Imaging of bone metastasis: An update

Soft tissue tumor imaging in adults: whole-body staging in sarcoma, non-malignant entities requiring special algorithms, pitfalls and special imaging aspects. Guidelines 2024 from the European Society of Musculoskeletal Radiology (ESSR)

OBJECTIVES

The revised European Society of Musculoskeletal Radiology (ESSR) consensus guidelines on soft tissue tumor imaging represent an update of 2015 after technical advancements, further insights into specific entities, and revised World Health Organization (2020) and AJCC (2017) classifications. This second of three papers covers algorithms once histology is confirmed: (1) standardized whole-body staging, (2) special algorithms for non-malignant entities, and (3) multiplicity, genetic tumor syndromes, and pitfalls.

MATERIALS AND METHODS

A validated Delphi method based on peer-reviewed literature was used to derive consensus among a panel of 46 specialized musculoskeletal radiologists from 12 European countries. Statements that had undergone interdisciplinary revision were scored online by the level of agreement (0 to 10) during two iterative rounds, that could result in 'group consensus', 'group agreement', or 'lack of agreement'.

RESULTS

The three sections contain 24 statements with comments. Group consensus was reached in 95.8% and group agreement in 4.2%. For whole-body staging, pulmonary MDCT should be performed in all high-grade sarcomas. Whole-body MRI is preferred for staging bone metastasis, with [18F]FDG-PET/CT as an alternative modality in PET-avid tumors. Patients with alveolar soft part sarcoma, clear cell sarcoma, and angiosarcoma should be screened for brain metastases. Special algorithms are recommended for entities such as rhabdomyosarcoma, extraskeletal Ewing sarcoma, myxoid liposarcoma, and neurofibromatosis type 1 associated malignant peripheral nerve sheath tumors. Satisfaction of search should be avoided in potential multiplicity.

CONCLUSION

Standardized whole-body staging includes pulmonary MDCT in all high-grade sarcomas; entity-dependent modifications and specific algorithms are recommended for sarcomas and non-malignant soft tissue tumors.

CLINICAL RELEVANCE STATEMENT

These updated ESSR soft tissue tumor imaging guidelines aim to provide support in decision-making, helping to avoid common pitfalls, by providing general and entity-specific algorithms, techniques, and reporting recommendations for whole-body staging in sarcoma and non-malignant soft tissue tumors.

KEY POINTS

An early, accurate, diagnosis is crucial for the prognosis of patients with soft tissue tumors. These updated guidelines provide best practice expert consensus for standardized imaging algorithms, techniques, and reporting. Standardization can improve the comparability examinations and provide databases for large data analysis.

Whole-body PET/MRI to detect bone metastases: comparison of the diagnostic performance of the sequences

BACKGROUND

Whole-body positron emission tomography/magnetic resonance imaging (WB-PET/MRI) is increasingly used in the initial evaluation of oncology patients. The purpose of this study was to compare the diagnostic performance of WB MRI sequences, attenuation-corrected raw data positron-emission tomography (AC PET), and PET/MRI fused images to detect bone metastases.

PATIENTS AND METHODS

We included 765 consecutive oncologic patients who received WB-PET/MRI from between January 2017 and September 2023. The presence of bone metastases was assessed using the individual sequences by two radiologists. Interobserver agreement was calculated. A receiver operating characteristic (ROC) analysis was performed to assess the performance of each individual sequence and fused images.

RESULTS

Interobserver agreement for the detection of bone metastases on all sequences ranged from good to very good. The reading of the combination of MRI sequences with PET images showed statistically significantly better performance than the reading of individual MRI sequences and PET component only. Contrast enhanced T1 W Volume-interpolated breath-hold examination (CE T1W VIBE) sequence superior to PET for the detection of bone metastasis, but the statistical significance was not as high as with T1W-PET and CE T1W-PET fused images. The highest performance was achieved by the fused CE T1W-PET images with sensitivity of 100%, specificity of 92%, PPV of 96%, and NPV of 100%.

