Robustness of intratumour 18F-FDG PET uptake heterogeneity quantification for therapy response prediction in oesophageal carcinoma

Can alternative PET reconstruction schemes improve the prognostic value of radiomic features in non-small cell lung cancer?

PURPOSE

To evaluate the potential benefit of using alternative reconstruction schemes of PET images for the prognostic value of radiomic features.

METHODS

Patients (n=91) with non-small cell lung cancer were prospectively included. All had a PET/CT examination before treatment. Three different PET images were reconstructed for each patient: the standard clinical protocol (i.e., 4×4×4 mm³ voxels, 5mm Gaussian filter, denoted '200G5'), as well as using smaller voxels (i.e., 2×2×2 mm³ with a larger reconstruction matrix, denoted 400G1) and/or 1mm post-reconstruction Gaussian filter, denoted 200G1). Metabolic volumes of the primary tumors were semi-automatically delineated on the PET images and IBSI compliant radiomic features (intensity, shape, textural) were extracted. First, the distributions of 200G1 and 400G1 features were compared to the reference clinical protocol (200G5) through Bland-Altman tests and the use of linear mixed models. Then, the prognostic value of the features from each of the 3 reconstructions was evaluated in a univariate analysis, through their stratification power in Kaplan-Meier curves through a threshold set at the median.

RESULTS

The 3 reconstructions led to different distributions for most of the features. The larger shifts and standard deviations of differences was observed between 200G5 and 400G1, which was also confirmed through linear mixed models. However, these relatively important differences in distributions did not translate into a significant impact on the stratification power of the features in terms of prognosis, although a trend in decreasing prognostic value could be observed (smaller number of features with HR above 2, overall lower HR values). Most prognostic features displayed high correlation with either volume or SUV_max, although there was great variability of prognostic value for similar levels of correlation with these basic metrics.

CONCLUSIONS

Using smaller voxels or less strong filtering options in the reconstruction settings of PET images compared to the standard clinical protocols led to different distributions of the resulting radiomic features. However, the hierarchy between patients according to these distributions remained overall the same and therefore the resulting stratification power of the radiomic features was not significantly altered. These results should be compared to those obtained in the context of other pathologies where radiomic features displaying lower correlation with volume or SUV_max may have predictive value, such as in cervical cancer.

Radiomics in PET/CT: Current Status and Future AI-Based Evolutions

This short review aims at providing the readers with an update on the current status, as well as future perspectives in the quickly evolving field of radiomics applied to the field of PET/CT imaging. Numerous pitfalls have been identified in study design, data acquisition, segmentation, features calculation and modeling by the radiomics community, and these are often the same issues across all image modalities and clinical applications, however some of these are specific to PET/CT (and SPECT/CT) imaging and therefore the present paper focuses on those. In most cases, recommendations and potential methodological solutions do exist and should therefore be followed to improve the overall quality and reproducibility of published studies. In terms of future evolutions, the techniques from the larger field of artificial intelligence (AI), including those relying on deep neural networks (also known as deep learning) have already shown impressive potential to provide solutions, especially in terms of automation, but also to maybe fully replace the tools the radiomics community has been using until now in order to build the usual radiomics workflow. Some important challenges remain to be addressed before the full impact of AI may be realized but overall the field has made striking advances over the last few years and it is expected advances will continue at a rapid pace.

Effect of uncertainties in quantitative 18 F-FDG PET/CT imaging feedback for intratumoral dose-response assessment and dose painting by number

PURPOSE

Intratumoral dose response can be detected using serial fluoro-2-deoxyglucose-(FDG) positron emission tomography (PET)/computed tomography (CT) imaging feedback during treatment and used to guide adaptive dose painting by number (DPbN). However, to reliably implement this technique, the effect of uncertainties in quantitative PET/CT imaging feedback on tumor voxel dose-response assessment and DPbN needs to be determined and reduced.

METHODS

Three major uncertainties, induced by (a) PET imaging partial volume effect (PVE) and (b) tumor deformable image registration (DIR), and (c) variation of the time interval between FDG injection and PET image acquisition (TI), were determined using serial FDG-PET/CT images acquired during chemoradiotherapy of 18 head and neck cancer patients. PET imaging PVE was simulated using the discrepancy between with and without iterative deconvolution-based PVE corrections. Effect of tumor DIR uncertainty was simulated using the discrepancy between two DIR algorithms, including one with and one without soft-tissue mechanical correction for the voxel displacement. The effect of TI variation was simulated using linear interpolation on the dual-point PET/CT images. Tumor voxel pretreatment metabolic activity (SUV₀ ) and dose-response matrix (DRM) discrepancies induced by each of the three uncertainties were quantified, respectively. Adverse effects of tumor voxel SUV₀ and DRM discrepancies on tumor control probability (TCP) in DPbN were assessed.

RESULTS

Partial volume effect and TI variations of 10 mins induced a mean ± standard deviation (SD) of tumor voxel SUV₀ discrepancies to be -0.7% ± 9.2% and 0% ± 4.8%, respectively. Tumor voxel DRM discrepancies induced by PVE, tumor DIR discrepancy, and TI variations were 0.6% ± 8.9%, 1.7% ± 9.1%, and 0% ± 7%, respectively. Partial volume effect induced SUV₀ and DRM discrepancies correlated significantly with the tumor shape and FDG uptake heterogeneity. Tumor DIR uncertainty-induced DRM discrepancy correlated significantly with the tumor volume and shrinkage during treatment. Among the three uncertainties, PVE dominated the adverse effects on the TCP, with a mean ± SD of TCP reduction to be 12.7% ± 9.8% for all tumors if no compensation was applied for.

CONCLUSIONS

Effect of uncertainties in quantitative FDG-PET/CT imaging feedback on intratumoral dose-response quantification was not negligible. These uncertainties primarily caused by PVE and tumor DIR were highly dependent on individual tumor shape, volume, shrinkage during treatment, and pretreatment SUV heterogeneity, which can be managed individually. The adverse effects of these uncertainties could be minimized by using proper PVE corrections and DIR methods and compensated for in the clinical implementation of DPbN.

Texture Feature Comparison Between Step-and-Shoot and Continuous-Bed-Motion 18F-FDG PET

Our objective was to investigate the differences in texture features between step-and-shoot (SS) and continuous-bed-motion (CBM) imaging in phantom and clinical studies. Methods: A National Electrical Manufacturers Association body phantom was filled with ¹⁸F-FDG solution at a sphere-to-background ratio of 4:1. SS and CBM were performed using the same acquisition duration, and the data were reconstructed using 3-dimensional ordered-subset expectation maximization with time-of-flight algorithms. Texture features were extracted using the software LIFEx. A volume of interest was delineated on the 22-, 28-, and 37-mm spheres with a threshold of 42% of the maximum SUV. The voxel intensities were discretized using 2 resampling methods, namely a fixed bin size and a fixed bin number discretization. The discrete resampling values were set to 64 and 128. In total, 31 texture features were calculated with gray-level cooccurrence matrix (GLCM), gray-level run length matrix, neighborhood gray-level different matrix, and gray-level zone length matrix. The texture features of the SS and CBM images were compared for all settings using the paired t test and the coefficient of variation. In a clinical study, 27 lesions from 20 patients were examined using the same acquisition and image processing as were used during the phantom study. The percentage difference (%Diff) and correlation between the texture features from SS and CBM images were calculated to evaluate agreement between the 2 scanning techniques. Results: In the phantom study, the 11 features exhibited no significant difference between SS and CBM images, and the coefficient of variation was no more than 10%, depending on resampling conditions, whereas entropy and dissimilarity from GLCM fulfilled the criteria for all settings. In the clinical study, the entropy and dissimilarity from GLCM exhibited a low %Diff and excellent correlation in all resampling conditions. The %Diff of entropy was lower than that of dissimilarity. Conclusion: Differences between the texture features of SS and CBM images varied depending on the type of feature. Because entropy for GLCM exhibits minimal differences between SS and CBM images irrespective of resampling conditions, entropy may be the optimal feature to reduce the differences between the 2 scanning techniques.

