Early-stage Lung Cancer Patients Research Articles

The novel DNA methylation marker FIBIN suppresses non-small cell lung cancer metastasis by negatively regulating ANXA2

ObjectivesThe clinical T1 stage solid lung cancer with metastasis is a serious threat to human life and health. In this study, we performed RNA sequencing on T1 advanced-stage lung cancer and adjacent tissues to identify a novel biomarker and explore its roles in lung cancer. MethodsQuantitative reversed-transcription PCR, reverse transcription PCR and Western blot, MSP and Methtarget were utilized to evaluate FIBIN expression levels at both the transcriptional and protein levels as well as its methylation status. Differential target protein was evaluated for relative and absolute quantitation by isobaric tags. Co-IP was performed to detect the interactions between target protein. Precise location and expression levels of target proteins were revealed by immunofluorescence staining and component protein extraction using specific kits, respectively. ResultsWe reported that FIBIN was frequently silenced due to promoter hypermethylation in lung cancer. Additionally, both in vitro and in vivo experiments confirmed the significant anti-proliferation and anti-metastasis capabilities of FIBIN. Mechanistically, FIBIN decreased the nuclear accumulation of β-catenin by reducing the binding activity of GSK3β with ANXA2 while promoting interaction between GSK3β and β-catenin. ConclusionOur findings firstly identify FIBIN is a tumor suppressor, frequently silenced due to promoter hypermethylation. FIBIN may serve as a predictive biomarker for progression or metastasis among early-stage lung cancer patients.

Identification of CT radiomic features robust to acquisition and segmentation variations for improved prediction of radiotherapy-treated lung cancer patient recurrence

The primary objective of the present study was to identify a subset of radiomic features extracted from primary tumor imaged by computed tomography of early-stage non-small cell lung cancer patients, which remain unaffected by variations in segmentation quality and in computed tomography image acquisition protocol. The robustness of these features to segmentation variations was assessed by analyzing the correlation of feature values extracted from lesion volumes delineated by two annotators. The robustness to variations in acquisition protocol was evaluated by examining the correlation of features extracted from high-dose and low-dose computed tomography scans, both of which were acquired for each patient as part of the stereotactic body radiotherapy planning process. Among 106 radiomic features considered, 21 were identified as robust. An analysis including univariate and multivariate assessments was subsequently conducted to estimate the predictive performance of these robust features on the outcome of early-stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. The univariate predictive analysis revealed that robust features demonstrated superior predictive potential compared to non-robust features. The multivariate analysis indicated that linear regression models built with robust features displayed greater generalization capabilities by outperforming other models in predicting the outcomes of an external validation dataset.

Application value of the automated machine learning model based on modified CT index combined with serological indices in the early prediction of lung cancer.

Accurately predicting the extent of lung tumor infiltration is crucial for improving patient survival and cure rates. This study aims to evaluate the application value of an improved CT index combined with serum biomarkers, obtained through an artificial intelligence recognition system analyzing CT features of pulmonary nodules, in early prediction of lung cancer infiltration using machine learning models. A retrospective analysis was conducted on clinical data of 803 patients hospitalized for lung cancer treatment from January 2020 to December 2023 at two hospitals: Hospital 1 (Affiliated Changshu Hospital of Soochow University) and Hospital 2 (Nantong Eighth People's Hospital). Data from Hospital 1 were used for internal training, while data from Hospital 2 were used for external validation. Five algorithms, including traditional logistic regression (LR) and machine learning techniques (generalized linear models [GLM], random forest [RF], gradient boosting machine [GBM], deep neural network [DL], and naive Bayes [NB]), were employed to construct models predicting early lung cancer infiltration and were analyzed. The models were comprehensively evaluated through receiver operating characteristic curve (AUC) analysis based on LR, calibration curves, decision curve analysis (DCA), as well as global and individual interpretative analyses using variable feature importance and SHapley additive explanations (SHAP) plots. A total of 560 patients were used for model development in the training dataset, while a dataset comprising 243 patients was used for external validation. The GBM model exhibited the best performance among the five algorithms, with AUCs of 0.931 and 0.99 in the validation and test sets, respectively, and accuracies of 0.857 and 0.955 in the validation and test groups, respectively, outperforming other models. Additionally, the study found that nodule diameter and average CT value were the most significant features for predicting lung cancer infiltration using machine learning models. The GBM model established in this study can effectively predict the risk of infiltration in early-stage lung cancer patients, thereby improving the accuracy of lung cancer screening and facilitating timely intervention for infiltrative lung cancer patients by clinicians, leading to early diagnosis and treatment of lung cancer, and ultimately reducing lung cancer-related mortality.

