ArticlePhysics in medicine and biology2016
A novel metric for quantification of homogeneous and heterogeneous tumors in PET for enhanced clinical outcome prediction.
Article in Physics in medicine and biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 25 citations in OpenAlex.
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- A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT.Cancers · 2023Article
- An Explainable Radiogenomic Framework to Predict Mutational Status of KRAS and EGFR in Lung Adenocarcinoma Patients.Bioengineering (Basel, Switzerland) · 2023Article
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- A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging.Diagnostics (Basel, Switzerland) · 2021Review
- Subregional Radiomics Analysis of PET/CT Imaging with Intratumor Partitioning: Application to Prognosis for Nasopharyngeal Carcinoma.Molecular imaging and biology · 2020Article
- Radiomics Analysis of PET and CT Components of PET/CT Imaging Integrated with Clinical Parameters: Application to Prognosis for Nasopharyngeal Carcinoma.Molecular imaging and biology · 2019Article
- Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features.European journal of radiology · 2019Article
- Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer.Scientific reports · 2017Article
- A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [Molecular imaging and biology · 2017Article
- Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images.NeuroImage. Clinical · 2017Article
- Applications and limitations of radiomics.Physics in medicine and biology · 2016Review
- Application of texture analysis to DAT SPECT imaging: Relationship to clinical assessments.NeuroImage. Clinical · 2016Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Oncologic PET images provide valuable information that can enable enhanced prognosis of disease. Nonetheless, such information is simplified significantly in routine clinical assessment to meet workflow constraints. Examples of typical FDG PET metrics include: (i) SUVmax, (2) total lesion glycolysis (TLG), and (3) metabolic tumor volume (MTV). We have derived and implemented a novel metric for tumor quantification, inspired in essence by a model of generalized equivalent uniform dose as used in radiation therapy. The proposed metric, denoted generalized effective total uptake (gETU), is attractive as it encompasses the abovementioned commonly invoked metrics, and generalizes them, for both homogeneous and heterogeneous tumors, using a single parameter a. We evaluated this new metric for improved overall survival (OS) prediction on two different baseline FDG PET/CT datasets: (a) 113 patients with squamous cell cancer of the oropharynx, and (b) 72 patients with locally advanced pancreatic adenocarcinoma. Kaplan-Meier survival analysis was performed, where the subjects were subdivided into two groups using the median threshold, from which the hazard ratios (HR) were computed in Cox proportional hazards regression. For the oropharyngeal cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak produced HR values of 1.86, 3.02, 1.34, 1.36 and 1.62, while the proposed gETU metric for a = 0.25 (greater emphasis on volume information) enabled significantly enhanced OS prediction with HR = 3.94. For the pancreatic cancer dataset, MTV, TLG, SUVmax, SUVmean and SUVpeak resulted in HR values of 1.05, 1.25, 1.42, 1.45 and 1.52, while gETU at a = 3.2 (greater emphasis on SUV information) arrived at an improved HR value of 1.61. Overall, the proposed methodology allows placement of differing degrees of emphasis on tumor volume versus uptake for different types of tumors to enable enhanced clinical outcome prediction.
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