes for resected early stage non-small cell lung cancers at 12 months.

8018 Background: ctDNA Lung DETECT is a multicentre investigator initiated prospective study at 3 thoracic surgery centres in the Greater Toronto Area assessing ctDNA detection and association with recurrence free survival (RFS) in patients with early stage non-small cell lung cancer (NSCLC) (NCT05254782). Patients who have ctDNA detected perioperatively are offered ctDNA Lung RCT, a randomized trial investigating the benefit of adjuvant chemo-immunotherapy in patients where the standard of care is observation alone after surgery (NCT04966663). Herein, we report on ctDNA outcomes at 12 months for patients with resected early stage NSCLC. Methods: Patients with stage I (T1-2N0) or multifocal T3-4 &lt; 4cm N0 NSCLC planned for resection at University Health Network consented to plasma ctDNA assessment before and after surgery, and at 12 months post-operatively or relapse using the tumor-informed RaDaR® assay, which detects up to 48 tumor-specific variants in plasma with a Limit of Detection (LoD₉₅) of 0.0011% variant allele fraction. Results: From July 2021 to January 2024, 178 patients were enrolled; 115 had sufficient tissue for assessment. Of these, 68/72 patients have 12 month post-resection ctDNA results available (3 withdrew, 1 sample failed). ctDNA was detected pre-operatively in 18 patients; 99% (71/72) had ctDNA clearance post-operatively, and 93% (62/67) remained ctDNA negative at 12 months. Median follow up time was 18.7 months (range 12.0– 28.3); 8/72 (11%) patients (5 stage I, 3 stage II) experienced lung cancer recurrence. Median time to recurrence was 13.9 months (range 6.2- 24.9). Of these, 3 had ctDNA detected on their preoperative and 12-month or recurrence sample, 1 had ctDNA detected at 12 months prior to relapse, 1 had ctDNA detected at 12 months and recurred around the same time, 2 had negative ctDNA samples and 1 missed sample collection preoperatively. The recurrence rate was 16.7% (3/18, 95% exact CI 3.6-41.4%) in patients with ctDNA detected pre-operatively vs. 7.5% (4/53, CI 2.1-18.2%) in those without. New lung cancers were diagnosed in 5/72 (median time to new primary 15.3 months, range 4.9-14.2) and 2/72 patients had new cancers diagnosed (ovarian/liposarcoma). For those with new lung primaries, 1 had ctDNA detected preoperatively but none had ctDNA detected at time of new primary diagnosis. Of 4 patients who have died, 2 were from recurrent lung cancer and 2 from new primaries (lung/sarcoma). Conclusions: This study represents one of the largest prospective cohorts of ctDNA kinetics in patients with resected lung cancer. Of patients with at least 12 months follow up, 8/72 experienced a lung cancer recurrence with a higher rate in those with pre-operative ctDNA detected (16.7% vs 7.5%). Pre-operative ctDNA detection may help identify patients with resected stage I NSCLC that could benefit from treatment intensification, currently under study in ctDNA Lung RCT (NCT04966663). Clinical trial information: NCT05254782 .

Ultrasensitive circulating tumor DNA (ctDNA) minimal residual disease (MRD) detection in early stage non-small cell lung cancer (NSCLC).

