The Image Biomarker Standardization Initiative: Standardized Convolutional Filters for Reproducible Radiomics and Enhanced Clinical Insights.

Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters. The first two phases focused on finding reference filtered images and reference feature values for commonly used convolutional filters: mean, Laplacian of Gaussian, Laws and Gabor kernels, separable and nonseparable wavelets (including decomposed forms), and Riesz transformations. In the first phase, 15 teams used digital phantoms to establish 33 reference filtered images of 36 filter configurations. In phase 2, 11 teams used a chest CT image to derive reference values for 323 of 396 features computed from filtered images using 22 filter and image processing configurations. Reference filtered images and feature values for Riesz transformations were not established. Reproducibility of standardized convolutional filters was validated on a public data set of multimodal imaging (CT, fluorodeoxyglucose PET, and T1-weighted MRI) in 51 patients with soft-tissue sarcoma. At validation, reproducibility of 486 features computed from filtered images using nine configurations × three imaging modalities was assessed using the lower bounds of 95% CIs of intraclass correlation coefficients. Out of 486 features, 458 were found to be reproducible across nine teams with lower bounds of 95% CIs of intraclass correlation coefficients greater than 0.75. In conclusion, eight filter types were standardized with reference filtered images and reference feature values for verifying and calibrating radiomics software packages. A web-based tool is available for compliance checking.

Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients.

Purpose: Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma. Methods and Materials: We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression. A recursive feature elimination random forest classifier was built using nested cross-validation without and with O6-methylguanine-DNA methyltransferase status to predict pseudoprogression. Results: A classifier constructed with cross-validation on the training cohort achieved an area under the receiver operating curve of 81% for predicting pseudoprogression. This was further improved to 89% with the addition of O6-methylguanine-DNA methyltransferase status into the classifier. Conclusions: Our results suggest that radiomic analysis of contrast T1-weighted images and magnetic resonance imaging perfusion images can assist the prompt diagnosis of pseudoprogression. Validation on external and independent data sets is necessary to verify these advanced analyses, which can be performed on routinely acquired clinical images and may help inform clinical treatment decisions.

Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer.

Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level. Toxicity scores using the Common Terminology Criteria for Adverse Events (CTCAE v4), spatial dose distributions, and usual clinical predictors for the toxicity of 102 patients treated with chemoradiotherapy followed by brachytherapy for LACC were used in this study. In addition to usual DVH parameters, 91 radiomic features were extracted from rectum, bladder and vaginal 3D dose distributions, after discretization into a fixed bin width of 1 Gy. They were evaluated for predictive modelling of rectal, genitourinary (GU) and vaginal toxicities (grade ≥ 2). Logistic Normal Tissue Complication Probability (NTCP) models were derived using clinical parameters only or combinations of clinical, DVH and radiomics. For rectal acute/late toxicities, the area under the curve (AUC) using clinical parameters was 0.53/0.65, which increased to 0.66/0.63, and 0.76/0.87, with the addition of DVH or radiomics parameters, respectively. For GU acute/late toxicities, the AUC increased from 0.55/0.56 (clinical only) to 0.84/0.90 (+DVH) and 0.83/0.96 (clinical + DVH + radiomics). For vaginal acute/late toxicities, the AUC increased from 0.51/0.57 (clinical only) to 0.58/0.72 (+DVH) and 0.82/0.89 (clinical + DVH + radiomics). The predictive performance of NTCP models based on radiomics features was higher than the commonly used clinical and DVH parameters. Dosimetric radiomics analysis is a promising tool for NTCP modelling in radiotherapy.

Radiogenomics in Colorectal Cancer.

The steady improvement of high-throughput technologies greatly facilitates the implementation of personalized precision medicine. Characterization of tumor heterogeneity through image-derived features-radiomics and genetic profile modifications-genomics, is a rapidly evolving field known as radiogenomics. Various radiogenomics studies have been dedicated to colorectal cancer so far, highlighting the potential of these approaches to enhance clinical decision-making. In this review, a general outline of colorectal radiogenomics literature is provided, discussing the current limitations and suggested further developments.

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.

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 SUVmax, 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 SUVmax may have predictive value, such as in cervical cancer.

Clinical utility of radiomics at baseline rectal MRI to predict complete response of rectal cancer after chemoradiation therapy.

