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

Impact of annual trend volume of low-dose computed tomography for lung cancer screening on overdiagnosis, overmanagement, and gender disparities

BACKGROUND

With the increasing prevalence of nonsmoking-related lung cancer in Asia, Asian countries have increasingly adopted low-dose computed tomography (LDCT) for lung cancer screening, particularly in private screening programs. This study examined how annual LDCT volume affects lung cancer stage distribution, overdiagnosis, and gender disparities using a hospital-based lung cancer database.

METHODS

This study analyzed the annual utilized LDCT volume, clinical characteristics of lung cancer, stage shift distribution, and potential overdiagnosis. At the individual level, this study also investigated the relationship between stage 0 lung cancer (potential strict definition regarding overdiagnosis) and the clinical characteristics of lung cancer.

RESULTS

This study reviewed the annual trend of 4971 confirmed lung cancer cases from 2008 to 2021 and conducted a link analysis with an LDCT imaging examination database over these years. As the volume of lung cancer screenings has increased over the years, the number and proportion of stage 0 lung cancers have increased proportionally. Our study revealed that the incidence of stage 0 lung cancer increased with increasing LDCT scan volume, particularly during the peak growth period from 2017 to 2020. Conversely, stage 4 lung cancer cases remained consistent across different time intervals. Furthermore, the increase in the lung cancer screening volume had a more pronounced effect on the increase in stage 0 lung cancer cases among females than it had among males. The estimated potential for overdiagnosis brought about by the screening process, compared to non-participating individuals, ranged from an odds ratio of 7.617 to one of 17.114. Both strict and lenient definitions of overdiagnosis (evaluating cases of stage 0 lung cancer and stages 0 to 1 lung cancer) were employed.

CONCLUSIONS

These results provide population-level evidence of potential lung cancer overdiagnosis in the Taiwanese population due to the growing use of LDCT screening, particularly concerning the strict definition of stage 0 lung cancer. The impact was greater in the female population than in the male population, especially among females younger than 40 years. To improve lung cancer screening in Asian populations, creating risk-based prediction models for smokers and nonsmokers, along with gender-specific strategies, is vital for ensuring survival benefits and minimizing overdiagnosis.

Sentinel Lymph Node Mapping in Lung Cancer: A Pilot Study for the Detection of Micrometastases in Stage I Non-Small Cell Lung Cancer

Lymphadenectomy represents a fundamental step in the staging and treatment of non-small cell lung cancer (NSCLC). To date, the extension of lymphadenectomy in early-stage NSCLC is a debated topic due to its possible complications. The detection of sentinel lymph nodes (SLNs) is a strategy that can improve the selection of patients in which a more extended lymphadenectomy is necessary. This pilot study aimed to refine lymph nodal staging in early-stage NSCLC patients who underwent robotic lung resection through the application of innovative intraoperative sentinel lymph node (SLN) identification and the pathological evaluation using one-step nucleic acid amplification (OSNA). Clinical N0 NSCLC patients planning to undergo robotic lung resection were selected. The day before surgery, all patients underwent radionuclide computed tomography (CT)-guided marking of the primary lung lesion and subsequently Single Photon Emission Computed Tomography (SPECT) to identify tracer migration and, consequently, the area with higher radioactivity. On the day of surgery, the lymph nodal radioactivity was detected intraoperatively using a gamma camera. SLN was defined as the lymph node with the highest numerical value of radioactivity. The OSNA amplification, detecting the mRNA of CK19, was used for the detection of nodal metastases in the lymph nodes, including SLN. From March to July 2021, a total of 8 patients (3 female; 5 male), with a mean age of 66 years (range 48-77), were enrolled in the study. No complications relating to the CT-guided marking or preoperative SPECT were found. An average of 5.3 lymph nodal stations were examined (range 2-8). N2 positivity was found in 3 out of 8 patients (37.5%). Consequently, pathological examination of lymph nodes with OSNA resulted in three upstages from the clinical IB stage to pathological IIIA stage. Moreover, in 1 patient (18%) with nodal upstaging, a positive node was intraoperatively identified as SLN. Comparing this protocol to the usual practice, no difference was found in terms of the operating time, conversion rate, and complication rate. Our preliminary experience suggests that sentinel lymph node detection, in association with the accurate pathological staging of cN0 patients achieved using OSNA, is safe and effective in the identification of metastasis, which is usually undetected by standard diagnostic methods.

