Journal Information
Share
Share
Download PDF
More article options
Visits
821
Original article
Full text access
Available online 15 April 2024
Personalised follow-up and management schema for patients with screen-detected pulmonary nodules: A dynamic modelling study
Visits
821
Z. Wanga,b, F. Xuea, X. Suic, W. Hana, W. Songc, J. Jianga,
Corresponding author
jingmeijiang@ibms.pumc.edu.cn

Corresponding author at: No. 5 Dongdansantiao Street, Dongcheng District, Beijing 100005, China.
a Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College. No. 5 Dongdansantiao Street, Dongcheng District, Beijing, China
b Peking University People's Hospital, Peking University Hepatology Institute, Beijing Key Laboratory of Hepatitis C and Immunotherapy for Liver Diseases. No. 11 Xizhimen South Street, Beijing, China
c Department of Radiology, Peking Union Medical College Hospital. No.1 Shuaifuyuan Street, Dongcheng District, Beijing, China
This item has received
Article information
Abstract
Full Text
Bibliography
Download PDF
Statistics
Figures (2)
Tables (2)
Table 1. Characteristics of patients with nodule(s).
Table 2. Comparison of guideline protocols and personalized schema in validation cohort.
Show moreShow less
Additional material (1)
Abstract
Background

Selecting the time target for follow-up testing in lung cancer screening is challenging. We aim to devise dynamic, personalized lung cancer screening schema for patients with pulmonary nodules detected through low-dose computed tomography.

Methods

We developed and validated dynamic models using data of pulmonary nodule patients (aged 55–74 years) from the National Lung Screening Trial. We predicted patient-specific risk profiles at baseline (R0) and updated the risk evaluation results in repeated screening rounds (R1 and R2). We used risk cutoffs to optimize time-dependent sensitivity at an early decision point (3 months) and time-dependent specificity at a late decision point (1 year).

Results

In validation, area under receiver operating characteristic curve for predicting 12-month lung cancer onset was 0.867 (95 % confidence interval: 0.827–0.894) and 0.807 (0.765–0.948) at R0 and R1-R2, respectively. The personalized schema, compared with National Comprehensive Cancer Network (NCCN) guideline and Lung-RADS, yielded lower rates of delayed diagnosis (1.7% vs. 1.7% vs. 6.9 %) and over-testing (4.9% vs. 5.6% vs. 5.6 %) at R0, and lower rates of delayed diagnosis (0.0% vs. 18.2% vs. 18.2 %) and over-testing (2.6% vs. 8.3% vs. 7.3 %) at R2. Earlier test recommendation among cancer patients was more frequent using the personalized schema (vs. NCCN: 29.8% vs. 20.9 %, p = 0.0065; vs. Lung-RADS: 33.2% vs. 22.8 %, p = 0.0025), especially for women, patients aged ≥65 years, and part-solid or non-solid nodules.

Conclusions

The personalized schema is easy-to-implement and more accurate compared with rule-based protocols. The results highlight value of personalized approaches in realizing efficient nodule management.

Keywords:
Lung cancer screening
Dynamic modelling
Risk prediction
Follow-up test planning
Full Text
Introduction

Lung cancer screening with low-dose computed tomography (LDCT) is routinely recommended for individuals at high risk for the disease.1,2 A quarter to half of screened individuals have at least one pulmonary nodule,3,4 a gateway to repeated imaging, diagnostic work-up and treatment including surgical resection. Benefits of early diagnosis and treatment of cancer largely depend on criteria and frequency of follow-up examinations.5 However, these benefits are often offset by high over-testing rates, resource waste, complications, and mental stress.6,7 Precisely planning follow-up testing is therefore critical to improving the effectiveness of screening programs.5,8,9

Selecting the time target for follow-up testing is clinically challenging. Current guidelines use flowcharts to classify nodules according to size and attenuation, whereupon immediate diagnostic work-up or recall in 3 months, 6 months, or 1 year is recommended.10–13 These rules have been proposed by different expert panels and therefore differ among existing guidelines,14 with varied practical effects and poor clinical adherence.15,16

In this work, we present a dynamic and easy-to-implement schema to personalize the time interval between tests for patients detected with pulmonary nodules in lung cancer screening. Compared with two rule-based guideline protocols,10,11 we demonstrated the capability of this personalized approach to maximize timely diagnosis and minimize over-testing, thereby improving the screening workflow.

