Natural History of Persistent Pulmonary Subsolid Nodules: Long-Term Observation of Different Interval Growth.

BACKGROUND: The long-term natural course and outcomes of subsolid nodules (SSNs) in terms of true growth, substantial growth, and stage shift need to be clarified. METHODS: Between 2002 and 2016, 128 subjects with persistent SSNs of 3cm or smaller were enrolled. The baseline and interval changes in the series computed tomography (CT) findings during the follow-up period were subsequently reviewed. RESULTS: The mean follow-up period was 3.57±2.93years. The cumulative percentage of growth nodules of the part-solid nodule (PSN) group was significantly higher than that of the ground-glass nodule (GGN) group by Kaplan-Meier estimation (all p<0.0001). For true SSN growth, GGNs usually take a median follow-up of 7 years to grow; PSNs usually take a median follow-up of 3 years to grow. For substantial SSN growth, GGNs usually take a median follow-up of 9 years to grow; PSNs usually take a median follow-up of 3 years to grow. For stage shift, GGNs usually take a median follow-up of 12 years to grow; PSNs usually take a median follow-up of 9 years to grow. CONCLUSIONS: The natural course in terms of true growth, substantial growth, and stage shift differed significantly according to their nodule type, which could contribute to the development of follow-up guidelines and management strategy of pulmonary SSNs.

Evaluating Efficiency and Adherence in Asian Lung Cancer Screening: Comparing Self-paid and Clinical Study Approaches in Taiwan.

RATIONALE AND OBJECTIVES: This study aimed to assess how different screening methods, specifically self-paid screening versus participation in clinical studies, affect screening efficiency and adherence in a real-world Asian lung cancer screening population. MATERIALS AND METHODS: This study collected 4166 participants from our hospital imaging database who underwent baseline low-dose computed tomography (LDCT) between January 2014 and August 2021. Adherence status was determined by counting CT scans, with one check indicating non-adherence and two or more checks indicating adherence. The primary objective was to investigate adherence to LDCT follow-up schedules among individuals with baseline pure ground-glass nodules (GGNs) based on different screening settings and to evaluate adherence status and CT follow-up clinical profiles. RESULTS: Of the 4166 participants in the study, 3619 in the self-paid group and 547 in the clinical study group were men, with an average follow-up period of 4.5 years. Significant differences were observed in the proportions of Lung-RADS 4 lesions, subsolid nodules, and pure GGN lesions between the self-paid and clinical trial groups. A significant difference was found in adherence rates between the self-paid screening group (60.5%) and the clinical study group (84.8%) (p < 0.001). Adherence status rates significantly increased with larger GGN sizes across categories (p < 0.001). Multivariate logistic regression revealed that age (odds ratio [OR], 1.025; p = 0.012), smoking habits (OR, 1.744; p = 0.036), and clinical study screening type (OR, 3.097; p < 0.001) significantly influenced the adherence status. CONCLUSION: The disparities in Asian lung cancer screening emphasize the need for increased efficacy, public awareness, and culturally sensitive approaches to mitigate overdiagnosis and enhance adherence among self-paying groups.

Prediction of the stage shift growth of early-stage lung adenocarcinomas by volume-doubling time.

Background: Prediction of subsolid nodule (SSN) interval growth is crucial for clinical management and decision making in lung cancer screening program. To the best of our knowledge, no study has investigated whether volume doubling time (VDT) is an independent factor for predicting SSN interval growth, or whether its predictive power is better than that of traditional semantic methods, such as nodular diameter or type. This study aimed to investigate whether VDT could provide added value in predicting the long-term natural course of SSNs (<3 cm) regarding stage shift. Methods: This retrospective study enrolled 132 patients with spectrum lesions of lung adenocarcinoma who underwent two consecutive computed tomography (CT) examinations before surgical tissue proofing between 2012 and 2021 in Kaohsiung Veterans General Hospital. The VDTs were manually calculated from the volumetric segmentation using Schwartz's approximation formula. We utilized logistic regression to identify predictors associated with stage shift progression based on the VDT parameter. Results: The average duration of follow-up period was 3.629 years. A VDT-based nomogram model (model 2) based on CT semantic features, clinical characteristics, and the VDT parameter yielded an area under the curve (AUC) of 0.877 [95% confidence interval (CI): 0.807-0.928]. Compared with model 1 (CT semantic features and clinical characteristics), model 2 exhibited the better predictive performance for stage shift (AUC model 1: 0.833 versus AUC model 2: 0.877, P=0.047). In model 2, significant predictors of stage shift growth included initial nodule size [odds ratio (OR) =4.074, 95% CI: 1.368-12.135; P=0.012], SSN classification (OR =0.042; 95% CI: 0.006-0.288; P=0.001), follow-up period (OR =1.692, 95% CI: 1.337-2.140; P<0.001), and VDT classification (OR =2.327, 95% CI: 1.368-3.958; P=0.002). For the stage shift, the mean progression time for the VDT (>400 d) group was 7.595 years, and median progression time was 7.430 years. Additionally, a VDT ≤400 d is an important prognostic factor associated with aggressive growth behavior with a stage shift. Conclusions: VDT is crucial for predicting SSN stage shift growth irrespective of clinical and CT semantic features. This highlights its significance in informing follow-up protocols and surgical planning, emphasizing its prognostic value in predicting SSN growth.

