Lung Cancer Screening

Early detection to save more lives

Lung cancer is one of the most frequent causes of cancer and the leading cause of cancer death globally1 . When patients present with symptoms, the disease often has already progressed, with limited options remaining for treatment. As a result, prognosis in lung cancer tends to be poor, despite advances in treatment. However, when the disease is detected at an early stage, the chances of survival increase dramatically2.

Consequently, there is a growing interest in lung cancer screening worldwide as the main opportunity to reduce lung cancer mortality. 

Canon Medical aims to be your partner in the fight against lung cancer and offers a unique set of lung cancer screening solutions. These include a suite of innovative technologies to enhance your clinical confidence everywhere, delivering high-quality imaging at low dose, streamlining workflows, and offering comprehensive diagnosis throughout the entire cycle of care.

"You only have a chance and a good outcome if you detect the cancer at a very, very early stage. And that is the purpose of lung cancer screening."

Prof. Cornelia M Schaefer- Prokop, MD, PhD
Appointed member of the Fleischner Society (international group of lung specialists)
Former president of the European Society of Thoracic Imaging (ESTI)

Meander Medical Centre, Amersfoort
Radboud University Medical Center (Radboudumc), Nijmegen the Netherlands

Current challenges

Lung cancer screening faces various challenges that hinder its widespread success.
As part of our Made for Life philosophy, Canon Medical is committed to providing support to address these challenges.

Our solutions

We are taking measures to increase the accessibility of lung cancer screening for the public, providing high-quality imaging at low dose, removing workflow inefficiencies, supporting the interpretation and communication of findings among clinicians, and streamlining follow-up procedures.
Together, we can improve the patient experience and ensure a high-quality and cost-effective screening program, while minimizing the burden of the lung cancer on patients worldwide.

Easy access to screening

Easy access from anywhere

One of the main challenges of lung cancer screening is reaching the people who may need it the most. Mobile imaging solutions allow you to provide healthcare and education where people live and work, and offer screening close to their homes. By removing barriers to access, mobile imaging services may play a significant role in improving lung cancer screening rates and contribute to more equitable healthcare. The design of our mobile medical equipment provides a comfortable and convenient environment for high-patient throughput lung cancer screening without any compromises on patient safety, workflow or image quality.

Learn more about mobile imaging solutions

"Developing a mobile program allowed delivery of healthcare to rural parts of our state with the ultimate goal of mitigating some of the cancer outcome disparities of our state and region."

Prof. Hannah Hazard- Jenkins, MD, FACS
Associate Professor of Surgery at the department of Surgery WVU School of Medicine
Director of the WVU Cancer Institute
Morgantown, West Virginia, USA

Efficient workflow

Scanning is easier than ever before

An efficient workflow is essential for lung cancer screening. It enhances accessibility, reduces waiting times, optimises resource utilisation, and improves overall patient care. By prioritising efficiency, we provide patient-centric end-to-end workflow solutions, incorporating intuitive design and automated features for improved efficiency through the INSTINX workflow.
INSTINX workflow
Canon's CT total workflow experience is redesigned from the ground up to set new standards in efficiency and consistency. Every detail of the workflow has been thoroughly refined and validated through clinical testing in medical centers around the world.

Learn more about INSTINX

Automatic scan planning for all chest routine scans
INSTINX features automatic scan planning to automatically plan all routine scans including scan ranges and parameters, including dose and exposure settings, using the unique 3D Landmark Scan data and Anatomical Landmark Detection technology.

High-quality images at low dose

A unique synergy of beam filtering and AI-enhanced reconstruction technologies

Lung cancer screening demands high patient throughput and low radiation dose while providing images that have the quality for a reliable assessment of nodule growth and malignancy. The unique combination of SilverBeam Filter with our specific Deep Learning imaging reconstruction results in the best possible image quality at typical lung cancer screening doses.

SilverBeam Filter
SilverBeam, a beam shaping energy filter, leverages the photon-attenuating properties of silver to selectively remove low energy photons from a polychromatic X-ray beam, leaving an energy spectrum optimised for lung cancer screening.

Discover SilverBeam

Advanced intelligent Clear-IQ Engine (AiCE)
AiCE is an innovative Deep Learning Reconstruction technology that has been trained to reduce noise and boost signal to deliver sharp, clear and distinct images at speed.

Learn more about AiCE

✓ Low Noise
✓ Natural Image Texture*1
✓ Twice the high contrast spatial resolution*2
✓ Clear Low Contrast Detectability
✓ Reduced dose for body imaging*3

*1 Natural defined as similar to FBP compared to MBIR
*2 AiCE Body Sharp compared to AIDR 3D Body Sharp at 10% MTF measured using the Teflon sensitometry module of Catphan600
*3 Compared to FBP

Small lung nodule detection

High-quality lung CT imaging at 0.54 mSv utilising the combination of SilverBeam Filter and AiCE.
In this patient, a small lung nodule is clearly visible in the upper right lobe.