MethodsStudy population

We based this study on the National Lung Screening Trial (NLST).17 All participants from 33 medical centers underwent baseline screening (R0) and subsequently, a maximum of two rounds of repeat annual screening (R1 and R2) if no lung cancer was diagnosed. Follow-up was conducted through the end of 2009, with the longest follow-up duration >8 years.

We accessed data from the LDCT arm (delivery ID: NLST-503) and used inclusion criteria as follows: individuals aged 55–74 years at R0 with at least a 30 pack-year smoking history and smoking cessation <15 years. Exclusion criteria were lung cancer history; CT examination within 18 months before participation; and no positive findings during R0–R2, defined as ≥1 non-calcified pulmonary nodule or mass detected on LDCT.

Patient selection is depicted in Fig A.1. We included all (809) lung cancer patients who had ≥1 diameter record, which is the primary variable for planning follow-up testing. We retrospectively selected a sample (1000) of cancer-free pulmonary nodule patients to lower the burden in nodule selection, linkage, and quantification. Sample size determination is detailed in Methods A.1. Using a 2:1 ratio, we divided the 1809 selected patients into two patient cohorts, one for schema development (1206) and another for validation (603).

The study was approved by the institutional review board of Institute of Basic Medical Sciences, Chinese Academy of Medical Science. Patient consent was exempt as only publicly available data was used.

Outcomes and predictors

We used a joint modelling framework and considered two classes of outcome for implementing dynamic prediction18,19: time-to-event outcomes, defined as a lung cancer diagnosis and its time interval since the most recent test; and longitudinal outcomes, i.e., trajectories of nodule diameter. We applied this simple image biomarker for ease of interpretation and clinical use, as well as for meaningful comparisons of our approach with rule-based protocols that largely rely on diameter measurement.10,11

Model predictors were selected according to statistical or clinical significance. These included epidemiological information (age, obesity, family history of lung cancer, smoking pack-years) and nodule information (attenuation and margin), coded as binary variables where appropriate. Height or weight data for determining obesity were missing in 7 (0.4 %) patients; these were imputed according to the sex mean.

Dynamic prediction

We developed a Cox proportional hazards model for a baseline screening scenario and joint models for a repeated screening scenario. Mathematical details are available in Methods A.3. The joint models first predicted the longitudinal outcome (diameter trajectory); this was then used, together with other predictors, to model the risk profiles regarding the time-to-event outcome. Between these sub-models, we used an association structure to account for the diameter measured at the present test and its rate of change over time; both are clinically important in determining cancer risk.20 A unique advantage of this approach is smoothing of nodule diameter measurement error, which can be as high as 25 % in LDCT screening.21

Time target recommendation

We selected two risk cut-offs to optimize accuracy in decisions about timing of the upcoming follow-up test. We based these choices on the analysis of a time-dependent receiver operating curve.22,23 Specifically, we selected one risk cut-off that allowed for sensitivity (t= 3 months) ≥0.95, and another cut-off that allowed for specificity (t= 12 months) ≥0.95. These cut-offs were then used to classify patients (per each screening round) as having high, middle, or low risk, whereupon recommendations for a follow-up test interval of 0 months (i.e., immediate work-up), 3 months, or 12 months (i.e., annual repeat screening) were made. The ≥0.95 criterion was intended to control delayed diagnosis (defined as false recommendation of annual repeat screening for those who develop lung cancer within 3 months) and over-testing (defined as false recommendation of immediate work-up for cancer-free patients) to a small probability (<0.05).

Schema benchmark

To demonstrate strengths and potential weaknesses of the proposed schema, we created a benchmark with two nodule management protocols that are in current use: the NCCN guideline (2022 V2)10 and the Lung CT Screening Reporting & Data System (Lung-RADS 2022).11 We examined delayed diagnosis and over-testing rates following these rule-based protocols versus our personalized schema in the validation cohort. We also investigated which lung cancer patient subgroups could benefit most from a personalized schema in terms of shorter delay in diagnosis.

Statistical analysis

Because of a right-skewed distribution of the nodule diameter, we conducted a natural logarithm transform before using this longitudinal outcome. We estimated parameters of the joint models using a Bayesian method, implemented with a Markov chain Monte Carlo algorithm (1 chain, 11,000 interactions with 1000 burn-ins discarded). We assessed model performance using time-dependent accuracy metrics and estimated 95 % confidence intervals (CIs) using a 1000-sample bootstrap approach.

We performed a log-rank test to examine between-group differences among high-, mid- and low-risk strata. We drew a contingency table to tabulate recommendations on the time target of follow-up testing and ground truth of the time-to-event outcome, whereupon rates of delayed diagnosis and over-testing (as defined above) were calculated. We used a paired-samples McNemar exact probability method to test for statistical significance of these rates.