PURPOSE: To investigate the value of T2-radiomics combined with anatomical MRI staging criteria from pre-treatment rectal MRI in predicting complete response to neoadjuvant chemoradiation therapy (CRT). METHODS: This retrospective study included patients with locally advanced rectal cancer who underwent rectal MRI before neoadjuvant CRT from October 2011 to January 2015 and then surgery. Surgical histopathologic analysis was used as the reference standard for pathologic complete response. Anatomical MRI staging criteria were extracted from our institutional standardized radiology report. In radiomics analysis, one radiologist manually segmented the primary tumor on T2-weighted images for all 102 patients (i.e., training set); two different radiologists independently segmented 66/102 patients (i.e., validation set). 108 radiomics features were extracted. Then, scanner-independent features were identified and least absolute shrinkage operator analysis was used to extract a radiomics score. Finally, a support vector machine model combining the radiomics score and anatomical MRI staging criteria was compared against both anatomical MRI-only and radiomics-only models using the deLong test. RESULTS: The study included 102 patients (42 women; median age = 61 years).The radiomics score produced an area under the curve (AUC) of 0.75. Comparable results were found using the validation set (AUCs = 0.75 and 0.71 for each radiologist, respectively). The anatomical MRI-only model had an accuracy of 67% (sensitivity 42%, specificity 72%); when adding the radiomics score, the accuracy increased to 74% (sensitivity 58%, specificity 77%). CONCLUSION: Combining T2-radiomics and anatomical MRI staging criteria from pre-treatment rectal MRI may help to stratify patients based on the prediction of treatment response to neoadjuvant therapy.

Volumetric analysis of IDH-mutant lower-grade glioma: a natural history study of tumor growth rates before and after treatment.

BACKGROUND: Lower-grade gliomas (LGGs) with isocitrate dehydrogenase 1 and/or 2 (IDH1/2) mutations have long survival times, making evaluation of treatment efficacy difficult. We investigated the volumetric growth rate of IDH mutant gliomas before and after treatment with established glioma therapies to determine whether a significant change in growth rate could be documented and perhaps be used in the future to evaluate treatment response to investigational agents in LGG trials. METHODS: In this multicenter retrospective study, 230 adult patients with IDH1/2 mutated LGGs (World Health Organization grade II or III) undergoing surgery, radiation, or chemotherapy for progressive non-enhancing tumor were identified. Subjects were required to have 3 MRI scans containing T2/fluid attenuated inversion recovery imaging spanning a minimum of 6 months prior to treatment. A mixed-effect model was used to estimate tumor growth prior to treatment. A subset of 95 patients who received chemotherapy, radiotherapy, or chemoradiotherapy and had 2 posttreatment imaging time points available were evaluated for change in pre- and posttreatment volumetric growth rates using a piecewise mixed model. RESULTS: The pretreatment volumetric growth rate across all 230 patients was 27.37%/180 days (95% CI: [23.36%, 31.51%]). In the 95 patients with both pre- and posttreatment scans available, there was a significant difference in volumetric growth rates before (26.63%/180 days, 95% CI: [19.31%, 34.40%]) and after treatment (-15.24% /180 days, 95% CI: [-21.37%, -8.62%]) (P < 0.0001). The growth rates for patient subgroup with 1p/19q codeletion (N = 118) was significantly slower than the rate of the 1p/19q non-codeleted group (N = 68) (22.84% vs 35.49%, P = 0.0108). CONCLUSION: In this study, we evaluated the growth rates of IDH mutant gliomas before and after standard therapy. Further study is needed to establish whether a change in growth rate is associated with patient survival and its use as a surrogate endpoint in clinical trials for IDH mutant LGGs.

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.

Qualitative and quantitative reproducibility of [18]fluoromethycholine PET/computed tomography in prostate cancer.