CONCLUSIONS

The combination of these CE T1W VIBE sequences with PET images have the highest diagnostic performance in detecting bone metastases in oncologic patients. This sequence should be integrated in WB-PET/MRI acquisitions for initial staging of cancer.

Clinical challenges in prostate cancer management: Metastatic bone-tropism and the role of circulating tumor cells

Prostate cancer (PCa) metastasis is one of the leading causes of cancer-related mortality in men worldwide, primarily due to its tendency to metastasize, with bones of axial skeleton being the favored target-site. PCa bone-metastasis (PCa-BM) presents significant clinical challenges, especially by the weakening of bone architecture, majorly due to the formation of osteoblastic lesions, leading to severe bone fractures. Another complication is that the disease predominantly affects elderly men. Further exploration is required to understand how the circulating tumor cells (CTCs) adapt to varying microenvironments and other biomechanical stresses encountered during the sequential steps in metastasis, finally resulting in colonization specifically in the bone niche, in PCa-BM. Deciphering how CTCs encounter and adapt to different biochemical, biomechanical and microenvironmental factors may improve the prospects of PCa diagnosis, development of novel therapeutics and prognosis. Moreover, the knowledge developed is expected to have broader implications for cancer research, paving the way for better therapeutic strategies and targeted therapies in the realm of metastatic cancer progression across different types of cancers. Our review begins with analyzing the challenges in PCa diagnosis, treatment and management, and delves into the formation and dynamics of CTCs, highlighting their role in PCa metastasis and bone-tropism. We further explore the pivotal role of individual factors in dictating the predisposition of tumors to metastasize to specific secondary sites, such as the noteworthy tendency of PCa bone-metastasis. Finally, we highlight the unresolved questions and potential avenues for further exploration.

Artificial intelligence in skeletal metastasis imaging

In the field of metastatic skeletal oncology imaging, the role of artificial intelligence (AI) is becoming more prominent. Bone metastasis typically indicates the terminal stage of various malignant neoplasms. Once identified, it necessitates a comprehensive revision of the initial treatment regime, and palliative care is often the only resort. Given the gravity of the condition, the diagnosis of bone metastasis should be approached with utmost caution. AI techniques are being evaluated for their efficacy in a range of tasks within medical imaging, including object detection, disease classification, region segmentation, and prognosis prediction in medical imaging. These methods offer a standardized solution to the frequently subjective challenge of image interpretation.This subjectivity is most desirable in bone metastasis imaging. This review describes the basic imaging modalities of bone metastasis imaging, along with the recent developments and current applications of AI in the respective imaging studies. These concrete examples emphasize the importance of using computer-aided systems in the clinical setting. The review culminates with an examination of the current limitations and prospects of AI in the realm of bone metastasis imaging. To establish the credibility of AI in this domain, further research efforts are required to enhance the reproducibility and attain robust level of empirical support.

AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images

Background/Objectives: Artificial intelligence (AI) is a promising tool for the enhancement of physician workflow and serves to further improve the efficiency of their diagnostic evaluations. This study aimed to assess the performance of an AI-based bone scan noise-reduction filter on noisy, low-count images in a routine clinical environment. Methods: The performance of the AI bone-scan filter (BS-AI filter) in question was retrospectively evaluated on 47 different patients' 99mTc-MDP bone scintigraphy image pairs (anterior- and posterior-view images), which were obtained in such a manner as to represent the diverse characteristics of the general patient population. The BS-AI filter was tested on artificially degraded noisy images-75, 50, and 25% of total counts-which were generated by binominal sampling. The AI-filtered and unfiltered images were concurrently appraised for image quality and contrast by three nuclear medicine physicians. It was also determined whether there was any difference between the lesions seen on the unfiltered and filtered images. For quantitative analysis, an automatic lesion detector (BS-AI annotator) was utilized as a segmentation algorithm. The total number of lesions and their locations as detected by the BS-AI annotator in the BS-AI-filtered low-count images was compared to the total-count filtered images. The total number of pixels labeled as lesions in the filtered low-count images in relation to the number of pixels in the total-count filtered images was also compared to ensure the filtering process did not change lesion sizes significantly. The comparison of pixel numbers was performed using the reduced-count filtered images that contained only those lesions that were detected in the total-count images. Results: Based on visual assessment, observers agreed that image contrast and quality were better in the BS-AI-filtered images, increasing their diagnostic confidence. Similarities in lesion numbers and sites detected by the BS-AI annotator compared to filtered total-count images were 89%, 83%, and 75% for images degraded to counts of 75%, 50%, and 25%, respectively. No significant difference was found in the number of annotated pixels between filtered images with different counts (p > 0.05). Conclusions: Our findings indicate that the BS-AI noise-reduction filter enhances image quality and contrast without loss of vital information. The implementation of this filter in routine diagnostic procedures reliably improves diagnostic confidence in low-count images and elicits a reduction in the administered dose or acquisition time by a minimum of 50% relative to the original dose or acquisition time.

[18F]FDG PET/CT performs better than CT in determining the bone biopsy site : randomized controlled clinical trial

BACKGROUND

Bone biopsy is the gold standard for diagnosing bone metastases. However, there is no clinical consensus regarding the optimal imaging test for determining the puncture site.

METHODS

We compared the performance of [18F]FDG PET/CT with CT in detecting bone metastases to achieve the highest biopsy efficiency. This registered prospective study enrolled 273 patients with bone lesions who were treated between January 2020 and March 2021. Patients were randomly assigned to undergo [18F]FDG PET/CT or CT to determine the puncture site before bone biopsy. The accuracy, sensitivity, specificity, second biopsy rate, diagnostic time and cost-effectiveness of the two imaging tests were compared.

RESULTS

The accuracy and sensitivity of [18F]FDG PET/CT group in detecting bone metastases were significantly higher than CT group(97.08% vs. 90.44%, 98.76% vs. 92.22%, P < 0.05). The second biopsy rate was significantly lower in the [18F]FDG PET/CT group (2.19% vs. 5.15%; P < 0.05). The diagnostic time of [18F]FDG PET/CT was 18.33 ± 2.08 days, which was significantly shorter than 21.28 ± 1.25 days in CT group ( P < 0.05). The cost of [18F] FDG PETCT is 11428.35 yuan, and the cost of CT is 13287.52 yuan; the incremental cost is 1859.17 yuan. SUVmax > 6.3 combined with ALP > 103 U/L showed a tendency for tumor metastases with an AUC of 0.901 (95%CI 0.839 to 0.946, P < 0.001).

CONCLUSION

[18F]FDG PET/CT has better performance and cost-effectiveness than CT in determining the bone biopsy site for suspect bone metastases.

TRIAL REGISTRATION

The prospective study was registered on 2018-04-10, and the registration number is ChiCTR1800015540.

Incidence, epidemiology, radiology, and classification of metastatic spine tumors: WFNS Spine Committee recommendations

Spinal metastasis (SMs) are the most encountered tumors of the spine. Their occurrence is expected roughly around one to two years after primary tumor diagnosis. Since the advent of Magnetic Resonance Imaging (MRI), this technology has been considered the gold standard for SMs diagnosis and characterization due to its precise ability to comprehend the rate of soft tissue compression/invasion (dural sac/nervous tissue), which is one of the main drivers of management strategies. Computed Tomography (CT) remains unbeatable when a detailed bony anatomy and instability assessment is searched. Nuclear medicine technologies may have a role in diagnosis when standard MR or CT study findings are inconclusive or when the extent of the systemic metastatic disease is studied. The main objective of this study is to offer an update on the epidemiology and radiology of spinal metastasis (SMs), endorsed by the WFNS Spine Committee. A systematic review of the literature of the last ten years gave 1531 results with "spine/spinal metastatic tumors/metastasis AND radiology OR imaging OR classification" as search strings in all fields, of which 56 papers were fully analyzed. The results were discussed and voted on in two consensus meetings of the WFNS (World Federation of Neurosurgical Societies) Spine Committee, reaching a positive or negative consensus using the Delphi method. The committee stated nine recommendations on two main topics: (1) Incidence and epidemiology of SMs; (2) Radiology and classifications of SMs.