We considered a two-sided p-value <0.05 to indicate statistical significance. We performed the analyses using SAS 9.4 (SAS Institute Inc., Cary, NC, USA) and R 4.1.2 with packages “JMbayes2 0.2–8”, “riskRegression 2022.09.23”, “tdROC 1.0” and “survminer 0.4.9” (R Project for Statistical Computing, Vienna, Austria).

ResultsPatient characteristics

Table 1 presents characteristics of the included patients. The mean age at R0 was 62.7 years; 58.7 % were men; 50.5 % had an associate's, bachelor's, or higher education degree; 23.7 % were obese; and 24.9 % of patients had a family history of lung cancer. Participants had a median 52.5 pack-year smoking history with a median starting age of 16 years, and half (51.4 %) had not quit smoking before participation. Median follow-up duration was 2197 days (6 years).

Table 1.

Characteristics of patients with nodule(s).

    Overall  Development  Validation  p-value 
Sample size1809  1206  603   
Age, years  Mean (SD)  62.7 (5.3)  62.5 (5.2)  63.2 (5.3)  0.0135 
Gender  Male  1062 (58.7)  717 (59.5)  345 (57.2)  0.3619 
  Female  747 (41.3)  489 (40.5)  258 (42.8)   
Education  11th grade or less  119 (6.6)  82 (6.8)  37 (6.1)  0.3654 
  High school graduate/GED  489 (27.0)  322 (26.7)  167 (27.7)   
  Post high school training  246 (13.6)  160 (13.3)  86 (14.3)   
  Bachelors / Associate degree  667 (36.9)  456 (37.8)  211 (35.0)   
  Graduate School  246 (13.6)  164 (13.6)  82 (13.6)   
  Other / missing  42 (2.3)  22 (1.8)  20 (3.3)   
Obesity (BMI ≥30 kg/m2No  1380 (76.3)  929 (77.0)  451 (74.8)  0.2913 
  Yes  429 (23.7)  277 (23.0)  152 (25.2)   
Family history of lung cancer  No  1359 (75.1)  896 (74.3)  463 (76.8)  0.2486 
  Yes  450 (24.9)  310 (25.7)  140 (23.2)   
Smoking status  Former  880 (48.7)  573 (47.5)  307 (50.9)  0.1726 
  Current  929 (51.4)  633 (52.5)  296 (49.1)   
Age starting smoking, years  Median (IQR)  16 (14–18)  16 (14–18)  16 (14–18)  0.7831 
Pack-year  Median (IQR)  52.5 (42.0–73.5)  52.5 (42.0–72.0)  52.5 (42.0–75.0)  0.8402 
Follow-up days  Median (IQR)  2197 (794–2463)  2212 (813–2480)  2142 (612–2436)  0.0010 
Diagnosis of lung cancer  No  1000 (55.3)  690 (57.2)  310 (51.4)  0.0193 
  Yes  809 (44.7)  516 (42.8)  293 (48.6)   
Time to diagnosis, days  Median (IQR)  735 (181–1300)  755 (203.5–1261)  644 (119–1344)  0.4655 
Pathological type  Adenocarcinoma  393 (49.1)  248 (48.3)  145 (50.5)  0.4714 
  Squamous cell carcinoma  169 (21.1)  114 (22.2)  55 (19.2)   
  Other non-small cell carcinoma  136 (17.0)  92 (17.9)  44 (15.3)   
  Small cell carcinoma  77 (9.6)  44 (8.6)  33 (11.5)   
  Bronchioloalveolar carcinoma/Carcinoid  25 (3.1)  15 (2.9)  10 (3.5)   
Stage at diagnosis  IA  370 (45.7)  243 (47.0)  127 (43.3)  0.4218 
  IB  88 (10.9)  60 (11.6)  28 (9.6)   
  IIA  28 (3.5)  17 (3.3)  11 (3.8)   
  IIB  23 (2.8)  17 (3.3)  6 (2.1)   
  IIIA  69 (8.5)  36 (7.0)  33 (11.3)   
  IIIB  74 (9.1)  47 (9.1)  27 (9.2)   
  IV  143 (17.7)  87 (16.8)  56 (19.1)   
  Not available  15 (1.9)  10 (1.9)  5 (1.7)   

BMI, body mass index; IQR, interquartile range; GED, General Educational Diploma; SD, standard deviation. BMI calculated as weight (kg) / height (m)2.

