Whole-body MRI: a practical guide for imaging patients with malignant bone disease

Deep bone oncology Diagnostics: Computed tomography based Machine learning for detection of bone tumors from breast cancer metastasis

Purpose

The objective of this study is to develop a novel diagnostic tool using deep learning and radiomics to distinguish bone tumors on CT images as metastases from breast cancer. By providing a more accurate and reliable method for identifying metastatic bone tumors, this approach aims to significantly improve clinical decision-making and patient management in the context of breast cancer.

Methods

This study utilized CT images of bone tumors from 178 patients, including 78 cases of breast cancer bone metastases and 100 cases of non-breast cancer bone metastases. The dataset was processed using the Medical Image Segmentation via Self-distilling TransUNet (MISSU) model for automated segmentation. Radiomics features were extracted from the segmented tumor regions using the Pyradiomics library, capturing various aspects of tumor phenotype. Feature selection was conducted using LASSO regression to identify the most predictive features. The model's performance was evaluated using ten-fold cross-validation, with metrics including accuracy, sensitivity, specificity, and the Dice similarity coefficient.

Results

The developed radiomics model using the SVM algorithm achieved high discriminatory power, with an AUC of 0.936 on the training set and 0.953 on the test set. The model's performance metrics demonstrated strong accuracy, sensitivity, and specificity. Specifically, the accuracy was 0.864 for the training set and 0.853 for the test set. Sensitivity values were 0.838 and 0.789 for the training and test sets, respectively, while specificity values were 0.896 and 0.933 for the training and test sets, respectively. These results indicate that the SVM model effectively distinguishes between bone metastases originating from breast cancer and other origins. Additionally, the average Dice similarity coefficient for the automated segmentation was 0.915, demonstrating a high level of agreement with manual segmentations.

Conclusion

This study demonstrates the potential of combining CT-based radiomics and deep learning for the accurate detection of bone metastases from breast cancer. The high-performance metrics indicate that this approach can significantly enhance diagnostic accuracy, aiding in early detection and improving patient outcomes. Future research should focus on validating these findings on larger datasets, integrating the model into clinical workflows, and exploring its use in personalized treatment planning.

Review of whole-body magnetic resonance imaging in multiple myeloma

Multiple Myeloma (MM) is a hematological malignancy affecting bone marrow, most frequently in elderly men. Imaging has a crucial role in this disease. Recently, whole-body MRI has been introduced and it has gained growing interest due to is high sensitivity and specificity in evaluating bone marrow involvement in MM. Diffusion-weighted sequences (DWI) with apparent diffusion coefficient (ADC) maps have emerged as the most sensitive technique to evaluate patients with MM, both in the pre- and post-treatment setting. Aim of this review is to provide an overview of the role and main imaging findings of whole-body MRI in MM.

A scan-specific quality control acquisition for clinical whole-body (WB) MRI protocols

Objective.Image quality in whole-body MRI (WB-MRI) may be degraded by faulty radiofrequency (RF) coil elements or mispositioning of the coil arrays. Phantom-based quality control (QC) is used to identify broken RF coil elements but the frequency of these acquisitions is limited by scanner and staff availability. This work aimed to develop a scan-specific QC acquisition and processing pipeline to detect broken RF coil elements, which is sufficiently rapid to be added to the clinical WB-MRI protocol. The purpose of this is to improve the quality of WB-MRI by reducing the number of patient examinations conducted with suboptimal equipment.Approach.A rapid acquisition (14 s additional acquisition time per imaging station) was developed that identifies broken RF coil elements by acquiring images from each individual coil element and using the integral body coil. This acquisition was added to one centre's clinical WB-MRI protocol for one year (892 examinations) to evaluate the effect of this scan-specific QC. To demonstrate applicability in multi-centre imaging trials, the technique was also implemented on scanners from three manufacturers.Main results. Over the course of the study RF coil elements were flagged as potentially broken on five occasions, with the faults confirmed in four of those cases. The method had a precision of 80% and a recall of 100% for detecting faulty RF coil elements. The coil array positioning measurements were consistent across scanners and have been used to define the expected variation in signal.Significance. The technique demonstrated here can identify faulty RF coil elements and positioning errors and is a practical addition to the clinical WB-MRI protocol. This approach was fully implemented on systems from two manufacturers and partially implemented on a third. It has potential to reduce the number of clinical examinations conducted with suboptimal hardware and improve image quality across multi-centre studies.

Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets

BACKGROUND

Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning.

METHODS

We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287).

RESULTS

The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 × 10-3 mm2/s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised.

CONCLUSION

Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.

Deep learning-based k-space-to-image reconstruction and super resolution for diffusion-weighted imaging in whole-spine MRI

PURPOSE

To assess the feasibility of deep learning (DL)-based k-space-to-image reconstruction and super resolution for whole-spine diffusion-weighted imaging (DWI).

METHOD

This retrospective study included 97 consecutive patients with hematologic and/or oncologic diseases who underwent DL-processed whole-spine MRI from July 2022 to March 2023. For each patient, conventional (CONV) axial single-shot echo-planar DWI (b = 50, 800 s/mm2) was performed, followed by DL reconstruction and super resolution processing. The presence of malignant lesions and qualitative (overall image quality and diagnostic confidence) and quantitative (nonuniformity [NU], lesion contrast, signal-to-noise ratio [SNR], contrast-to-noise ratio [CNR], and ADC values) parameters were assessed for DL and CONV DWI.

RESULTS

Ultimately, 67 patients (mean age, 63.0 years; 35 females) were analyzed. The proportions of vertebrae with malignant lesions for both protocols were not significantly different (P: [0.55-0.99]). The overall image quality and diagnostic confidence scores were higher for DL DWI (all P ≤ 0.002) than CONV DWI. The NU, lesion contrast, SNR, and CNR of each vertebral segment (P ≤ 0.04) but not the NU of the sacral segment (P = 0.51) showed significant differences between protocols. For DL DWI, the NU was lower, and lesion contrast, SNR, and CNR were higher than those of CONV DWI (median values of all segments; 19.8 vs. 22.2, 5.4 vs. 4.3, 7.3 vs. 5.5, and 0.8 vs. 0.7). Mean ADC values of the lesions did not significantly differ between the protocols (P: [0.16-0.89]).

CONCLUSIONS

DL reconstruction can improve the image quality of whole-spine diffusion imaging.

Magnetic Resonance Imaging as a Tool for Monitoring Intratibial Growth of Experimental Prostate Cancer Metastases in Mice

Bone metastases cause morbidity and mortality in several human cancer forms. Experimental models are used to unravel the mechanisms and identify possible treatment targets. The location inside the skeleton complicates accurate assessment. This study evaluates the performance of magnetic resonance imaging (MRI) of prostate cancer tumors growing intratibially in mice. MRI detected intratibial tumor lesions with a sensitivity and specificity of 100% and 89%, respectively, compared to histological evaluation. Location and some phenotypical features could also be readily detected with MRI. Regarding volume estimation, the correlation between MRI and histological assessment was high (p < 0.001, r = 0.936). In conclusion, this study finds MRI to be a reliable tool for in vivo, non-invasive, non-ionizing, real-time monitoring of intratibial tumor growth.

The use of MRI for the imaging of metastatic bone lesions

Skeletal metastatic disease accounts for significant overall morbidity in cancer patients. Accurate and accessible imaging forms an integral part of the investigation for patients with suspected or known skeletal metastatic disease; it is considered indispensable in making appropriate oncological treatment decisions. Magnetic resonance imaging (MRI) is a contemporary imaging modality that provides excellent spatial and contrast resolution for bone and soft tissues. Therefore, it is particularly useful for imaging patients suffering from metastatic skeletal disease. This review provides a fundamental overview of the physics and image generation of MRI. The most commonly used MRI sequences in the investigation of metastatic skeletal disease are also discussed. Additionally, a review of the pathophysiological basis of metastatic bone disease is presented, along with an introduction to the interpretation of MRI sequences obtained for metastatic bone disease. Finally, the strengths and drawbacks of MRI are considered in comparison to alternative imaging modalities for the investigation of this common and important oncological complication.

Whole-body MRI in children and adolescents: Can T2-weighted Dixon fat-only images replace standard T1-weighted images in the assessment of bone marrow?

