Journal of Bone Oncology最新文献

Groenlandicine is a protoberberine alkaloid isolated from Coptidis Rhizoma, a widely used traditional Chinese medicine known for its various biological activities. This study aims to validate groenlandicine’s effect on both cisplatin-sensitive and cisplatin-resistant osteosarcoma (OS) cells, along with exploring its potential molecular mechanism.

The ligand-based virtual screening (LBVS) method and molecular docking were employed to screen drugs. CCK-8 and FCM were used to measure the effect of groenlandicine on the OS cells transfected by lentivirus with over-expression or low-expression of TOP1. Cell scratch assay, CCK-8, FCM, and the EdU assay were utilized to evaluate the effect of groenlandicine on cisplatin-resistant cells. WB, immunofluorescence, and PCR were conducted to measure the levels of TOP1, Bcl-2, BAX, Caspase-9, and Caspase-3. Additionally, a subcutaneous tumor model was established in nude mice to verify the efficacy of groenlandicine.

Groenlandicine reduced the migration and proliferation while promoting apoptosis in OS cells, effectively damaging them. Meanwhile, groenlandicine exhibited weak cytotoxicity in 293T cells. Combination with cisplatin enhanced tumor-killing activity, markedly activating BAX, cleaved-Caspase-3, and cleaved-Caspase-9, while inhibiting the Bcl2 pathway in cisplatin-resistant OS cells. Moreover, the level of TOP1, elevated in cisplatin-resistant OS cells, was down-regulated by groenlandicine both in vitro and in vivo. Animal experiments confirmed that groenlandicine combined with cisplatin suppressed OS growth with lower nephrotoxicity.

Groenlandicine induces apoptosis and enhances the sensitivity of drug-resistant OS cells to cisplatin via the BAX/Bcl-2/Caspase-9/Caspase-3 pathway. Groenlandicine inhibits OS cells growth by down-regulating TOP1 level.Therefore, groenlandicine holds promise as a potential agent for reversing cisplatin resistance in OS treatment.

Objective

Variability exists in the subjective delineation of tumor areas in MRI scans of patients with spinal bone metastases. This research aims to investigate the efficacy of the nnUNet radiomics model for automatic segmentation and identification of spinal bone metastases.

Methods

A cohort of 118 patients diagnosed with spinal bone metastases at our institution between January 2020 and December 2023 was enrolled. They were randomly divided into a training set (n = 78) and a test set (n = 40). The nnUNet radiomics segmentation model was developed, employing manual delineations of tumor areas by physicians as the reference standard. Both methods were utilized to compute tumor area measurements, and the segmentation performance and consistency of the nnUNet model were assessed.

Results

The nnUNet model demonstrated effective localization and segmentation of metastases, including smaller lesions. The Dice coefficients for the training and test sets were 0.926 and 0.824, respectively. Within the test set, the Dice coefficients for lumbar and thoracic vertebrae were 0.838 and 0.785, respectively. Strong linear correlation was observed between the nnUNet model segmentation and physician-delineated tumor areas in 40 patients (R² = 0.998, P < 0.001).

Conclusions

The nnUNet model exhibits efficacy in automatically localizing and segmenting spinal bone metastases in MRI scans.

Objective

This study aims to explore the application of radiographic imaging and image recognition algorithms, particularly AlexNet and ResNet, in classifying malignancies for spinal bone tumors.

Methods

We selected a cohort of 580 patients diagnosed with primary spinal osseous tumors who underwent treatment at our hospital between January 2016 and December 2023, whereby 1532 images (679 images of benign tumors, 853 images of malignant tumors) were extracted from this imaging dataset. Training and validation follow a ratio of 2:1. All patients underwent X-ray examinations as part of their diagnostic workup. This study employed convolutional neural networks (CNNs) to categorize spinal bone tumor images according to their malignancy. AlexNet and ResNet models were employed for this classification task. These models were fine-tuned through training, which involved the utilization of a database of bone tumor images representing different categories.

Results

Through rigorous experimentation, the performance of AlexNet and ResNet in classifying spinal bone tumor malignancy was extensively evaluated. The models were subjected to an extensive dataset of bone tumor images, and the following results were observed. AlexNet: This model exhibited commendable efficiency during training, with each epoch taking an average of 3 s. Its classification accuracy was found to be approximately 95.6 %. ResNet: The ResNet model showed remarkable accuracy in image classification. After an extended training period, it achieved a striking 96.2 % accuracy rate, signifying its proficiency in distinguishing the malignancy of spinal bone tumors. However, these results illustrate the clear advantage of AlexNet in terms of proficiency despite a lower classification accuracy. The robust performance of the ResNet model is auspicious when accuracy is more favored in the context of diagnosing spinal bone tumor malignancy, albeit at the cost of longer training times, with each epoch taking an average of 32 s.

