Long-term cancer risk associated with lung nodules observed on low-dose screening CT scans

Low-Dose Computed Tomography Screening in Relatives With a Family History of Lung Cancer

INTRODUCTION

The role of a family history of lung cancer (LCFH) in screening using low-dose computed tomography (LDCT) has not been prospectively investigated with long-term follow-up.

METHODS

A multicenter prospective study with up to three rounds of annual LDCT screening was conducted to determine the detection rate of lung cancer (LC) in asymptomatic first- or second-degree relatives of LCFH.

RESULTS

From 2007 to 2011, there were 1102 participants enrolled, including 805 and 297 from simplex and multiplex families (MFs), respectively (54.2% women and 70.0% never-smokers). The last follow-up date was May 5, 2021. The overall LC detection rate was 4.5% (50 of 1102). The detection rate in MF was 9.4% (19 of 202) and 4.4% (4 of 91) in never-smokers and in those who smoked, respectively. The corresponding rates for simplex families were 3.7% (21 of 569) and 2.7% (6 of 223), respectively. Of these, 68.0% and 22.0% of cases with stage I and IV diseases, respectively. LC diagnoses within a 3-year interval from the initial screening tend to be younger, have a higher detection rate, and have stage I disease; thereafter, more stage III-IV disease and 66.7% (16 of 24) with negative or semipositive nodules in initial computed tomography scans. Within the 6-year interval, only maternal (modified rate ratio = 4.46, 95% confidence interval: 2.32-8.56) or maternal relative history of LC (modified rate ratio = 5.41, 95% confidence interval: 2.84-10.30) increased the risk of LC.

CONCLUSIONS

LCFH is a risk factor for LC and is increased with MF history, among never-smokers, younger adults, and those with maternal relatives with LC. Randomized controlled trials are needed to confirm the mortality benefit of LDCT screening in those with LCFH.

Recalibration of a Deep Learning Model for Low-Dose Computed Tomographic Images to Inform Lung Cancer Screening Intervals

Importance

Annual low-dose computed tomographic (LDCT) screening reduces lung cancer mortality, but harms could be reduced and cost-effectiveness improved by reusing the LDCT image in conjunction with deep learning or statistical models to identify low-risk individuals for biennial screening.

Objective

To identify low-risk individuals in the National Lung Screening Trial (NLST) and estimate, had they been assigned a biennial screening, how many lung cancers would have been delayed 1 year in diagnosis.

Design, Setting, and Participants

This diagnostic study included participants with a presumed nonmalignant lung nodule in the NLST between January 1, 2002, and December 31, 2004, with follow-up completed on December 31, 2009. Data were analyzed for this study from September 11, 2019, to March 15, 2022.

Exposures

An externally validated deep learning algorithm that predicts malignancy in current lung nodules using LDCT images (Lung Cancer Prediction Convolutional Neural Network [LCP-CNN]; Optellum Ltd) was recalibrated to predict 1-year lung cancer detection by LDCT for presumed nonmalignant nodules. Individuals with presumed nonmalignant lung nodules were hypothetically assigned annual vs biennial screening based on the recalibrated LCP-CNN model, Lung Cancer Risk Assessment Tool (LCRAT + CT [a statistical model combining individual risk factors and LDCT image features]), and the American College of Radiology recommendations for lung nodules, version 1.1 (Lung-RADS).

Main Outcomes and Measures

Primary outcomes included model prediction performance, the absolute risk of a 1-year delay in cancer diagnosis, and the proportion of people without lung cancer assigned a biennial screening interval vs the proportion of cancer diagnoses delayed.

Results

The study included 10 831 LDCT images from patients with presumed nonmalignant lung nodules (58.7% men; mean [SD] age, 61.9 [5.0] years), of whom 195 were diagnosed with lung cancer from the subsequent screen. The recalibrated LCP-CNN had substantially higher area under the curve (0.87) than LCRAT + CT (0.79) or Lung-RADS (0.69) to predict 1-year lung cancer risk (P < .001). If 66% of screens with nodules were assigned to biennial screening, the absolute risk of a 1-year delay in cancer diagnosis would have been lower for recalibrated LCP-CNN (0.28%) than LCRAT + CT (0.60%; P = .001) or Lung-RADS (0.97%; P < .001). To delay only 10% of cancer diagnoses at 1 year, more people would have been safely assigned biennial screening under LCP-CNN than LCRAT + CT (66.4% vs 40.3%; P < .001).

Conclusions and Relevance

In this diagnostic study evaluating models of lung cancer risk, a recalibrated deep learning algorithm was most predictive of 1-year lung cancer risk and had least risk of 1-year delay in cancer diagnosis among people assigned biennial screening. Deep learning algorithms could prioritize people for workup of suspicious nodules and decrease screening intensity for people with low-risk nodules, which may be vital for implementation in health care systems.

Potential Impact of Criteria Modifications on Race and Sex Disparities in Eligibility for Lung Cancer Screening

INTRODUCTION

Low-dose computed tomography (LDCT) screening reduces lung cancer mortality, but current eligibility criteria underestimate risk in women and racial minorities. We evaluated the impact of screening criteria modifications on LDCT eligibility and lung cancer detection.

METHODS

Using data from a Lung Nodule Program, we compared persons eligible for LDCT by the following: U.S. Preventive Services Task Force (USPSTF) 2013 criteria (55-80 y, ≥30 pack-years of smoking, and ≤15 y since cessation); USPSTF2021 criteria (50-80 y, ≥20 pack-years of smoking, and ≤15 y since cessation); quit duration expanded to less than or equal to 25 years (USPSTF2021-QD25); reducing the pack-years of smoking to more than or equal to 10 years (USPSTF2021-PY10); and both (USPSTF2021-QD25-PY10). We compare across groups using the chi-square test or analysis of variance.

RESULTS

The 17,421 individuals analyzed were of 56% female sex, 69% white, 28% black; 13% met USPSTF2013 criteria; 17% USPSTF2021; 18% USPSTF2021-QD25; 19% USPSTF2021-PY10; and 21% USPSTF2021-QD25-PY10. Additional eligible individuals by USPSTF2021 (n = 682) and USPSTF2021-QD25-PY10 (n = 1402) were 27% and 29% black, both significantly higher than USPSTF2013 (17%, p < 0.0001). These additional eligible individuals were 55% (USPSTF2021) and 55% (USPSTF2021-QD25-PY10) of female sex, compared with 48% by USPSTF2013 (p < 0.05). Of 1243 persons (7.1%) with lung cancer, 22% were screening eligible by USPSTF13. USPSTF2021-QD25-PY10 increased the total number of persons with lung cancer by 37%. These additional individuals with lung cancer were of 57% female sex (versus 48% with USPSTF2013, p = 0.0476) and 24% black (versus 20% with USPSTF2013, p = 0.3367).

CONCLUSIONS

Expansion of LDCT screening eligibility criteria to allow longer quit duration and fewer pack-years of exposure enriches the screening-eligible population for women and black persons.

Management of Lung Cancer Screening Results Based on Individual Prediction of Current and Future Lung Cancer Risks

OBJECTIVES

We propose a risk-tailored approach for management of lung cancer screening results. This approach incorporates individual risk factors and low-dose computed tomography (LDCT) image features into calculations of immediate and next-screen (1-y) risks of lung cancer detection, which in turn can recommend short-interval imaging or 1-year or 2-year screening intervals.

METHODS

We first extended the "LCRAT+CT" individualized risk calculator to predict lung cancer risk after either a negative or abnormal LDCT screen result. To develop the abnormal screen portion, we analyzed 18,129 abnormal LDCT results in the National Lung Screening Trial (NLST), including lung cancers detected immediately (n = 649) or at the next screen (n = 235). We estimated the potential impact of this approach among NLST participants with any screen result (negative or abnormal).

RESULTS

Applying the draft National Health Service (NHS) England protocol for lung screening to NLST participants referred 76% of participants to a 2-year interval, but delayed diagnosis for 40% of detectable cancers. The Lung Cancer Risk Assessment Tool+Computed Tomography (LCRAT+CT) risk model, with a threshold of less than 0.95% cumulative lung cancer risk, would also refer 76% of participants to a 2-year interval, but would delay diagnosis for only 30% of cancers, a 25% reduction versus the NHS protocol. Alternatively, LCRAT+CT, with a threshold of less than 1.7% cumulative lung cancer risk, would also delay diagnosis for 40% of cancers, but would refer 85% of participants for a 2-year interval, a 38% further reduction in the number of required 1-year screens beyond the NHS protocol.

CONCLUSIONS

Using individualized risk models to determine management in lung cancer screening could substantially reduce the number of screens or increase early detection.

Cost-effectiveness and health impact of lung cancer screening with low-dose computed tomography for never smokers in Japan and the United States: a modelling study

BACKGROUND

Never smokers in Asia have a higher incidence of lung cancer than in Europe and North America. We aimed to assess the cost-effectiveness of lung cancer screening with low-dose computed tomography (LDCT) for never smokers in Japan and the United States.

METHODS

We developed a state-transition model for three strategies: LDCT, chest X-ray (CXR), and no screening, using a healthcare payer perspective over a lifetime horizon. Sensitivity analyses were also performed. Main outcomes were costs, quality-adjusted life-years (QALYs), life expectancy life-years (LYs), incremental cost-effectiveness ratios (ICERs), and deaths from lung cancer. The willingness-to-pay level was US$100,000 per QALY gained.