Radiomics in medical imaging-"how-to" guide and critical reflection

Radiomics is a quantitative approach to medical imaging, which aims at enhancing the existing data available to clinicians by means of advanced mathematical analysis. Through mathematical extraction of the spatial distribution of signal intensities and pixel interrelationships, radiomics quantifies textural information by using analysis methods from the field of artificial intelligence. Various studies from different fields in imaging have been published so far, highlighting the potential of radiomics to enhance clinical decision-making. However, the field faces several important challenges, which are mainly caused by the various technical factors influencing the extracted radiomic features.The aim of the present review is twofold: first, we present the typical workflow of a radiomics analysis and deliver a practical "how-to" guide for a typical radiomics analysis. Second, we discuss the current limitations of radiomics, suggest potential improvements, and summarize relevant literature on the subject.

Quantitative accuracy of radiomic features of low-dose 18F-FDG PET imaging

Background

¹⁸F-FDG PET based radiomics is promising for precision oncology imaging. This work aims to explore quantitative accuracies of radiomic features (RFs) for low-dose ¹⁸F-FDG PET imaging.

Methods

Twenty lung cancer patients were prospectively enrolled and underwent ¹⁸F-FDG PET/CT scans. Low-dose PET situations (true counts: 20×10⁶, 15×10⁶, 10×10⁶, 7.5×10⁶, 5×10⁶, 2×10⁶, 1×10⁶, 0.5×10⁶, 0.25×10⁶) were simulated by randomly discarding counts from the acquired list-mode data. Each PET image was created using the scanner default reconstruction parameters. Each lesion volume of interest (VOI) was obtained via an adaptive contouring method with a threshold of 50% peak standardized uptake value (SUVpeak) in the PET images with full count data and VOIs were copied to the PET images at the reduced count level. Conventional SUV measures, features calculated from first-order statistics (FOS) and texture features (TFs) were calculated. Texture based RF include features calculated from gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), gray-level size zone matrix (GLSZM), neighboring gray-level dependence matrix (NGLDM) and neighbor gray-tone difference matrix (NGTDM). Bias percentage (BP) at different count levels for each RF was calculated.

Results

Fifty-seven lesions with a volume greater than 1.5 cm³ were found (mean volume, 25.7 cm³, volume range, 1.5–245.4 cm³). In comparison with normal total counts, mean SUV (SUVmean) in the lesions, normal lungs and livers, Entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the most robust features, with a BP of 5% at the count level of 1×10⁶ (equivalent to an effective dose of 0.04 mSv) RF including cluster shade from GLCM, long-run low grey-level emphasis, high grey-level run emphasis and short-run low grey-level emphasis from GLRM exhibited the worst performance with 50% of bias with 20×10⁶ counts (equivalent to an effective dose of 0.8 mSv).

Conclusions

In terms of the lesions included in this study, SUVmean, entropy and sum entropy from GLCM, busyness from NGTDM and run-length non-uniformity from GLRLM were the least sensitive features to lowering count.

Machine learning-based analysis of [18F]DCFPyL PET radiomics for risk stratification in primary prostate cancer

PURPOSE

Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [¹⁸F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features.

METHODS

In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [¹⁸F]DCFPyL PET-CT. Primary tumors were delineated using 50-70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC.

RESULTS

The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance.

CONCLUSION

Machine learning-based analysis of quantitative [¹⁸F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice.

2-[18F]FDG PET/CT radiomics in lung cancer: An overview of the technical aspect and its emerging role in management of the disease

Lung cancer is the most common cancer, worldwide, and a major health issue with a remarkable mortality rate. 2-[¹⁸F]fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (2-[¹⁸F]FDG PET/CT) plays an indispensable role in the management of lung cancer patients. Long-established quantitative parameters such as size, density, and metabolic activity have been and are being employed in the current practice to enhance interpretation and improve diagnostic and prognostic value. The introduction of radiomics analysis revolutionized the quantitative evaluation of medical imaging, revealing data within images beyond visual interpretation. The "big data" are extracted from high-quality images and are converted into information that correlates to relevant genetic, pathologic, clinical, or prognostic features. Technically advanced, diverse methods have been implemented in different studies. The standardization of image acquisition, segmentation and features analysis is still a debated issue. Importantly, a body of features has been extracted and employed for diagnosis, staging, risk stratification, prognostication, and therapeutic response. 2-[¹⁸F]FDG PET/CT-derived features show promising value in non-invasively diagnosing the malignant nature of pulmonary nodules, differentiating lung cancer subtypes, and predicting response to different therapies as well as survival. In this review article, we aimed to provide an overview of the technical aspects used in radiomics analysis in non-small cell lung cancer (NSCLC) and elucidate the role of 2-[¹⁸F]FDG PET/CT-derived radiomics in the diagnosis, prognostication, and therapeutic response.

Combined CT texture analysis and nodal axial ratio for detection of nodal metastasis in esophageal cancer

OBJECTIVE

To assess the accuracy of a combination of CT texture analysis (CTTA) and nodal axial ratio to detect metastatic lymph nodes (LNs) in esophageal squamous cell carcinoma (ESCC).

METHODS

The contrast-enhanced chest CT images of 78 LNs (40 metastasis, 38 benign) from 38 patients with ESCC were retrospectively analyzed. Nodal axial ratios (short-axis/long-axis diameter) were calculated. CCTA parameters (kurtosis, entropy, skewness) were extracted using commercial software (TexRAD) with fine, medium, and coarse spatial filters. Combinations of significant texture features and nodal axial ratios were entered as predictors in logistic regression models to differentiate metastatic from benign LNs, and the performance of the logistic regression models was analyzed using the area under the receiver operating characteristic curve (AUROC).

RESULTS

The mean axial ratio of metastatic LNs was significantly higher than that of benign LNs (0.81 ± 0.2 vs 0.71 ± 0.1, p = 0.005; sensitivity 82.5%, specificity 47.4%); namely, significantly more round than benign. The mean values of the entropy (all filters) and kurtosis (fine and medium) of metastatic LNs were significantly higher than those of benign LNs (all, p < 0.05). Medium entropy showed the best performance in the AUROC analysis with 0.802 (p < 0.001; sensitivity 85.0%, specificity 63.2%). A binary logistic regression analysis combining the nodal axial ratio, fine entropy, and fine kurtosis identified metastatic LNs with 87.5% sensitivity and 65.8% specificity (AUROC = 0.855, p < 0.001).