OBJECTIVE

When performing whole-body MRI for bone marrow assessment in children, optimizing scan time is crucial. The aim was to compare T2 Dixon fat-only and TSE T1-weighted sequences in the assessment of bone marrow high signal areas seen on T2 Dixon water-only in healthy children and adolescents.

MATERIALS AND METHODS

Whole-body MRIs from 196 healthy children and adolescents aged 6 to 19 years (mean 12.0) were obtained including T2 TSE Dixon and T1 TSE-weighted images. Areas with increased signal on T2 Dixon water-only images were scored using a novel, validated scoring system and classified into "minor" or "major" findings according to size and intensity, where "major" referred to changes easily being misdiagnosed as pathology in a clinical setting. Areas were assessed for low signal on T2 Dixon fat-only images and, after at least three weeks to avoid recall bias, on the T1-weighted sequence by two experienced pediatric radiologists.

RESULTS

1250 high signal areas were evaluated on T2 Dixon water-only images. In 1159/1250 (92.7%) low signal was seen on both T2 Dixon fat-only and T1-weighted sequences while in 24 (1.9%) it was not present on either sequence, with an absolute agreement of 94.6%. Discordant findings were found in 67 areas, of which in 18 (1.5%) low signal was visible on T1-weighted images alone and in 49 (3.9%) on T2 Dixon fat-only alone. The overall kappa value between the two sequences was 0.39. The agreement was higher for major as compared to minor findings (kappa values of 0.69 and 0.29, respectively) and higher for the older age groups.

CONCLUSION

T2 Dixon fat-only can replace T1-weighted sequence on whole-body MRI for bone marrow assessment in children over the age of nine, thus reducing scan time.

Imaging in metastatic breast cancer, CT, PET/CT, MRI, WB-DWI, CCA: review and new perspectives

BACKGROUND

Breast cancer is the most frequent cancer in women and remains the second leading cause of death in Western countries. It represents a heterogeneous group of diseases with diverse tumoral behaviour, treatment responsiveness and prognosis. While major progress in diagnosis and treatment has resulted in a decline in breast cancer-related mortality, some patients will relapse and prognosis in this cohort of patients remains poor. Treatment is determined according to tumor subtype; primarily hormone receptor status and HER2 expression. Menopausal status and site of disease relapse are also important considerations in treatment protocols.

MAIN BODY

Staging and repeated evaluation of patients with metastatic breast cancer are central to the accurate assessment of disease extent at diagnosis and during treatment; guiding ongoing clinical management. Advances have been made in the diagnostic and therapeutic fields, particularly with new targeted therapies. In parallel, oncological imaging has evolved exponentially with the development of functional and anatomical imaging techniques. Consistent, reproducible and validated methods of assessing response to therapy is critical in effectively managing patients with metastatic breast cancer.

CONCLUSION

Major progress has been made in oncological imaging over the last few decades. Accurate disease assessment at diagnosis and during treatment is important in the management of metastatic breast cancer. CT (and BS if appropriate) is generally widely available, relatively cheap and sufficient in many cases. However, several additional imaging modalities are emerging and can be used as adjuncts, particularly in pregnancy or other diagnostically challenging cases. Nevertheless, no single imaging technique is without limitation. The authors have evaluated the vast array of imaging techniques - individual, combined parametric and multimodal - that are available or that are emerging in the management of metastatic breast cancer. This includes WB DW-MRI, CCA, novel PET breast cancer-epitope specific radiotracers and radiogenomics.

Whole body imaging in musculoskeletal oncology: when, why, and how

The use of whole-body imaging has become increasingly popular in oncology due to the possibility of evaluating total tumor burden with a single imaging study. This is particularly helpful in cases of widespread disease where dedicated regional imaging would make the evaluation more expensive, time consuming, and prone to more risks. Different techniques can be used, including whole-body MRI, whole-body CT, and PET-CT. Common indications include surveillance of cancer predisposing syndromes, evaluation of osseous metastases and clonal plasma cell disorders such as multiple myeloma, and evaluation of soft tissue lesions, including peripheral nerve sheath tumors. This review focuses on advanced whole-body imaging techniques and their main uses in musculoskeletal oncology.

Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice

PURPOSE

This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice.

METHODS

We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK.

RESULTS

The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions.

CONCLUSIONS

We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.