The latest medical research on Lung Cancer

Various cutoffs have been used to diagnose computed tomography (CT)-defined low skeletal muscle mass; however, the impact of this variability on predicting physical functional limitations (PFL) remains unclear. In the present study we aimed to evaluate the diagnostic test metrics for predicting PFLs using a fixed cutoff value from previous reports and sought to create a prediction score that incorporated the skeletal muscle index (SMI) and other clinical factors.

In this cross-sectional study including 237 patients with lung cancer, the SMI was assessed using CT-determined skeletal muscle area at the third lumbar vertebra. Physical function was assessed using the short physical performance battery (SPPB) test, with PFL defined as an SPPB score ≤9. We analyzed the diagnostic metrics of the five previous cutoffs for CT-defined low skeletal muscle mass in predicting PFL.

The mean age of participants was 66.0 ± 10.4 years. Out of 237 patients, 158 (66.7%) had PFLs. A significant difference was observed in SMI between individuals with and without PFLs (35.7 cm2/m2 ± 7.8 vs. 39.5 cm2/m2 ± 8.4, p < 0.001). Diagnostic metrics of previous cutoffs in predicting PFL showed suboptimal sensitivity (63.29%-91.77%), specificity (11.39%-50.63%), and area under the receiver operating characteristic curve (AUC) values (0.516-0.592). Age and the SMI were significant predictors of PFL; therefore, a score for predicting PFL (age - SMI + 21) was constructed, which achieved an AUC value of 0.748.

Fixed cutoffs for CT-defined low skeletal muscle mass may inadequately predict PFLs, potentially overlooking declining physical functions in patients with lung cancer.

This study aimed to investigate predictors of thoracic aortic invasion in lung cancer patients using preoperative clinical and imaging characteristics and elucidate surgical outcomes in cases of aortic invasion.

Of the 4751 lung cancer patients who underwent surgery at our hospital, we included 126 (6.8%) who underwent left-sided surgery and in whom tumor appeared to be in contact with the thoracic aorta on preoperative imaging. The patients were divided into two groups: group A, 23 patients (18%) who underwent combined aortic resection (+); group B, 103 patients (82%) who did not undergo combined aortic resection (-).

The percentage of aortic invasion for tumor diameter <3 cm, 3-4 cm, 4-5 cm, 5-7 cm, and >7 cm was 0%, 13%, 23%, 16%, and 35%, respectively. The percentages of aortic invasion were 27%, 16%, and 0% for tumor localization in the upper division, S6, and S10, respectively. Multivariate analysis revealed that aortic depression due to tumor or loss of fatty tissue between tumor and mediastinum in the chest CT significantly predicted aortic invasion (odds ratio = 23.83, 16.66). Group A demonstrated significantly more blood loss, longer operative time, prolonged hospital stay, and increased percentage of recurrent nerve palsy (13%) compared to group B. The 1-, 3-, and 5-year survival rates for patients in group A were 53.4%, 24.3%, and 24.3%, respectively.

If the chest CT of a patient demonstrates aortic depression due to tumor or loss of fatty tissue between tumor and mediastinum, aortic complications should be considered when planning surgery.

Traditional cell line models are the commonly used preclinical models for lung cancer research. However, cell lines cannot recapitulate the complex tumor heterogeneity and cannot mimic the microenvironment of human cancer. Recently, 3D multicellular in vitro self-assembled models called "organoids" have been developed at a fast pace in the field of research, which can mimic the actual primary tumor. At present, several studies have reported on protocols of lung cancer organoids (LCOs) generation, and using LCOs can provide novel insight into the basic and translational research of lung cancer. However, the establishment of the LCO models remains challenging due to the complexity of lung cancer and the immaturity of organoid technology, so it is necessary to understand the influences of different methodologies on LCO generation and review the applications and limitations of LCO models.

In this review, we searched the literature in the recent ten years in the field of LCOs.

We summarized the methodology, the problems, and the solutions in the LCOs generation, its application and limitations, as well as proposing future challenges and perspectives.

Currently, LCOs are successfully generated via exploring the methodology by the researchers. Though there are still challenges in clinical application, LCOs are applied in some cancer studies including investigation of anti-cancer treatment response in vitro, modeling tumor immune microenvironment, and construction of organ chips, which are forging a promising path towards precision medicine.

Primary pulmonary lymphoepithelioma-like carcinoma (PPLELC) is a rare yet aggressive malignancy. This study aims to investigate a deep learning model based on hematological indices, referred to as haematological indices-based signature (HIBS), and propose multivariable predictive models for accurate prognosis prediction and assessment of therapeutic response to immunotherapy in PPLELC.

This retrospective study included 117 patients with PPLELC who received immunotherapy and were randomly divided into a training (n=82) and a validation (n=35) cohort. A total of 41 hematological features were extracted from routine laboratory tests and the least absolute shrinkage and selection operator (LASSO) algorithm were utilized to establish the HIBS. Additionally, we developed a nomogram using the HIBS and clinical characteristics through multivariate Cox regression analysis. To evaluate the nomogram's predictive performance, we used calibration curves and calculated the time-dependent area under the curve (AUC). Kaplan-Meier survival analysis was performed to estimate progression-free survival (PFS) in both cohorts.

The proposed HIBS comprised 14 hematological features and showed that patients who experienced disease progression had significantly higher HIBS scores compared to those who did not progress (P<0.001). Five prognostic factors, including HIBS, tumor-node-metastasis (TNM) stage, presence of bone metastasis and the specific immunotherapy regimen, were found to be independent factors and were used to construct a nomogram, which effectively categorized PPLELC patients into a high-risk and a low-risk group, with patients in the high-risk patients demonstrating worse PFS (7.0 vs. 18.0 months, P<0.001) and lower overall response rates (22.2% vs. 52.7%, P<0.001). The nomogram showed satisfactory discrimination for PFS, with AUC values of 0.837 and 0.855 in the training and validation cohorts, respectively.

The HIBS-based nomogram could effectively predict the PFS and response of patients with PPLELC regarding immunotherapy and serve as a valuable tool for clinical decision making.