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David Raymond Baldwin
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ES10 - Advances in Lung Cancer Screening Through Imaging and Data Analytics (ID 149)
- Event: WCLC 2020
- Type: Educational Session
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 1/30/2021, 09:15 - 10:15, Scientific Program Auditorium
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ES10.06 - Putting It Together: Current Best Practices in the Evaluation and Follow-Up of the Indeterminate Pulmonary Nodule (ID 3954)
09:15 - 10:15 | Presenting Author(s): David Raymond Baldwin
- Abstract
- Presentation
Abstract
In a sense, all pulmonary nodules are indeterminate until there has been a definitive diagnosis or long-term follow-up. Which nodules are included in the “indeterminate” category is determined by the definition applied, and this can differ between established guidelines. For the purposes of this talk, which relates specifically to the context of LDCT screening for lung cancer, the recommendations from five key guidelines on the management of pulmonary nodules have been reviewed1 - 5. A further key guideline, the Fleischner Society 2017 update refers only to nodules detected incidentally, rather than in a screening setting so is not included in the comparisons6.
Most comparisons of recommendations concentrate on the approach to nodules according to size categories because this is one of the major determinants of the probability that a nodule is both malignant and harmful. This is a useful approach but can produce some complicated tables. This talk approaches the subject from a slightly different and patient-centred angle. For participants in screening, it is important to think of the impact of management strategies and there are 3 key outcomes after either a prevalence or incident screen: return to the screening programme for the next scheduled screen; have additional low dose CT(s) before the next screen is due; and be referred to the hospital for further clinical work-up. The chance of harming the participant, both physically and psychologically, increases as we progress through these categories. It is thus important to avoid both additional LDCT and clinical work-up, unless beneficial. Guidelines aim to do this by managing the vast majority of nodules with minimal chance of harm whilst promptly investigating those nodules likely to be both malignant and harmful. It is important to appreciate that nodules that are malignant are not always harmful because they may be very slow growing and treating them may cause more harm. This can be the case for sub-solid nodules.
Comparisons are made for four situations: baseline (prevalence) detection, new nodules (incidence screen), subsolid nodules and growing nodules. The table shows an example of a comparison for baseline detection. The Lung RADS 1.1 and NCCN guidelines essentially make the same recommendations. Guidelines generally agree on the threshold for returning to the screening programme but specify an annual screen (1 year interval) except for the British Thoracic Society (BTS), where the interval can be longer. Guidelines differ somewhat on the management of the nodules that require a shorter interval. It can be seen that there is the potential for a stage shift from T1a (≤10mm) for some of the recommendations although caveats are added such as the option for PET-CT or very short interval CT for larger nodules. Another potentially important difference is the use of semi-automated volumetry, which is the preferred method to measure nodules and their growth in both the BTS guideline and the European Position Statement (EUPS). Both of these documents make a case for the better accuracy of volumetry as compared with manual diameter measurements. This also has implications for some of the other recommendations for growing nodules where management is recommended on the basis of volume doubling time (VDT). For example, both BTS and the EUPS recommend work-up on the basis of specific VDTs, with less invasive options for participants who have nodules with a VDT of 400-600 days. I-ELCAP defines growth sufficient to prompt work-up as a VDT of 180 days, in contrast to BTS and EUPS where it is 400 days.
BTS is the only guideline that firmly recommends the use of multivariable models in the management of people with nodules. The Brock / PanCan model is recommended at baseline to assist in deciding who should undergo PET-CT. This avoids this high-radiation dose scan in people who have low risk nodules. The Herder model is then used to classify nodules further. The use of PET-CT is generally recommended in the further assessment of nodules and the cut-off size is broadly >8-10mm diameter, reflecting the limitations of PET in nodules smaller than this.
Guidelines are cautious with the management of sub-solid nodules, reflecting their often indolent nature and therefore the potential to harm participants by over-zealous treatment. The is particularly the case for pure ground glass / non-solid nodules; most guidelines only recommend an invasive approach if there is a solid component.
Much progress has been made on the management of pulmonary nodules, and this is reflected in the much lower frequency of complications and invasive approaches in participants who do not have cancer. It will be important that screening centres adhere to guidelines to ensure participants experience the maximum benefit with least harm.
1. Callister ME, Baldwin DR, Akram AR, Barnard S, Cane P, Draffan J, et al. British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax. 2015;70 Suppl 2:ii1-ii54
2. Matthijs Oudkerk AD, Rozemarijn Vliegenthart, Thomas Henzler, Helmut Prosch, Claus P Heussel, Gorka Bastarrika, Nicola Sverzellati,, Mario Mascalchi SD, David R Baldwin, Matthew E Callister, Nikolaus Becker, Marjolein A Heuvelmans, Witold Rzyman,, Maurizio V Infante UP, Jesper H Pedersen, Eugenio Paci, Stephen W Duffy, Harry de Koning, John K Field. European position statement on lung cancer screening. Lancet Oncology. 2017;18(12): e754–e66.
