Baseline bone marrow ADC value of diffusion-weighted MRI: a potential independent predictor for progression and death in patients with newly diagnosed multiple myeloma

Quantitative Analysis of Whole-Body MRI for Accessing the Degree of Diffuse Infiltration Patterns and Identifying High Risk Cases of Newly Diagnosed Multiple Myeloma

BACKGROUND

Accurate identification of high-risk multiple myeloma (HRMM) is important for prognostication. The degree of diffuse infiltration patterns on magnetic resonance imaging (MRI) is associated with patient prognosis in multiple myeloma. However, objective indexes to determine the degree of diffuse infiltration patterns are unavailable.

PURPOSE

To investigate whether qualitative and quantitative evaluations of diffuse infiltration patterns on MRI could identify HRMM.

STUDY TYPE

Retrospective.

SUBJECTS

Totally, 180 patients (79 HRMM and 101 standard-risk MM) were assessed. The presence of del(17p), t(4;14), t(14;16), t(14;20), gain 1q, and/or p53 mutations was considered to indicate HRMM.

FIELD STRENGTH/SEQUENCE

3.0 T/diffusion-weighted whole-body imaging with background body signal suppression (DWIBS), modified Dixon chemical-shift imaging Quant (mDIXON Quant), and short TI inversion recovery (STIR).

ASSESSMENT

Qualitative analysis involved assessing the degree of diffuse marrow infiltration (mild, moderate, or severe), and quantitative analysis involved evaluating apparent diffusion coefficient (ADC), fat fraction (FF), and T2* values. Clinical data such as sex, age, hemoglobin, serum albumin, serum calcium, serum creatinine, serum lactate dehydrogenase, β2-microglobulin, and bone marrow plasma cells (BMPCs) were also included.

STATISTICAL TESTS

Univariate and multivariate analyses, receiver operating characteristic (ROC) curve. P < 0.05 was considered statistically significant.

RESULTS

The high-risk group had significantly higher ADC and T2* and lower FF compared with the standard-risk group. Multivariate analysis indicated BMPCs as a significant independent risk factor for HRMM (odds ratio (OR) = 1.019, 95% CI 1.004-1.033), while FF was a significant independent protective factor associated with HRMM (OR = 0.972, 95% CI 0.946-0.999). The combination of BMPCs and FF achieved the highest areas under the curve (AUC) of 0.732, with sensitivity and specificity of 70.9% and 68.3%, respectively.

DATA CONCLUSION

Compared with qualitative analysis, FF value was independently associated with HRMM. The quantitative features of diffuse marrow infiltration on MRI scans are more effective in detecting HRMM.

LEVEL OF EVIDENCE

3 TECHNICAL EFFICACY: Stage 2.

MRI-based bone marrow radiomics for predicting cytogenetic abnormalities in multiple myeloma

AIM

To develop a radiomics signature applied to magnetic resonance imaging (MRI)-images to predict cytogenetic abnormalities in multiple myeloma (MM).

MATERIALS AND METHODS

Patients with newly diagnosed MM were enrolled retrospectively from March 2019 to September 2022. They were categorised into the high-risk cytogenetics (HRC) group and standard-risk cytogenetics (SRC) group. The patients were allocated randomly at a ratio of 7:3 into training and validation cohorts. Volumes of interest (VOI) was drawn manually on fat suppression T2-weighted imaging (FS-T2WI) and copied to the same location of the T1-weighted imaging (T1WI) sequence. Radiomics features were extracted from two sequences and selected by reproducibility and redundant analysis. The least absolute shrinkage selection operation (LASSO) algorithm was applied to build the radiomics signatures. The performance of the radiomics signatures to distinguish HRC with SRC was evaluated by ROC curves. The area under the curve (AUC), specificity, and sensitivity were also calculated.

RESULTS

A total of 105 MM patients were enrolled in this study. The four and 11 most significant and relevant features were selected separately from T1WI and FS-T2WI sequences to build the radiomics signatures based on the training cohort. Compared to the T1WI sequence, the radiomics signature based on the FS-T2WI sequence achieved better performance with AUCs of 0.896 and 0.729 in the training and validation cohorts respectively. A sensitivity of 0.833, specificity of 0.667, and Youden index of 0.500 were achieved for the FS-T2WI radiomics signature in the validation cohort.

