Robot-assisted minimally invasive oesophagectomy versus thoracoscopic approach: multi-institutional study on short-term outcomes.

BACKGROUND: Robot-assisted minimally invasive oesophagectomy and conventional minimally invasive oesophagectomy are superior to open techniques. However, few studies have directly compared the outcomes of the two minimally invasive approaches. METHODS: A retrospective study of patients from six medical centres with oesophageal squamous cell carcinoma who underwent minimally invasive oesophagectomy between 2015 and 2022. Perioperative outcomes were compared after applying inverse probability of treatment weighting. RESULTS: The study included 577 patients (robot-assisted minimally invasive oesophagectomy: 206; conventional minimally invasive oesophagectomy: 371). After applying inverse probability of treatment weighting, robot-assisted minimally invasive oesophagectomy was found to yield a higher number of mediastinal nodes compared with conventional minimally invasive oesophagectomy (14.86 versus 12.66, P = 0.017). Robot-assisted minimally invasive oesophagectomy was notably effective in retrieving upper mediastinal left recurrent laryngeal nerve nodes, averaging 1.97 nodes versus 1.14 nodes harvested by conventional minimally invasive oesophagectomy (P < 0.001). This was coupled by a significant decrease in nerve palsy rates (13.9% versus 22.8%, P = 0.020). A significantly larger percentage of patients in the robot-assisted minimally invasive oesophagectomy group had an uncomplicated postoperative course (51.8% versus 34%, P < 0.001). Robot-assisted minimally invasive oesophagectomy also led to a reduction in pneumonia rates (8.6% versus 15.2%, P = 0.041) and was linked to a shorter length of stay (length of stay; 16.64 versus 21.14 days, P = 0.007). The advantage of robot-assisted minimally invasive oesophagectomy in reducing the length of stay was especially pronounced in patients with a high Charlson co-morbidity index (≥2, mean difference 8.46 days; P = 0.0069) and those who underwent neoadjuvant therapy (mean difference 5.63 days; P < 0.001). CONCLUSION: In oesophageal squamous cell carcinoma, the use of robot-assisted minimally invasive oesophagectomy led to fewer cases of pneumonia and faster recovery compared with conventional minimally invasive oesophagectomy. Additionally, robot-assisted minimally invasive oesophagectomy significantly improved the feasibility and safety of performing lymph node dissection along the recurrent laryngeal nerve.

IL-33/NF-κB/ST2L/Rab37 positive-feedback loop promotes M2 macrophage to limit chemotherapeutic efficacy in lung cancer.

IL-33 is a danger signal that binds to its receptor ST2L to promote tumor progression. This study identifies the IL-33/ST2L positive-feedback loop and the trafficking of ST2L membrane presentation in macrophages that contribute to lung tumor progression. Mechanistically, IL-33 induces ST2L upregulation by activating NF-κB, which binds to the promoter region of the ST2L gene. Moreover, Rab37, a small GTPase involved in membrane trafficking, mediates ST2L trafficking to the plasma membrane of M2 macrophages. This IL-33/NF-κB/ST2L/Rab37 axis promotes positive-feedback loops that enhance ST2L expression and membrane trafficking in M2 macrophages. Notably, neutralizing antibodies against IL-33 or ST2L block NF-κB activity, suppress M2 macrophage polarization, and synergistically inhibit tumor growth when combined with cisplatin treatment in vitro/vivo. Clinically, Rab37+/ST2L+/CD206+ tumor-infiltrating M2 macrophages correlate with advanced-stage lung cancer patients with poor response to chemotherapy. These findings unveil a positive-feedback mechanism and provide a basis for IL-33/ST2L-targeting therapy for cancer.

USP24-i-101 targeting of USP24 activates autophagy to inhibit drug resistance acquired during cancer therapy.