CONCLUSION

The combination of CTTA features and the axial ratio of LNs has the potential to differentiate metastatic from benign LNs and improves the sensitivity for detection of LN metastases in ESCC.

ADVANCES IN KNOWLEDGE

The combination of CTTA and nodal axial ratio has improved CT sensitivity (up to 87.5%) for the diagnosis of metastatic LNs in esophageal cancer.

Predicting response to radiotherapy in tumors with PET/CT: when and how?

Radiotherapy is one of the main methods for tumor treatment, with the improved radiotherapy delivery technique to combat cancer, there is a growing interest for finding effective and feasible ways to predict tumor radiosensitivity. Based on a series of changes in metabolism, microvessel density, hypoxic microenvironment, and cytokines of tumors after radiotherapy, a variety of radiosensitivity detection methods have been studied. Among the detection methods, positron emission tomography-computed tomography (PET/CT) is a feasible tool for response evaluation following definitive radiotherapy for cancers with a high negative predictive value. The prognostic or predictive value of PET/CT is currently being studied widely. However, there are many unresolved issues, such as the optimal probe of PET/CT for radiosensitivity prediction, the selection of the most useful PET/CT parameters and their optimal cut-offs such as total lesion glycolysis (TLG), metabolic tumor volume (MTV) and standardized uptake value (SUV), and the optimal timing of PET/CT pre-treatment, during or following RT. Different radiosensitivity of tumors, modes of radiotherapy action and fraction scheduling may complicate the appropriate choice. In this study, we will discuss the diverse methods for evaluating radiosensitivity, and will also focus on the selection of the optimal probe, timing, cut-offs and parameters of PET/CT for evaluating the radiotherapy response.

Transcriptomics in cancer revealed by Positron Emission Tomography radiomics

Metabolic images from Positron Emission Tomography (PET) are used routinely for diagnosis, follow-up or treatment planning purposes of cancer patients. In this study we aimed at determining if radiomic features extracted from 18F-Fluoro Deoxy Glucose (FDG) PET images could mirror tumor transcriptomics. In this study we analyzed 45 patients with locally advanced head and neck cancer (H&N) that underwent FDG-PET scans at the time of diagnosis and transcriptome analysis using RNAs from both cancer and healthy tissues on microarrays. Association between PET radiomics and transcriptomics was carried out with the Genomica software and a functional annotation was used to associate PET radiomics, gene expression and altered biological pathways. We identified relationships between PET radiomics and genes involved in cell-cycle, disease, DNA repair, extracellular matrix organization, immune system, metabolism or signal transduction pathways, according to the Reactome classification. Our results suggest that these FDG PET radiomic features could be used to infer tissue gene expression and cellular pathway activity in H&N cancers. These observations strengthen the value of radiomics as a promising approach to personalize treatments through targeting tumor-specific molecular processes.

Heterogeneity in tumours: Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool

AIM

The aim was to validate promising radiomic features (RFs)1 on 18F-flourodeoxyglucose positron emission tomography/computed tomography-scans (18F-FDG PET/CT) of non-small cell lung cancer (NSCLC) patients undergoing definitive chemo-radiotherapy.

METHODS

18F-FDG PET/CT scans performed for radiotherapy (RT) planning were retrieved. Auto-segmentation with visual adaption was used to define the primary tumour on PET images. Six pre-selected prognostic and reproducible PET texture -and shape-features were calculated using texture respectively shape analysis. The correlation between these RFs and metabolic active tumour volume (MTV)3, gross tumour volume (GTV)4 and maximum and mean of standardized uptake value (SUV)5 was tested with a Spearman's Rank test. The prognostic value of RFs was tested in a univariate cox regression analysis and a multivariate cox regression analysis with GTV, clinical stage and histology. P-value ≤ 0.05 were considered significant.

RESULTS

Image analysis was performed for 233 patients: 145 males and 88 females, mean age of 65.7 and clinical stage II-IV. Mean GTV was 129.87 cm3 (SD 130.30 cm3). Texture and shape-features correlated more strongly to MTV and GTV compared to SUV-measurements. Four RFs predicted PFS in the univariate analysis. No RFs predicted PFS in the multivariate analysis, whereas GTV and clinical stage predicted PFS (p = 0.001 and p = 0.008 respectively).

CONCLUSION

The pre-selected RFs were insignificant in predicting PFS in combination with GTV, clinical stage and histology. These results might be due to variations in technical parameters. However, it is relevant to question whether RFs are stable enough to provide clinically useful information.

The effects of volume of interest delineation on MRI-based radiomics analysis: evaluation with two disease groups

BACKGROUND

Manual delineation of volume of interest (VOI) is widely used in current radiomics analysis, suffering from high variability. The tolerance of delineation differences and possible influence on each step of radiomics analysis are not clear, requiring quantitative assessment. The purpose of our study was to investigate the effects of delineation of VOIs on radiomics analysis for the preoperative prediction of metastasis in nasopharyngeal carcinoma (NPC) and sentinel lymph node (SLN) metastasis in breast cancer.

METHODS

This study retrospectively enrolled two datasets (NPC group: 238 cases; SLN group: 146 cases). Three operations, namely, erosion, smoothing, and dilation, were implemented on the VOIs accurately delineated by radiologists to generate diverse VOI variations. Then, we extracted 2068 radiomics features and evaluated the effects of VOI differences on feature values by the intra-class correlation coefficient (ICC). Feature selection was conducted by Maximum Relevance Minimum Redundancy combined with 0.632+ bootstrap algorithms. The prediction performance of radiomics models with random forest classifier were tested on an independent validation cohort by the area under the receive operating characteristic curve (AUC).

RESULTS

The larger the VOIs changed, the fewer features with high ICCs. Under any variation, SLN group showed fewer features with ICC ≥ 0.9 compared with NPC group. Not more than 15% top-predictive features identical to the accurate VOIs were observed across feature selection. The differences of AUCs of models derived from VOIs across smoothing or dilation with 3 pixels were not statistically significant compared with the accurate VOIs (p > 0.05) except for T2-weighted fat suppression images (smoothing: 0.845 vs. 0.725, p = 0.001; dilation: 0.800 vs. 0.725, p = 0.042). Dilation with 5 and 7 pixels contributed to remarkable AUCs in SLN group but the opposite in NPC group. The radiomics models did not perform well when tested by data from other delineations.

CONCLUSIONS

Differences in delineation of VOIs affected radiomics analysis, related to specific disease and MRI sequences. Differences from smooth delineation or expansion with 3 pixels width around the tumors or lesions were acceptable. The delineation for radiomics analysis should follow a predefined and unified standard.

