Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, and early LC diagnosis can significantly improve outcomes and survival rates in affected patients. Implementation of LC screening programs using low-dose computed tomography CT in high-risk subjects aims to detect LC as early as possible, but so far, adoption of screening programs into routine clinical care has been very slow. In recent years, the use of CT has significantly increased the rate of incidentally detected pulmonary nodules. Although most of those incidental pulmonary nodules (IPNs) are benign, some of them represent early-stage LC. Given the large number of IPNs detected in the range of several millions each year, this represents an additional, maybe even larger, opportunity to drive stage shift in LC diagnosis, next to LC screening programs. Comprehensive evaluation and targeted work-up of IPNs are mandatory to identify the malignant nodules from the crowd, and several guidelines provide radiologists and physicians’ guidance on IPN assessment and management. However, IPNs still seem to be inadequately processed due to various reasons including insufficient reporting in the radiological report, missing communication between stakeholders, absence of patient tracking systems, and uncertainty regarding responsibilities for the IPN management. In recent years, several approaches such as lung nodule programs, patient tracking software, artificial intelligence, and communication software were introduced into clinical practice to address those shortcomings. This review evaluates the current situation of IPN management and highlights recent developments in process improvement to achieve first steps toward stage shift in LC diagnosis.

Lung cancer (LC) claims the lives of almost 400,000 people in Europe alone each year [1], and most patients to date still get diagnosed at an advanced stage with very low 5-year overall survival rates of less than 10% [2‒4]. This is particularly tragic as screening with low-dose computed tomography (CT) in a clearly defined high-risk population has demonstrated survival benefit already in 2011 in the National Lung Screening Trial (NLST) [5‒7] which was confirmed later in several randomized controlled trials in Europe [8‒13]. Screening of high-risk individuals thus has the potential to lead to a stage shift and to significantly increase 5-year overall survival rates within the next few years. However, as the experience from USA shows, it seems difficult to successfully implement a national screening program, with currently only less than 10% of individuals at risk making use of the offer [14‒16]. Similar challenges can be expected in other countries; therefore, it might take many years to see a stage shift happening, with improved survival rates in cancer registries as a result.

A second, even larger opportunity to achieve the needed stage shift is through the increasing number of incidentally detected lung nodules which must be clearly distinguished from those detected by screening. Incidental pulmonary nodules (IPNs) are one of the commonest abnormal findings in radiology, as they can occur on every CT scan that includes the lungs or parts of them. Since imaging of the chest is increasingly used in various indications [17‒24], detection of IPNs will steadily increase [19, 21, 25].

Although only a small number of IPNs correspond to early-stage LC, it is critical to find and select the malignant ones from the crowd [19, 21, 26, 27]. Accurate assessment and appropriate subsequent follow-up of IPNs might be at least as crucial as screening programs in improving LC mortality [28‒30].

This is particularly important, since IPNs often occur in individuals who generally do not match the population who would be eligible for LC screening due to their very heterogeneous sociodemographic and medical characteristics [19, 31] and therefore defining a separate group, different from the LC screening population. The major problem, however, is that in the USA, for example, estimated 1.1 million IPNs per year seem to be not appropriately followed up and worked up as shown by several analyses of US health data [19, 25, 32].

In recent years, specially designed IPN management programs have been developed, mainly in the USA, to improve the work-up process [28‒30, 33]. Implementation of such IPN management programs has shown that this can lead to a higher rate of early detected LC, especially in subjects who would not be eligible for LC screening [28‒30, 34, 35]. Therefore, a combined approach including both LC screening in high-risk individuals and proper follow-up programs for those with IPN seems to be a key factor for improving LC mortality covering a large population of potentially affected individuals.

So far, little to no data have been published regarding the real-world situation of IPNs in European countries. The reality of IPN management is a “black box,” and we can only speculate how many IPNs may be overlooked in Europe.

Given the dynamic in this field, and the high number of publications on this topic in recent years, this article reviews the current state of knowledge, summarizes recent guidelines, and gives an overview where the field is heading, based on latest global developments. The current status and outlook are intended to provide awareness and guidance for physicians in IPN management. Additionally, shortcomings in current management strategies and future areas of research are also discussed.

What Is Described as an IPN?

IPNs are nodules that were not explicitly looked for in a screening program but were found incidentally in CT scans which were performed for an unrelated reason such as trauma, pulmonary diseases, cardiac and mediastinal pathologies, or currently, the SARS-CoV-2 pneumonia [17‒24]. According to the glossary of term from chest imaging proposed by the Fleischner Society, an IPN is a collective term for space claims of the lungs >3 mm and ≤3 cm in diameter without assignment of a specific dignity [36]. IPNs can occur as single or multiple event [27, 37]. On the radiologic image, IPNs can be seen as more rounded structures having a sharp or indistinct border to the surrounding tissue [37]. They can be in contact with the pleura, but usually they are surrounded completely by aerated lung on CT scan [37]. In accordance with their radio-morphology, IPNs are classified as solid or subsolid nodules, whereby the subsolid nodules are further differentiated into pure ground-glass (pGG) nodules and mixed-solid (part-solid) nodules [31, 37, 38]. An overview about the classification is given in Figure 1.

Fig. 1.

Nodule classification by radio-morphology according to Fleischner Society guidelines. Shown are examples of solid, pure ground-glass, and part-solid nodules (blue arrows) detected on CT images in different patients.

Fig. 1.

Nodule classification by radio-morphology according to Fleischner Society guidelines. Shown are examples of solid, pure ground-glass, and part-solid nodules (blue arrows) detected on CT images in different patients.

