Baseline and annual repeat rounds of screening: implications for optimal regimens of screening

Estimates of the lead time in screening for bladder cancer

Some studies have suggested a survival benefit from early treatment of bladder cancer (BC). This benefit may be due in part to a "lead-time" bias (LT), i.e., the time interval between the detection of BC in asymptomatic individuals and the development of symptoms ("backward prolongation of survival"). To estimate the LT of BC, it was assumed that LT corresponds to the ratio between the prevalence of pre-symptomatic BC and the incidence of symptomatic BC. Data on the prevalence of pre-symptomatic BC were derived from published screening studies. Data on the annual incidence of symptomatic BC at the age and gender of the study populations were derived from national registries in the countries in the years in which the screening studies were conducted. The ratios of the prevalence of presymptomatic BC to the incidence of symptomatic BC ranged from 3.3 to 12.1 years when derived from screening for microhematuria, and from 1.8 to 5.3 years when derived from screening for urine cytology and cell markers. The estimates of the LT of BC derived from the ratios between its prevalence in asymptomatic persons and its incidence in the corresponding population were consistent with those previously reported in retrospective and prospective cohort studies. Since these estimates may account for the survival benefit from early treatment of BC, the gain of screening for BC remains uncertain and should be confirmed by controlled randomized trials.

Perspective on Management of Low-Dose Computed Tomography Findings on Low-Dose Computed Tomography Examinations for Lung Cancer Screening. From the International Association for the Study of Lung Cancer Early Detection and Screening Committee

Lung cancer screening using low-dose computed tomography (LDCT) carefully implemented has been found to reduce deaths from lung cancer. Optimal management starts with selection of eligibility criteria, counseling of screenees, smoking cessation, selection of the regimen of screening which specifies the imaging protocol, and workup of LDCT findings. Coordination of clinical, radiologic, and interventional teams and ultimately treatment of diagnosed lung cancers under screening determine the benefit of LDCT screening. Ethical considerations of who should be eligible for LDCT screening programs are important to provide the benefit to as many people at risk of lung cancer as possible. Unanticipated diseases identified on LDCT may offer important benefits through early detection of leading global causes of death, such as cardiovascular diseases and chronic obstructive pulmonary disease, as the latter may result from conditions such as emphysema and bronchiectasis, which can be identified early on LDCT. This report identifies the key components of the regimen of LDCT screening for lung cancer which include the need for a management system to provide data for continuous updating of the regimen and provides quality assurance assessment of actual screenings. Multidisciplinary clinical management is needed to maximize the benefit of early detection, diagnosis, and treatment of lung cancer. Different regimens have been evolving throughout the world as the resources and needs may be different, for countries with limited resources. Sharing of results, further knowledge, and incorporation of technologic advances will continue to accelerate worldwide improvements in the diagnostic and treatment approaches.

Comparison of lung cancer occurring in fibrotic versus non-fibrotic lung on chest CT

PURPOSE

Evaluate the behavior of lung nodules occurring in areas of pulmonary fibrosis and compare them to pulmonary nodules occurring in the non-fibrotic lung parenchyma.

METHODS

This retrospective review of chest CT scans and electronic medical records received expedited IRB approval and a waiver of informed consent. 4500 consecutive patients with a chest CT scan report containing the word fibrosis or a specific type of fibrosis were identified using the system M*Model Catalyst (Maplewood, Minnesota, U.S.). The largest nodule was measured in the longest dimension and re-evaluated, in the same way, on the follow-up exam if multiple time points were available. The nodule doubling time was calculated. If the patient developed cancer, the histologic diagnosis was documented.

RESULTS

Six hundred and nine patients were found to have at least one pulmonary nodule on either the first or the second CT scan. 274 of the largest pulmonary nodules were in the fibrotic tissue and 335 were in the non-fibrotic lung parenchyma. Pathology proven cancer was more common in nodules occurring in areas of pulmonary fibrosis compared to nodules occurring in areas of non-fibrotic lung (34% vs 15%, p < 0.01). Adenocarcinoma was the most common cell type in both groups but more frequent in cancers occurring in non-fibrotic tissue. In the non-fibrotic lung, 1 of 126 (0.8%) of nodules measuring 1 to 6 mm were cancer. In contrast, 5 of 49 (10.2%) of nodules in fibrosis measuring 1 to 6 mm represented biopsy-proven cancer (p < 0.01). The doubling time for squamous cell cancer was shorter in the fibrotic lung compared to non-fibrotic lung, however, the difference was not statistically significant (p = 0.24). 15 incident lung nodules on second CT obtained ≤ 18 months after first CT scan was found in fibrotic lung and eight (53%) were diagnosed as cancer.

CONCLUSIONS

Nodules occurring in fibrotic lung tissue are more likely to be cancer than nodules in the nonfibrotic lung. Incident pulmonary nodules in pulmonary fibrosis have a high likelihood of being cancer.

Characterizing Lung-RADS category 4 lesions in a university lung cancer screening program

OBJECTIVES

To assess the prevalence of lung cancer in Lung-RADS category 4 patients, and to elucidate if clinical or imaging features help differentiate benign lesions from lung cancer.

