2024 multidisciplinary consensus on image-guided lung tumor ablation from the Taiwan Academy of Tumor Ablation.

In this article, the multidisciplinary team of the Taiwan Academy of Tumor Ablation, who have expertise in treating lung cancer, present their perspectives on percutaneous image-guided thermal ablation (IGTA) of lung tumors. The modified Delphi technique was applied to reach a consensus on clinical practice guidelines concerning ablation procedures, including a comprehensive literature review, selection of panelists, creation of a rating form and survey, and arrangement of an in-person meeting where panelists agreed or disagreed on various points. The conclusion was a final rating and written summary of the agreement. The multidisciplinary expert team agreed on 10 recommendations for the use of IGTA in the lungs. These recommendations include terms and definitions, line of treatment planning, modality, facility rooms, patient anesthesia settings, indications, margin determination, post-ablation image surveillance, qualified centers, and complication ranges. In summary, IGTA is a safe and feasible approach for treating primary and metastatic lung tumors, with a relatively low complication rate. However, decisions regarding the ablation technique should consider each patient's specific tumor characteristics.

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.

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.

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.

Early drainage reduces the length of hospital stay in patients with lung abscess.

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.

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.

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.

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.

Pulmonary-function changes after uniportal video-assisted thoracoscopic anatomical lung resection.

OBJECTIVE: The superiority of segmentectomy over lobectomy with regard to preservation of pulmonary function is controversial. This study aimed to examine changes in pulmonary function after uniportal video-assisted thoracoscopic surgery (VATS) according to the number of resected segments. METHODS: We retrospectively reviewed 135 consecutive patients who underwent anatomical lung resection via uniportal VATS from April 2015 to December 2020. Pulmonary function loss was evaluated using forced vital capacity (FVC) and forced expiratory volume in 1 s (FEV1). Patients were grouped according to number of resected segments: one-segment (n = 33), two segments (n = 22), three segments (n = 40), four segments (n = 15), and five segments (n = 25). RESULTS: Clinical characteristics did not significantly differ between groups, except for tumor size. Mean follow-up was 8.96 ± 3.16 months. FVC loss was significantly greater in five-segment resection (10.8%) than one-segment (0.97%, p = 0.008) and two-segment resections (2.44%, p = 0.040). FEV1 loss was significantly greater in five-segment resection (15.02%) than one-segment (3.83%, p < 0.001), two-segment (4.63%, p = 0.001), and three-segment resections (7.63%, p = 0.007). Mean FVC loss and FEV1 loss increased linearly from one-segment resection to five-segment resection. Mean loss in FVC and FEV1 per segment resected was 2.16% and 3.00%, respectively. CONCLUSIONS: Anatomical lung resection of fewer segments was associated with better preservation of pulmonary function in patients undergoing uniportal VATS, and function loss was approximately 2%-3% per segment resected with linear relationship.

Tailored Surgical Stabilization of Rib Fractures Matters More Than the Number of Fractured Ribs.

BACKGROUND: Patients sustaining multiple rib fractures have a significant risk of developing morbidity and mortality. More evidence is emerging that the indication of surgical stabilization of rib fractures (SSRF) should expand beyond flail chest. Nevertheless, little is known about factors associated with poor outcomes after surgical fixation. We reviewed patients with rib fractures to further explore the role of SSRF; we matched two groups by propensity score (PS). METHOD: A comparison of patients with blunt thoracic trauma treated with SSRF between 2010 and 2020 was compared with those who received conservative treatment for rib fractures. Risk factors for poor outcomes were analyzed by multivariate regression analysis. RESULTS: After tailored SSRF, the number of fractured ribs was not associated with longer ventilator days (p = 0.617), ICU stay (p = 0.478), hospital stay (p = 0.706), and increased nonprocedure-related pulmonary complications (NPRCs) (p = 0.226) despite having experienced much more severe trauma. In the multivariate regression models, lower GCS, delayed surgery, thoracotomy, and flail chest requiring mechanical ventilation were factors associated with prolonged ventilator days. Lower GCS, higher ISS, delayed surgery, and flail chest requiring mechanical ventilation were factors associated with longer ICU stays. Lower GCS and older age were factors associated with increased NPRCs. In the PS model, NPRCs risk was reduced by SSRF. CONCLUSIONS: The risk of NPRCs was reduced once ribs were surgically fixed through an algorithmic approach, and poor consciousness and aging were independent risk factors for NPRCs.