Close modal

Solid IPNs are the most common kinds of nodules. An IPN is classified as solid if its solid fraction is at least 80%. On CT images, they may completely obscure bronchial and vascular structures pervading the IPNs (shown in Fig. 1) [39]. pGG nodules represent subsolid nodules with increased lung attenuation so that parenchymal structures are visible throughout them (shown in Fig. 1) [39, 40]. In contrast to that, part-solid nodules consist of ground-glass and solid components, and consequently, they do not completely obscure the underlying lung architecture (shown in Fig. 1) [39‒41]. pGG nodules <6 mm have a high prevalence in the population. They are often transient and usually associated with infection or hemorrhage, while persistent pGG nodules are suggested to have a higher risk of malignancy [42] as they can represent a premalignant precursor/progenitor of an adenocarcinoma [39].

Quantity – How Many IPNs Are Discovered Each Year?

Early reviews suggested that in the year 2000 pulmonary nodules were detected in approximately 150,000 Americans each year [43, 44]. In 2015, Gould et al. [19] retrospectively analyzed health data from US patients. In this analysis, the frequency of nodule detection raised from 24% to 31% for all scans performed between 2006 and 2012 [19] and was linked to the enhanced performance of chest CT scans during that time frame (from 1.3% to 1.9%) [19]. Results corresponded to an increase of the annual rate of nodule identification from 3.9 to 6.6 per 1,000 person-years [19]. In Europe, similar results have been observed. A study including patients from 5 regions of northeastern France whose data were analyzed between 2002 and 2005 revealed an incidence up to 12.6/100,000 person-years [45]. Consistent with Gould et al. [19], the incidence rate increased in the French study population (age-standardized rate men: from 16.4 to 17.7 per 100,000 person-years; women: from 4.9 to 8.2 per 100,000 person-years) [45]. In another European study, analyzing data from two large Dutch hospitals, the percentage of patients with an incidentally detected pulmonary nodule increased from 33% to 50% in 10 years corresponding to the augmented rate of chest CTs performed per year (plus 64% in 10 years) [46]. In China, a retrospective study including 64,168 patients undergoing chest CT revealed a significant portion of patients (59%) with at least one noncalcified IPN [47]. Although the proportion of patients with IPNs varies between 10% and 59% in the literature [25, 47], it is evident that IPNs are very common in the population, even in those subjects, who are not eligible for LC screening [19, 28]. Due to the steadily increasing use of abdominal and thoracic CTs [48], extrapolation of the data suggests that in 2015 ∼1.6 million IPNs were detected by CT scans in 1 year in the entire US population [19].

Management of IPNs Is Complex and Varies with Guidelines

In recent years, several strategies have been published that address IPNs as well as screening-detected nodules. The Fleischner Society [31] is one of the few guidelines that refer only to IPNs, whereas guidelines from other societies such as the British Thoracic Society (BTS) [37], the American College of Chest Physicians (ACCP) [49], and the National Comprehensive Cancer Network (NCCN) [50] include recommendations for both, incidentally and screening-detected nodules. All mentioned guidelines [31, 37, 49, 50] consider nodule characteristics found on actual and previous imaging scans and individual clinical risk factors for LC development to evaluate the probability of malignancy and to determine the most appropriate management strategy. In addition, there are local national guidelines, such as the newly revised 2022 German S3 guideline [51], in which a chapter with recommendations on IPNs has been newly added. These local guidelines are based on literature review, evidence assessment, and derived recommendations, most of which are consistent with the Fleischner Society and BTS guidelines. Differences between these two guidelines are shown in Table 1.

Table 1.

Differences between the Fleischner Society [31] and the BTS guidelines [37]

 Differences between the Fleischner Society [31] and the BTS guidelines [37]
 Differences between the Fleischner Society [31] and the BTS guidelines [37]

The American College of Radiology developed the Lung CT Screening Reporting & Data System (Lung-RADS) [52] which has become commonly used in screening practice and differs from the approach for incidental nodules. Thus, it is not mentioned in detail here.

What Do We Know about Cancer Risk of Nodules?

Estimating the probability of malignancy is the major task, as this is the basis for deciding on subsequent management. Morphology, location, size, multiplicity, and growth rate of the identified nodule(s); the assessment of present pulmonary diseases (e.g., emphysema and fibrosis); assessment of patient demographics (including age, sex, race, family history); and documentation of inhalation of toxic compounds (e.g., tobacco smoke, fumes, or dust) must be considered for this purpose [19, 31, 37].

In general, nodules with smooth border, total or partly calcification, solid density, very fast (<1 month) or rather long (>1 year) doubling time, and a size ≤6 mm in diameter are suggested to be benign [19, 53, 54]. Fatty degeneration and calcification are further suspected as benign characteristics in pulmonary nodules. However, the risk of malignancy appears to raise with increasing diameter [27, 55]. Solid nodules <6 mm in diameter are supposed to become rarely malign in contrast to solid nodules ≥6 mm in diameter [27, 31, 39, 56, 57]. Location also plays a role in risk assessment. Upper lobe location is associated with a higher risk. Small solid nodules in a perifissural or subpleural location often represent intrapulmonary lymph nodes and have a low risk of malignancy [58]. Regarding subsolid nodules, many are associated with benign conditions such as focal interstitial fibrosis, eosinophilic pneumonia, thoracic endometriosis, and focal hemorrhage [59]. However, pGG nodules with a larger size (diameter >10 mm) or bubbly transparency should receive certain awareness as they have a higher probability of developing malignancy [60]. Additionally, numerous studies have shown that pGG nodules ≥6 mm need in average 3–4 years to establish growth or to be diagnosed as developing invasive carcinoma showing that even smaller pGG nodules may become malign over time [61‒63]. Part-solid nodules are also known to have a high likelihood of malignancy as shown in the Early Lung Cancer Action Project (ELCAP) study where a malignancy rate for 63% was observed for part-solid nodules [42]. However, in general, the malignancy rates of subsolid nodules are markedly higher than that of solid nodules (34% vs. 7%) [42].