Abstract SY38-02: Clinical investigations of obesity in cancer: BMI and other confounders

Abstract SY38-02: Clinical investigations of obesity in cancer: BMI and other confounders

Personalized, tumor-informed, circulating tumor DNA assay for detecting minimal residual disease in non-small cell lung cancer patients receiving curative treatments.

Circulating tumor DNA (ctDNA) has emerged as a prognostic and predictive biomarker for detection of minimal residual disease (MRD), monitoring treatment response, and early detection of recurrence in cancer patients. In this study, we explored the utility of ctDNA-based MRD detection to predict recurrence in a real-world cohort of primarily early-stage non-small cell lung cancer (NSCLC) patients treated with curative intent. Longitudinal plasma samples were collected post curative-intent treatment from 36 patients with stage I-IV NSCLC. A personalized, tumor-informed assay was used to detect and quantify ctDNA in plasma samples. Of the 24 patients with plasma samples available during the MRD window (within 6 months of curative surgery and before adjuvant therapy), ctDNA was detectable in two patients. Patients with ctDNA-positivity during the MRD window were 15 times more likely to recur compared to ctDNA-negative patients (HR: 15.0, 95% CI: 1.0-253.0, p = 0.010). At any time post-curative intent treatment, ctDNA-positivity was associated with significantly poorer recurrence-free survival compared to persistently ctDNA-negative patients (p < 0.0001). Our real-world data indicate that longitudinal, personalized, tumor-informed ctDNA monitoring is a valuable tool in patients with NSCLC receiving curative treatment to identify patients at high risk for recurrence.

Molecular and immune characterization of Chinese early-stage non-squamous non-small cell lung cancer: a multi-omics cohort study.

Albeit considered with superior survival, around 30% of the early-stage non-squamous non-small cell lung cancer (Ns-NSCLC) patients relapse within 5 years, suggesting unique biology. However, the biological characteristics of early-stage Ns-NSCLC, especially in the Chinese population, are still unclear. Multi-omics interrogation of early-stage Ns-NSCLC (stage I-III), paired blood samples and normal lung tissues (n=76) by whole-exome sequencing (WES), RNA sequencing, and T-cell receptor (TCR) sequencing were conducted. An average of 128 exonic mutations were identified, and the most frequently mutant gene was EGFR (55%), followed by TP53 (37%) and TTN (26%). Mutations in MUC17, ABCA2, PDE4DIP, and MYO18B predicted significantly unfavorable disease-free survival (DFS). Moreover, cytobands amplifications in 8q24.3, 14q13.1, 14q11.2, and deletion in 3p21.1 were highlighted in recurrent cases. Higher incidence of human leukocyte antigen loss of heterozygosity (HLA-LOH), higher tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were identified in ever-smokers than never-smokers. HLA-LOH also correlated with higher TMB, TNB, intratumoral heterogeneity (ITH), and whole chromosomal instability (wCIN) scores. Interestingly, higher ITH was an independent predictor of better DFS in early-stage Ns-NSCLC. Up-regulation of immune-related genes, including CRABP2, ULBP2, IL31RA, and IL1A, independently portended a dismal prognosis. Enhanced TCR diversity of peripheral blood mononuclear cells (PBMCs) predicted better prognosis, indicative of a noninvasive method for relapse surveillance. Eventually, seven machine-learning (ML) algorithms were employed to evaluate the predictive accuracy of clinical, genomic, transcriptomic, and TCR repertoire data on DFS, showing that clinical and RNA features combination in the random forest (RF) algorithm, with area under the curve (AUC) of 97.5% and 83.3% in the training and testing cohort, respectively, significantly outperformed other methods. This study comprehensively profiled the genomic, transcriptomic, and TCR repertoire spectrums of Chinese early-stage Ns-NSCLC, shedding light on biological underpinnings and candidate biomarkers for prognosis development.

Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer.