MATERIALS/METHODS

A retrospective review of lung cancer screening (LCS) studies at a single university screening program between January 2018 and December 2021 identified all patients with Lung-RADS category 4 lesions. Patient demographics, symptoms within the prior 6 months, and imaging features were recorded.

RESULTS

During the defined period, 4819 baseline and annual LCS exams were performed; 7.6 % (n = 368) of exams had category 4 nodules and 59 (1.2 %) patients had biopsy-proven lung cancer. Distribution of Lung-RADS category 4 lesions and lung cancer diagnosis were as follows: 4A - 223 nodules, 6.3 % malignant; 4B - 114 nodules, 20.2 % malignant; and 4X - 31 nodules, 71.0 % malignant. Symptoms were reported in 9.0 % (n = 20) of category 4A (2 were malignant), 15.8 % (n = 18) category 4B (1 was malignant) and 22.6 % (n = 7) category 4X (5 were malignant). Imaging features associated with malignancy included endobronchial obstruction with distal atelectasis, pleural tethering, irregular shape, cavitation, and heterogeneous cystic appearance. Twenty-four nodules increased in size, however, only 7 were biopsy proven. Relative to the risk seen with 4A disease, multivariable logistic analyses showed that the odds of a malignancy increased significantly by 3.8 fold (95 % CI: 1.9, 7.9) and 39.2 fold (95 % CI: 14.9, 103.0) with 4B and 4X disease, respectively (p < 0.0001). A separate analysis involving only category 4A and 4B patients jointly showed that disease category (OR = 3.0; 95 % CI: 1.5, 6.4) and additional imaging features (OR = 3.2; 95 % CI: 1.4, 7.0) were significant predictors of malignancy. The presence of clinical symptoms was not statistically associated with lung cancer.

CONCLUSIONS

Lung-RADS 4 nodules were found in 7.6% of LCS examinations and 16% of these nodules were lung cancer. The probability of lung cancer increases from category 4A to 4X, and imaging features may help differentiate benign from malignant nodules in this LCS category.

A 20-year Follow-up of the International Early Lung Cancer Action Program (I-ELCAP)

Background The low-dose CT (≤3 mGy) screening report of 1000 Early Lung Cancer Action Program (ELCAP) participants in 1999 led to the International ELCAP (I-ELCAP) collaboration, which enrolled 31 567 participants in annual low-dose CT screening between 1992 and 2005. In 2006, I-ELCAP investigators reported the 10-year lung cancer-specific survival of 80% for 484 participants diagnosed with a first primary lung cancer through annual screening, with a high frequency of clinical stage I lung cancer (85%). Purpose To update the cure rate by determining the 20-year lung cancer-specific survival of participants diagnosed with first primary lung cancer through annual low-dose CT screening in the expanded I-ELCAP cohort. Materials and Methods For participants enrolled in the HIPAA-compliant prospective I-ELCAP cohort between 1992 and 2022 and observed until December 30, 2022, Kaplan-Meier survival analysis was used to determine the 10- and 20-year lung cancer-specific survival of participants diagnosed with first primary lung cancer through annual low-dose CT screening. Eligible participants were aged at least 40 years and had current or former cigarette use or had never smoked but had been exposed to secondhand tobacco smoke. Results Among 89 404 I-ELCAP participants, 1257 (1.4%) were diagnosed with a first primary lung cancer (684 male, 573 female; median age, 66 years; IQR, 61-72), with a median smoking history of 43.0 pack-years (IQR, 29.0-60.0). Median follow-up duration was 105 months (IQR, 41-182). The frequency of clinical stage I at pretreatment CT was 81% (1017 of 1257). The 10-year lung cancer-specific survival of 1257 participants was 81% (95% CI: 79, 84) and the 20-year lung cancer-specific survival was 81% (95% CI: 78, 83), and it was 95% (95% CI: 91, 98) for 181 participants with pathologic T1aN0M0 lung cancer. Conclusion The 10-year lung cancer-specific survival of 80% reported in 2006 for I-ELCAP participants enrolled in annual low-dose CT screening and diagnosed with a first primary lung cancer has persisted, as shown by the updated 20-year lung cancer-specific survival for the expanded I-ELCAP cohort. © RSNA, 2023 See also the editorials by Grenier and by Sequist and Olazagasti in this issue.

Microwave ablation for the treatment of peripheral ground-glass nodule-like lung cancer: Long-term results from a multi-center study

Introduction

Microwave ablation (MWA) is an effective and safe approach for the treatment of ground-glass nodule (GGN)-like lung cancer, but long-term follow-up is warranted. Therefore, this multi-center retrospective study aimed to evaluate the results of MWA for the treatment of peripheral GGN-like lung cancer with a long-term follow-up.

Materials and Methods

From June 2013 to January 2018, a total of 87 patients (47 males and 40 females, mean age 64.6 ± 10.2 years) with 87 peripheral lung cancer lesions showing GGN (mean long axis diameter, 17 ± 5 mm) underwent computed tomography (CT)-guided percutaneous MWA. All GGN-like lung cancers were histologically verified. The primary endpoints were local progression-free survival (LPFS) and overall survival (OS). The secondary endpoints were cancer-specific survival (CSS) and complications.