Drug resistance in cancer therapy is the major reason for poor prognosis. Addressing this clinically unmet issue is important and urgent. In this study, we found that targeting USP24 by the specific USP24 inhibitors, USP24-i and its analogues, dramatically activated autophagy in the interphase and mitotic periods of lung cancer cells by inhibiting E2F4 and TRAF6, respectively. USP24 functional knockout, USP24C1695A, or targeting USP24 by USP24-i-101 inhibited drug resistance and activated autophagy in gefitinib-induced drug-resistant mice with doxycycline-induced EGFRL858R lung cancer, but this effect was abolished after inhibition of autophagy, indicating that targeting USP24-mediated induction of autophagy is required for inhibition of drug resistance. Genomic instability and PD-L1 levels were increased in drug resistant lung cancer cells and were inhibited by USP24-i-101 treatment or knockdown of USP24. In addition, inhibition of autophagy by bafilomycin-A1 significantly abolished the effect of USP24-i-101 on maintaining genomic integrity, decreasing PD-L1 and inhibiting drug resistance acquired in chemotherapy or targeted therapy. In summary, an increase in the expression of USP24 in cancer cells is beneficial for the induction of drug resistance and targeting USP24 by USP24-i-101 optimized from USP24-i inhibits drug resistance acquired during cancer therapy by increasing PD-L1 protein degradation and genomic stability in an autophagy induction-dependent manner.

A scenario-based web app to facilitate patient education in lung tumor patients undergoing video-assisted thoracoscopic surgery: Development and usability testing.

Background: Patient education (PE) is essential for improving patients' knowledge, anxiety, and satisfaction, and supporting their postoperative recovery. However, the advantages of video-assisted thoracoscopic surgery (VATS)-smaller incisions and faster recovery-can result in shorter hospital stays, making PE more challenging to implement effectively. Multimedia PE can potentially enhance PE, but its effectiveness for patients undergoing VATS is unclear. Objective: This study developed a scenario-based PE web app for lung tumor patients undergoing VATS (SPE-VATS) to facilitate the PE process and evaluated its usability through a clinical trial. Methods: The SPE-VATS provided the experimental group (EG: 32 participants) with interactive scenario, query guidance, diagnostic analysis, experience sharing, and active reminder, while the control group (CG: 32 participants) used pamphlets and videos. The usability of SPE-VATS in terms of postoperative anxiety reduction and patient satisfaction with the app was evaluated using self-reported questionnaires based on the state-trait anxiety inventory, technology acceptance model, system usability scale, and task load index. Results: There was no statistically significant difference in postoperative anxiety reduction between the EG and CG, possibly because 90% of the participants underwent a low-risk surgical type, and VATS is known to be advantageous in alleviating surgical anxiety. However, females and higher educated EG participants showed a non-significant but favorable reduction than their CG counterparts. Moreover, the EG was highly satisfied with the app (rated 4.2 to 4.4 out of 5.0), with no significant gender and education level difference. They particularly valued the interactive scenario, experience sharing, and diagnostic analysis features of SPE-VATS. Conclusions: The SPE-VATS demonstrated its usability and high patient satisfaction, particularly for female and higher educated patients. Low-risk patient predominance and VATS's advantages may explain non-significant postoperative anxiety reduction, warranting further studies on high-risk patients to evaluate the impact of SPE-VATS on clinical practice.

Lymph node volume predicts survival in esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery.