3. American College Radiology. Lung RADS v 1.1 2019 [Available from: https://www.acr.org/-/media/ACR/Files/RADS/Lung-RADS/LungRADSAssessmentCategoriesv1-1.pdf?la=en.
4. National Comprehensive Cancer Network. NCCN Lung Cancer Screening Version 1 2021 [Available from: https://www.nccn.org/professionals/physician_gls/pdf/lung_screening.pdf.
5. International Early Lung Cancer Action Program Investigators Group. Protocol Documents 2016 [Available from: https://www.ielcap.org/sites/default/files/I-ELCAP-protocol-summary.pdf.
6. 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;284(1):228-43.
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MA05 - Lung Cancer Screening (ID 174)
- Event: WCLC 2020
- Type: Mini Oral
- Track: Screening and Early Detection
- Presentations: 1
- Moderators:
- Coordinates: 1/30/2021, 11:45 - 12:45, Scientific Program Auditorium
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MA05.06 - Lung Cancer Screening – Cumulative Results from Five UK-Based Programmes (ID 3527)
11:45 - 12:45 | Author(s): David Raymond Baldwin
- Abstract
- Presentation
Introduction
Lung cancer remains the leading cause of cancer related death globally. Low-dose CT (LDCT) screening of high-risk individuals reduces lung cancer specific mortality. An important requirement for any screening programme is to minimise harms, especially in those who do not have cancer. Data from randomised controlled trials (RCT) is often used as the primary source from which to extrapolate risks of harm but they do not reflect modern, real-world practice. In this paper we present cumulative data on screening harms from five UK-based lung cancer screening programmes.
Methods
In the United Kingdom (UK), several implementation pilots and research studies have demonstrated that screening can be successfully delivered within or aligned to the NHS. These include: UK Lung Cancer Screening Trial (UKLS), Lung Screen Uptake Trial, Manchester Lung Health Checks, Liverpool Healthy Lung Project and Nottingham Lung Health MOT. Most sites, other than UKLS, used the British Thoracic Society (BTS) nodule management guidelines. Positive screening results were defined as those referred for more than a repeat LDCT. False positives were those positive screens without an eventual diagnosis of lung cancer. Harms were categorised according to the need for further imaging, invasive investigations and/or surgery. Complications were categorised as per the National Lung Screening Trial (NLST).
Results
A total of 11,815 screening LDCTs were performed across the five projects between 2016 and 2020. Overall, 85.5% of screening scans were categorised as negative, 10.5% as indeterminate and 4% as positive. Lung cancer detection was 2.1%, ranging from 1.7% to 4.4% across sites. The surgical resection rate was 66.0%. Details of the cumulative reported harms are summarised in Table 1.
Table 1. Details of cumulative reported harms
Reported screening related harm
Total % (n)
Per 1000 screening scans
False positive rate
As a proportion of all LDCT scans
1.9% (219)
17
As a proportion of all positive scans
(i.e. false discovery rate)
46.7% (219)
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Invasive investigation* for benign disease (excluding surgery)
0.5% (61)
5
Surgical resection for benign disease
As a proportion of all surgeries
4.6% (8)
1
As a proportion of all LDCT scans
0.07% (8)
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Major complication+ from invasive
investigation/treatment for benign disease
0% (0)
0
Deaths from invasive investigation/treatment for benign disease
0% (0)
0
*image guide biopsies or bronchoscopic procedures; +as defined by NLST
Conclusion
Discussion: This collaborative work provides up-to-date data on lung cancer screening performance and harms. The rate of positive (4%) and false positive (1.9%) screening results were significantly lower than NLST and the majority of European screening trials other than NELSON. Harms from investigation and treatment of non-malignant disease was minimised with no reported major complications or deaths. This provides reassurance that with the use of evidence-based practice and experienced lung MDTs, harms from false positive results can be minimised within screening. This information is important in the planning of larger scale implementation of lung cancer screening within the UK and beyond.
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P42 - Screening and Early Detection - Risk Modelling and Artificial Intelligence (ID 177)
- Event: WCLC 2020
- Type: Posters
- Track: Screening and Early Detection
- Presentations: 2
- Moderators:
- Coordinates: 1/28/2021, 00:00 - 00:00, ePoster Hall
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P42.01 - AI Assistance for Pulmonary Nodule Stratification: An Multiple-Reader Multiple-Case Study (ID 3481)
00:00 - 00:00 | Author(s): David Raymond Baldwin
- Abstract
Introduction
Improving stratification of patients with indeterminate pulmonary nodules (IPNs) can lead both to earlier diagnosis of lung cancer and to reduced scanning and reduced intervention in cases of benign disease. AI-based decision support software has been shown to outperform conventional risk models at classifying IPNs as low or high risk, but its performance in addition to clinician assessment has yet to be investigated. We report the results of a Multiple-Reader Multiple-Case reader evaluation comparing reader performance for both radiologists and pulmonologists on an IPN risk stratification task with and without AI assistance from the previously-published Lung Cancer Prediction Convolutional Neural Network (LCP-CNN).