Results

During a median follow-up of 65 months, both the 3-year and 5-year LPFS rates were 96.6% and 96.6%. The OS rate was 94.3% at 3 years and 84.9% at 5 years, whereas the 3-year and 5-year CSS rates were 100% and 100%, respectively. No periprocedural deaths were observed. Complications were observed in 49 patients (51.6%). Grade 3 or higher complications included pneumothorax, pleural effusion, hemorrhage, and pulmonary infection, which were identified in ten (10.5%), two (2.1%), two (2.1%), and one (1.1%) patient, respectively.

Conclusions

CT-guided percutaneous MWA is an effective, safe, and potentially curative treatment regimen for GGN-like lung cancer.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning

PURPOSE

This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs).

METHODS

Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts.

RESULTS

Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228-0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074-0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance.

CONCLUSIONS

The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.

Computed tomography findings, associated factors, and management of pulmonary nodules in 54,326 healthy individuals

Introduction

To investigate the pulmonary nodules detected by low-dose computed tomography (LDCT), identified factors affecting the size and number of pulmonary nodules (single or multiple), and the pulmonary nodules diagnosed and management as lung cancer in healthy individuals.

Methods

A retrospective analysis was conducted on 54,326 healthy individuals who received chest LDCT screening. According to the results of screening, the detection rates of pulmonary nodules, grouped according to the size and number of pulmonary nodules (single or multiple), and the patients' gender, age, history of smoking, hypertension, and diabetes were statistically analyzed to determine the correlation between each factor and the characteristics of the nodules. The pulmonary nodules in healthy individuals diagnosed with lung cancer were managed with differently protocols.

Results

The detection rate of pulmonary nodules was 38.8% (21,055/54,326). The baseline demographic characteristics of patients with pulmonary nodules were: 58% male and 42% female patients, 25.7% smoking and 74.3% nonsmoking individuals, 40-60 years old accounted for 49%, 54.8% multiple nodules, and 45.2% single nodules, and ≤5-mm size accounted for 80.4%, 6-10 mm for 18.2%, and 11-30 mm for 1.4%. Multiple pulmonary nodules were more common in hypertensive patients. Diabetes is not an independent risk factor for several pulmonary nodules. Of all patients with lung nodules, 26 were diagnosed with lung cancer, accounting for 0.1% of all patients with pulmonary nodules, 0.6% with nodules ≥5 mm, and 2.2% with nodules ≥8 mm, respectively. Twenty-six patients with lung cancer were treated with surgical resection (57.7%), microwave ablation (MWA, 38.5%), and follow-up (3.8%).

Conclusions

LDCT was suitable for large-scale pulmonary nodules screening in healthy individuals, which was helpful for the early detection of suspicious lesions in the lung. In addition to surgical resection, MWA is an option for early lung cancer treatment.

A brain-like classification method for computed tomography images based on adaptive feature matching dual-source domain heterogeneous transfer learning

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed for the preoperative aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples. The method includes two parts: (1) feature extraction and (2) feature classification. In the feature extraction part, first, By simulating the feature selection mechanism of the human brain in the process of drawing inferences about other cases from one instance, an adaptive selected-based dual-source domain feature matching network is proposed to determine the matching weight of each pair of feature maps and each pair of convolution layers between the two source networks and the target network, respectively. These two weights can, respectively, adaptive select the features in the source network that are conducive to the learning of the target task, and the destination of feature transfer to improve the robustness of the target network. Meanwhile, a target network based on diverse branch block is proposed, which made the target network have different receptive fields and complex paths to further improve the feature expression ability of the target network. Second, the convolution kernel of the target network is used as the feature extractor to extract features. In the feature classification part, an ensemble classifier based on sparse Bayesian extreme learning machine is proposed that can automatically decide how to combine the output of base classifiers to improve the classification performance. Finally, the experimental results (the AUCs were 0.9542 and 0.9356, respectively) on the data of two center data show that this method can provide a better diagnostic reference for doctors.

Associations of systemic inflammation markers with identification of pulmonary nodule and incident lung cancer in Chinese population

Objectives

Neutrophil‐to‐lymphocyte ratio (NLR), platelet‐to‐lymphocyte ratio (PLR), and systemic immune‐inflammation index (SII), easily accessible systemic inflammation response parameters, were reported to associate with poor lung cancer prognosis. However, research on the effects of these markers on the risk of positive nodules (PNs) and lung cancer is limited.

Methods

Participants in this retrospective study were those who had their first computed tomography (CT) screening at Jiangsu Province Hospital's Health Promotion Center between January 1, 2017 and December 31, 2020. We identified PNs (≥6 mm in diameter) from free text of CT reports and lung cancer from medical records. Multivariate logistic analysis was used to assess the association between NLR, PLR, or SII and PNs or lung cancer.

Results

The detected rate of PNs was 9.60% among the 96,476 participants. Age, smoking and body mass index were possible influencing factors for PNs. We observed linear dose‐effect relationship between NLR, PLR, or SII and PNs (p_non‐linear > 0.05). Compared with low quintile, participants with top quintiles of NLR, PLR or SII had an increased risk of PNs, with the adjusted ORs of 1.19 (1.11–1.28), 1.11 (1.04–1.19) or 1.11 (1.03–1.18), respectively. Meanwhile, NLR showed the U‐shaped relationship with lung cancer, with adjusted ORs of 1.40 (1.08–1.81) comparing highest NLR quintile to the third quintile. The high PLR and SII showed significantly associated with lung cancer with adjusted ORs of 1.29 (0.99–1.68) and 1.35 (1.04–1.74) comparing to the lowest quintile.