8078 Background: Translation of ctDNA MRD in NSCLC has been hampered by suboptimal sensitivity of 1st-generation assays. Here, we explore how improvements in analytical sensitivity can drive improved clinical sensitivity for MRD after surgery in NSCLC. Methods: To understand MRD kinetics, we analyzed longitudinal ctDNA in early stage NSCLC using data from the TRACERx study. Patient-specific mathematical models were generated to predict MRD levels at the postsurgical landmark and estimate the impact of an assay’s 95% limit of detection (LOD95) on clinical sensitivity. To test these predictions, tumor-informed ctDNA testing was performed using an SNV-based assay (CAPP-Seq) and a phased variant-based assay (PhasED-Seq, Foresight Diagnostics) on 269 samples from 46 patients. We also assessed MRD performance for predicting 1) patient outcomes at the landmark timepoint and 2) the effect of adjuvant treatment. Results: ctDNA MRD dynamics were assessed in 23 patients from TRACERx with ≥3 consecutive samples with detectable ctDNA without intervening therapy. MRD kinetics strongly correlated with exponential growth, with a median ctDNA doubling time of 51 days. Extrapolating MRD levels to the postsurgical landmark predicted that improving MRD assay LOD95 from 100 ppm (0.01%) to 1 ppm could increase clinical sensitivity by 2.1-fold. We then evaluated 46 NSCLC cases using two assays. The median LOD95 was 1 ppm for PhasED-Seq and 84 ppm for CAPP-Seq. Twelve cases were MRD+ by PhasED-Seq, all of whom recurred (100%), with MRD levels as low as 0.19 ppm. In contrast, 6 cases were MRD+ by the SNV-based approach, of whom 5 (83%) recurred. Accordingly, PhasED-Seq had a higher clinical sensitivity than the SNV-based method (12/18 [67%] vs. 5/18 [28%], P = 0.022). Kaplan Meier analysis for freedom from recurrence revealed significantly worse outcomes for patients with detectable MRD regardless of the method used; however, outcomes were better stratified using PhasED-Seq (HR 3.1 vs. 11.4). Patients who were MRD- after surgery by both assays had similar outcomes regardless of adjuvant therapy (chemotherapy and/or radiotherapy). However, MRD+ patients by PhasED-Seq receiving adjuvant therapy had significantly better outcomes than those who did not (HR 8.2, P = 0.00035). A similar benefit for adjuvant therapy was not observed with the SNV-based assay. Using PhasED-Seq, 80% (4/5) of MRD+ patients receiving adjuvant therapy cleared their MRD, compared to 0% (0/3) without adjuvant treatment. Conclusions: Ultrasensitive ctDNA detection improved the clinical sensitivity of MRD at key landmarks in early stage NSCLC. PhasED-Seq detected MRD at levels below 1 ppm and was associated with significantly better outcomes, revealing potential benefits of adjuvant therapy in MRD+ patients. This suggests that ultrasensitive MRD detection is promising for use in risk-adapted trials in early stage NSCLC.

Improving LDCT screening rates within a large multidisciplinary community based practice using integrated ACR accredited imaging centers.

e13582 Background: Lung cancer remains the leading cause of cancer-related death in the US, accounting for about 22% of such deaths. Lung cancer detected by low-dose computed tomography (LDCT) screening is discovered at an early stage 64-85% of the time. The benefits of LDCT screening have been demonstrated in numerous trials, but there has been difficulty in establishing programs that screen a high percentage of eligible patients. We present data from the first 18 months of our lung cancer screening program that is designed to provide the highest quality screening, follow-up and access to prevention, diagnosis, and treatment including clinical trials within their local communities. Methods: New York Cancer and Blood Specialists (NYCBS) is a community practice with over 50 locations across NY. While most lung cancer screening programs are hospital-based, NYCBS has a series of American College of Radiology (ACR) accredited imaging centers that perform these screenings and are closely affiliated with oncology, primary care, Ob-Gyn, nephrology, urology and other specialty medical and surgical practices. These centers are strategically located in areas that have high rates of tobacco use as suggested by heat map data from the NY City Community Health Survey . Upon initial patient presentation, all NYCBS and affiliated sites screen patients with tablet-based questionnaires that prompt additional questions during follow-up appointments. Patients with positive findings are discussed during a monthly, system-wide multidisciplinary lung nodule review conference. Follow-up interventions include referrals to pulmonology, interventional radiology, or further imaging with PET/CT. Per ACR guidelines, patients not requiring acute intervention continue with follow-up CT. Results: In 2023, 29,501 patients were screened with an in-office questionnaire. Of those patients, 3,606 were eligible for screening, 225 declined, and 896 (29%) high risk patients proceeded to undergo LDCT screening. In screened patients, 128 underwent follow-up CT, 36 patients had findings requiring immediate intervention, and 6 were diagnosed with lung cancer. 5 of these patients were diagnosed with stage I and 1 patient was diagnosed with stage IV lung cancer. Conclusions: LDCT screening is a proven method to discover early stage lung cancer and improve the survival rate of diagnosed patients but has not been well adopted in practice. With access to a diverse array of multidisciplinary practices across NY and integrated accredited imaging centers, we have increased the accessibility of lung cancer screening and improved transitions to follow-up care. Nationally, only 4.5% of patients at high risk are screened, however, we found that with our approach to patient care, we have achieved among the highest reported screening rates in eligible individuals.