      Courtesy of Fujita Health University Hospital, Japan

More detail in comparison to X-ray

In contrast to the chest X-ray, the presence of lung cancer is clearly visible in the left upper lobe. Utilising the combination of SilverBeam Filter and AiCE, the effective dose of this scan was only 0.18 mSv, a dose level closer to a chest X-ray3


      Courtesy of Dr. Russell Bull, Royal Bournemouth Hospital, Bournemouth, UK

Follow-up imaging at low dose

SilverBeam Filter combined with AiCE delivers high-quality images of the lungs at a dose closer to a chest X-ray.
In this patient, 94% dose reduction was achieved at follow-up through the implementation of our SilverBeam Filter as compared to the initial scan using a standard filter, while nodule conspicuity was maintained.


      Courtesy NHLBI, National Institutes of Health, USA

"I have had patients who are reluctant to come in for a chest CT because of the fears of radiation. However, with Canon Medical technology, that barrier is removed. And now, we are able to offer ultra-low dose chest CT for screening for lung cancer, and that's a game changer."

Dr. Marcus Chen
Director of Cardiothoracic Imaging at the National
Institutes for Health (NIH), Maryland, US

Risk stratification and diagnosis

Standardised lung nodule assessment and comprehensive characterisation of nodules over time

A key aspect of any lung cancer screening program is the accurate and timely reporting of scans followed by streamlined nodule management and lung disease management. However, without appropriate software solutions, the reporting of lung cancer examinations can be a time- consuming task that is prone to error.

The comprehensive lung cancer screening solution in Vitrea Advanced Visualisation contains automated tools that support you in the detection and characterisation of different lung nodule types including the assessment of growth patterns. To facilitate workflow and structured reporting, findings are automatically transferred to your reporting template with quick reference to guidelines such as Lung-RADS and the Fleischner Criteria.

Learn more about Vitrea lung cancer solutions

Automatically detects potentially actionable lung nodules and quantitative assessment of growth.

Scientific Papers

Find our latest scientific evidence on lung cancer screening solutions using Canon technologies here.

Hamada, A et al. | Comparison of deep-learning image reconstruction with hybrid iterative reconstruction for evaluating lung nodules with high-resolution computed tomography| Journal of Computer Assisted Tomography (2023)

Oshima, Y et al. | Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X- ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods|European Journal of Radiology (2023)

Goto, M et al. | Lung- optimised deep-learning-based reconstruction for ultralow-dose CT| Academic Radiology (2023)

Hamabuchi N et al. | Effectiveness of deep learning reconstruction on standard to ultra-low-dose high-definition chest CT images | Japanese Journal of Radiology (2023)

K. Boedeker et al. | Technical Evaluation of a Low Dose Lung Cancer Screening Computed Tomography Protocol using a Beam- Shaping Silver Filter and Deep Learning Reconstruction.

Watanabe, S et al.| Pulmonary nodule volumetric accuracy of a deep learning- based reconstruction algorithm in low- dose computed tomography: A phantom study | Physica Medica (2022)

Mikayama, R et al. | Deep-learning reconstruction for ultra- low- dose lung CT: volumetric measurement accuracy and reproducibility of artificial groundglass nodules in a phantom study|The British Journal of Radiology (2022)

Keiichi Nomura et al. | Radiation Dose Reduction for Computed Tomography Localiser Radiography Using an Ag Additional Filter | J Comput Assist Tomogr (2021)

Ortlieb, A. C et al. | Impact of Morphotype on Image Quality and Diagnostic Performance of Ultra-Low-Dose Chest CT | Journal of Clinical Medicine (2021)

Singh R et al. | Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT | American Journal of Roentgenology (2020)

Yanagawa, M et al. | Lung adenocarcinoma at CT with 0.25- mm section thickness and a 2048 matrix: high-spatial-resolution imaging for predicting invasiveness |Radiology (2020)

Tsubamoto, M et al. | Ultra high-resolution computed tomography with 1024-matrix: Comparison with 512-matrix for the evaluation of pulmonary nodules. |European Journal of Radiology (2020)

Lucia J M Kroft et al. | Added Value of Ultra-Low-Dose Computed Tomography, Dose Equivalent to Chest x-Ray Radiography, for Diagnosing Chest Pathology | J Thorac Imaging (2019)

Fujita, M et al. | Lung cancer screening with ultra-low dose CT using full iterative reconstruction | Japanese journal of radiology (2017)

Meyer, E. et al. | Wide-volume versus helical acquisition in unenhanced chest CT: prospective intra-patient comparison of diagnostic accuracy and radiation dose in an ultra- low-dose setting |European Radiology (2019)

Schaal, M. et al. | Diagnostic Performance of Ultra-Low-Dose Computed Tomography for Detecting Asbestos-Related Pleuropulmonary Diseases: Prospective Study in a Screening Setting| PLOS One (2016)

Kakinuma R et al. | Ultra-High-Resolution Computed Tomography of the Lung: Image Quality of a Prototype Scanner | PLoS ONE (2015)

Nomura, Y et al. | Effects of iterative reconstruction algorithms on computer-assisted detection (CAD) software for lung nodules in ultra- low- dose CT for lung cancer screening |Academic Radiology (2017)

General references

1. Globocan 2020.

2. Oudkerk M. et al | Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives | Nature Reviews Clinical Oncology (2021)

3. | Radiation Dose from X-Ray and CT Exams | (2022)

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