Of 809 patients diagnosed with lung cancer, the median time to diagnosis was 735 days (2 years); the range was as wide as 4–2499 days. High cancer heterogeneity was also demonstrated in diverse pathological types (9.6 % small cell, 49.1 % adenocarcinoma, 21.1 % squamous cell, 19.9 % other) and stages (e.g., 71.4 % stages IA-IIIA, 26.8 % stages IIIB-IV), suggesting a need for personalized optimization of diagnostic testing.

The above patient characteristics did not differ between the cohorts used for model development and schema validation, except for negligible differences in mean age (62.5 vs. 63.2 years, p = 0.0135) and median follow-up duration (2212 vs. 2142 days, p = 0.0010).

Model performance

The multi-stage models are summarized in Table A.1, and were used to predict onset of lung cancer within a time interval of interest. Results of time-dependent predictive performance of the models are available in Table A.2.

Validation results: the area under the receiver operating curve (AUC) (t= 3 months) was 0.879 (95 % CI: 0.842, 0.917) at R0 and 0.845 (95 % CI: 0.801, 0.892) at R1–R2; the AUC (t= 12 months) was 0.867 (95 % CI: 0.827, 0.894) for R0 and 0.807 (0.765, 0.948) for R1–R2. These were comparable to the development cohort, thus demonstrating the validity of the model performance.

Risk cut-offs selected according to the development cohort yielded high sensitivity (t= 3 months): 0.983 (95 % CI: 0.946, 1.000) for R0; 0.957 (95 % CI: 0.901, 1.000) for R1–R2, and moderately high specificity (t= 12 months): 0.909 (95 % CI: 0.881, 0.938) for R0; 0.936 0.936 (95 % CI: 0.914, 0.958) for R1–R2 in the validation cohort.

In Fig 1, we present risk strata according to the selected cut-offs. In the development and validation cohorts, patients determined as high-, mid- or low-risk had significantly different curves for the cumulative risk of lung cancer (p < 0.0001 at each screening round).

Fig. 1.

Risk stratification effectiveness.

(0.49MB).
Schema benchmark

We compared the personalized schema with the NCCN and Lung-RADS protocols. The results obtained from the validation cohort are shown in Table 2.

Table 2.

Comparison of guideline protocols and personalized schema in validation cohort.

