MRI in multiple myeloma : a pictorial review of diagnostic and post-treatment findings

Magnetic resonance imaging (MRI) is increasingly being used in the diagnostic work-up of patients with multiple myeloma. Since 2014, MRI findings are included in the new diagnostic criteria proposed by the International Myeloma Working Group. Patients with smouldering myeloma presenting with more than one unequivocal focal lesion in the bone marrow on MRI are considered having symptomatic myeloma requiring treatment, regardless of the presence of lytic bone lesions. However, bone marrow evaluation with MRI offers more than only morphological information regarding the detection of focal lesions in patients with MM. The overall performance of MRI is enhanced by applying dynamic contrast-enhanced MRI and diffusion weighted imaging sequences, providing additional functional information on bone marrow vascularization and cellularity. This pictorial review provides an overview of the most important imaging findings in patients with monoclonal gammopathy of undetermined significance, smouldering myeloma and multiple myeloma, by performing a 'total' MRI investigation with implications for the diagnosis, staging and response assessment. Main message aEuro cent Conventional MRI diagnoses multiple myeloma by assessing the infiltration pattern. aEuro cent Dynamic contrast-enhanced MRI diagnoses multiple myeloma by assessing vascularization and perfusion. aEuro cent Diffusion weighted imaging evaluates bone marrow composition and cellularity in multiple myeloma. aEuro cent Combined morphological and functional MRI provides optimal bone marrow assessment for staging. aEuro cent Combined morphological and functional MRI is of considerable value in treatment follow-up

An update on the management of sporadic desmoid-type fibromatosis: A European Consensus Initiative between Sarcoma PAtients EuroNet (SPAEN) and European Organization for Research and Treatment of Cancer (EORTC)/Soft Tissue and Bone Sarcoma Group (STBSG)

Desmoid-type fibromatosis is a rare and locally aggressive monoclonal, fibroblastic proliferation characterized by a variable and often unpredictable clinical course. Currently, there is no established or evidence-based treatment approach available for this disease. Therefore, in 2015 the European Desmoid Working Group published a position paper giving recommendations on the treatment of this intriguing disease. Here, we present an update of this consensus approach based on professionals' AND patients' expertise following a round table meeting bringing together sarcoma experts from the European Organization for Research and Treatment of Cancer/Soft Tissue and Bone Sarcoma Group with patients and patient advocates from Sarcoma PAtients EuroNet. In this paper, we focus on new findings regarding the prognostic value of mutational analysis in desmoid-type fibromatosis patients and new systemic treatment options

The patient perspective in the era of personalized medicine: What about scanxiety?

Frequency of scanning has accelerated in the era of personalized medicine and is related, but not restricted, to the exploding number of clinical trials for new cancer treatments. Particularly in drug trials, but also in clinical practice, patients are followed up by scans frequently, which may vary from every 6 to 12 weeks until progression. The authors aimed to raise awareness for this underreported but widely present "Sword of Damocles" scan-related issue also referred to as 'scanxiety.

Imaging in myeloma with focus on advanced imaging techniques.

In recent years, there have been major advances in the imaging of myeloma with whole body MRI incorporating diffusion-weighted imaging, emerging as the most sensitive modality. Imaging is now a key component in the work-up of patients with a suspected diagnosis of myeloma. The International Myeloma Working Group now specifies that more than one focal lesion on MRI or lytic lesion on whole body low-dose CT or fludeoxyglucose (FDG) PET/CT fulfil the criteria for bone damage requiring therapy. The recent National Institute for Health and Care Excellence myeloma guidelines recommend imaging in all patients with suspected myeloma. In addition, there is emerging data supporting the use of functional imaging techniques (WB-DW MRI and FDG PET/CT) to predict outcome and evaluate response to therapy. This review summarises the imaging modalities used in myeloma, the latest guidelines relevant to imaging and future directions

Detection of avascular necrosis on routine diffusion-weighted whole body MRI in patients with multiple myeloma.