Bone scintigraphy based on deep learning model and modified growth optimizer

Bone scintigraphy is recognized as an efficient diagnostic method for whole-body screening for bone metastases. At the moment, whole-body bone scan image analysis is primarily dependent on manual reading by nuclear medicine doctors. However, manual analysis needs substantial experience and is both stressful and time-consuming. To address the aforementioned issues, this work proposed a machine-learning technique that uses phases to detect Bone scintigraphy. The first phase in the proposed model is the feature extraction and it was conducted based on integrating the Mobile Vision Transformer (MobileViT) model in our framework to capture highly complex representations from raw medical imagery using two primary components including ViT and lightweight CNN featuring a limited number of parameters. In addition, the second phase is named feature selection, and it is dependent on the Arithmetic Optimization Algorithm (AOA) being used to improve the Growth Optimizer (GO). We evaluate the performance of the proposed FS model, named GOAOA using a set of 18 UCI datasets. Additionally, the applicability of Bone scintigraphy for real-world application is evaluated using 2800 bone scan images (1400 normal and 1400 abnormal). The results and statistical analysis revealed that the proposed GOAOA algorithm as an FS technique outperforms the other FS algorithms employed in this study.

Differentiated Thyroid Cancer after Thyroidectomy

The widespread use of neck US and other imaging modalities has contributed to a phenomenon of increased detection of differentiated thyroid cancer (DTC). Most of these cancers remain indolent, without requiring surgical intervention. Nonetheless, a subset of patients who require surgical treatment experience subsequent disease recurrence. This most commonly occurs in the cervical lymph nodes and thyroid bed, followed by distant metastasis to the lungs and bones. Because imaging is an integral part of postoperative surveillance, radiologists play a central role in the detection of recurrent tumors and in guiding treatment in these patients. US is the primary imaging modality used for postoperative evaluation. Other modalities such as CT, MRI, radioactive iodine imaging, and PET/CT aid in the accurate diagnosis and characterization of recurrent disease. Therefore, radiologists must have a thorough understanding of the utility of these imaging techniques and the imaging characteristics of recurrent DTC when interpreting these multimodality studies. The interpretation of imaging findings should also be correlated with the clinical status of patients and their biochemical markers to minimize interpretative errors. The authors present a broad overview of the postoperative evaluation of DTC, including its initial primary management, staging, and prognostication; clinical risk stratification for recurrent disease; postoperative surveillance with imaging and evaluation of biochemical markers; and management of recurrent DTC. Published under a CC BY 4.0 license. Supplemental material is available for this article.

Artificial intelligence and machine learning applications for the imaging of bone and soft tissue tumors

Recent advancements in artificial intelligence (AI) and machine learning offer numerous opportunities in musculoskeletal radiology to potentially bolster diagnostic accuracy, workflow efficiency, and predictive modeling. AI tools have the capability to assist radiologists in many tasks ranging from image segmentation, lesion detection, and more. In bone and soft tissue tumor imaging, radiomics and deep learning show promise for malignancy stratification, grading, prognostication, and treatment planning. However, challenges such as standardization, data integration, and ethical concerns regarding patient data need to be addressed ahead of clinical translation. In the realm of musculoskeletal oncology, AI also faces obstacles in robust algorithm development due to limited disease incidence. While many initiatives aim to develop multitasking AI systems, multidisciplinary collaboration is crucial for successful AI integration into clinical practice. Robust approaches addressing challenges and embodying ethical practices are warranted to fully realize AI's potential for enhancing diagnostic accuracy and advancing patient care.