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.

Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.

Quantifying the Value of Reduced Health Disparities: Low-Dose Computed Tomography Lung Cancer Screening of High-Risk Individuals Within the United States

OBJECTIVE

This study aimed to measure the value of increasing lung cancer screening rates for high-risk individuals and its impact on health disparities.

METHODS

The model estimated changes in health economic outcomes if low-dose computed tomography screening increased from current to 100% compliance, following clinical guidelines. Current low-dose computed tomography screening rates were estimated by income, education, and race, using 2017-2019 Behavioral Risk Factor Surveillance System data. The model contained a decision tree module to segment the population by screening outcomes and a Markov chain module to estimate cancer progression over time. Model parameters included information on survival, quality of life, and costs related to cancer diagnosis, treatment, and adverse events. Distributional cost-effectiveness analysis estimated the net monetary value from reduced health disparities-measured using quality-adjusted life expectancy-across income, education, and race groups. Outcomes were assessed over 30 years.

RESULTS

Lung cancer screening eligibility using US Preventive Services Task Force guidelines was higher for individuals with income <$15 000 (47.2%) and without a high-school education (46.1%) than individuals with income >$50 000 (16.6%) and with a college degree (13.5%), respectively. Increasing lung cancer screening to 100% compliance was cost-effective ($64 654 per quality-adjusted life-year) and produced economic value by up to $560 per person ($182.1 billion for United States overall). Up to 32.2% of the value was due to reductions in health disparities.

CONCLUSIONS

Significant value in increasing lung cancer screening rates derived from reducing health disparities. Policy makers and clinicians may not be appropriately prioritizing cancer screening if value from reducing health disparities is unconsidered.

Construction and validation of a predictive model of invasive adenocarcinoma in pure ground-glass nodules less than 2 cm in diameter

OBJECTIVES

In this study, we aimed to develop a multiparameter prediction model to improve the diagnostic accuracy of invasive adenocarcinoma in pulmonary pure glass nodules.

METHOD

We included patients with pulmonary pure glass nodules who underwent lung resection and had a clear pathology between January 2020 and January 2022 at the Qilu Hospital of Shandong University. We collected data on the clinical characteristics of the patients as well as their preoperative biomarker results and computed tomography features. Thereafter, we performed univariate and multivariate logistic regression analyses to identify independent risk factors, which were then used to develop a prediction model and nomogram. We then evaluated the recognition ability of the model via receiver operating characteristic (ROC) curve analysis and assessed its calibration ability using the Hosmer-Lemeshow test and calibration curves. Further, to assess the clinical utility of the nomogram, we performed decision curve analysis.

RESULT

We included 563 patients, comprising 174 and 389 cases of invasive and non-invasive adenocarcinoma, respectively, and identified seven independent risk factors, namely, maximum tumor diameter, age, serum amyloid level, pleural effusion sign, bronchial sign, tumor location, and lobulation. The area under the ROC curve was 0.839 (95% CI: 0.798-0.879) for the training cohort and 0.782 (95% CI: 0.706-0.858) for the validation cohort, indicating a relatively high predictive accuracy for the nomogram. Calibration curves for the prediction model also showed good calibration for both cohorts, and decision curve analysis showed that the clinical prediction model has clinical utility.

CONCLUSION

The novel nomogram thus constructed for identifying invasive adenocarcinoma in patients with isolated pulmonary pure glass nodules exhibited excellent discriminatory power, calibration capacity, and clinical utility.

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.

Ten-year trends of the clinicopathological characteristics, surgical treatments and survival outcomes of operable lung cancer patients in monocenter: a retrospective cohort study

Background

Lung cancer is one of the cancers with the highest morbidity and mortality. During the last decade, the trends of clinical characteristics, surgical treatments and survival of lung cancer patients in China have remained unclear.

Methods

All lung cancer patients operated on from 2011 to 2020 were identified in a prospectively maintained database of Sun Yat-sen University Cancer Center.

