Purpose: Sphericity has been proposed as a parameter for characterizing PET tumour volumes, with complementary prognostic value with respect to SUV and volume in both head and neck cancer and lung cancer. The objective of the present study was to investigate its dependency on tumour delineation and the resulting impact on its prognostic value.
Methods: Five segmentation methods were considered: two thresholds (40% and 50% of SUV), ant colony optimization, fuzzy locally adaptive Bayesian (FLAB), and gradient-aided region-based active contour. The accuracy of each method in extracting sphericity was evaluated using a dataset of 176 simulated, phantom and clinical PET images of tumours with associated ground truth. The prognostic value of sphericity and its complementary value with respect to volume for each segmentation method was evaluated in a cohort of 87 patients with stage II/III lung cancer.
Results: Volume and associated sphericity values were dependent on the segmentation method. The correlation between segmentation accuracy and sphericity error was moderate (|ρ| from 0.24 to 0.57). The accuracy in measuring sphericity was not dependent on volume (|ρ| < 0.4). In the patients with lung cancer, sphericity had prognostic value, although lower than that of volume, except for that derived using FLAB for which when combined with volume showed a small improvement over volume alone (hazard ratio 2.67, compared with 2.5). Substantial differences in patient prognosis stratification were observed depending on the segmentation method used.
Conclusion: Tumour functional sphericity was found to be dependent on the segmentation method, although the accuracy in retrieving the true sphericity was not dependent on tumour volume. In addition, even accurate segmentation can lead to an inaccurate sphericity value, and vice versa. Sphericity had similar or lower prognostic value than volume alone in the patients with lung cancer, except when determined using the FLAB method for which there was a small improvement in stratification when the parameters were combined.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s00259-017-3865-3 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Editorial: Reviews in: nuclear medicine 2023.
Front Med (Lausanne)
December 2024
Department of Physics, Faculty of Science, Minia University, Minia, Egypt.
Pheochromocytoma: an updated scoping review from clinical presentation to management and treatment.
Front Endocrinol (Lausanne)
December 2024
One Health Research Group, Universidad de las Americas, Quito, Ecuador.
Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors derived from chromaffin cells, with 80-85% originating in the adrenal medulla and 15-20% from extra-adrenal chromaffin tissues (paragangliomas). Approximately 30-40% of PPGLs have a hereditary component, making them one of the most genetically predisposed tumor types. Recent advances in genetic research have classified PPGLs into three molecular clusters: pseudohypoxia-related, kinase-signaling, and -signaling pathway variants.
View Article and Find Full Text PDFPredicting epidermal growth factor receptor mutation status of lung adenocarcinoma based on PET/CT images using deep learning.
Front Oncol
December 2024
Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Background: The aim of this study is to develop deep learning models based on F-fluorodeoxyglucose positron emission tomography/computed tomographic (F-FDG PET/CT) images for predicting individual epidermal growth factor receptor () mutation status in lung adenocarcinoma (LUAD).
Methods: We enrolled 430 patients with non-small-cell lung cancer from two institutions in this study. The advanced Inception V3 model to predict EGFR mutations based on PET/CT images and developed CT, PET, and PET + CT models was used.
The deep learning radiomics nomogram helps to evaluate the lymph node status in cervical adenocarcinoma/adenosquamous carcinoma.
Front Oncol
December 2024
Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Objectives: The accurate assessment of lymph node metastasis (LNM) can facilitate clinical decision-making on radiotherapy or radical hysterectomy (RH) in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC). This study aims to develop a deep learning radiomics nomogram (DLRN) to preoperatively evaluate LNM in cervical AC/ASC.
Materials And Methods: A total of 652 patients from a multicenter were enrolled and randomly allocated into primary, internal, and external validation cohorts.
Sleep discrepancy and brain glucose metabolism in community-dwelling older adults.
Aging Brain
November 2024
School of Psychological Science, University of Western Australia, Crawley, Western Australia, Australia.
Sleep discrepancy (negative discrepancy reflects worse self-reported sleep than objective measures, such as actigraphy, and positive discrepancy the opposite) has been linked to adverse health outcomes. This study is first to investigate the relationship between sleep discrepancy and brain glucose metabolism (assessed globally and regionally via positron emission tomography), and to evaluate the contribution of insomnia severity and depressive symptoms to any associations. Using data from cognitively unimpaired community-dwelling older adults ( = 68), cluster analysis was used to characterise sleep discrepancy (for total sleep time (TST), wake after sleep onset (WASO), and sleep efficiency (SE)), and logistic regression was used to explore sleep discrepancy's associations with brain glucose metabolism, while controlling for insomnia severity and depressive symptoms.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!