BACKGROUND: Recurrence occurs in more than 50% of prostate cancer. To be effective, treatments require precise localization of tumor cells. [F]fluoromethylcholine ([18F]FCH) PET/computed tomography (CT) is currently used to restage disease in cases of biochemical relapse. To be used for therapy response as has been suggested, repeatability limits of PET derived indices need to be established. OBJECTIVE: The aim of our study was to prospectively assess the qualitative and quantitative reproducibility [18F]FCH PET/CT in prostate cancer. METHODS: Patients with histologically proven prostate cancer referred for initial staging or restaging were prospectively included. All patients underwent two [18F]FCH PET/CTs in the same conditions within a maximum of 3 weeks' time. We studied the repeatability of the visual report and the repeatability of SUVmax and its evolution over the acquisition time in lesions, liver and vascular background. Statistical analysis was performed using the Bland-Altman approach. RESULTS: Twenty-one patients were included. Reporting repeatability was excellent with 97.8% of concordance. Mean repeatability of SUVmax considering all times and all lesions was 2.2% ± 20. Evolution of SUVmax was unpredictable, either increasing or decreasing over the acquisition time, both for lesions and for physiological activity. CONCLUSION: Our study demonstrated that visual report of [18F]FCH PET/CT was very reproducible and that the repeatability limits of SUVmax was similar to those of other PET radiotracers. An SUVmax difference of more than 40% should be considered as representing a treatment response effect. Change of SUVmax during the acquisition time varied and should not be considered as an interpretation criterion.

MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors.

BACKGROUND: Melanoma brain metastases historically portend a dismal prognosis, but recent advances in immune checkpoint inhibitors (ICIs) have been associated with durable responses in some patients. There are no validated imaging biomarkers associated with outcomes in patients with melanoma brain metastases receiving ICIs. We hypothesized that radiomic analysis of magnetic resonance images (MRIs) could identify higher-order features associated with survival. METHODS: Between 2010 and 2019, we retrospectively reviewed patients with melanoma brain metastases who received ICI. After volumes of interest were drawn, several texture and edge descriptors, including first-order, Haralick, Gabor, Sobel, and Laplacian of Gaussian (LoG) features were extracted. Progression was determined using Response Assessment in Neuro-Oncology Brain Metastases. Univariate Cox regression was performed for each radiomic feature with adjustment for multiple comparisons followed by Lasso regression and multivariate analysis. RESULTS: Eighty-eight patients with 196 total brain metastases were identified. Median age was 63.5 years (range, 19-91 y). Ninety percent of patients had Eastern Cooperative Oncology Group performance status of 0 or 1 and 35% had elevated lactate dehydrogenase. Sixty-three patients (72%) received ipilimumab, 11 patients (13%) received programmed cell death protein 1 blockade, and 14 patients (16%) received nivolumab plus ipilimumab. Multiple features were associated with increased overall survival (OS), and LoG edge features best explained the variation in outcome (hazard ratio: 0.68, P = 0.001). In multivariate analysis, a similar trend with LoG was seen, but no longer significant with OS. Findings were confirmed in an independent cohort. CONCLUSION: Higher-order MRI radiomic features in patients with melanoma brain metastases receiving ICI were associated with a trend toward improved OS.

Revisiting the identification of tumor sub-volumes predictive of residual uptake after (chemo)radiotherapy: influence of segmentation methods on 18F-FDG PET/CT images.

Our aim was to evaluate the impact of the accuracy of image segmentation techniques on establishing an overlap between pre-treatment and post-treatment functional tumour volumes in 18FDG-PET/CT imaging. Simulated images and a clinical cohort were considered. Three different configurations (large, small or non-existent overlap) of a single simulated example was used to elucidate the behaviour of each approach. Fifty-four oesophageal and head and neck (H&N) cancer patients treated with radiochemotherapy with both pre- and post-treatment PET/CT scans were retrospectively analysed. Images were registered and volumes were determined using combinations of thresholds and the fuzzy locally adaptive Bayesian (FLAB) algorithm. Four overlap metrics were calculated. The simulations showed that thresholds lead to biased overlap estimation and that accurate metrics are obtained despite spatially inaccurate volumes. In the clinical dataset, only 17 patients exhibited residual uptake smaller than the pre-treatment volume. Overlaps obtained with FLAB were consistently moderate for esophageal and low for H&N cases across all metrics. Overlaps obtained using threshold combinations varied greatly depending on thresholds and metrics. In both cases overlaps were variable across patients. Our findings do not support optimisation of radiotherapy planning based on pre-treatment 18FDG-PET/CT image definition of high-uptake sub-volumes. Combinations of thresholds may have led to overestimation of overlaps in previous studies.

Radiomics: Data Are Also Images.

The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated oncology applications. The main pitfalls were identified in study design, data acquisition, segmentation, feature calculation, and modeling; however, in most cases, potential solutions are available and existing recommendations should be followed to improve the overall quality and reproducibility of published radiomics studies. The techniques from the field of deep learning have some potential to provide solutions, especially in terms of automation. Some important challenges remain to be addressed but, overall, striking advances have been made in the field in the last 5 y.

Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets.

Recent advances in radiomics have enhanced the value of medical imaging in various aspects of clinical practice, but a crucial component that remains to be investigated further is the robustness of quantitative features to imaging variations and across multiple institutions. In the case of MRI, signal intensity values vary according to the acquisition parameters used, yet no consensus exists on which preprocessing techniques are favorable in reducing scanner-dependent variability of image-based features. Hence, the purpose of this study was to assess the impact of common image preprocessing methods on the scanner dependence of MRI radiomic features in multi-institutional glioblastoma multiforme (GBM) datasets. Two independent GBM cohorts were analyzed: 50 cases from the TCGA-GBM dataset and 111 cases acquired in our institution, and each case consisted of 3 MRI sequences viz. FLAIR, T1-weighted, and T1-weighted post-contrast. Five image preprocessing techniques were examined: 8-bit global rescaling, 8-bit local rescaling, bias field correction, histogram standardization, and isotropic resampling. A total of 420 features divided into eight categories representing texture, shape, edge, and intensity histogram were extracted. Two distinct imaging parameters were considered: scanner manufacturer and scanner magnetic field strength. Wilcoxon tests identified features robust to the considered acquisition parameters under the selected image preprocessing techniques. A machine learning-based strategy was implemented to measure the covariate shift between the analyzed datasets using features computed using the aforementioned preprocessing methods. Finally, radiomic scores (rad-scores) were constructed by identifying features relevant to patients' overall survival after eliminating those impacted by scanner variability. These were then evaluated for their prognostic significance through Kaplan-Meier and Cox hazards regression analyses. Our results demonstrate that overall, histogram standardization contributes the most in reducing radiomic feature variability as it is the technique to reduce the covariate shift for three feature categories and successfully discriminate patients into groups of different survival risks.

Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features.

PURPOSE: The use of radiomic features as biomarkers of treatment response and outcome or as correlates to genomic variations requires that the computed features are robust and reproducible. Segmentation, a crucial step in radiomic analysis, is a major source of variability in the computed radiomic features. Therefore, we studied the impact of tumor segmentation variability on the robustness of MRI radiomic features. METHOD: Fluid-attenuated inversion recovery (FLAIR) and contrast-enhanced T1-weighted (T1WICE ) MRI of 90 patients diagnosed with glioblastoma were segmented using a semiautomatic algorithm and an interactive segmentation with two different raters. We analyzed the robustness of 108 radiomic features from five categories (intensity histogram, gray-level co-occurrence matrix, gray-level size-zone matrix (GLSZM), edge maps, and shape) using intra-class correlation coefficient (ICC) and Bland and Altman analysis. RESULTS: Our results show that both segmentation methods are reliable with ICC ≥ 0.96 and standard deviation (SD) of mean differences between the two raters (SDdiffs ) ≤ 30%. Features computed from the histogram and co-occurrence matrices were found to be the most robust (ICC ≥ 0.8 and SDdiffs  ≤ 30% for most features in these groups). Features from GLSZM were shown to have mixed robustness. Edge, shape, and GLSZM features were the most impacted by the choice of segmentation method with the interactive method resulting in more robust features than the semiautomatic method. Finally, features computed from T1WICE and FLAIR images were found to have similar robustness when computed with the interactive segmentation method. CONCLUSION: Semiautomatic and interactive segmentation methods using two raters are both reliable. The interactive method produced more robust features than the semiautomatic method. We also found that the robustness of radiomic features varied by categories. Therefore, this study could help motivate segmentation methods and feature selection in MRI radiomic studies.

Preoperative MRI-radiomics features improve prediction of survival in glioblastoma patients over MGMT methylation status alone.

