DCR Identifies Pleural Invasion of Lung Tumors
|
By MedImaging International staff writers Posted on 15 Feb 2021 |

Image: Examples of DCR images (Photo courtesy of Rie Tanaka/ Kanazawa University)
Dynamic chest radiography (DCR) holds promise for assessment of tumor invasion and adhesion in the parietal pleura, according to a new study.
Researchers at Kanazawa University (Japan), Duke University (Durham NC, USA), and Yokohama City University (Japan) conducted a proof-of-concept study to investigate the feasibility of preoperative evaluation of pleural invasion/adhesion of lung tumors with DCR, using a four‐dimensional (4D) extended cardiac‐torso (XCAT) computational phantom with simulated respiratory and cardiac motions; to simulate lung tumors, a 30‐mm diameter sphere was inserted into each lobe of the phantom.
The virtual patient during respiration was virtually projected using an X‐ray simulator in posteroanterior (PA) and oblique directions, and sequential bone suppression (BS) images were created. Measurement points (tumor, rib, and diaphragm) were automatically tracked on the simulated images by a template matching technique. The researchers then calculated five quantitative metrics related to the movement distance and directions of the targeted tumor, and evaluated whether DCR could distinguish between tumors with and without pleural invasion/adhesion.
The results showed precise tracking of the targeted tumor, without undue influence of rib shadows. There was a significant difference in all five quantitative metrics between the lung tumors with and without pleural invasion. Metrics related to movement distance were effective for tumors in the middle and lower lobes, while those related to movement directions were effective for tumors close to the frontal chest wall on the oblique projection view. The oblique views were useful for the evaluation of the space between the chest wall and a moving tumor. The study was published on February 3, 2021, in Medical Physics.
“Imaging modalities like cine MRI or breathing chest CT can be used to assess tumor invasion and adhesion, but both are more costly than x-ray; MRI often isn't available, while CT carries a higher radiation burden than radiography,” said lead author Rie Tanaka, PhD, of Kanazawa University. “With DCR, a series of sequential chest radiographs are obtained of a standing patient through a 10-second respiratory cycle. Except for the breathing pattern, images are acquired in the same manner as a conventional radiograph.”
Preoperative assessment of tumor invasion and adhesion is imperative when planning surgical excision of lung tumors. If the tumor moves independent of parietal or mediastinal pleura, it means that it is not invasive or strongly adheres to lung tissue, and therefore may be easier to remove surgically.
Related Links:
Kanazawa University
Duke University
Yokohama City University
Researchers at Kanazawa University (Japan), Duke University (Durham NC, USA), and Yokohama City University (Japan) conducted a proof-of-concept study to investigate the feasibility of preoperative evaluation of pleural invasion/adhesion of lung tumors with DCR, using a four‐dimensional (4D) extended cardiac‐torso (XCAT) computational phantom with simulated respiratory and cardiac motions; to simulate lung tumors, a 30‐mm diameter sphere was inserted into each lobe of the phantom.
The virtual patient during respiration was virtually projected using an X‐ray simulator in posteroanterior (PA) and oblique directions, and sequential bone suppression (BS) images were created. Measurement points (tumor, rib, and diaphragm) were automatically tracked on the simulated images by a template matching technique. The researchers then calculated five quantitative metrics related to the movement distance and directions of the targeted tumor, and evaluated whether DCR could distinguish between tumors with and without pleural invasion/adhesion.
The results showed precise tracking of the targeted tumor, without undue influence of rib shadows. There was a significant difference in all five quantitative metrics between the lung tumors with and without pleural invasion. Metrics related to movement distance were effective for tumors in the middle and lower lobes, while those related to movement directions were effective for tumors close to the frontal chest wall on the oblique projection view. The oblique views were useful for the evaluation of the space between the chest wall and a moving tumor. The study was published on February 3, 2021, in Medical Physics.
“Imaging modalities like cine MRI or breathing chest CT can be used to assess tumor invasion and adhesion, but both are more costly than x-ray; MRI often isn't available, while CT carries a higher radiation burden than radiography,” said lead author Rie Tanaka, PhD, of Kanazawa University. “With DCR, a series of sequential chest radiographs are obtained of a standing patient through a 10-second respiratory cycle. Except for the breathing pattern, images are acquired in the same manner as a conventional radiograph.”
Preoperative assessment of tumor invasion and adhesion is imperative when planning surgical excision of lung tumors. If the tumor moves independent of parietal or mediastinal pleura, it means that it is not invasive or strongly adheres to lung tissue, and therefore may be easier to remove surgically.
