Dataset Information

A Systematic Review of PET Textural Analysis and Radiomics in Cancer.

ABSTRACT: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20-1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1-286). PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.

SUBMITTER: Pineiro-Fiel M

PROVIDER: S-EPMC7926413 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer.

Piñeiro-Fiel Manuel M Moscoso Alexis A Pubul Virginia V Ruibal Álvaro Á Silva-Rodríguez Jesús J Aguiar Pablo P

Diagnostics (Basel, Switzerland) 20210223 2

<h4>Background</h4>Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies.<h4>Methods</h4>PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statis ...[more]

PMID: 33672285

Similar Datasets

Project description:PURPOSE:Image texture is increasingly used to discriminate tissues and lesions in PET/CT. For quantification or in computer-aided diagnosis, textural feature analysis must produce robust and comparable values. Because statistical feature values depend on image count statistics, we investigated in depth the stability of Haralick features values as functions of acquisition duration, and for common image resolutions and reconstructions. METHODS:A homogeneous cylindrical phantom containing 9.6 kBq/ml Ge-68 was repeatedly imaged on a Siemens Biograph mCT, with acquisition durations ranging from three seconds to three hours. Images with 1.5, 2, and 4 mm isometrically spaced voxels were reconstructed with filtered back-projection (FBP), ordered subset expectation maximization (OSEM), and the Siemens TrueX algorithm. We analysed Haralick features derived from differently quantized (3 to 8-bit) grey level co-occurrence matrices (GLCMs) as functions of exposure E, which we defined as the product of activity concentration in a volume of interest (VOI) and acquisition duration. The VOI was a 50 mm wide cube at the centre of the phantom. Feature stability was defined for df/dE → 0. RESULTS:The most stable feature values occurred in low resolution FBPs, whereas some feature values from 1.5 mm TrueX reconstructions ranged over two orders of magnitude. Within the same reconstructions, most feature value-exposure curves reached stable plateaus at similar exposures, regardless of GLCM quantization. With 8-bit GLCM, median time to stability was 16 s and 22 s for FBPs, 18 s and 125 s for OSEM, and 23 s, 45 s, and 76 s for PSF reconstructions, with longer durations for higher resolutions. Stable exposures coincided in OSEM and TrueX reconstructions with image noise distributions converging to a Gaussian. In FBP, the occurrence of stable values coincided the disappearance of negatives image values in the VOI. CONCLUSIONS:Haralick feature values depend strongly on exposure, but invariance exists within defined domains of exposure. Here, we present an easily replicable procedure to identify said stable exposure domains, where image noise does not substantially add to textural feature values. Only by imaging at predetermined feature-invariant exposure levels and by adjusting exposure to expected activity concentrations, can textural features have a quantitative use in PET/CT. The necessary exposure levels are attainable by modern PET/CT systems in clinical routine.

