Lung Cancer Screening, Towards a Multidimensional Approach: Why and How?

Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath

BACKGROUND

Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD.

RESEARCH QUESTION

Can eNose technology be used for prospective detection of early lung cancer in patients with COPD?

STUDY DESIGN AND METHODS

BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis.

RESULTS

Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95).

INTERPRETATION

Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

Matched tissue and liquid biopsies for advanced non-small cell lung cancer patients A potentially indispensable complementary approach

The introduction of liquid biopsies (LB) has brought forth a number of therapeutic opportunities into the domain of thoracic oncology. Many of which have been adopted for care of patients presenting with advanced non-squamous non-small cell lung cancer (aNS-NSCLC). For example, one of the most frequent indications to perform a LB in these patients, at least in Europe, is for patients treated with tyrosine kinase inhibitors (TKIs) targeting EGFR and ALK genomic alterations when the tumor progresses. A tissue biopsy (TB) must then be taken, ideally from a site of a tumor that progresses, in particular if the LB does not permit detection of a mechanism of resistance to TKI. A LB from a patient with aNS-NSCLC is recommended before first-line therapy if no tissue and/or cytological material is accessible or if the extracted nucleic acid is insufficient in amount and/or of poor quality. At present a LB and a TB are rarely performed simultaneously before treatment and/or on tumor progression. This complementary/matched testing approach is still controversial but needs to be better evaluated to determine the true benefit to care of patients. This review provides an update on the complementarity of the LB and TB method for care of patients presenting with aNS-NSCLC.

The XRCC1 and TP53 gene polymorphisms are associated with advanced-stage disease and early distant metastasis at diagnosis in non-small cell lung cancer

Background

Studies on single nucleotide polymorphisms (SNPs) in non-small cell lung cancer (NSCLC) suggest that DNA repair capacity may have prognostic implications for disease recurrence and survival. However, there is no study investigating the relationship between SNPs and the risk of metastasis at the time of initial diagnosis in patients with NSCLC.

Objective

This study aimed to investigate the potential predictive value of SNPs in detecting the risk of metastasis at the time of initial diagnosis and poor prognosis in patients with NSCLC.

Material and Methods

In this prospective cohort study, we evaluated 275 patients with NSCLC. Analysis of SNPs from peripheral blood cells was performed by a polymerase chain reaction. Excision repair cross-complementing group 1 (ERCC1)- Asn118Asn, excision repair cross-complementing group 2 (ERCC2)-Lys751Gln, X-ray repair cross-complementing group 1 (XRCC1)-Arg399Gln, and tumor protein 53 (TP53)-Arg72Pro polymorphisms were evaluated in conjunction with the development of metastasis.

Results

The ERCC1 normal genotype, ERCC2 heterozygote genotype, XRCC1 normal genotype, and TP53 normal genotype were associated with a higher stage and more advanced-stage disease at the time of initial diagnosis (P = 0.027, 0.005, <0.001, and 0.006, respectively). Also, XRCC1 normal genotype and TP53 normal genotype were associated with the risk of metastasis at the time of initial diagnosis (P = <0.001 and 0.002, respectively). Moreover, the XRCC1 normal genotype was associated with the risk of brain metastasis at the time of initial diagnosis (P = 0.031).

Conclusions

We showed that SNPs are related to a higher stage and more advanced-stage disease at the time of initial diagnosis in patients with NSCLC, and XRCC1 and TP53 gene polymorphisms are associated with the risk of metastasis. These results may contribute to the identification of high-risk groups and may help to earlier diagnosis and treatment in patients with NSCLC.

Retrospective review of patients with lung cancer presenting emergently

BACKGROUND

A significant proportion of lung cancer patients receive their diagnosis as part of an emergency presentation (EPs) to emergency departments (EDs).

OBJECTIVES

This study aimed to describe EPs of lung cancer at a safety-net hospital system.

METHODS

We conducted a retrospective analysis of patients with lung cancer at a safety-net ED. EP was defined as a diagnosis of lung cancer due to an acute presentation with symptoms of undiagnosed lung cancer (e.g., cough, hemoptysis, shortness of breath). Non-EPs were the result of either incidental findings (trauma pan-scan) or as part of lung cancer screening.