Large primary tumor volume has been identified as a poor prognostic factor of esophageal squamous cell carcinoma (ESCC) treated with definitive concurrent chemoradiotherapy (CCRT). However, when neoadjuvant CCRT and surgery are adopted, the prognostic impact of primary tumor and lymph node (LN) volume on clinical outcomes in ESCC remains to be elucidated. This study included 107 patients who received neoadjuvant CCRT and surgery for ESCC. The volume of the primary tumor and LN was measured using radiotherapy planning computed tomography scans, and was correlated with overall survival (OS), disease-free survival (DFS), and cancer failure pattern. The median OS was 24.2 months (IQR, 11.1-93.9) after a median follow-up of 18.4 months (IQR, 8.1-40.7). The patients with a baseline LN volume > 7.7 ml had a significantly worse median OS compared to those with smaller LN volume (18.8 vs. 46.9 months, p = 0.049), as did those with tumor regression grade (TRG) 3-5 after CCRT (13.9 vs. 86.7 months, p < 0.001). However, there was no association between OS and esophageal tumor volume (p = 0.363). Multivariate analysis indicated that large LN volume (HR 1.753, 95% CI 1.015-3.029, p = 0.044) and high TRG (HR 3.276, 95% CI 1.556-6.898, p = 0.002) were negative prognostic factors for OS. Furthermore, large LN volume was linked to increased locoregional failure (p = 0.033) and decreased DFS (p = 0.041). In conclusion, this study demonstrated that large LN volume is correlated with poor OS, DFS, and locoregional control in ESCC treated with neoadjuvant CCRT and esophagectomy.

Development of a modified 3D region proposal network for lung nodule detection in computed tomography scans: a secondary analysis of lung nodule datasets.

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.

A modified 3D RPN for detecting lung nodules on CT images that exhibited greater sensitivity and CPM than did several previously reported CAD detection models was established.

Baseline Blood Levels of Mucin-1 Are Associated with Crucial On-Treatment Adverse Outcomes in Patients with Idiopathic Pulmonary Fibrosis Receiving Antifibrotic Pirfenidone.

Mucin-1 is a multi-functional glycoprotein expressed by type II alveolocytes and may be detectable in the circulation following pulmonary fibrosis. The prognostic utility of baseline pre-treatment blood levels of mucin-1 in patients with idiopathic pulmonary fibrosis (IPF) receiving antifibrotics has not yet been fully established. We retrospectively studied a cohort of patients (from two hospitals) with IPF who were receiving pirfenidone for >12 weeks. Baseline blood mucin-1 levels were measured via sandwich enzyme-linked immunosorbent assays. We investigated the performance of mucin-1 levels in longitudinally predicting the risks of acute exacerbation of IPF (AE-IPF) and severe adverse outcomes (SAO), including lung transplantation and death. Seventy patients were included; 20 developed AE-IPF; and 31 had SAO during the follow-up period. Patients with baseline mucin-1 levels ≥2.5 ng/mL had enhanced risks of AE-IPF (adjusted hazard ratio [aHR], 14.07; 95% confidence interval [CI], 4.26-46.49) and SAO within 2 years (aHR, 7.87; 95% CI, 2.86-21.70) and anytime during the follow-up (aHR, 4.68; 95% CI, 2.11-10.39). The risks increased across subgroups with increasing mucin-1 levels. Patients in the "mucin-1 ≥ 2.5" group also exhibited an accelerated decline in DLCO. This study supports baseline blood mucin-1 levels as a biomarker for IPF that predicts adverse outcomes during pirfenidone treatment.

Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?

The study aimed to develop machine learning (ML) classification models for differentiating patients who needed direct surgery from patients who needed core needle biopsy among patients with prevascular mediastinal tumor (PMT). Patients with PMT who received a contrast-enhanced computed tomography (CECT) scan and initial management for PMT between January 2010 and December 2020 were included in this retrospective study. Fourteen ML algorithms were used to construct candidate classification models via the voting ensemble approach, based on preoperative clinical data and radiomic features extracted from the CECT. The classification accuracy of clinical diagnosis was 86.1%. The first ensemble learning model was built by randomly choosing seven ML models from a set of fourteen ML models and had a classification accuracy of 88.0% (95% CI = 85.8 to 90.3%). The second ensemble learning model was the combination of five ML models, including NeuralNetFastAI, NeuralNetTorch, RandomForest with Entropy, RandomForest with Gini, and XGBoost, and had a classification accuracy of 90.4% (95% CI = 87.9 to 93.0%), which significantly outperformed clinical diagnosis (p < 0.05). Due to the superior performance, the voting ensemble learning clinical-radiomic classification model may be used as a clinical decision support system to facilitate the selection of the initial management of PMT.

