Differential diagnostic value of 18F-FDG PET/CT in osteolytic lesions

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.

Diagnostic value of machine learning-based computed tomography texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton

OBJECTIVES

To report the diagnostic performance of machine learning-based CT texture analysis for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton.

METHODS

We retrospectively evaluated 172 patients with multiple myeloma (n = 70) and osteolytic metastatic bone lesions (n = 102) in the peripheral skeleton. Two radiologists individually used two-dimensional manual segmentation to extract texture features from non-contrast CT. In total, 762 radiomic features were extracted. Dimension reduction was performed in three stages: inter-observer agreement analysis, collinearity analysis, and feature selection. Data were randomly divided into training (n = 120) and test (n = 52) groups. Eight machine learning algorithms were used for model development. The primary performance metrics were the area under the receiver operating characteristic curve and accuracy.

RESULTS

In total, 476 of the 762 texture features demonstrated excellent interobserver agreement. The number of features was reduced to 22 after excluding those with strong collinearity. Of these features, six were included in the machine learning algorithms using the wrapper-based classifier-specific technique. When all eight machine learning algorithms were considered for differentiating multiple myeloma from osteolytic metastatic bone lesions in the peripheral skeleton, the area under the receiver operating characteristic curve and accuracy were 0.776-0.932 and 78.8-92.3%, respectively. The k-nearest neighbors model performed the best, with the area under the receiver operating characteristic curve and accuracy values of 0.902 and 92.3%, respectively.

CONCLUSION

Machine learning-based CT texture analysis is a promising method for discriminating multiple myeloma from osteolytic metastatic bone lesions.

An MRI-based radiomics nomogram for differentiating spinal metastases from multiple myeloma

BACKGROUND

Spinal metastasis and multiple myeloma share many overlapping conventional radiographic imaging characteristics, thus, their differentiation may be challenging. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the differentiation of spinal metastasis and multiple myeloma.

MATERIALS AND METHODS

A total of 312 patients (training set: n = 146, validation set: n = 65, our center; external test set: n = 101, two other centers) with spinal metastasis (n = 196) and multiple myeloma (n = 116) were retrospectively enrolled. Demographics and MRI findings were assessed to build a clinical factor model. Radiomics features were extracted from MRI images. A radiomics model was constructed by the least absolute shrinkage and selection operator method. A radiomics nomogram combining the radiomics signature and independent clinical factors was constructed. And, one experienced radiologist reviewed the MRI images for all case. The diagnostic performance of the different models was evaluated by receiver operating characteristic curves.

RESULTS

A clinical factors model was built based on heterogeneous appearance and shape. Twenty-one features were used to build the radiomics signature. The area under the curve (AUC) values of the radiomics nomogram (0.853 and 0.762, respectively) were significantly higher than that of the clinical factor model (0.692 and 0.540, respectively) in both validation (p = 0.048) and external test (p < 0.001) sets. The AUC values of the radiomics nomogram model were higher than that of radiologist in training, validation and external test sets (all p < 0.05). Moreover, no significant difference in AUC values of radiomics nomogram model was found between the validation set and external test set (p = 0.212).

CONCLUSION

The radiomics nomogram can differentiate spinal metastasis and multiple myeloma with a moderate to good performance, and may be as a valuable method to assist in the clinical diagnosis and preoperative decision-making.

Hypermetabolic Subserosal Uterine Leiomyoma With Synchronous Atypical Multiple Myeloma Mimicking Ovarian Malignancy With Multiple Bone Metastases on 18F-FDG PET/CT

ABSTRACT

Subserosal cystic myoma with intense FDG uptake can resemble malignant cystic ovarian tumor and may lead to a false-positive diagnosis. A 49-year-old woman presented with chest pain for 4 months, and the initial chest CT showed multiple bone lesions. 18F-FDG PET/CT revealed not only multiple osteolytic lesions with FDG uptake but also a highly FDG-avid mass abutting the right side of the uterus. Ovarian malignancy with multiple bone metastases was considered initially. Subsequent biopsy confirmed multiple myeloma, and a subserosal uterine myoma was diagnosed by transvaginal sonography.

