Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer

Role of textural and radiomic analysis parameters in predicting histopathological parameters of the tumor in breast cancer patients

INTRODUCTION

Texture and radiomic analysis characterizes the tumor's phenotype and evaluates its microenvironment in quantitative terms. This study aims to investigate the role of textural and radiomic analysis parameters in predicting histopathological factors in breast cancer patients.

MATERIALS AND METHODS

Two hundred and twelve primary breast cancer patients underwent 18 F-FDG PET/computed tomography for staging. The images were processed in a commercially available textural analysis software. ROI was drawn over the primary tumor with a 40% threshold and was processed further to derive textural and radiomic parameters. These parameters were then compared with histopathological factors of tumor. Receiver-operating characteristic analysis was performed with a P -value <0.05 for statistical significance. The significant parameters were subsequently utilized in various machine learning models to assess their predictive accuracy.

RESULTS

A retrospective study of 212 primary breast cancer patients was done. Among all the significant parameters, SUVmin, SUVmean, SUVstd, SUVmax, discretized HISTO_Entropy, and gray level co-occurrence matrix_Contrast were found to be significantly associated with ductal carcinoma type. Four parameters (SUVmin, SUVmean, SUVstd, and SUVmax) were significant in differentiating the luminal subtypes of the tumor. Five parameters (SUVmin, SUVmean, SUVstd, SUVmax, and SUV kurtosis) were significant in predicting the grade of the tumor. These parameters showcased robust capabilities in predicting multiple histopathological parameters when tested using machine learning algorithms.

CONCLUSION

Though textural analysis could not predict hormonal receptor status, lymphovascular invasion status, perineural invasion status, microcalcification status of tumor, and all the molecular subtypes of the tumor, it could predict the tumor's histologic type, triple-negative subtype, and score of the tumor noninvasively.

Pretreatment CT and 18 F-FDG PET-based radiomic model predicting pathological complete response and loco-regional control following neoadjuvant chemoradiation in oesophageal cancer

INTRODUCTION

To develop a radiomic-based model to predict pathological complete response (pCR) and outcome following neoadjuvant chemoradiotherapy (NACRT) in oesophageal cancer.

METHODS

We analysed 68 patients with oesophageal cancer treated with NACRT followed by esophagectomy, who had staging 18F-fluorodeoxyglucose (¹⁸ F-FDG) positron emission tomography (PET) and computed tomography (CT) scans performed at our institution. An in-house data-chjmirocterization algorithm was used to extract 3D-radiomic features from the segmented primary disease. Prediction models were constructed and internally validated. Composite feature, F_c  = α * F_PET  + (1 - α) * F_CT , 0 ≤ α ≤ 1, was constructed for each corresponding CT and PET feature. Loco-regional control (LRC), recurrence-free survival (RFS), metastasis-free survival (MFS) and overall survival (OS) were estimated by Kaplan-Meier analysis, and compared using log-rank test.

RESULTS

Median follow-up was 59 months. pCR was achieved in 34 (50%) patients. Five-year RFS, LRC, MFS and OS were 67.1%, 88.5%, 75.6% and 57.6%, respectively. Tumour Regression Grade (TRG) 0-1 indicative of complete response or minimal residual disease was significantly associated with improved 5-year LRC [93.7% vs 71.8%; P = 0.020; HR 0.19, 95% CI 0.04-0.85]. Four sepjmirote pCR predictive models were built for CT alone, PET alone, CT+PET and composite. CT, PET and CT+PET models had AUC 0.73 ± 0.08, 0.66 ± 0.08 and 0.77 ± 0.07, respectively. The composite model resulted in an improvement of pCR predicting power with AUC 0.87 ± 0.06. Stratifying patients with a low versus high radiomic score showed clinically relevant improvement in 5-year LRC favouring low-score group (91.1% vs. 80%, 95% CI 0.09-1.77, P = 0.2).

CONCLUSION

The composite CT/PET radiomics model was highly predictive of pCR following NACRT. Validation in larger data sets is warranted to determine whether the model can predict clinical outcomes.

PET Imaging of Tumor Perfusion: A Potential Cancer Biomarker?

Perfusion, as measured by imaging, is considered a standard of care biomarker for the evaluation of many tumors. Measurements of tumor perfusion may be used in a number of ways, including improving the visual detection of lesions, differentiating malignant from benign findings, assessing aggressiveness of tumors, identifying ischemia and by extension hypoxia within tumors, and assessing treatment response. While most clinical perfusion imaging is currently performed with CT or MR, a number of methods for PET imaging of tumor perfusion have been described. The inert PET radiotracer 15O-water PET represents the recognized gold standard for absolute quantification of tissue perfusion in both normal tissue and a variety of pathological conditions including cancer. Other cancer PET perfusion imaging strategies include the use of radiotracers with high first-pass uptake, analogous to those used in cardiac perfusion PET. This strategy produces more visually pleasing high-contrast images that provide relative rather than absolute perfusion quantification. Lastly, multiple timepoint imaging of PET tracers such as 18F-FDG, are not specifically optimized for perfusion, but have advantages related to availability, convenience, and reimbursement. Multiple obstacles have thus far blocked the routine use of PET imaging for tumor perfusion, including tracer production and distribution, image processing, patient body coverage, clinical validation, regulatory approval and reimbursement, and finally feasible clinical workflows. Fortunately, these obstacles are being overcome, especially within larger imaging centers, opening the door for PET imaging of tumor perfusion to become standard clinical practice. In the foreseeable future, it is possible that whole-body PET perfusion imaging with 15O-water will be able to be performed in a single imaging session concurrent with standard PET imaging techniques such as 18F-FDG-PET. This approach could establish an efficient clinical workflow. The resultant ability to measure absolute tumor blood flow in combination with glycolysis will provide important complementary information to inform prognosis and clinical decisions.

