Localized FDG loss in lung cancer lesions
Purpose: Analysis of [18F]-Fluorodeoxyglucose (FDG) kinetics in cancer has been most often limited to the evaluation of the average uptake over relatively large volumes. Nevertheless, tumor lesion almost contains inflammatory infiltrates whose cells are characterized by a significant radioactivity washout due to the hydrolysis of FDG-6P catalyzed by glucose-6P phosphatase. The present study aimed to verify whether voxel-wise compartmental analysis of dynamic imaging can identify tumor regions characterized by tracer washout. Materials & Methods: The study included 11 patients with lung cancer submitted to PET/CT imaging for staging purposes. Tumor was defined by drawing a volume of interest loosely surrounding the lesion and considering all inside voxels with standardized uptake value (SUV) >40% of the maximum. After 20 minutes dynamic imaging centered on the heart, eight whole body scans were repeated. Six parametric maps were progressively generated by computing six regression lines that considered all eight frames, the last seven ones, and so on, up to the last three. Results: Progressively delaying the starting point of regression line computation identified a progressive increase in the prevalence of voxels with a negative slope. Conclusions: The voxel-wise parametric maps provided by compartmental analysis permits to identify a measurable volume characterized by radioactivity washout. The spatial localization of this pattern is compatible with the recognized preferential site of inflammatory infiltrates populating the tumor stroma and might improve the power of FDG imaging in monitoring the effectiveness of treatments aimed to empower the host immune response against the cancer.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
Lung cancer is responsible for 21% of cancer deaths in the UK and five-year survival rates are heavily influenced by the stage the cancer was identified at. Recent studies have demonstrated the capability of AI methods f…
Lung Cancer DiagnosisLung Nodule ClassificationA deep learning-facilitated radiomics solution for the prediction of lung lesion shrinkage in non-small cell lung cancer trials
Herein we propose a deep learning-based approach for the prediction of lung lesion response based on radiomic features extracted from clinical CT scans of patients in non-small cell lung cancer trials. The approach start…
Leveraging object detection for the identification of lung cancer
Lung cancer poses a significant global public health challenge, emphasizing the importance of early detection for improved patient outcomes. Recent advancements in deep learning algorithms have shown promising results in…
Computational EfficiencyMedical Image AnalysisObjectobject-detection+1Hierarchical Classification of Pulmonary Lesions: A Large-Scale Radio-Pathomics Study
Diagnosis of pulmonary lesions from computed tomography (CT) is important but challenging for clinical decision making in lung cancer related diseases. Deep learning has achieved great success in computer aided diagnosis…
Computed Tomography (CT)Decision MakingGeneral ClassificationLung Cancer DiagnosisDeep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer
The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficien…
Segmentation