The value of intratumoral heterogeneity of (18)F-FDG uptake to differentiate between primary benign and malignant musculoskeletal tumours on PET/CT

Whole-tumoral metabolic heterogeneity in 18F-FDG PET/CT is a novel prognostic marker for neuroblastoma

BACKGROUND

Neuroblastoma (NB) is a highly heterogeneous tumor, and more than half of newly diagnosed NB are associated with extensive metastases. Accurately characterizing the heterogeneity of whole-body tumor lesions remains clinical challenge. This study aims to quantify whole-tumoral metabolic heterogeneity (WMH) derived from whole-body tumor lesions, and investigate the prognostic value of WMH in NB.

METHODS

We retrospectively enrolled 95 newly diagnosed pediatric NB patients in our department. Traditional semi-quantitative PET/CT parameters including the maximum standardized uptake value (SUVmax), the mean standardized uptake value (SUVmean), the peak standardized uptake value (SUVpeak), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were measured. These PET/CT parameters were expressed as PSUVmax, PSUVmean, PSUVpeak, PMTV, PTLG for primary tumor, WSUVmax, WSUVmean, WSUVpeak, WMTV, WTLG for whole-body tumor lesions. The metabolic heterogeneity was quantified using the areas under the curve of the cumulative SUV-volume histogram index (AUC-CSH index). Intra-tumoral metabolic heterogeneity (IMH) and WMH were extracted from primary tumor and whole-body tumor lesions, respectively. The outcome endpoints were overall survival (OS) and progression-free survival (PFS). Survival analysis was performed utilizing the univariate and multivariate Cox proportional hazards regression. The optimal cut-off values for metabolic parameters were obtained by receiver operating characteristic curve (ROC).

RESULTS

During follow up, 27 (28.4%) patients died, 21 (22.1%) patients relapsed and 47 (49.5%) patients remained progression-free survival, with a median follow-up of 35.0 months. In survival analysis, WMTV and WTLG were independent indicators of PFS, and WMH was an independent risk factor of PFS and OS. However, IMH only showed association with PFS and OS. In addition to metabolic parameters, the International Neuroblastoma Staging System (INSS) was identified as an independent risk factor for PFS, and neuron-specific enolase (NSE) served as an independent predictor of OS.

CONCLUSION

WMH was an independent risk factor for PFS and OS, suggesting its potential as a novel prognostic marker for newly diagnosed NB patients.

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

Background

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

Methods

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

Results

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

Conclusion

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

Radiomics Analysis of [18F]-Fluorodeoxyglucose-Avid Thyroid Incidentalomas Improves Risk Stratification and Selection for Clinical Assessment

Background: [¹⁸F]-Fluorodeoxyglucose (FDG)-avid thyroid lesions incidentally detected on positron emission tomography/computed tomography (PET/CT) scans represent a tumor lesion in about 30% of cases. The present study evaluated the ability of PET metrics and radiomics features to predict final diagnosis of [¹⁸F]FDG thyroid incidentalomas (TIs). Methods: A total of 104 patients with 107 TIs were retrospectively studied; 30 nodules (28%) were diagnosed as malignant. After volumetric segmentation of each thyroid lesion, metabolic tumor volume (MTV), total lesion glycolysis (TLG), standardized uptake values (SUVs), and metabolic heterogeneity were estimated, and 107 radiomics features were extracted following a standard protocol. Results: MTV, TLG, SUVmax, SUVmean, and SUVpeak among functional PET parameters, and gray-level co-occurrence matrix (GLCM)_InverseDifferenceMoment, shape_Sphericity, GLCM_SumSquares, firstorder_Maximum2DDiameterSlice, firstorder_Energy, and GLCM_Contrast among nonredundant radiomics features, showed significantly different values between malignant and benign TIs (Mann-Whitney U-test, p < 0.01 for all). Univariate logistic regression revealed that these parameters demonstrated good ability to predict final diagnosis of TIs (p < 0.02 for all). Shape_Sphericity was the best predictor classifying 82% of TIs correctly (p < 0.0001). Only TLG, SUVmax, and shape_Sphericity retained significance (p < 0.0001) by multivariate analysis. Malignant lesion prevalence increased from 7% to 100% in accordance with the number (score, 0-3) of the three positive parameters present (χ² trend, p < 0.0001). A score of 0 excludes malignant TIs with a negative predictive value of 93%, while a score of 3 predicted malignancy with a positive predictive value of 100%. Conclusions: PET metrics and radiomics analysis can improve identification of [¹⁸F]FDG-avid TIs at high risk of malignancy. A model based on TLG, SUVmax, and shape_Sphericity may allow prediction of a final diagnosis, providing useful information for the management of TIs.

Differentiation of soft tissue and bone sarcomas from benign lesions utilizing 18F-FDG PET/CT-derived parameters

Background

Accurate differentiation between malignant and benign changes in soft tissue and bone lesions is essential for the prevention of unnecessary biopsies and surgical resection. Nevertheless, it remains a challenge and a standard diagnosis modality is urgently needed. The objective of this study was to evaluate the usefulness of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT-derived parameters to differentiate soft tissue sarcoma (STS) and bone sarcoma (BS) from benign lesions.

Methods

Patients who had undergone pre-treatment ¹⁸F-FDG PET/CT imaging and subsequent pathological diagnoses to confirm malignant (STS and BS, n = 37) and benign (n = 33) soft tissue and bone lesions were retrospectively reviewed. The tumor size, PET and low-dose CT visual characteristics, maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneous factor (HF) of each lesion were measured. Univariate and multivariate logistic regression analyses were conducted to determine the significant risk factors to distinguish sarcoma from benign lesions. To establish a regression model based on independent risk factors, and the receiver operating characteristic curves (ROCs) of individual parameters and their combination were plotted and compared. Conventional imaging scans were re-analyzed, and the diagnostic performance compared with the regression model.

