Exploring feature-based approaches in PET images for predicting cancer treatment outcomes

By Issam El-Naqa, Perry Grigsby, A. Apte, E. Kidd, E. Donnelly, D. Khullar, S. Chaudhari, Deshan Yang, Martin Schmitt, R. Laforest and 2 more. Pattern Recognition, 2009

479 citations recorded by the source corpus.

Accumulating evidence suggests that characteristics of pre-treatment FDG-PET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensity-volume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.

Subfields: Artificial Intelligence, Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/73c5ac2e88c43707da0314dcbd496429ecf6114a

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Citations in the corpus

Radiomics: Extracting more information from medical images using advanced feature analysis (2012, background): “There was a study of El Naqa et al.15, investigating the predictive power of intensity–volume histogram (IVH) metrics, shape and texture features to assess response to treatment of a limited set of patients with head and neck and cervix cancers.”
QIN “Radiomics: The Process and the Challenges” (2012, background): “, mean, median, min, max, range, skewness, kurtosis), but also more complex values, such as metabolic volume above an absolute SUV of 5 or the fraction of highdensity tissue measured with CT [60,61].”
Characterization of PET/CT images using texture analysis: the past, the present… any future? (2016, unknown): “models [14–16], intensity histogram analysis [15, 17] and filtering combined with statistical and frequency-based methods [18].”
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer (2011, background): “Until now, only 1 study has considered the use of some textural features to predict treatment outcome from baseline 18F-FDG PET images, with encouraging results in cervical and head and neck cancer (23), and the assessment of spatial heterogeneity was also shown to be significantly associated with s”
Applications and limitations of radiomics (2016, methodology): “Prior to radiomic feature computation, voxel intensities within tumour volumes need to be discretized to a limited range of intensity values in order to efficiently and practically compute the radiomic features (Cheng et al 2013b, Leijenaar et al 2013). For instance, an image with 1024 discrete inte”
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, methodology): “First-order statistics are used to study the distribution of voxel values without considering spatial relationships [24]; second-order statistics characterize spatial relationships between voxels, initially described by Haralick [25] such as the cooccurrence matrix (GLCM), gray-level run length matr”
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “There were also studies based on PET [11, 12] and US [13] images utilizing similar strategy.”
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation (2014, background): “Several studies have shown the importance of imaging features for treatment monitoring and outcome prediction in lung and other cancer types [5–7].”
A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change. (2018, methodology): “Ongoing efforts at standardization are paramount in the following areas: (1) medical imaging acquisition and reconstruction; (2) workflow of computation of radiomics features proposed by the Image Biomarker Standardization Initiative (19); and (3) statistical analysis.”
The role of quantitative PET in predicting cancer treatment outcomes (2014, background): “These image-based features can provide better spatial characterization of uptake heterogeneity than the simple SUV descriptors currently employed [38] and might therefore result in improved ability to predict treatment outcomes [41].”