Tumor Texture Analysis in PET: Where Do We Stand?

Intratumoral metabolic heterogeneity by 18F-FDG PET/CT to predict prognosis for patients with thymic epithelial tumors

BACKGROUND

The aim of the present study was to evaluate the impact of intratumoral metabolic heterogeneity and quantitative 18F-FDG PET/CT imaging parameters in predicting patient outcomes in thymic epithelial tumors (TETs).

METHODS

This retrospective study included 100 patients diagnosed with TETs who underwent pretreatment 18F-FDG PET/CT. The maximum and mean standardized uptake values (SUVmax and SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) on PET/CT were measured. Heterogeneity index-1 (HI-1; standard deviation [SD] divided by SUVmean) and heterogeneity index-2 (HI-2; linear regression slopes of the MTV according with different SUV thresholds), were evaluated as heterogeneity indices. Associations between these parameters and patient survival outcomes were analyzed.

RESULTS

The univariate analysis showed that Masaoka stage, TNM stage, WHO classification, SUVmax, SUVmean, TLG, and HI-1 were significant prognostic factors for progression-free survival (PFS), while MTV, HI-2, age, gender, presence of myasthenia gravis, and maximum tumor diameter were not. Subsequently, multivariate analyses showed that HI-1 (p < 0.001) and TNM stage (p = 0.002) were independent prognostic factors for PFS. For the overall survival analysis, TNM stage, WHO classification, SUVmax, and HI-1 were significant prognostic factors in the univariate analysis, while TNM stage remained an independent prognostic factor in multivariate analyses (p = 0.024). The Kaplan Meier survival analyses showed worse prognoses for patients with TNM stages III and IV and HI-1 ≥ 0.16 compared to those with stages I and II and HI-1 < 0.16 (log-rank p < 0.001).

CONCLUSION

HI-1 and TNM stage were independent prognostic factors for progression-free survival in TETs. HI-1 generated from baseline 18F-FDG PET/CT might be promising to identify patients with poor prognosis.

Primary tumor heterogeneity on pretreatment 18F-FDG PET/CT to predict outcome in patients with rectal cancer who underwent surgery after neoadjuvant therapy

PURPOSE

This retrospective study aimed to investigate the value of texture features of primary tumors in pretreatment ¹⁸F-FDG PET/CT in the prediction of response to treatment, progression, and overall survival in patients with rectal cancer who underwent surgery after neoadjuvant therapy(NAT).

METHODS

Patients with rectal cancer who had pretreatment ¹⁸F-FDG PET/CT, and underwent surgery after NAT were included in this study. Clinicopathologic features, date of last follow-up, progression, and death were recorded. Textural and conventional PET parameters(maximum standardized uptake value-SUVmax, metabolic tumor volume-MTV, total lesion glycolysis-TLG) were obtained from PET/CT images using LifeX program. Parameters were grouped using Youden index in ROC analysis. Factors predicting the pathological response to treatment, progression, and overall survival were determined using logistic regression and Cox regression analyses.

RESULTS

Forty-four patients (26(59%) male, 18(41%) female; 60.1±11.4 years) with rectal cancer were included in this study. The numbers of patients with responders and non-responders to NAT were 15(34.9%) and 28(65.1%), respectively. One patient' pathology report did not contain the response status to NAT. The median of follow-up duration was 29.9 months. 9(20.5%) showed disease progression, and 8(18.2%) died during the follow-up period. Difference entropy_GLCM and correlation_GLCM parameters were found as independent predictors for response to NAT. The positivity of surgical margin, intensity interquartile range_CONV and AUC-CSH_DISC texture parameters were independent predictors of progression, while normalized inverse difference_GLCM and LZLGE_GLZLM parameters were independent predictors of mortality.

CONCLUSION

The texture parameters obtained from pretreatment ¹⁸F-FDG PET/CT have presented a more robust predictive value than conventional parameters in patients with rectal cancer who underwent surgery after NAT.

The prognostic value of 18F-FDG PET/CT intra-tumoural metabolic heterogeneity in pretreatment neuroblastoma patients

Background

Neuroblastoma (NB) is the most common tumour in children younger than 5 years old and notable for highly heterogeneous. Our aim was to quantify the intra-tumoural metabolic heterogeneity of primary tumour lesions by using ¹⁸F-FDG PET/CT and evaluate the prognostic value of intra-tumoural metabolic heterogeneity in NB patients.

Methods

We retrospectively enrolled 38 pretreatment NB patients in our study. ¹⁸F-FDG PET/CT images were reviewed and analyzed using 3D slicer software. The semi-quantitative metabolic parameters of primary tumour were measured, including the maximum standard uptake value (SUVmax), metabolic tumour volume (MTV), and total lesion glycolysis (TLG). The areas under the curve of cumulative SUV-volume histogram index (AUC-CSH index) was used to quantify intra-tumoural metabolic heterogeneity. The median follow-up was 21.3 months (range 3.6 - 33.4 months). The outcome endpoint was event-free survival (EFS), including progression-free survival and overall survival. Survival analysis was performed using Cox regression models and Kaplan Meier survival plots.

Results

In all 38 newly diagnosed NB patients, 2 patients died, and 17 patients experienced a relapse. The AUC-CSH_total (r=0.630, P<0.001) showed moderate correlation with the AUC-CSH_40%. In univariate analysis, chromosome 11q deletion (P=0.033), Children's Oncology Group (COG) risk grouping (P=0.009), bone marrow involvement (BMI, P=0.015), and AUC-CSH_total (P=0.007) were associated with EFS. The AUC-CSH_total (P=0.036) and BMI (P=0.045) remained significant in multivariate analysis. The Kaplan Meier survival analyses demonstrated that patients with higher intra-tumoural metabolic heterogeneity and BMI had worse outcomes (log-rank P=0.002).

Conclusion

The intra-tumoural metabolic heterogeneity of primary lesions in NB was an independent prognostic factor for EFS. The combined predictive effect of intra-tumoural metabolic heterogeneity and BMI provided prognostic survival information in NB patients.

Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma

Purpose

This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE.

Patients and Methods

The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan–Meier (K-M) analysis and the Log rank test.

Results

The median OS was 439 days (95% CI: 215.795–662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172–932.828), 395 days (95% CI: 309.415–480.585), respectively (P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285–3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310–2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496–1.000) and combined model (C-index: 0.770, 95% CI: 0.581–0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508–0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort.

Conclusion

Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.

