Journal of Bone Oncology最新文献

Rationale and Objectives

Radiomics has demonstrated potential in predicting the cytogenetic status of multiple myeloma (MM). However, the role of single-sequence radiomic nomograms in predicting the high-risk cytogenetic (HRC) status of MM remains underexplored. This study aims to develop and validate radiomic nomograms based on fat-suppressed T2-weighted images (T2WI-FS) for predicting MM’s HRC status, facilitating pre-treatment decision-making and prognostic assessment.

Materials and methods

A cohort of 159 MM patients was included, comprising 71 HRC and 88 non-HRC cases. Regions of interest within the most significant tumor lesions on T2WI-FS images were manually delineated, yielding 1688 features. Fourteen radiomic features were selected using 10-fold cross-validation, employing methods such as variance thresholds, Student’s t-test, redundancy analysis, and least absolute shrinkage and selection operator (LASSO). Logistic regression was utilized to develop three prediction models: a clinical model (model 1), a T2WI-FS radiomic model (model 2), and a combined clinical-radiomic model (model 3). Receiver operating characteristic (ROC) curves evaluated and compared the diagnostic performance of these models. Kaplan-Meier survival analysis and log-rank tests assessed the prognostic value of the radiomic nomograms.

Results

Models 2 and 3 demonstrated significantly greater diagnostic efficacy compared to model 1 (p < 0.05). The areas under the ROC curve for models 1, 2, and 3 were as follows: training set—0.650, 0.832, and 0.846; validation set—0.702, 0.730, and 0.757, respectively. Kaplan-Meier survival analysis indicated comparable prognostic values between the radiomic nomogram and MM cytogenetic status, with log-rank test results (p < 0.05) and concordance indices of 0.651 and 0.659, respectively; z-score test results were not statistically significant (p = 0.153). Additionally, Kaplan-Meier analysis revealed that patients in the non-HRC group, low-RS group, and aged ≤ 60 years exhibited the longest overall survival, while those in the HRC group, high-RS group, and aged > 60 years demonstrated the shortest overall survival (p = 0.004, Log-rank test).

Conclusions

Radiomic nomograms are capable of predicting the HRC status in MM. The cytogenetic status, radiomics model Rad score, and age collectively influence the overall survival of MM patients. These factors potentially contribute to pre-treatment clinical decision-making and prognostic assessment.

Objective

To develop a model combining clinical and radiomics features from CT scans for a preoperative noninvasive evaluation of Huvos grading of neoadjuvant chemotherapy in patients with HOS.

Methods

183 patients from center A and 42 from center B were categorized into training and validation sets. Features derived from radiomics were obtained from unenhanced CT scans.Following dimensionality reduction, the most optimal features were selected and utilized in creating a radiomics model through logistic regression analysis. Integrating clinical features, a composite clinical radiomics model was developed, and a nomogram was constructed. Predictive performance of the model was evaluated using ROC curves and calibration curves. Additionally, decision curve analysis was conducted to assess practical utility of nomogram in clinical settings.

Results

LASSO LR analysis was performed, and finally, three selected image omics features were obtained.Radiomics model yielded AUC values with a good diagnostic effect for both patient sets (AUCs: 0.69 and 0.68, respectively). Clinical models (including sex, age, pre-chemotherapy ALP and LDH levels, new lung metastases within 1 year after surgery, and incidence) performed well in terms of Huvos grade prediction, with an AUC of 0.74 for training set. The AUC for independent validation set stood at 0.70. Notably, the amalgamation of radiomics and clinical features exhibited commendable predictive prowess in training set, registering an AUC of 0.78. This robust performance was subsequently validated in the independent validation set, where the AUC remained high at 0.75. Calibration curves of nomogram showed that the predictions were in good agreement with actual observations.

Conclusion

Combined model can be used for Huvos grading in patients with HOS after preoperative chemotherapy, which is helpful for adjuvant treatment decisions.

Background

Osteosarcoma is the most common primary bone malignancy. It has classically been described as having a bimodal incidence by age. We sought to identify whether the bimodal incidence distribution still exists for osteosarcoma using the SEER and NIS databases.

Methods

Incidence rates of primary osteosarcoma between 2000–2021 were analyzed by age at diagnosis, year of occurrence, sex, and tumor site from the SEER Research Data, 17 Registries, Nov 2023 Sub (2000–2021). The incidence of cases in 35–64 year-olds and 65 and above was compared statistically to determine if there is an increased incidence in the later ages. Incidence of tumors of the long bones of the lower limbs from the NIS discharge database 2012–2019 was also analyzed for comparison.

Results

Overall, 5,129 cases of osteosarcoma were reported in the SEER database. Across the 22 calendar year span, a consistent first peak appeared in the second decade of life. There was no consistent second peak in the 35+ age group. There were 86,100 discharges with long bone tumors analyzed in the NIS data which exhibited nearly identical patterns.