Currently, the following individual risk factors in patients with newly identified lung nodules have been identified (shown in Table 2) [31, 64] which are almost congruent with the risk factors postulated by others [19, 27, 31, 37, 64, 65]. Based on these independent risk factors (shown in Table 2), a pre-test probability of malignancy of IPN can be calculated [37, 49]. Additionally, the BTS guideline endorses [37] the use of the prediction models from Herder et al. [66] and Brock model [27] as additional decision aid for nodule judgment in patients with newly identified lung nodules. In the Brock model [27], for example, the individual risk of malignant disease is estimated as a percentage. Based on this model, a risk profile of more or less than 10% is the cut-off between noninvasive CT follow-up and invasive diagnostics. However, the application of these models to clinical practice should take into account the population characteristics from which each model was derived. As all risk models have been validated in LC screening populations, they may not be appropriate for every individual patient with IPNs [27, 37].

Table 2.

Risk factors for malignancy [27, 63]

 Risk factors for malignancy [27, 63]
 Risk factors for malignancy [27, 63]

Technical Aspects of Imaging

Imaging standards for the optimal assessment and follow-up of nodules have been defined and described by scientific societies and are included in current guidelines [31, 37, 51]. It is important to emphasize, that all follow-up examinations should be performed in low-radiation technique, and that thin slice reconstruction should be used (≤1.5 mm, ideally 1.0 mm). Another important aspect is the use of three-dimensional measurements (volumetry), instead of diameter. The measurement of the maximal transversal diameter often leads to an overestimation of nodule size because lung nodules usually do not have a perfectly spherical shape which then results in an increased number of false-positive cases. The NELSON trial has demonstrated the advantage of using volume change to measure tumor growth with lower false-positive scan rates (e.g., 1.2% vs. 23.3% in the NLST) than in other trials resulting in less invasive diagnostic procedures [5, 8]. Another positive aspect of volumetry is a faster detection of growth which is not possible using the diameter measurement since it is difficult to detect a critical diameter growth of 25% before the IPN has doubled its volume [67].

What Do We Know about Adherence to Guidelines?

Although currently available guidelines from the major medical societies provide recommendations for management of pulmonary nodules, several studies suggested that not all stakeholders are familiar with them and/or used them consistently at their work [68‒73], even among radiologists the level of awareness and utilization of the guidelines varies considerably between countries from 27% to 61% [70‒73]. Among hospitalists practicing internal medicine, up to 42% are not familiar with the guidelines from the Fleischner Society, although IPNs are frequently seen in their clinical routine (at least one IPN/6 months) [73]. The wide disparities in adherence to the guidelines among both radiologists and other involved physicians such as pulmonologists, internists, and general practitioners indicate that there is still room for refinement. A significant improvement in guideline compliance was already seen with simple approaches such as printing the Fleischner Society guidelines on small cards and attaching them to workstations where radiologists or other physicians interpret CT examinations [74] or adding the Fleischner recommendations to radiological report templates [25, 75‒77] or to chest CT reports to encourage their usage by radiologists and general practitioners [78].

Although every year, hundreds of thousands of IPNs are detected on CT scans, follow-up appears to be insufficient in most of the newly detected nodules since rates of follow-up care ranged from 29% to 39% [25, 32, 79] raising the question of why approximately 2 out of 3 patients with IPNs do not seem to receive a proper radiological and clinical follow-up [25]. These results are rather interesting as majority of the radiological reports (up to 68%) had recommended a follow-up of the pulmonary nodules [25] indicating a significant lack of adequate nodule work-up in many patients with potential early-stage cancer. Several pitfalls are described in the literature that may be responsible for inadequate management of IPNs [25, 33, 79‒81] showing that, even if the radiologist initiates the IPN management process by documenting them in the radiology report, he is not solely responsible that IPNs are often neglected. Different health care providers and patients also represent important factors in the success of IPN management. Figure 2 shows exemplary a patient journey from the onset of pulmonary nodule discovery with potential pitfalls encountered in the evaluation of IPNs, highlighting the challenges in clinical practice and the high demands placed on radiologists.

Fig. 2.

Exemplary patient journey including pitfalls in assessment of IPNs and in their follow-up management [25, 33, 77‒79, 82].

Fig. 2.

Exemplary patient journey including pitfalls in assessment of IPNs and in their follow-up management [25, 33, 77‒79, 82].

Close modal

Process Improvement – What Is Technically Possible so far and Where Is the Journey Heading?

In recent years, several approaches were developed to address current shortcomings of nodule management. One approach is the modification of the radiological report template to ensure concise and guideline compliant description of incidentally detected nodules by the radiologist (see section What Do We Know about Guideline Adherence?) [25, 76, 77]. Aase et al. [77] demonstrated that the inclusion of six key nodule descriptors (average diameter, location, density, suspicious features of the dominant nodule, total number of nodules, and risk stratification) in the radiological report template significantly increased the number of reports including all descriptors from 12% to 47% (p < 0.001).

Other approaches comprise computerized closed-loop communication and multistage tracking systems, such as Alert Notification of Critical Results (ANCR) [83, 83] and Result Alert and Development of Automated Resolution (RADAR) [84], to control completeness of follow-up imaging, and to provide reminders to patients and/or physicians if the recommended follow-up has not been performed [82, 85‒89]. The ANCR system includes creation, communication, and acknowledgment of alerts as well as their management including reminder and escalation. Responses are created automatically by the system and communicated via paging or secure mails to involved parties [83, 90]. Several studies demonstrated that ANCR usage reduced medical errors, increased the adherence to institutional guidelines for communication of critical results (improvement from 91% to 95%, p < 0.0001), and reduced workflow interruptions: all properties that improve the quality of patients’ care [83, 90].

The RADAR system is another example of how closed-loop system integration can manage recommendations in accordance with the Fleischner Society guidelines [84, 85]. RADAR is embeddable in the picture archiving and communication system [91] generally used by radiologists. A retrospective analysis in a tertiary academic center with more than 600,000 radiology examinations annually revealed that in all RADAR alerts, imaging modality and follow-up time frame were stated whereas in non-RADAR alerts, only 71% of them had a complete documentation (p value <0.001) [84]. Additionally, after implementation of RADAR, the timely follow-up rate was increased (from 65% to 84%, p = 0.0001) and the agreement between primary care provider and radiologists regarding IPN management plan was high (87%) [85].