Conclusions

The high levels of systemic inflammation markers were associated with the risk of positive pulmonary nodules and lung cancer, which suggested systemic immune response may be an important pre‐clinical feature for the early identification of diseases.

Content-based image retrieval with a Convolutional Siamese Neural Network: Distinguishing lung cancer and tuberculosis in CT images

BACKGROUND

CT findings of lung cancer and tuberculosis are sometimes similar, potentially leading to misdiagnosis. This study aims to combine deep learning and content-based image retrieval (CBIR) to distinguish lung cancer (LC) from nodular/mass atypical tuberculosis (NMTB) in CT images.

METHODS

This study proposes CBIR with a convolutional Siamese neural network (CBIR-CSNN). First, the lesion patches are cropped out to compose LC and NMTB datasets and the pairs of two arbitrary patches form a patch-pair dataset. Second, this patch-pair dataset is utilized to train a CSNN. Third, a test patch is treated as a query. The distance between this query and 20 patches in both datasets is calculated using the trained CSNN. The patches closest to the query are used to give the final prediction by majority voting. One dataset of 719 patients is used to train and test the CBIR-CSNN. Another external dataset with 30 patients is employed to verify CBIR-CSNN.

RESULTS

The CBIR-CSNN achieves excellent performance at the patch level with an mAP (Mean Average Precision) of 0.953, an accuracy of 0.947, and an area under the curve (AUC) of 0.970. At the patient level, the CBIR-CSNN correctly predicted all labels. In the external dataset, the CBIR-CSNN has an accuracy of 0.802 and AUC of 0.858 at the patch level, and 0.833 and 0.902 at the patient level.

CONCLUSIONS

This CBIR-CSNN can accurately and automatically distinguish LC from NMTB using CT images. CBIR-CSNN has excellent representation capability, compatibility with few-shot learning, and visual explainability.

Characterization of Newly Detected Costal Pleura-attached Noncalcified Nodules at Annual Low-Dose CT Screenings

Background Solid costal pleura-attached noncalcified nodules (CP-NCNs) less than 10.0 mm with lentiform, oval, or semicircular (LOS) or triangular shapes and smooth margins on baseline low-dose CT scans from the Mount Sinai Early Lung and Cardiac Action Program (MS-ELCAP) were reviewed, and it was determined that they can be followed up at the first annual screening rather than having a shorter-term work-up. Purpose To determine whether the same criteria could be used for solid CP-NCNs newly identified at annual screening examinations. Materials and Methods With use of the same MS-ELCAP database, all new solid CP-NCNs measuring 30.0 mm or less were identified at 4425 annual screening examinations between 2010 and 2019. In addition, to ensure that no malignant CP-NCNs met the criteria, all solid malignant CP-NCNs of 30.0 mm or less in the International Early Lung Cancer Action Program, or I-ELCAP, database of 111 102 annual screening examinations from the 76 participating institutions between 1992 and 2019 were identified; Mount Sinai is one of these institutions. All identified solid CP-NCNs were reviewed-with the radiologists blinded to diagnosis-for shape (triangular, LOS, polygonal, round, or irregular), margin (smooth or nonsmooth), pleural attachment (broad or narrow), and the presence of emphysema and/or fibrosis within 10.0 mm of each CP-NCN. Intra- and interreader readings were performed, and agreements were determined by using the B-statistic. Results Of the 76 new solid CP-NCNs, 21 were lung cancers. Benign CP-NCNs were smaller than malignant ones (median diameter, 4.2 mm vs 11 mm; P < .001), had a different shape distributions, more frequently had smooth margins (67% vs 14%; P < .001), and less frequently had emphysema (38% vs 81%; P = .003) or fibrosis (3.6% vs 19%; P = .045) within a 10.0 mm radius. All 22 solid CP-NCNs less than 10.0 mm in average diameter with triangular or LOS shapes and smooth margins were benign, and none of the 21 solid malignant CP-NCNs had these characteristics. Intra- and interobserver agreement for triangular or LOS-shaped CP-NCNs with smooth margins was almost perfect (0.77 and 0.69, respectively). Conclusion The same follow-up recommendation developed for baseline costal pleura-attached noncalcified nodules (CP-NCNs) can be used for CP-NCNs newly identified at annual screening rounds. © RSNA, 2021.

Interobserver variability in Lung CT Screening Reporting and Data System categorisation in subsolid nodule-enriched lung cancer screening CTs

OBJECTIVES

To assess interobserver agreement in Lung CT Screening Reporting and Data System (Lung-RADS) categorisation in subsolid nodule-enriched low-dose screening CTs.

METHODS

A retrospective review of low-dose screening CT reports from 2013 to 2017 using keyword searches for subsolid nodules identified 54 baseline CT scans. With an additional 108 negative screening CT scans, a total of 162 CT scans were categorised according to the Lung-RADS by two fellowship-trained thoracic radiologists in consensus. We randomly selected 20, 20, 10, and 10 scans from categories 1/2, 3, 4A, and 4B CT scans, respectively, to ensure balanced category representation. Five radiologists classified the 60 CT scans into Lung-RADS categories. The frequencies of concordance and minor and major discordance were calculated, with major discordance defined as at least 6 months of management discrepancy. We used Cohen's κ statistics to analyse reader agreement.

RESULTS

An average of 60.3% (181 of 300) of all cases and 45.0% (90 of 200) of positive screens were correctly categorised. The minor and major discordance rates were 12.3% and 27.3% overall and 18.5% and 36.5% in positive screens, respectively. The concordance rate was significantly higher among experienced thoracic radiologists. Overall, the interobserver agreement was moderate (mean κ, 0.45; 95% confidence interval: 0.40-0.51). The proportion of part-solid risk-dominant nodules was significantly higher in cases with low rates of accurate categorisation.

CONCLUSION

This retrospective study observed variable accuracy and moderate interobserver agreement in radiologist categorisation of subsolid nodules in screening CTs. This inconsistency may affect management recommendations for lung cancer screening.

KEY POINTS

• Diagnostic performance for Lung-RADS categorisation is variable among radiologists with fair to moderate interobserver agreement in subsolid nodule-enriched CT scans. • Experienced thoracic radiologists showed more accurate and consistent Lung-RADS categorisation than radiology residents. • The relative abundance of part-solid nodules was a potential factor related to increased disagreement in Lung-RADS categorisation.

Implementation of low-dose CT screening in two different health care systems: Mount Sinai Healthcare System and Phoenix VA Health Care System

Implementation of lung screening (LS) programs is challenging even among health care organizations that have the motivation, the resources, and more importantly, the goal of providing for life-saving early detection, diagnosis, and treatment of lung cancer. We provide a case study of LS implementation in different healthcare systems, at the Mount Sinai Healthcare System (MSHS) in New York City, and at the Phoenix Veterans Affairs Health Care System (PVAHCS) in Phoenix, Arizona. This will illustrate the commonalities and differences of the LS implementation process in two very different health care systems in very different parts of the United States. Underlying the successful implementation of these LS programs was the use of a comprehensive management system, the Early Lung Cancer Action Program (ELCAP) Management System^TM. The collaboration between MSHS and PVAHCS over the past decade led to the ELCAP Management System^TM being gifted by the Early Diagnosis and Treatment Research Foundation to the PVAHCS, to develop a “VA-ELCAP” version. While there remain challenges and opportunities to continue improving LS and its implementation, there is an increasing realization that most patients who are diagnosed with lung cancer as a result of annual LS can be cured, and that of all the possible risks associated with LS, the greater risk of all is for heavy cigarette smokers not to be screened. We identified 10 critical components in implementing a LS program. We provided the details of each of these components for the two healthcare systems. Most importantly, is that continual re-evaluation of the screening program is needed based on the ongoing quality assurance program and database of the actual screenings. At minimum, there should be an annual review and updating. As early diagnosis of lung cancer must be followed by optimal treatment to be effective, treatment advances for small, early lung cancers diagnosed as a result of screening also need to be assessed and incorporated into the entire screening and treatment program.

Duration of lead time in screening for lung cancer

BACKGROUND

Screening for lung cancer has used chest radiography (CR), low dose computed tomography (LDCT) and sputum cytology (SC). Estimates of the lead time (LT), i.e., the time interval from detection of lung cancer by screening to the development of symptoms, have been derived from longitudinal studies of populations at risk, tumor doubling time (DT), the ratio between its prevalence at the first round of screening and its annual incidence during follow-up, and by probability modeling derived from the results of screening trials.

OBJECTIVE

To review and update the estimates of LT of lung cancer.

METHODS

A non-systematic search of the literature for estimates of LT and screening trials. Search of the reference sections of the retrieved papers for additional relevant studies. Calculation of LTs derived from these studies.

RESULTS

LT since detection by CR was 0.8-1.1 years if derived from longitudinal studies; 0.6-2.1 years if derived from prevalence / incidence ratios; 0.2 years if derived from the average tumor DT; and 0.2-1.0 if derived from probability modeling. LT since detection by LDCT was 1.1-3.5 if derived from prevalence / incidence ratios; 3.9 if derived from DT; and 0.9 if derived from probability modeling. LT since detection of squamous cell cancer by SC in persons with normal CR was 1.3-1.5 if derived from prevalence/incidence ratios; and 2.1 years if derived from the DT of squamous cell cancer.

CONCLUSIONS

Most estimates of the LT yield values of 0.2-1.5 years for detection by CR; of 0.9-3.5 years for detection by LDCT; and about 2 years or less for detection of squamous cell cancer by SC in persons with normal CR. The heterogeneity of the screening trials and methods of derivation may account for the variability of LT estimates.

The Regimen of Computed Tomography Screening for Lung Cancer: Lessons Learned Over 25 Years From the International Early Lung Cancer Action Program

We learned many unanticipated and valuable lessons since we started planning our study of low-dose computed tomography (CT) screening for lung cancer in 1991. The publication of the baseline results of the Early Lung Cancer Action Project (ELCAP) in Lancet 1999 showed that CT screening could identify a high proportion of early, curable lung cancers. This stimulated large national screening studies to be quickly started. The ELCAP design, which provided evidence about screening in the context of a clinical program, was able to rapidly expand to a 12-institution study in New York State (NY-ELCAP) and to many international institutions (International-ELCAP), ultimately working with 82 institutions, all using the common I-ELCAP protocol. This expansion was possible because the investigators had developed the ELCAP Management System for screening, capturing data and CT images, and providing for quality assurance. This advanced registry and its rapid accumulation of data and images allowed continual assessment and updating of the regimen of screening as advances in knowledge and new technology emerged. For example, in the initial ELCAP study, introduction of helical CT scanners had allowed imaging of the entire lungs in a single breath, but the images were obtained in 10 mm increments resulting in about 30 images per person. Today, images are obtained in submillimeter slice thickness, resulting in around 700 images per person, which are viewed on high-resolution monitors. The regimen provides the imaging acquisition parameters, imaging interpretation, definition of positive result, and the recommendations for further workup, which now include identification of emphysema and coronary artery calcifications. Continual updating is critical to maximize the benefit of screening and to minimize potential harms. Insights were gained about the natural history of lung cancers, identification and management of nodule subtypes, increased understanding of nodule imaging and pathologic features, and measurement variability inherent in CT scanners. The registry also provides the foundation for assessment of new statistical techniques, including artificial intelligence, and integration of effective genomic and blood-based biomarkers, as they are developed.

Lung cancer screening intervals based on cancer risk

OBJECTIVES

As low-dose CT screening is gaining acceptance, focus is on increasing the efficiency of screening. One major consideration is to reduce the total number of annual rounds by increasing the interval between screening rounds. It has been suggested that longer intervals could be used for individuals who are at lower risk of lung cancer. In this study, we explored whether eligible participants in a program of LDCT screening who are at lower risk of lung cancer have less aggressive cancers than those at higher risk.

METHODS

We retrospectively identified 118 participants in I-ELCAP database between 1992-2019 who had been screened using HIPAA-compliant protocols and had solid lung cancers diagnosed on an annual round of screening, 7-18 months after the prior round. Volume doubling time (VDT) for each cancer was calculated. Estimated risk of developing lung cancer was calculated using PLCO_M2012 model. The strength of the relationship between VDT and individual PLCO_M2012 scores was assessed by Pearson(r) and Spearman (ρ) correlation coefficients.

RESULTS

VDTs were significantly different by cell-type (p < 0.0001); median VDT for small cell was 34.0 days, followed by other cell-types (61.8 days), squamous-cell (73.3 days), and adenocarcinoma (135.7 days). The median VDT for the 78 (66.1 %) Stage I lung cancers was significantly longer than the 40 Stage II + lung cancers (101.4 days vs. 45.5 days, p < 0.0001). None of the established lung cancer risk indicators (age, pack-years of smoking, or PLCO_M2012 scores) were significant predictors of VDT or lung cancer stage.

CONCLUSION

No significant relationship was demonstrated between risk of developing lung cancer (measured by risk models, age or smoking history) and lung cancer aggressiveness (measured by VDT, cell-type and Stage). This suggests that there is no evidence for determining intervals between repeat screenings using risk-based characteristics. It does not, however, exclude the possibility that future models may establish such a relationship.

Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas

OBJECTIVES

To evaluate the differential diagnostic performance of a computed tomography (CT)-based deep learning nomogram (DLN) in identifying tuberculous granuloma (TBG) and lung adenocarcinoma (LAC) presenting as solitary solid pulmonary nodules (SSPNs).

METHODS

Routine CT images of 550 patients with SSPNs were retrospectively obtained from two centers. A convolutional neural network was used to extract deep learning features from all lesions. The training set consisted of data for 218 patients. The least absolute shrinkage and selection operator logistic regression was used to create a deep learning signature (DLS). Clinical factors and CT-based subjective findings were combined in a clinical model. An individualized DLN incorporating DLS, clinical factors, and CT-based subjective findings was constructed to validate the diagnostic ability. The performance of the DLN was assessed by discrimination and calibration using internal (n = 140) and external validation cohorts (n = 192).

RESULTS

DLS, gender, age, and lobulated shape were found to be independent predictors and were used to build the DLN. The combination showed better diagnostic accuracy than any single model evaluated using the net reclassification improvement method (p < 0.05). The areas under the curve in the training, internal validation, and external validation cohorts were 0.889 (95% confidence interval [CI], 0.839-0.927), 0.879 (95% CI, 0.813-0.928), and 0.809 (95% CI, 0.746-0.862), respectively. Decision curve analysis and stratification analysis showed that the DLN has potential generalization ability.

CONCLUSIONS

The CT-based DLN can preoperatively distinguish between LAC and TBG in patients presenting with SSPNs.