Radiomics and Texture Analysis in Laryngeal Cancer. Looking for New Frontiers in Precision Medicine through Imaging Analysis

Radiomics and texture analysis represent a new option in our biomarkers arsenal. These techniques extract a large number of quantitative features, analyzing their properties to incorporate them in clinical decision-making. Laryngeal cancer represents one of the most frequent cancers in the head and neck area. We hypothesized that radiomics features can be included as a laryngeal cancer precision medicine tool, as it is able to non-invasively characterize the overall tumor accounting for heterogeneity, being a prognostic and/or predictive biomarker derived from routine, standard of care, imaging data, and providing support during the follow up of the patient, in some cases avoiding the need for biopsies. The larynx represents a unique diagnostic and therapeutic challenge for clinicians due to its complex tridimensional anatomical structure. Its complex regional and functional anatomy makes it necessary to enhance our diagnostic tools in order to improve decision-making protocols, aimed at better survival and functional results. For this reason, this technique can be an option for monitoring the evolution of the disease, especially in surgical and non-surgical organ preservation treatments. This concise review article will explain basic concepts about radiomics and discuss recent progress and results related to laryngeal cancer.

Prognostic Value of Functional Parameters of 18F-FDG-PET Images in Patients with Primary Renal/Adrenal Lymphoma

Objectives

The aim of this study is to explore the textural features that may identify the morphological changes in the lymphoma region and predict the prognosis of patients with primary renal lymphoma (PRL) and primary adrenal lymphoma (PAL).

Methods

This retrospective study comprised nineteen non-Hodgkin's lymphoma (NHL) patients undergoing ¹⁸F-FDG-PET/CT at West China Hospital from December 2013 to May 2017. ¹⁸F-FDG-PET images were reviewed independently by two board certificated radiologists of nuclear medicine, and the texture features were extracted from LifeX packages. The prognostic value of PET FDG-uptake parameters, patients' baseline characteristics, and textural parameters were analyzed using Kaplan–Meier analysis. Cox regression analysis was used to identify the independent prognostic factors among the imaging and clinical features.

Results

The overall survival of included patients was 18.84 ± 13.40 (mean ± SD) months. Univariate Cox analyses found that the tumor stage, GLCM (gray-level co-occurrence matrix) entropy, GLZLM_GLNU (gray-level nonuniformity), and GLZLM_ZLNU (zone length nonuniformity), values were significant predictors for OS. Among them, GLRLM_RLNU ≥216.6 demonstrated association with worse OS at multivariate analysis (HR 9.016, 95% CI 1.041–78.112, p=0.046).

Conclusions

The texture analysis of ¹⁸F-FDG-PET images could potentially serve as a noninvasive strategy to predict the overall survival of patients with PRL and PAL.

Prediction of Chemotherapy Response of Osteosarcoma Using Baseline 18F-FDG Textural Features Machine Learning Approaches with PCA

Purpose

Patients with high-grade osteosarcoma undergo several chemotherapy cycles before surgical intervention. Response to chemotherapy, however, is affected by intratumor heterogeneity. In this study, we assessed the ability of a machine learning approach using baseline ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emitted tomography (PET) textural features to predict response to chemotherapy in osteosarcoma patients.

Materials and Methods

This study included 70 osteosarcoma patients who received neoadjuvant chemotherapy. Quantitative characteristics of the tumors were evaluated by standard uptake value (SUV), total lesion glycolysis (TLG), and metabolic tumor volume (MTV). Tumor heterogeneity was evaluated using textural analysis of ¹⁸F-FDG PET scan images. Assessments were performed at baseline and after chemotherapy using ¹⁸F-FDG PET; ¹⁸F-FDG textural features were evaluated using the Chang-Gung Image Texture Analysis toolbox. To predict the chemotherapy response, several features were chosen using the principal component analysis (PCA) feature selection method. Machine learning was performed using linear support vector machine (SVM), random forest, and gradient boost methods. The ability to predict chemotherapy response was evaluated using the area under the receiver operating characteristic curve (AUC).

Results

AUCs of the baseline ¹⁸F-FDG features SUVmax, TLG, MTV, 1st entropy, and gray level co-occurrence matrix entropy were 0.553, 0538, 0.536, 0.538, and 0.543, respectively. However, AUCs of the machine learning features linear SVM, random forest, and gradient boost were 0.72, 0.78, and 0.82, respectively.

Conclusion

We found that a machine learning approach based on ¹⁸F-FDG textural features could predict the chemotherapy response using baseline PET images. This early prediction of the chemotherapy response may aid in determining treatment plans for osteosarcoma patients.

MRI-based radiomics signature is a quantitative prognostic biomarker for nasopharyngeal carcinoma

This study aimed to develop prognosis signatures through a radiomics analysis for patients with nasopharyngeal carcinoma (NPC) by their pretreatment diagnosis magnetic resonance imaging (MRI). A total of 208 radiomics features were extracted for each patient from a database of 303 patients. The patients were split into the training and validation cohorts according to their pretreatment diagnosis date. The radiomics feature analysis consisted of cluster analysis and prognosis model analysis for disease free-survival (DFS), overall survival (OS), distant metastasis-free survival (DMFS) and locoregional recurrence-free survival (LRFS). Additionally, two prognosis models using clinical features only and combined radiomics and clinical features were generated to estimate the incremental prognostic value of radiomics features. Patients were clustered by non-negative matrix factorization (NMF) into two groups. It showed high correspondence with patients' T stage (p < 0.00001) and overall stage information (p < 0.00001) by chi-squared tests. There were significant differences in DFS (p = 0.0052), OS (p = 0.033), and LRFS (p = 0.037) between the two clustered groups but not in DMFS (p = 0.11) by log-rank tests. Radiomics nomograms that incorporated radiomics and clinical features could estimate DFS with the C-index of 0.751 [0.639, 0.863] and OS with the C-index of 0.845 [0.752, 0.939] in the validation cohort. The nomograms improved the prediction accuracy with the C-index value of 0.029 for DFS and 0.107 for OS compared with clinical features only. The DFS and OS radiomics nomograms developed in our study demonstrated the excellent prognostic estimation for NPC patients with a noninvasive way of MRI. The combination of clinical and radiomics features can provide more information for precise treatment decision.

CT-based radiomic signatures for prediction of pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant chemoradiotherapy

The objective of this study was to build models to predict complete pathologic response (pCR) after neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC) patients using radiomic features. A total of 55 consecutive patients pathologically diagnosed as having ESCC were included in this study. Patients were divided into a training cohort (44 patients) and a testing cohort (11 patients). The logistic regression analysis using likelihood ratio forward selection was performed to select the predictive clinical parameters for pCR, and the least absolute shrinkage and selection operator (LASSO) with logistic regression to select radiomic predictors in the training cohort. Model performance in the training and testing groups was evaluated using the area under the receiver operating characteristic curves (AUC). The multivariate logistic regression analysis identified no clinical predictors for pCR. Thus, only radiomic features selected by LASSO were used to build prediction models. Three logistic regression models for pCR prediction were developed in the training cohort, and they were able to predict pCR well in both the training (AUC, 0.84-0.86) and the testing cohorts (AUC, 0.71-0.79). There were no differences between these AUCs. We developed three predictive models for pCR after nCRT using radiomic parameters and they demonstrated good model performance.

Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis

Radiomics in nuclear medicine is rapidly expanding. Reproducibility of radiomics studies in multicentre settings is an important criterion for clinical translation. We therefore performed a meta-analysis to investigate reproducibility of radiomics biomarkers in PET imaging and to obtain quantitative information regarding their sensitivity to variations in various imaging and radiomics-related factors as well as their inherent sensitivity. Additionally, we identify and describe data analysis pitfalls that affect the reproducibility and generalizability of radiomics studies. After a systematic literature search, 42 studies were included in the qualitative synthesis, and data from 21 were used for the quantitative meta-analysis. Data concerning measurement agreement and reliability were collected for 21 of 38 different factors associated with image acquisition, reconstruction, segmentation and radiomics-specific processing steps. Variations in voxel size, segmentation and several reconstruction parameters strongly affected reproducibility, but the level of evidence remained weak. Based on the meta-analysis, we also assessed inherent sensitivity to variations of 110 PET image biomarkers. SUV_mean and SUV_max were found to be reliable, whereas image biomarkers based on the neighbourhood grey tone difference matrix and most biomarkers based on the size zone matrix were found to be highly sensitive to variations, and should be used with care in multicentre settings. Lastly, we identify 11 data analysis pitfalls. These pitfalls concern model validation and information leakage during model development, but also relate to reporting and the software used for data analysis. Avoiding such pitfalls is essential for minimizing bias in the results and to enable reproduction and validation of radiomics studies.

Inter-observer and segmentation method variability of textural analysis in pre-therapeutic FDG PET/CT in head and neck cancer

Aim

Characterizing tumor heterogeneity with textural indices extracted from 18F-fluorodeoxyglucose positron emission tomography (FDG PET/CT) is of growing interest in oncology. Several series showed promising results to predict survival in patients with head and neck squamous cell carcinoma (HNSCC), analyzing various tumor segmentation methods and textural indices. This preliminary study aimed at assessing the inter-observer and inter-segmentation method variability of textural indices in HNSCC pre-therapeutic FDG PET/CT.

Materials and methods

Consecutive patients with HNSCC referred in our department for a pre-therapeutic FDG PET/CT from January to March 2016 were retrospectively included. Two nuclear medicine physicians separately segmented all tumors using 3 different segmentation methods: a relative standardized uptake value (SUV) threshold (40%SUVmax), a signal-to-noise adaptive SUV threshold (DAISNE) and an image gradient-based method (PET-EDGE). SUV and metabolic tumor volume were recorded. Thirty-one textural indices were calculated using LIFEx software (www.lifexsoft.org). After correlation analysis, selected indices’ inter-segmentation method and inter-observer variability were calculated.

Results

Forty-three patients (mean age 63.8±9.3y) were analyzed. Due to a too small segmented tumor volume of interest, textural analysis could not be performed in 6, 11 and 15 cases with respectively DAISNE, 40%SUVmax and PET-EDGE segmentation methods. Five independent textural indices were selected (Homogeneity, Correlation, Entropy, Busyness and LZLGE). There was a high inter-contouring method variability for Homogeneity, Correlation, Entropy and LZLGE (p<0.0001 for each index). The inter-observer reproducibility analysis revealed an excellent agreement for 3 indices (Homogeneity, Correlation and Entropy) with an intraclass correlation coefficient higher than 0.90 for the 3 methods.

Conclusions

This preliminary study showed a high variability of 4 out of 5 textural indices (Homogeneity, Correlation, Entropy and LZLGE) extracted from pre-therapeutic FDG PET/CT in HNSCC using 3 different contouring methods. However, for each method, there was an excellent agreement between observers for 3 of these textural indices (Homogeneity, Correlation and Entropy).

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.

Response-to-repeatability of quantitative imaging features for longitudinal response assessment

Quantitative imaging biomarkers (QIBs) are often selected and ranked based on their repeatability performance. In the context of treatment response assessment, however, one must also consider how sensitive a QIB is to measuring changes in the tumour. This work introduces response-to-repeatability ratio (R/R), which weighs the ability of a QIB to detect significant changes with respect to its measurement repeatability and applies it to the case of PET texture features. R/R is evaluated as the proportion of measurable changes from baseline to follow-up for each candidate QIB. We analyse 47 texture features extracted from lesions in bone-metastatic prostate cancer patients who received double baseline and/or baseline to treatment follow-up ¹⁸F-NaF PET/CT scans. R/R evaluates the proportion of follow-up changes outside of the 95% limits of agreement (LOA) defined by test-retest values. Intraclass correlation coefficient (ICC) and coefficient of variation (CV) are calculated for each feature. Relationship between ICC and R/R are evaluated with the Spearman's correlation coefficient. R/R varied significantly across texture features: 41/47 (87%) features demonstrated R/R  >  5%; 21/47 (45%) features demonstrated R/R  >  10%, and 11/47 (23%) features demonstrated R/R  >  20%. LOA of features ranged from [0.998, 1.001] to [0.22, 4.86]. Repeatability alone did not qualify a feature for its efficacy at detecting measurable change at follow-up, as shown by weak correlations between R/R and both CV and ICC (ρ  =  0.23 and ρ  =  0.40, respectively). Three features demonstrated excellent ICC (ICC  >  0.75) and R/R greater than that of SUV_max (R/R  =  41.8%): skewness (ICC  =  0.92, R/R  =  75.4%), kurtosis (ICC  =  0.88, R/R  =  47.0%) and diagonal moment (ICC  =  0.88, R/R  =  45.5%). R/R characterizes the sensitivity of candidate QIBs to detect measurable changes at follow-up. R/R supplements existing precision performance metrics (e.g. CV, ICC, and LOA) as an index to assess the utility of QIBs for response assessment.

Radiomics in Oncological PET/CT: a Methodological Overview

Radiomics is a medical imaging analysis approach based on computer-vision. Metabolic radiomics in particular analyses the spatial distribution patterns of molecular metabolism on PET images. Measuring intratumoral heterogeneity via image is one of the main targets of radiomics research, and it aims to build a image-based model for better patient management. The workflow of radiomics using texture analysis follows these steps: 1) imaging (image acquisition and reconstruction); 2) preprocessing (segmentation & quantization); 3) quantification (texture matrix design & texture feature extraction); and 4) analysis (statistics and/or machine learning). The parameters or conditions at each of these steps are effect on the results. In statistical testing or modeling, problems such as multiple comparisons, dependence on other variables, and high dimensionality of small sample size data should be considered. Standardization of methodology and harmonization of image quality are one of the most important challenges with radiomics methodology. Even though there are current issues in radiomics methodology, it is expected that radiomics will be clinically useful in personalized medicine for oncology.

Repeatability of 18 F-FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method

Background

¹⁸F‐fluoro‐2‐deoxy‐D‐Glucose positron emission tomography (¹⁸F‐FDG PET) radiomics has the potential to guide the clinical decision making in cancer patients, but validation is required before radiomics can be implemented in the clinical setting. The aim of this study was to explore how feature space reduction and repeatability of ¹⁸F‐FDG PET radiomic features are affected by various sources of variation such as underlying data (e.g., object size and uptake), image reconstruction methods and settings, noise, discretization method, and delineation method.