Another tracking system developed by Dyer et al. [88] is coupled with a computerized registry using tracker phrases for nodule description which then automatically generate follow-up recommendations for the radiological report in accordance with the Fleischner Society guidelines. Besides the autonomous calculation of patients’ LC risk, the system is also able to check whether the recommended follow-up has taken place and issues appropriate reminders if examinations are still pending [88]. The implementation of the system increased the timely follow-up rate from 46% before to 55% after implementation of the system [88].

Over the past few years, artificial intelligence (AI) has been increasingly used to aid automatic detection and characterization of lung nodules [92, 93]. In addition, the malignancy of nodules can also be assessed using nodule volumetry. Computer-aided detection has the potential to analyze nodule morphology and improve the workflow of follow-up assessment [93].

Thus, the use of AI in diagnostic imaging relieves the burden on radiologists and is associated with an increased accuracy of IPN assessment which in turn enables a more frequent detection of early-stage LC, possibly resulting in a stage shift in LC diagnosis [93, 94]. Recent evidence has demonstrated that deep learning algorithms have similar model performance to radiologists in predicting LC risk to even better performance especially for less skilled readers [95‒98].

Although there are already commercially available AI software solutions on the market that have already received FDA clearance and CE mark [99], AI solutions have not yet found their way into everyday clinical practice. Most important is the full integration of these tools into the clinical radiology workflow, and it is precisely this factor that is one of the main reasons for the lack of acceptance of already available computer-aided detection tools [100]. However, we expect that the coming years will bring significant progress and that these technologies will then become the standard of care and revolutionize radiologists' workflows.

The Impact of Incidental Lung Nodule Programs on Stage Shift and Cancer Survival

While in European countries, IPNs are usually managed by the respective specialty departments, specialized pulmonary nodule clinics have already been established in the USA providing work-up and treatment of lung nodules in an optimized surrounding [29]. Key factor in such nodule clinics is the so-called lung navigators who are responsible for the timely performance of designated procedures and patient support [29]. Pulmonary nodule management is a complex process often confusing affected patient. Lung navigators can effectively address this problem by educating patients and easing their transition from the multidisciplinary clinic to individual specialists, which helps prevent patients from getting lost in the complicated follow-up process. Further, consequent support by specialized nurses improves adherence to recommendations as there is a certain kind of guidance and control for the patients.

In addition to improving patient compliance with follow-up recommendations [28], the most important outcome achieved through the establishment of pulmonary nodule programs and specialized lung nodule clinics is the stage shift in LC detection [28‒30]. In one study, implementation of the nodule management program enabled that 33% of the patients with LC received an early-stage diagnosis (IA or IB) [30]. In another lung nodule program, the percentage of patients with early-stage LC at time of detection increased from 23% to 38% within 2 years [28]. First results of a specialized lung nodule clinic showed that 94% of the patients had stage I or stage II at the time of diagnosis enabling a promising prognosis [29].

Lung nodule programs, patient tracking software, AI, and communication software to engage referring clinicians are efficient and effective tools to ensure planning, organization, and control of adequate follow-up of IPNs in clinical practice and significantly improve patient care, but it has to be considered that such approaches require well-organized teamwork [28‒30, 33‒35, 47, 81, 82, 86‒88, 90‒97]. However, outcomes of several studies showed that utilization of such tools is first step to achieve a true stage shift in LC diagnosis [28‒30].

To improve LC mortality, it is urgently needed to achieve a stage shift toward earlier disease. Two parallel approaches are necessary to drive this stage shift: first, implementation of national LC screening programs in high-risk populations, and second, appropriate follow-up and management of the increasing number of incidentally detected nodules. Regarding IPNs, data suggest that a high number of patients are lost to follow-up and, thus, miss the chance for early diagnosis in cases where the nodule is malignant. Reasons for insufficient follow-up are numerous. Recent experience from USA has demonstrated that follow-up rates can be increased by structured nodule management programs, which in some cases has resulted in a stage shift. Critical factors for a successful and timely follow-up include the use of AI software for nodule evaluation, comprehensive reporting of the findings, effective communication between involved parties including patients’ awareness to the findings, establishment of patient tracking systems, clear responsibilities for scheduling and managing the follow-up, e.g., through a lung nodule navigator or nurse. Additionally, comprehensive education, raising awareness, and adherence to guidelines on the part of the physicians and persons involved such as general practitioners, radiologists, thoracic surgeons, oncologists, and pulmonologists are essential to ensure adequate IPN management.

In the future, in addition to the introduction of LC screening programs, the focus should continue to be on the development and optimization of specific IPN management programs for incidentally detected lung nodules, especially in those countries where little has been initiated to this end (e.g., Europe). The two approaches work complementarily, and together, they will reduce the disparities that exist in access to LC screening programs by enabling different at-risk populations to participate in LC screening and appropriate treatment. As a result, it will be possible to increasingly diagnose LC at early stages and, finally, to improve survival rates from this devastating disease.

The authors would like to thank Dr. Katharina Bakhaus, Alcedis GmbH, Giessen, Germany, for medical writing assistance.

Gerald Schmid-Bindert and Joana Fink are employees of AstraZeneca. However, there are no conflicts of interests in this context, since this work is part of the Lung Ambition Alliance. All other authors have no conflicts of interest to declare.

AstraZeneca is supporting this article in the context of the Lung Ambition Alliance (LAA) initiative, where AZ and IASLC are partnering with the goal to support projects that have the potential to improve lung cancer 5-year-survival rates, independent of drug development. The medical writing assistance for this review was therefore funded by AstraZeneca, Hamburg, Germany.