Development of a real-time RGB-D visual feedback-assisted pulmonary rehabilitation system.

Background: Following surgery, perioperative pulmonary rehabilitation (PR) is important for patients with early-stage lung cancer. However, current inpatient programs are often limited in time and space, and outpatient settings have access barriers. Therefore, we aimed to develop a background-free, zero-contact thoracoabdominal movement-tracking model that is easily set up and incorporated into a pre-existing PR program or extended to home-based rehabilitation and remote monitoring. We validated its effectiveness in providing preclinical real-time RGB-D (colour-depth camera) visual feedback. Methods: Twelve healthy volunteers performed deep breathing exercises following audio instruction for three cycles, followed by audio instruction and real-time visual feedback for another three cycles. In the visual feedback system, we used a RealSense™ D415 camera to capture RGB and depth images for human pose-estimation with Google MediaPipe. Target-tracking regions were defined based on the relative position of detected joints. The processed depth information of the tracking regions was visualised on a screen as a motion bar to provide real-time visual feedback of breathing intensity. Pulmonary function was simultaneously recorded using spirometric measurements, and changes in pulmonary volume were derived from respiratory airflow signals. Results: Our movement-tracking model showed a very strong correlation (r = 0.90 ± 0.05) between thoracic motion signals and spirometric volume, and a strong correlation (r = 0.73 ± 0.22) between abdominal signals and spirometric volume. Displacement of the chest wall was enhanced by RGB-D visual feedback (23 vs 20 mm, P = 0.034), and accompanied by an increased lung volume (2.58 vs 2.30 L, P = 0.003). Conclusion: We developed an easily implemented thoracoabdominal movement-tracking model and reported the positive impact of real-time RGB-D visual feedback on self-promoted external chest wall expansion, accompanied by increased internal lung volumes. This system can be extended to home-based PR.

Robot-assisted surgery outperforms video-assisted thoracoscopic surgery for anterior mediastinal disease: a multi-institutional study.

Anterior mediastinal procedures are increasingly being performed using robot-assisted thoracic surgery (RATS) or video-assisted thoracoscopic surgery (VATS). While both approaches have shown superior outcomes compared to open surgery, their comparative benefits are not as distinct. The aim of this retrospective study was to bridge this knowledge gap using a multicenter dataset. Patients who underwent elective minimally invasive surgery for anterior mediastinal disease between 2015 and 2022 were deemed eligible. The study participants were grouped based on whether a robot was used or not, and perioperative outcomes were compared. To mitigate selection bias, inverse probability of treatment weighting (ITPW) was applied using the propensity score. The final analysis included 312 patients (RATS = 120; VATS = 192). Following the application of IPTW, RATS was found to be associated with a longer operating time (215.3 versus 139.31 min, P < 0.001), fewer days with a chest tube (1.96 versus 2.61 days, P = 0.047), and a shorter hospital stay (3.03 versus 3.91 days, P = 0.041) compared to VATS. Subgroup analyses indicated that the benefit of RATS in reducing the length of hospital stay was particularly pronounced in patients with tumors larger than 6 cm (mean difference [MD] = - 2.28 days, P = 0.033), those diagnosed with myasthenia gravis (MD = - 3.84 days, P = 0.002), and those who underwent a trans-subxiphoid surgical approach (MD = - 0.81 days, P = 0.04). Both VATS and RATS are safe and effective approaches for treating anterior mediastinal disease. However, RATS holds distinct advantages over VATS including shorter hospital stays and reduced chest tube drainage periods.

Combined model integrating deep learning, radiomics, and clinical data to classify lung nodules at chest CT.

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.

Low neutrophil-to-lymphocyte ratio predicts overall survival benefit in advanced NSCLC patients with low PD-L1 expression and receiving chemoimmunotherapy.