Conclusion

Integrating deep learning and CNN-based image recognition technology offers a promising solution for qualitatively classifying bone tumors. This research underscores the potential of these models in enhancing the diagnosis and treatment processes for patients, benefiting both patients and medical professionals alike. The study highlights the significance of selecting appropriate models, such as ResNet, to improve accuracy in image recognition tasks.

The gold standard treatment for primary bone sarcomas has been surgical resection with wide margins. However, there is no consensus regarding an optimal method for limb salvage reconstruction. In 2005, a technique for recycling resected bone after intraoperative treatment with liquid nitrogen was described. This technique has been reported to have a spectrum of advantages; nonetheless, acceptance for routine use has been limited, primarily for fear of local recurrence. A systematic search of the literature using PubMed and Google Scholar was performed. Full-text articles published between 2008 and 2023 were included if the study presented sufficient information regarding patients with a diagnosis of a primary bone sarcoma of the limbs or pelvis who had undergone reconstruction with liquid nitrogen recycled autografts. Sixteen studies that included 286 patients met criteria for analyses. Local recurrence occurred in 25 patients (8.7 %) during the first 4 years following limb salvage reconstruction using recycled autografts for treatment of primary bone sarcomas, which compares favorably to the 15–30 % local recurrence rates reported for patients undergoing limb salvage reconstruction using artificial implants. Systematic synthesis of the current evidence regarding local recurrence rates following use of the liquid nitrogen recycled autograft technique for limb salvage reconstruction after bone sarcoma resection suggests a favorable comparison to other limb salvage reconstruction options. As such, this technique warrants further consideration as a viable option for indicated patients based on relative advantages regarding costs, availability, and biologic and surgical reconstruction benefits.

Objective

Bone tumors, known for their infrequent occurrence and diverse imaging characteristics, require precise differentiation into benign and malignant categories. Existing diagnostic approaches heavily depend on the laborious and variable manual delineation of tumor regions. Deep learning methods, particularly convolutional neural networks (CNNs), have emerged as a promising solution to tackle these issues. This paper introduces an enhanced deep-learning model based on AlexNet to classify femoral bone tumors accurately.

Methods

This study involved 500 femoral tumor patients from July 2020 to January 2023, with 500 imaging cases (335 benign and 165 malignant). A CNN was employed for automated classification. The model framework encompassed training and testing stages, with 8 layers (5 Conv and 3 FC) and ReLU activation. Essential architectural modifications included Batch Normalization (BN) after the first and second convolutional filters. Comparative experiments with various existing methods were conducted to assess algorithm performance in tumor staging. Evaluation metrics encompassed accuracy, precision, sensitivity, specificity, F-measure, ROC curves, and AUC values.

Results

The analysis of precision, sensitivity, specificity, and F1 score from the results demonstrates that the method introduced in this paper offers several advantages, including a low feature dimension and robust generalization (with an accuracy of 98.34 %, sensitivity of 97.26 %, specificity of 95.74 %, and an F1 score of 96.37). These findings underscore its exceptional overall detection capabilities. Notably, when comparing various algorithms, they generally exhibit similar classification performance. However, the algorithm presented in this paper stands out with a higher AUC value (AUC=0.848), signifying enhanced sensitivity and more robust specificity.

Conclusion

This study presents an optimized AlexNet model for classifying femoral bone tumor images based on convolutional neural networks. This algorithm demonstrates higher accuracy, precision, sensitivity, specificity, and F1-score than other methods. Furthermore, the AUC value further confirms the outstanding performance of this algorithm in terms of sensitivity and specificity. This research makes a significant contribution to the field of medical image classification, offering an efficient automated classification solution, and holds the potential to advance the application of artificial intelligence in bone tumor classification.

Secondary metastases, accounting for 90 % of cancer-related deaths, pose a formidable challenge in cancer treatment, with bone being a prevalent site. Importantly, tumours may relapse, often in the skeleton even after successful eradication of the primary tumour, indicating that tumour cells may lay dormant within bone for extended periods of time. This review summarises recent findings in the mechanisms underlying tumour cell dormancy and the role of bone cells in this process. Hematopoietic stem cell (HSC) niches in bone provide a model for understanding regulatory microenvironments. Dormant tumour cells have been shown to exploit similar niches, with evidence suggesting interactions with osteoblast-lineage cells and other stromal cells via CXCL12-CXCR4, integrins, and TAM receptor signalling, especially through GAS6-AXL, led to dormancy, with exit of dormancy potentially regulated by osteoclastic bone resorption and neuronal signalling. A comprehensive understanding of dormant tumour cell niches and their regulatory mechanisms is essential for developing targeted therapies, a critical step towards eradicating metastatic tumours and stopping disease relapse.