Intrabronchial schwannoma with right lower lobe obstructive bronchiectasis and organizing pneumonia: A case report.

We present the case of a 60-year-old female patient with no prior history of any systemic disease. She suffered from a prolonged cough that lasted more than 3 months, associated with poor appetite and weight loss of 5 kg. The pathology report of the pre-operative transbronchial needle biopsy was consistent with a neurogenic tumour. Chest computed tomography (CT) revealed a right lower lobe (RLL) mass-like consolidation of 8.67 cm with obstructive pneumonitis and suspicious posterior mediastinal invasion. The tumour was surgically resected with bronchial reconstruction, and the pathological diagnosis was intrabronchial schwannoma located inside the bronchus, a rare tumour that should be included as one of the differential diagnoses of primary bronchial tumours. The possibility of a surgical completed resection should be considered in patients with airway obstruction symptoms.

Long-term follow-up of non-myasthenic patients with early-stage thymoma who underwent extended thymectomy or limited resection.

BACKGROUD: The standard resection for early-stage thymoma is total thymectomy and complete tumour excision with or without myasthenia gravis but the optimal surgery mode for patients with early-stage non-myasthenic thymoma is debatable. This study analysed the oncological outcomes for non-myasthenic patients with early-stage thymoma treated by thymectomy or limited resection in the long term. METHODS: Patients who had resections of thymic neoplasms at Taipei Veteran General Hospital, Taiwan between December 1997 and March 2013 were recruited, exclusive of those combined clinical evidence of myasthenia gravis were reviewed. A total of 113 patients were retrospectively reviewed with pathologic early stage (Masaoka stage I and II) thymoma who underwent limited resection or extended thymectomy to compare their long-term oncologic and surgical outcomes. RESULTS: The median observation time was 134.1 months [interquartile range (IQR) 90.7-176.1 months]. In our cohort, 52 patients underwent extended thymectomy and 61 patients underwent limited resection. Shorter duration of surgery (p < 0.001) and length of stay (p = 0.006) were demonstrated in limited resection group. Six patients experienced thymoma recurrence, two of which had combined myasthenia gravis development after recurrence. There was no significant difference (p = 0.851) in freedom-from-recurrence, with similar 10-year freedom-from-recurrence rates between the limited resection group (96.2 %) and the thymectomy group (93.2 %). Tumour-related survival was also not significantly different between groups (p = 0.726).result CONCLUSION: Patients with early-stage non-myasthenic thymoma who underwent limited resection without complete excision of the thymus achieved similar oncologic outcomes during the long-term follow-up and better peri-operative results compared to those who underwent thymectomy.

Comparison between one-stage and two-stage CT-guided localization of lung nodules with surgical resection: a single medical center experience.

BACKGROUND: This retrospective study aimed to compare the efficacy and safety of one-stage computed tomography (OSCT)- to that of two-stage computed tomography (TSCT)-guided localization for the surgical removal of small lung nodules. METHODS: We collected data from patients with ipsilateral pulmonary nodules who underwent localization before surgical removal at Veteran General Hospital Kaohsiung between October 2017 and January 2022. The patients were divided into the OSCT and TSCT groups. RESULTS: We found that OSCT significantly reduced the localization time and risky time compared to TSCT, and the success rate of localization and incidence of pneumothorax were similar in both groups. However, the time spent under general anesthesia was longer in the OSCT group than in the TSCT group. CONCLUSIONS: The OSCT-guided approach to localize pulmonary nodules in hybrid operation room is a safe and effective technique for the surgical removal of small lung nodules.