Results of Lung Cancer Screening with Low-dose Computed Tomography and Exploration of Risk Factors in Guangzhou

Both of lung cancer incidence and mortality rank first among all cancers in China. Previous lung cancer screening trials were mostly selective screening for high-risk groups such as smokers. Non-smoking women accounted for a considerable proportion of lung cancer cases in Asia. This study aimed to evaluate the outcome of community-based mass screening in Guangzhou and identify the high-risk factors for lung cancer. Residents aged 40-74 years in Guangzhou were screened with low-dose computed tomography (LDCT) for lung cancer and the pulmonary nodules were classified and managed according to China National Lung Cancer Screening Guideline with Low-dose Computed Tomography (2018 version). The detection rate of positive nodules was calculated. Before the LDCT examination, residents were required to complete a "lung cancer risk factors questionnaire". The risk factors of the questionnaire were analyzed by least absolute shrinkage and selection operator (LASSO) penalized Logistic regression analysis. A total of 6256 residents were included in this study. 1228 positive nodules (19.63%) and 117 lung cancers were confirmed, including 6 cases of Tis, 103 cases of stage I (accounting for 88.03% of lung cancer). The results of LASSO penalized Logistic regression analysis indicated that age ≥50 yr (OR=1.07, 95%CI: 1.06-1.07), history of cancer (OR=3.29, 95%CI: 3.22-3.37), textile industry (OR=1.10, 95%CI: 1.08-1.13), use coal for cooking in childhood (OR=1.14, 95%CI: 1.13-1.16) and food allergy (OR=1.10, 95%CI: 1.07-1.13) were risk factors of lung cancer for female in this district. This study highlighted that numerous early stages of lung cancer cases were detected by LDCT, which could be applied to screening of lung cancer in women. Besides, age ≥50 yr, personal history of cancer, textile industry and use coal for cooking in childhood are risk factors for women in this district, which suggested that it's high time to raise the awareness of early lung cancer screening in this group.

Impact of OLD/Emphysema in LC Mortality Risk in Screening Programs: An Analysis of NLST and P-IELCAP

IntroductionThe impact of obstructive lung disease (OLD) and emphysema on lung cancer (LC) mortality in patients undergoing LC screening is controversial. MethodsPatients with spirometry and LC diagnosed within the first three rounds of screening were selected from the National Lung Screening Trial (NLST) and from the Pamplona International Early Lung Cancer Detection Program (P-IELCAP). Medical and demographic data, tumor characteristics, comorbidities and presence of emphysema were collected. The effect of OLD and emphysema on the risk of overall survival was assessed using unadjusted and adjusted Cox models, competing risk regression analysis, and propensity score matching. ResultsData from 353 patients with LC, including 291 with OLD and/or emphysema and 62 with neither, were analyzed. The median age was 67.3 years-old and 56.1% met OLD criteria, predominantly mild (1: 28.3%, 2: 65.2%). Emphysema was present in 69.4% of the patients. Patients with OLD and/or emphysema had worse survival on univariate analysis (HR: 1.40; 95% CI: 0.86–2.31; p=0.179). However, after adjusting for LC stage, age, and sex, the HR was 1.02 (95% CI: 0.61–1.70; p=0.952). Specific LC survival between both groups showed an adjusted HR of 0.90 (95% CI: 0.47–1.72; p=0.76). Propensity score matching found no statistically significant difference in overall survival (HR: 1.03; 95% CI: 0.59–1.9; p=0.929). ConclusionThe survival of LC patients diagnosed in the context of screening is not negatively impacted by the coexistence of mild OLD and/or emphysema.