Methods

The NEMA image quality phantom was scanned with various sphere‐to‐background ratios (SBR), simulating different activity uptakes, including spheres with low uptake, that is, SBR smaller than 1. Furthermore, images of a phantom containing 3D printed inserts reflecting realistic heterogeneity uptake patterns were acquired. Data were reconstructed using various matrix sizes, reconstruction algorithms, and scan durations (noise). For every specific reconstruction and noise level, ten statistically equal replicates were generated. The phantom inserts were delineated using CT and PET‐based segmentation methods. A total of 246 radiomic features was extracted from each image dataset. Images were discretized with a fixed number of 64 bins (FBN) and a fixed bin width (FBW) of 0.25 for the high and a FBW of 0.05 for the low uptake data. In terms of feature reduction, we determined the impact of these factors on the composition of feature clusters, which were defined on the basis of Spearman's correlation matrices. To assess feature repeatability, the intraclass correlation coefficient was calculated over the ten replicates.

Results

In general, larger spheres with high uptake resulted in better repeatability compared to smaller low uptake spheres. In terms of repeatability, features extracted from heterogeneous phantom inserts were comparable to features extracted from bigger high uptake spheres. For example, for an EARL‐compliant reconstruction, larger and smaller high uptake spheres yielded good repeatability for 32% and 30% of the features, while the heterogeneous inserts resulted in 34% repeatable features. For the low uptake spheres, this was the case for 22% and 20% of the features for bigger and smaller spheres, respectively. Images reconstructed with point‐spread‐function (PSF) resulted in the highest repeatability when compared with OSEM or time‐of‐flight, for example, 53%, 30%, and 32% of repeatable features, respectively (for unsmoothed data, discretized with FBN, 300 s scan duration). Reducing image noise (increasing scan duration and smoothing) and using CT‐based segmentation for the low uptake spheres yielded improved repeatability. FBW discretization resulted in higher repeatability than FBN discretization, for example, 89% and 35% of the features, respectively (for the EARL‐compliant reconstruction and larger high uptake spheres).

Conclusion

Feature space reduction and repeatability of ¹⁸F‐FDG PET radiomic features depended on all studied factors. The high sensitivity of PET radiomic features to image quality suggests that a high level of image acquisition and preprocessing standardization is required to be used as clinical imaging biomarker.

Investigating the histopathologic correlates of 18F-FDG PET heterogeneity in non-small-cell lung cancer

PURPOSE

Despite the growing use of fluorine-18-fluorodeoxyglucose (F-FDG) PET texture analysis to measure intratumoural heterogeneity in cancer research, the biologic basis of F-FDG PET-derived texture variables is poorly understood. We aimed to assess correlations between F-FDG PET-derived texture variables and whole-slide image (WSI)-derived metrics of tumour cellularity and spatial heterogeneity.

PATIENTS AND METHODS

Twenty-two patients with non-small-cell lung cancer prospectively underwent F-FDG PET imaging before tumour resection. We tested nine F-FDG PET parameters: metabolically active tumour volume, total lesion glycolysis, mean standardized uptake value (SUVmean), first-order entropy, energy, skewness, kurtosis, grey-level co-occurrence matrix entropy and lacunarity (SUV-lacunarity). From the haematoxylin and eosin-stained WSIs, we derived mean tumour-cell density (MCD) and lacunarity (path-lacunarity). Spearman's correlation analysis and agglomerative hierarchical clustering were performed to assess variable associations.

RESULTS

Tumour volumes ranged from 2.2 to 74 cm (median: 17.9 cm). MCD correlated positively with total lesion glycolysis (rs: 0.46, P: 0.007) and SUVmean (rs : 0.55; P: 0.008) and negatively with skewness and kurtosis (rs: -0.47 for both; P: 0.028 and 0.026, respectively). SUV-lacunarity and path-lacunarity were positively correlated (rs: 0.5; P: 0.018). On cluster analysis, larger tumours trended towards higher SUVmean and entropy with a predominance of tightly concentrated high SUV-voxels (negative skewness and low kurtosis on the histogram); on WSI analysis such larger tumours also displayed generally higher MCD and low SUV-lacunarity and path-lacunarity.

CONCLUSION

Our data suggest that histopathological MCD and lacunarity are associated with several commonly used F-FDG PET-derived indices including SUV-lacunarity, metabolically active tumour volume, SUVmean, entropy, skewness, and kurtosis, and thus may explain the biological basis of F-FDG PET-uptake heterogeneity in non-small-cell lung cancer.

Challenges and Promises of PET Radiomics

PURPOSE

Radiomics describes the extraction of multiple, otherwise invisible, features from medical images that, with bioinformatic approaches, can be used to provide additional information that can predict underlying tumor biology and behavior.

METHODS AND MATERIALS

Radiomic signatures can be used alone or with other patient-specific data to improve tumor phenotyping, treatment response prediction, and prognosis, noninvasively. The data describing 18F-fluorodeoxyglucose positron emission tomography radiomics, often using texture or heterogeneity parameters, are increasing rapidly.

RESULTS

In relation to radiation therapy practice, early data have reported the use of radiomic approaches to better define tumor volumes and predict radiation toxicity and treatment response.

CONCLUSIONS

Although at an early stage of development, with many technical challenges remaining and a need for standardization, promise nevertheless exists that PET radiomics will contribute to personalized medicine, especially with the availability of increased computing power and the development of machine-learning approaches for imaging.

Variability and Repeatability of Quantitative Uptake Metrics in 18F-FDG PET/CT of Non-Small Cell Lung Cancer: Impact of Segmentation Method, Uptake Interval, and Reconstruction Protocol

There is increased interest in various new quantitative uptake metrics beyond SUV in oncologic PET/CT studies. The purpose of this study was to investigate the variability and test-retest ratio (TRT) of metabolically active tumor volume (MATV) measurements and several other new quantitative metrics in non-small cell lung cancer using ¹⁸F-FDG PET/CT with different segmentation methods, user interactions, uptake intervals, and reconstruction protocols. Methods: Ten patients with advanced non-small cell lung cancer received 2 series of 2 whole-body ¹⁸F-FDG PET/CT scans at 60 min after injection and at 90 min after injection. PET data were reconstructed with 4 different protocols. Eight segmentation methods were applied to delineate lesions with and without a tumor mask. MATV, SUV_max, SUV_mean, total lesion glycolysis, and intralesional heterogeneity features were derived. Variability and repeatability were evaluated using a generalized-estimating-equation statistical model with Bonferroni adjustment for multiple comparisons. The statistical model, including interaction between uptake interval and reconstruction protocol, was applied individually to the data obtained from each segmentation method. Results: Without masking, none of the segmentation methods could delineate all lesions correctly. MATV was affected by both uptake interval and reconstruction settings for most segmentation methods. Similar observations were obtained for the uptake metrics SUV_max, SUV_mean, total lesion glycolysis, homogeneity, entropy, and zone percentage. No effect of uptake interval was observed on TRT metrics, whereas the reconstruction protocol affected the TRT of SUV_max Overall, segmentation methods showing poor quantitative performance in one condition showed better performance in other (combined) conditions. For some metrics, a clear statistical interaction was found between the segmentation method and both uptake interval and reconstruction protocol. Conclusion: All segmentation results need to be reviewed critically. MATV and other quantitative uptake metrics, as well as their TRT, depend on segmentation method, uptake interval, and reconstruction protocol. To obtain quantitative reliable metrics, with good TRT performance, the optimal segmentation method depends on local imaging procedure, the PET/CT system, or reconstruction protocol. Rigid harmonization of imaging procedure and PET/CT performance will be helpful in mitigating this variability.

18F-FDG-PET/CT features of primary tumours for predicting the risk of recurrence in thyroid cancer after total thyroidectomy: potential usefulness of combination of the SUV-related, volumetric, and heterogeneous texture parameters

OBJECTIVE:

This retrospective study examined whether the primary tumour 18F-FDG uptake features could predict the high-risk of recurrence in differentiated thyroid cancer (DTC) patients.

METHODS:

The enrolled 114 DTC patients underwent preoperative ¹⁸F-FDG-PET/CT. SUVmax, SUVmean, metabolic tumour volume (MTV), total lesion glycolysis (TLG) and 6 texture parameters were obtained. Because the texture features can be confounded by the tumour volume effects, ¹⁸F-FDG-avid tumour patients were divided into two groups (tumours with MTV ≤ 10.0 cm³ and >10.0 cm³). Diagnostic performance for predicting the high-risk was evaluated by the area under the curve (AUC) by the ROC curve analysis.

RESULTS:

Eighty eight ¹⁸F-FDG-avid tumours revealed more advanced-risk classification (p = 0.015 → 0.02) than 26 ¹⁸F-FDG-nonavid tumours, which yielded no high-risk patients. In the 44 MTV > 10.0 cm^{3 18}F-FDG-avid tumour patients, 8 high-risk patients revealed significantly higher SUVmax, SUVmean, MTV, TLG, intensity variability and size-zone variability, and lower zone percentage than 36 non-high-risk patients (p < 0.001-0.016). Their AUC (diagnostic accuracy) ranged between 0.77 (66%) and 0.92 (91%). When each parameter was scored as 0 (negative for high-risk) or 1 (positive for high-risk) according to each threshold criterion, and the 7 parameter summed score ≥5 was defined as high-risk, the accuracy was 93.2% (AUC: 0.98) in the MTV > 10.0 cm^{3 18}F-FDG-avid tumour patients.

CONCLUSION:

For primary MTV > 10.0 cm^{3 18}F-FDG-avid DTCs, the combined use of SUV-related, volumetric, and texture parameters may be more useful to identify high-risk patients than the individual parameters.

ADVANCES IN KNOWLEDGE:

Combined use of SUV-related, volumetric, and texture parameters may be useful to identify high-risk DTC patients.

Role of textural heterogeneity parameters in patient selection for 177Lu-PSMA therapy via response prediction

Purpose

Prostate cancer is most common tumor in men causing significant patient mortality and morbidity. In newer diagnostic/therapeutic agents PSMA linked ones are specifically important. Analysis of textural heterogeneity parameters is associated with determination of innately aggressive and therapy resistant cell lines thus emphasizing their importance in therapy planning. The objective of current study was to assess predictive ability of tumor textural heterogeneity parameters from baseline ⁶⁸Ga-PSMA PET prior to ¹⁷⁷Lu-PSMA therapy.

Results

Entropy showed a negative correlation (r_s = −0.327, p = 0.006, AUC = 0.695) and homogeneity showed a positive correlation (r_s = 0.315, p = 0.008, AUC = 0.683) with change in pre and post therapy PSA levels.

Conclusions

Study showed potential for response prediction through baseline PET scan using textural features. It suggested that increase in heterogeneity of PSMA expression seems to be associated with an increased response to PSMA radionuclide therapy.

Materials and Methods

Retrospective analysis of 70 patients was performed. All patients had metastatic prostate cancer and were planned to undergo ¹⁷⁷Lu-PSMA therapy. Pre-therapeutic ⁶⁸Ga- PSMA PET scans were used for analysis. 3D volumes (VOIs) of 3 lesions each in bones and lymph nodes were manually delineated in static PET images. Five PET based textural heterogeneity parameters (COV, entropy, homogeneity, contrast, size variation) were determined. Results obtained were then compared with clinical parameters including pre and post therapy PSA, alkaline phosphate, bone specific alkaline phosphate levels and ECOG criteria. Spearman correlation was used to determine statistical dependence among variables. ROC analysis was performed to estimate the optimal cutoff value and AUC.

A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change

The adoption of enterprise digital imaging, along with the development of quantitative imaging methods and the re-emergence of statistical learning, has opened the opportunity for more personalized cancer treatments through transformative data science research. In the last 5 years, accumulating evidence has indicated that noninvasive advanced imaging analytics (i.e., radiomics) can reveal key components of tumor phenotype for multiple lesions at multiple time points over the course of treatment. Many groups using homegrown software have extracted engineered and deep quantitative features on 3-dimensional medical images for better spatial and longitudinal understanding of tumor biology and for the prediction of diverse outcomes. These developments could augment patient stratification and prognostication, buttressing emerging targeted therapeutic approaches. Unfortunately, the rapid growth in popularity of this immature scientific discipline has resulted in many early publications that miss key information or use underpowered patient data sets, without production of generalizable results. Quantitative imaging research is complex, and key principles should be followed to realize its full potential. The fields of quantitative imaging and radiomics in particular require a renewed focus on optimal study design and reporting practices, standardization, interpretability, data sharing, and clinical trials. Standardization of image acquisition, feature calculation, and statistical analysis (i.e., machine learning) are required for the field to move forward. A new data-sharing paradigm enacted among open and diverse participants (medical institutions, vendors and associations) should be embraced for faster development and comprehensive clinical validation of imaging biomarkers. In this review and critique of the field, we propose working principles and fundamental changes to the current scientific approach, with the goal of high-impact research and development of actionable prediction models that will yield more meaningful applications of precision cancer medicine.

Radiomics of the primary tumour as a tool to improve 18F-FDG-PET sensitivity in detecting nodal metastases in endometrial cancer

Background

A radiomic approach was applied in 18F-FDG PET endometrial cancer, to investigate if imaging features computed on the primary tumour could improve sensitivity in nodal metastases detection. One hundred fifteen women with histologically proven endometrial cancer who underwent preoperative 18F-FDG PET/CT were retrospectively considered. SUV, MTV, TLG, geometrical shape, histograms and texture features were computed inside tumour contours. On a first group of 86 patients (DB1), univariate association with LN metastases was computed by Mann-Whitney test and a neural network multivariate model was developed. Univariate and multivariate models were assessed with leave one out on 20 training sessions and on a second group of 29 patients (DB2). A unified framework combining LN metastases visual detection results and radiomic analysis was also assessed.

Results

Sensitivity and specificity of LN visual detection were 50% and 99% on DB1 and 33% and 95% on DB2, respectively. A unique heterogeneity feature computed on the primary tumour (the zone percentage of the grey level size zone matrix, GLSZM ZP) was able to predict LN metastases better than any other feature or multivariate model (sensitivity and specificity of 75% and 81% on DB1 and of 89% and 80% on DB2). Tumours with LN metastases are in fact generally characterized by a lower GLSZM ZP value, i.e. by the co-presence of high-uptake and low-uptake areas. The combination of visual detection and GLSZM ZP values in a unified framework obtained sensitivity and specificity of 94% and 67% on DB1 and of 89% and 75% on DB2, respectively.

Conclusions

The computation of imaging features on the primary tumour increases nodal staging detection sensitivity in 18F-FDG PET and can be considered for a better patient stratification for treatment selection. Results need a confirmation on larger cohort studies.

Influence of inter-observer delineation variability on radiomics stability in different tumor sites

BACKGROUND

Radiomics is a promising methodology for quantitative analysis and description of radiological images using advanced mathematics and statistics. Tumor delineation, which is still often done manually, is an essential step in radiomics, however, inter-observer variability is a well-known uncertainty in radiation oncology. This study investigated the impact of inter-observer variability (IOV) in manual tumor delineation on the reliability of radiomic features (RF).

METHODS

Three different tumor types (head and neck squamous cell carcinoma (HNSCC), malignant pleural mesothelioma (MPM) and non-small cell lung cancer (NSCLC)) were included. For each site, eleven individual tumors were contoured on CT scans by three experienced radiation oncologists. Dice coefficients (DC) were calculated for quantification of delineation variability. RF were calculated with an in-house developed software implementation, which comprises 1404 features: shape (n = 18), histogram (n = 17), texture (n = 137) and wavelet (n = 1232). The IOV of RF was studied using the intraclass correlation coefficient (ICC). An ICC >0.8 indicates a good reproducibility. For the stable RF, an average linkage hierarchical clustering was performed to identify classes of uncorrelated features.

RESULTS

Median DC was high for NSCLC (0.86, range 0.57-0.90) and HNSCC (0.72, 0.21-0.89), whereas it was low for MPM (0.26, 0-0.9) indicating substantial IOV. Stability rate of RF correlated with DC and depended on tumor site, showing a high stability in NSCLC (90% of total parameters), acceptable stability in HNSCC (59% of total parameters) and low stability in MPM (36% of total parameters). Shape features showed the weakest stability across all tumor types. Hierarchical clustering revealed 14 groups of correlated and stable features for NSCLC and 6 groups for both HNSCC and MPM.

CONCLUSION

Inter-observer delineation variability has a relevant influence on radiomics analysis and is strongly influenced by tumor type. This leads to a reduced number of suitable imaging features.

Voxel size and gray level normalization of CT radiomic features in lung cancer

Radiomic features are potential imaging biomarkers for therapy response assessment in oncology. However, the robustness of features with respect to imaging parameters is not well established. Previously identified potential imaging biomarkers were found to be intrinsically dependent on voxel size and number of gray levels (GLs) in a recent texture phantom investigation. Here, we validate the voxel size and GL in-phantom normalizations in lung tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were analyzed. To compare with patient data, phantom scans were acquired on eight different scanners. Twenty four previously identified features were extracted from lung tumors. The Spearman rank (r_s) and interclass correlation coefficient (ICC) were used as metrics. Eight out of 10 features showed high (r_s > 0.9) and low (r_s < 0.5) correlations with number of voxels before and after normalizations, respectively. Likewise, texture features were unstable (ICC < 0.6) and highly stable (ICC > 0.8) before and after GL normalizations, respectively. We conclude that voxel size and GL normalizations derived from a texture phantom study also apply to lung tumors. This study highlights the importance and utility of investigating the robustness of radiomic features with respect to CT imaging parameters in radiomic phantoms.

Repeatability and Reproducibility of Radiomic Features: A Systematic Review

Purpose:

An ever-growing number of predictive models used to inform clinical decision making have included quantitative, computer-extracted imaging biomarkers, or “radiomic features.” Broadly generalizable validity of radiomics-assisted models may be impeded by concerns about reproducibility. We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features, derived from a systematic review of published peer-reviewed literature.

Methods and Materials:

The PubMed electronic database was searched using combinations of the broad Haynes and Ingui filters along with a set of text words specific to cancer, radiomics (including texture analyses), reproducibility, and repeatability. This review has been reported in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. From each full-text article, information was extracted regarding cancer type, class of radiomic feature examined, reporting quality of key processing steps, and statistical metric used to segregate stable features.

Results:

Among 624 unique records, 41 full-text articles were subjected to review. The studies primarily addressed non-small cell lung cancer and oropharyngeal cancer. Only 7 studies addressed in detail every methodologic aspect related to image acquisition, preprocessing, and feature extraction. The repeatability and reproducibility of radiomic features are sensitive at various degrees to processing details such as image acquisition settings, image reconstruction algorithm, digital image preprocessing, and software used to extract radiomic features. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features; however, coarseness and contrast appeared among the least reproducible.

Conclusions:

Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. Reporting quality could be improved regarding details of feature extraction software, digital image manipulation (preprocessing), and the cutoff value used to distinguish stable features.

We offer a qualitative synthesis of 41 studies that specifically investigated the repeatability and reproducibility of radiomic features. The repeatability and reproducibility of radiomic features are sensitive at various degrees to image quality and to software used to extract radiomic features. Investigations of feature repeatability and reproducibility are currently limited to a small number of cancer types. No consensus was found regarding the most repeatable and reproducible features with respect to different settings.

Radiomics in Oncological PET/CT: Clinical Applications

18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is widely used for staging, evaluating treatment response, and predicting prognosis in malignant diseases. FDG uptake and volumetric PET parameters such as metabolic tumor volume have been used and are still used as conventional PET parameters to assess biological characteristics of tumors. However, in recent years, additional features derived from PET images by computational processing have been found to reflect intratumoral heterogeneity, which is related to biological tumor features, and to provide additional predictive and prognostic information, which leads to the concept of radiomics. In this review, we focus on recent clinical studies of malignant diseases that investigated intratumoral heterogeneity on PET/CT, and we discuss its clinical role in various cancers.

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.

Evaluation of prognostic models developed using standardised image features from different PET automated segmentation methods

Background

Prognosis in oesophageal cancer (OC) is poor. The 5-year overall survival (OS) rate is approximately 15%. Personalised medicine is hoped to increase the 5- and 10-year OS rates. Quantitative analysis of PET is gaining substantial interest in prognostic research but requires the accurate definition of the metabolic tumour volume. This study compares prognostic models developed in the same patient cohort using individual PET segmentation algorithms and assesses the impact on patient risk stratification.

Consecutive patients (n = 427) with biopsy-proven OC were included in final analysis. All patients were staged with PET/CT between September 2010 and July 2016. Nine automatic PET segmentation methods were studied. All tumour contours were subjectively analysed for accuracy, and segmentation methods with < 90% accuracy were excluded. Standardised image features were calculated, and a series of prognostic models were developed using identical clinical data. The proportion of patients changing risk classification group were calculated.

Results

Out of nine PET segmentation methods studied, clustering means (KM2), general clustering means (GCM3), adaptive thresholding (AT) and watershed thresholding (WT) methods were included for analysis. Known clinical prognostic factors (age, treatment and staging) were significant in all of the developed prognostic models. AT and KM2 segmentation methods developed identical prognostic models. Patient risk stratification was dependent on the segmentation method used to develop the prognostic model with up to 73 patients (17.1%) changing risk stratification group.

Conclusion

Prognostic models incorporating quantitative image features are dependent on the method used to delineate the primary tumour. This has a subsequent effect on risk stratification, with patients changing groups depending on the image segmentation method used.

Electronic supplementary material

The online version of this article (10.1186/s13550-018-0379-3) contains supplementary material, which is available to authorized users.

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.