Objectives: The study aimed to develop a combined model that integrates deep learning (DL), radiomics, and clinical data to classify lung nodules into benign or malignant categories, and to further classify lung nodules into different pathological subtypes and Lung Imaging Reporting and Data System (Lung-RADS) scores. Materials and methods: The proposed model was trained, validated, and tested using three datasets: one public dataset, the Lung Nodule Analysis 2016 (LUNA16) Grand challenge dataset (n = 1004), and two private datasets, the Lung Nodule Received Operation (LNOP) dataset (n = 1027) and the Lung Nodule in Health Examination (LNHE) dataset (n = 1525). The proposed model used a stacked ensemble model by employing a machine learning (ML) approach with an AutoGluon-Tabular classifier. The input variables were modified 3D convolutional neural network (CNN) features, radiomics features, and clinical features. Three classification tasks were performed: Task 1: Classification of lung nodules into benign or malignant in the LUNA16 dataset; Task 2: Classification of lung nodules into different pathological subtypes; and Task 3: Classification of Lung-RADS score. Classification performance was determined based on accuracy, recall, precision, and F1-score. Ten-fold cross-validation was applied to each task. Results: The proposed model achieved high accuracy in classifying lung nodules into benign or malignant categories in LUNA 16 with an accuracy of 92.8%, as well as in classifying lung nodules into different pathological subtypes with an F1-score of 75.5% and Lung-RADS scores with an F1-score of 80.4%. Conclusion: Our proposed model provides an accurate classification of lung nodules based on the benign/malignant, different pathological subtypes, and Lung-RADS system.

Background: Low-dose computed tomography (LDCT) has been shown useful in early lung cancer detection. This study aimed to develop a novel deep learning model for detecting pulmonary nodules on chest LDCT images. Methods: In this secondary analysis, three lung nodule datasets, including Lung Nodule Analysis 2016 (LUNA16), Lung Nodule Received Operation (LNOP), and Lung Nodule in Health Examination (LNHE), were used to train and test deep learning models. The 3D region proposal network (RPN) was modified via a series of pruning experiments for better predictive performance. The performance of each modified deep leaning model was evaluated based on sensitivity and competition performance metric (CPM). Furthermore, the performance of the modified 3D RPN trained on three datasets was evaluated by 10-fold cross validation. Temporal validation was conducted to assess the reliability of the modified 3D RPN for detecting lung nodules. Results: The results of pruning experiments indicated that the modified 3D RPN composed of the Cross Stage Partial Network (CSPNet) approach to Residual Network (ResNet) Xt (CSP-ResNeXt) module, feature pyramid network (FPN), nearest anchor method, and post-processing masking, had the optimal predictive performance with a CPM of 92.2%. The modified 3D RPN trained on the LUNA16 dataset had the highest CPM (90.1%), followed by the LNOP dataset (CPM: 74.1%) and the LNHE dataset (CPM: 70.2%). When the modified 3D RPN trained and tested on the same datasets, the sensitivities were 94.6%, 84.8%, and 79.7% for LUNA16, LNOP, and LNHE, respectively. The temporal validation analysis revealed that the modified 3D RPN tested on LNOP test set achieved a CPM of 71.6% and a sensitivity of 85.7%, and the modified 3D RPN tested on LNHE test set had a CPM of 71.7% and a sensitivity of 83.5%. Conclusion: A modified 3D RPN for detecting lung nodules on LDCT scans was designed and validated, which may serve as a computer-aided diagnosis system to facilitate lung nodule detection and lung cancer diagnosis.