Intratumor heterogeneity: A new perspective on colorectal cancer research

Colorectal cancers generally consist of multiple subclones. These subclones have their own unique characteristics, resulting in intratumor heterogeneity (ITH). As the discussion of ITH has advanced, a model describing the relationship of ITH to the tumor has gradually emerged. ITH can be divided into two types of intraprimary tumor heterogeneity and intraindividual tumor heterogeneity, the former for further understanding of tumor composition, and the latter for providing more information about evolutionary patterns. With the rapid development of new methods, such as next‐generation, polyguanine region sequencing, and Image detection, researchers may unravel the secrets underlying ITH. The higher the ITH of the tumor, the richer the interaction between the subclones maybe, or the greater the chance of the tumor getting more powerful subclones may be, thus increasing the malignant potential of the tumor. Existing evidence suggests that ITH may increase the ability of tumors to resist treatment and can be used as an independent influence on the prognosis of colorectal cancer. We reviewed 80 recent studies to give researchers a new perspective on colorectal cancer. There is still a limited amount of research in this area. Further study of the relationship between ITH and clinical endpoints may lead to the development of new treatment strategies.

Metabolic Imaging Phenotype Using Radiomics of [18F]FDG PET/CT Associated with Genetic Alterations of Colorectal Cancer

PURPOSE

To understand the association between genetic mutations and radiomics of 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography (PET)/x-ray computed tomography (CT) in patients with colorectal cancer (CRC).

PROCEDURES

This study included 74 CRC patients who had undergone preoperative [¹⁸F]FDG PET/CT. A total of 65 PET/CT-related features including intensity, volume-based, histogram, and textural features were calculated. High-resolution melting methods were used for genetic mutation analysis.

RESULTS

Genetic mutants were found in 21 KRAS tumors (28 %), 31 TP53 tumors (42 %), and 17 APC tumors (23 %). Tumors with a mutated KRAS had an increased value at the 25th percentile of maximal standardized uptake value (SUV_max) within their metabolic tumor volume (MTV) (P < .0001; odds ratio [OR] 1.99; 95 % confidence interval [CI] 1.37-2.90) and their contrast from the gray-level cooccurrence matrix (P = .005; OR 1.52; 95 % CI 1.14-2.04). A mutated TP53 was associated with an increased value of short-run low gray-level emphasis derived from the gray-level run length matrix (P = .001; OR 243006.0; 95 % CI 59.2-996,872,313). APC mutants exhibited lower low gray-level zone emphasis derived from the gray-level zone length matrix (P = .006; OR < .0001; 95 % CI 0.000-0.22).

CONCLUSION

PET/CT-derived radiomics can provide supplemental information to determine KRAS, TP53, and APC genetic alterations in CRC.

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

INTRODUCTION

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

OBJECTIVE

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

Noninvasive assessment and quantification of tumor vascularization using [18F]FDG-PET/CT and CE-CT in a tumor model with modifiable angiogenesis-an animal experimental prospective cohort study

Background

This study investigated the noninvasive assessment of tumor vascularization with clinical F-18-fluorodeoxyglucose positron emission tomography/computed tomography and contrast-enhanced computed tomography ([18F]FDG-PET/CT and CE-CT) in experimental human xenograft tumors with modifiable vascularization and compared results to histology. Tumor xenografts with modifiable vascularization were established in 71 athymic nude rats by subcutaneous transplantation of human non-small-cell lung cancer (NSCLC) cells. Four different groups were transplanted with two different tumor cell lines (either A549 or H1299) alone or tumors co-transplanted with rat glomerular endothelial (RGE) cells, the latter to increase vascularization. Tumors were assessed noninvasively by [18F]FDG PET/CT and contrast-enhanced CT (CE-CT) using clinical scanners. This was followed by histological examinations evaluating tumor vasculature (CD-31 and intravascular fluorescent beads).

Results

In both tumor lines (A549 and H1299), co-transplantation of RGE cells resulted in faster growth rates [maximal tumor diameter of 20 mm after 22 (± 1.2) as compared to 45 (± 1.8) days, p < 0.001], higher microvessel density (MVD) determined histologically after CD-31 staining [171.4 (± 18.9) as compared to 110.8 (± 11) vessels per mm², p = 0.002], and higher perfusion as indicated by the number of beads [1.3 (± 0.1) as compared to 1.1 (± 0.04) beads per field of view, p = 0.001]. In [18F]FDG-PET/CT, co-transplanted tumors revealed significantly higher standardized uptake values [SUVmax, 2.8 (± 0.2) as compared to 1.1 (± 0.1), p < 0.001] and larger metabolic active volumes [2.4 (± 0.2) as compared to 0.4 (± 0.2) cm³, p < 0.001] than non-co-transplanted tumors. There were significant correlations for vascularization parameters derived from histology and [18F]FDG PET/CT [beads and SUVmax, r = 0.353, p = 0.005; CD-31 and SUVmax, r = 0.294, p = 0.036] as well as between CE-CT and [18F]FDG PET/CT [contrast enhancement and SUVmax, r = 0.63, p < 0.001; vital CT tumor volume and metabolic PET tumor volume, r = 0.919, p < 0.001].

Conclusions

In this study, a human xenograft tumor model with modifiable vascularization implementable for imaging, pharmacological, and radiation therapy studies was successfully established. Both [18F]FDG-PET/CT and CE-CT are capable to detect parameters closely connected to the degree of tumor vascularization, thus they can help to evaluate vascularization in tumors noninvasively. [18F]FDG-PET may be considered for characterization of tumors beyond pure glucose metabolism and have much greater contribution to diagnostics in oncology.