  Recommendation  Overall  Cancer diagnosed within 3 mo  Cancer diagnosed within 3–12 mo  Cancer diagnosed after 12 mo  Cancer-free 
R0  No. of patients  365  58  44  121  142 
NCCN  Immediate work-up  87 (23.8)  39 (67.2)  21 (47.7)  19 (15.7)  8 (5.6) 
  LDCT in 3 mo or PET/CT  89 (24.4)  12 (20.7)  12 (27.3)  39 (32.2)  26 (18.3) 
  LDCT in 6 mo  76 (20.8)  6 (10.3)  7 (15.9)  30 (24.8)  33 (23.2) 
  Annual LDCT  113 (31.0)  1 (1.7)  4 (9.1)  33 (27.3)  75 (52.8) 
Lung-RADS  Immediate work-up  84 (23.0)  37 (63.8)  21 (47.7)  18 (14.9)  8 (5.6) 
  LDCT in 3 mo  81 (22.2)  14 (24.1)  12 (27.3)  33 (27.3)  22 (15.5) 
  LDCT in 6 mo  59 (16.2)  3 (5.2)  5 (11.4)  30 (24.8)  21 (14.8) 
  Annual LDCT  141 (38.6)  4 (6.9)  6 (13.6)  40 (33.1)  91 (64.1) 
personalized  Immediate work-up  84 (23.0)  41 (70.7)  19 (43.2)  17 (14.1)  7 (4.9) 
  LDCT in 3 mo  173 (47.4)  16 (27.6)  22 (50.0)  76 (62.8)  59 (41.6) 
  Annual LDCT  108 (29.6)  1 (1.7)  3 (6.8)  28 (23.1)  76 (53.5) 
R1  No. of patients  343  24  29  84  206 
NCCN  Immediate work-up  91 (26.5)  20 (83.3)  17 (58.6)  21 (25.0)  33 (16.0)** 
  LDCT in 3 mo or PET/CT  30 (8.7)  0 (0.0)  4 (13.8)  9 (10.7)  17 (8.3) 
  LDCT in 6 mo  72 (21.0)  0 (0.0)  3 (10.3)  10 (11.9)  59 (28.6) 
  Annual LDCT  150 (43.7)  4 (16.7)  5 (17.2)  44 (52.4)  97 (47.1) 
Lung-RADS  Immediate work-up  76 (22.2)  18 (75.0)  16 (55.2)  18 (21.4)  24 (11.7)** 
  LDCT in 3 mo  37 (10.8)  2 (8.3)  4 (13.8)  9 (10.7)  22 (10.7) 
  LDCT in 6 mo  80 (23.3)  1 (4.2)  6 (20.7)  19 (22.6)  54 (26.2) 
  Annual LDCT  150 (43.7)  3 (12.5)  3 (10.3)  38 (45.2)  106 (51.5) 
personalized  Immediate work-up  43 (12.5)  12 (50.0)  9 (31.0)  11 (13.1)  11 (5.3) 
  LDCT in 3 mo  149 (43.4)  10 (41.7)  15 (51.7)  52 (61.9)  72 (35.0) 
  Annual LDCT  151 (44.0)  2 (8.3)  5 (17.2)  21 (25.0)  123 (59.7) 
R2  No. of patients  303  22  24  64  193 
NCCN  Immediate work-up  56 (18.5)  15 (68.2)  10 (41.7)  15 (23.4)  16 (8.3)** 
  LDCT in 3 mo or PET/CT  27 (8.9)  1 (4.6)  2 (8.3)  8 (12.5)  16 (8.3) 
  LDCT in 6 mo  49 (16.2)  2 (9.1)  3 (12.5)  8 (12.5)  36 (18.7) 
  Annual LDCT  171 (56.4)  4 (18.2)*  9 (37.5)  33 (51.6)  125 (64.8) 
Lung-RADS  Immediate work-up  52 (17.2)  15 (68.2)  9 (37.5)  14 (21.9)  14 (7.3)* 
  LDCT in 3 mo  21 (6.9)  0 (0.0)  1 (4.2)  8 (12.5)  12 (6.2) 
  LDCT in 6 mo  52 (17.2)  3 (13.6)  5 (20.8)  10 (15.6)  34 (17.6) 
  Annual LDCT  178 (58.7)  4 (18.2)*  9 (37.5)  32 (50.0)  133 (68.9) 
personalized  Immediate work-up  28 (9.2)  7 (31.8)  7 (29.2)  9 (14.1)  5 (2.6) 
  LDCT in 3 mo  154 (50.8)  15 (68.2)  16 (66.7)  36 (56.3)  87 (45.1) 
  Annual LDCT  121 (39.9)  0 (0.0)  1 (4.2)  19 (29.7)  101 (52.3) 

*p < 0.05 or ** p < 0.01 indicates statistical significance compared with personalized schema in a paired-samples test.

Including one patient recommended LDCT at 6 months or excision or resection.

LDCT, low-dose computed tomography; Lung-RADS, Lung CT Screening Reporting & Data System; NCCN, National Comprehensive Cancer Network; PET, positron emission computed tomography.

In R0, the three protocols performed equally well at controlling delayed diagnosis (rates: 1.7% vs. 6.9% vs. 1.7 % following NCCN, Lung-RADS, and our schema) and over-testing (5.6% vs. 5.6% vs. 4.9 %); all p > 0.05.

In R1–R2, the personalized schema outperformed the rule-based protocols. The rate of delayed diagnosis associated with the NCCN, Lung-RADS, and our schema was 16.7 % versus 12.5 % versus 8.3 % in R1, and 18.2 % versus 18.2 % versus 0.0 % in R2; the rate of over-testing was 16.0 % versus 11.7 % versus 5.3 % in R1, and 8.3 % versus 7.3 % versus 2.6 % in R2 (statistical significance shown in Table 2).

Differences in cancer subgroups

Among 470 available decision time points for 293 patients with lung cancer in the validation cohort, 232 (49.4 %) and 207 (44.0 %) follow-up testing recommendations were consistent between NCCN and the personalized schema and between Lung-RADS and the personalized schema, respectively. Earlier test recommendation was less frequent using NCCN versus the personalized schema: 98 (20.9 %) versus 140 (29.8 %); p = 0.0065; or using Lung-RADS versus the personalized schema: 107 (22.8 %) versus 156 (33.2 %); p = 0.0025. Subgroup analyses (Fig 2) identified several subgroups of patients with lung cancer who were more likely to benefit from the personalized schema than the NCCN protocol and the Lung-RADS protocol (patients aged ≥65 years, women, former smokers, and patients with part-solid or non-solid attenuation, adenocarcinoma cancer, and stage IIIB-IV; all p < 0.05).