Gerald Schmid-Bindert, Jens Vogel-Claussen, Sylvia Gütz, Joana Fink, Hans Hoffmann, Martin Eichhorn, and Felix J.F. Herth were involved in the conceptualization of the manuscript. Gerald Schmid-Bindert and Joana Fink prepared the original draft preparation. Gerald Schmid-Bindert, Jens Vogel-Claussen, Sylvia Gütz, Joana Fink, Hans Hoffmann, Martin Eichhorn, and Felix J.F. Herth were involved in the review, writing, and editing of the manuscript. All authors have read and agreed to the final version of this review article.

1.
World Health Organization-International Agency for Research on Cancer. GlobocanLungFact Sheet 2020 [cited 2022 Jan 24]. Available from: https://gco.iarc.fr/today/data/factsheets/cancers/15-Lung-fact-sheet.pdf.
2.
Blandin Knight S, Crosbie PA, Balata H, Chudziak J, Hussell T, Dive C. Progress and prospects of early detection in lung cancer. Open Biol. 2017 Sep;7(9):170070.
3.
Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2021. CA Cancer J Clin. 2021 Jan;71(1):7–33.
4.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021 May;71(3):209–49.
5.
National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395–409.
6.
Pinsky PF, Church TR, Izmirlian G, Kramer BS. The National Lung Screening Trial: results stratified by demographics, smoking history, and lung cancer histology. Cancer. 2013 Nov 15;119(22):3976–83.
7.
National Lung Screening Trial Research Team. Lung cancer incidence and mortality with extended follow-up in the National Lung Screening Trial. J Thorac Oncol. 2019 Oct;14(10):1732–42.
8.
Horeweg N, van Rosmalen J, Heuvelmans MA, van der Aalst CM, Vliegenthart R, Scholten ET, et al. Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening. Lancet Oncol. 2014 Nov;15(12):1332–41.
9.
Wille MMW, Dirksen A, Ashraf H, Saghir Z, Bach KS, Brodersen J, et al. Results of the Randomized Danish Lung Cancer Screening Trial with focus on high-risk profiling. Am J Respir Crit Care Med. 2016 Mar 1;193(5):542–51.
10.
Paci E, Puliti D, Lopes Pegna A, Carrozzi L, Picozzi G, Falaschi F, et al. Mortality, survival and incidence rates in the ITALUNG randomised lung cancer screening trial. Thorax. 2017 Sep;72(9):825–31.
11.
Pastorino U, Silva M, Sestini S, Sabia F, Boeri M, Cantarutti A, et al. Prolonged lung cancer screening reduced 10-year mortality in the MILD trial: new confirmation of lung cancer screening efficacy. Ann Oncol. 2019 Jul 1;30(7):1162–9. Erratum in: Ann Oncol. 2019 Oct 1;30(10):1672
12.
Becker N, Motsch E, Trotter A, Heussel CP, Dienemann H, Schnabel PA, et al. Lung cancer mortality reduction by LDCT screening-results from the randomized German LUSI trial. Int J Cancer. 2020 Mar 15;146(6):1503–13.
13.
de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, et al. Reduced lung-cancer mortality with volume CT screening in a randomized trial. N Engl J Med. 2020 Feb 6;382(6):503–13.
14.
Doria-Rose VP, White MC, Klabunde CN, Nadel MR, Richards TB, McNeel TS, et al. Use of lung cancer screening tests in the United States: results from the 2010 National Health Interview Survey. Cancer Epidemiol Biomarkers Prev. 2012 Jul;21(7):1049–59.
15.
Jemal A, Fedewa SA. Lung cancer screening with low-dose computed tomography in the United States-2010 to 2015. JAMA Oncol. 2017;3(9):1278–81.
16.
Pham D, Bhandari S, Pinkston C, Oechsli M, Kloecker G. Lung cancer screening registry reveals low-dose CT screening remains heavily underutilized. Clin Lung Cancer. 2020 May;21(3):e206–11.
17.
Adam A, Dixon A, Gillard JH, Schaefer-Prokop CM. Grainger and Allison’s diagnostic radiology. 7th ed, revised. Amsterdam: Elsevier; 2020.
18.
Smith-Bindman R, Miglioretti DL, Johnson E, Lee C, Feigelson HS, Flynn M, et al. Use of diagnostic imaging studies and associated radiation exposure for patients enrolled in large integrated health care systems, 1996–2010. JAMA. 2012 Jun 13;307(22):2400–9.
19.
Gould MK, Tang T, Liu ILA, Lee J, Zheng C, Danforth KN, et al. Recent trends in the identification of incidental pulmonary nodules. Am J Respir Crit Care Med. 2015 Nov 15;192(10):1208–14.
20.
Larici AR, Farchione A, Franchi P, Ciliberto M, Cicchetti G, Calandriello L, et al. Lung nodules: size still matters. Eur Respir Rev. 2017 Dec 20;26(146):170025.
21.
Anderson IJ, Davis AM. Incidental pulmonary nodules detected on CT images. JAMA. 2018 Dec 4;320(21):2260–1.
22.
Karius P, Lembcke A, Sokolowski FC, Gandara IDP, Rodríguez A, Hamm B, et al. Extracardiac findings on coronary computed tomography angiography in patients without significant coronary artery disease. Eur Radiol. 2019 Apr;29(4):1714–23.
23.
Smith-Bindman R, Kwan ML, Marlow EC, Theis MK, Bolch W, Cheng SY, et al. Trends in use of medical imaging in US health care systems and in ontario, Canada, 2000–2016. JAMA. 2019 Sep 3;322(9):843–56.
24.
Tabatabaei SMH, Talari H, Gholamrezanezhad A, Farhood B, Rahimi H, Razzaghi R, et al. A low-dose chest CT protocol for the diagnosis of COVID-19 pneumonia: a prospective study. Emerg Radiol. 2020 Dec;27(6):607–15,