Protein functionalised self assembled monolayer based biosensor for colon cancer detection

We report results of the studies relating to the fabrication of a surface plasmon resonance (SPR) based label-free immunosensor for real-time monitoring of endothelin-1 (ET-1), a colon cancer biomarker. A gold disk modified with a self-assembled monolayer (SAM) of 11-mercaptoundecanoic acid (11-MUA) was functionalised via covalent immobilization of monoclonal anti-ET-1 antibodies using EDC-NHS (1-(3-(dimethylamine)-propyl)-3-ethylcarbodiimide hydrochloride, N-hydroxy succinimide) chemistry. This immunosensing platform (ethanolamine/anti-ET-1/11-MUA/Au) was characterized via atomic force microscopy (AFM), contact angle (CA) and Fourier transform infrared (FT-IR) spectroscopic techniques. The fabricated SPR electrode was further used to detect ET-1 in the broad concentration range 2-100 pg mL^-1, with a detection limit of 0.30 pg mL^-1 and remarkable sensitivity of 2.18 m^o pg^-1mL. The adsorption mechanism was studied using monophasic model and the values of association (k_a) and dissociation (k_d) constants for anti-ET-1 and ET-1 binding were calculated to be 4.4 ± 0.4 × 10⁵ M^-1 s^-1 and 2.04 ± 0.0003 × 10^-3 s^-1, respectively. The results obtained via analysis of serum samples of colorectal cancer patients were found to be in good agreement with those obtained from enzyme-linked immunosorbent assay (ELISA) technique. Further, electrochemical studies were performed to prove the efficacy of the fabricated platform as a point of care device for the detection of ET-1.

Direct comparison of 2‑amino[3‑11C]isobutyric acid and 2‑amino[11C]methyl‑isobutyric acid uptake in eight lung cancer xenograft models

The non‑natural amino acid positron emission tomography tracers, 2‑amino[3‑11C]isobutyric acid ([3‑11C]AIB) and 2‑amino[11C]methyl‑isobutyric acid ([11C]MeAIB), are metabolically stable in vivo and accumulate in tumors. [3‑11C]AIB is transported into cells mainly via the amino acid transport system A and partially via systems L and ASC, whereas [11C]MeAIB is transported into cells specifically via system A. How transport via the different systems affects the tumor uptake of these tracers, however, is unclear. In the present study, the tumor uptake of the two tracers was directly compared in eight lung cancer models (A549, H82, H441, H460, H1299, H1650, PC14, and SY), and the correlation of tumor uptake with several factors (amino acid transporter expression, contribution of amino acid transport systems to AIB uptake and tumor proliferation indices) was analyzed. Biodistribution analyses revealed that the tumor uptake of [3‑11C]AIB (4.9 to 19.2% injected dose per gram [ID/g]) was higher than that of [11C]MeAIB (3.1 to 15.9% ID/g) in all eight tumors, with a statistically significant difference in three tumors (P<0.01 in H441 and H460 tumors, P<0.05 in H82 tumors). A significant correlation was observed between the tumor uptake of the two tracers (r=0.95, P<0.01). The mRNA expression levels of the amino acid transporters of system A (SLC38A1 and SLC38A2), system L (SLC7A5) and system ASC (SLC1A5) were higher in all eight tumors than in the normal lung, with widely varying expression patterns. Although the contributions of the amino acid transport systems, Ki‑67 indices and tumor doubling times greatly differed among the eight models, these factors did not correlate with the tumor uptake of either tracer. The higher tumor uptake of [3‑11C]AIB and the correlation of tumor uptake between [3‑11C]AIB and [11C]MeAIB warrant further investigation in clinical studies in order to clarify the role of [3‑11C]AIB PET in oncology imaging.

Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features

OBJECTIVE

We aimed to identify optimal machine-learning methods for preoperative differentiation of sacral chordoma (SC) and sacral giant cell tumour (SGCT) based on 3D non-enhanced computed tomography (CT) and CT-enhanced (CTE) features.

METHODS

A total of 95 patients were divided into a training set and a validation set. Three best feature selection methods (Relief, least absolute shrinkage and selection operator (LASSO) and Random Forest (RF)) and three classification methods, including generalised linear models (GLM), support vector machines (SVM) and RF, were compared for their performance in distinguishing SC and SGCT. The performance of the radiomics model was investigated via area under the receiver-operating characteristic curve (AUC) and accuracy (ACC) analysis.

RESULTS

The selection method LASSO + classifier GLM had the highest AUC of 0.984 and ACC of 0.897 in the validating set, followed by Relief + GLM (AUC = 0.909, ACC = 0.862) and LASSO + SVM (AUC = 0.900, ACC = 0.862) based on CTE features. For CT features, RF + GLM had the highest AUC of 0.889, while LASSO + GLM achieved a high ACC of 0.793 in the validating set. Regardless of the methods, CTE features significantly outperformed those from CT for the differentiation of SC and SGCT (Z_AUC = -3.029, Z_ACC = -4.553; p < 0.05).

CONCLUSIONS

Our study demonstrated CTE features performed better than CT features. The selection method LASSO + classifier GLM had the best performance in differentiation of SC and SGCT, which could enhance the application of radiomics methods in sacral tumours.

KEY POINTS

• Sacral chordoma and sacral giant cell tumour are the two most common primary tumours of the sacrum with many common clinical and imaging characteristics. • A radiomics model helps clinicians to identify the histology of a sacral tumour. • CTE features should be preferred.

Advanced Imaging Techniques in Evaluation of Colorectal Cancer

Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. ^©RSNA, 2018.

Robustness versus disease differentiation when varying parameter settings in radiomics features: application to nasopharyngeal PET/CT

OBJECTIVES

To investigate the impact of parameter settings as used for the generation of radiomics features on their robustness and disease differentiation (nasopharyngeal carcinoma (NPC) versus chronic nasopharyngitis (CN) in FDG PET/CT imaging).

METHODS

We studied 106 patients (69/37 NPC/CN, pathology confirmed), and extracted 57 radiomics features under different parameter settings. Robustness was assessed by the intra-class correlation coefficient (ICC). Logistic regression with leave-one-out cross validation was used to generate classification probabilities, and diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC).

RESULTS

Varying averaging strategies and symmetry, 4/26 GLCM features showed poor range of pairwise ICCs of 0.02-0.98, while depicting good AUCs of 0.82-0.91. Varying distances, 5/26 GLCM features showed ICCs of 0.82-0.99 while corresponding AUCs were 0.52-0.91. 6/13 GLRLM features showed both high AUC (0.81-0.89) and high ICC (0.85-0.99) regarding to averaging strategies. 7/13 GLSZM features showed AUCs of 0.81-0.90 while having ICCs of 0.01-0.99 under different neighbourhoods. 2/5 NGTDM features showed AUCs of 0.81-0.85 while having ICCs of 0.19-0.89 for different window sizes. Differentiating a subset of NPC (stages I-II) form CN, both SumEntropy and SZLGE achieved significantly higher AUCs than metabolically active tumour volume (AUC: 0.91 vs. 0.72, p<0.01).

CONCLUSIONS

Radiomics features depicting poor absolute-scale robustness regarding to parameter settings can still lead to good diagnostic performance. As such, robustness of radiomics features should not be overemphasized for removal of features towards assessment of clinical tasks. For differentiating NPC from CN, some radiomics features (e.g. SumEntropy, SZLGE, LGZE) outperformed conventional metrics.

KEY POINTS

• Poor robustness did not necessarily translate into poor differentiation performance. • Absolute-scale robustness of radiomics features should not be overemphasized. • Radiomics features SumEntropy, SZLGE and LGZE outperformed conventional metrics.

A Novel Framework for Automated Segmentation and Labeling of Homogeneous Versus Heterogeneous Lung Tumors in [18F]FDG-PET Imaging

PURPOSE

Determination of intra-tumor high-uptake area using 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography (PET) imaging is an important consideration for dose painting in radiation treatment applications. The aim of our study was to develop a framework towards automated segmentation and labeling of homogeneous vs. heterogeneous tumors in clinical lung [¹⁸F]FDG-PET with the capability of intra-tumor high-uptake region delineation.

PROCEDURES

We utilized and extended a fuzzy random walk PET tumor segmentation algorithm to delineate intra-tumor high-uptake areas. Tumor textural feature (TF) analysis was used to find a relationship between tumor type and TF values. Segmentation accuracy was evaluated quantitatively utilizing 70 clinical [¹⁸F]FDG-PET lung images of patients with a total of 150 solid tumors. For volumetric analysis, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) measures were extracted with respect to gold-standard manual segmentation. A multi-linear regression model was also proposed for automated tumor labeling based on TFs, including cross-validation analysis.

RESULTS

Two-tailed t test analysis of TFs between homogeneous and heterogeneous tumors revealed significant statistical difference for size-zone variability (SZV), intensity variability (IV), zone percentage (ZP), proposed parameters II and III, entropy and tumor volume (p < 0.001), dissimilarity, high intensity emphasis (HIE), and SUV_min (p < 0.01). Lower statistical differences were observed for proposed parameter I (p = 0.02), and no significant differences were observed for SUV_max and SUV_mean. Furthermore, the Spearman rank analysis between visual tumor labeling and TF analysis depicted a significant correlation for SZV, IV, entropy, parameters II and III, and tumor volume (0.68 ≤ ρ ≤ 0.84) and moderate correlation for ZP, HIE, homogeneity, dissimilarity, parameter I, and SUV_min (0.22 ≤ ρ ≤ 0.52), while no correlations were observed for SUV_max and SUV_mean (ρ < 0.08). The multi-linear regression model for automated tumor labeling process resulted in R ² and RMSE values of 0.93 and 0.14, respectively (p < 0.001), and generated tumor labeling sensitivity and specificity of 0.93 and 0.89. With respect to baseline random walk segmentation, the results showed significant (p < 0.001) mean DSC, HD, and SUV_mean error improvements of 21.4 ± 11.5 %, 1.4 ± 0.8 mm, and 16.8 ± 8.1 % in homogeneous tumors and 7.4 ± 4.4 %, 1.5 ± 0.6 mm, and 7.9 ± 2.7 % in heterogeneous lesions. In addition, significant (p < 0.001) mean DSC, HD, and SUV_mean error improvements were observed for tumor sub-volume delineations, namely 5 ± 2 %, 1.5 ± 0.6 mm, and 7 ± 3 % for the proposed Fuzzy RW method compared to RW segmentation.

CONCLUSION

We proposed and demonstrated an automatic framework for significantly improved segmentation and labeling of homogeneous vs. heterogeneous tumors in lung [¹⁸F]FDG-PET images.

Investigating multi-radiomic models for enhancing prediction power of cervical cancer treatment outcomes

Quantitative image features, also known as radiomic features, have shown potential for predicting treatment outcomes in several body sites. We quantitatively analyzed ¹⁸Fluorine-fluorodeoxyglucose (¹⁸F-FDG) Positron Emission Tomography (PET) uptake heterogeneity in the Metabolic Tumor Volume (MTV) of eighty cervical cancer patients to investigate the predictive performance of radiomic features for two treatment outcomes: the development of distant metastases (DM) and loco-regional recurrent disease (LRR). We aimed to fit the highest predictive features in multiple logistic regression models (MLRs). To generate such models, we applied backward feature selection method as part of Leave-One-Out Cross Validation (LOOCV) within a training set consisting of 70% of the original patient cohort. The trained MLRs were tested on an independent set consisted of 30% of the original cohort. We evaluated the performance of the final models using the Area under the Receiver Operator Characteristic Curve (AUC). Accordingly, six models demonstrated superior predictive performance for both outcomes (four for DM and two for LRR) when compared to both univariate-radiomic feature models and Standard Uptake Value (SUV) measurements. This demonstrated approach suggests that the ability of the pre-radiochemotherapy PET radiomics to stratify patient risk for DM and LRR could potentially guide management decisions such as adjuvant systemic therapy or radiation dose escalation.

[18F]FDG-PET/CT texture analysis in thyroid incidentalomas: preliminary results

Background

significance of incidental thyroid 2-deoxy-2-[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) uptake on positron emission tomography/computed tomography (PET/CT) scans remains controversial. We aimed to evaluate the ability of [¹⁸F]FDG-PET/CT texture analysis to predict final diagnosis in thyroid incidentaloma.

Methods

We retrospectively evaluated medical records of all patients who performed a [¹⁸F]FDG-PET/CT from January 2012 to October 2016. Those patients who presented a thyroid incidentaloma described in the medical records and performed a fine needle aspiration in our institution were considered for the analysis. Cytological and/or histological results were used as reference standard to define the final diagnosis. In case of negative cytology, the nodule was considered benign. In case of non-diagnostic or inconclusive results ultrasound, follow-up and further cytology/histology were used as final diagnosis. For suspected or positive cytological result, histology was used as reference standard. PET images were segmented using a General Electric AW workstation running PET VCAR software (GE Healthcare, Waukesha, WI, USA) settled with a threshold of 40% SUV_max. LifeX software (http://www.lifexsoft.org) was used to perform texture analysis. Statistical analysis was performed with R package (https://www.r-project.org).

Results

We identified 55 patients with incidental thyroid [¹⁸F]FDG uptake. Five patients were excluded from the analysis because a final diagnosis was not available. Thirty-two out of 50 patients had benign nodules while in 18/50 cases a malignancy (primary thyroid cancer = 15, metastases = 3) was diagnosed. Conventional PET parameters and histogram-based features were calculated for all 50 patients, while other matrices-based features were available for 28/50 patients. SUV_max and skewness resulted significantly different in benign and malignant nodules (p = 0.01 and = 0.02, respectively). Using ROC analysis, seven features were identified as potential predictors. Among all the textural features tested, skewness showed the best area under the curve (= 0.66). SUV-based parameters resulted in the highest specificity while MTV, TLG, skewness and kurtosis, as well as correlation_GLCM resulted better in sensitivity.

Conclusions

[¹⁸F]FDG-PET/CT texture analysis seems to be a promising approach to stratify the patients with thyroid incidentaloma identified on PET scans, with respect to the risk of the diagnosis of a malignant thyroid nodule and thus, could refine the selection of the patients to be referred for cytology.

Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms

Site‐specific investigations of the role of radiomics in cancer diagnosis and therapy are emerging. We evaluated the reproducibility of radiomic features extracted from ¹⁸Flourine–fluorodeoxyglucose (¹⁸F‐FDG) PET images for three parameters: manual versus computer‐aided segmentation methods, gray‐level discretization, and PET image reconstruction algorithms. Our cohort consisted of pretreatment PET/CT scans from 88 cervical cancer patients. Two board‐certified radiation oncologists manually segmented the metabolic tumor volume (MTV₁ and MTV₂) for each patient. For comparison, we used a graphical‐based method to generate semiautomated segmented volumes (GBSV). To address any perturbations in radiomic feature values, we down‐sampled the tumor volumes into three gray‐levels: 32, 64, and 128 from the original gray‐level of 256. Finally, we analyzed the effect on radiomic features on PET images of eight patients due to four PET 3D‐reconstruction algorithms: maximum likelihood‐ordered subset expectation maximization (OSEM) iterative reconstruction (IR) method, fourier rebinning‐ML‐OSEM (FOREIR), FORE‐filtered back projection (FOREFBP), and 3D‐Reprojection (3DRP) analytical method. We extracted 79 features from all segmentation method, gray‐levels of down‐sampled volumes, and PET reconstruction algorithms. The features were extracted using gray‐level co‐occurrence matrices (GLCM), gray‐level size zone matrices (GLSZM), gray‐level run‐length matrices (GLRLM), neighborhood gray‐tone difference matrices (NGTDM), shape‐based features (SF), and intensity histogram features (IHF). We computed the Dice coefficient between each MTV and GBSV to measure segmentation accuracy. Coefficient values close to one indicate high agreement, and values close to zero indicate low agreement. We evaluated the effect on radiomic features by calculating the mean percentage differences (d¯) between feature values measured from each pair of parameter elements (i.e. segmentation methods: MTV₁‐MTV₂, MTV₁‐GBSV, MTV₂‐GBSV; gray‐levels: 64‐32, 64‐128, and 64‐256; reconstruction algorithms: OSEM‐FORE‐OSEM, OSEM‐FOREFBP, and OSEM‐3DRP). We used |d¯| as a measure of radiomic feature reproducibility level, where any feature scored |d¯| ±SD ≤ |25|% ± 35% was considered reproducible. We used Bland–Altman analysis to evaluate the mean, standard deviation (SD), and upper/lower reproducibility limits (U/LRL) for radiomic features in response to variation in each testing parameter. Furthermore, we proposed U/LRL as a method to classify the level of reproducibility: High— ±1% ≤ U/LRL ≤ ±30%; Intermediate— ±30% < U/LRL ≤ ±45%; Low— ±45 < U/LRL ≤ ±50%. We considered any feature below the low level as nonreproducible (NR). Finally, we calculated the interclass correlation coefficient (ICC) to evaluate the reliability of radiomic feature measurements for each parameter. The segmented volumes of 65 patients (81.3%) scored Dice coefficient >0.75 for all three volumes. The result outcomes revealed a tendency of higher radiomic feature reproducibility among segmentation pair MTV₁‐GBSV than MTV₂‐GBSV, gray‐level pairs of 64‐32 and 64‐128 than 64‐256, and reconstruction algorithm pairs of OSEM‐FOREIR and OSEM‐FOREFBP than OSEM‐3DRP. Although the choice of cervical tumor segmentation method, gray‐level value, and reconstruction algorithm may affect radiomic features, some features were characterized by high reproducibility through all testing parameters. The number of radiomic features that showed insensitivity to variations in segmentation methods, gray‐level discretization, and reconstruction algorithms was 10 (13%), 4 (5%), and 1 (1%), respectively. These results suggest that a careful analysis of the effects of these parameters is essential prior to any radiomics clinical application.

Limits of radiomic-based entropy as a surrogate of tumor heterogeneity: ROI-area, acquisition protocol and tissue site exert substantial influence

Entropy is a promising quantitative imaging biomarker for characterizing cancer imaging phenotype. Entropy has been associated with tumor gene expression, tumor metabolism, tumor stage, patient prognosis, and treatment response. Our hypothesis states that tumor-specific biomarkers such as entropy should be correlated between synchronous metastases. Therefore, a significant proportion of the variance of entropy should be attributed to the malignant process. We analyzed 112 patients with matched/paired synchronous metastases (SM#1 and SM#2) prospectively enrolled in the MOSCATO-01 clinical trial. Imaging features were extracted from Regions Of Interest (ROI) delineated on CT-scan using TexRAD software. We showed that synchronous metastasis entropy was correlated across 5 Spatial Scale Filters: Spearman's Rho ranged between 0.41 and 0.59 (P = 0.0001, Bonferroni correction). Multivariate linear analysis revealed that entropy in SM#1 is significantly associated with (i) primary tumor type; (ii) entropy in SM#2 (same malignant process); (iii) ROI area size; (iv) metastasis site; and (v) entropy in the psoas muscle (reference tissue). Entropy was a logarithmic function of ROI area in normal control tissues (aorta, psoas) and in mathematical models (P < 0.01). We concluded that entropy is a tumor-specific metric only if confounding factors are corrected.

Prediction of Recurrence by Preoperative Intratumoral FDG Uptake Heterogeneity in Endometrioid Endometrial Cancer

PURPOSE: To investigate the prognostic value of preoperative intratumoral ¹⁸F-FDG uptake heterogeneity (IFH) derived from positron emission tomography (PET)/computed tomography (CT) in patients with endometrioid endometrial cancer. METHODS: We retrospectively evaluated clinicopathological data from patients with pathologically proven endometrioid endometrial cancer who had undergone ¹⁸F-FDG PET/CT scans before surgery. Patients were divided into two groups according to their IFH. The main outcome measure was disease-free survival (DFS). RESULTS: Between January 2010 and January 2015, data from 72 patients were available for analysis. The median duration of DFS was 23 months (range, 6 to 57 months), and 4 (5.6%) patients experienced recurrence. There were significant differences in tumor size, IFH, and DFS between patients with and without recurrence. In regression analysis, high IFH value [P = .007, hazard ratio (HR) 2.545, 95% confidence interval (CI) 1.468-8.674] was the only independent risk factor for recurrence. The Kaplan-Meier survival graphs showed that DFS significantly differed in groups categorized based on IFH (P < .001, log-rank test). CONCLUSIONS: Preoperative IFH measured by ¹⁸F-FDG PET/CT was associated with recurrence of endometrioid endometrial cancer. The finding supports evidence that FDG-based heterogeneity can be a novel and useful predictor of endometrioid endometrial cancer recurrence.

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.

DiFranco M, Opposits G, K. Krizsan A, Garai I, Czernin J, Varga J, Tron L, Balkay L. A Study on the Basic Criteria for Selecting Heterogeneity Parameters of F18-FDG PET Images

Textural analysis might give new insights into the quantitative characterization of metabolically active tumors. More than thirty textural parameters have been investigated in former F18-FDG studies already. The purpose of the paper is to declare basic requirements as a selection strategy to identify the most appropriate heterogeneity parameters to measure textural features. Our predefined requirements were: a reliable heterogeneity parameter has to be volume independent, reproducible, and suitable for expressing quantitatively the degree of heterogeneity. Based on this criteria, we compared various suggested measures of homogeneity. A homogeneous cylindrical phantom was measured on three different PET/CT scanners using the commonly used protocol. In addition, a custom-made inhomogeneous tumor insert placed into the NEMA image quality phantom was imaged with a set of acquisition times and several different reconstruction protocols. PET data of 65 patients with proven lung lesions were retrospectively analyzed as well. Four heterogeneity parameters out of 27 were found as the most attractive ones to characterize the textural properties of metabolically active tumors in FDG PET images. These four parameters included Entropy, Contrast, Correlation, and Coefficient of Variation. These parameters were independent of delineated tumor volume (bigger than 25-30 ml), provided reproducible values (relative standard deviation< 10%), and showed high sensitivity to changes in heterogeneity. Phantom measurements are a viable way to test the reliability of heterogeneity parameters that would be of interest to nuclear imaging clinicians.

Value of 18F-FDG heterogeneity for discerning metastatic from benign lymph nodes in nasopharyngeal carcinoma patients with suspected recurrence

OBJECTIVE

This study investigated the value of fluorine-18 fludeoxyglucose (¹⁸F-FDG) heterogeneity as an indicator of metastatic lymph nodes (LNs) in patients with nasopharyngeal carcinoma (NPC). We further assessed whether addition of this parameter improves diagnostic performance beyond that provided by maximum standardized uptake value (SUV_max).

METHODS

We analyzed 74 LNs that were suspicious for metastasis. These LNs were measured for coefficient of variation (CV) of ¹⁸F-FDG uptake, which was used as a parameter for ¹⁸F-FDG heterogeneity.

RESULTS

Multivariate logistic regression analyses revealed that a high CV (hazard ratio, 20.97; 95% confidence interval, 2.26-194.62; p = 0.007) was an independent predictor of metastatic LNs. However, receiver-operating characteristic curve analysis (p = 0.278) and net reclassification (p = 0.539) were unable to show improved diagnostic performance by addition of CV to SUV_max.

CONCLUSION

High CV of ¹⁸F-FDG uptake is an independent risk factor for metastatic LNs in patients with NPC displaying suspicious LNs following treatment. Advances in knowledge: Heterogeneity of ¹⁸F-FDG uptake has a potential as a biomarker of metastatic LNs.

Applications and limitations of radiomics

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.

Comparison of Tumor Uptake Heterogeneity Characterization Between Static and Parametric 18F-FDG PET Images in Non-Small Cell Lung Cancer

(18)F-FDG PET is well established in the field of oncology for diagnosis and staging purposes and is increasingly being used to assess therapeutic response and prognosis. Many quantitative indices can be used to characterize tumors on (18)F-FDG PET images, such as SUVmax, metabolically active tumor volume (MATV), total lesion glycolysis, and, more recently, the proposed intratumor uptake heterogeneity features. Although most PET data considered within this context concern the analysis of activity distribution using images obtained from a single static acquisition, parametric images generated from dynamic acquisitions and reflecting radiotracer kinetics may provide additional information. The purpose of this study was to quantify differences between volumetry, uptake, and heterogeneity features extracted from static and parametric PET images of non-small cell lung carcinoma (NSCLC) in order to provide insight on the potential added value of parametric images.

METHODS

Dynamic (18)F-FDG PET/CT was performed on 20 therapy-naive NSCLC patients for whom primary surgical resection was planned. Both static and parametric PET images were analyzed, with quantitative parameters (MATV, SUVmax, SUVmean, heterogeneity) being extracted from the segmented tumors. Differences were investigated using Spearman rank correlation and Bland-Altman analysis.

RESULTS

MATV was slightly smaller on static images (-2% ± 7%), but the difference was not significant (P = 0.14). All derived parameters, including those characterizing tumor functional heterogeneity, correlated strongly between static and parametric images (r = 0.70-0.98, P ≤ 0.0006), exhibiting differences of less than ±25%.

CONCLUSION

In NSCLC primary tumors, parametric and static baseline (18)F-FDG PET images provided strongly correlated quantitative features for both standard (MATV, SUVmax, SUVmean) and heterogeneity quantification. Consequently, heterogeneity quantification on parametric images does not seem to provide significant complementary information compared with static SUV images.

Respiration-Averaged CT for Attenuation Correction of PET Images - Impact on PET Texture Features in Non-Small Cell Lung Cancer Patients

PURPOSE

We compared attenuation correction of PET images with helical CT (PET/HCT) and respiration-averaged CT (PET/ACT) in patients with non-small-cell lung cancer (NSCLC) with the goal of investigating the impact of respiration-averaged CT on 18F FDG PET texture parameters.

MATERIALS AND METHODS

A total of 56 patients were enrolled. Tumors were segmented on pretreatment PET images using the adaptive threshold. Twelve different texture parameters were computed: standard uptake value (SUV) entropy, uniformity, entropy, dissimilarity, homogeneity, coarseness, busyness, contrast, complexity, grey-level nonuniformity, zone-size nonuniformity, and high grey-level large zone emphasis. Comparisons of PET/HCT and PET/ACT were performed using Wilcoxon signed-rank tests, intraclass correlation coefficients, and Bland-Altman analysis. Receiver operating characteristic (ROC) curves as well as univariate and multivariate Cox regression analyses were used to identify the parameters significantly associated with disease-specific survival (DSS). A fixed threshold at 45% of the maximum SUV (T45) was used for validation.

RESULTS

SUV maximum and total lesion glycolysis (TLG) were significantly higher in PET/ACT. However, texture parameters obtained with PET/ACT and PET/HCT showed a high degree of agreement. The lowest levels of variation between the two modalities were observed for SUV entropy (9.7%) and entropy (9.8%). SUV entropy, entropy, and coarseness from both PET/ACT and PET/HCT were significantly associated with DSS. Validation analyses using T45 confirmed the usefulness of SUV entropy and entropy in both PET/HCT and PET/ACT for the prediction of DSS, but only coarseness from PET/ACT achieved the statistical significance threshold.

CONCLUSIONS

Our results indicate that 1) texture parameters from PET/ACT are clinically useful in the prediction of survival in NSCLC patients and 2) SUV entropy and entropy are robust to attenuation correction methods.

Development of a nomogram combining clinical staging with (18)F-FDG PET/CT image features in non-small-cell lung cancer stage I-III

PURPOSE

Our goal was to develop a nomogram by exploiting intratumour heterogeneity on CT and PET images from routine (18)F-FDG PET/CT acquisitions to identify patients with the poorest prognosis.

METHODS

This retrospective study included 116 patients with NSCLC stage I, II or III and with staging (18)F-FDG PET/CT imaging. Primary tumour volumes were delineated using the FLAB algorithm and 3D Slicer™ on PET and CT images, respectively. PET and CT heterogeneities were quantified using texture analysis. The reproducibility of the CT features was assessed on a separate test-retest dataset. The stratification power of the PET/CT features was evaluated using the Kaplan-Meier method and the log-rank test. The best standard metric (functional volume) was combined with the least redundant and most prognostic PET/CT heterogeneity features to build the nomogram.

RESULTS

PET entropy and CT zone percentage had the highest complementary values with clinical stage and functional volume. The nomogram improved stratification amongst patients with stage II and III disease, allowing identification of patients with the poorest prognosis (clinical stage III, large tumour volume, high PET heterogeneity and low CT heterogeneity).

CONCLUSION

Intratumour heterogeneity quantified using textural features on both CT and PET images from routine staging (18)F-FDG PET/CT acquisitions can be used to create a nomogram with higher stratification power than staging alone.

The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis

FDG-PET-derived textural features describing intra-tumor heterogeneity are increasingly investigated as imaging biomarkers. As part of the process of quantifying heterogeneity, image intensities (SUVs) are typically resampled into a reduced number of discrete bins. We focused on the implications of the manner in which this discretization is implemented. Two methods were evaluated: (1) R_D, dividing the SUV range into D equally spaced bins, where the intensity resolution (i.e. bin size) varies per image; and (2) R_B, maintaining a constant intensity resolution B. Clinical feasibility was assessed on 35 lung cancer patients, imaged before and in the second week of radiotherapy. Forty-four textural features were determined for different D and B for both imaging time points. Feature values depended on the intensity resolution and out of both assessed methods, R_B was shown to allow for a meaningful inter- and intra-patient comparison of feature values. Overall, patients ranked differently according to feature values–which was used as a surrogate for textural feature interpretation–between both discretization methods. Our study shows that the manner of SUV discretization has a crucial effect on the resulting textural features and the interpretation thereof, emphasizing the importance of standardized methodology in tumor texture analysis.

Do clinical, histological or immunohistochemical primary tumour characteristics translate into different (18)F-FDG PET/CT volumetric and heterogeneity features in stage II/III breast cancer?

PURPOSE

The aim of this retrospective study was to determine if some features of baseline (18)F-FDG PET images, including volume and heterogeneity, reflect clinical, histological or immunohistochemical characteristics in patients with stage II or III breast cancer (BC).

METHODS

Included in the present retrospective analysis were 171 prospectively recruited patients with stage II/III BC treated consecutively at Saint-Louis hospital. Primary tumour volumes were semiautomatically delineated on pretreatment (18)F-FDG PET images. The parameters extracted included SUVmax, SUVmean, metabolically active tumour volume (MATV), total lesion glycolysis (TLG) and heterogeneity quantified using the area under the curve of the cumulative histogram and textural features. Associations between clinical/histopathological characteristics and (18)F-FDG PET features were assessed using one-way analysis of variance. Areas under the ROC curves (AUC) were used to quantify the discriminative power of the features significantly associated with clinical/histopathological characteristics.

RESULTS

T3 tumours (>5 cm) exhibited higher textural heterogeneity in (18)F-FDG uptake than T2 tumours (AUC <0.75), whereas there were no significant differences in SUVmax and SUVmean. Invasive ductal carcinoma showed higher SUVmax values than invasive lobular carcinoma (p = 0.008) but MATV, TLG and textural features were not discriminative. Grade 3 tumours had higher FDG uptake (AUC 0.779 for SUVmax and 0.694 for TLG), and exhibited slightly higher regional heterogeneity (AUC 0.624). Hormone receptor-negative tumours had higher SUV values than oestrogen receptor-positive (ER-positive) and progesterone receptor-positive tumours, while heterogeneity patterns showed only low-level variation according to hormone receptor expression. HER-2 status was not associated with any of the image features. Finally, SUVmax, SUVmean and TLG significantly differed among the three phenotype subgroups (HER2-positive, triple-negative and ER-positive/HER2-negative BCs), but MATV and heterogeneity metrics were not discriminative.

CONCLUSION

SUV parameters, MATV and textural features showed limited correlations with clinical and histopathological features. The three main BC subgroups differed in terms of SUVs and TLG but not in terms of MATV and heterogeneity. None of the PET-derived metrics offered high discriminative power.

Correlation Between F-18 Fluorodeoxyglucose Positron Emission Tomography Metabolic Parameters and Dynamic Contrast-Enhanced MRI-Derived Perfusion Data in Patients with Invasive Ductal Breast Carcinoma

PURPOSE

The aim of this study was to establish possible relationships among the metabolic and vascular characteristics of breast cancer using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) imaging.

METHODS

Sixty-seven female patients with invasive ductal breast carcinoma (age 32-79 years) who underwent FDG PET/CT and DCE-MRI prior to cancer treatment were included in the study. The maximum standardized uptake value (SUVmax), metabolic tumor volume, total lesion glycolysis (TLG), and heterogeneity factor (HF) were derived from FDG PET/CT. The DCE-MRI parameters K trans, K ep, and V e were obtained for all tumors, and relationships between the metabolic and perfusion parameters were sought via Spearman's rank correlation analysis. The prognostic significance of clinicopathological and imaging parameters in terms of recurrence-free survival (RFS) was also evaluated.

RESULTS

No significant correlation between perfusion and metabolic parameters (p > 0.05) was found, except between SUVmax and V e (p = 0.001, rho = -0.391). Recurrence developed in 12 of the 67 patients (17.9 %, follow-up period 8-41 months). Age (p = 0.016) and HF (p = 0.027) were significant independent predictors of recurrence-free survival (RFS) upon multivariate analysis. The RFS of patients under 40 years of age was significantly poorer than that of older patients (p < 0.001). Survival of patients with more heterogeneous tumors (HF less than -0.12) was poorer than those with relatively homogenous tumors (p = 0.033).

CONCLUSIONS

Tumors with higher levels of glucose metabolism (SUVmax values) exhibited higher tumor cellularities (V e values). Also, of the various metabolic and perfusion parameters available, tumor heterogeneity measured via FDG PET/CT (HF) may be useful in predicting RFS in breast cancer patients.