BACKGROUND: Glioblastoma (GBM) is the most common malignant central nervous system tumor, and MGMT promoter hypermethylation in this tumor has been shown to be associated with better prognosis. We evaluated the capacity of radiomics features to add complementary information to MGMT status, to improve the ability to predict prognosis. METHODS: 159 patients with untreated GBM were included in this study and divided into training and independent test sets. 286 radiomics features were extracted from the magnetic resonance images acquired prior to any treatments. A least absolute shrinkage selection operator (LASSO) selection followed by Kaplan-Meier analysis was used to determine the prognostic value of radiomics features to predict overall survival (OS). The combination of MGMT status with radiomics was also investigated and all results were validated on the independent test set. RESULTS: LASSO analysis identified 8 out of the 286 radiomic features to be relevant which were then used for determining association to OS. One feature (edge descriptor) remained significant on the external validation cohort after multiple testing (p=0.04) and the combination with MGMT identified a group of patients with the best prognosis with a survival probability of 0.61 after 43 months (p=0.0005). CONCLUSION: Our results suggest that combining radiomics with MGMT is more accurate in stratifying patients into groups of different survival risks when compared to with using these predictors in isolation. We identified two subgroups within patients who have methylated MGMT: one with a similar survival to unmethylated MGMT patients and the other with a significantly longer OS.

Use of baseline 18F-FDG PET scan to identify initial sub-volumes with local failure after concomitant radio-chemotherapy in head and neck cancer.

INTRODUCTION: Head and neck squamous cell carcinoma (HNSCC) treated by radio-chemotherapy have a significant local recurrence rate. It has been previously suggested that 18F-FDG PET could identify the high uptake areas that can be potential targets for dose boosting. The purpose of this study was to compare the location of initial hypermetabolic regions on baseline scans with the metabolic relapse sites after radio-chemotherapy in HNSCC. RESULTS: The initial functional tumor volume was significantly higher for patients with proven local recurrence or residual disease (23.5 cc vs. 8.9 cc; p = 0.0005). The overlap between baseline and follow-up sub-volumes were moderate with an overlap fraction ranging from 0.52 to 0.39 between R40 and I30 to I60. CONCLUSION: In our study the overlap between baseline and post-therapeutic metabolic tumor sub-volumes was only moderate. These results need to be investigated in a larger cohort acquired with a more standardized patient repositioning protocol for sequential PET imaging. METHODS: Pre and post treatment PET/CT scans of ninety four HNSCC patients treated with radio-chemotherapy were retrospectively reviewed. Follow-up 18F-FDG PET/CT images were registered to baseline scans using a rigid body transformation. Seven metabolic tumor sub-volumes were obtained on the baseline scans using a fixed percentage of SUVmax (I30, I40, I50, I60, I70, I80, and I90) and were subsequently compared with two post-treatment sub-volumes (R40, R90) in 38 cases of local recurrence or residual metabolic disease. Overlap fraction, Dice and Jaccard indices, common volume/baseline volume and common volume/recurrent volume were used to determine the overlap of the different estimated sub-volumes.

Characterization of PET/CT images using texture analysis: the past, the present any future?

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

Reliability of PET/CT Shape and Heterogeneity Features in Functional and Morphologic Components of Non-Small Cell Lung Cancer Tumors: A Repeatability Analysis in a Prospective Multicenter Cohort.

The main purpose of this study was to assess the reliability of shape and heterogeneity features in both the PET and the low-dose CT components of PET/CT. A secondary objective was to investigate the impact of image quantization. Methods: A Health Insurance Portability and Accountability Act-compliant secondary analysis of deidentified prospectively acquired PET/CT test-retest datasets of 74 patients from multicenter Merck and American College of Radiology Imaging Network trials was performed. Metabolically active volumes were automatically delineated on PET with a fuzzy locally adaptive bayesian algorithm. Software was used to semiautomatically delineate the anatomic volumes on the low-dose CT component. Two quantization methods were considered: a quantization into a set number of bins (quantization B) and an alternative quantization with bins of fixed width (quantization W). Four shape descriptors, 10 first-order metrics, and 26 textural features were evaluated. Bland-Altman analysis was used to quantify repeatability. Features were subsequently categorized as very reliable, reliable, moderately reliable, or poorly reliable with respect to the corresponding volume variability. Results: Repeatability was highly variable among features. Numerous metrics were identified as poorly or moderately reliable. Others were reliable or very reliable in both modalities and in all categories (shape and first-, second-, and third-order metrics). Image quantization played a major role in feature repeatability. Features were more reliable in PET with quantization B, whereas quantization W showed better results in CT. Conclusion: The test-retest repeatability of shape and heterogeneity features in PET and low-dose CT varied greatly among metrics. The level of repeatability also depended strongly on the quantization step, with different optimal choices for each modality. The repeatability of PET and low-dose CT features should be carefully considered when selecting metrics to build multiparametric models.