Although combination therapy including chemotherapy and immune checkpoint inhibitors (ICIs) improves overall survival (OS) of patients with non-small-cell lung cancer (NSCLC), there is a higher incidence of adverse events and treatment discontinuation. Since programmed death-ligand 1 (PD-L1) could not serve as a predictive biomarker, we investigated the neutrophil-to-lymphocyte ratio (NLR) as a predictive biomarker. In our previous research, we demonstrated that a low NLR could predict survival benefits when patients with high PD-L1 expression (> 50%) received chemoimmunotherapy as opposed to immunotherapy alone. In this current study, our objective is to evaluate this predictive capacity in patients with low PD-L1 expression (< 50%). A total of 142 patients were enrolled, 28 receiving combination therapy and 114 receiving chemotherapy alone. Progression-free survival (PFS) and OS were estimated using the Kaplan-Meier method and compared using the log-rank test. Patients who received combination therapy had significantly better PFS and OS than those who received monotherapy. In the subgroup of patients with low NLR, those who received combination therapy exhibited extended PFS and OS with clinical significance, which was also confirmed by multivariate Cox regression analysis. Our study demonstrates the potential use of NLR as a biomarker for predicting survival benefits when receiving combination therapy with chemotherapy and ICIs in patients with advanced NSCLC and low PD-L1 expression.

Risk factor analysis of postoperative complications after adjunctive pulmonary resection in patients with multidrug-resistant tuberculosis: A multi-institutional study.

BACKGROUND AND OBJECTIVE: Multidrug-resistant tuberculosis (MDR-TB) requires extended treatment with regimens with multiple side effects, resulting in high treatment failure rates. Adjunctive lung resection combined with anti-tubercular agents improves outcomes. However, few studies have evaluated the potential harm from surgery and determined the optimal conditions for surgery. We aimed to analyze perioperative conditions to assess risk factors for postoperative complications in a multi-institutional setting. METHODS: This retrospective study included 44 patients with MDR-TB who underwent adjunctive lung resection at three management groups of the Taiwan MDR-TB consortium between January 2007 and December 2020. Demographic data, clinical characteristics, radiological findings, sputum culture status before surgery, primary or acquired drug resistance, surgical procedure, complications, and treatment outcomes were collected and analyzed. Multivariate logistic regression was used to identify risk factors for postoperative complications. RESULTS: Twenty-seven patients (61.4%) underwent lung resection using video-assisted thoracic surgery (VATS). The overall surgical complication rate was 20.5%, and the surgical mortality rate was 9.1%. Postsurgical hemothorax was the most common complication (11.4%). According to the univariate analysis, hilum involvement in images, positive preoperative sputum culture, and thoracotomy approach were unfavorable factors. VATS approach [adjusted OR, 0.088 (95% CI, 0.008-0.999)] was the only favorable factor identified by multivariate analysis. CONCLUSION: The minimally invasive approach is a growing trend, and lobectomies and sublobar resections were the main procedures for MDR-TB. The VATS approach significantly reduced the surgical complication rate. Postsurgical hemothorax was noteworthy, and meticulous hemostasis of the chest wall and residual lung surface is critical for successful resections.

Asia expert consensus on segmentectomy in non-small cell lung cancer: A modified Delphi study.

Objective: Segmentectomy as a parenchymal-sparing surgical approach has been recommended over lobectomy in select patients with early-stage non-small cell lung cancer. This study aimed to address 3 aspects of segmentectomy ("patient indication"; "segmentectomy approaches"; "lymph node assessment") where there is limited clinical guidance. Methods: A modified Delphi approach comprising 3 anonymous surveys and 2 expert discussions was used to establish consensus on the aforementioned topics among 15 thoracic surgeons (2 Steering Committee; 2 Task Force; 11 Voting Experts) from Asia who have extensive segmentectomy experience. Statements were developed by the Steering Committee and Task Force based on their clinical experience, published literature (rounds 1-3), and comments received from Voting Experts through surveys (rounds 2-3). Voting Experts indicated their agreement with each statement on a 5-point Likert scale. Consensus was defined as ≥70% of Voting Experts selecting either "Agree"/"Strongly Agree" or "Disagree"/"Strongly Disagree." Results: Consensus from the 11 Voting Experts was reached on 36 statements (11 "patient indication" statements; 19 "segmentation approaches" statements; 6 "lymph node assessment" statements). In rounds 1, 2, and 3, consensus was reached on 48%, 81%, and 100% of drafted statements, respectively. Conclusions: A recent phase 3 trial reported significantly improved 5-year overall survival rates for segmentectomy compared with lobectomy, proposing thoracic surgeons to consider segmentectomy as a surgical option in suitable patients. This consensus serves as a guidance to thoracic surgeons considering segmentectomy in patients with early non-small cell lung cancer, outlining key principles that surgeons should consider in surgical decision-making.