RESULTS

LDCT yielded the greatest benefits with the lowest cost in Japan, but the ICERs of LDCT compared with CXR were US$3,001,304 per QALY gained for American men and US$2,097,969 per QALY gained for American women. Cost-effectiveness was sensitive to the incidence of lung cancer. Probabilistic sensitivity analyses demonstrated that LDCT was cost-effective 99.3-99.7% for Japanese, no screening was cost-effective 77.7% for American men, and CXR was cost-effective 93.2% for American women. Compared with CXR, LDCT has the cumulative lifetime potential for 60-year-old Japanese to save US$117 billion, increase 2,339,349 QALYs and 3,020,102 LYs, and reduce 224,749 deaths, and the potential for 60-year-old Americans to cost US$120 billion, increase 48,651 QALYs and 67,988 LYs, and reduce 2,309 deaths.

CONCLUSIONS

This modelling study suggests that LDCT screening for never smokers has the greatest benefits and cost savings in Japan, but is not cost-effective in the United States. Assessing the risk of lung cancer in never smokers is important for introducing population-based LDCT screening.

Lung cancer risk following previous abnormal chest radiographs: A 27-year follow-up study of a Chinese lung screening cohort

Background

Chest radiograph (CXR) is still one of the most commonly used diagnostic tools for chest diseases. In this cohort study, we attempted to investigate the magnitude and temporal pattern of lung cancer risk following abnormal CXR findings.

Methods

We conducted an extended follow‐up of an occupational screening cohort in Yunnan, China. The associations between abnormal CXR results from baseline screening, the first four consecutive rounds of CXR screening, all previous rounds of screening and lung cancer risk were analyzed using time‐varying coefficient Cox regression model. The associations of lung cancer risk and previous CXR‐screening results according to histology were also considered. Sensitivity analyses were conducted to assess the robustness of the previous abnormal CXR findings on subsequent lung cancer risk.

Results

Abnormal CXR findings were associated with a significantly increased lung cancer risk. This relative hazard significantly decreased over time. Compared to negative screening results, the adjusted hazard ratios (HR) of baseline abnormal CXR results, and at least one abnormal result in the first four consecutive screening rounds during the first 5 years of follow‐up were 17.06 (95% CI: 11.74–24.79) and 13.77 (95%: 9.58–17.79), respectively. This significantly increased lung cancer risk continued over the next 5 years. These associations were stronger for persistent abnormal findings, and abnormal findings identified in recent screening rounds.

Conclusions

The increased risk was significant for both squamous cell carcinoma and adenocarcinoma. Although decreased over time, an increased lung cancer risk relative to abnormal CXR findings can continue for 10 years.

Long-term Lung Cancer Risk Associated with Sputum Atypia: A 27-Year Follow-up Study of an Occupational Lung Screening Cohort in Yunnan, China

BACKGROUND

Sputum cytologic atypia is associated with increased lung cancer risk. However, little is known about the long-term magnitude and temporal trend of this risk.

METHODS

An extended follow-up was conducted in a prospective screening cohort among occupational tin miners in Yunnan, China. Sputum samples were collected prospectively at baseline and 7 annual screenings since enrollment. The associations between sputum cytologic results from baseline screening, the first 4 consecutive rounds of sputum screening, and lung cancer risk were analyzed by time-varying covariate Cox regression model.

RESULTS

A moderate or worse cytologic result was associated with a significantly increased lung cancer risk. This relative hazard significantly decreased over time. Compared with negative screening results, the adjusted hazard ratios of baseline-moderate or worse atypia, at least one moderate or worse atypia in the first 4 consecutive screening rounds during the first 10 years of follow-up were 3.11 [95% confidence interval (CI): 2.37-4.07], 3.25 (95% CI: 2.33-4.54) respectively. This association was stronger for persistent atypia (adjusted hazard ratio = 17.55, 95% CI: 8.32-37.03); atypia identified in the recent screening rounds (adjusted HR = 4.14, 95% CI: 2.70-6.35), and those were old in age, had higher level of smoking, occupational radon, and arsenic exposure. In terms of histology, this increased risk was significant for squamous cell carcinoma and small cell lung cancer.

CONCLUSIONS

Although decreasing over time, an increased lung cancer risk concerning moderate or worse sputum atypia can continue at least for 10 years.

IMPACT

Sputum atypia might be helpful for identifying high-risk individuals for screening, surveillance, or chemoprevention of lung cancer.