25.
Blagev DP, Lloyd JF, Conner K, Dickerson J, Adams D, Stevens SM, et al. Follow-up of incidental pulmonary nodules and the radiology report. J Am Coll Radiol. 2014 Apr;11(4):378–83,
26.
Bach PB, Mirkin JN, Oliver TK, Azzoli CG, Berry DA, Brawley OW, et al. Benefits and harms of CT screening for lung cancer: a systematic review. JAMA. 2012 Jun 13;307(22):2418–29. Erratum in: JAMA. 2012 Oct 3;308(13):1324. Erratum in: JAMA. 2013 Jun 5;309(21):2212.
27.
McWilliams A, Tammemagi MC, Mayo JR, Roberts H, Liu G, Soghrati K, et al. Probability of cancer in pulmonary nodules detected on first screening CT. N Engl J Med. 2013 Sep 5;369(10):910–9.
28.
LeMense GP, Waller EA, Campbell C, Bowen T. Development and outcomes of a comprehensive multidisciplinary incidental lung nodule and lung cancer screening program. BMC Pulm Med. 2020 Apr 29;20(1):115.
29.
Roberts TJ, Lennes IT, Hawari S, Sequist LV, Park ER, Willers H, et al. Integrated, multidisciplinary management of pulmonary nodules can streamline care and improve adherence to recommendations. Oncologist. 2020 May;25(5):431–7.
30.
Van Gerpen R. Creating an incidental pulmonary nodule safety-net program. Chest. 2021 Jun;159(6):2477–82.
31.
MacMahon H, Naidich DP, Goo JM, Lee KS, Leung ANC, Mayo JR, et al. Guidelines for management of incidental pulmonary nodules detected on CT images: from the fleischner society 2017. Radiology. 2017 Jul;284(1):228–43.
32.
Pyenson BS, Bazell CM, Bellanich MJ, Caplen MA, Zulueta JJ. No apparent workup for most new indeterminate pulmonary nodules in US Commercially-Insured patients. J Health Econ Outcomes Res. 2019 May 8;6(3):118–29.
33.
Mahajan AB. “Where do incidental lung nodule programs fit in the age of lung cancer screening?”. ECPRM. 2019;8.5:430–1.
34.
Smeltzer M, Liao W, Meadows-Taylor M, Faris N, Fehnel C, Goss J, et al. Early detection of lung cancer with an incidental lung nodule program (ILNP). J Clin Oncol. 2021:39(15 Suppl):8553.
35.
Osarogiagbon RU, Liao W, Faris NR, Meadows-Taylor M, Fehnel C, Lane J, et al. Lung cancer diagnosed through screening, lung nodule, and neither program: a prospective observational study of the detecting early lung cancer (DELUGE) in the Mississippi delta cohort. J Clin Oncol. 2022 Mar:JCO2102496. Epub ahead of print.
36.
Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner Society: glossary of terms for thoracic imaging. Radiology. 2008 Mar;246(3):697–722.
37.
Callister MEJ, Baldwin DR, Akram AR, Barnard S, Cane P, Draffan J, et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules: accredited by NICE. Thorax. 2015 Aug;70 Suppl 2:ii1–54.
38.
Bankier AA, MacMahon H, Goo JM, Rubin GD, Schaefer-Prokop CM, Naidich DP. Recommendations for measuring pulmonary nodules at CT: a statement from the fleischner society. Radiology. 2017 Nov;285(2):584–600.
39.
Schaefer-Prokop C. Management des pulmonalen Rundherdes. Onkologie up2date. 2020;2(4):295–307.
40.
Godoy MCB, Naidich DP. Overview and strategic management of subsolid pulmonary nodules. J Thorac Imaging. 2012 Jul;27(4):240–8.
41.
Naidich DP, Bankier AA, MacMahon H, Schaefer-Prokop CM, Pistolesi M, Goo JM, et al. Recommendations for the management of subsolid pulmonary nodules detected at CT: a statement from the Fleischner Society. Radiology. 2013 Jan;266(1):304–17.
42.
Henschke CI, Yankelevitz DF, Mirtcheva R, McGuinness G, McCauley D, Miettinen OS, et al. CT screening for lung cancer: frequency and significance of part-solid and nonsolid nodules. AJR Am J Roentgenol. 2002 May;178(5):1053–7.
43.
Ost D, Fein A. Evaluation and management of the solitary pulmonary nodule. Am J Respir Crit Care Med. 2000 Sep;162(3):782–7.
44.
Ost D, Fein AM, Feinsilver SH. Clinical practice. The solitary pulmonary nodule. N Engl J Med. 2003 Jun 19;348(25):2535–42.
45.
Marrer É, Jolly D, Arveux P, Lejeune C, Woronoff-Lemsi MC, Jégu J, et al. Incidence of solitary pulmonary nodules in Northeastern France: a population-based study in five regions. BMC Cancer. 2017 Jan 11;17(1):47.
46.
Hendrix W, Hendrix N, Prokop M, Scholten E, Van Ginneken B, Rutten M, et al. Trend in the incidence of pulmonary nodules in chest computed tomography: 10-year results from two Dutch hospitals. Poster, ECR. 2021.
47.
Cui S, Ming S, Lin Y, Chen F, Shen Q, Li H, et al. Development and clinical application of deep learning model for lung nodules screening on CT images. Sci Rep. 2020 Aug 12;10(1):13657.
48.
Hess EP, Haas LR, Shah ND, Stroebel RJ, Denham CR, Swensen SJ. Trends in computed tomography utilization rates: a longitudinal practice-based study. J Patient Saf. 2014 Mar;10(1):52–8.
49.
Gould MK, Donington J, Lynch WR, Mazzone PJ, Midthun DE, Naidich DP, et al. Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines. Chest. 2013 May;143(5 Suppl):e93S–120S.
50.
Wood DE, Kazerooni EA, Baum SL, Eapen GA, Ettinger DS, Hou L, et al. Lung cancer screening, version 3.2018, NCCN clinical practice guidelines in oncology. J Natl Compr Canc Netw. 2018 Apr;16(4):412–41.