OBJECTIVE:Current therapies for multiple myeloma, which include corticosteroids, increase risk of avascular necrosis. The aim of this study was to assess incidental detection of femoral head avascular necrosis on routine whole body MRI including diffusion weighted MRI. METHODS:All whole body MRI studies, performed on patients with known multiple myeloma between 1 January 2010 to 1 May 2017 were assessed for features of avascular necrosis. RESULTS:650 whole body MR scans were analysed. 15 patients (6.6%) had typical MR features of avascular necrosis: 2/15 (13.3%) had femoral head collapse, 4/15 (26.7%) had bilateral avascular necrosis and 9/15 (60%) were asymptomatic. CONCLUSION:This is the first report of avascular necrosis detected on routine whole body MRI in patients with multiple myeloma. Targeted review of femoral heads in multiple myeloma patients undergoing whole body MR is recommended, including in patients without symptoms. ADVANCES IN KNOWLEDGE:Whole body MR which includes diffusion-weighted MRI is extremely sensitive for evaluation of bone marrow. Although whole body MRI is primarily used for evaluation of multiple myeloma disease burden, it also presents an unique opportunity to evaluate the femoral heads for signs of avascular necrosis which can predate symptoms

Deep-learned estimation of uncertainty in measurements of apparent diffusion coefficient from whole-body diffusion-weighted MRI.

PURPOSE: To use deep learning to calculate the uncertainty in apparent diffusion coefficient (σADC) voxel-wise measurements to clinically impact the monitoring of treatment response and improve the quality of ADC maps. MATERIALS AND METHODS: We use a uniquely designed diffusion-weighted imaging (DWI) acquisition protocol that provides gold-standard measurements of σADC to train a deep learning model on two separate cohorts: 16 patients with prostate cancer and 28 patients with mesothelioma. Our network was trained with a novel cost function, which incorporates a perception metric and a b-value regularisation term, on ADC maps calculated by combinations of 2 or 3 b-values (e.g. 50/600/900, 50/900, 50/600, 600/900 s/mm2). We compare the accuracy of the deep-learning based approach for estimation of σADC with gold-standard measurements. RESULTS: The model accurately predicted the σADC for every b-value combination in both cohorts. Mean values of σADC within areas of active disease deviated from those measured by the gold-standard by 4.3% (range, 2.87-6.13%) for the prostate and 3.7% (range, 3.06-4.54%) for the mesothelioma cohort. We also showed that the model can easily be adapted for a different DWI protocol and field-of-view with only a few images (as little as a single patient) using transfer learning. CONCLUSION: Deep learning produces maps of σADC from standard clinical diffusion-weighted images (DWI) when 2 or more b-values are available

Virtual Biopsy in Soft Tissue Sarcoma. How Close Are We?

A shift in radiology to a data-driven specialty has been unlocked by synergistic developments in imaging biomarkers (IB) and computational science. This is advancing the capability to deliver "virtual biopsies" within oncology. The ability to non-invasively probe tumour biology both spatially and temporally would fulfil the potential of imaging to inform management of complex tumours; improving diagnostic accuracy, providing new insights into inter- and intra-tumoral heterogeneity and individualised treatment planning and monitoring. Soft tissue sarcomas (STS) are rare tumours of mesenchymal origin with over 150 histological subtypes and notorious heterogeneity. The combination of inter- and intra-tumoural heterogeneity and the rarity of the disease remain major barriers to effective treatments. We provide an overview of the process of successful IB development, the key imaging and computational advancements in STS including quantitative magnetic resonance imaging, radiomics and artificial intelligence, and the studies to date that have explored the potential biological surrogates to imaging metrics. We discuss the promising future directions of IBs in STS and illustrate how the routine clinical implementation of a virtual biopsy has the potential to revolutionise the management of this group of complex cancers and improve clinical outcomes

Deep Learning Framework with Multi-Head Dilated Encoders for Enhanced Segmentation of Cervical Cancer on Multiparametric Magnetic Resonance Imaging.

T2-weighted magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) are essential components of cervical cancer diagnosis. However, combining these channels for the training of deep learning models is challenging due to image misalignment. Here, we propose a novel multi-head framework that uses dilated convolutions and shared residual connections for the separate encoding of multiparametric MRI images. We employ a residual U-Net model as a baseline, and perform a series of architectural experiments to evaluate the tumor segmentation performance based on multiparametric input channels and different feature encoding configurations. All experiments were performed on a cohort of 207 patients with locally advanced cervical cancer. Our proposed multi-head model using separate dilated encoding for T2W MRI and combined b1000 DWI and apparent diffusion coefficient (ADC) maps achieved the best median Dice similarity coefficient (DSC) score, 0.823 (confidence interval (CI), 0.595-0.797), outperforming the conventional multi-channel model, DSC 0.788 (95% CI, 0.568-0.776), although the difference was not statistically significant (p > 0.05). We investigated channel sensitivity using 3D GRAD-CAM and channel dropout, and highlighted the critical importance of T2W and ADC channels for accurate tumor segmentation. However, our results showed that b1000 DWI had a minor impact on the overall segmentation performance. We demonstrated that the use of separate dilated feature extractors and independent contextual learning improved the model's ability to reduce the boundary effects and distortion of DWI, leading to improved segmentation performance. Our findings could have significant implications for the development of robust and generalizable models that can extend to other multi-modal segmentation applications