Low-dose CT screening among never-smokers with or without a family history of lung cancer in Taiwan: a prospective cohort study.

BACKGROUND: In Taiwan, lung cancers occur predominantly in never-smokers, of whom nearly 60% have stage IV disease at diagnosis. We aimed to assess the efficacy of low-dose CT (LDCT) screening among never-smokers, who had other risk factors for lung cancer. METHODS: The Taiwan Lung Cancer Screening in Never-Smoker Trial (TALENT) was a nationwide, multicentre, prospective cohort study done at 17 tertiary medical centres in Taiwan. Eligible individuals had negative chest radiography, were aged 55-75 years, had never smoked or had smoked fewer than 10 pack-years and stopped smoking for more than 15 years (self-report), and had one of the following risk factors: a family history of lung cancer; passive smoke exposure; a history of pulmonary tuberculosis or chronic obstructive pulmonary disorders; a cooking index of 110 or higher; or cooking without using ventilation. Eligible participants underwent LDCT at baseline, then annually for 2 years, and then every 2 years up to 6 years thereafter, with follow-up assessments at each LDCT scan (ie, total follow-up of 8 years). A positive scan was defined as a solid or part-solid nodule larger than 6 mm in mean diameter or a pure ground-glass nodule larger than 5 mm in mean diameter. Lung cancer was diagnosed through invasive procedures, such as image-guided aspiration or biopsy or surgery. Here, we report the results of 1-year follow-up after LDCT screening at baseline. The primary outcome was lung cancer detection rate. The p value for detection rates was estimated by the χ2 test. Univariate and multivariable logistic regression analyses were used to assess the association between lung cancer incidence and each risk factor. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of LDCT screening were also assessed. This study is registered with ClinicalTrials.gov, NCT02611570, and is ongoing. FINDINGS: Between Dec 1, 2015, and July 31, 2019, 12 011 participants (8868 females) were enrolled, of whom 6009 had a family history of lung cancer. Among 12 011 LDCT scans done at baseline, 2094 (17·4%) were positive. Lung cancer was diagnosed in 318 (2·6%) of 12 011 participants (257 [2·1%] participants had invasive lung cancer and 61 [0·5%] had adenocarcinomas in situ). 317 of 318 participants had adenocarcinoma and 246 (77·4%) of 318 had stage I disease. The prevalence of invasive lung cancer was higher among participants with a family history of lung cancer (161 [2·7%] of 6009 participants) than in those without (96 [1·6%] of 6002 participants). In participants with a family history of lung cancer, the detection rate of invasive lung cancer increased significantly with age, whereas the detection rate of adenocarcinoma in situ remained stable. In multivariable analysis, female sex, a family history of lung cancer, and age older than 60 years were associated with an increased risk of lung cancer and invasive lung cancer; passive smoke exposure, cumulative exposure to cooking, cooking without ventilation, and a previous history of chronic lung diseases were not associated with lung cancer, even after stratification by family history of lung cancer. In participants with a family history of lung cancer, the higher the number of first-degree relatives affected, the higher the risk of lung cancer; participants whose mother or sibling had lung cancer were also at an increased risk. A positive LDCT scan had 92·1% sensitivity, 84·6% specificity, a PPV of 14·0%, and a NPV of 99·7% for lung cancer diagnosis. INTERPRETATION: TALENT had a high invasive lung cancer detection rate at 1 year after baseline LDCT scan. Overdiagnosis could have occurred, especially in participants diagnosed with adenocarcinoma in situ. In individuals who do not smoke, our findings suggest that a family history of lung cancer among first-degree relatives significantly increases the risk of lung cancer as well as the rate of invasive lung cancer with increasing age. Further research on risk factors for lung cancer in this population is needed, particularly for those without a family history of lung cancer. FUNDING: Ministry of Health and Welfare of Taiwan.

An integrated nomogram combined semantic-radiomic features to predict invasive pulmonary adenocarcinomas in subjects with persistent subsolid nodules.