Results

Univariate analysis results revealed that tumor size, SUVmax, MTV, TLG, and HF of ¹⁸F-FDG PET/CT imaging in the STS and BS group were all higher than in the benign lesions group (all P values were < 0.01). The differences in the visual characteristics between the two groups were also all statistically significant (P < 0.05). However, the multivariate regression model only included SUVmax and HF as independent risk factors, for which the odds ratios were 1.135 (95%CI: 1.026 ~ 1.256, P = 0.014) and 7.869 (95%CI: 2.119 ~ 29.230, P = 0.002), respectively. The regression model was constructed using the following expression: Logit (P) = − 2.461 + 0.127SUVmax + 2.063HF. The area under the ROC was 0.860, which was higher than SUVmax (0.744) and HF (0.790). The diagnostic performance of the regression model was superior to those of individual parameters and conventional imaging.

Conclusion

The regression model including SUVmax and HF based on ¹⁸F-FDG PET/CT imaging may be useful for differentiating STS and BS from benign lesions.

Imaging Analyses of Bone Tumors

Despite the evolution in imaging, especially the introduction of advanced imaging technologies, radiographs still are the key for the initial assessment of a bone tumor. Important aspects to be considered in radiographs are the location, shape and size or volume, margins, periosteal reaction, and internal mineralization of the tumor's matrix; careful evaluation of these may provide for accurate diagnosis in >80% of cases. Computed tomography and magnetic resonance imaging are often diagnostic for lesions with typical findings such as the nidus of osteoid osteoma and bone destruction such as in Ewing sarcoma and lymphoma that may be difficult to detect with radiographs; they may also be used for surgical planning. Magnetic resonance imaging accurately determines the intraosseous extent and articular and vascular involvement by the tumor. This article summarizes the diagnostic accuracy of imaging analyses in bone tumors and emphasizes the specific radiographic findings for optimal radiographic diagnosis of the patients with these tumors.

Metabolic heterogeneity on baseline 18FDG-PET/CT scan is a predictor of outcome in primary mediastinal B-cell lymphoma

An important unmet need in the management of primary mediastinal B-cell lymphoma (PMBCL) is to identify the patients for whom first-line therapy will fail to intervene before the lymphoma becomes refractory. High heterogeneity of intratumoral 18F-fluorodeoxyglucose (18FDG) uptake distribution on positron emission tomography/computed tomography (PET/CT) scans has been suggested as a possible marker of chemoresistance in solid tumors. In the present study, we investigated the prognostic value of metabolic heterogeneity (MH) in 103 patients with PMBCL prospectively enrolled in the International Extranodal Lymphoma Study Group (IELSG) 26 study, aimed at clarifying the role of PET in this lymphoma subtype. MH was estimated using the area under curve of cumulative standardized uptake value-volume histogram (AUC-CSH) method. Progression-free survival at 5 years was 94% vs 73% in low- and high-MH groups, respectively (P = .0001). In a Cox model of progression-free survival including dichotomized MH, metabolic tumor volume, total lesion glycolysis (TLG), international prognostic index, and tumor bulk (mediastinal mass > 10 cm), as well as age as a continuous variable, only TLG (P < .001) and MH (P < .001) retained statistical significance. Using these 2 features to construct a simple prognostic model resulted in early and accurate (positive predictive value, 89%; negative predictive value, ≥90%) identification of patients at high risk for progression at a point that would allow the use of risk-adapted treatments. This may provide an important opportunity for the design of future trials aimed at helping the minority of patients who harbor chemorefractory PMBCL. The study is registered at ClinicalTrials.gov as NCT00944567.

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.

Characterisation of malignant peripheral nerve sheath tumours in neurofibromatosis-1 using heterogeneity analysis of 18F-FDG PET

PURPOSE

Measurement of heterogeneity in 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) images is reported to improve tumour phenotyping and response assessment in a number of cancers. We aimed to determine whether measurements of 18F-FDG heterogeneity could improve differentiation of benign symptomatic neurofibromas from malignant peripheral nerve sheath tumours (MPNSTs).

METHODS

18F-FDG PET data from a cohort of 54 patients (24 female, 30 male, mean age 35.1 years) with neurofibromatosis-1 (NF1), and clinically suspected malignant transformation of neurofibromas into MPNSTs, were included. Scans were performed to a standard clinical protocol at 1.5 and 4 h post-injection. Six first-order [including three standardised uptake value (SUV) parameters], four second-order (derived from grey-level co-occurrence matrices) and four high-order (derived from neighbourhood grey-tone difference matrices) statistical features were calculated from tumour volumes of interest. Each patient had histological verification or at least 5 years clinical follow-up as the reference standard with regards to the characterisation of tumours as benign (n = 30) or malignant (n = 24).

RESULTS

There was a significant difference between benign and malignant tumours for all six first-order parameters (at 1.5 and 4 h; p < 0.0001), for second-order entropy (only at 4 h) and for all high-order features (at 1.5 h and 4 h, except contrast at 4 h; p < 0.0001-0.047). Similarly, the area under the receiver operating characteristic curves was high (0.669-0.997, p < 0.05) for the same features as well as 1.5-h second-order entropy. No first-, second- or high-order feature performed better than maximum SUV (SUVmax) at differentiating benign from malignant tumours.

CONCLUSIONS

18F-FDG uptake in MPNSTs is higher than benign symptomatic neurofibromas, as defined by SUV parameters, and more heterogeneous, as defined by first- and high-order heterogeneity parameters. However, heterogeneity analysis does not improve on SUVmax discriminative performance.