Corrigendum: Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.

[This corrects the article DOI: 10.3389/fonc.2023.1105100.].

Chemotherapy outcomes in EGFR-TKI resistant patients with common and uncommon EGFR mutation: An exploratory retrospective cohort study.

BACKGROUND: Some prospective studies have shown that second-generation tyrosine kinase inhibitors (TKIs) provide better control in patients with non-small cell lung cancer (NSCLC) with uncommon epidermal growth factor receptor (EGFR) mutations. However, studies comparing second-line chemotherapy efficacy between NSCLC patients with common and uncommon EGFR mutations remain rare. This retrospective study compared treatment outcomes in these patients. METHODS: Patients with EGFR-mutated advanced-stage NSCLC who received first-line EGFR-TKIs in a tertiary referral center were retrospectively reviewed between January 2010 and August 2022. Patients with a negative T790M test at disease progression who received second-line chemotherapy were enrolled. We compared progression-free (PFS) and overall (OS) survival between advanced NSCLC patients with common and uncommon EGFR mutations using Kaplan-Meier and log-rank tests. RESULTS: In total, 209 (54.8%) patients had a negative T790M mutation test and received second-line chemotherapy, of which 192 (91.8%) had a common EGFR mutation (exon 19 deletion or exon 21 L858R substitution), and 17 (8.2%) had an uncommon EGFR mutation. Patients with common EGFR mutations had significantly longer PFS than those with uncommon EGFR mutations (4.57 vs. 2.57 months, p = 0.031). A Cox proportional hazard regression analysis controlling for potential confounding factors indicated that an uncommon EGFR mutation was an independent prognostic factor for PFS. CONCLUSION: This study suggests that patients with uncommon EGFR mutations have poorer chemotherapy responses and shorter survival than those with common EGFR mutations. The development of new treatment strategies for these patients remains an unmet need.

Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan.

Purpose: To compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs). Methods: A retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models. Result: In the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275). Conclusion: Our study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.

Effect of BIM expression on the prognostic value of PD-L1 in advanced non-small cell lung cancer patients treated with EGFR-TKIs.

The role of Programmed Cell Death Ligand 1 (PD-L1) expression in predicting epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKIs) efficacy remains controversial. Recent studies have highlighted that tumor-intrinsic PD-L1 signaling can be modulated by STAT3, AKT, MET oncogenic pathway, epithelial-mesenchymal transition, or BIM expression. This study aimed to investigate whether these underlying mechanisms affect the prognostic role of PD-L1. We retrospectively enrolled patients with EGFR mutant advanced stage NSCLC who received first-line EGFR-TKI between January 2017 and June 2019, the treatment efficacy of EGFR-TKI was assessed. Kaplan-Meier analysis of progression-free survival (PFS) revealed that patients with high BIM expression had shorter PFS, regardless of PD-L1 expression. This result was also supported by the COX proportional hazard regression analysis. In vitro, we further proved that the knockdown of BIM, instead of PDL1, induced more cell apoptosis following gefitinib treatment. Our data suggest that among the pathways affecting tumor-intrinsic PD-L1 signaling, BIM is potentially the underlying mechanism that affects the role of PD-L1 expression in predicting response to EGFR TKI and mediates cell apoptosis under treatment with gefitinib in EGFR-mutant NSCLC. Further prospective studies are required to validate these results.