CONCLUSIONS

The radiomics signature based on MRI provides a non-invasive and convenient tool to predict cytogenetic abnormalities in MM patients.

3D CNN-based Deep Learning Model-based Explanatory Prognostication in Patients with Multiple Myeloma using Whole-body MRI

Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted MRI data using three-dimensional (3D) convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI, to predict prognosis and explore the factors involved in prediction. We retrospectively analyzed the MRI data of a total of 142 patients with MM obtained from two medical centers. We defined the occurrence of progressive disease after MRI evaluation within 12 months as a poor prognosis and constructed a 3D CNN-based deep learning model to predict prognosis. Images from 111 cases were used as the training and internal validation data; images from 31 cases were used as the external validation data. Internal validation of the AI model with stratified 5-fold cross-validation resulted in a significant difference in progression-free survival (PFS) between good and poor prognostic cases (2-year PFS, 91.2% versus [vs.] 61.1%, P = 0.0002). The AI model clearly stratified good and poor prognostic cases in the external validation cohort (2-year PFS, 92.9% vs. 55.6%, P = 0.004), with an area under the receiver operating characteristic curve of 0.804. According to Grad-CAM, the MRI signals of the spleen and bones of the vertebrae and pelvis contributed to prognosis prediction. This study is the first to show that image analysis of whole-body MRI using a 3D CNN without any other clinical data is effective in predicting the prognosis of patients with MM.

Applications of Diffusion-Weighted MRI to the Musculoskeletal System

Diffusion-weighted imaging (DWI) is an established MRI technique that can investigate tissue microstructure at the scale of a few micrometers. Musculoskeletal tissues typically have a highly ordered structure to fulfill their functions and therefore represent an optimal application of DWI. Even more since disruption of tissue organization affects its biomechanical properties and may indicate irreversible damage. The application of DWI to the musculoskeletal system faces application-specific challenges on data acquisition including susceptibility effects, the low T2 relaxation time of most musculoskeletal tissues (2-70 msec) and the need for sub-millimetric resolution. Thus, musculoskeletal applications have been an area of development of new DWI methods. In this review, we provide an overview of the technical aspects of DWI acquisition including diffusion-weighting, MRI pulse sequences and different diffusion regimes to study tissue microstructure. For each tissue type (growth plate, articular cartilage, muscle, bone marrow, intervertebral discs, ligaments, tendons, menisci, and synovium), the rationale for the use of DWI and clinical studies in support of its use as a biomarker are presented. The review describes studies showing that DTI of the growth plate has predictive value for child growth and that DTI of articular cartilage has potential to predict the radiographic progression of joint damage in early stages of osteoarthritis. DTI has been used extensively in skeletal muscle where it has shown potential to detect microstructural and functional changes in a wide range of muscle pathologies. DWI of bone marrow showed to be a valuable tool for the diagnosis of benign and malignant acute vertebral fractures and bone metastases. DTI and diffusion kurtosis have been investigated as markers of early intervertebral disc degeneration and lower back pain. Finally, promising new applications of DTI to anterior cruciate ligament grafts and synovium are presented. The review ends with an overview of the use of DWI in clinical routine. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 3.

Imaging of Multiple Myeloma: Present and Future

Multiple myeloma (MM) is the second most common adult hematologic malignancy, and early intervention increases survival in asymptomatic high-risk patients. Imaging is crucial for the diagnosis and follow-up of MM, as the detection of bone and bone marrow lesions often dictates the decision to start treatment. Low-dose whole-body computed tomography (CT) is the modality of choice for the initial assessment, and dual-energy CT is a developing technique with the potential for detecting non-lytic marrow infiltration and evaluating the response to treatment. Magnetic resonance imaging (MRI) is more sensitive and specific than 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) for the detection of small focal lesions and diffuse marrow infiltration. However, FDG-PET/CT is recommended as the modality of choice for follow-up. Recently, diffusion-weighted MRI has become a new technique for the quantitative assessment of disease burden and therapy response. Although not widespread, we address current proposals for structured reporting to promote standardization and diminish variations. This review provides an up-to-date overview of MM imaging, indications, advantages, limitations, and recommended reporting of each technique. We also cover the main differential diagnosis and pitfalls and discuss the ongoing controversies and future directions, such as PET-MRI and artificial intelligence.

