Evaluating the Optimal Time Between Diagnosis and Surgical Intervention for Early-Stage Lung Cancer

Disparities across the continuum of lung cancer care: a review of recent literature

PURPOSE OF REVIEW

Lung cancer remains the leading cause of cancer mortality worldwide. Health disparities have long been noted in lung cancer incidence and survival and persist across the continuum of care. Understanding the gaps in care that arise from disparities in lung cancer risk, screening, treatment, and survivorship are essential to guiding efforts to achieve equitable care.

RECENT FINDINGS

Recent literature continues to show that Black people, women, and people who experience socioeconomic disadvantage or live in rural areas experience disparities throughout the spectrum of lung cancer care. Contributing factors include structural racism, lower education level and health literacy, insurance type, healthcare facility accessibility, inhaled carcinogen exposure, and unmet social needs. Promising strategies to improve lung cancer care equity include policy to reduce exposure to tobacco smoke and harmful pollutants, more inclusive lung cancer screening eligibility criteria, improved access and patient navigation in lung cancer screening, diagnosis and treatment, more deliberate offering of appropriate surgical and medical treatments, and improved availability of survivorship and palliative care.

SUMMARY

Given ongoing disparities in lung cancer care, research to determine best practices for narrowing these gaps and to guide policy change are an essential focus of future lung cancer research.

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

Background and objective

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.

Patients and methods

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.

Results

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.

Conclusion

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.

Three-Dimensional Imaging-Guided Lung Anatomic Segmentectomy: A Single-Center Preliminary Experiment

Background and objectives: VATS segmentectomy has been proven to be effective in the treatment of stage I NSCLC, but its technical complexity remains one of the most challenging aspects for thoracic surgeons. Furthermore, 3D-CT reconstruction images can help in planning and performing surgical procedures. In this paper, we present our personal experience of 11 VATS anatomical resections performed after accurate pre-operative planning with 3D reconstructions. Materials and methods: A 3D virtual model of the lungs, airways, and vasculature was obtained, starting from a 1.25 mm 3-phase contrast CT scan, and the original images were used for the semi-automatic segmentation of the lung parenchyma, airways, and tumor. Results: Six males and five females were included in this study. The median diameter of the pulmonary lesion at the pre-operative chest CT scan was 20 mm. The surgical indication was confirmed in seven patients: in three cases, a lobectomy, instead of a segmentectomy, was needed due to intraoperative findings of nodal metastasis. Meanwhile, only in one case, we performed a lobectomy because of inadequate surgical resection margins. Skin-to-skin operative average time was 142 (IQR 1-3 105-182.5) min. The median post-operative stay was 6 (IQR 1-3 3.5-7) days. The mean value of the closest surgical margin was 13.7 mm. Conclusion: Image-guided reconstructions are a useful tool for surgeons to perform complex resections in order to spare healthy parenchyma and to ensure disease-free margins. Nevertheless, human skill and surgeon experience still remain fundamental for the final decisions regarding the proper resection to perform.