Novel MRI scoring system to assess osseous malignancy in soft tissue sarcoma patients following radiotherapy

PURPOSE

Radiation induced changes in bone such as radiation osteitis are commonly identified on magnetic resonance imaging (MRI) in patients who receive radiotherapy for soft tissue sarcoma (STS) management. This study proposes a novel MRI scoring system to assess osseous lesions and predict potential for malignancy based on MRI score in STS patients who received radiotherapy.

METHODS

The MRI score consisted of 3 parameters: morphology, signal intensity, and progression. Interobserver reliability between MRI scores were analyzed with Cohen's kappa coefficient. Receiver operating curve (ROC) analysis was performed to determine a predictive MRI score for malignancy.

RESULTS

156 MRI's from 30 STS patients who received radiotherapy were retrospectively reviewed. Two (6.7 %) patients developed regional osseous metastasis identified on MRI. The kappa coefficient of the scoring system was 0.785 demonstrating substantial interobserver agreement (p < 0.001). ROC analysis demonstrated that the optimal cut-off value for malignant lesion on MRI was 5.5 (area under the curve 0.998; p < 0.001).

CONCLUSIONS

This novel MRI scoring system recommends lesions with a score of six and above to be biopsied to distinguish if malignancy is present. We believe this scoring system can be utilized by multidisciplinary care teams to guide clinical recommendations for patients with STS and MRI findings concerning for malignancy versus radiation induced changes.

Advances in Imaging for Metastatic Epidural Spinal Cord Compression: A Comprehensive Review of Detection, Diagnosis, and Treatment Planning

Metastatic epidural spinal cord compression (MESCC) is a critical oncologic emergency caused by the invasion of metastatic tumors into the spinal epidural space, leading to compression of the spinal cord. If not promptly diagnosed and treated, MESCC can result in irreversible neurological deficits, including paralysis, significantly impacting the patient's quality of life. Early detection and timely intervention are crucial to prevent permanent damage. Imaging modalities play a pivotal role in the diagnosis, assessment of disease extent, and treatment planning for MESCC. Magnetic resonance imaging (MRI) is the current gold standard due to its superior ability to visualize the spinal cord, epidural space, and metastatic lesions. However, recent advances in imaging technologies have enhanced the detection and management of MESCC. Innovations such as functional MRI, diffusion-weighted imaging (DWI), and hybrid techniques like positron emission tomography-computed tomography (PET-CT) and PET-MRI have improved the accuracy of diagnosis, particularly in detecting early metastatic changes and guiding therapeutic interventions. This review provides a comprehensive analysis of the evolution of imaging techniques for MESCC, focusing on their roles in detection, diagnosis, and treatment planning. It also discusses the impact of these advances on clinical outcomes and future research directions in imaging modalities for MESCC. Understanding these advancements is critical for optimizing the management of MESCC and improving patient prognosis.

Assessing visibility and bone changes of spinal metastases in CT scans: a comprehensive analysis across diverse cancer types

OBJECTIVES

To analyze the characteristics of spinal metastasis in CT scans across diverse cancers for effective diagnosis and treatment, using MRI as the gold standard.

METHODS

A retrospective study of 309 patients from four centers, who underwent concurrent CT and spinal MRI, revealing spinal metastasis, was conducted. Data on metastasis including total number, volume, visibility on CT (visible, indeterminate, or invisible), and type of bone change were collected. Through chi-square and Mann-Whitney U tests, we characterized the metastasis across diverse cancers and investigated the variation in the intra-individual ratio representing the percentage of lesions within each category for each patient.