Deep Learning for Automatic Bone Marrow Apparent Diffusion Coefficient Measurements From Whole-Body Magnetic Resonance Imaging in Patients With Multiple Myeloma: A Retrospective Multicenter Study

OBJECTIVES

Diffusion-weighted magnetic resonance imaging (MRI) is increasingly important in patients with multiple myeloma (MM). The objective of this study was to train and test an algorithm for automatic pelvic bone marrow analysis from whole-body apparent diffusion coefficient (ADC) maps in patients with MM, which automatically segments pelvic bones and subsequently extracts objective, representative ADC measurements from each bone.

MATERIALS AND METHODS

In this retrospective multicentric study, 180 MRIs from 54 patients were annotated (semi)manually and used to train an nnU-Net for automatic, individual segmentation of the right hip bone, the left hip bone, and the sacral bone. The quality of the automatic segmentation was evaluated on 15 manually segmented whole-body MRIs from 3 centers using the dice score. In 3 independent test sets from 3 centers, which comprised a total of 312 whole-body MRIs, agreement between automatically extracted mean ADC values from the nnU-Net segmentation and manual ADC measurements from 2 independent radiologists was evaluated. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated. In 56 patients with newly diagnosed MM who had undergone bone marrow biopsy, ADC measurements were correlated with biopsy results using Spearman correlation.

RESULTS

The ADC-nnU-Net achieved automatic segmentations with mean dice scores of 0.92, 0.93, and 0.85 for the right pelvis, the left pelvis, and the sacral bone, whereas the interrater experiment gave mean dice scores of 0.86, 0.86, and 0.77, respectively. The agreement between radiologists' manual ADC measurements and automatic ADC measurements was as follows: the bias between the first reader and the automatic approach was 49 × 10 -6 mm 2 /s, 7 × 10 -6 mm 2 /s, and -58 × 10 -6 mm 2 /s, and the bias between the second reader and the automatic approach was 12 × 10 -6 mm 2 /s, 2 × 10 -6 mm 2 /s, and -66 × 10 -6 mm 2 /s for the right pelvis, the left pelvis, and the sacral bone, respectively. The bias between reader 1 and reader 2 was 40 × 10 -6 mm 2 /s, 8 × 10 -6 mm 2 /s, and 7 × 10 -6 mm 2 /s, and the mean absolute difference between manual readers was 84 × 10 -6 mm 2 /s, 65 × 10 -6 mm 2 /s, and 75 × 10 -6 mm 2 /s. Automatically extracted ADC values significantly correlated with bone marrow plasma cell infiltration ( R = 0.36, P = 0.007).

CONCLUSIONS

In this study, a nnU-Net was trained that can automatically segment pelvic bone marrow from whole-body ADC maps in multicentric data sets with a quality comparable to manual segmentations. This approach allows automatic, objective bone marrow ADC measurements, which agree well with manual ADC measurements and can help to overcome interrater variability or nonrepresentative measurements. Automatically extracted ADC values significantly correlate with bone marrow plasma cell infiltration and might be of value for automatic staging, risk stratification, or therapy response assessment.

Diffusion-Weighted Imaging Distinguishes Between Osteomyelitis, Bone Marrow Edema, and Healthy Bone on Forefoot Magnetic Resonance Imaging

BACKGROUND

Diagnosis of osteomyelitis by imaging can be challenging. The feasibility of diffusion-weighted imaging (DWI) as ancillary sequence was evaluated in this study.

PURPOSE

To evaluate DWI for differentiation between osteomyelitis, bone marrow edema, and healthy bone on forefoot magnetic resonance imaging (MRI).

STUDY TYPE

Prospective.

SUBJECTS

A total of 60 consecutive patients undergoing forefoot MRI divided into three study groups (20 subjects each): osteomyelitis, bone marrow edema, and healthy bone.

FIELD STRENGTH/SEQUENCE

A 1.5T and 3T MRI scanners; readout-segmented multishot echo planar DWI.

ASSESSMENT

Two independent radiologists measured apparent diffusion coefficient (ADC) values within abnormal or healthy bone.

STATISTICAL TESTS

ADC values were compared between groups (pairwise t-test with Bonferroni-Holm correction for multiple testing). Intraclass correlation coefficient (ICC) was calculated to assess inter-reader agreement. Threshold ADC values were determined as the cutoffs that maximized the sum of sensitivity and specificity. Receiver operating characteristic (ROC) analysis was performed with statistical threshold of P < 0.05.

RESULTS

Inter-reader agreement was 0.92 in the healthy bone group and 0.78 in both the edema and osteomyelitis groups. Average ADC values were significantly different between groups: 1432 ± 222 × 10^-6  mm² /sec (osteomyelitis), 1071 ± 196 × 10^-6  mm² /sec (bone marrow edema), and 277 ± 89 × 10^-6  mm² /sec (healthy bone). A threshold ADC value of 534 × 10^-6  mm² /sec distinguishes between healthy and abnormal bone with specificity and sensitivity of 100% each. For distinction between osteomyelitis and bone marrow edema, two cutoff values were determined: a 95%-specificity cutoff indicating osteomyelitis (>1320 × 10^-6  mm² /sec) and a 95%-sensitivity cutoff indicating bone marrow edema (<1155 × 10^-6  mm² /sec). Diagnostic accuracy of 95% was achieved for 73% (29/40) of the subjects.

DATA CONCLUSION

DWI with ADC maps distinguishes between healthy and abnormal bone on forefoot MRI. Calculated cutoff values allow confirmation or exclusion of osteomyelitis in a high proportion of subjects.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Whole-body MRI in oncology: can a single anatomic T2 Dixon sequence replace the combination of T1 and STIR sequences to detect skeletal metastasis and myeloma?

OBJECTIVES

To compare the diagnostic accuracy of a single T2 Dixon sequence to the combination T1+STIR as anatomical sequences used for detecting tumoral bone marrow lesions in whole-body MRI (WB-MRI) examinations.

METHODS

Between January 2019 and January 2020, seventy-two consecutive patients (55 men, 17 women, median age = 66 years) with solid (prostate, breast, neuroendocrine) cancers at high risk of metastasis or proven multiple myeloma (MM) prospectively underwent a WB-MRI examination including coronal T1, STIR, T2 Dixon and axial diffusion-weighted imaging sequences. Two radiologists independently assessed the combination of T1+STIR sequences and the fat+water reconstructions from the T2 Dixon sequence. The reference standard was established by consensus reading of WB-MRI and concurrent imaging available at baseline and at 6 months. Repeatability and reproducibility of MRI scores (presence and semi-quantitative count of lesions), image quality (SNR: signal-to-noise, CNR: contrast-to-noise, CRR: contrast-to-reference ratios), and diagnostic characteristics (Se: sensitivity, Sp: specificity, Acc: accuracy) were assessed per-skeletal region and per-patient.

RESULTS

Repeatability and reproducibility were at least good regardless of the score, region, and protocol (0.67 ≤ AC1 ≤ 0.98). CRR was higher on T2 Dixon fat compared to T1 (p < 0.0001) and on T2 Dixon water compared to STIR (p = 0.0128). In the per-patient analysis, Acc of the T2 Dixon fat+water was higher than that of T1+STIR for the senior reader (Acc = +0.027 [+0.025; +0.029], p < 0.0001) and lower for the junior reader (Acc = -0.029 [-0.031; -0.027], p < 0.0001).

CONCLUSIONS

A single T2 Dixon sequence with fat+water reconstructions offers similar reproducibility and diagnostic accuracy as the recommended combination of T1+STIR sequences and can be used for skeletal screening in oncology, allowing significant time-saving.

KEY POINTS

• Replacement of the standard anatomic T1 + STIR WB-MRI protocol by a single T2 Dixon sequence drastically shortens the examination time without loss of diagnostic accuracy. • A protocol based on fat + water reconstructions from a single T2 Dixon sequence offers similar inter-reader agreement and a higher contrast-to-reference ratio for detecting lesions compared to the standard T1 + STIR protocol. • Differences in the accuracy between the two protocols are marginal (+ 3% in favor of the T2 Dixon with the senior reader; -3% against the T2 Dixon with the junior reader).

Review of diffusion-weighted imaging and dynamic contrast-enhanced MRI for multiple myeloma and its precursors (monoclonal gammopathy of undetermined significance and smouldering myeloma)

The last decades, increasing research has been conducted on dynamic contrast-enhanced and diffusion-weighted MRI techniques in multiple myeloma and its precursors. Apart from anatomical sequences which are prone to interpretation errors due to anatomical variants, other pathologies and subjective evaluation of signal intensities, dynamic contrast-enhanced and diffusion-weighted MRI provide additional information on microenvironmental changes in bone marrow and are helpful in the diagnosis, staging and follow-up of plasma cell dyscrasias. Diffusion-weighted imaging provides information on diffusion (restriction) of water molecules in bone marrow and in malignant infiltration. Qualitative evaluation by visually assessing images with different diffusion sensitising gradients and quantitative evaluation of the apparent diffusion coefficient are studied extensively. Dynamic contrast-enhanced imaging provides information on bone marrow vascularisation, perfusion, capillary resistance, vascular permeability and interstitial space, which are systematically altered in different disease stages and can be evaluated in a qualitative and a (semi-)quantitative manner. Both diffusion restriction and abnormal dynamic contrast-enhanced MRI parameters are early biomarkers of malignancy or disease progression in focal lesions or in regions with diffuse abnormal signal intensities. The added value for both techniques lies in better detection and/or characterisation of abnormal bone marrow otherwise missed or misdiagnosed on anatomical MRI sequences. Increased detection rates of focal lesions or diffuse bone marrow infiltration upstage patients to higher disease stages, provide earlier access to therapy and slower disease progression and allow closer monitoring of high-risk patients. Despite promising results, variations in imaging protocols, scanner types and post-processing methods are large, thus hampering universal applicability and reproducibility of quantitative imaging parameters. The myeloma response assessment and diagnosis system and the international myeloma working group provide a systematic multicentre approach on imaging and propose which parameters to use in multiple myeloma and its precursors in an attempt to overcome the pitfalls of dynamic contrast-enhanced and diffusion-weighted imaging.Single sentence summary statementDiffusion-weighted imaging and dynamic contrast-enhanced MRI provide important additional information to standard anatomical MRI techniques for diagnosis, staging and follow-up of patients with plasma cell dyscrasias, although some precautions should be taken on standardisation of imaging protocols to improve reproducibility and application in multiple centres.

MRI-Based Bone Marrow Radiomics Nomogram for Prediction of Overall Survival in Patients With Multiple Myeloma

Purpose

To develop and validate a radiomics nomogram for predicting overall survival (OS) in multiple myeloma (MM) patients.

Material and Methods

A total of 121 MM patients was enrolled and divided into training (n=84) and validation (n=37) sets. The radiomics signature was established by the selected radiomics features from lumbar MRI. The radiomics signature and clinical risk factors were integrated in multivariate Cox regression model for constructing radiomics nomogram to predict MM OS. The predictive ability and accuracy of the nomogram were evaluated by the index of concordance (C-index) and calibration curves, and compared with other four models including the clinical model, radiomics signature model, the Durie-Salmon staging system (D-S) and the International Staging System (ISS). The potential association between the radiomics signature and progression-free survival (PFS) was also explored.

Results

The radiomics signature, 1q21 gain, del (17p), and β2-MG≥5.5 mg/L showed significant association with MM OS. The predictive ability of radiomics nomogram was better than the clinical model, radiomics signature model, the D-S and the ISS (C-index: 0.793 vs. 0.733 vs. 0.742 vs. 0.554 vs. 0.671 in training set, and 0.812 vs. 0.799 vs.0.717 vs. 0.512 vs. 0.761 in validation set). The radiomics signature lacked the predictive ability for PFS (log-rank P=0.001 in training set and log-rank P=0.103 in validation set), whereas the 1-, 2- and 3-year PFS rates all showed significant difference between the high and low risk groups (P ≤ 0.05).

Conclusion

The MRI-based bone marrow radiomics may be an additional useful tool for MM OS prediction.

