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M.J.A.M. Van Putten
Author of
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ORAL 24 - CT Detected Nodules - Predicting Biological Outcome (ID 122)
- Event: WCLC 2015
- Type: Oral Session
- Track: Screening and Early Detection
- Presentations: 1
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ORAL24.02 - Quantification of Growth Patterns of Screen-Detected Lung Cancers: The NELSON Trial (ID 1455)
10:56 - 11:07 | Author(s): M.J.A.M. Van Putten
- Abstract
Background:
A wait-and-see principle is not commonly used when lung cancer is suspected, because of the aggressiveness of the disease. In-vivo information on growth patterns of lung cancers, from small nodules barely detectable by imaging techniques to histologically proven lung cancers, is therefore scarce. In low-dose computed tomography (LDCT) lung screening, lung nodules, usually benign, are found in the majority of screenees. Follow-up CT examinations are performed to determine nodule growth, in order to differentiate between benign and malignant nodules. Growth is often defined in terms of volume-doubling time (VDT), under the assumption of exponential growth. However, this pattern has never been quantified in actual patient data. Our purpose was to evaluate and quantify growth patterns of lung cancers detected in LDCT lung cancer screening, in order to elucidate the development and progression of early lung cancer.
Methods:
The study was based on data of the Dutch-Belgian randomized lung cancer screening trial (NELSON trial). Solid lung cancers detected at ≥3 LDCT examinations before referral and diagnosis were included. Nodule volume was calculated by semi-automated software (LungCARE, Siemens, Erlangen). We fitted lung cancer volume (V) growth curves with a single exponential, expressed as V=V~1~exp(t/τ), with t time from baseline (days), V~1~ estimated volume at baseline (mm[3]) and τ estimated time constant. Overall VDT per lung cancer for all time points combined was calculated using τ*log(2). We used R[2] coefficient of determination as a measure for goodness of fit, where a perfect fit results in R[2]=1. A normalized growth curve for all lung cancers combined was created by plotting normalized volume (V/V~1~), on a logarithmic y-axis as a function of normalized time, t*=t/τ. Statistical analyses were performed using SPSS 20.0 and Octave (www.octave.org).
Results:
Forty-seven lung cancers in 46 participants were included. Seven participants were female (13.0%); mean age 61.7 ±6.2 years. Median follow-up time before lung cancer was diagnosed, was 770 days (IQR: 383-1102 days). One cancer (2.1%) was diagnosed after six LDCTs, six (12.8%) after five LDCTs, 14 (29.8%) after four LDCTs, and 26 cancers (55.3%) after three LDCTs. Most lung cancers were stage I disease (35/47, 74.5%) at diagnosis. The majority concerned adenocarcinoma (38/48, 80.9%). Median overall VDT was 348 days (IQR: 222-492). Overall VDT for adenocarcinomas versus other histological cancer types were similar (median 338 days [IQR: 225-470 days] versus 348 days [IQR: 153-558 days], respectively [p=NS]). Good fit to exponential growth was confirmed by the high R[2] coefficient of determination for the individual cancer growth curves (median 0.98; IQR: 0.94-0.99). After normalization, we found linear growth on a logarithmic scale, according to exponential growth, for almost all nodules. Not all cancers showed an exponential growth immediately from baseline; five cancers were identified with constant (low) volume for >500 days before growth expansion occurred. However, when these dormant lung cancers started growing, they followed the exponential function with excellent fit (median 1.00; IQR: 0.98-1.00).
Conclusion:
Screen-detected lung cancers usually evolve at an exponential growth rate. This makes VDT a powerful imaging biomarker to stratify prevalent lung nodules to growth rates.