An Adaptive Learning Model for Multiscale Texture Features in Polyp Classification via Computed Tomographic Colonography

Objective: As an effective lesion heterogeneity depiction, texture information extracted from computed tomography has become increasingly important in polyp classification. However, variation and redundancy among multiple texture descriptors render a challenging task of integrating them into a general characterization. Considering these two problems, this work proposes an adaptive learning model to integrate multi-scale texture features. Methods: To mitigate feature variation, the whole feature set is geometrically split into several independent subsets that are ranked by a learning evaluation measure after preliminary classifications. To reduce feature redundancy, a bottom-up hierarchical learning framework is proposed to ensure monotonic increase of classification performance while integrating these ranked sets selectively. Two types of classifiers, traditional (random forest + support vector machine)- and convolutional neural network (CNN)-based, are employed to perform the polyp classification under the proposed framework with extended Haralick measures and gray-level co-occurrence matrix (GLCM) as inputs, respectively. Experimental results are based on a retrospective dataset of 63 polyp masses (defined as greater than 3 cm in largest diameter), including 32 adenocarcinomas and 31 benign adenomas, from adult patients undergoing first-time computed tomography colonography and who had corresponding histopathology of the detected masses. Results: We evaluate the performance of the proposed models by the area under the curve (AUC) of the receiver operating characteristic curve. The proposed models show encouraging performances of an AUC score of 0.925 with the traditional classification method and an AUC score of 0.902 with CNN. The proposed adaptive learning framework significantly outperforms nine well-established classification methods, including six traditional methods and three deep learning ones with a large margin. Conclusions: The proposed adaptive learning model can combat the challenges of feature variation through a multiscale grouping of feature inputs, and the feature redundancy through a hierarchal sorting of these feature groups. The improved classification performance against comparative models demonstrated the feasibility and utility of this adaptive learning procedure for feature integration.

Effects of tracer uptake time in non-small cell lung cancer 18F-FDG PET radiomics

Positron emission tomography (PET) radiomics applied to oncology allows the measurement of intra-tumoral heterogeneity. This quantification can be affected by image protocols hence there is an increased interest in understanding how radiomic expression on PET images is affected by different imaging conditions. To address that, this study explores how radiomic features are affected by changes in ¹⁸F-FDG uptake time, image reconstruction, lesion delineation, and radiomics binning settings. Methods: Ten non-small cell lung cancer (NSCLC) patients underwent ¹⁸F-FDG PET scans on two consecutive days. On each day, scans were obtained at 60min and 90min post-injection and reconstructed following EARL version 1 (EARL1) and with point-spread-function resolution modelling (PSF-EARL2). Lesions were delineated using thresholds at SUV=4.0, 40% of SUV_max, and with a contrast-based isocontour. PET image intensity was discretized with both fixed bin width (FBW) and fixed bin number (FBN) before the calculation of the radiomic features. Repeatability of features was measured with intraclass correlation (ICC), and the change in feature value over time was calculated as a function of its repeatability. Features were then classified on use-case scenarios based on their repeatability and susceptibility to tracer uptake time. Results: With PSF-EARL2 reconstruction, 40% of SUV_max lesion delineation, and FBW intensity discretization, most features (94%) were repeatable at both uptake times (ICC>0.9), 39% being classified for dual-time-point use-case for being sensitive to changes in uptake time, 39% were classified for cross-sectional studies with unclear dependency on time, 20% classified for cross-sectional use while being robust to tracer uptake time changes, and 6% were discarded for poor repeatability. EARL1 images had one less repeatable feature than PSF-EARL2 (Neighborhood Gray-Level Different Matrix Coarseness), the contrast-based delineation had the poorest repeatability of the delineation methods with 45% features being discarded, and FBN resulted in lower repeatability than FBW (45% and 6% features were discarded, respectively). Conclusion: Repeatability was maximized with PSF-EARL2 reconstruction, lesion delineation at 40% of SUV_max, and FBW intensity discretization. Based on their susceptibility to tracer uptake time, radiomic features were classified into specific NSCLC PET radiomics use-cases.

Measuring Heterogeneity in 18F-Fluorodeoxyglucose Positron Emission Tomography Images for Classifying Metastatic and Benign Bone Lesions in Patients with Cervical Cancer

Purpose

Heterogeneity assessment can be applied for medical imaging analysis. Here, we evaluated first-order and texture analysis (TA) metrics in 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) imaging for classification of metastatic and benign bone lesions in patients with cervical cancer.

Methods

The data of 18F-FDG PET studies performed on a specific PET/CT system from 2016 to 2018 in patients with cervical cancer were retrieved. The data of bone lesions extracted from studies over 2016–2017 and 2018 were used as training and validation datasets, respectively. Metastatic bone lesions were identified in each dataset, with an equal number of benign bone lesions selected. Cuboid volume of interest (VOI) consisting of 3 × 3 × 5 reconstructed voxels was applied for first-order metrics, and cubic VOI consisting of smaller voxels with trilinear interpolation of standardized uptake value (SUV) was adopted for TA metrics. First-order metrics included the maximum SUV (SUVmax) of lesions and the mean voxel SUV and its standard deviation (SUVsd), skewness, and kurtosis in VOI. In total, 4464 TA metrics based on 62 texture features were evaluated. Logistic regression was used for classification with area under the receiver operating characteristic curve (AUC) as the performance measure.

Results

From the training and validation datasets, 98 and 42 metastatic bone lesions were identified, respectively. SUVsd demonstrated higher performance than did SUVmax in both the training (AUC .798 vs .732, P = .001) and validation (AUC .786 vs .684, P < .001) datasets. Top-performing TA metrics demonstrated significantly higher performance in the training dataset, but not in the validation dataset.

Conclusion

A simple first-order measure of heterogeneity, SUVsd, was found to be superior to SUVmax for the classification of metastatic and benign bone lesions. Multiple hypothesis testing can result in false-positive findings in TA with multiple features and parameters; careful validation is required.

Radiomics in PET Imaging:: A Practical Guide for Newcomers

Radiomics has undergone considerable development in recent years. In PET imaging, very promising results concerning the ability of handcrafted features to predict the biological characteristics of lesions and to assess patient prognosis or response to treatment have been reported in the literature. This article presents a checklist for designing a reliable radiomic study, gives an overview of the steps of the pipeline, and outlines approaches for data harmonization. Tips are provided for critical reading of the content of articles. The advantages and limitations of handcrafted radiomics compared with deep-learning approaches for the characterization of PET images are also discussed.

Radiomics and Digital Image Texture Analysis in Oncology (Review)

One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology.

The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered.

Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.