Modern imaging techniques for monitoring patients with multiple myeloma

Bone disease is a serious problem for many patients, often causing pathological bone fractures. A spinal collapse is a condition that affects the quality of life. It is the most frequent feature of multiple myeloma (MM), used in establishing the diagnosis and the need to start treatment. Because of these complications, imaging plays a vital role in the diagnosis and workup of myeloma patients. For many years, conventional radiography has been considered the gold standard for detecting bone lesions. The main reasons are the wide availability, low cost, the relatively low radiation dose and the ability of this imaging method to cover the entire bone system. Because of its incapacity to evaluate the response to therapy, more sophisticated techniques such as whole-body low-dose computed tomography (WBLDCT), whole-body magnetic resonance imaging, and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (PET/CT) are used. In this review, some of the advantages, indications and applications of the three techniques in managing patients with MM will be discussed. The European Myeloma Network guidelines have recommended WBLDCT as the imaging modality of choice for the initial assessment of MM-related lytic bone lesions. Magnetic resonance imaging is the gold-standard imaging modality for the detection of bone marrow involvement. One of the modern imaging methods and PET/CT can provide valuable prognostic data and is the preferred technique for assessing response to therapy.

Application of 18F-FDG PET-CT Images Based Radiomics in Identifying Vertebral Multiple Myeloma and Bone Metastases

Purpose

The purpose of this study was to explore the application of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) image radiomics in the identification of spine multiple myeloma (MM) and bone metastasis (BM), and whether this method could improve the classification diagnosis performance compared with traditional methods.

Methods

This retrospective study collected a total of 184 lesions from 131 patients between January 2017 and January 2021. All images were visually evaluated independently by two physicians with 20 years of experience through the double-blind method, while the maximum standardized uptake value (SUVmax) of each lesion was recorded. A total of 279 radiomics features were extracted from the region of interest (ROI) of CT and PET images of each lesion separately by manual method. After the reliability test, the least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation were used to perform dimensionality reduction and screening of features. Two classification models of CT and PET were derived from CT images and PET images, respectively and constructed using the multivariate logistic regression algorithm. In addition, the ComModel was constructed by combining the PET model and the conventional parameter SUVmax. The performance of the three classification diagnostic models, as well as the human experts and SUVmax, were evaluated and compared, respectively.

Results

A total of 8 and 10 features were selected from CT and PET images for the construction of radiomics models, respectively. Satisfactory performance of the three radiomics models was achieved in both the training and the validation groups (Training: AUC: CT: 0.909, PET: 0.949, ComModel: 0.973; Validation: AUC: CT: 0.897, PET: 0.929, ComModel: 0.948). Moreover, the PET model and ComModel showed significant improvement in diagnostic performance between the two groups compared to the human expert (Training: P = 0.01 and P = 0.001; Validation: P = 0.018 and P = 0.033), and no statistical difference was observed between the CT model and human experts (P = 0.187 and P = 0.229, respectively).

Conclusion

The radiomics model constructed based on 18F-FDG PET/CT images achieved satisfactory diagnostic performance for the classification of MM and bone metastases. In addition, the radiomics model showed significant improvement in diagnostic performance compared to human experts and PET conventional parameter SUVmax.

What's new in the management of metastatic bone disease

Metastatic bone disease is a common complication of malignant tumours. As cancer treatment improves the overall survival of patients, the number of patients with bone metastases is expected to increase. The treatments for bone metastases include surgery, radiotherapy, and bone-modifying agents, with patients with a short expected prognosis requiring less invasive treatment. Patients with metastatic bone disease show greatly varying primary tumour histology, metastases sites and numbers, and comorbidities. Therefore, randomised clinical trials are indispensable to compare treatments for these patients. This editorial reviews recent findings on the diagnosis and prognosis prediction and discusses the current treatment of patients with metastatic bone disease.