RESULTS

Out of 3333 spinal metastases from 309 patients, 55% were visible, 21% indeterminate, and 24% invisible. Sclerotic and lytic lesions made up 47% and 43% of the visible and indeterminate categories, respectively. Renal cell carcinoma (RCC), prostate cancer, and hepatocellular carcinoma (HCC) had the highest visibility at 86%, 73%, and 67% (p < 0.0001, p < 0.0001, and p = 0.003), while pancreatic cancer was lowest at 29% (p < 0.0001). RCC and HCC had significantly high lytic metastasis ratios (interquartile range (IQR) 0.96-1.0 and 0.31-1.0, p < 0.001 and p = 0.005). Prostate cancer exhibited a high sclerotic lesion ratio (IQR 0.52-0.97, p < 0.001). About 39% of individuals had invisible or indeterminate lesions, even with a single visible lesion on CT. The intra-individual ratio for indeterminate and invisible metastases surpassed 18%, regardless of the maximal size of the visible metastasis.

CONCLUSIONS

This study highlights the variability in characteristics of spinal metastasis based on the primary cancer type through unique lesion-centric analysis.

Multiparametric Whole-Body MRI: A Game Changer in Metastatic Prostate Cancer

Prostate cancer ranks among the most prevalent tumours globally. While early detection reduces the likelihood of metastasis, managing advanced cases poses challenges in diagnosis and treatment. Current international guidelines support the concurrent use of 99Tc-Bone Scintigraphy and Contrast-Enhanced Chest and Abdomen CT for the staging of metastatic disease and response assessment. However, emerging evidence underscores the superiority of next-generation imaging techniques including PSMA-PET/CT and whole-body MRI (WB-MRI). This review explores the relevant scientific literature on the role of WB-MRI in metastatic prostate cancer. This multiparametric imaging technique, combining the high anatomical resolution of standard MRI sequences with functional sequences such as diffusion-weighted imaging (DWI) and bone marrow relative fat fraction (rFF%) has proved effective in comprehensive patient assessment, evaluating local disease, most of the nodal involvement, bone metastases and their complications, and detecting the increasing visceral metastases in prostate cancer. It does have the advantage of avoiding the injection of contrast medium/radionuclide administration, spares the patient the exposure to ionizing radiation, and lacks the confounder of FLARE described with nuclear medicine techniques. Up-to-date literature regarding the diagnostic capabilities of WB-MRI, though still limited compared to PSMA-PET/CT, strongly supports its widespread incorporation into standard clinical practice, alongside the latest nuclear medicine techniques.

Analysis of the effectiveness and efficiency of the Indonesian metastatic bone disease of unknown origin algorithm (INA-MBD): time to diagnosis and cost to diagnosis : Quasi-experimental study

Background

Patients with Metastatic Bone Disease (MBD) often present with complaints of pain and multiple osteolytic lesions findings. Remarkably, 30% of these cases exhibit an undetected primary lesion. Hence, categorizing them as MBD of unknown origin. The diagnostic processes of patients with MBD of unknown origin typically takes up to four months, rendering it as a catastrophic disease with the second-highest financial burden. Given its urgency, it is necessary to develop a evidence-based consensus for managing cases of MBD with an unknown origin.

Purpose

This study aimed to enhance the effectiveness and efficiency of treating patients with MBD of unknown origin through the application of the INA-MBD algorithm.

Research method

A quasi-experimental study with a pretest and post-test design was conducted with a total of 128 patients who met the inclusion and exclusion criteria. The patients were consecutively enrolled and categorized into two groups: the intervention group with the INA-MBD algorithm and the non-intervention group without the INA-MBD algorithm. The primary outcomes were the cost and time to diagnose MBD of unknown origin. The proposed measuring tool was the INA-MBD algorithm. Furthermore, for the cost-to-diagnosis variable, an extra measurement tool was used, which were summaries of the patient's medical bill including hospital stays and medical procedures. The analysis of data related to the time-to-diagnosis variable was conducted using the Log Rank regression test, and cost-to-diagnosis variable was carried out using co-variance test.