Characteristics of malignant thyroid lesions on [18F] fluorodeoxyglucose (FDG)-Positron emission tomography (PET)/Computed tomography (CT)

Objectives

To determine the imaging variables that can best differentiate malignant from benign thyroid lesions incidentally found on F-18 FDG PET/CT scans.

Methods

All F-18 FDG PET/CT studies starting from 2011 to end of 2016 were reviewed for incidental thyroid lesions or metabolic abnormalities. Only patients who were found to have FNAB or histopathology were included. Patients with known thyroid malignancy were excluded. Patients were analyzed for age, sex, SUVmax, non-enhanced CT tissue density in mean Hounsfield units (HU), uptake pattern (focal or diffuse) and gland morphology (MNG or diffuse). A control group of 15 patients with normal thyroid glands were used to assess the tissue density in HU for normal thyroid tissue. Sensitivity, specificity, PPV, NPV and accuracy to detect malignancy were calculated. Pearson Chi-square test was used to compare categorical variables while unpaired T-test and one way ANOVA test were used to compare means of continuous variables. ROC analysis was used to assess the best cut off points for SUVmax and HU. Regression analysis was used to detect the independent predictors for malignant lesions.

Results

Biopsy was unsatisfactory or indeterminate in 4/48 patients (8%). Only 44 patients (mean age 55.2 ± 14.7; 30 females (68 %)) with unequivocal FNAB or histopathology were included for further analysis. MNG was noted in 17/44 patients (38.6 %). Thyroid malignancy was found in 16/44 (36.4 %), benign thyroid lesions in 28/44 (63.6 %). Thyroid malignancies were 12 papillary, 1 follicular, 1 Hurthle cell neoplasm and 2 lymphoma. Benign lesions were 23 benign follicular or colloid nodules and 5 autoimmune thyroiditis. Focal FDG uptake pattern was more frequently associated with malignant lesions compared to benign lesions (75 % vs. 43 %; p = 0.039). The mean SUVmax and tissue density (HU) were both higher in malignant than benign lesions (8.8 ± 8.3 vs. 3.6 ± 1.9, p = 0.024) and (48.9 ± 12.7 vs. 32.9 ± 17.5, p = 0.003) respectively. The mean HU in the control group with normal thyroid tissue was 90 ± 7.4 significantly higher than in both the benign and malignant lesions (p < 0.001). ROC analysis revealed SUVmax cutoff of >4.7 and HU cutoff of >42 to best differentiate malignant from benign lesions. The sensitivity, specificity, PPV, NPV and accuracy to detect malignancy for SUVmax>4.7 were 68.8 %, 78.6 %, 64.8 %, 81.5 & 75.0 % (p = 0.002), for HU > 42 were 81.3.0 %, 75.0 %, 65.0 %, 87.5 & 77.3 % (p = 0.0003) and for both parameters combined were 87.5 %, 60.7 %, 56.0 %, 89.5 % and accuracy of 70.5 % (p = 0.002) respectively. Only HU > 42 and SUVmax>4.7 were independent predictors for malignancy with odd ratios 8.98 and 4.93 respectively.

Conclusion

A higher tissue density (HU > 42) and SUVmax>4.7 as well as tendency for focal uptake pattern are the most significant characteristics associated with malignant thyroid lesions occasionally detected on PET/CT.

A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging

Nasopharyngeal carcinoma (NPC) is one of the most common malignant tumours of the head and neck, and improving the efficiency of its diagnosis and treatment strategies is an important goal. With the development of the combination of artificial intelligence (AI) technology and medical imaging in recent years, an increasing number of studies have been conducted on image analysis of NPC using AI tools, especially radiomics and artificial neural network methods. In this review, we present a comprehensive overview of NPC imaging research based on radiomics and deep learning. These studies depict a promising prospect for the diagnosis and treatment of NPC. The deficiencies of the current studies and the potential of radiomics and deep learning for NPC imaging are discussed. We conclude that future research should establish a large-scale labelled dataset of NPC images and that studies focused on screening for NPC using AI are necessary.

Additional value of volumetric and texture analysis on FDG PET assessment in paediatric Hodgkin lymphoma: an Italian multicentric study protocol

INTRODUCTION

Assessment of response to therapy in paediatric patients with Hodgkin lymphoma (HL) by 18F-fluorodeoxyglucose positron emission tomography/CT has become a powerful tool for the discrimination of responders from non-responders. The addition of volumetric and texture analyses can be regarded as a valuable help for disease prognostication and biological characterisation. Based on these premises, the Hodgkin Lymphoma Study Group of the Associazione Italiana Ematologia Oncologia Pediatrica (AIEOP) has designed a prospective evaluation of volumetric and texture analysis in the Italian cohort of patients enrolled in the EuroNet-PHL-C2.

METHODS AND ANALYSIS

The primary objective is to compare volumetric assessment in patiens with HL at baseline and during the course of therapy with standard visual and semiquantitative analyses. The secondary objective is to identify the impact of volumetric and texture analysis on bulky masses. The tertiary objective is to determine the additional value of multiparametric assessment in patients having a partial response on morphological imaging.The overall cohort of the study is expected to be round 400-500 patients, with approximately half presenting with bulky masses. All PET scans of the Italian cohort will be analysed for volumetric assessment, comprising metabolic tumour volume and total lesion glycolysis at baseline and during the course of therapy. A dedicated software will delineate semiautomatically contours using different threshold methods, and the impact of each segmentation techniques will be evaluated. Bulky will be defined on contiguous lymph node masses ≥200 mL on CT/MRI. All bulky masses will be outlined and analysed by the same software to provide textural features. Morphological assessment will be based on RECIL 2017 for response definition.

ETHICS AND DISSEMINATION

The current study has been ethically approved (AIFA/SC/P/27087 approved 09/03/2018; EudraCT 2012-004053-88, EM-04). The results of the different analyses performed during and after study completion the will be actively disseminated through peer-reviewed journals, conference presentations, social media, print media and internet.

Performance evaluation of the mouse version of the LabPET II PET scanner

The LabPET II is a new positron emission tomography technology platform designed to achieve submillimetric spatial resolution imaging using fully pixelated avalanche photodiodes-based detectors and highly integrated parallel front-end processing electronics. The detector was designed as a generic building block to develop devices for preclinical imaging of small to mid-sized animals and for clinical imaging of the human brain. The aim of this work is to assess the physical characteristics and imaging performance of the mouse version of LabPET II scanner following the NEMA NU4-2008 standard and using high resolution phantoms and in vivo imaging applications. A reconstructed spatial resolution of 0.78 mm (0.5 μ l) is measured close to the center of the radial field of view. With an energy window of 350 650 keV, the system absolute sensitivity is 1.2% and its maximum noise equivalent count rate reaches 61.1 kcps at 117 MBq. Submillimetric spatial resolution is achieved in a hot spot phantom and tiny bone structures were resolved with unprecedented contrast in the mouse. These results provide convincing evidence of the capabilities of the LabPET II technology for biomolecular imaging in preclinical research.