Use of the Archimedes navigation system to diagnose peripheral pulmonary lesions: preliminary Italian results.

Diagnosis of peripheral pulmonary lesions (PPL) is one of the most challenging fields in early lung cancer diagnosis. Despite novel techniques and new approaches to the periphery of the lung, almost 25% of PPL remains undiagnosed. Virtual bronchoscopy navigation (VBN) potentially allows to sample PPL previously not reachable with conventional bronchoscopy. In this preliminary report, we described nine cases of PPL (in which conventional bronchoscopy did not reach the lesion) sampled with VBN, from which we obtained a diagnosis in seven out of nine cases (77.8%), consistent with other reported results in literature. More large-scale data are needed to whether VBN can increase diagnostic yield (DY) of PPL.

ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework

CD47-mediated immune evasion in early-stage lung cancer progression

Alveolar and interstitial macrophages play crucial roles in eradicating pathogens and transformed cells in the lungs. The immune checkpoint CD47, found on normal and malignant cells, interacts with the SIRPα ligand on macrophages, inhibiting phagocytosis, antigen presentation, and promoting immune evasion. In this study, we demonstrated that CD47 is not only a transmembrane protein, but that it is also highly concentrated in extracellular vesicles from lung cancer cell lines and patient plasma. Abundant CD47 was observed in the cytoplasm of lung cancer cells, aligning with our finding that it was packed into extracellular vesicles for physiological and pathological functions. In our clinical cohort, extracellular vesicle CD47 was significantly higher in the patients with early-stage lung cancer, emphasizing innate immunity inactivation in early tumor progression. To validate our hypothesis, we established an orthotopic xenograft model mimicking lung cancer development, which showed increased serum soluble CD47 and elevated IL-10/TNF-α ratio, indicating an immune-suppressive tumor microenvironment. CD47 expression led to reduced tumor-infiltrating macrophages during progression, while there was a post-xenograft increase in tumor-associated macrophages. In conclusion, CD47 is pivotal in early lung cancer progression, with soluble CD47 emerging as a key pathological effector.

Weakly-supervised preclinical tumor localization associated with survival prediction from lung cancer screening Chest X-ray images

In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction. Such multi-class model allows us to use post-hoc interpretability methods, such as Grad-CAM, to localize image regions of preclinical tumors. For the experiments, we propose a new benchmark based on the National Lung Cancer Screening Trial (NLST) dataset to test weakly-supervised preclinical tumor localization and survival prediction models, and results suggest that our proposed method shows state-of-the-art C-index survival prediction and weakly-supervised preclinical tumor localization results. To our knowledge, this constitutes a pioneer approach in the field that is able to produce visual explanations of preclinical events associated with survival prediction results.

Long-term outcomes of PDT for centrally-located early lung cancers with tumor diameters > 2.0 cm

BackgroundPhotodynamic therapy (PDT) is used for the treatment of centrally-located early lung cancers (CLELCs) and is recommended for tumors ≤ 1.0 cm in diameter. We previously reported that PDT using talaporfin sodium, second-generation photosensitizer, for tumors > 1.0 cm but ≤ 2.0 cm in diameter was able to achieve a therapeutic outcome comparable to that of tumors with a diameter of ≤ 1.0 cm. However, the effectiveness of PDT using talaporfin sodium for tumors > 2.0 cm in diameter remains unclear. We conducted a retrospective analysis of cases in which PDT was performed for flat-type CLELCs with tumor diameters of > 2.0 cm. MethodsWe retrospectively analyzed seven cases (eight lesions) with tumor diameters > 2.0 cm and no evidence of extracartilaginous invasion or lymph node metastasis. ResultsAll the patients underwent multiple PDT sessions. The PDT treatment results over the study period were partial response in one case (14.3 %), stable disease (SD) in three cases (42.9 %), and progressive disease (PD) in three cases (42.9 %). At the time of writing this report, five of seven cases (71.4 %) are still undergoing treatment. The duration of SD—the time from the start of treatment until the criteria for PD were met (SD or better maintained)—ranged from 7 to 52 months (mean, 25.3 months). Conclusions“Maintenance PDT” for CLELCs > 2.0 cm in diameter has the potential to inhibit tumor progression in the long term while maintaining quality of life, rather than simply aiming only for a quick radical cure.