Related Links:
Kanazawa University
Duke University
Yokohama City University
Latest Radiography News
- Routine Mammograms Could Predict Future Cardiovascular Disease in Women
- AI Detects Early Signs of Aging from Chest X-Rays
- X-Ray Breakthrough Captures Three Image-Contrast Types in Single Shot
- AI Generates Future Knee X-Rays to Predict Osteoarthritis Progression Risk
- AI Algorithm Uses Mammograms to Accurately Predict Cardiovascular Risk in Women
- AI Hybrid Strategy Improves Mammogram Interpretation
- AI Technology Predicts Personalized Five-Year Risk of Developing Breast Cancer
- RSNA AI Challenge Models Can Independently Interpret Mammograms
- New Technique Combines X-Ray Imaging and Radar for Safer Cancer Diagnosis
- New AI Tool Helps Doctors Read Chest X‑Rays Better
- Wearable X-Ray Imaging Detecting Fabric to Provide On-The-Go Diagnostic Scanning
- AI Helps Radiologists Spot More Lesions in Mammograms
- AI Detects Fatty Liver Disease from Chest X-Rays
- AI Detects Hidden Heart Disease in Existing CT Chest Scans
- Ultra-Lightweight AI Model Runs Without GPU to Break Barriers in Lung Cancer Diagnosis
- AI Radiology Tool Identifies Life-Threatening Conditions in Milliseconds
Channels
MRI
view channel
Novel Imaging Approach to Improve Treatment for Spinal Cord Injuries
Vascular dysfunction in the spinal cord contributes to multiple neurological conditions, including traumatic injuries and degenerative cervical myelopathy, where reduced blood flow can lead to progressive... Read more
AI-Assisted Model Enhances MRI Heart Scans
A cardiac MRI can reveal critical information about the heart’s function and any abnormalities, but traditional scans take 30 to 90 minutes and often suffer from poor image quality due to patient movement.... Read more
AI Model Outperforms Doctors at Identifying Patients Most At-Risk of Cardiac Arrest
Hypertrophic cardiomyopathy is one of the most common inherited heart conditions and a leading cause of sudden cardiac death in young individuals and athletes. While many patients live normal lives, some... Read moreUltrasound
view channel
Wearable Ultrasound Imaging System to Enable Real-Time Disease Monitoring
Chronic conditions such as hypertension and heart failure require close monitoring, yet today’s ultrasound imaging is largely confined to hospitals and short, episodic scans. This reactive model limits... Read more
Ultrasound Technique Visualizes Deep Blood Vessels in 3D Without Contrast Agents
Producing clear 3D images of deep blood vessels has long been difficult without relying on contrast agents, CT scans, or MRI. Standard ultrasound typically provides only 2D cross-sections, limiting clinicians’... Read moreNuclear Medicine
view channel
PET Imaging of Inflammation Predicts Recovery and Guides Therapy After Heart Attack
Acute myocardial infarction can trigger lasting heart damage, yet clinicians still lack reliable tools to identify which patients will regain function and which may develop heart failure.... Read more
Radiotheranostic Approach Detects, Kills and Reprograms Aggressive Cancers
Aggressive cancers such as osteosarcoma and glioblastoma often resist standard therapies, thrive in hostile tumor environments, and recur despite surgery, radiation, or chemotherapy. These tumors also... Read more
New Imaging Solution Improves Survival for Patients with Recurring Prostate Cancer
Detecting recurrent prostate cancer remains one of the most difficult challenges in oncology, as standard imaging methods such as bone scans and CT scans often fail to accurately locate small or early-stage tumors.... Read moreGeneral/Advanced Imaging
view channel
AI-Based Tool Accelerates Detection of Kidney Cancer
Diagnosing kidney cancer depends on computed tomography scans, often using contrast agents to reveal abnormalities in kidney structure. Tumors are not always searched for deliberately, as many scans are... Read more
New Algorithm Dramatically Speeds Up Stroke Detection Scans
When patients arrive at emergency rooms with stroke symptoms, clinicians must rapidly determine whether the cause is a blood clot or a brain bleed, as treatment decisions depend on this distinction.... Read moreImaging IT
view channel
New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible
Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Global AI in Medical Diagnostics Market to Be Driven by Demand for Image Recognition in Radiology
The global artificial intelligence (AI) in medical diagnostics market is expanding with early disease detection being one of its key applications and image recognition becoming a compelling consumer proposition... Read moreIndustry News
view channel
GE HealthCare and NVIDIA Collaboration to Reimagine Diagnostic Imaging
GE HealthCare (Chicago, IL, USA) has entered into a collaboration with NVIDIA (Santa Clara, CA, USA), expanding the existing relationship between the two companies to focus on pioneering innovation in... Read morePatient-Specific 3D-Printed Phantoms Transform CT Imaging
New research has highlighted how anatomically precise, patient-specific 3D-printed phantoms are proving to be scalable, cost-effective, and efficient tools in the development of new CT scan algorithms... Read more
Siemens and Sectra Collaborate on Enhancing Radiology Workflows
Siemens Healthineers (Forchheim, Germany) and Sectra (Linköping, Sweden) have entered into a collaboration aimed at enhancing radiologists' diagnostic capabilities and, in turn, improving patient care... Read more