[Potential of radiomics and artificial intelligence in myeloma imaging : Development of automatic, comprehensive, objective skeletal analyses from whole-body imaging data]

CLINICAL/METHODICAL ISSUE

Multiple myeloma can affect the complete skeleton, which makes whole-body imaging necessary. With the current assessment of these complex datasets by radiologists, only a small part of the accessible information is assessed and reported.

STANDARD RADIOLOGICAL METHODS

Depending on the question and availability, computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET) is performed and the results are then visually examined by radiologists.

METHODOLOGICAL INNOVATIONS

A combination of automatic skeletal segmentation using artificial intelligence and subsequent radiomics analysis of each individual bone have the potential to provide automatic, comprehensive, and objective skeletal analyses.

PERFORMANCE

A few automatic skeletal segmentation algorithms for CT already show promising results. In addition, first studies indicate correlations between radiomics features of bone and bone marrow with established disease markers and therapy response.

ACHIEVEMENTS

Artificial intelligence (AI) and radiomics algorithms for automatic skeletal analysis from whole-body imaging are currently in an early phase of development.

3D MRI of the Hand and Wrist: Technical Considerations and Clinical Applications

In the last few years, major developments have been observed in the field of magnetic resonance imaging (MRI). Advances in both scanner hardware and software technologies have witnessed great leaps, enhancing the diagnostic quality and, therefore, the value of MRI. In musculoskeletal radiology, three-dimensional (3D) MRI has become an integral component of the diagnostic pathway at our institutions. This technique is particularly relevant in patients with hand and wrist symptoms, due to the intricate nature of the anatomical structures and the wide range of differential diagnoses for most presentations. We review the benefits of 3D MRI of the hand and wrist, commonly used pulse sequences, clinical applications, limitations, and future directions. We offer guidance for enhancing the image quality and tips for image interpretation of 3D MRI of the hand and wrist.

Diffusion-weighted magnetic resonance imaging of mandibular bone marrow: do apparent diffusion coefficient values of the cervical vertebrae and mandible correlate with age?

OBJECTIVES

The objective of this investigation was to assess the correlation between the mandible and cervical vertebrae bone marrow apparent diffusion coefficient (ADC), obtained by diffusion-weighted magnetic resonance imaging (DWI), with age; to verify the correlation between ADC values from the mandible and the cervical vertebrae; to describe and assess the differences between ADC values obtained from DWI examinations of distinct mandible areas as well as cervical vertebrae.

METHODS

Thirty imaging examinations with DWI for that included the mandible and C1, C2, C3, and C4 vertebrae in the same examination were included. ADC values were collected from 7 distinct areas in the mandible and the cervical vertebrae. Differences between ADC values and non-parametric correlations were performed.

RESULTS

A total of 270 regions were assessed. No significant difference was found between ADC values of all areas tested. An inverse correlation was found between C2, C3, and C4 vertebrae ADC values and age. The significant correlation of anatomic area ADC values and age were presented as graphics to verify if the linear trend of ADC values and age are in accordance with the literature CONCLUSIONS: The mandible area that most correlates with the cervical vertebrae, using ADC values, is the posterior trabecular area, below the inferior molars. Also, C2, C3, and C4 vertebrae ADC values inversely correlate with age, which demonstrates the bone qualitative changes in bone composition. ADC values may be useful for the qualitative assessment of bone quality to screen patients at osteoporosis risk.

Positron Emission Tomography (PET) Imaging of Multiple Myeloma in a Post-Treatment Setting

2-deoxy-2-[18F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (FDG PET/CT) has an established clinical value in the diagnosis and initial staging of multiple myeloma (MM). In the last ten years, a vast body of literature has shown that this tool can also be of high relevance for monitoring therapy responses, making it the recommended imaging approach in this field. Starting from the strengths and weaknesses of radiological imaging in MM, the present review aims to analyze FDG PET/CT’s current clinical value focusing on therapy response assessment and objective interpretation criteria for therapy monitoring. Given the potential occurrence of patients with MM showing non-FDG-avid bone disease, new opportunities can be provided by non-FDG PET tracers. Accordingly, the potential role of non-FDG PET tracers in this setting has also been discussed.