Results

A total of 7,800 lung cancer patients were included in this study. Within the past 10 years, the average age at diagnosis of the patients remained stable, the proportion of asymptomatic, female and nonsmoking patients increased, and the average tumor size decreased from 3.766 to 2.300 cm. In addition, the proportion of early stage and adenocarcinoma increased, while that of squamous cell carcinoma decreased. Among the patients, the proportion of patients having video-assisted thoracic surgery increased. More than 80% of the patients underwent lobectomy and systematic nodal dissection over the 10 years. Additionally, both the average postoperative length of stay and 1-, 3-, and 6-month postoperative mortality decreased. Moreover, the 1-, 3-, and 5-year overall survival (OS) rates of all the operable patients increased from 89.8, 73.9, and 63.8% to 99.6, 90.7, and 80.8%, respectively. The 5-year OS rates of the patients with stage I, II, and III lung cancer were 87.6, 79.9, and 59.9%, respectively, which were higher than those in other published data.

Conclusion

There were significant changes in the clinicopathological characteristics, surgical treatments and survival outcomes of the patients with operable lung cancer from 2011 to 2020.

Potential of artificial intelligence based on chest computed tomography to predict the nature of part-solid nodules

BACKGROUND

The potential of artificial intelligence (AI) to predict the nature of part-solid nodules based on chest computed tomography (CT) is still under exploration.

OBJECTIVE

To determine the potential of AI to predict the nature of part-solid nodules.

METHODS

Two hundred twenty-three patients diagnosed with part-solid nodules (241) by chest CT were retrospectively collected that were divided into benign group (104) and malignant group (137). Intraclass correlation coefficient (ICC) was used to assess the agreement in predicting malignancy, and the predictive effectiveness was compared between AI and senior radiologists. The parameters measured by AI and the size of solid components measured by senior radiologists were compared between two groups. Receiver operating characteristic (ROC) curve was chosen for calculating the Youden index of each quantitative parameter, which has statistical significance between two groups. Binary logistic regression performed on the significant indicators to suggest predictors of malignancy.

RESULTS

AI was in moderate agreement with senior radiologists (ICC = 0.686). The sensitivity, specificity and accuracy of two groups were close (p > 0.05). The longest diameter, volume and mean CT attenuation value and the largest diameter of solid components between benign and malignant groups were different significantly (p < 0.001). Logistic regression analysis showed that the longest diameter and mean CT attenuation value and the largest diameter of solid components were indicators for malignant part-solid nodules, the threshold of which were 9.45 mm, 425.0 HU and 3.45 mm, respectively.

CONCLUSION

Potential of quantitative parameter measured by AI to predict malignant part-solid nodules can provide a certain value for the clinical management.

The natural growth history of persistent pulmonary subsolid nodules: Radiology, genetics, and clinical management

The high detection rate of pulmonary subsolid nodules (SSN) is an increasingly crucial clinical issue due to the increased number of screening tests and the growing popularity of low-dose computed tomography (LDCT). The persistence of SSN strongly suggests the possibility of malignancy. Guidelines have been published over the past few years and guide the optimal management of SSNs, but many remain controversial and confusing for clinicians. Therefore, in-depth research on the natural growth history of persistent pulmonary SSN can help provide evidence-based medical recommendations for nodule management. In this review, we briefly describe the differential diagnosis, growth patterns and rates, genetic characteristics, and factors that influence the growth of persistent SSN. With the advancement of radiomics and artificial intelligence (AI) technology, individualized evaluation of SSN becomes possible. These technologies together with liquid biopsy, will promote the transformation of current diagnosis and follow-up strategies and provide significant progress in the precise management of subsolid nodules in the early stage of lung cancer.

Feasibility of Using High-Resolution Computed Tomography Features for Invasiveness Differentiation of Malignant Nodules Manifesting as Ground-Glass Nodules

Ground-glass nodule (GGN)-like adenocarcinoma is a special subtype of lung cancer. The invasiveness of the nodule correlates well with the patient’s prognosis. This study aimed to establish a radiomic model for invasiveness differentiation of malignant nodules manifesting as ground glass on high-resolution computed tomography (HRCT). Between January 2014 and July 2019, 276 pulmonary nodules manifesting as GGNs on preoperative HRCTs, whose histological results were available, were collected. The nodules were randomly classified into training (n = 221) and independent testing (n = 55) cohorts. Three logistic models using features derived from HRCT were fit in the training cohort and validated in both aforementioned cohorts for invasive adenocarcinoma and preinvasive-minimally invasive adenocarcinoma (MIA) differentiation. The model with the best performance was presented as a nomogram and was validated using a calibration curve before performing a decision curve analysis. The benefit of using the proposed model was also shown by groups of management strategies recommended by The Fleischner Society. The combined model showed the best differentiation performance (area under the curve (AUC), training set = 0.89, and testing set = 0.92). The quantitative texture model showed better performance (AUC, training set = 0.87, and testing set = 0.91) than the semantic model (AUC, training set = 0.83, and testing set = 0.79). Of the 94 type 2 nodules that were IACs, 66 were identified by this model. Models using features derived from imaging are effective for differentiating between preinvasive-MIA and IACs among lung adenocarcinomas appearing as GGNs on CT images.