Comparison of three freeware software packages for 18F-FDG PET texture feature calculation

PURPOSE

To compare texture feature estimates obtained from ¹⁸F-FDG-PET images using three different software packages.

METHODS

PET images from 15 patients with head and neck cancer were processed with three different freeware software: CGITA, LIFEx, and Metavol. For each lesion, 38 texture features were extracted from each software package. To evaluate the statistical agreement among the features across packages a non-parametric Kruskal-Wallis test was used. Differences in the features between each couple of software were assessed using a subsequent Dunn test. Correlation between texture features was evaluated via the Spearman coefficient.

RESULTS

Twenty-three of 38 features showed a significant agreement across the three software (P < 0.05). The agreement was better between LIFEx vs. Metavol (36 of 38) and worse between CGITA and Metavol (24 of 38), and CGITA vs. LIFEx (23 of 38). All features resulted correlated (ρ >  = 0.70, P < 0.001) in comparing LIFEx vs. Metavol. Seven of 38 features were found not in agreement and slightly or not correlated (ρ < 0.70, P < 0.001) in comparing CGITA vs. LIFEx, and CGITA vs. Metavol.

CONCLUSION

Some texture discrepancies across software packages exist. Our findings reinforce the need to continue the standardization process, and to succeed in building a reference dataset to be used for comparisons.

Influence of Magnetic Field Strength on Magnetic Resonance Imaging Radiomics Features in Brain Imaging, an In Vitro and In Vivo Study

BACKGROUND

The development and clinical adoption of quantitative imaging biomarkers (radiomics) has established the need for the identification of parameters altering radiomics reproducibility. The aim of this study was to assess the impact of magnetic field strength on magnetic resonance imaging (MRI) radiomics features in neuroradiology clinical practice.

METHODS

T1 3D SPGR sequence was acquired on two phantoms and 10 healthy volunteers with two clinical MR devices from the same manufacturer using two different magnetic fields (1.5 and 3T). Phantoms varied in terms of gadolinium concentrations and textural heterogeneity. 27 regions of interest were segmented (phantom: 21, volunteers: 6) using the LIFEX software. 34 features were analyzed.

RESULTS

In the phantom dataset, 10 (67%) out of 15 radiomics features were significantly different when measured at 1.5T or 3T (student's t-test, p < 0.05). Gray levels resampling, and pixel size also influence part of texture features. These findings were validated in healthy volunteers.

CONCLUSIONS

According to daily used protocols for clinical examinations, radiomic features extracted on 1.5T should not be used interchangeably with 3T when evaluating texture features. Such confounding factor should be adjusted when adapting the results of a study to a different platform, or when designing a multicentric trial.

Differentiating gastric cancer and gastric lymphoma using texture analysis (TA) of positron emission tomography (PET)

Background:

Texture analysis (TA) can quantify intra-tumor heterogeneity using standard medical images. The present study aimed to assess the application of positron emission tomography (PET) TA in the differential diagnosis of gastric cancer and gastric lymphoma.

Methods:

The pre-treatment PET images of 79 patients (45 gastric cancer, 34 gastric lymphoma) between January 2013 and February 2018 were retrospectively reviewed. Standard uptake values (SUVs), first-order texture features, and second-order texture features of the grey-level co-occurrence matrix (GLCM) were analyzed. The differences in features among different groups were analyzed by the two-way Mann-Whitney test, and receiver operating characteristic (ROC) analysis was used to estimate the diagnostic efficacy.

Results:

Inertia_GLCM was significantly lower in gastric cancer than that in gastric lymphoma (4975.61 vs. 11,425.30, z = −3.238, P = 0.001), and it was found to be the most discriminating texture feature in differentiating gastric lymphoma and gastric cancer. The area under the curve (AUC) of inertia_GLCM was higher than the AUCs of SUVmax and SUVmean (0.714 vs. 0.649 and 0.666, respectively). SUVmax and SUVmean were significantly lower in low-grade gastric lymphoma than those in high grade gastric lymphoma (3.30 vs. 11.80, 2.40 vs. 7.50, z = −2.792 and −3.007, P = 0.005 and 0.003, respectively). SUVs and first-order grey-level intensity features were not significantly different between low-grade gastric lymphoma and gastric cancer. Entropy_GLCM12 was significantly lower in low-grade gastric lymphoma than that in gastric cancer (6.95 vs. 9.14, z = −2.542, P = 0.011) and had an AUC of 0.770 in the ROC analysis of differentiating low-grade gastric lymphoma and gastric cancer.

Conclusions:

Inertia_GLCM and entropy_GLCM were the most discriminating features in differentiating gastric lymphoma from gastric cancer and low-grade gastric lymphoma from gastric cancer, respectively. PET TA can improve the differential diagnosis of gastric neoplasms, especially in tumors with similar degrees of fluorodeoxyglucose uptake.

Radiomics for the Prediction of Treatment Outcome and Survival in Patients With Colorectal Cancer: A Systematic Review

Prediction of outcome in patients with colorectal cancer (CRC) is challenging as a result of lack of a robust biomarker and heterogeneity between and within tumors. The aim of this review was to assess the current possibilities and limitations of radiomics (on computed tomography [CT], magnetic resonance imaging [MRI], and positron emission tomography [PET]) for the prediction of treatment outcome and long-term outcome in CRC. Medline/PubMed was searched up to August 2020 for studies that used radiomics for the prediction of response to treatment and survival in patients with CRC (based on pretreatment imaging). The Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool and Radiomics Quality Score (RQS) were used for quality assessment. A total of 76 studies met the inclusion criteria and were included for further analysis. Radiomics analyses were performed on MRI in 41 studies, on CT in 30 studies, and on ¹⁸F-FDG-PET/CT in 10 studies. Heterogeneous results were reported regarding radiomics methods and included features. High-quality studies (n = 13), consisting mainly of MRI-based radiomics to predict response in rectal cancer, were able to predict response with good performance. Radiomics literature in CRC is highly heterogeneous, but it nonetheless holds promise for the prediction of outcome. The most evidence is available for MRI-based radiomics in rectal cancer. Future radiomics research in CRC should focus on independent validation of existing models rather than on developing new models.

