Lung cancer screening: tell me more about post-test risk

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

Urinary Biomarkers for Early Diagnosis of Lung Cancer

Lung cancer is the leading cause of cancer deaths worldwide. Its early detection has the potential to significantly impact the burden of the disease. The screening and diagnostic techniques in current use suffer from limited specificity. The need therefore arises for a reliable biomarker to identify the disease earlier, which can be integrated into a test. This test would also allow for the recurrence risk after surgery to be stratified. In this context, urine could represent a non-invasive alternative matrix, with the urinary metabolomic profile offering a potential source for the discovery of diagnostic biomarkers. This paper aims to examine the current state of research and the potential for translation into clinical practice.

Validation of multivariable lung cancer risk prediction models for the personalized assignment of optimal screening frequency: a retrospective analysis of data from the German Lung Cancer Screening Intervention Trial (LUSI)

BACKGROUND

Current guidelines for lung cancer screening via low-dose computed tomography recommend annual screening for all candidates meeting basic eligibility criteria. However, lung cancer risk of eligible screening participants can vary widely, and further risk stratification could be used to individually optimize screening intervals in view of expected benefits, possible harms and financial costs. To this effect, models have been developed in the US National Lung Screening Trial based on self-reported lung cancer risk factors and imaging data. We evaluated these models using data from an independent screening trial in Germany.

METHODS

We examined the Polynomial model by Schreuder et al., the Lung Cancer Risk Assessment Tool extended by CT characteristics (LCRAT + CT) by Robbins et al., and a criterion of presence vs. absence of pulmonary nodules ≥4 mm (Patz et al.), applied to sub-sets of screening participants according to eligibility criteria. Discrimination was evaluated via the receiver operating characteristic curve. Delayed diagnoses and false positive results were calculated at various thresholds of predicted risk. Model calibration was assessed by comparing mean predicted risk versus observed incidence.

RESULTS

One thousand five hundred and six participants were eligible for the validation of the LCRAT + CT model, and 1,889 for the validation of the Polynomial model and Patz criterion, yielding areas under the receiver operating characteristic curve of 0.73 (95% CI: 0.63, 0.82), 0.75 (0.67, 0.83), and 0.56 (0.53, 0.72) respectively. Skipping 50% annual screenings (participants within the 5 lowest risk deciles by LCRAT + CT in any round or by the Polynomial model; baseline screening round), would have avoided 75% (21.9%, 98.7%) and 40% (21.8%, 61.1%) false positive screen tests and delayed 10% (1.8%, 33.1%) or no (0%, 32.1%) diagnoses, respectively. Using the Patz criterion, referring 63.2% (61.0% to 65.4%) of participants to biennial screening would have avoided 4% (0.2% to 22.3%) of false positive screen tests but delayed 55% (24.6% to 81.9%) diagnoses.

CONCLUSIONS

In this German trial, the LCRAT + CT and Polynomial models showed useful discrimination of screening participants for one-year lung cancer risk following CT examination. Our results illustrate the remaining heterogeneity in risk within screening-eligible subjects and the trade-off between a low-frequency screening approach and delayed detection.

Lung Cancer Screening by Low-Dose Computed Tomography: Part 2 - Key Elements for Programmatic Implementation of Lung Cancer Screening

PURPOSE

 For screening with low-dose CT (LDCT) to be effective, the benefits must outweigh the potential risks. In large lung cancer screening studies, a mortality reduction of approx. 20 % has been reported, which requires several organizational elements to be achieved in practice.

MATERIALS AND METHODS

 The elements to be set up are an effective invitation strategy, uniform and quality-assured assessment criteria, and computer-assisted evaluation tools resulting in a nodule management algorithm to assign each nodule the needed workup intensity. For patients with confirmed lung cancer, immediate counseling and guideline-compliant treatment in tightly integrated regional expert centers with expert skills are required. First, pulmonology contacts as well as CT facilities should be available in the participant's neighborhood. IT infrastructure, linkage to clinical cancer registries, quality management as well as epidemiologic surveillance are also required.

RESULTS

 An effective organization of screening will result in an articulated structure of both widely distributed pulmonology offices as the participants' primary contacts and CT facilities as well as central expert facilities for supervision of screening activities, individual clarification of suspicious findings, and treatment of proven cancer.

CONCLUSION

 In order to ensure that the benefits of screening more than outweigh the potential harms and that it will be accepted by the public, a tightly organized structure is needed to ensure wide availability of pulmonologists as first contacts and CT facilities with expert skills and high-level equipment concentrated in central facilities.

KEY POINTS

  · For lung cancer screening, elements must function optimally and be tightly organized.. · Lung cancer screening requires a network of expert centers and collaborating facilities.. · IT infrastructure, QM, epidemiological surveillance, and linkage to cancer registries are essential..

CITATION FORMAT

· Delorme S, Kaaks R: Lung Cancer Screening by Low-Dose Computed Tomography: Part 2 - Key Elements for Programmatic Implementation of Lung Cancer Screening. Fortschr Röntgenstr 2021; 193: 644 - 651.