Assessing the efficiency of eligibility criteria for low-dose computed tomography lung screening in China according to current guidelines.

BACKGROUND: Evidence from observational studies indicates that lung cancer screening (LCS) guidelines with high rates of lung cancer (LC) underdiagnosis, and although current screening guidelines have been updated and eligibility criteria for screening have been expanded, there are no studies comparing the efficiency of LCS guidelines in Chinese population. METHODS: Between 2005 and 2022, 31,394 asymptomatic individuals were screened using low-dose computed tomography (LDCT) at our institution. Demographic data and relevant LC risk factors were collected. The efficiency of the LCS for each guideline criteria was expressed as the efficiency ratio (ER). The inclusion rates, eligibility rates, LC detection rates, and ER based on the different eligibility criteria of the four guidelines were comparatively analyzed. The four guidelines were as follows: China guideline for the screening and early detection of lung cancer (CGSL), the National Comprehensive Cancer Network (NCCN), the United States Preventive Services Task Force (USPSTF), and International Early Lung Cancer Action Program (I-ELCAP). RESULTS: Of 31,394 participants, 298 (155 women, 143 men) were diagnosed with LC. For CGSL, NCCN, USPSTF, and I-ELCAP guidelines, the eligibility rates for guidelines were 13.92%, 6.97%, 6.81%, and 53.46%; ERe for eligibility criteria were 1.46%, 1.64%, 1.51%, and 1.13%, respectively; and for the inclusion rates, they were 19.0%, 9.5%, 9.3%, and 73.0%, respectively. LCs which met the screening criteria of CGSL, NCCN, USPSTF, and I-ELCAP guidelines were 29.2%, 16.4%, 14.8%, and 86.6%, respectively. The age and smoking criteria for CGSL were stricter, hence resulting in lower rates of LC meeting the screening criteria. The CGSL, NCCN, and USPSTF guidelines showed the highest underdiagnosis in the 45-49 age group (17.4%), while the I-ELCAP guideline displayed the highest missed diagnosis rate (3.0%) in the 35-39 age group. Males and females significantly differed in eligibility based on the criteria of the four guidelines (P < 0.001). CONCLUSIONS: The I-ELCAP guideline has the highest eligibility rate for both males and females. But its actual efficiency ratio for those deemed eligible by the guideline was the lowest. Whereas the NCCN guideline has the highest ERe value for those deemed eligible by the guideline.

Radiomic-signature changes after early treatment improve the prediction of progression-free survival in patients with advanced anaplastic lymphoma kinase-positive non-small cell lung cancer.

BACKGROUND: The prognosis of lung cancer varies widely, even in cases wherein the tumor stage, genetic mutation, and treatment regimens are the same. Thus, an effective means for risk stratification of patients with lung cancer is needed. PURPOSE: To develop and validate a combined model for predicting progression-free survival and risk stratification in patients with advanced anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) treated with ensartinib. MATERIAL AND METHODS: We analyzed 203 tumor lesions in 114 patients and evaluated average radiomic feature measures from all lesions at baseline and changes in these features after early treatment (Δradiomic features). Combined models were developed by integrating clinical with radiomic features. The prediction performance and clinical value of the proposed models were evaluated using receiver operating characteristic analysis, calibration curve, decision curve analysis (DCA), and Kaplan-Meier survival analysis. RESULTS: Both the baseline and delta combined models achieved predictive efficacy with a high area under the curve. The calibration curve and DCA indicated the high accuracy and clinical usefulness of the combined models for tumor progression prediction. In the Kaplan-Meier analysis, the delta and baseline combined models, Δradiomic signature, and two selected clinical features could distinguish patients with a higher progression risk within 42 weeks. The delta combined model had the best performance. CONCLUSION: The combination of clinical and radiomic features provided a prognostic value for survival and progression in patients with NSCLC receiving ensartinib. Radiomic-signature changes after early treatment could be more valuable than those at baseline alone.

Long-term follow-up of persistent pulmonary pure ground-glass nodules with deep learning-assisted nodule segmentation.

OBJECTIVE: To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation. METHODS: Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth. RESULTS: The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth. CONCLUSIONS: Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow. KEY POINTS: • The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339-8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290-38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116-2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.

Different Clinicopathologic and Computed Tomography Imaging Characteristics of Primary and Acquired EGFR T790M Mutations in Patients with Non-Small-Cell Lung Cancer.

PURPOSE: Although patients with primary and acquired epidermal growth factor receptor (EGFR) T790M positive non-small-cell lung cancer (NSCLC) respond to osimertinib treatment, the optimal treatment strategy differs for these two groups of patients. This study aimed to compare the clinicopathologic and computed tomography (CT) imaging characteristics between primary and acquired EGFR T790M mutations in patients with NSCLC before treatment. PATIENTS AND METHODS: We enrolled two groups of patients with primary or acquired EGFR T790M mutation NSCLC (n = 103 per group) from January 2012 to December 2019. We analyzed their clinicopathologic and CT characteristics and differences between the groups. The groups were further categorized based on 21L858R and 19del to exclude the effect of coexistent mutations. RESULTS: Primary, compared to acquired, T790M mutation tends to coexist with 21L858R (P < 0.001), exhibiting earlier tumor stage (P < 0.001), higher differentiation (P = 0.029), higher proportion of lepidic subtype adenocarcinoma (P < 0.001), and significant associations with some CT features (multiple primary lung cancers, ground-glass opacity, air bronchogram, and vacuole sign [all P < 0.001]). The combined model, composed of clinicopathologic and conventional CT signature and CT-radiomic signature, showed good discriminative ability with the area under the receiver operating characteristic curve 0.90 and 0.91 in the training and validation datasets, respectively. The T790M mutation contributed to these differences independently of coexistent mutations. CONCLUSION: We identified clinicopathologic and CT imaging differences between primary and acquired T790M mutations. These findings provide insights into developing future personalized T790M mutation status-based theranostic strategies.