Conclusions

Our analysis shows that the incidence of osteosarcoma is no longer bimodally distributed but rather unimodally distributed.

Obesity contributes to many cancers, including breast cancer and multiple myeloma, two cancers that often colonize the bone marrow (BM). Obesity often causes metabolic disease, but at the cellular level, there is uncertainty regarding how these shifts affect cellular phenotypes. Evidence is building that different types of fuel affect tumor cell metabolism, mitochondrial function, and signaling pathways differently, but tumor cells are also flexible and adapt to less-than ideal metabolic conditions, suggesting that single-pronged attacks on tumor metabolism may not be efficacious enough to be effective clinically. In this review, we describe the newest research at the pre-clinical level on how tumor metabolic pathways and energy sources affect cancer cells, with a special focus on multiple myeloma (MM). We also describe the known forward-feedback loops between bone marrow adipocytes (BMAds) and local tumor cells that support tumor growth. We describe how metabolic targets and transcription factors related to fatty acid (FA) oxidation, FA biosynthesis, glycolysis, oxidative phosphorylation (OXPHOS), and other pathways hold great promise as new vulnerabilities in myeloma cells. Specifically, we describe the importance of the acetyl-CoA synthetase (ACSS) and the acyl-CoA synthetase long chain (ACSL) families, which are both involved in FA metabolism. We also describe new data on the importance of lactate metabolism and lactate transporters in supporting the growth of tumor cells in a hypoxic BM microenvironment. We highlight new data showing the dependency of myeloma cells on the mitochondrial pyruvate carrier (MPC), which transports pyruvate to the mitochondria to fuel the tricarboxylic acid (TCA) cycle and electron transport chain (ETC), boosting OXPHOS. Inhibiting the MPC affects myeloma cell mitochondrial metabolism and growth, and synergizes with proteosome inhibitors in killing myeloma cells. We also describe how metabolic signaling pathways intersect established survival and proliferation pathways; for example, the fatty acid binding proteins (FABPs) affect MYC signaling and support growth, survival, and metabolism of myeloma cells. Our goal is to review the current the field so that novel, metabolic-focused therapeutic interventions and treatments can be imagined, developed and tested to decrease the burden of MM and related cancers.

The skeleton is a common site of cancer metastasis and malignancy with the resultant lesions often being incurable. Interactions between metastatic cancer cells and the bone microenvironment are critical for cancer cell survival, outgrowth, and progression. Mesenchymal Stem Cells (MSCs) are an essential stromal cell type in bone that are appreciated for their impacts on cancer-induced bone disease, however, newer evidence suggests that MSCs possess extensive roles in cancer-bone crosstalk, including cancer cell dormancy, metabolic demands, and immune-oncology. Emerging evidence has also identified the importance of MSC tissue source and the influence of ageing when studying MSC biology. Combining these considerations together with developing technologies such as spatial transcriptomics will contribute to defining the molecular mechanisms underlying complex stroma-cancer interactions in bone and assist with identification of therapeutically tractable targets.

Interleukin-1B (IL-1B) is a potent pro-inflammatory cytokine that plays multiple, pivotal roles, in the complex interplay between breast cancer cells and the bone microenvironment. IL-1B is involved in the growth of the primary tumours, regulation of inflammation within the tumour microenvironment, promotion of epithelial to mesenchymal transition (EMT), migration and invasion. Moreover, when breast cancer cells arrive in the bone microenvironment there is an upregulation of IL-1B which promotes the creation of a conducive niche for metastatic breast cancer cells as well as stimulating initiation of the vicious cycle of bone metastasis. Pre-clinical studies have demonstrated that inhibition of IL-1 signalling reduces bone metastasis from oestrogen receptor positive/triple-negative breast cancers in various mouse models. However, effects on primary tumours and soft tissue metastasis remain controversial with some studies showing increased tumour growth in these sites, whilst others show no effects. Notably, combining anti-IL-1 therapy with standard-of-care treatments, such as chemotherapy and immunotherapy, has been demonstrated to reduce the growth of primary tumours, bone metastasis, as well as metastatic outgrowth in other organs. This review focuses on the mechanisms by which IL-1B promotes breast cancer bone metastasis.

Objective

This study aims to explore an optimized deep-learning model for automatically classifying spinal osteosarcoma and giant cell tumors. In particular, it aims to provide a reliable method for distinguishing between these challenging diagnoses in medical imaging.

Methods

This research employs an optimized DenseNet model with a self-attention mechanism to enhance feature extraction capabilities and reduce misclassification in differentiating spinal osteosarcoma and giant cell tumors. The model utilizes multi-scale feature map extraction for improved classification accuracy. The paper delves into the practical use of Gradient-weighted Class Activation Mapping (Grad-CAM) for enhancing medical image classification, specifically focusing on its application in diagnosing spinal osteosarcoma and giant cell tumors. The results demonstrate that the implementation of Grad-CAM visualization techniques has improved the performance of the deep learning model, resulting in an overall accuracy of 85.61%. Visualizations of images for these medical conditions using Grad-CAM, with corresponding class activation maps that indicate the tumor regions where the model focuses during predictions.