Project description:BackgroundRadiomics refers to the extraction of a large number of image biomarker describing the tumor phenotype displayed in a medical image. Extracted from positron emission tomography (PET) images, radiomics showed diagnostic and prognostic value for several cancer types. However, a large number of radiomic features are nonreproducible or highly correlated with conventional PET metrics. Moreover, radiomic features used in the clinic should yield relevant information about tumor texture. In this study, we propose a framework to identify technical and clinical meaningful features and exemplify our results using a PET non-small cell lung cancer (NSCLC) dataset.Materials and methodsThe proposed selection procedure consists of several steps. A priori, we only include features that were found to be reproducible in a multicenter setting. Next, we apply a voxel randomization step to identify features that reflect actual textural information, that is, that yield in 90% of the patient scans a value significantly different from random texture. Finally, the remaining features were correlated with standard PET metrics to further remove redundancy with common PET metrics. The selection procedure was performed for different volume ranges, that is, excluding lesions with smaller volumes in order to assess the effect of tumor size on the results. To exemplify our procedure, the selected features were used to predict 1-yr survival in a dataset of 150 NSCLC patients. A predictive model was built using volume as predictive factor for smaller, and one of the selected features as predictive factor for bigger lesions. The prediction accuracy of the both models were compared with the prediction accuracy of volume.ResultsThe number of selected features depended on the lesion size included in the analysis. When including the whole dataset, from 19 features reflecting actual texture only two were found to be not strongly correlated with conventional PET metrics. When excluding lesions smaller than 11.49 and 33.10 mL (25 and 50 percentile of the dataset), four out of 27 features and 13 out of 29 features remained after eliminating features highly correlated with standard PET metrics. When excluding lesions smaller than 103.9 mL (75 percentile), 33 out of 53 features remained. For larger lesions, some of these features outperformed volume in terms of classification accuracy (increase of 4-10%). The combination of using volume as predictor for smaller and one of the selected features for larger lesions also improved the accuracy when compared with volume only (increase from 72% to 76%).ConclusionWhen performing radiomic analysis for smaller lesions, it should be first carefully investigated if a textural feature reflects actual heterogeneity information. Next, verification of the absence of correlation with all conventional PET metrics is essential in order to assess the additional value of radiomic features. Radiomic analysis with lesions larger than 11.4 mL might give additional information to conventional metrics while at the same time reflecting actual tumor texture. Using a combination of volume and one of the selected features for prediction yields promise to increase accuracy and reliability of a radiomic model.

Project description:Purpose: Recent studies suggest that 68Ga-FAPI PET/CT demonstrated superiority over 18F-FDG PET/CT in the evaluation of various cancer types, especially in gastric cancer (GC). By comprehensively reviewing and analysing the differences between 68Ga-FAPI and 18F-FDG in GC, some evidence is provided to foster the broader clinical application of FAPI PET imaging. Methods: In this review, studies published up to July 3, 2023, that employed radionuclide labelled FAPI as a diagnostic radiotracer for PET in GC were analysed. These studies were sourced from both the PubMed and Web of Science databases. Our statistical analysis involved a bivariate meta-analysis of the diagnostic data and a meta-analysis of the quantitative metrics. These were performed using R language. Results: The meta-analysis included 14 studies, with 527 patients, of which 358 were diagnosed with GC. Overall, 68Ga-FAPI showed higher pooled sensitivity (0.84 [95% CI 0.67-0.94] vs. 0.46 [95% CI 0.32-0.60]), specificity (0.91 [95% CI 0.76-0.98] vs. 0.88 [95% CI 0.74-0.96]) and area under the curve (AUC) (0.92 [95% CI 0.77-0.98] vs. 0.52 [95% CI 0.38-0.86]) than 18F-FDG. The evidence showed superior pooled sensitivities of 68Ga-FAPI PET over 18F-FDG for primary tumours, local recurrence, lymph node metastases, distant metastases, and peritoneal metastases. Furthermore, 68Ga-FAPI PET provided higher maximum standardized uptake value (SUVmax) and tumour-to-background ratios (TBR). For bone metastases, while 68Ga-FAPI PET demonstrated slightly lower patient-based pooled sensitivity (0.93 vs. 1.00), it significantly outperformed 18F-FDG in the lesion-based analysis (0.95 vs. 0.65). However, SUVmax (mean difference [MD] 1.79 [95% CI -3.87-7.45]) and TBR (MD 5.01 [95% CI -0.78-10.80]) of bone metastases showed no significant difference between 68Ga-FAPI PET/CT and 18F-FDG PET/CT. Conclusion: Compared with 18F-FDG, 68Ga-FAPI PET imaging showed improved diagnostic accuracy in the evaluation of GC. It can be effectively applied to the early diagnosis, initial staging, and detection of recurrence/metastases of GC. 68Ga-FAPI may have the potential of replacing 18F-FDG in GC in future applications.

Dataset Information

A Systematic Review of PET Textural Analysis and Radiomics in Cancer.

Publications

A Systematic Review of PET Textural Analysis and Radiomics in Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets