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Piñeiro Fiel, M. PET Radiomics. Encyclopedia. Available online: https://encyclopedia.pub/entry/8228 (accessed on 19 January 2026).
Piñeiro Fiel M. PET Radiomics. Encyclopedia. Available at: https://encyclopedia.pub/entry/8228. Accessed January 19, 2026.
Piñeiro Fiel, Manuel. "PET Radiomics" Encyclopedia, https://encyclopedia.pub/entry/8228 (accessed January 19, 2026).
Piñeiro Fiel, M. (2021, March 24). PET Radiomics. In Encyclopedia. https://encyclopedia.pub/entry/8228
Piñeiro Fiel, Manuel. "PET Radiomics." Encyclopedia. Web. 24 March, 2021.
PET Radiomics
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This entry is adapted from the peer-reviewed paper 10.3390/diagnostics11020380

PET radiomics is a new medical imaging field exploiting image features to develop novel diagnostic, predictive and prognostic multiparametric models to support personalized clinical decisions and improve individualized treatment selection.

PET radiomics heterogeneity textural analysis cancer

1. Introduction

Decades of research on cancer biology have revealed that tumors are heterogeneous entities at all scales (macroscopic, physiological, microscopic and genetic) [1][2][3][4], with different regions showing distinct morphological and phenotypic profiles [5][6][7]. Nowadays, it is widely accepted that tumor heterogeneity has profound implications in tumor development, therapeutic outcomes and survival [8][9][10][11], making it essential to develop methods for studying tumor heterogeneity in vivo [12].

In this context, non-invasive imaging techniques, such as magnetic resonance (MR), computed tomography (CT) and positron emission tomography (PET) become relevant due to their ability to provide information on the whole tumor in one acquisition [13]. Nowadays, imaging is central to cancer management, having applications in screening, diagnosis, staging, prognosis and treatment response, among others [14][15][16][17][18]. Mainly, PET has emerged as the predominant imaging modality, overperforming conventional imaging techniques for the evaluation of blood [19], head and neck [20] or lung cancer [21]; and it has been increasingly proposed as an ideal tool for characterizing the biology of tumors at the macroscopic scale [13][22][23][24][25]. Over the last years, there has been an increasing interest in extracting quantitative information from PET images using image analysis [26][27]. Thus, semi-quantitative parameters such as the standard uptake value (SUV), the metabolic tumor volume (MTV) or the total lesion glycolysis (TLG) [28] obtained from 18F-fluorodeoxyglucose PET (FDG-PET) images, have been demonstrated to provide relatively objective information useful for the diagnosis, earlier evaluation and monitoring of treatment response [24][25][28]. These parameters are now fully incorporated into clinical guidelines and commonly measured at most hospitals in developed countries [29].

Several research studies have pointed out that high-order textural features derived from PET images can provide information about tumor heterogeneity, expanding the information available from clinical reports, laboratory tests and genomic or proteomic assays [26][27][30]. This has led to the incorporation of PET imaging to radiomics, a new medical imaging field exploiting image features to develop novel diagnostic, predictive and prognostic multiparametric models to support personalized clinical decisions and improve individualized treatment selection [27][31][32] (Figure 1). Textural analysis has long been applied in CT [33] and MRI [34], but it had not been introduced in PET until the last decade. Since then, increasing numbers of studies have suggested that PET textural features would be correlated with tumor biology and heterogeneity [35][36][37][38][39][40][41][42], providing valuable information for tailoring individual treatments [13][18][23][43][44][45][46].

Figure 1. Example of image processing for radiomics. (a) Image acquisition and tumor segmentation. (b) Extraction of different shape, intensity, and textural features from the segmented tumor. (c) Development of prediction models using imaging features.

Despite the promising early reports, numerous studies have highlighted the challenges to be addressed before the use of PET radiomics becomes reliable and interpretable [46]. In addition to issues common to PET imaging itself, such as noise or partial volume effects [47][48][49], radiomics must deal with standardization issues related to the differences in acquisition and reconstruction parameters, post-processing techniques, tumor segmentation methods or even texture calculations [46][50][51][52][53]. The complex formulation of radiomics makes it challenging to explain pervasive findings such as the correlations displayed between different texture indices [54][55][56][57][58][59][60], as well as strong correlations between textural indices and tumor volume [61][62][63], which compromise the added value of these parameters in comparison with SUV or MTV. Furthermore, the variability in definitions and nomenclature of heterogeneity metrics themselves complicate any evaluation and comparison of published results [64]. Finally, there is no consensus about how many patients are needed, which textures must be extracted, or what methodologies should be applied for proper validation [24][46][52].