Fig. 2.

Subgroup analysis of patients with lung cancer.

*p < 0.05 or ** p < 0.01 indicates statistical significance in a paired-samples test.

Lung-RADS, Lung CT Screening Reporting & Data System; NCCN, National Comprehensive Cancer Network.

(0.46MB).
Clinical application

We provide a web application (available at http://www.biostatpumc.com:3838/pred_risk_2.Rmd) for computer or cell phone users to check and update their follow-up recommendations generated by the personalized schema. We illustrate its use in two example cases from our institute and preliminarily examine applicability in NLST-ineligible patients (Fig A.2.).

The schema can be adapted according to patient and physician preferences. Tables A.3A.5 illustrate that decreasing the criteria of sensitivity(t) or specificity(t) (e.g., from ≥0.95 to ≥0.90) would result in more conservative recommendations (i.e., fewer recommendations for immediate work-up and more for annual screening); in contrast, increasing these criteria would mean more aggressive recommendations.

Discussion

A National Cancer Institute review states that available evidence that supports guidelines on the time target for follow-up after a positive screening is low across cancers, and very low regarding lung cancer.24 Here, we present a personalized solution to this challenge. Compared with two rule-based guideline protocols used frequently in clinical settings, the personalized schema showed better capacity in terms of securing a timely diagnosis while reducing costs and resource use related to avoidable testing. In particular, it demonstrated strength regarding early testing for several subgroups of patients with lung cancer including women, former smokers, and patients with part-solid or non-solid nodules.

The valuable role of risk prediction models in personalizing lung cancer screening has been evidenced in some publications on selecting individuals for screening.25–27 The epidemiological and nodule information that comprised our models were largely the same as existing single-stage models for evaluating lung cancer risk.28–30 This makes our approach open to model comparison, validation, and re-calibration in different populations. The dynamic property, i.e., time-dependent prediction horizon and its associated outputs, sets our approach apart from other models. Because translating risk into a diagnostic decision can lead to error, particularly in the setting of population screening where harm related to mis- or missed diagnosis can be substantially augmented, our models are intended for recommendations regarding a time interval for an upcoming test rather than predicting benignity or malignancy. Our work therefore pertains to longitudinal rather than one-off cancer screening and provides a vehicle to personalize patients’ visit schedules.

Studies have identified that accuracy of Lung-RADS recommendations improve when there is an initial screen to compare against.31 Therefore, it is important to consider time target decision strategies separately in baseline and repeated screening scenarios. In a previous proof-of-concept study, we put forward a radiomics model for follow-up timing after baseline screening, which demonstrated better performance than existing guidelines in a small-sized patient sample.32 As to the application of multiple tests in repeated screening, Tammemägi et al used combinations of positive or negative results throughout R0–R2 among NLST participants and predicted whether a patient would be diagnosed with lung cancer after R2.33 The question is more complicated when it comes to dynamically analyzing the nodule trajectory as an individual's disease history unfolds. Although cancer heterogeneity makes it difficult to identify an optimal solution, our results showed that the proposed schema works better than guideline protocols in repeated screening rounds. This demonstrate that personalized approaches could provide a unique way to deepen understanding as well as a better means (compared with arbitrary cut-offs in nodule size or its increase) to inform follow-up decisions.

Several features of our personalized schema make it distinct from existing rule-based guidelines. First, we did not consider a follow-up interval of 6 months, which neither reduces avoidable tests nor promotes an early diagnosis. Second, the rule-based guidelines differ regarding the management of solid, sub-solid, and non-solid nodules. We have simplified this categorization because its clinical judgment is sometimes challenging and can vary moderately or substantially.34 Third, nodule diameter measurement is prone to error in LDCT and varies among radiologists.21 The joint modelling approach used in this study has unique advantages in avoiding these problems. Nevertheless, the moderate agreement observed between the rule-based and personalized approaches suggest that they can complement each other and be used to generate stronger confidence when recommendations are consistent.

There are several limitations in the study that warrant consideration. First, the extensively validated NLST dataset provides a strong basis for devising follow-up plans in the NLST-eligible population, i.e., individuals aged 55–74 years having a 30 pack-year smoking history; the applicability of our findings in other populations (e.g., younger, or passively smoking) is unclear. Second, prospective and cost-effectiveness studies are needed before integrating the personalized schema into public health programs given discrepancies in region-specific lung cancer epidemic levels and eligibility criteria for screening. Third, despite our efforts to link nodule observations over repeat scans, errors may persist because of insufficient annotation data.35 Fourth, we treated nodules newly detected during R1–R2 in an equal manner as those detected in R0, although the biological properties of incident versus prevalent cancers may vary.36

Conclusions

The personalized lung cancer screening schema is easy-to-implement and more accurate compared with rule-based protocols. Further research is needed to examine its value in precision screening for lung cancer in diverse populations and settings.

Data availability

Data supporting this work is publicly available through the Cancer Imaging Achieve at: https://www.cancerimagingarchive.net.

Ethics approval

Not applicable.

Patient consent

Not applicable.

Declaration of generative AI in scientific writing

None.

Funding

This study was supported by the National Natural Science Foundation of China [grant number 82304215], the CAMS Fund for Medical Sciences [grant number 2021- 1-I 2M-022], and the National High Level Hospital Clinical Research Funding [grant number 2022-PUMCH-A-034]. The funders had no role in the study design, in the collection, analysis or interpretation of data, in the writing of the report, or in the decision to submit the article for publication.

References
[1]
United States Preventive Services Task Force.
Screening for lung cancer US preventive services task force recommendation statement.
JAMA, 325 (2021), pp. 962-970
[2]
P.J. Mazzone, G.A. Silvestri, L.H. Souter, et al.
Screening for lung cancer: CHEST guideline and expert panel report.
Chest, 160 (2021), pp. e427-e494
[3]
M.A. Heuvelmans, J.E. Walter, R.B. Peters, et al.
Relationship between nodule count and lung cancer probability in baseline CT lung cancer screening: the NELSON study.
Lung Cancer, 113 (2017), pp. 45-50
[4]
G. Ji, T. Bao, Z. Li, et al.
Current lung cancer screening guidelines may miss high-risk population: a real-world study.
BMC Cancer, 21 (2021), pp. 50
[5]
F. Farjah, S.E. Monsell, M.K. Gould, et al.
Association of the intensity of diagnostic evaluation with outcomes in incidentally detected lung nodules.
JAMA Intern Med, 181 (2021), pp. 480-489
[6]
M. Oudkerk, S. Liu, M. Heuvelmans, et al.
Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives.
Nat Rev Clin Oncol, 18 (2021), pp. 135-151
[7]
J. Sands, M.C. Tammemägi, S. Couraud, et al.
Lung screening benefits and challenges: a review of the data and outline for implementation.
J Thorac Oncol, 16 (2021), pp. 37-53
[8]
M. Bastani, I. Toumazis, J. Hedou', et al.
Evaluation of alternative diagnostic follow-up intervals for lung reporting and data system criteria on the effectiveness of lung cancer screening.
J Am Coll Radiol, 18 (2021), pp. 1614-1623
[9]
M.P. Rivera, D.D. Durham, J.M. Long, et al.
Receipt of recommended follow-up care after a positive lung cancer screening examination.
JAMA Netw Open, 5 (2022),
[10]
Wood D.E., Kazerooni E.A., Aberle D., et al. Lung cancer screening, version 2.2022. NCCN clinical practice guidelines in oncology. Available at: NCCN.org. Accessed July 23, 2022.
[11]
American College of Radiology. Lung CT screening reporting & data system (Lung-RADS). https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Lung-Rads. Accessed July 23, 2022.
[12]
M.T. Jaklitsch, F.L. Jacobson, J.H. Austinet, et al.
The American Association for Thoracic Surgery guidelines for lung cancer screening using low-dose computed tomography scans for lung cancer survivors and other high-risk groups.
J Thorac Cardiovasc Surg, 144 (2012), pp. 33-38
[13]
Respiratory Branch of Chinese Medical Association.
Chinese expert consensus on diagnosis of early lung cancer (2023 Edition).
Chinese J Tuberculosis Respir Dis, 46 (2023), pp. 1-18
[14]
Z.Y. Li, L. Luo, Y.H. Hu, et al.
Lung cancer screening: a systematic review of clinical practice guidelines.
Int J Clin Pract, 70 (2016), pp. 20-30
[15]
C.I. Henschke, R. Yip, T. Ma, et al.
CT screening for lung cancer: comparison of three baseline screening protocols.
Eur Radiol, 29 (2019), pp. 5217-5226
[16]
R.S. Wiener, M.K. Gould, C.G. Slatore, et al.
Resource use and guideline concordance in evaluation of pulmonary nodules for cancer: too much and too little care.
JAMA Intern Med, 174 (2014), pp. 871-880
[17]
D.R. Aberle, A.M. Adams, C.D. Berg, et al.