KEY POINTS

• The deep learning nomogram was developed to preoperatively differentiate TBG from LAC in patients with SSPNs. • The performance of the deep learning feature was superior to that of the radiomics feature. • The deep learning nomogram achieved superior performance compared to the deep learning signature, the radiomics signature, or the clinical model alone.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

Recommendations for Implementing Lung Cancer Screening with Low-Dose Computed Tomography in Europe

Lung cancer screening (LCS) with low-dose computed tomography (LDCT) was demonstrated in the National Lung Screening Trial (NLST) to reduce mortality from the disease. European mortality data has recently become available from the Nelson randomised controlled trial, which confirmed lung cancer mortality reductions by 26% in men and 39-61% in women. Recent studies in Europe and the USA also showed positive results in screening workers exposed to asbestos. All European experts attending the "Initiative for European Lung Screening (IELS)"-a large international group of physicians and other experts concerned with lung cancer-agreed that LDCT-LCS should be implemented in Europe. However, the economic impact of LDCT-LCS and guidelines for its effective and safe implementation still need to be formulated. To this purpose, the IELS was asked to prepare recommendations to implement LCS and examine outstanding issues. A subgroup carried out a comprehensive literature review on LDCT-LCS and presented findings at a meeting held in Milan in November 2018. The present recommendations reflect that consensus was reached.

The growth feature and its diagnostic value for benign and malignant pulmonary nodules met in routine clinical practice

Background

Growth rate is an independent risk factor for lung cancer in screened pulmonary nodules. This study aimed to clarify growth characteristics of pulmonary nodules in routine clinical practice and examine whether volume doubling time (VDT) can predict the malignancy of these nodules.

Methods

We retrospectively enrolled patients with 5-30-mm-sized pulmonary nodules that had been surgically resected after a follow-up of at least 3 months. Two follow-up computed tomography (CT) images with similar thickness and long interval were obtained. Then, three-dimensional (3D) manual segmentation for all nodules was performed on two follow-up CT scans. Subsequently, VDT was calculated for nodules with a change in volume of at least 25%.

Results

Overall, 305 pulmonary nodules in 305 patients (men, 36.7%; median age, 57) were included. The mean increased diameter, mass, and volume of benign (n=86) and malignant (n=219) nodules were 0.09 vs. 2.37 mm, 0.10 vs. 0.66 g, and 32.74 vs. 1,871.28 mm3, respectively (P<0.05). In total, 24 of 86 benign nodules (28%, 18 grew and 6 shrank) and 121 of 219 malignant nodules (55%, 114 grew and 7 shrank) changed over time. The median VDTs of growing benign and malignant nodules were 389 and 526 days, respectively, (P=0.18), and the area under the receiver operating characteristic (ROC) curve was 0.67 (0.55-0.78), with a sensitivity and specificity of 69% and 58%, respectively. The median VDT for growing nodules was 339 days for inflammatory pseudotumors, 226 days for granulomas, 640 days for benign tumors, 1,541 days for enlarged lymph nodes, 762 days for adenocarcinoma in situ, 954 days for microinvasive adenocarcinoma, 534 days for invasive adenocarcinoma, and 118 days for squamous cell carcinoma.

Conclusions

In routine clinical practice, many malignant nodules could grow slowly or even remain stable over time. Regarding growing nodules, the diagnostic value of VDT was limited.

A population-based cohort study to evaluate the effectiveness of lung cancer screening using low-dose CT in Hitachi city, Japan

Objectives

To evaluate the effectiveness of lung cancer screening using low-dose computed tomography for the general population, we conducted a retrospective cohort study of screening for participants among Hitachi residents.

Materials and Methods

Citizens aged 50-74 who underwent low-dose computed tomography screening at least once during 1998-2006 were defined as the computed tomography group, and those who underwent X-ray screening at least once during the same period, but did not receive low-dose computed tomography screening throughout the follow-up period, were defined as the XP group. We investigated the lung cancer incidence rate, mortality rate and all-cause mortality rate for both groups from the first lung cancer screening to the end of 2012.

Results

In the computed tomography group (17 935 residents; 9790 males and 8145 females), 273 cases of lung cancer (1.5%), 72 cases of lung cancer death (0.4%), and 885 cases of all-cause death (4.9%) were observed. On the other hand, 164 cases (1.1%) of lung cancer, 80 cases (0.5%) of lung cancer death and 1188 cases (7.6%) of all-cause death were observed in the XP group (15 548 residents; 6526 males and 9022 females). The hazard ratios of the computed tomography group to the XP group adjusted for gender, age and smoking history were 1.23 for lung cancer incidence rate, 0.49 for lung cancer mortality rate and 0.57 for all-cause mortality rate. Non-smokers and light smokers (<30 pack-years) had a significantly lower lung cancer mortality (0.41 and 0.21, respectively).

Conclusion

low-dose computed tomography screening for a population including non-smokers and light smokers may be effective.

CT screening for lung cancer: comparison of three baseline screening protocols

PURPOSE

Clinical management decisions arising from the baseline round for lung cancer screening are the most challenging, as findings have accumulated over a lifetime and may be of no clinical concern. To minimize unnecessary harms and costs of workup prior to the first, annual repeat screening, workup should be limited to participants with the highest suspicion of lung cancer while still aiming to identify small, early lung cancers.

METHODS

We compared recommendations for immediate, delayed (by 3 or 6 months) workup to assess growth at a malignant rate, and the resulting overall and potential biopsies of three baseline screening protocols: I-ELCAP, the two scenarios of ACR-LungRADS, and the European Consortium. For each protocol, the efficiency ratio (ER) of each recommendation was calculated by dividing the number of participants recommended for that workup by the number of resulting lung cancer diagnoses. The ER for potential biopsies was calculated, assuming that biopsies were performed on all participants recommended for immediate workup as well as those diagnosed with lung cancer after delayed workup.

