Purpose: To study the impact of fused (18)F-fluoro-deoxy-D-glucose (FDG)-hybrid positron emission tomography (PET) and computed tomography (CT) images on conformal radiation therapy (CRT) planning for patients with esophageal carcinoma.
Patients And Methods: Thirty-four patients with esophageal carcinoma were referred for concomitant radiotherapy and chemotherapy with radical intent. Each patient underwent CT and FDG-hybrid PET for simulation treatment in the same radiation treatment position. PET-images were coregistered using five fiducial markers. Target delineation was initially performed on CT images and the corresponding PET data were subsequently used as an overlay to CT data to define the target volume.
Results: FDG-PET identified previously undetected distant metastatic disease in 2 patients, making them ineligible for curative CRT. The Gross Tumor Volume (GTV) was decreased by CT and FDG image fusion in 12 patients (35%) and was increased in 7 patients (20.5%). The GTV reduction was >or=25% in 4 patients due to reduction of the length of the esophageal tumor. The GTV increase was >or=25% with FDG-PET in 2 patients due to the detection of occult mediastinal lymph node involvement in one patient and an increased length of the esophageal tumor in the other patient. Modifications of the GTV affected the planning treatment volume (PTV) in 18 patients. Modifications of delineation of GTV and displacement of the isocenter of PTV by FDG-PET also affected the percentage of total lung volume receiving more than 20 Gy (VL20) in 25 patients (74%), with a dose reduction in 12 patients and a dose increase in 13 patients.
Conclusion: In our study, CT and FDG-PET image fusion appeared to have an impact on treatment planning and management of patients with esophageal carcinoma related to modifications of GTV. The impact on treatment outcome remains to be demonstrated.
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http://dx.doi.org/10.1016/j.canrad.2005.04.001 | DOI Listing |
J Microsc
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Ningbo Key Laboratory of Micro-Nano Motion and Intelligent Control, Ningbo University, Ningbo, PR China.
The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image optimisation and deep learning. Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution.
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Department of Spinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China.
Objectives: The advent of O-arm navigation optimized the oblique lumbar interbody fusion (OLIF) procedure, allowing the operator to simultaneously perform OLIF and percutaneous posterior pedicle screw implantation without patient position change, thus improving the fluency and accuracy of the OLIF procedure (called as OLIF360). Nevertheless, a consensus regarding its suitability for patients with severe spinal stenosis remains elusive. This study aims to investigate the clinical efficacy of OLIF360 and its imaging changes in severe lumbar spinal stenosis cases.
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Cixi Biomedical Research Institute, Wenzhou Medical University, Ningbo, China.
Background: Vessel segmentation in fundus photography has become a cornerstone technique for disease analysis. Within this field, Ultra-WideField (UWF) fundus images offer distinct advantages, including an expansive imaging range, detailed lesion data, and minimal adverse effects. However, the high resolution and low contrast inherent to UWF fundus images present significant challenges for accurate segmentation using deep learning methods, thereby complicating disease analysis in this context.
View Article and Find Full Text PDFJ Med Radiat Sci
January 2025
Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Introduction: Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. Despite advancements in early detection and treatment, postsurgical recurrence remains a significant challenge, occurring in 30%-55% of patients within 5 years after surgery. This review analysed existing studies on the utilisation of artificial intelligence (AI), incorporating CT, PET, and clinical data, for predicting recurrence risk in early-stage NSCLCs.
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