Association of anaplastic lymphoma kinase variants and alterations with ensartinib response duration in non-small cell lung cancer.

BACKGROUND: Here, we aimed to assess the association of ALK variants and alterations with ensartinib response duration in NSCLC, and explore the potential value of computed tomography (CT) radiomic features in predicting progression-free survival (PFS). METHODS: We enrolled 88 patients with identified ALK variant NSCLC in a multicenter phase 2 trial, and assessed the impact of ALK variants and secondary ALK alterations on the clinical outcome (response duration) of patients receiving ensartinib. We also established a multifactorial model of clinicopathological and quantitative CT radiomic features to predict PFS and risk stratification. Kaplan-Meier analysis was conducted to identify risk factors for tumor progression. RESULTS: Univariate analysis indicated a statistical difference (p = 0.035) in PFS among ALK variants in three classifications (V1, V3, and other variants). Secondary ALK alterations were adversely associated with PFS both in univariate (p = 0.008) and multivariate (p = 0.04) analyses and could identify patients at high risk for early progression in the Kaplan-Meier analysis (p = 0.002). Additionally, response duration to crizotinib <1 year and liver metastasis were adversely associated with PFS. The combined model, composed of clinicopathological signature and CT radiomic signature, showed good prediction ability with the area under the receiver operating characteristic curve being 0.85, and 0.89 in the training and validation dataset respectively. CONCLUSIONS: Our study showed that secondary ALK alterations were adversely associated with ensartinib efficacy, and that ALK variants might not correlate with PFS. The quantitative radiomic signature provided added prognostic prediction value to the clinicopathological features.

MRI radiomic signature predicts intracranial progression-free survival in patients with brain metastases of ALK-positive non-small cell lung cancer.

BACKGROUND: Intracranial progression is considered an important cause of treatment failure in anaplastic lymphoma kinase (ALK)-positive non-small cell lung cancer (NSCLC) patients. Recent advances in targeted therapy and radiomics have generated considerable interest for the exploration of prognostic imaging biomarkers to predict the clinical course. Here, we developed a magnetic resonance imaging (MRI) radiomic signature that can stratify survival and intracranial progression. METHODS: We analyzed 87 brain metastatic lesions in 24 ALK-positive NSCLC patients undergoing ALK-inhibitor ensartinib therapy and divided them into training (n=61) and validation (n=26) sets. Radiomic features were extracted and screened from contrast-enhanced MR images. Combined with these selected features, the Rad-score was calculated with multivariate logistic regression. The predictive model and Rad-score performance were assessed in the training set and validated in the validation set; decision curve analysis was performed with the combined training and validation sets to estimate Rad-score's patient-stratification ability. RESULTS: The prediction model constructed with nine selected radiomic features could predict intracranial progression within 51 weeks (AUC =0.84 and 0.85 in the training and validation sets, respectively), while clinical and regular MRI characteristics were independent of progression (P>0.05). The decision-curve analysis showed that the radiomic prediction model was clinically useful. The Kaplan-Meier analysis showed that the progression-free survival (PFS) difference between the high- and low-risk groups distinguished by the Rad-score was significant (P=0.017). CONCLUSIONS: Radiomics may provide prognostic information and improve pretreatment risk stratification in ALK-positive NSCLC patients with brain metastases undergoing ensartinib treatment, allowing follow-up and treatment to be tailored to the patient's individual risk profile.

First measurements of the radiation dose on the lunar surface.

Human exploration of the Moon is associated with substantial risks to astronauts from space radiation. On the surface of the Moon, this consists of the chronic exposure to galactic cosmic rays and sporadic solar particle events. The interaction of this radiation field with the lunar soil leads to a third component that consists of neutral particles, i.e., neutrons and gamma radiation. The Lunar Lander Neutrons and Dosimetry experiment aboard China's Chang'E 4 lander has made the first ever measurements of the radiation exposure to both charged and neutral particles on the lunar surface. We measured an average total absorbed dose rate in silicon of 13.2 ± 1 µGy/hour and a neutral particle dose rate of 3.1 ± 0.5 µGy/hour.

Lung cancer imaging methods in China from 2005 to 2014: A national, multicenter study.

BACKGROUND: The study was conducted to examine changes in diagnostic and staging imaging methods for lung cancer in China over a 10-year period and to determine the relationships between such changes and socioeconomic development. METHODS: This was a hospital-based, nationwide, multicenter retrospective study of primary lung cancer cases. The data were extracted from the 10-year primary lung cancer databases at eight tertiary hospitals from various geographic areas in China. The chi-squared test was used to assess the differences and the Cochran-Armitage trend test was used to estimate the trends of changes. RESULTS: A total of 7184 lung cancer cases were analyzed. Over the 10-year period, the utilization ratio of diagnostic imaging methods, such as chest computed tomography (CT) and chest magnetic resonance imaging (MRI), increased from 65.79% to 81.42% and from 0.73% to 1.96%, respectively, while the utilization ratio of chest X-ray declined from 50.15% to 30.93%. Staging imaging methods, such as positron emission tomography-CT, neck ultrasound, brain MRI, bone scintigraphy, and bone MRI increased from 0.73% to 9.29%, 22.95% to 47.92%, 8.77% to 40.71%, 42.40% to 62.22%, and 0.88% to 4.65%, respectively; abdominal ultrasound declined from 83.33% to 59.9%. These trends were more notable in less developed areas than in areas with substantial economic development. CONCLUSION: Overall, chest CT was the most common radiological diagnostic method for lung cancer in China. Imaging methods for lung cancer tend to be used in a diverse, rational, and regionally balanced manner.