Repeatability of quantitative individual lesion and total disease multiparametric whole-body MRI measurements in prostate cancer bone metastases.

OBJECTIVES: To assess the repeatability of quantitative multiparametric whole-body MRI (mpWB-MRI) parameters in advanced prostate cancer (APC) bone metastases. METHODS: 1.5T MRI was performed twice on the same day in 10 APC patients. MpWB-MRI-included diffusion weighted imaging (DWI) and T1-weighted gradient-echo 2-point Dixon sequences. ADC and relative fat-fraction percentage (rFF%) maps were calculated, respectively. A radiologist delineated up to 10 target bone metastases per study. Means of ADC, b900 signal intensity(SI), normalised b900 SI, rFF% and maximum diameter (MD) for each target lesion and overall parameter averages across all targets per patient were recorded. The total disease volume (tDV in ml) was manually delineated on b900 images and mean global (g)ADC was derived. Bland-Altman analyses were performed with calculation of 95% repeatability coefficients (RC). RESULTS: Seventy-three individual targets (median MD 26 mm) were included. Lesion mean ADC RC was 12.5%, mean b900 SI RC 137%, normalised mean b900 SI RC 110%, rFF% RC 3.2 and target MD RC 5.5 mm (16.3%). Patient target lesion average mean ADC RC was 6.4%, b900 SI RC 104% and normalised mean b900 SI RC 39.6%. Target average rFF% RC was 1.8, average MD RC 1.3 mm (4.8%). tDV segmentation RC was 6.4% and mean gADC RC 5.3%. CONCLUSIONS: APC bone metastases' ADC, rFF% and maximum diameter, tDV and gADC show good repeatability. ADVANCES IN KNOWLEDGE: APC bone metastases' mean ADC and rFF% measurements of single lesions and global disease volumes are repeatable, supporting their potential role as quantitative biomarkers in metastatic bone disease

Developing and testing a robotic MRI/CT fusion biopsy technique using a purpose-built interventional phantom.

BACKGROUND: Magnetic resonance imaging (MRI) can be used to target tumour components in biopsy procedures, while the ability to precisely correlate histology and MRI signal is crucial for imaging biomarker validation. Robotic MRI/computed tomography (CT) fusion biopsy offers the potential for this without in-gantry biopsy, although requires development. METHODS: Test-retest T1 and T2 relaxation times, attenuation (Hounsfield units, HU), and biopsy core quality were prospectively assessed (January-December 2021) in a range of gelatin, agar, and mixed gelatin/agar solutions of differing concentrations on days 1 and 8 after manufacture. Suitable materials were chosen, and four biopsy phantoms were constructed with twelve spherical 1-3-cm diameter targets visible on MRI, but not on CT. A technical pipeline was developed, and intraoperator and interoperator reliability was tested in four operators performing a total of 96 biopsies. Statistical analysis included T1, T2, and HU repeatability using Bland-Altman analysis, Dice similarity coefficient (DSC), and intraoperator and interoperator reliability. RESULTS: T1, T2, and HU repeatability had 95% limits-of-agreement of 8.3%, 3.4%, and 17.9%, respectively. The phantom was highly reproducible, with DSC of 0.93 versus 0.92 for scanning the same or two different phantoms, respectively. Hit rate was 100% (96/96 targets), and all operators performed robotic biopsies using a single volumetric acquisition. The fastest procedure time was 32 min for all 12 targets. CONCLUSIONS: A reproducible biopsy phantom was developed, validated, and used to test robotic MRI/CT-fusion biopsy. The technique was highly accurate, reliable, and achievable in clinically acceptable timescales meaning it is suitable for clinical application