Background: Patients with persistent pulmonary subsolid nodules have a relatively high incidence of lung adenocarcinoma. Preoperative early diagnosis of invasive pulmonary adenocarcinoma spectrum lesions could help avoid extensive advanced cancer management and overdiagnosis in lung cancer screening programs. Methods: In total, 260 consecutive patients with persistent subsolid nodules ≤30 mm (n=260) confirmed by surgical pathology were retrospectively investigated from February 2016 to August 2020 at the Kaohsiung Veterans General Hospital. All patients underwent surgical resection within 3 months of the chest CT exam. The study subjects were divided into a training cohort (N=195) and a validation cohort (N=65) at a ratio of 3:1. The purpose of our study was to develop and validate a least absolute shrinkage and selection operator-derived nomogram integrating semantic-radiomic features in differentiating preinvasive and invasive pulmonary adenocarcinoma lesions, and compare its predictive value with clinical-semantic, semantic, and radiologist's performance. Results: In the training cohort of 195 subsolid nodules, 106 invasive lesions and 89 preinvasive lesions were identified. We developed a least absolute shrinkage and selection operator-derived combined nomogram prediction model based on six predictors (nodular size, CTR, roundness, GLCM_Entropy_log10, HISTO_Entropy_log10, and CONVENTIONAL_Humean) to predict the invasive pulmonary adenocarcinoma lesions. Compared with other predictive models, the least absolute shrinkage and selection operator-derived model showed better diagnostic performance with an area under the curve of 0.957 (95% CI: 0.918 to 0.981) for detecting invasive pulmonary adenocarcinoma lesions with balanced sensitivity (92.45%) and specificity (88.64%). The results of Hosmer-Lemeshow test showed P values of 0.394 and 0.787 in the training and validation cohorts, respectively, indicating good calibration power. Conclusions: We developed a least absolute shrinkage and selection operator-derived model integrating semantic-radiomic features with good calibration. This nomogram may help physicians to identify invasive pulmonary adenocarcinoma lesions for guidance in personalized medicine and make more informed decisions on managing subsolid nodules.

Prediction of Interval Growth of Lung Adenocarcinomas Manifesting as Persistent Subsolid Nodules ≤3 cm Based on Radiomic Features.

RATIONALES AND OBJECTIVES: To investigate the prognostic value of the radiomic-based prediction model in predicting the interval growth rate of persistent subsolid nodules (SSNs) with an initial size of ≤ 3 cm manifesting as lung adenocarcinomas. MATERIALS AND METHODS: A total of 133 patients (mean age, 59.02 years; male, 37.6%) with 133 SSNs who underwent a series of CT examinations at our hospital between 2012 and 2022 were included in this study. Forty-one radiomic features were extracted from each volumetric region of interest. Radiomic features combined with conventional clinical and semantic parameters were then selected for radiomic-based model building. To investigate the model performance in terms of substantial SSN growth and stage shift growth, the model performance was compared by the area under the curve (AUC) obtained by receiver operating characteristic analysis. RESULTS: The mean follow-up period was 3.62 years. For substantial SSN growth, a radiomic-based model (Model 2) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.869 (95% CI: 0.799-0.922). In comparison with Model 1 (clinical characteristics and CT semantic features), Model 2 performed better than Model 1 for substantial SSN growth (AUC model 1:0.793 versus AUC model 2:0.869, p = 0.028). A radiomic-based nomogram combining sex, follow-up period, and three radiomic features was built for substantial SSN growth prediction. For the stage shift growth, a radiomic-based model (Model 4) based on clinical characteristics, CT semantic features, and radiomic features yielded an AUCs of 0.883 (95% CI: 0.815-0.933). Compared with Model 3 (clinical characteristics and CT semantic features), Model 4 performed better than the model 3 for stage shift growth (AUC model 1: 0.769 versus AUC model 2: 0.883, p = 0.006). A radiomic-based nomogram combining the initial nodule size, SSN classification, follow-up period, and three radiomic features was built to predict the stage shift growth. CONCLUSION: Radiomic-based models have superior utility in estimating the prognostic interval growth of patients with early lung adenocarcinomas (≤ 3 cm) than conventional clinical-semantic models in terms of substantial interval growth and stage shift growth, potentially guiding clinical decision-making with follow-up strategies of SSNs in personalized precision medicine.

Managing Persistent Subsolid Nodules in Lung Cancer: Education, Decision Making, and Impact of Interval Growth Patterns.

With the popularization of lung cancer screening, many persistent subsolid nodules (SSNs) have been identified clinically, especially in Asian non-smokers. However, many studies have found that SSNs exhibit heterogeneous growth trends during long-term follow ups. This article adopted a narrative approach to extensively review the available literature on the topic to explore the definitions, rationale, and clinical application of different interval growths of subsolid pulmonary nodule management and follow-up strategies. The development of SSN growth thresholds with different growth patterns could support clinical decision making with follow-up guidelines to reduce over- and delayed diagnoses. In conclusion, using different SSN growth thresholds could optimize the follow-up management and clinical decision making of SSNs in lung cancer screening programs. This could further reduce the lung cancer mortality rate and potential harm from overdiagnosis and over management.

Comparison of the Outcomes between Systematic Lymph Node Dissection and Lobe-Specific Lymph Node Dissection for Stage I Non-small Cell Lung Cancer.

BACKGROUND: This study compares the surgical and long-term outcomes, including disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS), between lobe-specific lymph node dissection (L-SND) and systematic lymph node dissection (SND) among patients with stage I non-small cell lung cancer (NSCLC). METHODS: In this retrospective study, 107 patients diagnosed with clinical stage I NSCLC undergoing video-assisted thoracic surgery lobectomy (exclusion of the right middle lobe) from January 2011 to December 2018 were enrolled. The patients were assigned to the L-SND (n = 28) and SND (n = 79) groups according to the procedure performed on them. Demographics, perioperative data, and surgical and long-term oncological outcomes were collected and compared between the L-SND and SND groups. RESULTS: The mean follow-duration was 60.6 months. The demographic data and surgical outcomes and long-term oncological outcomes were not significantly different between the two groups. The 5-year OS of the L-SND and SND groups was 82% and 84%, respectively. The 5-year DFS of the L-SND and SND groups was 70% and 65%, respectively. The 5-year CSS of the L-SND and SND groups was 80% and 86%, respectively. All the surgical and long-term outcomes were not statistically different between the two groups. CONCLUSION: L-SND showed comparable surgical and oncologic outcomes with SND for clinical stage I NSCLC. L-SND could be a treatment choice for stage I NSCLC.

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.].

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.

Radiomics in Early Lung Cancer Diagnosis: From Diagnosis to Clinical Decision Support and Education.

Lung cancer is the most frequent cause of cancer-related death around the world. With the recent introduction of low-dose lung computed tomography for lung cancer screening, there has been an increasing number of smoking- and non-smoking-related lung cancer cases worldwide that are manifesting with subsolid nodules, especially in Asian populations. However, the pros and cons of lung cancer screening also follow the implementation of lung cancer screening programs. Here, we review the literature related to radiomics for early lung cancer diagnosis. There are four main radiomics applications: the classification of lung nodules as being malignant/benign; determining the degree of invasiveness of the lung adenocarcinoma; histopathologic subtyping; and prognostication in lung cancer prediction models. In conclusion, radiomics offers great potential to improve diagnosis and personalized risk stratification in early lung cancer diagnosis through patient-doctor cooperation and shared decision making.

Effectiveness of Perioperative Cardiopulmonary Rehabilitation in Patients With Lung Cancer Undergoing Video-Assisted Thoracic Surgery.

Objectives: Patients with lung cancer pose a high risk of morbidity and mortality after lung resection. Those who receive perioperative cardiopulmonary rehabilitation (PRCR) have better prognosis. Peak oxygen consumption (peak VO2), VO2 at the ventilatory threshold (VO2 at VT), and slope of minute ventilation to carbon dioxide production (VE/VCO2 slope) measured during pre-surgical cardiopulmonary exercise testing (CPET) have prognostic values after lung resection. We aimed to investigate the influence of individualized PRCR on postoperative complications in patients undergoing video-assisted thoracic surgery (VATS) for lung cancer with different pre-surgical risks. Methods: This was a retrospective study. We recruited 125 patients who underwent VATS for lung cancer between 2017 and 2021. CPET was administered before surgery to evaluate the risk level and PRCR was performed based on the individual risk level defined by peak VO2, VO2 at VT, and VE/VCO2 slope, respectively. The primary outcomes were intensive care unit (ICU) and hospital lengths of stay, endotracheal intubation time (ETT), and chest tube insertion time (CTT). The secondary outcomes were postoperative complications (PPCs), including subcutaneous emphysema, pneumothorax, pleural effusion, atelectasis, infection, and empyema. Results: Three intergroup comparisons based on the risk level by peak VO2 (3 groups), VO2 at VT (2 groups), and VE/VCO2 slope (3 groups) were done. All of the comparisons showed no significant differences in both the primary and secondary outcomes (p = 0.061-0.910). Conclusion: Patients with different risk levels showed comparable prognosis and PPCs after undergoing CPET-guided PRCR. PRCR should be encouraged in patients undergoing VATS for lung cancer.

Impact of low-dose computed tomography for lung cancer screening on lung cancer surgical volume: The urgent need in health workforce education and training.

ABSTRACT: This study aimed to investigate the time trend variation in the surgical volume and prognostic outcome of patients with lung cancer after the gradual prolonged implementation of a low-dose computed tomography (LDCT) lung cancer screening program.Using the hospital-based cancer registry data on number of patients with lung cancer and deaths from 2008 to 2017, we conducted a retrospective study using a hospital-based cohort to investigate the relationship between changes in lung cancer surgical volume, the proportion of lung-sparing surgery, and prolonged prognostic outcomes after the gradual implementation of the LDCT lung cancer screening program in recent years.From 2008 to 2017, 3251 patients were diagnosed with lung cancer according to the hospital-based cancer registry. The 5-year mortality rate decreased gradually from 83.54% to 69.44% between 2008 and 2017. The volume of total lung cancer surgical procedures and proportion of lung-sparing surgery performed gradually increased significantly from 2008 to 2017, especially from 2014 to 2017 after implementation of a large volume of LDCT lung cancer screening examinations. In conclusion, our real-world data suggest that there will be an increase in cases of operable early-stage lung cancers, which in turn will increase the surgical volume and proportion of lung-sparing surgery, after the gradual implementation of the LDCT lung cancer screening program in recent years. These findings suggest the importance of a successful national policy regarding LDCT screening programs, regulation of shortage of thoracic surgeons, thoracic radiologist workforce training positions, and education programs.

A comparative study to evaluate CT-based semantic and radiomic features in preoperative diagnosis of invasive pulmonary adenocarcinomas manifesting as subsolid nodules.

This study aims to predict the histological invasiveness of pulmonary adenocarcinoma spectrum manifesting with subsolid nodules ≦ 3 cm using the preoperative CT-based radiomic approach. A total of 186 patients with 203 SSNs confirmed with surgically pathologic proof were retrospectively reviewed from February 2016 to March 2020 for training cohort modeling. The validation cohort included 50 subjects with 57 SSNs confirmed with surgically pathologic proof from April 2020 to August 2020. CT-based radiomic features were extracted using an open-source software with 3D nodular volume segmentation manually. The association between CT-based conventional features/selected radiomic features and histological invasiveness of pulmonary adenocarcinoma status were analyzed. Diagnostic models were built using conventional CT features, selected radiomic CT features and experienced radiologists. In addition, we compared diagnostic performance between radiomic CT feature, conventional CT features and experienced radiologists. In the training cohort of 203 SSNs, there were 106 invasive lesions and 97 pre-invasive lesions. Logistic analysis identified that a selected radiomic feature named GLCM_Entropy_log10 was the predictor for histological invasiveness of pulmonary adenocarcinoma spectrum (OR: 38.081, 95% CI 2.735-530.309, p = 0.007). The sensitivity and specificity for predicting histological invasiveness of pulmonary adenocarcinoma spectrum using the cutoff value of CT-based radiomic parameter (GLCM_Entropy_log10) were 84.8% and 79.2% respectively (area under curve, 0.878). The diagnostic model of CT-based radiomic feature was compared to those of conventional CT feature (morphologic and quantitative) and three experienced radiologists. The diagnostic performance of radiomic feature was similar to those of the quantitative CT feature (nodular size and solid component, both lung and mediastinal window) in prediction invasive pulmonary adenocarcinoma (IPA). The AUC value of CT radiomic feature was higher than those of conventional CT morphologic feature and three experienced radiologists. The c-statistic of the training cohort model was 0.878 (95% CI 0.831-0.925) and 0.923 (0.854-0.991) in the validation cohort. Calibration was good in both cohorts. The diagnostic performance of CT-based radiomic feature is not inferior to solid component (lung and mediastinal window) and nodular size for predicting invasiveness. CT-based radiomic feature and nomogram could help to differentiate IPA lesions from preinvasive lesions in the both independent training and validation cohorts. The nomogram may help clinicians with decision making in the management of subsolid nodules.

Prognostic Factor of Completely Resected and Pathologic T3 N0 M0 Thymic Epithelial Tumor.

BACKGROUND: A multiinstitutional study was conducted to analyze prognosticators of completely resected and pathologic T3 N0 M0 (pT3 N0 M0) stage thymic epithelial tumors. METHODS: A total of 607 patients with surgically treated thymic epithelial tumors between June 1988 and December 2017 were enrolled. A Cox proportional hazards model and an inverse probability of treatment weighting-adjusted analysis using the propensity score were performed. RESULTS: A total of 394 patients with thymoma and 130 patients with thymic carcinoma underwent complete tumor resections. Forty-one thymomas and 49 thymic carcinomas were confirmed as pT3 N0 M0 stage tumors. Postoperative adjuvant radiotherapy was associated with improved disease-free and overall survival in patients with thymoma (hazard ratio [HR], 0.40; 95% confidence interval [CI], 0.23 to 0.69; and HR, 0.24; 95% CI, 0.11 to 0.52, respectively) and in patients with thymic carcinoma (HR, 0.15; 95% CI, 0.07 to 0.33; and HR, 0.12; 95% CI, 0.05 to 0.31, respectively). Although lung invasion was associated with poor disease-free survival (HR, 3.28; 95% CI, 1.90 to 5.89) and overall survival (HR, 2.60; 95% CI, 1.21 to 6.07), male sex (HR, 1.88; 95% CI, 1.10 to 3.18), older age (HR, 2.77; 95% CI, 1.29 to 5.70), and advanced histologic features (HR, 3.84; 95% CI, 1.42 to 14.51) were associated with poor overall survival in patients with pT3 N0 M0 thymoma. Adjuvant chemotherapy was associated with improved disease-free survival (HR, 0.11; 95% CI, 0.03 to 0.41) and overall survival (HR, 0.11; 95% CI, 0.06 to 0.20) in patients with pT3 N0 M0 thymic carcinoma with superior vena cava or innominate vein invasion. CONCLUSIONS: Postoperative radiotherapy was associated with improved survival in patients with pT3 N0 M0 thymic epithelial tumors. Lung invasion was associated with poor survival in patients with pT3 N0 M0 thymoma. Adjuvant chemotherapy was associated with improved survival in patients with pT3 N0 M0 thymic carcinoma with superior vena cava or innominate vein invasion.

Prognostic effect of implementation of the mass low-dose computed tomography lung cancer screening program: a hospital-based cohort study.

Low-dose computed tomography lung cancer screening aims to detect early-stage lung cancers in order to decrease the incidence of advanced-stage lung cancers and to reduce lung cancer mortality. We analyzed the time trends of lung cancer stage distribution and mortality rates after the gradual implementation of the low-dose computed tomography lung cancer screening in a hospital-based cohort. Using the hospital-based cancer registry data on lung cancer number and death from 2007 to 2014, we aim to evaluate the trends in stage distribution and mortality rate after the gradual implementation of low-dose computed tomography lung cancer screening program over recent years. From 2007 to 2014, overall 2542 cases of lung cancers were diagnosed according to hospital-based cancer registry. For the 1-year mortality rate, the mortality rate decreased gradually from 48.16 to 37.04% between 2007 and 2014. For the 5-year mortality rate, the mortality rate decreased gradually from 88.49 to 69.44% between 2007 and 2014. There was a gradual decrease in stage IV lung cancer with the corresponding sharp increase in stage I early lung cancer after following the implementation of the large volume of the low-dose computed tomography examination between the years 2011 and 2014. In conclusion, these results suggest that the gradual implementation of low-dose computed tomography lung screening program could lead to a remarkable decrease in lung cancer mortality and a remarkable stage shift in the trend over time in this hospital-based cohort.