Lung cancer remains the leading cause of cancer death globally, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Multidrug resistance (MDR), often caused by ATP-binding cassette (ABC) transporters, represents a significant obstacle in the treatment of NSCLC. While genetic profiling has an important role in personalized therapy, functional assays that measure cellular responses to drugs are gaining in importance. We developed an automated microplate-based immunofluorescence assay for the evaluation of MDR markers ABCB1, ABCC1, and ABCG2 in cells obtained from NSCLC patients through high-content imaging and image analysis, as part of a functional diagnostic approach. This assay effectively discriminated cancer from non-cancer cells within mixed cultures, which is vital for accurate assessment of changes in MDR marker expression in different cell populations in response to anticancer drugs. Validation was performed using established drug-sensitive (NCI-H460) and drug-resistant (NCI-H460/R) NSCLC cell lines, demonstrating the assay’s capacity to distinguish and evaluate different MDR profiles. The obtained results revealed wide-ranging effects of various chemotherapeutic agents on MDR marker expression in different patient-derived NSCLC cultures, emphasizing the need for MDR diagnostics in NSCLC. In addition to being a valuable tool for assessing drug effects on MDR markers in different cell populations, the assay can complement genetic profiling to optimize treatment. Further assay adaptations may extend its application to other cancer types, improving treatment efficacy while minimizing the development of resistance.

Beside the well known predominance of distant vs. loco-regional relapse, several aspects of the relapse pattern still have not been fully elucidated.

The pattern and factors influencing the lung function recovery in the first postoperative days are still not fully elucidated, especially in patients at increased risk.

Background and Objectives: Lung cancer is the second most common form of cancer in the world for both men and women as well as the most common cause of cancer-related deaths worldwide. The aim of this study is to summarize the radiological characteristics between primary lung adenocarcinoma subtypes and to correlate them with FDG uptake on PET-CT. Materials and Methods: This retrospective study included 102 patients with pathohistologically confirmed lung adenocarcinoma. A PET-CT examination was performed on some of the patients and the values of SUVmax were also correlated with the histological and morphological characteristics of the masses in the lungs. Results: The results of this analysis showed that the mean size of AIS-MIA (adenocarcinoma in situ and minimally invasive adenocarcinoma) cancer was significantly lower than for all other cancer types, while the mean size of the acinar cancer was smaller than in the solid type of cancer. Metastases were significantly more frequent in solid adenocarcinoma than in acinar, lepidic, and AIS-MIA cancer subtypes. The maximum standardized FDG uptake was significantly lower in AIS-MIA than in all other cancer types and in the acinar predominant subtype compared to solid cancer. Papillary predominant adenocarcinoma had higher odds of developing contralateral lymph node involvement compared to other types. Solid adenocarcinoma was associated with higher odds of having metastases and with higher SUVmax. AIS-MIA was associated with lower odds of one unit increase in tumor size and ipsilateral lymph node involvement. Conclusions: The correlation between histopathological and radiological findings is crucial for accurate diagnosis and staging. By integrating both sets of data, clinicians can enhance diagnostic accuracy and determine the optimal treatment plan.

Background: Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) accounting for the majority of cases. Radiomics and artificial intelligence (AI) have emerged as promising tools for quantitative imaging analysis and precision staging. This study aimed to evaluate the ability of an AI-based radiomics model to preoperatively predict tumor (T) and nodal (N) stage, lymphovascular invasion (LVI), and postoperative complications in patients with early-stage NSCLC. Material and Methods: This retrospective study included 51 consecutive patients who underwent anatomical lobectomy with systematic lymph node dissection between 2019 and 2024, at the Clinic for Thoracic Surgery of the University Clinical Center of Serbia. Quantitative imaging features were extracted from preoperative CT scans using the Lesion Scout with Auto ID module (syngo.via VB50 MM, Siemens Healthineers). Radiomics and clinical predictors were analyzed using regularized logistic regression (LASSO) with five-fold cross-validation. Model performance was assessed using AUC, accuracy, sensitivity, specificity, precision, and F1 score, and calibration was evaluated using the Hosmer–Lemeshow test. Groups were compared using parametric and non-parametric tests. Correlation between the variables was assessed using Spearman’s rank correlation coefficient. All p-values less than 0.05 were considered significant. Results: The AI-based model showed excellent performance for predicting the T component (training AUC = 0.89; test AUC = 0.86; F1 = 0.81) and acceptable calibration (p = 0.41). Nodal metastasis (OR = 0.108; 95% CI: 0.011–1.069; p = 0.057) and LVI (OR = 0.519; 95% CI: 0.139–1.937; p = 0.329) were not significantly predicted. Emphysema was identified as a significant independent predictor of postoperative complications (χ² = 5.13; p = 0.024). Conclusions: The AI-driven radiomics model demonstrated strong predictive ability for the T component and identified emphysema as a clinically relevant predictor of postoperative complications.