The surgical resection of the primary tumor increases survival in patients with EGFR-mutant advanced non-small cell lung cancer: a tertiary center cohort study.

Tumor resection could increase treatment efficacy of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI) in patients with advanced EGFR-mutant non-small cell lung cancer (NSCLC). This study aimed to retrospectively analyze patients with advanced EGFR-mutant NSCLC from a Taiwanese tertiary center and receiving EGFR-TKI treatment with or without tumor resection. A total of 349 patients were enrolled. After propensity score matching, 53 EGFR-TKI treated patients and 53 EGFR-TKI treated patients with tumor resection were analyzed. The tumor resection group showed improved progression-free survival (PFS) (52.0 vs. 9.8 months; hazard ratio [HR] = 0.19; p < 0.001) and overall survival (OS) (not reached vs. 30.6 months; HR = 0.14; p < 0.001) compared to the monotherapy group. In the subgroup analysis of patients with newly-diagnosed NSCLC, the tumor resection group showed longer PFS (52.0 vs. 9.9 months; HR = 0.14; p < 0.001) and OS (not reached vs. 32.6 months; HR = 0.12; p < 0.001) than the monotherapy group. In conclusion. the combination of EGFR-TKI and tumor resection provided better PFS and OS than EGFR-TKI alone, and patients who underwent tumor resection within six months had fewer co-existing genomic alterations and better PFS.

Diagnostic value of apparent diffusion coefficient in predicting pathological T stage in patients with thymic epithelial tumor.

PURPOSES: This study aimed to evaluate the diagnostic capacity of apparent diffusion coefficient (ADC) in predicting pathological Masaoka and T stages in patients with thymic epithelial tumors (TETs). METHODS: Medical records of 62 patients who were diagnosed with TET and underwent diffusion-weighted imaging (DWI) prior to surgery between August 2017 and July 2021 were retrospectively analyzed. ADC values were calculated from DWI images using b values of 0, 400, and 800 s/mm2. Pathological stages were determined by histological examination of surgical specimens. Cut-off points of ADC values were calculated via receiver operating characteristic (ROC) analysis. RESULTS: Patients had a mean age of 56.3 years. Mean ADC values were negatively correlated with pathological Masaoka and T stages. Higher values of the area under the ROC curve suggested that mean ADC values more accurately predicated pathological T stages than pathological Masaoka stages. The optimal cut-off points of mean ADC were 1.62, 1.31, and 1.48 × 10-3 mm2/sec for distinguishing pathological T2-T4 from pathological T1, pathological T4 from pathological T1-T3, and pathological T3-T4 from pathological T2, respectively. CONCLUSION: ADC seems to more precisely predict pathological T stages, compared to pathological Masaoka stage. The cut-off values of ADC identified may be used to preoperatively predict pathological T stages of TETs.

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors.

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast-enhanced MRI (DCE-MRI)-derived perfusion parameters. The clinical data and preoperative DCE-MRI images of 62 PMT patients, including 17 patients with lymphoma, 31 with thymoma, and 14 with thymic carcinoma, were retrospectively analyzed. Six perfusion parameters were calculated as candidate predictors. Univariate receiver-operating-characteristic curve analysis was performed to evaluate the performance of the prediction models. A predictive model was built based on multi-class classification, which detected lymphoma, thymoma, and thymic carcinoma with sensitivity of 52.9%, 74.2%, and 92.8%, respectively. In addition, two predictive models were built based on binary classification for distinguishing Hodgkin from non-Hodgkin lymphoma and for distinguishing invasive from noninvasive thymoma, with sensitivity of 75% and 71.4%, respectively. In addition to two perfusion parameters (efflux rate constant from tissue extravascular extracellular space into the blood plasma, and extravascular extracellular space volume per unit volume of tissue), age and tumor volume were also essential parameters for predicting PMT subtypes. In conclusion, our machine learning-based predictive model, constructed with clinical data and perfusion parameters, may represent a useful tool for differential diagnosis of PMT subtypes.