RESULTS

For I-ELCAP, ACR-LungRADS Scenario 1, ACR-LungRADS Scenario 2, and the European consortium, the overall ER was 13.9, 18.3, 18.3, and 31.9, respectively, and for potential biopsies, it was 2.2, 8.1, 3.2, and 4.4, respectively. ER for immediate workup was 2.9, 8.6, 3.9, and 5.6, respectively, and for delayed workup was 36.1, 160.3, 57.8, and 111.9, respectively.

CONCLUSIONS

I-ELCAP recommendations had the lowest ER values for overall, immediate, and delayed workup, and for potential biopsies.

KEY POINTS

• Small differences in protocol thresholds can lead to many unnecessary diagnostic workups. • I-ELCAP recommendations were the most efficient for immediate and overall workup, and potential biopsies. • Definition of a "positive result" and recommendations for further workup in the baseline round needs to be continually reevaluated and updated.

Long-Term Active Surveillance of Screening Detected Subsolid Nodules is a Safe Strategy to Reduce Overtreatment

INTRODUCTION

Lung cancer presenting as subsolid nodule (SSN) can show slow growth, hence treating SSN is controversial. Our aim was to determine the long-term outcome of subjects with unresected SSNs in lung cancer screening.

METHODS

Since 2005, the Multicenter Italian Lung Detection (MILD) screening trial implemented active surveillance for persistent SSN, as opposed to early resection. Presence of SSNs was related to diagnosis of cancer at the site of SSN, elsewhere in the lung, or in the body. The risk of overall mortality and lung cancer mortality was tested by Cox proportional hazards model.

RESULTS

SSNs were found in 16.9% (389 of 2303) of screenees. During 9.3 ± 1.2 years of follow-up, the hazard ratio of lung cancer diagnosis in subjects with SSN was 6.77 (95% confidence interval: 3.39-13.54), with 73% (22 of 30) of cancers not arising from SSN (median time to diagnosis 52 months from SSN). Lung cancer-specific mortality in subjects with SSN was significantly increased (hazard ratio = 3.80; 95% confidence interval: 1.24-11.65) compared to subjects without lung nodules. Lung cancer arising from SSN did not lead to death within the follow-up period.

CONCLUSIONS

Subjects with SSN in the MILD cohort showed a high risk of developing lung cancer elsewhere in the lung, with only a minority of cases arising from SSN, and never representing the cause of death. These results show the safety of active surveillance for conservative management of SSN until signs of solid component growth and the need for prolonged follow-up because of high risk of other cancers.

The narrow path to organized LDCT lung cancer screening programs in Europe

A recent position statement by a group of European experts reviewed the current evidence for low-dose computed tomography (LDCT) lung cancer screening, based on the outcomes and screening performance of the published randomized trials and identified actions needed for eventual future implementation. After the National Lung Screening Trial (NLST) outcome publication, guidelines changed in USA and Canada, but there are still problems in real-world screening practice. In Europe any decision was postponed to the publication of the European randomized trial outcomes and recommendations continue to discourage screening for lung cancer in all member countries. The NELSON randomized controlled trial (RCT), the largest one in Europe, outcome results are still waited, whereas the MILD, DANTE, DLSCT and ITALUNG (all with small sample size) RCTs have published mortality and incidence data with adequate follow up. The implementation of an organized screening in Europe is conditioned by a health technology assessment process at European level. According with the European policy, confirmed in the recent European Cancer Code [2015], screening is transferred in current public-health practice according with evidence-based recommendations and based on organized, usually population-based, programs. Guidelines, standard indicators of performance, training of dedicated radiologists and professionals and a comprehensive quality assurance system is requested in European countries to implement nationally a public health screening program. Waiting the NELSON randomized trial results, key issues as modality for selection of high risk subjects and recruitment, integration of screening and smoking cessation, optimal screening regimen and related research on biomarkers should be assessed, discussed and reviewed. Informed decision making, promotion of primary prevention and integration of screening and smoking cessation are all essential components of a comprehensive risk reduction policy. The path to an Evidence-based screening practice is narrow and, in the absence of a well-established decision-making process, the risk of a spontaneous, uncontrolled use of LDCT screening or, on the other side, an oversight of the screening opportunity is high.

Progress in the Management of Early-Stage Non-Small Cell Lung Cancer in 2017

The landscape of care for early-stage non-small cell lung cancer continues to evolve. While some of the developments do not seem as dramatic as what has occurred in advanced disease in recent years, there is a continuous improvement in our ability to diagnose disease earlier and more accurately. We have an increased understanding of the diversity of early-stage disease and how to better tailor treatments to make them more tolerable without impacting efficacy. The International Association for the Study of Lung Cancer and the Journal of Thoracic Oncology publish this annual update to help readers keep pace with these important developments. Experts in the care of early-stage lung cancer patients have provided focused updates across multiple areas including screening, pathology, staging, surgical techniques and novel technologies, adjuvant therapy, radiotherapy, surveillance, disparities, and quality of life. The source for information includes large academic meetings, the published literature, or novel unpublished data from other international oncology assemblies.