Background: Although percutaneous transthoracic catheter drainage (PCD) has been proven effective in lung abscesses, the optimal timing of PCD is still unclear. The study aimed to evaluate the safety and efficacy of early versus delayed drainage in patients with lung abscesses.Methods: This retrospective study included 103 consecutive patients with liquefied lung abscesses more than 3 cm confirmed by a CT scan received CT-guided PCD over 16 years, from July 2005 to September 2021, in a single institution were reviewed. Early drainage was defined as PCD within one week after a lung abscess was diagnosed. The primary outcome was 90-day mortality. The secondary outcomes included perioperative complications and patients’ length of hospital stay (LoS). Factors associated with 90-day mortality and LoS were also analyzed. The key statistical methods were Chi-square test, Fisher’s exact test, Student t-test, and Pearson correlation.Results: Amount the 103 patients, there were 64 patients who received early PCD, and 39 patients received delayed PCD. Between the two groups, there were no significant differences in clinical characteristics, 90-day mortality, or perioperative complications. The LoS was significantly shortened in early PCD group (28.6 ± 25.5 vs. 39.3 ± 26.8 (days), p = 0.045). Higher Charlson comorbidity index, secondary lung abscess, and liver cirrhosis were associated with higher mortality (all p < 0.05). Positive sputum culture significantly increased the LoS (coefficient 19.35 (10.19, 28.50), p < 0.001).Conclusion: The 90-day mortality and complications were similar for early PCD and delayed PCD patients, but LoS was significantly shortened in early PCD patient.

Osimertinib is the main treatment choice for pretreated patients with advanced non-small cell lung cancer (NSCLC) harbouring epidermal growth factor receptor (EGFR) T790M mutations. However, the choice of subsequent therapy when progressive disease has developed after osimertinib treatment remains a major therapeutic challenge. This study evaluated the efficacy of osimertinib-based combination therapies in patients who developed progressive disease after treatment with osimertinib. We enrolled NSCLC patients harbouring T790M mutations pretreated with first- or second-generation EGFR tyrosine-kinase inhibitors and were receiving osimertinib at two tertiary referral centres between August 2015 and July 2019, and the subsequent treatment efficacy was assessed. Osimertinib-based combination therapy yielded better overall survival (OS) than chemotherapy alone (not achieved vs. 7.8 months; hazard ratio, 0.39; 95 % confidence interval 0.17-0.89; P = 0.025) according to the Cox proportional hazards model adjusted for possible confounders. Synergism (combination index <1) between AZD9291 and chemotherapy and a higher proportion of apoptosis cells in combination treatment were also demonstrated in the T790M-positive PC9 cell line with acquired resistance to AZD9291. Our data supported the hypothesis that osimertinib-based combination therapy is associated with improved OS among patients with clinical progression following the use of osimertinib. These findings warrant further validation in a randomised controlled study.

Background: It is challenging to proceed thoracoscopic anatomic resection when encountering severe pleural adhesion or calcified peribronchial lymphadenopathy. Compared with multiple-port video-assisted thoracoscopic surgery (MP-VATS), how to overcome these challenges in single-port (SP-) VATS is still an intractable problem. In the present study, we reported the surgical results of chronic inflammatory lung disease and shared some useful SP-VATS techniques. Methods: We retrospectively assessed the surgical results of chronic inflammatory lung disease, primarily bronchiectasis, and mycobacterial infection, at our institution between 2010 and 2018. The patients who underwent SP-VATS anatomic resection were compared with those who underwent MP-VATS procedures. We analyzed the baseline characteristics, perioperative data, and postoperative outcomes, and illustrated four special techniques depending on the situation: flexible hook electrocautery, hilum-first technique, application of Satinsky vascular clamp, and staged closure of bronchial stump method. Results: We classified 170 consecutive patients undergoing thoracoscopic anatomic resection into SP and MP groups, which had significant between-group differences in operation time and overall complication rate (P = 0.037 and 0.018, respectively). Compared to the MP-VATS group, the operation time of SP-VATS was shorter, and the conversion rate of SP-VATS was relatively lower (3.1% vs. 10.5%, P = 0.135). The most common complication was prolonged air leakage (SP-VATS, 10.8%; MP-VATS, 2.9%, P = 0.045). Conclusions: For chronic inflammatory lung disease, certain surgical techniques render SP-VATS anatomic resection feasible and safe with a lower conversion rate.