Methods
A pool of 12 readers interpreted 100 non-contrast chest CTs each with at least one IPN. The reader breakdown was 7 radiologists, 5 pulmonologists. 7 were UK and 5 US, with representation from both specialties in both geographic zones. They ranged in experience from registrar (resident) to consultant (attending). Readers interacted with viewing software in which they could scroll through axial slices, and adjust window/level settings, but were blinded to patient clinical information. Readers first estimated the likelihood of malignancy for each nodule independently (solo-LoM) as a percentage. The reader was then provided with the AI score as a number from 1-10 and allowed to update assessment of LoM (assisted-LoM). The dataset comprised 50 histologically-diagnosed primary lung cancers (median 10.1mm, IQR 8-13mm) and 50 benign nodules (median 8.8mm, IQR 7-11mm). The nodules were detected incidentally at six EU centres, and were all 5-15mm in size at detection, in patients of 18+ years without a history of malignancy in the past 5 years. Performance was analysed comparing the Area Under the ROC curve (AUC) for the solo-LoM and assisted-LoM. CIs and P-values were calculated using bootstrapping.
Results
The average pre-AI AUC over all readers was 76.6 (95%CI 68.7-83.7), and 84.3 (77.2-90.6) when assisted by AI (P<.0001). The mean improvement over the set of readers is 7.7 points of AUC (95%CI 4.6-11.0). Table 1 shows the performance on a per-reader basis. All readers improved when assisted by the AI, and the improvement was significant in 10/12 readers at the 0.05 level.
Table 1: Performance on per-reader basis
ConclusionReader #
AUC pre-AI
AUC post-AI
AUC improvement
P value
1
81.1 (72.1-89.1)
88.0 (81.0-93.8)
6.9 (1.8-12.4)
.004
2
74.5 (64.5-83.5)
81.9 (73.4-89.2)
7.4 (3.2-12.3)
<.001
3
80.7 (71.3-89.1)
83.1 (74.0-91.2)
2.5 (0.0-5.4)
.024
4
76.5 (66.4-85.7)
87.6 (80.3-93.7)
11.2 (4.3-18.6)
<.001
5
80.2 (71.3-87.9)
82.1 (73.6-89.3)
1.9 (-0.6-4.4)
.066
6
71.6 (61.2-81.0)
83.7 (75.5-90.7)
12.1 (6.7-18.3)
<.001
7
70.6 (60.1-80.0)
85.5 (77.8-92.2)
14.9 (7.3-23.3)
<.001
8
76.4 (66.6-85.1)
85.2 (77.4-91.9)
8.8 (1.2-16.7)
.012
9
80.3 (71.3-88.3)
82.3 (73.8-89.8)
2.0 (-1.2-5.1)
.105
10
78.1 (68.6-86.5)
85.4 (77.5-92.1)
7.3 (2.6-12.3)
<.001
11
72.0 (61.6-81.3)
82.6 (74.1-90.1)
10.6 (5.0-16.8)
<.001
12
76.8 (67.2-85.5)
84.1 (76.0-91.1)
7.3 (2.9-12.2)
<.001
Radiologists and pulmonologists were able to significantly improve their assessment of the likelihood of malignancy for an IPN when assisted by AI score.
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P42.07 - Comparative Performance of Lung Cancer Risk Models to Define Lung Screening Eligibility in the United Kingdom (ID 905)
00:00 - 00:00 | Author(s): David Raymond Baldwin
- Abstract
Introduction
The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the United Kingdom.
Methods
We analysed current and former smokers aged 40-80 in the UK Biobank (N=217,199), EPIC-UK (N=30,982), and Generations Study (N=25,849). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC).
Results
Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC=0.82, 95%CI=0.81-0.84), followed by the LCRAT (AUC=0.81, 95%CI=0.79-0.82) and the Bach model (AUC=0.80, 95%CI=0.79-0.81) (Figure). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.30 for PLCOm2012 (95%CI=1.23-1.36) to 2.16 for LLPv2 (95%CI=2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF criteria classified 50.6% of future cases as screening-eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.1%), and LLPv2 (53.6%).
Conclusion
Discrimination of lung cancer risk models in UK cohorts was highest for LCDRAT and LCRAT, and lowest for LLPv2. Our results highlight the importance of context-specific validation for prediction tools.