Results

The model achieves an overall accuracy of 80% or higher, with sensitivity exceeding 80% and specificity surpassing 80%. The average area under the curve AUC for spinal osteosarcoma and giant cell tumors is 0.814 and 0.882, respectively. The model significantly supports orthopedics physicians in developing treatment and care plans.

Conclusion

The DenseNet-based automatic classification model accurately distinguishes spinal osteosarcoma from giant cell tumors. This study contributes to medical image analysis, providing a valuable tool for clinicians in accurate diagnostic classification. Future efforts will focus on expanding the dataset and refining the algorithm to enhance the model's applicability in diverse clinical settings.

Background

Primary malignant bone cancers have extremely low incidence, resulting in poor evaluation of their epidemiological characteristics. The objective of this study was to investigate trends in the incidence of primary malignant bone cancers and related mortality.

Materials and methods

Data from patients diagnosed with malignant bone cancers from 2000 to 2017 in the Surveillance Epidemiology and End Results database were retrospectively analyzed. Annual age-adjusted incidence and mortality were calculated, and the annual percentage change analyzed. Further, characteristics including patient age and sex, as well as the primary site and stage of different tumor types, were analyzed.

Results

The overall age-adjusted incidence rate of primary malignant bone cancers was 7.70 per million people per year, and incidence rates had increased in patients between 60 and 79 years old, or with tumor size ≥ 8 cm. The incidence of chordoma increased significantly (annual percentage change (APC), 3.0 % per year), while those of WHO grade I and II primary bone cancers decreased. During 2000–2017, the mortality rate attributable to malignant bone cancers across the entire United States was 4.41 per million people per year. A positive mortality trend was observed during the study period (APC = 0.7 %, 95 % confidence interval: 0.0 %–1.5 %). Patients with osteosarcoma, and those who were female or of white ethnicity showed significant increasing trends in mortality rate.

Conclusions

Different tumor types have variable epidemiological manifestations, in terms of incidence and mortality, and exhibited altered trends over recent years. These variables can provide guidance to inform allocation of medical resources.

Osteosarcoma (OS), a malignant tumor, originates from the bone marrow. Currently, treatment for OS remains limited, making it urgent to understand the immune response in the tumor microenvironment of patients with OS. A comprehensive bioinformatics analysis was performed, including cell clustering subgroups, differential expression genes screening, proposed temporal order, and genomic variant analysis on single-cell RNA-sequencing data, from ten pre-chemotherapy patients and eleven post-chemotherapy patients. Subsequently, we analyzed the differentiation trajectories of osteoblasts, osteoclasts, fibroblasts, myeloid cells, and tumor-infiltrating lymphocytes (TILs) in detail to compare the changes in cell proportions and differential genes pre- and post-chemotherapy. The nine cell types were identified, including fibroblasts, myeloid cells, osteoblasts, TILs, osteoclasts, proliferative osteoblasts, pericytes, endothelial cells, and B cells. Post-chemotherapy treatment, the proportions of myeloid cells and TILs in OS were declined, while the number of osteoblasts was elevated. Besides, a decrease was observed in CD74, FTL, FTH1, MT1X and MT2A, and an increase in PTN, COL3A1, COL1A1, IGFBP7 and FN1. Meanwhile, EMT, DNA repair, G2M checkpoint, and E2F targets were highly enriched post-chemotherapy. Furthermore, there was a down-regulation in the proportions of CD14 monocytes, Tregs, NK cells and CD1C-/CD141-DCs, while an up-regulation was observed in the proportions of SELENOP macrophages, IL7R macrophages, COL1A1 macrophages, CD1C DCs, CD4+ T cells and CD8+ T cells. Overall, these findings revealed changes in the tumor microenvironment of OS post-chemotherapy treatment, providing a new direction for investigating OS treatment.

Chondrosarcoma (CHS) is a malignant bone tumor with insensitivity to both radiotherapy and chemotherapy, and a high recurrence rate. However, the latent mechanism of recurrent CHS (Re-CHS) remains elusive. Here, we discovered that FBXO22 was highly expressed in clinical samples of Re-CHS. FBXO22 played a significant role in various cancers. However, the role of FBXO22 in Re-CHS remained unclear. Our research demonstrated that suppressing FBXO22 abated the proliferation and migration of CHS cells and facilitated their apoptosis. In addition, suppressing FBXO22 raised the expression of PD-L1 in Re-CHS. All these findings provide new evidence for using FBXO22 and PD-L1 as combined targets to prevent and treat Re-CHS, which may prove to be a novel strategy for immunotherapy of CHS, especially Re-CHS.