Development of Deep Learning-based Automatic Scan Range Setting Model for Lung Cancer Screening Low-dose CT Imaging

RATIONALE AND OBJECTIVES

To develop an automatic setting of a deep learning-based system for detecting low-dose computed tomography (CT) lung cancer screening scan range and compare its efficiency with the radiographer's performance.

MATERIALS AND METHODS

This retrospective study was performed using 1984 lung cancer screening low-dose CT scans obtained between November 2019 and May 2020. Among 1984 CT scans, 600 CT scans were considered suitable for an observational study to explore the relationship between the scout landmarks and the actual lung boundaries. Further, 1144 CT scans data set was used for the development of a deep learning-based algorithm. This data set was split into an 8:2 ratio divided into a training set (80%, n = 915) and a validation set (20%, n = 229). The performance of the deep learning algorithm was evaluated in the test set (n = 240) using actual lung boundaries and radiographers' scan ranges.

RESULTS

The mean differences between the upper and lower boundaries of the deep learning-based algorithm and the actual lung boundaries were 4.72 ± 3.15 mm and 16.50 ± 14.06 mm, respectively. The accuracy and over-scanning of the scan ranges generated by the system were 97.08% (233/240) and 0% (0/240) for the upper boundary, and 96.25% (231/240) and 29.58% (71/240) for the lower boundary.

CONCLUSION

The developed deep learning-based algorithm system can effectively predict lung cancer screening low-dose CT scan range with high accuracy using only the frontal scout.

Radiomics Signature to Predict Prognosis in Early-Stage Lung Adenocarcinoma (≤3 cm) Patients with No Lymph Node Metastasis

Objectives: To investigate the predictive ability of radiomics signature to predict the prognosis of early-stage primary lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I). Materials and Methods: This study included consecutive patients with lung adenocarcinoma (≤3 cm) with no lymph node metastasis (pathological stage I) and divided them into two groups: good prognosis group and poor prognosis group. The association between the radiomics signature and prognosis was explored. An integrative radiomics model was constructed to demonstrate the value of the radiomics signature for individualized prognostic prediction. Results: Six radiomics features were significantly different between the two prognosis groups and were used to construct a radiomics model. On the training and test sets, the area under the receiver operating characteristic curve value of the radiomics model in discriminating between the two groups were 0.946 and 0.888, respectively, and those of the pathological model were 0.761 and 0.798, respectively. A radiomics nomogram combining sex, tumor size and rad-score was built. Conclusion: The radiomics signature has potential utility in estimating the prognosis of patients with pathological stage I lung adenocarcinoma (≤3 cm), potentially enabling a step forward in precision medicine.

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.

Differences in detection patterns, characteristics, and outcomes of central and peripheral lung cancers in low-dose computed tomography screening

Background

Although low-dose computed tomography (LDCT) screening is known to be effective for the detection of lung cancers localized in peripheral lung regions at a curable stage, limited data is available regarding the characteristics and outcomes of central lung cancers diagnosed in a screening cohort. This study aimed to determine whether LDCT screening could effectively detect central lung cancers at an early stage and offer survival benefits.

Methods

We analyzed 52,615 adults who underwent lung cancer screening with LDCT between May 2003 and Dec 2019 at a tertiary center in South Korea. Characteristics and outcomes of those diagnosed with lung cancer, stratified by screen-detection status and cancer location, were evaluated.