Reduced lung-cancer mortality with low-dose computed tomographic screening.
N Engl J Med, 365 (2011), pp. 395-409
[18]
M. Alsefri, M. Sudell, M. García-Fiñana, et al.
Bayesian joint modelling of longitudinal and time to event data: a methodological review.
BMC Med Res Methodol, 20 (2020), pp. 94
[19]
M. Schumacher, S. Hieke, G. Ihorst, et al.
Dynamic prediction: a challenge for biostatisticians, but greatly needed by patients, physicians and the public.
Biom J, 62 (2020), pp. 822-835
[20]
M.A. Heuvelmans, M. Oudkerk, G.H. de Bock, et al.
Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals.
Eur Radiol, 23 (2013), pp. 1836-1845
[21]
P. Maisonneuve, V. Bagnardi, M. Bellomi, et al.
Lung cancer risk prediction to select smokers for screening CT–a model based on the Italian COSMOS trial.
Cancer Prev Res, 4 (2011), pp. 1778-1789
[22]
A.N. Kamarudin, T. Cox, R. Kolamunnage-Dona.
Time-dependent ROC curve analysis in medical research: current methods and applications.
BMC Med Res Methodol, 17 (2017), pp. 53
[23]
A. Bansal, P.J. Heagerty.
A tutorial on evaluating the time-varying discrimination accuracy of survival models used in dynamic decision making.
Med Decis Making, 38 (2018), pp. 904-916
[24]
C.A. Doubeni, N.B. Gabler, C.M. Wheeler, et al.
Timely follow-up of positive cancer screening results: a systematic review and recommendations from the PROSPR Consortium.
CA Cancer J Clin, 68 (2018), pp. 199-216
[25]
H.A. Katki, S.A. Kovalchik, et al.
Implications of nine risk prediction models for selecting eversmokers for computed tomography lung cancer screening.
Ann Intern Med, 169 (2018), pp. 10-19
[26]
M.C. Tammemägi, H.A. Katki, W.G. Hocking, et al.
Selection criteria for lung-cancer screening.
N Engl J Med, 368 (2013), pp. 728-736
[27]
K. Ten Haaf, J. Jeon, M.C. Tammemägi, et al.
Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study.
PLoS Med, 14 (2017),
[28]
I. Toumazis, M. Bastani, S.S. Han, et al.
Risk-Based lung cancer screening: a systematic review.
Lung cancer, 147 (2020), pp. 154-186
[29]
A. McWilliams, M.C. Tammemagi, J.R. Mayo, et al.
Probability of cancer in pulmonary nodules detected on first screening CT.
N Engl J Med, 369 (2013), pp. 910-919
[30]
Z. Wu, F. Wang, W. Cao, et al.
Lung cancer risk prediction models based on pulmonary nodules: a systematic review.
Thorac Cancer, 13 (2022), pp. 664-677
[31]
Q. Li, Y. Balagurunathan, Y. Liu, et al.
Comparison between radiological semantic features and Lung-RADS in predicting malignancy of screen-detected lung nodules in the national lung screening trial.
Clin Lung Cancer, 19 (2018),
[32]
Z. Wang, N. Li, F. Zheng, et al.
Optimizing the timing of diagnostic testing after positive findings in lung cancer screening: a proof of concept radiomics study.
J Transl Med, 19 (2021), pp. 191
[33]
M.C. Tammemägi, K. Ten Haaf, I. Toumazis, et al.
Development and validation of a multivariable lung cancer risk prediction model that includes low-dose computed tomography screening results: a secondary analysis of data from the national lung screening trial.
JAMA Netw Open, 2 (2019),
[34]
S.J. van Riel, C.I. Sánchez, A.A. Bankier, et al.
Observer variability for classification of pulmonary nodules on low-dose CT images and its effect on nodule management.
Radiology, 277 (2015), pp. 863-871
[35]
P. Petousis, A. Winter, W. Speier, et al.
Using sequential decision making to improve lung cancer screening performance.
IEEE Access, 7 (2019), pp. 119403-119419
[36]
J. Cho, J. Kim, K.J. Lee, et al.
Incidence lung cancer after a negative CT screening in the national lung screening trial: deep learning-based detection of missed lung cancers.
J Clin Med, 9 (2020), pp. 3908
Download PDF
Pulmonology
Article options
Tools
Supplemental materials

Are you a health professional able to prescribe or dispense drugs?