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-IIIrespectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

Disparities in Access to Multidisciplinary Cancer Consultations and Treatment for Patients With Early-Stage Non-Small Cell Lung Cancer: A SEER-Medicare Analysis

PurposeDisparities in access to multidisciplinary cancer consultations (MDCc) persist, and the role of physician relationships remains understudied. This study examined the extent to which multilevel factors, including patient characteristics and patient-sharing network measures reflecting the structure of physician relationships, are associated with MDCc and receipt of stereotactic body radiation therapy (SBRT) versus surgery among early-stage non-small cell lung cancer (NSCLC) patients. Materials and MethodsIn this cross-sectional study, we analyzed Surveillance, Epidemiology, and End-Result (SEER)-Medicare data for patients diagnosed with stage I-IIA NSCLC in 2016-2017. We assembled patient-sharing networks and identified cancer specialists who were locally unique for their specialty, herein referred to as “linchpins”. The proportion of linchpin cancer specialists for each hospital referral region (HRR) was calculated as a network-based measure of specialist scarcity. We used multilevel multinomial logistic regression to estimate associations between study variables and the receipt of MDCc and multilevel logistic regression to examine the relationship between MDCc and patient's first treatment. ResultsOur study included 6,120 patients with stage I-IIA NSCLC, of which 751 (12.3%) received MDCc, 1,729 (28.3%) only consulted a radiation oncologist, 2,010 (32.8%) only consulted a surgeon, and 1,630 (26.6%) had no consultations with either specialist within two months following diagnosis. Compared with patients residing in an HRR with a low proportion of linchpin surgeons, those in an HRR with a high proportion of linchpin surgeons had a 2.99 (95% CI: 1.87-4.78) greater relative risk of exclusively consulting a radiation oncologist (vs. MDCc) and a 2.70 (95% CI: 1.68-4.35) greater relative risk consulting neither specialist (vs. MDCc). Patients who received MDCc were 5.32 (95% CI: 4.27-6.63) times more likely to receive SBRT (vs. surgery). ConclusionsPhysician networks are associated with receipt of MDCc and treatment, underscoring the potential for leveraging patient-sharing network analysis to improve access to lung cancer care.

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients. In the realm of predictive healthcare, machine learning algorithms have been instrumental in anticipating various surgical outcomes, including SSI. However, accurately predicting infection after minimally invasive surgery remains a clinical challenge due to the multitude of physiological and surgical factors associated with it. Furthermore, clinical patient data, in addition to being high-dimensional, often exists the long-tail problem, posing difficulties for traditional machine learning algorithms in effectively processing such data. Based on this insight, we propose a novel approach called meta-lasso for infection prediction following minimally invasive surgery. Our approach leverages the sparse learning algorithm lasso regression to select informative features and introduces a meta-learning framework to mitigate bias towards the dominant class. We conducted a retrospective cohort study on patients who had undergone minimally invasive surgery for lung cancer at Shanghai Chest Hospital between 2018 and 2020. The evaluation encompassed key performance metrics, including sensitivity, specificity, precision (PPV), negative predictive value (NPV), and accuracy. Our approach has surpassed the performance of logistic regression, random forest, Naive Bayes classifier, gradient boosting decision tree, ANN, and lasso regression, with sensitivity at 0.798, specificity at 0.779, precision at 0.789, NPV at 0.798, and accuracy at 0.788 and has greatly improved the classification performance of the inferior class.

Abstract PR011: Comprehensive longitudinal tracking of lung cancer evolutionary clonal dynamics during therapy using circulating tumour DNA