Contrast-Enhanced CT-based Textural Parameters as Potential Prognostic Factors of Survival for Colorectal Cancer Patients Receiving Targeted Therapy

PURPOSE

This study was designed to estimate the clinical significance of the contrast-enhanced computed tomography (CT) textural features for prediction of survival in colorectal cancer (CRC) patients receiving targeted therapy (bevacizumab and cetuximab).

PROCEDURES

The LifeX software was used to extract the textural parameters of the tumor lesions in the contrast-enhanced CT. We used the least absolute shrinkage and selection operator (LASSO) Cox regression and random forest method to screen the non-redundant radiomic features and constructed the CT imaging score. Univariate and multivariate analyses through the Cox proportional hazards model were performed to assess the prognostic clinical factor. Based on the result of multivariate analysis and CT imaging score, combined nomogram model was constructed to predict the overall survival (OS) of patients. Decision curves analysis was employed to evaluate the performance of the combined model and clinical model.

RESULTS

After comparative analysis of the area under curve of the receiver operating characteristic (ROC) curve, we chose the result of random forest model as CT imaging score. Considering the clinical practice and the result of analysis, age, surgery, and lactate dehydrogenase (LDH) level have been introduced into clinical model. Based on the result of analysis and the CT imaging score, we constructed the nomogram combined model. C-index and calibration curve verified the goodness of fit and discrimination of the combined model. Decision curve analysis (DCA) demonstrated that the combined model showed the better net benefit for a 3-year OS than clinical model.

CONCLUSIONS

In conclusion, the study provides preliminary evidences that several radiomic parameters of tumor lesions derived from CT images were prognostic factors and predictive markers for CRC patients who are candidates for targeted therapy (bevacizumab and cetuximab).

From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community

Big data are no longer an obstacle; now, by using artificial intelligence (AI), previously undiscovered knowledge can be found in massive data collections. The radiation oncology clinic daily produces a large amount of multisource data and metadata during its routine clinical and research activities. These data involve multiple stakeholders and users. Because of a lack of interoperability, most of these data remain unused, and powerful insights that could improve patient care are lost. Changing the paradigm by introducing powerful AI analytics and a common vision for empowering big data in radiation oncology is imperative. However, this can only be achieved by creating a clinical data science community in radiation oncology. In this work, we present why such a community is needed to translate multisource data into clinical decision aids.

Predictive value of quantitative 18F-FDG-PET radiomics analysis in patients with head and neck squamous cell carcinoma

BACKGROUND

Radiomics is aimed at image-based tumor phenotyping, enabling application within clinical-decision-support-systems to improve diagnostic accuracy and allow for personalized treatment. The purpose was to identify predictive 18-fluor-fluoro-2-deoxyglucose (¹⁸F-FDG) positron-emission tomography (PET) radiomic features to predict recurrence, distant metastasis, and overall survival in patients with head and neck squamous cell carcinoma treated with chemoradiotherapy.

METHODS

Between 2012 and 2018, 103 retrospectively (training cohort) and 71 consecutively included patients (validation cohort) underwent ¹⁸F-FDG-PET/CT imaging. The 434 extracted radiomic features were subjected, after redundancy filtering, to a projection resulting in outcome-independent meta-features (factors). Correlations between clinical, first-order ¹⁸F-FDG-PET parameters (e.g., SUVmean), and factors were assessed. Factors were combined with ¹⁸F-FDG-PET and clinical parameters in a multivariable survival regression and validated. A clinically applicable risk-stratification was constructed for patients' outcome.

RESULTS

Based on 124 retained radiomic features from 103 patients, 8 factors were constructed. Recurrence prediction was significantly most accurate by combining HPV-status, SUVmean, SUVpeak, factor 3 (histogram gradient and long-run-low-grey-level-emphasis), factor 4 (volume-difference, coarseness, and grey-level-non-uniformity), and factor 6 (histogram variation coefficient) (CI = 0.645). Distant metastasis prediction was most accurate assessing metabolic-active tumor volume (MATV)(CI = 0.627). Overall survival prediction was most accurate using HPV-status, SUVmean, SUVmax, factor 1 (least-axis-length, non-uniformity, high-dependence-of-high grey-levels), and factor 5 (aspherity, major-axis-length, inversed-compactness and, inversed-flatness) (CI = 0.764).

CONCLUSIONS

Combining HPV-status, first-order ¹⁸F-FDG-PET parameters, and complementary radiomic factors was most accurate for time-to-event prediction. Predictive phenotype-specific tumor characteristics and interactions might be captured and retained using radiomic factors, which allows for personalized risk stratification and optimizing personalized cancer care.

TRIAL REGISTRATION

Trial NL3946 (NTR4111), local ethics commission reference: Prediction 2013.191 and 2016.498. Registered 7 August 2013, https://www.trialregister.nl/trial/3946.

CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review

Texture analysis in medical imaging is a promising tool that is designed to improve the characterization of abnormal images from patients, to ultimately serve as a predictive or prognostic biomarker. However, the nature of image acquisition itself implies variability in each pixel/voxel value that could jeopardize the usefulness of texture analysis in the medical field. In this review, a search was performed to identify current published data for computed tomography (CT) texture reproducibility and variability. On the basis of this analysis, the critical steps were identified with a view of using texture analysis as a reliable tool in medical imaging. The need to specify the CT scanners used and the associated parameters in published studies is highlighted. Harmonizing acquisition parameters between studies is a crucial step for future texture analysis.

Evaluation of CT-based radiomics signature and nomogram as prognostic markers in patients with laryngeal squamous cell carcinoma

Background

The aim of this study was to evaluate the prognostic value of radiomics signature and nomogram based on contrast-enhanced computed tomography (CT) in patients after surgical resection of laryngeal squamous cell carcinoma (LSCC).

Methods

All patients (n = 136) were divided into the training cohort (n = 96) and validation cohort (n = 40). The LASSO regression method was performed to construct radiomics signature from CT texture features. Then a radiomics nomogram incorporating the radiomics signature and clinicopathologic factors was established to predict overall survival (OS). The validation of nomogram was evaluated by calibration curve, concordance index (C-index) and decision curve.

Results

Based on three selected texture features, the radiomics signature showed high C-indexes of 0.782 (95%CI: 0.656–0.909) and 0.752 (95%CI, 0.614–0.891) in the two cohorts. The radiomics nomogram had significantly better discrimination capability than cancer staging in the training cohort (C-index, 0.817 vs. 0.682; P = 0.009) and validation cohort (C-index, 0.913 vs. 0.699; P = 0.019), as well as a good agreement between predicted and actual survival in calibration curves. Decision curve analysis also suggested improved clinical utility of radiomics nomogram.