The establishment and progression of bone metastatic breast cancer is supported by immunosuppressive myeloid populations that enable tumor growth by dampening the innate and adaptive immune response. Much work remains to understand how to target these tumor-myeloid interactions to improve treatment outcomes. Noncanonical Hedgehog signaling is an essential component of bone metastatic tumor progression, and prior literature suggests a potential role for Hedgehog signaling and its downstream effector Gli2 in modulating immune responses. In this work, we sought to identify if inhibition of noncanonical Hedgehog signaling alters the cytokine profile of osteolytic breast cancer cells and the subsequent communication between the tumor cells and myeloid cells. Examination of large patient databases revealed significant relationships between Gli2 expression and expression of markers of myeloid maturation and activation as well as cytokine expression. We found that treatment with HPI-1 reduced tumor cell expression of numerous cytokine genes, including CSF1, CSF2, and CSF3, as well as CCL2 and IL6. Secreted CSF-1 (M−CSF) was also reduced by treatment. Changes in tumor-secreted factors resulted in polarization of THP-1 monocytes toward a proinflammatory phenotype, characterized by increased CD14 and CD40 surface marker expression. We therefore propose M−CSF as a novel target of Hedgehog inhibition with potential future applications in altering the immune microenvironment in addition to its known roles in reducing tumor-induced bone disease.

Purpose

This study aims to devise and assess an automated measurement framework for lumbar pedicle screw parameters leveraging preoperative computed tomography (CT) scans and a deep learning algorithm.

Methods

A deep learning model was constructed employing a dataset comprising 1410 axial preoperative CT images of lumbar pedicles sourced from 282 patients. The model was trained to predict several screw parameters, including the axial angle and width of pedicles, the length of pedicle screw paths, and the interpedicular distance. The mean values of these parameters, as determined by two radiologists and one spinal surgeon, served as the reference standard.

Results

The deep learning model achieved high agreement with the reference standard for the axial angle of the left pedicle (ICC = 0.92) and right pedicle (ICC = 0.93), as well as for the length of the left pedicle screw path (ICC = 0.82) and right pedicle (ICC = 0.87). Similarly, high agreement was observed for pedicle width (left ICC = 0.97, right ICC = 0.98) and interpedicular distance (ICC = 0.91). Overall, the model’s performance paralleled that of manual determination of lumbar pedicle screw parameters.

Conclusion

The developed deep learning-based model demonstrates proficiency in accurately identifying landmarks on preoperative CT scans and autonomously generating parameters relevant to lumbar pedicle screw placement. These findings suggest its potential to offer efficient and precise measurements for clinical applications.

Background

The Radiotherapy IBandronate (RIB) trial compared single dose radiotherapy and a single infusion of ibandronate in 470 bisphosphonate naïve patients with metastatic bone pain from prostate cancer randomised into a non-inferiority two arm study. Results for the primary endpoint of pain score response at 4 weeks showed that the ibandronate arm was non-inferior to single dose radiotherapy.

Patients and method

In addition to pain assessments including analgesic use made at baseline, 4, 8, 12, 26 and 52 weeks, urine was collected at baseline, 4 and 12 weeks. It was subsequently analysed for urinary N-telopeptide (NTx) and cystatin C. Linear regression models were used to compare the continuous outcome measures for urinary markers within treatment arms and baseline measurements were included as covariates. Interaction terms were fitted to allow for cross-treatment group comparisons.

Results

The primary endpoint of the RIB trial was worst pain response at 4 weeks and there was no treatment difference seen. Urine samples and paired pain scores at 4 weeks were available for 273 patients (radiotherapy 168; ibandronate 159)

The baseline samples measured for the RIB trial had an average concentration of 193 nM BCE/mM creatinine (range of 7.3–1871) compared to the quoted normal range of 33 nM BCE/mM creatinine (3 to 63). In contrast the average value of Cystatin C was 66 ng/ml (ranges ND – 1120 ng/ml) compared to the quoted normal range of 62.9 ng/ml (ranges 12.6–188 ng/ml). A statistically significant reduction in NTx concentrations between baseline and 4 weeks was seen in the ibandronate arm but not in the radiotherapy arm. No correlation between pain response and urinary marker concentration was seen in either the ibandronate or radiotherapy cohort at any time point.

Conclusion

NTx was significantly raised compared to the normal range consistent with a role as a biomarker for bone metastases from prostate cancer. A significant reduction in NTx 4 weeks after ibandronate is consistent with its action in osteoclast inhibition which was not seen after radiotherapy implying a different mode of action for radiation. There was no correlation between bone biomarker levels and pain response.