Abstract Background: Outgrowth of resistance cancer cell populations is a common mechanism of therapy failure in oncology. Effective personalised medicine relies on targeting of ubiquitous aberrations; however, tumours are highly heterogenous. Liquid biopsies have the potential to provide representative tumour sampling at regular intervals through disease course, but extensive interrogation of longitudinal clonal evolution through therapy has been lacking, particularly for patients on a curative disease pathway. Methods: We analysed 1102 plasma samples from 202 early-stage non-small cell lung cancer patients who underwent multiregional whole exome sequencing of primary tumour, relapse and, where available, comprehensive post-mortem samples. We designed personalised panels targeting a median of 200 mutations for each patient and sequenced cell-free DNA (cfDNA) around these variant sites to saturation. For four patients we designed personalised panels including all clonal and subclonal mutations discovered (up to 600). One of these four patients received a personalised neoantigen vaccine. We used ECLIPSE to determine the clonal composition of low tumour fraction ctDNA. Results: We found a directly proportional correlation between the cancer cell fraction (CCF) of subclones estimated in plasma and multiregional tissue samples at surgery. Lower plasma CCFs were observed for clonal illusion mutations from randomly selected single samples, one for each TRACERx patient, when compared to truly clonal mutations (P &amp;lt; 0.001, OR = 0.44), distinguishing appropriate therapeutic targets without multiregional sampling. In 24 patients with both cfDNA and tissue available at relapse we detected 28/29 relapse tissue subclones in the cfDNA with an additional 8 cfDNA unique subclones. We found a strong bias for cfDNA unique subclones to be present in only subset of cells (P = 0.008, OR = 5.5), consistent with insufficient tissue sampling. We observed longitudinal clonal dynamics which were often, but not always, associated with treatment. Metastatic competent subclones had higher CCFs in the primary tumour, as measured in pre-operative plasma (P &amp;lt; 0.001, OR = 4.5). Upon receiving a personalised neoantigen vaccine a patient had regression of clones harbouring a targeted EGFR mutation. However, copy number loss of the targeted EGFR mutation occurred in a liver lesion which grew over the treatment period on surveillance radiology alongside a novel ctDNA subclone. Upon cessation vaccine treatment the resistance-associated subclone and liver lesion underwent regression. EGFR mutant clones become dominant in the tumour thereafter suggesting a fitness cost associated with vaccine resistant EGFR mutation loss in this patient, measured using ctDNA. Conclusions: We find evidence that comprehensive tracking of subclonal mutations in plasma can accurately profile the clonal structure of tumours over time, revealing clonal dynamics in response to treatment and biological determinants of metastasis with the potential to guide personalised medicine. Citation Format: Alexander M. Frankell, Christopher Abbosh, Kristiana Grigoriadis, Emilia Lim, Selvaraju Veeriah, Sophia Ward, Tak Karasaki, James R. M. Black, Mariam Jamal-Hanjani, Nicholas McGranahan, Charles Swanton. Comprehensive longitudinal tracking of lung cancer evolutionary clonal dynamics during therapy using circulating tumour DNA [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr PR011.

A novel neoadjuvant therapy for early-stage non-small cell lung cancer in a mouse model.

Lung cancer is the common malignancy with high mortality rate in the world. Even with curative resection for early-stage lung cancer patients, the rate of postoperative recurrence and metastasis is still high. Neoadjuvant nivolumab combined with chemotherapy leads to improved pathological complete response rate and event-free survival in resectable non-small cell lung cancer (NSCLC) patients. However, the neoadjuvant therapy is not only accompanied by grade 3 or above adverse events which resulting in the potential missing out on the window for curative surgery for the patients, but also has low efficacy especially in patients with low programmed death ligand 1 (PD-L1) expression. Hence, it is particularly important to explore innovative ways to inhibit tumour recurrence and metastasis. In the present study, we investigated whether neoadjuvant therapy with intralesional Rose Bengal (RB) elicited specific immune responses compared with control group, and then the lung cancer mouse model was used to evaluated the immunological mechanism. The secondary Lewis lung cancer cells (LLCs) tumour growth was significantly suppressed by RB intralesional injection into subcutaneous tumour; the formation rate of secondary tumours induced by the B16 melanoma cell injection was 100%. Intralesional RB neoadjuvant therapy before surgical resection exhibited effectively enhanced T central memory cells (Tcm) and T memory stem cells (Tscm) + naïve T cells (Tn) infiltration, elicited stronger cytotoxic T lymphocyte (CTL) responses against LLCs, and displayed markedly higher proportions of splenic lymphocytes that produce tumour necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ) upon restimulation in a lung cancer mouse model. Based on our preclinical data, neoadjuvant therapy with intralesional RB injection generated immune memory and prevented the recurrence and metastasis of tumour in a lung cancer mouse model, which provides a new strategy for neoadjuvant treatment of early-stage NSCLC.

Factors associated with cavity formation after stereotactic body radiation therapy for peripheral early-stage lung cancer.