Conclusions

Radiomics signature and nomogram showed favorable prediction accuracy for OS, which might facilitate the individualized risk stratification and clinical decision-making in LSCC patients.

Interdisciplinarity: An essential requirement for translation of radiomics research into clinical practice -a systematic review focused on thoracic oncology

BACKGROUND

Recently, evidence has accumulated that demonstrates the potential for future applications of radiomics in many clinical settings, including thoracic oncology. Methodological reasons for the immaturity of image mining (radiomics and artificial intelligence-based) studies have been identified. However, data on the influence of the composition of the research team on the quality of investigations in radiomics are lacking.

AIM

This review aims to evaluate the interdisciplinarity within studies on radiomics in thoracic oncology in order to assess its influence on the quality of research (QUADAS-2 score) in the image mining field.

METHODS

We considered for inclusion radiomics investigations with objectives relating to clinical practice in thoracic oncology. Subsequently, we interviewed the corresponding authors. The field of expertise and/or educational degree was then used to assess interdisciplinarity. Subsequently, all studies were evaluated applying the QUADAS-2 score and assigned to a research phase from 0 to IV.

RESULTS

Overall, 27 studies were included. The study quality according to the QUADAS-2 score was low (score ≤5) in 8, moderate (=6) in 12, and high (≥7) in 7 papers. An interdisciplinary team (at least 3 different expertise categories) was involved in half of the papers without any type of validation and in all papers with independent validation. Clinicians were not involved in phase 0 studies while they contributed to all papers classified as phase I and to 4/5 papers classified as phase II with independent validation.

CONCLUSIONS

The composition of the research team influences the quality of investigations in radiomics. Also, growth in interdisciplinarity appears to reflect research development from the early phase to a more mature, clinically oriented stage of investigation.

A no-reference respiratory blur estimation index in nuclear medicine for image quality assessment

Few indexes are available for nuclear medicine image quality assessment, particularly for respiratory blur assessment. A variety of methods for the identification of blur parameters has been proposed in literature mostly for photographic pictures but these methods suffer from a high sensitivity to noise, making them unsuitable to evaluate nuclear medicine images. In this paper, we aim to calibrate and test a new blur index to assess image quality.Blur index calibration was evaluated by numerical simulation for various lesions size and intensity of uptake. Calibrated blur index was then tested on gamma-camera phantom acquisitions, PET phantom acquisitions and real-patient PET images and compared to human visual evaluation.For an optimal filter parameter of 9, non-weighted and weighted blur index led to an automated classification close to the human one in phantom experiments and identified each time the sharpest image in all the 40 datasets of 4 images. Weighted blur index was significantly correlated to human classification (ρ = 0.69 [0.45;0.84] P < .001) when used on patient PET acquisitions.The provided index allows to objectively characterize the respiratory blur in nuclear medicine acquisition, whether in planar or tomographic images and might be useful in respiratory gating applications.

Metabolic Biomarker-Based BRAFV600 Mutation Association and Prediction in Melanoma

The aim of this study was to associate and predict B-rapidly accelerated fibrosarcoma valine 600 (BRAFV600) mutation status with both conventional and radiomics ¹⁸F-FDG PET/CT features, while exploring several methods of feature selection in melanoma radiomics. Methods: Seventy unresectable stage III-IV melanoma patients who underwent a baseline ¹⁸F-FDG PET/CT scan were identified. Patients were assigned to the BRAFV600 group or BRAF wild-type group according to mutational status. ¹⁸F-FDG uptake quantification was performed by semiautomatic lesion delineation. Four hundred eighty radiomics features and 4 conventional PET features (SUV_max, SUV_mean, SUV_peak, and total lesion glycolysis) were extracted per lesion. Six different methods of feature selection were implemented, and 10-fold cross-validated predictive models were built for each. Model performances were evaluated with areas under the curve (AUCs) for the receiver operating characteristic curves. Results: Thirty-five BRAFV600 mutated patients (100 lesions) and 35 BRAF wild-type patients (79 lesions) were analyzed. AUCs predicting the BRAFV600 mutation varied from 0.54 to 0.62 and were susceptible to feature selection method. The best AUCs were achieved by feature selection based on literature, a penalized binary logistic regression model, and random forest model. No significant difference was found between the BRAFV600 and BRAF wild-type group in conventional PET features or predictive value. Conclusion: BRAFV600 mutation status is not associated with, nor can it be predicted with, conventional PET features, whereas radiomics features were of low predictive value (AUC = 0.62). We showed feature selection methods to influence predictive model performance, describing and evaluating 6 unique methods. Detecting BRAFV600 status in melanoma based on ¹⁸F-FDG PET/CT alone does not yet provide clinically relevant knowledge.

AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics

INTRODUCTION

The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.

OBJECTIVE

The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

Impact of intensity discretization on textural indices of [18F]FDG-PET tumour heterogeneity in lung cancer patients

Quantifying tumour heterogeneity from [¹⁸F]FDG-PET images promises benefits for treatment selection of cancer patients. Here, the calculation of texture parameters mandates an initial discretization step (binning) to reduce the number of intensity levels. Typically, three types of discrimination methods are used: lesion relative resampling (LRR) with fixed bin number, lesion absolute resampling (LAR) and absolute resampling (AR) with fixed bin widths. We investigated the effects of varying bin widths or bin number using 27 commonly cited local and regional texture indices (TIs) applied on lung tumour volumes. The data set were extracted from 58 lung cancer patients, with three different and robust tumour segmentation methods. In our cohort, the variations of the mean value as the function of the bin widths were similar for TIs calculated with LAR and AR quantification. The TI histograms calculated by LRR method showed distinct behaviour and its numerical values substantially effected by the selected bin number. The correlations of the AR and LAR based TIs demonstrated no principal differences between these methods. However, no correlation was found for the interrelationship between the TIs calculated by LRR and LAR (or AR) discretization method. Visual classification of the texture was also performed for each lesion. This classification analysis revealed that the parameters show statistically significant correlation with the visual score, if LAR or AR discretization method is considered, in contrast to LRR. Moreover, all the resulted tendencies were similar regardless the segmentation methods and the type of textural features involved in this work.

NCTN Assessment on Current Applications of Radiomics in Oncology

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.