Results

A total of 352 individuals (281 screen-detected, 71 non-screen-detected) were diagnosed with lung cancer. Compared to screen-detected cancers, non-screen-detected cancers tended to be centrally-located (11.4% vs. 64.8%, P<0.001). Most non-screen-detected central cancers (89.1%) had a negative result on prior LDCT screening. Multivariable regression analyses revealed that for peripheral cancers, screen-detection was associated with a significantly lower probability of diagnosis at an advanced stage [III/IV, odds ratio (OR) =0.15, 95% confidence interval (CI): 0.05-0.45] and mortality [hazard ratio (HR) =0.33, 95% CI: 0.13-0.84]; however, the association was insignificant for central cancers. For screen-detected cancers, central location, compared to peripheral location, was significantly associated with a higher risk of diagnosis at an advanced stage (OR =20.83, 95% CI: 6.67-64.98) and mortality (HR =4.98, 95% CI: 2.26-10.97).

Conclusions

Unlike for peripheral cancers, LDCT screening did not demonstrate an improvement in outcomes of central lung cancers, indicating an important limitation of LDCT screening and the need for developing novel modalities to screen and treat central lung cancer.

Impact of Smoking Status on Lung Cancer Characteristics and Mortality Rates between Screened and Non-Screened Lung Cancer Cohorts: Real-World Knowledge Translation and Education

This was a retrospective hospital-based cohort study of participants diagnosed with lung cancer in the lung cancer register database, and our goal was to evaluate the impact of smoking and screening status on lung cancer characteristics and clinical outcomes. According to the hospital-based lung cancer register database, a total of 2883 lung cancers were diagnosed in 2883 patients between January 2007 and September 2017, which were divided into four groups according to smoking and screening status. A comparison was performed in terms of clinical characteristics and outcomes of lung cancer between the four groups. For non-smokers, age, gender, screened status, tumor size, targeted therapy, and curative surgery were independent prognostic factors of overall survival for lung cancer subjects. However, screened status and gender were not significant prognostic factors for lung cancer survival in smokers with lung cancer. For the non-smoker group, about 4.9% of lung cancer subjects (N = 81) were detected by screening. However, only 0.97% of lung cancer subjects (N = 12) were detected by screening in smokers. This could be attributed to smokers' negative attitudes and low socioeconomic status preventing LDCT lung cancer screening. In summary, our real-world data suggest that effectively encouraging smokers to be more willing to participate in lung cancer screening programs with screening allowance and educational training in the future is an important issue.

Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: a multi-parameter prediction model

Background

Patients with consistent lung pure ground-glass nodules (pGGNs) have a high incidence of lung adenocarcinoma that can be classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Regular follow-up is recommended for AIS and MIA, while surgical resection should be considered for IAC. This study sought to develop a multi-parameter prediction model to increase the diagnostic accuracy in discriminating between IAC and AIS or MIA.

Methods

The training data set comprised consecutive patients with lung pGGNs who underwent resection from January to December 2017 at the Zhongshan Hospital. Of the 370 resected pGGNs, 344 were pathologically confirmed to be AIS, MIA, or IAC and were included in the study. The 26 benign pGGNs were excluded. We compared differences in the clinical features (e.g., age and gender), the content of serum tumor biomarkers, the computed tomography (CT) parameters (e.g., nodule size and the maximal CT value), and the morphologic characteristics of nodules (e.g., lobulation, spiculation, pleura indentation, vacuole sign, and normal vessel penetration or abnormal vessel) between the pathological subtypes of AIS, MIA, and IAC. An abnormal vessel was defined as “vessel curve” or “vessel enlargement”. Statistical analyses were performed using the chi-square test, analysis of variance (ANOVA), and rank test. The IAC prediction model was constructed via a multivariate logistical regression. Our prediction model for lung pGGNs was further validated in a data set comprising consecutive patients from multiple medical centers in China from July to December 2018. In total, 345 resected pGGNs were pathologically diagnosed as lung adenocarcinoma in the validation data set.

Results

In the training data set, patients with pGGNs ≥10 mm in size had a high incidence (74.5%) of IAC. The maximal CT value of IAC [–416.1±121.2 Hounsfield unit (HU)] was much higher than that of MIA (–507.7±138.0 HU) and AIS (–602.6±93.3 HU) (P<0.001). IAC was more common in pGGNs that displayed any of the following CT manifestations: lobulation, spiculation, pleura indentation, vacuole sign, and vessel abnormality. The IAC prediction model was constructed using the parameters that were assessed as risk factors (i.e., the nodule size, maximal CT value, and CT signs). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of this model for diagnosing IAC was 0.910, which was higher than that of the AUC for nodule size alone (0.891) or the AUC for the maximal CT value alone (0.807) (P<0.05, respectively). A multicenter validation data set was used to validate the performance of our prediction model in diagnosing IAC, and our model was found to have an AUC of 0.883, which was higher than that of the AUC of 0.827 for the module size alone model or the AUC of 0.791 for the maximal CT value alone model (P<0.05, respectively).

Conclusions

Our multi-parameter prediction model was more accurate at diagnosing IAC than models that used only nodule size or the maximal CT value alone. Thus, it is an efficient tool for identifying the IAC of malignant pGGNs and deciding if surgery is needed.

Cost Utility Analysis of a Pilot Study for the Korean Lung Cancer Screening Project

Purpose

The aim of this study was to evaluate the cost utility of a pilot study of Korean Lung Cancer Screening Project.

Materials and Methods

We constructed a Markov model consisting of 26 states based on the natural history of lung cancer according to the Surveillance, Epidemiology, and End Results summary stage (localized, regional, distant). In the base case, people aged 55–74 years were under consideration for annual screening. Costs and quality-adjusted life years were simulated to calculate the incremental cost utility ratio. Sensitivity analyses were performed on the uncertainty associated with screening target ages, stage distribution, cost, utility, mortality, screening duration, and discount rate.

Results

The base case (US$25,383 per quality-adjusted life year gained) was cost-effective compared to the scenario of no screening and acceptable considering a willingness-to-pay threshold of US$27,000 per quality-adjusted life years gained. In terms of the target age of screening, the age between 60 and 74 years was the most cost-effective. Lung cancer screening was still cost-effective in the sensitivity analyses on the cost for treatment, utility, mortality, screening duration, and less than 5% discount rates, although the result was sensitive to a rise in positive rates or variation of stage distribution.

Conclusion

Our results showed the cost-effectiveness of annual low-dose computed tomography screening for lung cancer in high-risk populations.

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

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.

Performance and educational training of radiographers in lung nodule or mass detection: Retrospective comparison with different deep learning algorithms

The aim of this investigation was to compare the diagnostic performance of radiographers and deep learning algorithms in pulmonary nodule/mass detection on chest radiograph.A test set of 100 chest radiographs containing 53 cases with no pathology (normal) and 47 abnormal cases (pulmonary nodules/masses) independently interpreted by 6 trained radiographers and deep learning algorithems in a random order. The diagnostic performances of both deep learning algorithms and trained radiographers for pulmonary nodules/masses detection were compared.QUIBIM Chest X-ray Classifier, a deep learning through mass algorithm that performs superiorly to practicing radiographers in the detection of pulmonary nodules/masses (AUCMass: 0.916 vs AUCTrained radiographer: 0.778, P < .001). In addition, heat-map algorithm could automatically detect and localize pulmonary nodules/masses in chest radiographs with high specificity.In conclusion, the deep-learning based computer-aided diagnosis system through 4 algorithms could potentially assist trained radiographers by increasing the confidence and access to chest radiograph interpretation in the age of digital age with the growing demand of medical imaging usage and radiologist burnout.

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.

Association of cancer screening and residing in a coal-polluted East Asian region with overall survival of lung cancer patients: a retrospective cohort study

Lung cancer is the leading cause of cancer death worldwide. The Xuanwei-Fuyuan (XF) region of Yunnan, China has a high incidence of lung cancer from coal-related pollution. Effort to raise public awareness screening for lung cancer has been ongoing. We retrospectively analyzed overall survival (OS) of lung cancer patients of a tertiary cancer center in Yunnan to investigate screening and regional residential status as predictive factors. Consecutive cases of newly diagnosed lung cancer were reviewed. The lung cancer cases diagnosed by screening were more likely to be early-staged and treated by surgery than those diagnosed not by screening. In patients diagnosed not by screening, XF residential status was a significant predictor of improved OS. Frailty model detected significant heterogeneity associated with region of residence in unscreened patients. Potential biases associated with screening were examined by Monte Carlo simulations and sensitivity analyses. Focused effort in cancer screening and increased public awareness of pollution-related lung cancer in XF might have led to early diagnosis and improved OS, and increased investment in health care resources in high risk areas may have produced additional unobserved factors that underlay the association of XF residential status with improved OS in patients diagnosed not by screening.