This retrospective study aimed to identify the factors associated with cavity formation after SBRT in peripheral early-stage lung cancer patients. We analyzed the occurrence of cavity changes after SBRT. We examined 99 cases with T1-T2aN0 peripheral non-small cell lung cancer treated with SBRT from 2004 to 2021. Patients underwent respiratory function tests, including diffusing capacity for carbon monoxide (DLco), before treatment. The median observation period was 35months (IQR 18-47.5months). Treatment involved fixed multi-portal irradiation in 67% of cases and VMAT in 33%. The total radiation doses ranged from 42 to 55Gy, delivered over 4 to 5 fractions. Cavity formation occurred in 14 cases (14.1%), appearing a median of 8months after SBRT. The cavity disappeared in a median of 4months after formation. High DLco and total radiation dose were identified as factors significantly associated with cavity formation. There have been no confirmed recurrences to date, but one patient developed a lung abscess. Although cavity formation after SBRT for peripheral early-stage lung cancer is infrequent, it can occur. This study showed high DLco and total radiation dose to be factors significantly associated with cavity formation. These findings can be applied to optimizing radiation therapy (RT) and improving patient outcomes. Further research is needed to determine the optimal radiation dose for patients with near-normal DLco for whom surgery is an option. This study provides valuable insights into image changes after RT.

A PET/CT radiomics model for predicting distant metastasis in early-stage non–small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study

ObjectivesStereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT.MethodsPatients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups.ResultsIn total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001).ConclusionsThe proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making.Plain language summaryIn this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.

Comparison of Treatment Outcomes Between Thoracoscopic Surgery and Stereotactic Body Radiotherapy for Early-Stage Non-Small Cell Lung Cancer.

Objective: To compare the risk of death, tumor recurrence, metastasis, and disease progression in early-stage non-small cell lung cancer (NSCLC) patients treated with thoracoscopic surgery and stereotactic body radiotherapy (SBRT). Methods: Patients who underwent radical surgery and SBRT for NSCLC between April 2010 and November 2021 were retrospectively analyzed. Continuous and categorical variables were compared using the Mann-Whitney U and Chi-square test, respectively. Kaplan-Meier curves were used to evaluate the survival outcomes of each patient group. Cox proportional hazard regression analyses were performed to estimate the risk of death, tumor recurrence, metastasis, and disease progression. Results: A total of 167 patients were enrolled, of whom 75 and 92 underwent SBRT and surgery, respectively. The median follow-up was 45 months (range, 4-105 months). SBRT patients were observed to be significantly older (median, 76.0 vs 67.0 years; P < .001), and associated with significantly higher mortality rate (42.7% vs 26.1%, P = .024). However, no significant difference in overall survival duration was seen between the SBRT and surgery groups (45.0 vs 41.0 months; P = .199). SBRT patients demonstrated significantly lower rates of metastasis (12.0% vs 30.4%, P = .004), and significantly longer metastasis-free survival (39.0 months vs 35.5 months, P = .020). The remaining outcomes, including tumor recurrence and disease progression rates, were similar between the groups. Compared to surgery, SBRT did not significantly associate with death, recurrence, or disease progression. Kaplan-Meier curves showed significant differences in overall, tumor recurrence-free, and disease progression-free survival between the groups (log-rank P < .05). Conclusions: SBRT demonstrated similar overall survival compared to radical surgery, and associated with significantly reduced risk of tumor metastasis. Our study thereby suggests SBRT as the best treatment option for patients with inoperable NSCLC.

Preoperative splenic area as a prognostic biomarker of early-stage non-small cell lung cancer

BackgroundThe correlation between the preoperative splenic area measured on CT scans and the overall survival (OS) of early-stage non-small cell lung cancer (NSCLC) patients remains unclear.MethodsA retrospective discovery cohort and validation cohort consisting of consecutive NSCLC patients who underwent resection and preoperative CT scans were created. The patients were divided into two groups based on the measurement of their preoperative splenic area: normal and abnormal. The Cox proportional hazard model was used to analyse the correlation between splenic area and OS.ResultsThe discovery and validation cohorts included 2532 patients (1374 (54.27%) males; median (IQR) age 59 (52–66) years) and 608 patients (403 (66.28%) males; age 69 (62–76) years), respectively. Patients with a normal splenic area had a 6% higher 5-year OS (n = 727 (80%)) than patients with an abnormal splenic area (n = 1805 (74%)) (p = 0.007) in the discovery cohort. A similar result was obtained in the validation cohort. In the univariable analysis, the OS hazard ratios (HRs) for the patients with abnormal splenic areas were 1.32 (95% confidence interval (CI): 1.08, 1.61) in the discovery cohort and 1.59 (95% CI: 1.01, 2.50) in the validation cohort. Multivariable analysis demonstrated that abnormal splenic area was independent of shorter OS in the discovery (HR: 1.32, 95% CI: 1.08, 1.63) and validation cohorts (HR: 1.84, 95% CI: 1.12, 3.02).ConclusionPreoperative CT measurements of the splenic area serve as a prognostic indicator for early-stage NSCLC patients, offering a novel metric with potential implications for personalized therapeutic strategies in top-tier oncology research.