Impact of computed tomographic patterns and extent on clinical management and outcomes of patients with organising pneumonia.

Background: Organising pneumonia (OP) has variable clinical and radiographic presentations and unstandardised treatments. Most patients with OP have favourable outcomes, but some develop respiratory insufficiency, experience recurrence or die. In this study we investigated the impact of computed tomographic (CT) patterns and extent of OP on the diagnostic and therapeutic management that patients received, and that on the therapeutic response and prognosis (particularly the risk of respiratory insufficiency and death). Methods: We retrospectively studied 156 patients with OP followed at our hospital between 2010 and 2021. The diagnosis was confirmed histologically and verified by multidisciplinary specialists. We performed Firth's logistic regression to determine the relationship between CT features and aetiologies, management and outcomes including the risk of severe disease (defined as the need for supplemental oxygen or mechanical ventilation). We conducted Kaplan-Meier analyses to assess survival differences. Results: Patients exhibiting multilobe involvement or mixed patterns, or both, were more likely to have secondary OP and receive immunosuppressants. Higher proportions of these patients experienced recurrence. Compared to patients with single-lobe involvement and single-pattern, they also had an enhanced risk of severe disease (the adjusted odds ratio for patients who simultaneously had multilobe involvement and mixed patterns was 27.64; 95% confidence interval 8.25-127.44). Besides, these patients had decreased survival probabilities. Conclusion: Different CT features of OP impact patients' management and prognosis. When treating patients with OP exhibiting multilobe involvement or mixed patterns, or both, it is important to identify the possible causative aetiology and follow closely for adverse outcomes.

Improving puncture accuracy in percutaneous CT-guided needle insertion with wireless inertial measurement unit: a phantom study.

OBJECTIVES: A novel method applying inertial measurement units (IMUs) was developed to assist CT-guided puncture, which enables real-time displays of planned and actual needle trajectories. The method was compared with freehand and laser protractor-assisted methods. METHODS: The phantom study was performed by three operators with 8, 2, and 0 years of experience in CT-guided procedure conducted five consecutive needle placements for three target groups using three methods (freehand, laser protractor-assisted, or IMU-assisted method). The endpoints included mediolateral angle error and caudocranial angle error of the first pass, the procedure time, the total number of needle passes, and the radiation dose. RESULTS: There was a significant difference in the number of needle passes (IMU 1.2 ± 0.42, laser protractor 2.9 ± 1.6, freehand 3.6 ± 2.0 time, p < 0.001), the procedure time (IMU 3.0 ± 1.2, laser protractor 6.4 ± 2.9, freehand 6.2 ± 3.1 min, p < 0.001), the mediolateral angle error of the first pass (IMU 1.4 ± 1.2, laser protractor 1.6 ± 1.3, freehand 3.7 ± 2.5 degree, p < 0.001), the caudocranial angle error of the first pass (IMU 1.2 ± 1.2, laser protractor 5.3 ± 4.7, freehand 3.9 ± 3.1 degree, p < 0.001), and the radiation dose (IMU 250.5 ± 74.1, laser protractor 484.6 ± 260.2, freehand 561.4 ± 339.8 mGy-cm, p < 0.001) among three CT-guided needle insertion methods. CONCLUSION: The wireless IMU improves the angle accuracy and speed of CT-guided needle punctures as compared with laser protractor guidance and freehand techniques. KEY POINTS: • The IMU-assisted method showed a significant decrease in the number of needle passes (IMU 1.2 ± 0.42, laser protractor 2.9 ± 1.6, freehand 3.6 ± 2.0 time, p < 0.001). • The IMU-assisted method showed a significant decrease in the procedure time (IMU 3.0 ± 1.2, laser protractor 6.4 ± 2.9, freehand 6.2 ± 3.1 min, p < 0.001). • The IMU-assisted method showed a significant decrease in the mediolateral angle error of the first pass and the caudocranial angle error of the first pass.