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NCT07057648
What is this study about? This study tests a new robotic technology to take tissue samples from lung nodules (small spots in the lungs). Some lung nodules are cancer, but doctors need a tissue sample to know for sure. What is the problem? Current methods to get tissue from lung nodules only work about 7 out of 10 times. When they don't work, doctors may need riskier procedures. What is the new technology? The new technology is called robotic bronchoscopy (ssRAB). It uses a robot with special sensors to guide a thin tube more accurately to lung nodules than current methods. Who can join? Adults aged 19 or older who have lung nodules that need tissue sampling and are healthy enough for the procedure. What happens? Participants will have the robotic procedure while asleep under anesthesia. The robot guides a thin tube to the lung nodule to take a small tissue sample. Participants are watched for problems and followed for 6 months. What are the risks and benefits? The new technology may be more accurate and safer than current methods. The main risks are small chance of lung collapse or bleeding, similar to regular procedures. Why is this important? This study will show if the new robotic technology works well and is safe in Korea. If successful, it could help diagnose lung cancer earlier and more accurately. This study will include 100 people at Ulsan University Hospital in Korea.
NCT07300072
The goal of this observational study is to learn about the pneumothorax risk associated with the Pleural-Depth-Trimmed Hookwire (PDTH) technique in patients undergoing Preoperative CT-Guided Lung Nodule Localization (POCTGL). The main question it aims to answer is: Does the specialized PDTH technique increase the risk of iatrogenic pneumothorax compared to dye-only localization in a setting utilizing advanced puncture guidance?. Participants were a retrospective cohort of patients who underwent POCTGL procedures between 2015 and 2022, and their procedural data and post-procedural complications were analyzed.
NCT07035977
The goal of this clinical trial is to learn if the DeepPriorCBCT model ensure high-quality CBCT image while reducing CBCT radiation dose. The main questions it aims to answer are: * Can DeepPriorCBCT reduce CBCT radiation doses? * Is the image quality of DeepPriorCBCT reconstruction consistent with that of existing clinical reconstruction method? Researchers will compare CBCT images quality reconstruction with DeepPriorCBCT model to CBCT image quality reconstruction with existing clinical protocols to see if DeepPriorCBCT model can improve the CBCT image quality while reducing radiation dose. Participants will: * Receive a single low dose (1/6 of existing clinical protocols) CBCT scan * Receive a single conventional dose CBCT scan
NCT05994898
The purpose of this study is to assess the feasibility of UHFUS on detection of GGOs in excised lung tissue and investigate UHFUS features of GGO in vitro. Each GGO was detected by palpation, UHFUS and open biopsy in sequence. The comparison of detection rate and time consumption were analyzed respectively. The Bland-Altman analysis was used to estimate the agreement of tumor size measured by CT, UHFUS and pathology.
NCT06282068
Research Objectives To use AI computer-aided detection software to assist physicians in reading CT scans of lung nodules, providing auxiliary diagnostic tools for medical decision-making. The software can mark nodule locations and related information during routine physician reading. This study will obtain prospective consent to use patient CT images for software reading and compare with clinical physician diagnosis, in order to enhance software training and improve recognition of lung lesions for early diagnosis and treatment. Study Design Collect CT images of untreated lung nodules 4-30mm in size that are scheduled for surgery. No limits on age, gender, disease type, with image resolution \<2.5mm. AI and clinicians will judge nodule characteristics separately. Surgical resection followed by comparison with pathology reports will evaluate diagnostic accuracy. Study Procedures A double-blinded method will be used. AI and physicians will record nodules as likely benign or malignant separately. After surgical resection, the lesions will undergo pathological staging and the diagnostic accuracy of both groups will be compared. Expected Results Compare the diagnostic accuracy of AI and clinicians to improve AI training quality, achieve early diagnosis and treatment goals, and provide patients with better medical care quality. Monitoring Method AI and clinicians will read separately, adhering to shared decision making without affecting patient access to diagnosis and treatment. Keywords: lung nodules, early lung cancer, artificial intelligence, chest CT, minimally invasive surgery, lung image analysis software
NCT00613041
In recent years, more and more people are having lung CT scans performed to screen for various cancers. Many of them have small abnormalities detected, called "nodules", which - for a variety of reasons - doctors are unable to biopsy. As a result, many patients have their CT scans repeated on a regular basis to see if their nodules grow. This process can last several years. Many patients experience significant anxiety during this process, when they are aware of a spot in the lung, but are not told any specific cause. Researchers at Memorial Sloan-Kettering have developed a new way to look at lung nodules in three dimensions. The purpose of this project is to see if any change in the nodules can be detected sooner by this method than by traditional CT scans.
NCT00188409
A CT scan is performed after a lung biopsy in order to detect a Pneumothorax. We postulate that CT is more useful than chest radiography