Exploring published and novel pre-treatment CT and PET radiomics to stratify risk of progression among early-stage non-small cell lung cancer patients treated with stereotactic radiation

PurposeDisease progression after definitive stereotactic body radiation therapy (SBRT) for early-stage non-small cell lung cancer (NSCLC) occurs in 20–40% of patients. Here, we explored published and novel pre-treatment CT and PET radiomics features to identify patients at risk of progression. Materials/MethodsPublished CT and PET features were identified and explored along with 15 other CT and PET features in 408 consecutively treated early-stage NSCLC patients having CT and PET < 3 months pre-SBRT (training/set-aside validation subsets: n = 286/122). Features were associated with progression-free survival (PFS) using bootstrapped Cox regression (Bonferroni-corrected univariate predictor: p ≤ 0.002) and only non-strongly correlated predictors were retained (|Rs|<0.70) in forward-stepwise multivariate analysis. ResultsTumor diameter and SUVmax were the two most frequently reported features associated with progression/survival (in 6/20 and 10/20 identified studies). These two features and 12 of the 15 additional features (CT: 6; PET: 6) were candidate PFS predictors. A re-fitted model including diameter and SUVmax presented with the best performance (c-index: 0.78; log-rank p-value < 0.0001). A model built with the two best additional features (CTspiculation1 and SUVentropy) had a c-index of 0.75 (log-rank p-value < 0.0001). ConclusionsA re-fitted pre-treatment model using the two most frequently published features – tumor diameter and SUVmax – successfully stratified early-stage NSCLC patients by PFS after receiving SBRT.

EP07.05-09 Oncologic Outcomes of Stereotactic Body Radiotherapy in Early-Stage Non-Small Cell Lung Cancer Patients with Interstitial Lung Disease

EP07.05-09 Oncologic Outcomes of Stereotactic Body Radiotherapy in Early-Stage Non-Small Cell Lung Cancer Patients with Interstitial Lung Disease

EP07.01-27 The Impact of Comorbidities on Exercise Level after Treatment for Early-Stage Lung Cancer Patients

EP07.01-27 The Impact of Comorbidities on Exercise Level after Treatment for Early-Stage Lung Cancer Patients

Impact of time-to-treatment on survival for early-stage non-small cell lung cancer in The Netherlands-a nationwide observational cohort study.

Varied outcomes on the relation between time-to-treatment and survival in early-stage non-small cell lung cancer (NSCLC) patients are reported. We examined this relation in a large multicentric retrospective cohort study and identified factors associated with extended time-to-treatment. We included 9,536 patients with clinical stage I-II NSCLC, diagnosed and treated in 2014-2019, from the Netherlands Cancer Registry that includes nation-wide data. Time-to-treatment was defined as the number of days between first outpatient visit for suspected lung cancer and start of treatment. The effect of extended time-to-treatment beyond the first quartile and survival was studied with Cox proportional hazard regression. Analyses were stratified for stage and type of therapy. Time-to-treatment was adjusted for multiple covariates including performance status and socioeconomic status. Factors associated with treatment delay were identified by multilevel logistic regression. Median time-to-treatment was 47 days [interquartile range (IQR): 34-65] for stage I and 46 days (IQR: 34-62) for stage II. The first quartile extended to 33 days for both stages. Risk of death increased significantly with extended time-to-treatment for surgical treatment of clinical stage II patients [adjusted hazard ratio (aHR) >33 days: 1.36, 95% confidence intervals (CI): 1.09-1.70], but not in stage II patients treated with radiotherapy or in stage I patients. Causes of prolonged time-to-treatment were multifactorial including diagnostic tests, such as endoscopic ultrasound (EUS) or endobronchial ultrasound (EBUS). Clinical stage II patients benefit from fast initiation of surgical treatment. Surprisingly this appears to be accounted for by patients who are clinically stage II but pathologically stage I. Further study is needed on characterizing these patients and the significance of lymph node- or distant micrometastasis in guiding time-to-treatment and treatment strategy.

Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier.

Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models. A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence. Activated natural killer (NK) cells, M0 macrophages, and M1 macrophages showed a positive correlation with progression. Conversely, T CD4+ memory resting cells were negatively correlated. In the PPI network, TNF and IL6 emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation. Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.