Due to the prominent role of quantitative image analysis in the diagnosis, tumor characterization and prognosis of cancer patients, the development of reliable and well-validated image analysis methods is of paramount importance. Particularly, parallel with the popularization of PET radiomics, several authors have highlighted the methodological and statistical issues in their works.

2. PET Textural Analysis and Radiomics in Cancer

Lung, head and neck, breast and gynecological cancers emerged as the most studied cancer types. It is well-known that radiomics is a demanding technique in terms of data, and thus we assume that this is a consequence of the prevalence of these types of cancer [65]. The average number of patients per study was 114, and we observed that 65% of the evaluated publications included data from less than 100 patients (29%, less than 50 patients). Previous works evaluating radiomic methodologies suggested a proportion of five patients per evaluated feature to avoid model overfitting [52][66] or a minimum of 100 patients [31]. Based on this and considering that the average number of high-order features was 31 (median = 26, range, 1–286), we would recommend including larger numbers of patients in further works. In addition to this, a limited number of publications (8%) provided a validation of the proposed models using independent databases or a validation in an independent subsample of the initial dataset (28%). We assume that this fact is related to the small amount of data available. Publications with higher numbers of patients used resources such as The Cancer Imaging Archive (TCIA) [67]. We expect that the continuous growth of public imaging databases will improve these numbers in the future and provide a common playground where algorithms could be compared and validated. The rise of collaborative models such as federated learning [68][69] where different centers share models validated across centers while keeping their own data anonymous will also generate more robust algorithms in the near future.

Furthermore, we observed that very few papers aimed at externally validating previously developed models [70][71]. We believe that this should be a common practice in the field. Nevertheless, we observed that most of the publications did not provide easy access to the developed models. As for data sharing, accessibility of software is a substantial concern, reducing the applicability and potential impact of published studies and models [72]. The collaboration between centers and researchers should be improved for further model validation, which is needed to generalize PET radiomics. In this regards, van Griethuysen et al. recently released PyRadiomics, an open modular platform to provide the community with standard tools for promoting further independent development and evaluation [73].

Regarding the objectives of the evaluated publications, the evaluation of prognosis or treatment response was the most common (46%), followed by diagnosis and staging (21%), tumor characterization (articles aimed at predicting the biological characteristics of tumors traditionally obtained by other means (i.e., histology, genetics)) (18%) and the evaluation of technical factors (15%). This result is not surprising, as textures are intended to measure heterogeneity, well-known to be related to aggressiveness and poor outcomes [74][75][76]. We believe that more papers focused on tumor characterization and technical factors would be useful in this regard. On the one hand, although heterogeneity itself was proposed as a biomarker in the past [77][78] it has been reported that heterogeneity is related to biological characteristics such as tumor microenvironment [79][80], genetic expression [81] and, macroscopically, tumor grades or cancer subtypes [82]; thus, linking radiomic features with particular biological characteristics could provide additional evidence for the field. On the other hand, further work is still needed to evaluate the impact of image acquisition, reconstruction, post-processing and feature calculation [83]. In an exemplary work, Bodowicz et al. investigated an association of PET radiomics with local tumor control after radiochemotherapy in head and neck cancer, developing radiomic implementations with two different software packages. Of the 649 features calculated, only 12% were reproducible between the two software implementations, and, although both models were similarly predictive, they included different sets of features, pointing to the need of further harmonization on feature calculation. Harmonization initiatives, such as the Image Biomarker Standardization Initiative [64], would be extremely helpful for this process, as reliable and reproducible measurements are of paramount importance for biomarkers to progress on their validation.

Most of the evaluated studies (78%) concluded that the inclusion of textural features improved the results of models developed with clinical or first-order imaging metrics, which is a powerful conclusion. Nevertheless, it must be interpreted with caution since, as mentioned above, most of the evaluated studies included an insufficient number of patients or were not extensively validated. Many studies reported models including both first and high-order features, which were compared usually with counterparts including a reduced number of features, so an improvement of the result can be caused by overfitting [84][85]. Several papers from our group [63] and others [60][86][87] have suggested a strong correlation between the most used textural features and conventional parameters such as MTV and SUV, which must be untangled.

In summary, PET radiomics is an up-and-coming field, and PET radiomics might have a role in clinical practice in the future. In this regard, the results of the most relevant papers are very appealing. Nevertheless, we have identified several methodological concerns related to the validation of the purposed algorithms, the number of patients included, the lack of data and software accessibility and a need for further methodological standardization. We have also observed that the community is already developing solutions to overcome these limitations.

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Subjects: Oncology
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