RESULTS

A total of 333 patient charts were reviewed who had lung cancer. Of those, 248 (74.5%) were defined as having an EP. EPs were more likely stage IV than non-EPs (50.4% vs 32.9%). The percent mortality was higher for EP versus non-EP, 60.0% vs 49.4%. which is driven by a high mortality rate for stage IV EPs (77.5%). Most patients with an EP were seen in the ED (177, 71.4%) as the location of initial visit that had a workup concerning for lung cancer. Most of the EPs were admitted for completion of either their diagnostic work up and/or for symptom management (117, 66.5%). Logistic regression identified significant predictors for an EP including stage IV at diagnosis (OR 2.49, 95% CI 1.39-4.48) and lack of primary care (OR 0.07, 95% CI 0.009-0.53).

CONCLUSION

Most patients with lung cancer present acutely as an EP with advanced stage in a safety-net health care setting. The ED plays an important role in the initial diagnosis of lung cancer and coordinating subsequent cancer care.

Benign-malignant classification of pulmonary nodules by low-dose spiral computerized tomography and clinical data with machine learning in opportunistic screening

BACKGROUND

Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology.

METHODS

The subjects' demographic data, baseline examination results, and annual follow-up low-dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model.

RESULTS

A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%-0.46%, which was lower than the baseline level except the follow-up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re-examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p < 0.001), and the proportion of highly suspicious nodules was not different between the baseline screening and other follow-up years (p > 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67-0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively.

CONCLUSIONS

It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow-up.

Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis

BACKGROUND

Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in healthcare, including in the assessment of medical images. A growing number of reviews studied the application of AI in lung cancer screening, but no overarching meta-analysis has examined the diagnostic test accuracy (DTA) of AI-based imaging for lung cancer screening.

OBJECTIVE

To systematically review the DTA of AI-based imaging for lung cancer screening.

METHODS

PubMed, EMBASE, Cochrane Library, CINAHL, IEEE Xplore, Web of Science, ACM Digital Library, Scopus, PsycINFO, and ProQuest Dissertations and Theses were searched from inception to date. Studies that were published in English and that evaluated the performance of AI-based imaging for lung cancer screening were included. Two independent reviewers screened titles and abstracts and used the Quality Assessment of Diagnostic Accuracy Studies-2 tool to appraise the quality of selected studies. Grading of Recommendations Assessment, Development, and Evaluation to diagnostic tests was used to assess the certainty of evidence.

RESULTS

Twenty-six studies with 150,721 imaging data were included. Hierarchical summary receiver-operating characteristic model used for meta-analysis demonstrated that the pooled sensitivity for AI-based imaging for lung cancer screening was 94.6 % (95 % CI: 91.4 % to 96.7 %) and specificity was 93.6 % (95 % CI: 88.5 % to 96.6 %). Subgroup analyses revealed that similar results were found among different types of AI, region, data source, and year of publication, but the overall quality of evidence was very low.

CONCLUSION

AI-based imaging could effectively detect lung cancer and be incorporated into lung cancer screening programs. Further high-quality DTA studies on large lung cancer screening populations are required to validate AI's role in early lung cancer detection.

Patient-Derived Lung Tumoroids—An Emerging Technology in Drug Development and Precision Medicine

Synthetic 3D multicellular systems derived from patient tumors, or tumoroids, have been developed to complete the cancer research arsenal and overcome the limits of current preclinical models. They aim to represent the molecular and structural heterogeneity of the tumor micro-environment, and its complex network of interactions, with greater accuracy. They are more predictive of clinical outcomes, of adverse events, and of resistance mechanisms. Thus, they increase the success rate of drug development, and help clinicians in their decision-making process. Lung cancer remains amongst the deadliest of diseases, and still requires intensive research. In this review, we analyze the merits and drawbacks of the current preclinical models used in lung cancer research, and the position of tumoroids. The introduction of immune cells and healthy regulatory cells in autologous tumoroid models has enabled their application to most recent therapeutic concepts. The possibility of deriving tumoroids from primary tumors within reasonable time has opened a direct approach to patient-specific features, supporting their future role in precision medicine.

Assisted versus Manual Interpretation of Low-Dose CT Scans for Lung Cancer Screening: Impact on Lung-RADS Agreement

Purpose To compare the inter- and intraobserver agreement and reading times achieved when assigning Lung Imaging Reporting and Data System (Lung-RADS) categories to baseline and follow-up lung cancer screening studies by using a dedicated CT lung screening viewer with integrated nodule detection and volumetric support with those achieved by using a standard picture archiving and communication system (PACS)-like viewer. Materials and Methods Data were obtained from the National Lung Screening Trial (NLST). By using data recorded by NLST radiologists, scans were assigned to Lung-RADS categories. For each Lung-RADS category (1 or 2, 3, 4A, and 4B), 40 CT scans (20 baseline scans and 20 follow-up scans) were randomly selected for 160 participants (median age, 61 years; interquartile range, 58-66 years; 61 women) in total. Seven blinded observers independently read all CT scans twice in a randomized order with a 2-week washout period: once by using the standard PACS-like viewer and once by using the dedicated viewer. Observers were asked to assign a Lung-RADS category to each scan and indicate the risk-dominant nodule. Inter- and intraobserver agreement was analyzed by using Fleiss κ values and Cohen weighted κ values, respectively. Reading times were compared by using a Wilcoxon signed rank test. Results The interobserver agreement was moderate for the standard viewer and substantial for the dedicated viewer, with Fleiss κ values of 0.58 (95% CI: 0.55, 0.60) and 0.66 (95% CI: 0.64, 0.68), respectively. The intraobserver agreement was substantial, with a mean Cohen weighted κ value of 0.67. The median reading time was significantly reduced from 160 seconds with the standard viewer to 86 seconds with the dedicated viewer (P < .001). Conclusion Lung-RADS interobserver agreement increased from moderate to substantial when using the dedicated CT lung screening viewer. The median reading time was substantially reduced when scans were read by using the dedicated CT lung screening viewer. Keywords: CT, Thorax, Lung, Computer Applications-Detection/Diagnosis, Observer Performance, Technology Assessment Supplemental material is available for this article. © RSNA, 2021.

Curative-Intent Treatment with Durvalumab in Early-Stage Cancers

The introduction of immunotherapy has fundamentally transformed the treatment landscape in cancer, providing long-term survival benefit for patients with advanced disease across multiple tumor types, including non-small cell lung cancer (NSCLC). In the placebo-controlled phase 3 PACIFIC trial, the PD-L1 inhibitor durvalumab demonstrated significant improvements in progression-free survival and overall survival in patients with unresectable, stage III NSCLC who had not progressed after platinum-based chemoradiotherapy (CRT). These findings have led to the widespread acceptance of the 'PACIFIC regimen' (durvalumab after CRT) as the standard of care in this setting. Moreover, the PACIFIC trial is the first study to demonstrate a proven survival advantage with an immunotherapy in a curative-intent setting, thereby providing a strong rationale for further investigation of durvalumab in early-stage cancers. Herein, we describe the extensive clinical development program for durvalumab across multiple tumor types in curative-intent settings, outlining the scientific rationale(s) for its use and highlighting the innovative research (e.g., personalized cancer monitoring) advanced by these trials.

Artificial Intelligence Tools for Refining Lung Cancer Screening

Nearly one-quarter of all cancer deaths worldwide are due to lung cancer, making this disease the leading cause of cancer death among both men and women. The most important determinant of survival in lung cancer is the disease stage at diagnosis, thus developing an effective screening method for early diagnosis has been a long-term goal in lung cancer care. In the last decade, and based on the results of large clinical trials, lung cancer screening programs using low-dose computer tomography (LDCT) in high-risk individuals have been implemented in some clinical settings, however, this method has various limitations, especially a high false-positive rate which eventually results in a number of unnecessary diagnostic and therapeutic interventions among the screened subjects. By using complex algorithms and software, artificial intelligence (AI) is capable to emulate human cognition in the analysis, interpretation, and comprehension of complicated data and currently, it is being successfully applied in various healthcare settings. Taking advantage of the ability of AI to quantify information from images, and its superior capability in recognizing complex patterns in images compared to humans, AI has the potential to aid clinicians in the interpretation of LDCT images obtained in the setting of lung cancer screening. In the last decade, several AI models aimed to improve lung cancer detection have been reported. Some algorithms performed equal or even outperformed experienced radiologists in distinguishing benign from malign lung nodules and some of those models improved diagnostic accuracy and decreased the false-positive rate. Here, we discuss recent publications in which AI algorithms are utilized to assess chest computer tomography (CT) scans imaging obtaining in the setting of lung cancer screening.

Liquid biopsy for lung cancer screening: Usefulness of circulating tumor cells and other circulating blood biomarkers

Screening for lung cancer has become a reality in certain countries, most notably the United States, but its implementation currently is under discussion and not established in many nations, including France. Screening for lung cancer currently is proposed using a low-dose computed tomography scanner. However, this approach lacks sensitivity and specificity, and could be improved when combined with a blood test (so-called "liquid biopsy"). Such tests attempt to detect biomarker (s) of early cancer development. Thus, numerous studies performed within the last few years have examined different blood components including circulating tumor cells and free DNA and other circulating elements such as microRNAs, exosomes, antibodies, and proteins. Recent studies have highlighted the value of seeking a signature for the methylation of circulating free DNA, which can be specific for certain solid tumors, including lung carcinoma. The current study describes some recent developments in the use of liquid biopsies for the detection of early-stage lung cancers, even those that are not yet visible using a low-dose computed tomography scanner.

Circulating tumour cells as a potential biomarker for lung cancer screening: a prospective cohort study

BACKGROUND

Lung cancer screening with low-dose chest CT (LDCT) reduces the mortality of eligible individuals. Blood signatures might act as a standalone screening tool, refine the selection of patients at risk, or help to classify undetermined nodules detected on LDCT. We previously showed that circulating tumour cells (CTCs) could be detected, using the isolation by size of epithelial tumour cell technique (ISET), long before the cancer was diagnosed radiologically. We aimed to test whether CTCs could be used as a biomarker for lung cancer screening.

METHODS

We did a prospective, multicentre, cohort study in 21 French university centres. Participants had to be eligible for lung cancer screening as per National Lung Screening Trial criteria and have chronic obstructive pulmonary disease with a fixed airflow limitation defined as post-bronchodilator FEV1/FVC ratio of less than 0·7. Any cancer, other than basocellular skin carcinomas, detected within the previous 5 years was the main exclusion criterion. Participants had three screening rounds at 1-year intervals (T0 [baseline], T1, and T2), which involved LDCT, clinical examination, and a blood test for CTCs detection. Participants and investigators were masked to the results of CTC detection, and cytopathologists were masked to clinical and radiological findings. Our primary objective was to test the diagnostic performance of CTC detection using the ISET technique in lung cancer screening, compared with cancers diagnosed by final pathology, or follow up if pathology was unavailable as the gold standard. This study is registered with ClinicalTrials.gov identifier, number NCT02500693.

FINDINGS

Between Oct 30, 2015, and Feb 2, 2017, we enrolled 614 participants, predominantly men (437 [71%]), aged 65·1 years (SD 6·5), and heavy smokers (52·7 pack-years [SD 21·5]). 81 (13%) participants dropped out between baseline and T1, and 56 (11%) did between T1 and T2. Nodules were detected on 178 (29%) of 614 baseline LDCTs. 19 participants (3%) were diagnosed with a prevalent lung cancer at T0 and 19 were diagnosed with incident lung cancer (15 (3%) of 533 at T1 and four (1%) of 477 at T2). Extrapulmonary cancers were diagnosed in 27 (4%) of participants. Overall 28 (2%) of 1187 blood samples were not analysable. At baseline, the sensitivity of CTC detection for lung cancer detection was 26·3% (95% CI 11·8-48·8). ISET was unable to predict lung cancer or extrapulmonary cancer development.

INTERPRETATION

CTC detection using ISET is not suitable for lung cancer screening.

FUNDING

French Government, Conseil Départemental 06, Fondation UNICE, Fondation Aveni, Fondation de France, Ligue Contre le Cancer-Comité des Alpes-Maritimes, ARC (Canc'Air Genexposomics), Claire de Divonne-Pollner, Enca Faidhi, Basil Faidhi, Fabienne Mourou, Michel Mourou, Leonid Fridlyand, cogs4cancer, and the Fondation Masikini.

Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration of DL in hospitals and medical centers. This paper reviews the development of deep learning applications in medical image analysis targeting pulmonary imaging and giving insights of contributions to COVID-19. It covers more than 160 contributions and surveys in this field, all issued between February 2017 and May 2020 inclusively, highlighting various deep learning tasks such as classification, segmentation, and detection, as well as different pulmonary pathologies like airway diseases, lung cancer, COVID-19 and other infections. It summarizes and discusses the current state-of-the-art approaches in this research domain, highlighting the challenges, especially with COVID-19 pandemic current situation.

Lung cancer increases H2O2 concentration in the exhaled breath condensate, extent of mtDNA damage, and mtDNA copy number in buccal mucosa

We have shown that the H2O2 concentration in exhaled breath condensate (EBC) in lung cancer patients increases significantly compared to the EBC of healthy people and revealed the correlation between the H2O2 level in the EBC and amount of mtDNA damage in buccal mucosa cells. The H2O2 hyper-production may trigger mitochondrial biogenesis, thereby resulting in an increase in mtDNA copy number. However, we did not observe a significant difference in the studied parameters between smokers and non-smokers. Overall, our data suggest that H2O2 concentration in the EBC, the extent of mtDNA damage, and mtDNA copy number in buccal mucosa could be potential as an early diagnostic marker of lung cancer.

Research progress of computer aided diagnosis system for pulmonary nodules in CT images

BACKGROUND AND OBJECTIVE

Since CAD (Computer Aided Diagnosis) system can make it easier and more efficient to interpret CT (Computer Tomography) images, it has gained much attention and developed rapidly in recent years. This article reviews recent CAD techniques for pulmonary nodule detection and diagnosis in CT Images.

METHODS

CAD systems can be classified into computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems. This review reports recent researches of both systems, including the database, technique, innovation and experimental results of each work. Multi-task CAD systems, which can handle segmentation, false positive reduction, malignancy prediction and other tasks at the same time. The commercial CAD systems are also briefly introduced.

RESULTS

We have found that deep learning based CAD is the mainstream of current research. The reported sensitivity of deep learning based CADe systems ranged between 80.06% and 94.1% with an average 4.3 false-positive (FP) per scan when using LIDC-IDRI dataset, and between 94.4% and 97.9% with an average 4 FP/scan when using LUNA16 dataset, respectively. The overall accuracy of deep learning based CADx systems ranged between 86.84% and 92.3% with an average AUC of 0.956 reported when using LIDC-IDRI dataset.

CONCLUSIONS

We summarized the current tendency and limitations as well as future challenges in this field. The development of CAD needs to meet the rigid clinical requirements, such as high accuracy, strong robustness, high efficiency, fine-grained analysis and classification, and to provide practical clinical functions. This review provides helpful information for both engineering researchers and radiologists to learn the latest development of CAD systems.

Management of incidental nodules in lung cancer screening: ready for prime-time?

Current clinical management of lung nodule patients is inefficient and therefore causes patient misclassification, which increases healthcare expenses. A precise and robust lung nodule classifier could minimise healthcare costs and discomfort for patients. http://bit.ly/2oMIEwQ.

Thermal Liquid Biopsy (TLB): A Predictive Score Derived from Serum Thermograms as a Clinical Tool for Screening Lung Cancer Patients

Risk population screening programs are instrumental for advancing cancer management and reducing economic costs of therapeutic interventions and the burden of the disease, as well as increasing the survival rate and improving the quality of life for cancer patients. Lung cancer, with high incidence and mortality rates, is not excluded from this situation. The success of screening programs relies on many factors, with some of them being the appropriate definition of the risk population and the implementation of detection techniques with an optimal discrimination power and strong patient adherence. Liquid biopsy based on serum or plasma detection of circulating tumor cells or DNA/RNA is increasingly employed nowadays, but certain limitations constrain its wide application. In this work, we present a new implementation of thermal liquid biopsy (TLB) for lung cancer patients. TLB provides a prediction score based on the ability to detect plasma/serum proteome alterations through calorimetric thermograms that strongly correlates with the presence of lung cancer disease (91% accuracy rate, 90% sensitivity, 92% specificity, diagnostic odds ratio 104). TLB is a quick, minimally-invasive, low-risk technique that can be applied in clinical practice for evidencing lung cancer, and it can be used in screening and monitoring actions.

Histopathological Imaging⁻Environment Interactions in Cancer Modeling

Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological imaging features and clinical/environmental risk factors in cancer modeling. In this article, we explore such a possibility and conduct both marginal and joint interaction analysis. Novel statistical methods, which are “borrowed” from gene–environment interaction analysis, are employed. Analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data is conducted. More specifically, we examine a biomarker of lung function as well as overall survival. Possible interaction effects are identified. Overall, this study can suggest an alternative way of cancer modeling that innovatively combines histopathological imaging and clinical/environmental data.

The OncoAge Consortium: Linking Aging and Oncology from Bench to Bedside and Back Again

It is generally accepted that carcinogenesis and aging are two biological processes, which are known to be associated. Notably, the frequency of certain cancers (including lung cancer), increases significantly with the age of patients and there is now a wealth of data showing that multiple mechanisms leading to malignant transformation and to aging are interconnected, defining the so-called common biology of aging and cancer. OncoAge, a consortium launched in 2015, brings together the multidisciplinary expertise of leading public hospital services and academic laboratories to foster the transfer of scientific knowledge rapidly acquired in the fields of cancer biology and aging into innovative medical practice and silver economy development. This is achieved through the development of shared technical platforms (for research on genome stability, (epi)genetics, biobanking, immunology, metabolism, and artificial intelligence), clinical research projects, clinical trials, and education. OncoAge focuses mainly on two pilot pathologies, which benefit from the expertise of several members, namely lung and head and neck cancers. This review outlines the broad strategic directions and key advances of OncoAge and summarizes some of the issues faced by this consortium, as well as the short- and long-term perspectives.