Human epididymis protein 4, a novel potential biomarker for diagnostic and prognosis monitoring of lung cancer.

This study aimed to explore the application value of human epididymis protein 4 (HE4) in diagnosing and monitoring the prognosis of lung cancer. First, TCGA (The Cancer Genome Atlas) databases were used to analyze whey-acidic-protein 4-disulfide bond core domain 2 (WFDC2) gene expression levels in lung cancer tissues. Then, a total of 160 individuals were enrolled, categorized into three groups: the lung cancer group (n = 80), the benign lesions group (n = 40), and the healthy controls group (n = 40). Serum HE4 levels and other biomarkers were quantified using an electro-chemiluminescent immunoassay. Additionally, the expression of HE4 in tissues was analyzed through immunohistochemistry (IHC). In vitro cultures of human airway epithelial (human bronchial epithelial [HBE]) cells and various lung cancer cell lines (SPC/PC9/A594/H520) were utilized to detect HE4 levels via western blot (WB). Analysis of the TCGA and UALCAN (The University of Alabama at Birmingham Cancer Data Analysis Portal) databases showed that WFDC2 gene expression levels were upregulated in lung cancer tissues (p < 0.01). Compared with the control group and the benign group, HE4 was significantly higher in the serum of patients with lung cancer (p < 0.001). Receiver operating characteristic (ROC) analysis confirmed that HE4 had better diagnostic efficacy than classical markers in the differential diagnosis of lung cancer and benign lesions and had the highest diagnostic value in lung adenocarcinoma (area under the ROC curve [AUC] = 0.826). HE4 increased in early lung cancer and positively correlated with poor prognosis (p < 0.001). Moreover, the results of WB and IHC revealed that the expression of HE4 was increased in lung cancer cells (SPC/A549/H520) and lung cancer tissues but decreased in PC9 cells with a lack of exon EGFR19 (p < 0.05). Serum HE4 emerges as a promising novel biomarker for the diagnosis and prognosis assessment of lung cancer.

Single-cell analyzing of tumor microenvironment and cell adhesion between early and late-stage lung cancer

Lung adenocarcinoma (LUAD) is a highly heterogeneous disease that threaten human life with serious incidence and high mortality. High heterogeneity of tumor microenvironment (TME) was reported in multiple studies. However, the factor of controlling the tumor migration progression between eary and late-stage LUAD is still not fully understood. In this study, we conducted a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data of LUAD obtained from the GEO database. The identification of cell clusters revealed significant expansion of epithelial cells in late-stage patients. Interpretation of the cell-cell communication results between early-stage and late-stage patient samples indicated that early tumor cells may interact with epithelial cells through the TGF-β pathway to promote tumor progression. The cell cycle analysis demonstrated a significant increase in the number of cells in the G2 and M phases in late-stage lung cancer. Further analysis using Non-negative Matrix Factorization (NMF) revealed early-stage cell-specific gene features involved in cell adhesion-related biological processes. Among these, the Tensin (TNS) gene family, particularly TNS1, showed high expression in epithelial cells and fibroblasts of early-stage samples, specifically associated with cell adhesion. Survival analysis using TCGA database for LUAD demonstrated that patients with high expression of TNS1 exhibited significantly higher overall survival rates compared to those with low expression. Immunofluorescence experiments have demonstrated co-expression of TNS1 with fibroblast and tumor cell markers (α-SMA and EPCAM). Immunohistochemistry experiments further validated the significantly higher expression levels of TNS1 in early-stage LUAD tissues compared to late-stage lung cancer tissues (P<0.05). Pathway experiments have shown that early-stage tumor patients with high expression of TNS1 exhibit stronger phosphorylation levels of Akt and mTOR, indicating a more potent activation of the Akt/mTOR signaling pathway. In conclusion, the results of this study demonstrate that TNS1 is an adhesive molecule in the immune microenvironment of early-stage tumor cells, and it may serve as a novel prognostic marker for lug cancer.

Deep Learning-Based Lung Medical Image Recognition

Pulmonary nodules serve as critical indicators for early lung cancer diagnosis, making their detection and classification essential. The prevalent use of transfer learning in recognition algorithms often encounters a significant disparity between source and target datasets, which hampers effective feature extraction from pulmonary nodules and degrades performance. An enhanced neural network model leveraging convolutional neural networks is introduced to address this issue. This model integrates a pre-trained GoogLeNet Inception V3 network with a custom-designed feature fusion layer, improving the network’s ability to extract features. To ascertain the optimal configuration, the models were evaluated based on accuracy in various combinations. The experiments conducted on the LUNA16 pulmonary nodule dataset revealed that the refined network model achieved an accuracy of 88.78% and a sensitivity of 87.18%. This represents an increase of 2.7 and 2.22 percentage points in accuracy and sensitivity, respectively, compared to the GoogLeNet Inception V3 algorithm. Further tests across different dataset proportions also yielded superior outcomes, demonstrating enhanced generalization capabilities. These findings can offer objective benchmarks for clinical diagnosis.

Development of a predictive score to discriminate community acquired pneumonia with underlying lung cancer: A retrospective case – control study

BackgroundA pneumonic infiltrate might hide an occult lung cancer (LC). This awareness depends on each clinician personal experience, turning definitive LC diagnosis challenging and possibly delayed. In this study we aimed to develop a clinical score to better identify those cases. Materials and methodsWe conducted a retrospective case–control study, including previously undiagnosed LC patients admitted in our institution, with a presumptive suspicious of community acquired pneumonia (CAP). Cases were compared with random CAP inpatient controls, using a matched 2:1 ratio. Demographic, clinical, and laboratorial variables were assessed for a possible association with the presence of a CAP with underlying LC (CAP–uLC). ResultsAmong 535 hospitalized LC patients, 43 cases had a presentation compatible with CAP and were compared with 86 CAP controls. A scoring system was built using 6 independent variables, which positively correlated with CAP–uLC: smoking history (OR: 8.3 [1.9–36.2]; p = 0.005); absence of fever (6.5 [2.0–21.5]; p = 0.002); sputum with blood (5.9 [1.2–29.9]; p = 0.033); platelet count ≥ 232x103/μL (5.8 [1.6–20.6]; p = 0.006); putative alternative diagnosis than CAP (4.6 [1.5–14.7]; p = 0.009); and duration of symptoms ≥ 10 days (3.7 [1.1–13.0]; p = 0.037). Our score presented an AUC of 0.910 (95 % CI, 0.852–0.967; p < 0.001), a sensitivity of 88.1 % and specificity of 84.7 %, in predicting the risk of presenting a CAP–uLC, when set to a cutoff of 18. ConclusionWe propose a novel risk score aimed to aid clinicians identifying patients with CAP–uLC in the acute setting, possibly prompting early LC diagnosis.

Lung Cancer Detection Using CNN VGG19 + Model

In the last many years, lung cancer has become a major public health concern. To examine cell breakdown in the lungs in its starting stages, doctors often use imaging modalities such as X-ray chest films, CT scans, MRIs, etc. The timing of diagnosis determines the course of therapy. Artificial intelligence (AI) is a hotspot for developing computational models of human intellect. In this research, we aim to enhance the detection and classification of lung nodules from CT images using a novel deep learning approach. Our study builds upon an extensive review of existing lung cancer detection methods, highlighting their strengths and weaknesses. This LIDC/IDRI dataset has been used in over half of the most recent research on lung cancer diagnosis. While several Convolutional Neural Networks (CNNs) architecture are adequate to process medical field data using technology solutions to discover and diagnose Lung cancer, here we proposed model VGG-19+ model. It is known for its strong feature extraction capabilities. It outflanks the ongoing model on a few exhibition measures, including exactness, accuracy, responsiveness/review, and f1-score.This research using VGG-19+ model could lead to more widespread utilization in cancer diagnosis by enhancing early lung cancer detection and developing the field of medical image analysis.