Definitive Radiation for Stage I Lung Cancer in a Screened Population: Results From the I-ELCAP

Deep Learning Radiomics Features of Mediastinal Fat and Pulmonary Nodules on Lung CT Images Distinguish Benignancy and Malignancy

This study investigated the relationship between mediastinal fat and pulmonary nodule status, aiming to develop a deep learning-based radiomics model for diagnosing benign and malignant pulmonary nodules. We proposed a combined model using CT images of both pulmonary nodules and the fat around the chest (mediastinal fat). Patients from three centers were divided into training, validation, internal testing, and external testing sets. Quantitative radiomics and deep learning features from CT images served as predictive factors. A logistic regression model was used to combine data from both pulmonary nodules and mediastinal adipose regions, and personalized nomograms were created to evaluate the predictive performance. The model incorporating mediastinal fat outperformed the nodule-only model, with C-indexes of 0.917 (training), 0.903 (internal testing), 0.942 (external testing set 1), and 0.880 (external testing set 2). The inclusion of mediastinal fat significantly improved predictive performance (NRI = 0.243, p < 0.05). A decision curve analysis indicated that incorporating mediastinal fat features provided greater patient benefits. Mediastinal fat offered complementary information for distinguishing benign from malignant nodules, enhancing the diagnostic capability of this deep learning-based radiomics model. This model demonstrated strong diagnostic ability for benign and malignant pulmonary nodules, providing a more accurate and beneficial approach for patient care.

Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee

Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches.

Prospective Cohort Study to Compare Long-Term Lung Cancer-Specific and All-Cause Survival of Clinical Early Stage (T1a-b; ≤20 mm) NSCLC Treated by Stereotactic Body Radiation Therapy and Surgery

INTRODUCTION

We aimed to compare outcomes of patients with first primary clinical T1a-bN0M0 NSCLC treated with surgery or stereotactic body radiation therapy (SBRT).

METHODS

We identified patients with first primary clinical T1a-bN0M0 NSCLCs on last pretreatment computed tomography treated by surgery or SBRT in the following two prospective cohorts: International Early Lung Cancer Action Program (I-ELCAP) and Initiative for Early Lung Cancer Research on Treatment (IELCART). Lung cancer-specific survival and all-cause survival after diagnosis were compared using Kaplan-Meier analysis. Propensity score matching was used to balance baseline demographics and comorbidities and analyzed using Cox proportional hazards regression.

RESULTS

Of 1115 patients with NSCLC, 1003 had surgery and 112 had SBRT; 525 in I-ELCAP in 1992 to 2021 and 590 in IELCART in 2016 to 2021. Median follow-up was 57.6 months. Ten-year lung cancer-specific survival was not significantly different: 90% (95% confidence interval: 87%-92%) for surgery versus 88% (95% confidence interval: 77%-99%) for SBRT, p = 0.55. Cox regression revealed no significant difference in lung cancer-specific survival for the combined cohorts (p = 0.48) or separately for I-ELCAP (p = 1.00) and IELCART (p = 1.00). Although 10-year all-cause survival was significantly different (75% versus 45%, p < 0.0001), after propensity score matching, all-cause survival using Cox regression was no longer different for the combined cohorts (p = 0.74) or separately for I-ELCAP (p = 1.00) and IELCART (p = 0.62).

CONCLUSIONS

This first prospectively collected cohort analysis of long-term survival of small, early NSCLCs revealed that lung cancer-specific survival was high for both treatments and not significantly different (p = 0.48) and that all-cause survival after propensity matching was not significantly different (p = 0.74). This supports SBRT as an alternative treatment option for small, early NSCLCs which is especially important with their increasing frequency owing to low-dose computed tomography screening. Furthermore, treatment decisions are influenced by many different factors and should be personalized on the basis of the unique circumstances of each patient.