[Lung Cancer Screening Study in Macao Smoking Individuals]

背景与目的

中国澳门肺癌发病率逐年上升，吸烟人群是肺癌的高发人群，本研究旨在了解中国澳门长期吸烟人群的肺癌发病情况及胸部低剂量计算机断层扫描(low-dose computed tomography, LDCT)肺结节特点。

方法

通过澳门中华医学会会员私家医生推荐及宣传招募中国澳门无症状长期吸烟人士，行胸部LDCT检查，分析肺癌、肺部结节检出率及影像学特点。

结果

符合纳入条件者291例，检出肺癌10例，检出率3.44%(95%CI: 2.78%-4.01%)，其中，肺腺癌5例，鳞癌、小细胞肺癌各2例，腺鳞癌1例。早期肺癌4例，占40%。212例检出肺结节，肺结节总检出率72.9%(95%CI: 67.8%-78.0%); 疑似肺癌结节44例，检出率15.1%(95%CI: 11.0%-19.2%)。单发结节51例，无肺癌检出; 多发结节161例，检出肺癌9例，两组肺癌检出率无统计学差异(P > 0.05)。 < 6 mm实性结节与 < 5 mm非实性结节组168例，未检出肺癌; ≥6 mm实性结节与≥5 mm非实性结节组44例，检出肺癌9例，两组比较有统计学差异(P < 0.05)。

结论

长期吸烟人群中肺癌检出率高，类型以腺癌为主，肺部结节发生率高，当实性结节≥6 mm或非实性结节≥5 mm时，肺癌检出率增高。建议在符合高危因素的男性吸烟人群中推行胸部LDCT筛查肺癌，女性肺癌筛查，应重新界定高危因素。

TRAP1 Shows Clinical Significance in the Early Diagnosis of Small Cell Lung Cancer

Purpose

To explore the clinical significance of tumor necrosis factor receptor-associated protein (TRAP1), mitotic arrest deficient 2 (mad2) and anti-nuclear mitotic spindle apparatus antibody (MSA) in the diagnosis of small cell lung cancer (SCLC).

Patients and Methods

Serum concentrations of TRAP1 and MSA were determined by enzyme-linked immunosorbent assay (ELISA), including SCLC group (Num.=86), non-small cell lung cancer (NSCLC) group (Num.=105), pulmonary nodules (PN) group (Num.=94), and 60 healthy subjects as control group (Num.=60). Whereas fluorescence quantitative PCR (qt-PCR) method was used to detect the expression of mad2.

Results

The expression of TRAP1 was low in SCLC and NSCLC compared with the other two groups, and was the lowest in SCLC, which was negatively correlated with the occurrence of the disease (P<0.05); the sensitivity and specificity of TRAP1 for SCLC were 75.29%, 93.33%, and the area under SCLC curve was 0.903; compared with the other three groups, the level of MSA was the highest in the SCLC, and the results were significantly different (P<0.05), while the area under the SCLC curve was 0.856, and the sensitivity and specificity were 62.78% and 95.24%, respectively. Mad2 is overexpressed in SCLC, but not in PN. The area under the SCLC curve is 0.835, and the sensitivity and specificity are 56.98% and 92.38%; TRAP1 levels are negatively correlated with SCLC tumor stage, the level of TRAP1 was significantly lower in stage III–IV than in stage I–II (P<0.05); combined analysis of TRAP1 and MAD2 and MSA showed that the sensitivity and specificity for SCLS were 95.35% and 99.05%, respectively.

Conclusion

TRAP1 is of great value in the early diagnosis of SCLC as well as differential diagnosis with NSCLC. TRAP1 combined with MAD2 and MSA improved the sensitivity and specificity and provided a new idea for the clinical diagnosis of SCLC.

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

Acute pneumothorax due to immunotherapy administration in non-small cell lung cancer

Nowadays we have novel therapies for advanced stage non-small cell lung cancer. Immunotherapy has been introduced in the market for several years and until now its administration is mostly based on the programmed death-ligand 1. First line treatment with immunotherapy can be administered alone if programmed death-ligand 1 expression is ≥ 50%. All therapies for advanced stage disease have advantages and disadvantages, immunotherapy until now has presented mild adverse effects when compared to chemotherapy. However; it is known to induce inflammatory response to different tissues within the body. In our case acute pneumothorax was induced after immunotherapy administration.

RASSF1A: A promising target for the diagnosis and treatment of cancer

The Ras association domain family 1 isoform A (RASSF1A), a tumor suppressor, regulates several tumor-related signaling pathways and interferes with diverse cellular processes. RASSF1A is frequently demonstrated to be inactivated by hypermethylation in numerous types of solid cancers. It is also associated with lymph node metastasis, vascular invasion, and chemo-resistance. Therefore, reactivation of RASSF1A may be a viable strategy to block tumor progress and reverse drug resistance. In this review, we have summarized the clinical value of RASSF1A for screening, staging, and therapeutic management of human malignancies. We also highlighted the potential mechanism of RASSF1A in chemo-resistance, which may help identify novel drugs in the future.