Radiomics: Principles and radiotherapy applications

Radiomics is defined as the extraction of a large quantity of quantitative image features. The different radiomic indexes that have been proposed in the literature are described as well as the various factors that have an impact on the robustness of these indexes. We will see that several hundred quantitative features can be extracted per lesion and imaging modality. The ever-growing number of features studied raises the question of the statistical method of analysis used. This review addresses the research supporting the clinical use of radiomics in oncology in the staging of disease, discrimination between healthy and pathological tissues, the identification of genetic features, the prediction of patient survival, the response to treatment, the recurrence after radiotherapy and chemoradiotherapy and the side effects. Based on the existing literature, it remains difficult to identify features that should be used for current clinical practice.

Impact of acquisition count statistics reduction and SUV discretization on PET radiomic features in pediatric 18F-FDG-PET/MRI examinations

PURPOSE

The evaluation of features robustness with respect to acquisition and post-processing parameter changes is fundamental for the reliability of radiomics studies. The aim of this study was to investigate the sensitivity of PET radiomic features to acquisition statistics reduction and standardized-uptake-volume (SUV) discretization in PET/MRI pediatric examinations.

METHODS

Twenty-seven lesions were detected from the analysis of twenty-one 18F-FDG-PET/MRI pediatric examinations. By decreasing the count-statistics of the original list-mode data (3 MBq/kg), injected activity reduction was simulated. Two SUV discretization approaches were applied: 1) resampling lesion SUV range into fixed bins numbers (FBN); 2) rounding lesion SUV into fixed bin size (FBS). One hundred and six radiomic features were extracted. Intraclass Correlation Coefficient (ICC), Spearman correlation coefficient and coefficient-of-variation (COV) were calculated to assess feature reproducibility between low tracer activities and full tracer activity feature values.

RESULTS

More than 70% of Shape and first order features, and around 70% and 40% of textural features, when using FBS and FBN methods respectively, resulted robust till 1.2 MBk/kg. Differences in median features reproducibility (ICC) between FBS and FBN datasets were statistically significant for every activity level independently from bin number/size, with higher values for FBS. Differences in median Spearman coefficient (i.e. patient ranking according to feature values) were not statistically significant, varying the intensity resolution (i.e. bin number/size) for either FBS and FBN methods.

CONCLUSIONS

For each simulated count-statistic level, robust PET radiomic features were determined for pediatric PET/MRI examinations. A larger number of robust features were detected when using FBS methods.

Activity painting: PET images of freely defined activity distributions applying a novel phantom technique

The aim of this work was to develop a novel phantom that supports the construction of highly reproducible phantoms with arbitrary activity distributions for PET imaging. It could offer a methodology for answering questions related to texture measurements in PET imaging. The basic idea is to move a point source on a 3-D trajectory in the field of view, while continuously acquiring data. The reconstruction results in a 3-D activity concentration map according to the pathway of the point source. A 22Na calibration point source was attached to a high precision robotic arm system, where the 3-D movement was software controlled. 3-D activity distributions of a homogeneous cube, a sphere, a spherical shell and a heart shape were simulated. These distributions were used to measure uniformity and to characterize reproducibility. Two potential applications using the lesion simulation method are presented: evaluation in changes of textural properties related to the position in the PET field of view; scanner comparison based on visual and quantitative evaluation of texture features. A lesion with volume of 50x50x50 mm3 can be simulated during approximately 1 hour. The reproducibility of the movement was found to be >99%. The coefficients of variation of the voxels within a simulated homogeneous cube was 2.34%. Based on 5 consecutive and independent measurements of a 36 mm diameter hot sphere, the coefficient of variation of the mean activity concentration was 0.68%. We obtained up to 18% differences within the values of investigated textural indexes, when measuring a lesion in different radial positions of the PET field of view. In comparison of two different human PET scanners the percentage differences between heterogeneity parameters were in the range of 5-55%. After harmonizing the voxel sizes this range reduced to 2-16%. The general activity distributions provided by the two different vendor show high similarity visually. For the demonstration of the flexibility of this method, the same pattern was also simulated on a small animal PET scanner giving similar results, both quantitatively and visually. 3-D motion of a point source in the PET field of view is capable to create an irregular shaped activity distribution with high reproducibility.

Quantitative radiomics: Validating image textural features for oncological PET in lung cancer

BACKGROUND AND PURPOSE

Radiomics textural features derived from PET imaging are of broad and current interest due to recent evidence of their prognostic value during cancer management. An inherent assumption is the link between these imaging features and the underlying tumoral phenotypic spatial heterogeneity. The purpose of this work was to validate this assumption for tumors within the lung through a comparison of image based textural features and the ground truth activity distribution from which the images were created. A second purpose was to assess the level at which PET imaging introduces spatial texture not present in the associated ground truth activity distribution.

MATERIALS AND METHODS

25 lung lesions were created using an anthropomorphic phantom. Ten of the lesions had a spherical shape with a uniform activity distribution. The remaining 15 had an irregular shape with a heterogeneous activity distribution. PET images were created for each lesion using Monte Carlo simulation. 79 textural features related to the gray-level intensity histograms, co-occurrence matrices, neighborhood difference matrices, run length, and size zone matrices were derived from both the simulated PET images and ground truth activity maps. A comparison was made between the two datasets using statistical analysis.

RESULTS

For homogenous lesions, features extracted from the PET images were largely irrelevant to the underlying uniform activity distribution. Additionally, the majority of these features assumed substantial values implying that an extensive amount of spatial texture had been introduced into the final imaging data. For heterogeneous lesions, complex trends were observed in the deviation between features extracted from PET images and those extracted from the ground truth activity maps. Moreover, the extent of both the deviation and the associated dynamic range was seen to be greatly feature-dependent.

CONCLUSION

The use of image based textural features as a surrogate for tumoral phenotypic spatial heterogeneity could not be clearly validated. The association between the two is complex and a significant amount of uncertainty exist due to the introduction of incidental texture during image acquisition and reconstruction.

Positron emission tomography-based assessment of metabolic gradient and other prognostic features in sarcoma

Intratumoral heterogeneity biomarkers derived from positron emission tomography (PET) imaging with fluorodeoxyglucose (FDG) are of interest for a number of cancers, including sarcoma. A range of radiomic texture variables, adapted from general methodologies for image analysis, has shown promise in the setting. In the context of sarcoma, our group introduced an alternative model-based approach to the measurement of heterogeneity. In this approach, the heterogeneity of a tumor is characterized by the extent to which the 3-D FDG uptake pattern deviates from a simple elliptically contoured structure. By using a nonparametric analysis of the uptake profile obtained from this spatial model, a variable assessing the metabolic gradient of the tumor is developed. The work explores the prognostic potential of this new variable in the context of FDG-PET imaging of sarcoma. A mature clinical series involving 197 patients, 88 of whom have complete time-to-death information, is used. Texture variables based on the imaging data are also evaluated in this series and a range of appropriate machine learning methodologies are then used to explore the complementary prognostic roles for structure and texture variables. We conclude that both texture-based and model-based variables can be combined to achieve enhanced prognostic assessments of outcome for patients with sarcoma based on FDG-PET imaging information.