51.
Leitlinienprogramm Onkologie (Deutsche Krebsgesellschaft, Deutsche Krebshilfe, AWMF): Prävention, Diagnostik, Therapie und Nachsorge des Lungenkarzinoms (German S3 Guideline Version 2.01 Mai 2022, preliminary version) [cited 2022 June 30]. Available from: https://www.leitlinienprogramm-onkologie.de/fileadmin/user_upload/LL_Lungenkarzinom_Langversion_2.01.pdf.
52.
Chelala L, Hossain R, Kazerooni EA, Christensen JD, Dyer DS, White CS. Lung-RADS version 1.1: challenges and a look ahead, from the AJR special Series on radiology reporting and data systems. AJR Am J Roentgenol. 2021 Jun;216(6):1411–22.
53.
Swensen SJ, Jett JR, Hartman TE, Midthun DE, Sloan JA, Sykes AM, et al. Lung cancer screening with CT: Mayo Clinic experience. Radiology. 2003 Mar;226(3):756–61.
54.
Kikano GE, Fabien A, Schilz R. Evaluation of the solitary pulmonary nodule. Am Fam Physician. 2015 Dec 15;92(12):1084–91.
55.
Wahidi MM, Govert JA, Goudar RK, Gould MK, McCrory DC; American College of Chest Physicians. Evidence for the treatment of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition). Chest. 2007 Sep;132(3 Suppl):94S–107.
56.
Samet JM, Avila-Tang E, Boffetta P, Hannan LM, Olivo-Marston S, Thun MJ, et al. Lung cancer in never smokers: clinical epidemiology and environmental risk factors. Clin Cancer Res. 2009 Sep 15;15(18):5626–45.
57.
Horeweg N, van der Aalst CM, Vliegenthart R, Zhao Y, Xie X, Scholten ET, et al. Volumetric computed tomography screening for lung cancer: three rounds of the NELSON trial. Eur Respir J. 2013 Dec;42(6):1659–67.
58.
de Hoop B, van Ginneken B, Gietema H, Prokop M. Pulmonary perifissural nodules on CT scans: rapid growth is not a predictor of malignancy. Radiology. 2012 Nov;265(2):611–6.
59.
Kim H, Park CM, Koh JM, Lee SM, Goo JM. Pulmonary subsolid nodules: what radiologists need to know about the imaging features and management strategy. Diagn Interv Radiol. 2014;20(1):47–57.
60.
Xing Y, Li Z, Jiang S, Xiang W, Sun X. Analysis of pre-invasive lung adenocarcinoma lesions on thin-section computerized tomography. Clin Respir J. 2015 Jul;9(3):289–96.
61.
Kobayashi Y, Mitsudomi T. Management of ground-glass opacities: should all pulmonary lesions with ground-glass opacity be surgically resected? Transl Lung Cancer Res. 2013 Oct;2(5):354–63.
62.
Aoki T. Growth of pure ground-glass lung nodule detected at computed tomography. J Thorac Dis. 2015 Sep;7(9):E326–8.
63.
Kakinuma R, Muramatsu Y, Kusumoto M, Tsuchida T, Tsuta K, Maeshima AM, et al. Solitary pure ground-glass nodules 5 mm or smaller: frequency of growth. Radiology. 2015 Sep;276(3):873–82.
64.
Loverdos K, Fotiadis A, Kontogianni C, Iliopoulou M, Gaga M. Lung nodules: a comprehensive review on current approach and management. Ann Thorac Med. 2019 Oct–Dec;14(4):226–38.
65.
Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES. The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med. 1997 Apr 28;157(8):849–55.
66.
Herder GJ, van Tinteren H, Golding RP, Kostense PJ, Comans EF, Smit EF, et al. Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. Chest. 2005 Oct;128(4):2490–6.
67.
Heuvelmans MA, Vliegenthart R, Oudkerk M. Contributions of the European trials (European randomized screening group) in computed tomography lung cancer screening. J Thorac Imaging. 2015 Mar;30(2):101–7.
68.
Eisenberg RL, Bankier AA, Boiselle PM. Compliance with Fleischner Society guidelines for management of small lung nodules: a survey of 834 radiologists. Radiology. 2010 Apr;255(1):218–24.
69.
Esmaili A, Munden RF, Mohammed TLH. Small pulmonary nodule management: a survey of the members of the Society of Thoracic Radiology with comparison to the Fleischner Society guidelines. J Thorac Imaging. 2011 Feb;26(1):27–31.
70.
Lacson R, Desai S, Landman A, Proctor R, Sumption S, Khorasani R. Impact of a health information technology intervention on the follow-up management of pulmonary nodules. J Digit Imaging. 2018 Feb;31(1):19–25.
71.
Masciocchi M, Wagner B, Lloyd B. Quality review: Fleischner criteria adherence by radiologists in a large community hospital. J Am Coll Radiol. 2012 May;9(5):336–9.
72.
Rampinelli C, Cicchetti G, Cortese G, Polverosi R, Farchione A, Iezzi R, et al. Management of incidental pulmonary nodule in CT: a survey by the Italian College of Chest Radiology. Radiol Med. 2019 Jul;124(7):602–12.
73.
Umscheid CA, Wilen J, Garin M, Goldstein JD, Cook TS, Liu Y, et al. National Survey of Hospitalists’ Experiences with incidental pulmonary nodules. J Hosp Med. 2019 Jun 1;14(6):353–6.
74.
Eisenberg RL; Fleischner Society. Ways to improve radiologists’ adherence to Fleischner Society guidelines for management of pulmonary nodules. J Am Coll Radiol. 2013 Jun;10(6):439–41.
75.
M Elias R, G Sykes AM, M Knudsen J, Morgenthaler TI. Impact of a standardized recommendation and electronic prompts on follow-up of indeterminate pulmonary nodules found on computed tomography. J Pulm Respir Med. 2012;2(1).
76.