The impact of driver mutation on the treatment outcome of early-stage lung cancer patients receiving neoadjuvant immunotherapy and chemotherapy.

Neoadjuvant immunotherapy and chemotherapy have improved the major pathological response (MPR) in patients with early-stage operable non-small cell lung cancer (NSCLC). This study aimed to assess whether the presence of targetable driver mutations affects the efficacy of the combination of immunotherapy and chemotherapy. We enrolled patients with early-stage operable NSCLC who received preoperative neoadjuvant therapy between January 1, 2017, and December 30, 2020. Neoadjuvant therapy was delivered with platinum-doublet chemotherapy; moreover, pembrolizumab was added at the attending physician's discretion based on patient's request. Pathological responses were assessed; moreover, disease-free survival was estimated. Next-generation sequencing was performed in case sufficient preoperative biopsy specimens were obtained. We included 23 patients; among them, 11 received a combination of neoadjuvant immunotherapy and chemotherapy while 12 received neoadjuvant chemotherapy alone. The MPR and pathological complete response rates were 54.5% and 27.3%, respectively, in patients who received a combination of neoadjuvant immunotherapy and chemotherapy. These rates were significantly higher than those in patients who only received neoadjuvant chemotherapy. Three patients in the combination group experienced disease recurrence during the follow-up period even though two of them showed an MPR. These three patients had targetable driver mutations, including an EGFR exon 20 insertion, EGFR exon 21 L858R substitution, and MET exon 14 skipping. Only one patient who remained disease-free had a targetable driver mutation. Among patients with early-stage operable NSCLC requiring neoadjuvant therapy, comprehensive genomic profiling is crucial before the administration of the combination of neoadjuvant immunotherapy and chemotherapy.

Partial Anomalous Pulmonary Venous Return.

Online supplemental material is available for this article.

Improved survival in patients with unresectable stage III EGFR-mutant adenocarcinoma with upfront EGFR-tyrosine kinase inhibitors.

BACKGROUND: Although epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) have been the standard treatment for advanced EGFR-mutant adenocarcinoma, the effects of upfront EGFR-TKI use in unresectable stage III EGFR-mutant adenocarcinoma remain unexplored. Here, we conducted a retrospective study to compare different treatment strategies in these patients. METHODS: From October 2010 to June 2019, patients with unresectable stage III adenocarcinoma who received treatment at a tertiary referral center were enrolled. Patients were classified into three groups: EGFR-mutant adenocarcinoma treated with concurrent chemoradiotherapy (group 1) or EGFR-TKI (group 2) and EGFR wild-type adenocarcinoma treated with concurrent chemoradiotherapy (group 3). Progression-free survival, progression-free survival-2, and overall survival were estimated and compared using Kaplan-Meier and log-rank tests. RESULTS: A total of 92 patients were enrolled; 10, 40, and 42 patients were assigned to groups 1, 2, and 3, respectively. Patients with EGFR mutations who received upfront EGFR-TKIs had significantly longer progression-free and overall survival than those who received upfront concurrent chemoradiotherapy (hazard ratio 0.33 vs. 0.34, p = 0.006 vs. 0.031) according to a Cox model adjusted for possible confounders. Moreover, upfront concurrent chemoradiotherapy did not lead to higher survival rates in patients with EGFR mutations than in those with EGFR wild-type adenocarcinoma (progression-free survival; hazard ratio 0.37, p = 0.036; overall survival; hazard ratio 0.35, p = 0.080) by Cox regression analysis. CONCLUSION: This current study suggests that EGFR-TKIs is a better choice for patients with unresectable stage III EGFR-mutant adenocarcinoma. However, further randomized studies are required to validate the results.