Radiomics in Nuclear Medicine Applied to Radiation Therapy: Methods, Pitfalls, and Challenges

Radiomics is a recent area of research in precision medicine and is based on the extraction of a large variety of features from medical images. In the field of radiation oncology, comprehensive image analysis is crucial to personalization of treatments. A better characterization of local heterogeneity and the shape of the tumor, depicting individual cancer aggressiveness, could guide dose planning and suggest volumes in which a higher dose is needed for better tumor control. In addition, noninvasive imaging features that could predict treatment outcome from baseline scans could help the radiation oncologist to determine the best treatment strategies and to stratify patients as at low risk or high risk of recurrence. Nuclear medicine molecular imaging reflects information regarding biological processes in the tumor thanks to a wide range of radiotracers. Many studies involving ¹⁸F-fluorodeoxyglucose positron emission tomography suggest an added value of radiomics compared with the use of conventional PET metrics such as standardized uptake value for both tumor diagnosis and prediction of recurrence or treatment outcome. However, these promising results should not hide technical difficulties that still currently prevent the approach from being widely studied or clinically used. These difficulties mostly pertain to the variability of the imaging features as a function of the acquisition device and protocol, the robustness of the models with respect to that variability, and the interpretation of the radiomic models. Addressing the impact of the variability in acquisition and reconstruction protocols is needed, as is harmonizing the radiomic feature calculation methods, to ensure the reproducibility of studies in a multicenter context and their implementation in a clinical workflow. In this review, we explain the potential impact of positron emission tomography radiomics for radiation therapy and underline the various aspects that need to be carefully addressed to make the most of this promising approach.

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor phenotypes and surrogate markers of response by transforming medical images into objects that yield quantifiable summary statistics to which regression and machine learning algorithms may be applied for statistical interrogation. Recent literature has identified clinicopathological association based on textural features deriving from gray-level co-occurrence matrices (GLCM) which facilitate evaluations of gray-level spatial dependence within a delineated region of interest. GLCM-derived features, however, tend to contribute highly redundant information. Moreover, when reporting selected feature sets, investigators often fail to adjust for multiplicities and commonly fail to convey the predictive power of their findings. This article presents a Bayesian probabilistic modeling framework for the GLCM as a multivariate object as well as describes its application within a cancer detection context based on computed tomography. The methodology, which circumvents processing steps and avoids evaluations of reductive and highly correlated feature sets, uses latent Gaussian Markov random field structure to characterize spatial dependencies among GLCM cells and facilitates classification via predictive probability. Correctly predicting the underlying pathology of 81% of the adrenal lesions in our case study, the proposed method outperformed current practices which achieved a maximum accuracy of only 59%. Simulations and theory are presented to further elucidate this comparison as well as ascertain the utility of applying multivariate Gaussian spatial processes to GLCM objects.

Prediction of cervical cancer recurrence using textural features extracted from 18F-FDG PET images acquired with different scanners

Objectives

To identify an imaging signature predicting local recurrence for locally advanced cervical cancer (LACC) treated by chemoradiation and brachytherapy from baseline ¹⁸F-FDG PET images, and to evaluate the possibility of gathering images from two different PET scanners in a radiomic study.

Methods

118 patients were included retrospectively. Two groups (G1, G2) were defined according to the PET scanner used for image acquisition. Eleven radiomic features were extracted from delineated cervical tumors to evaluate: (i) the predictive value of features for local recurrence of LACC, (ii) their reproducibility as a function of the scanner within a hepatic reference volume, (iii) the impact of voxel size on feature values.

Results

Eight features were statistically significant predictors of local recurrence in G1 (p < 0.05). The multivariate signature trained in G2 was validated in G1 (AUC=0.76, p<0.001) and identified local recurrence more accurately than SUV_max (p=0.022). Four features were significantly different between G1 and G2 in the liver. Spatial resampling was not sufficient to explain the stratification effect.

Conclusion

This study showed that radiomic features could predict local recurrence of LACC better than SUV_max. Further investigation is needed before applying a model designed using data from one PET scanner to another.

Significance of the impact of motion compensation on the variability of PET image features

In lung cancer, quantification by positron emission tomography/computed tomography (PET/CT) imaging presents challenges due to respiratory movement. Our primary aim was to study the impact of motion compensation implied by retrospectively gated (4D)-PET/CT on the variability of PET quantitative parameters. Its significance was evaluated by comparison with the variability due to (i) the voxel size in image reconstruction and (ii) the voxel size in image post-resampling. The method employed for feature extraction was chosen based on the analysis of (i) the effect of discretization of the standardized uptake value (SUV) on complementarity between texture features (TF) and conventional indices, (ii) the impact of the segmentation method on the variability of image features, and (iii) the variability of image features across the time-frame of 4D-PET. Thirty-one PET-features were involved. Three SUV discretization methods were applied: a constant width (SUV resolution) of the resampling bin (method RW), a constant number of bins (method RN) and RN on the image obtained after histogram equalization (method EqRN). The segmentation approaches evaluated were 40[Formula: see text] of SUV_max and the contrast oriented algorithm (COA). Parameters derived from 4D-PET images were compared with values derived from the PET image obtained for (i) the static protocol used in our clinical routine (3D) and (ii) the 3D image post-resampled to the voxel size of the 4D image and PET image derived after modifying the reconstruction of the 3D image to comprise the voxel size of the 4D image. Results showed that TF complementarity with conventional indices was sensitive to the SUV discretization method. In the comparison of COA and 40[Formula: see text] contours, despite the values not being interchangeable, all image features showed strong linear correlations (r  >  0.91, [Formula: see text]). Across the time-frames of 4D-PET, all image features followed a normal distribution in most patients. For our patient cohort, the compensation of tumor motion did not have a significant impact on the quantitative PET parameters. The variability of PET parameters due to voxel size in image reconstruction was more significant than variability due to voxel size in image post-resampling. In conclusion, most of the parameters (apart from the contrast of neighborhood matrix) were robust to the motion compensation implied by 4D-PET/CT. The impact on parameter variability due to the voxel size in image reconstruction and in image post-resampling could not be assumed to be equivalent.