Woloshin S, Schwartz LM, Dann E, Black WC. Using radiology reports to encourage evidence-based practice in the evaluation of small, incidentally detected pulmonary nodules. A preliminary study. Ann Am Thorac Soc. 2014 Feb;11(2):211–4.
77.
Aase A, Fabbrini AE, White KM, Averill S, Gravely A, Melzer AC. Implementation of a standardized template for reporting of incidental pulmonary nodules: feasibility, acceptability, and outcomes. J Am Coll Radiol. 2020 Feb;17(2):216–23.
78.
McDonald JS, Koo CW, White D, Hartman TE, Bender CE, Sykes AMG. Addition of the Fleischner Society Guidelines to chest CT examination interpretive reports improves adherence to recommended follow-up care for incidental pulmonary nodules. Acad Radiol. 2017 Mar;24(3):337–44.
79.
Sloan CE, Chadalavada SC, Cook TS, Langlotz CP, Schnall MD, Zafar HM. Assessment of follow-up completeness and notification preferences for imaging findings of possible cancer: what happens after radiologists submit their reports? Acad Radiol. 2014 Dec;21(12):1579–86.
80.
Cho JK, Zafar HM, Lalevic D, Cook TS. Patient factor disparities in imaging follow-up rates after incidental abdominal findings. AJR Am J Roentgenol. 2019 Mar;212(3):589–95.
81.
Kapoor S, Deppen SA, Paulson AB, Haddad D, Cook JP, Sandler KL. Education level predicts appropriate follow-up of incidental findings from lung cancer screening. J Am Coll Radiol. 2020 May;17(5):613–9.
82.
Azour L, Ko JP, Washer SL, Lanier A, Brusca-Augello G, Alpert JB, et al. Incidental lung nodules on cross-sectional imaging: current reporting and management. Radiol Clin North Am. 2021 Jul;59(4):535–49,
83.
Lacson R, O’Connor SD, Andriole KP, Prevedello LM, Khorasani R. Automated critical test result notification system: architecture, design, and assessment of provider satisfaction. AJR Am J Roentgenol. 2014 Nov;203(5):W491–6.
84.
Desai S, Kapoor N, Hammer MM, Levie A, Sivashanker K, Lacson R, et al. RADAR: a closed-loop quality improvement initiative leveraging a safety net model for incidental pulmonary nodule management. Jt Comm J Qual Patient Saf. 2021 May;47(5):275–81.
85.
Hammer MM, Kapoor N, Desai SP, Sivashanker KS, Lacson R, Demers JP, et al. Adoption of a closed-loop communication tool to establish and execute a collaborative follow-up plan for incidental pulmonary nodules. AJR Am J Roentgenol. 2019 Feb;212:1077–81.
86.
Baccei SJ, Chinai SA, Reznek M, Henderson S, Reynolds K, Brush DE. System-level process change improves communication and follow-up for emergency department patients with incidental radiology findings. J Am Coll Radiol. 2018 Apr;15(4):639–47.
87.
Wandtke B, Gallagher S. Reducing delay in diagnosis: multistage recommendation tracking. AJR Am J Roentgenol. 2017 Nov;209(5):970–5.
88.
Dyer DS, Zelarney PT, Carr LL, Kern EO. Improvement in follow-up imaging with a patient tracking system and computerized registry for lung nodule management. J Am Coll Radiol. 2021 Jul;18(7):937–46.
89.
Shelver J, Wendt CH, McClure M, Bell B, Fabbrini AE, Rector T, et al. Effect of an automated tracking registry on the rate of tracking failure in incidental pulmonary nodules. J Am Coll Radiol. 2017 Jun;14(6):773–7.
90.
Lacson R, Prevedello LM, Andriole KP, O’Connor SD, Roy C, Gandhi T, et al. Four-year impact of an alert notification system on closed-loop communication of critical test results. AJR Am J Roentgenol. 2014 Nov;203(5):933–8.
91.
Dreyer KJ, Thrall JH, Hirschhorn DS, Mehta A. PACS – a guide to the digital revolution. 2nd ed. New York: Springer. 2006.
92.
Waterfield Price N, Janes S, Gleeson F, Massion PP, Lehman J. Prediction of adenocarcinoma among other subtypes of lung cancer from CT using deep learning. J Clin Oncol. 2021 May;39(15 Suppl):3057.
93.
Goo JM. A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective. Korean J Radiol. 2011 Mar–Apr;12(2):145–55.
94.
Kim R, Arteta C, Pickup L, Chometon Q, Munden RF, Dotson TL, et al. An artificial intelligence lung cancer prediction tool improves clinicians' risk assessment of indeterminate pulmonary nodules. Am J Respir Crit Care Med. 2021;203:A1081.
95.
Joy Mathew C, David AM, Joy Mathew CM. Artificial intelligence and its future potential in lung cancer screening. EXCLI J. 2020;19:1552–62.
96.
Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019 Jun;25(6):954–61,
97.
Heuvelmans MA, van Ooijen PMA, Ather S, Silva CF, Han D, Heussel CP, et al. Lung cancer prediction by deep learning to identify benign lung nodules. Lung Cancer. 2021 Apr;154:1–4.
98.
Gong J, Liu J, Hao W, Nie S, Zheng B, Wang S, et al. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images. Eur Radiol. 2020 Apr;30(4):1847–55.
99.
AI for Radiology Products. Subspeciality Chest, Modality CT. [cited 2022 Mar 14]. Available from: https://grand-challenge.org/aiforradiology/?subspeciality=Chest&modality=CT&ce_under=All&ce_class=All&fda_class=All&sort_by=ce%20certification&search=.
100.
van Ginneken B, Schaefer-Prokop CM, Prokop M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology. 2011;261(3):719–32.