Bone Cancer Research Articles

Abstract A004: E2F5 conditional knockout in mammary epithelium drives organotropic metastasis in breast cancer

Abstract Breast cancer ranks as the most commonly diagnosed cancer in women with 90% of associated deaths caused by distant metastasis. The metastatic lesions in brain, lung, liver, bone and lymph nodes in human breast cancer are not arbitrary, but rather are a complex mechanism of extrinsic and intrinsic regulatory factors controlling organ-specific colonization. Specialized mechanisms driving bone, brain and lung cancer have been described. However, the studies of liver and lymph node metastatic drivers are limited despite the prevalence of metastases in these organs. A significant limitation is the lack of genetically engineered mouse models that develop mammary tumors that spontaneously metastasize to liver and lymph nodes. Our previous research identified the role for E2F5 in both mammary gland development and breast cancer. Loss of E2F5 resulted in tumorigenesis after a latency of one year and 70% of mice showed metastatic lesions in lung, lymph node and liver. Transplantation of these spontaneous tumors into FVB MMTV Cre mice recapitulated the metastatic pattern observed in the primary tumors. We propose that E2F5 regulates drivers of metastasis and potentiates organ tropism to lymph nodes and the liver. To address this hypothesis, we conducted serial transplantations of tumors originating from the lymph nodes and liver back into the mammary gland. This enrichment study led to a significant increase in organ-specific metastasis, with 60% and 77% of penetrance observed in two distinct mouse lineages with liver (LVM) and lymph node tropism (LNM). Immunohistochemical studies confirmed a complete epithelial-mesenchymal transition (EMT) phenotype in LVM that is conserved in the cell lines generated. LNM showed a partial EMT with a more epithelial phenotype in cell lines. We observed the presence of an immune enriched environment in primary and metastatic tumors in both lineages. Bulk-RNA sequencing studies uncovered the presence of unique organotropic pathways associated with liver and lymph node metastasis. We observed enriched pathways from the primary tumor as major component of our organotropic drivers with a clear distinction between liver and lymph node enriched lineages. Additionally, a gradual acquisition of distant organ-specific markers and signaling pathways suggests that phenotypic mimicry and tumor plasticity are key for successful events of colonization to liver and lymph nodes in our model. With these results, we unravel potential mechanisms responsible for the organotropic metastases in breast cancer under the absence of the transcription factor E2F5. Citation Format: Jesus A. Garcia Lerena, Briana To, Jing-Ru Jhan, Eran Andrechek. E2F5 conditional knockout in mammary epithelium drives organotropic metastasis in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A004.

BCDNet: A Deep Learning Model with Improved Convolutional Neural Network for Efficient Detection of Bone Cancer Using Histology Images

Among the several types of cancer, bone cancer is the most lethal prevailing in the world. Its prevention is better than cure. Besides early detection of bone cancer has potential to have medical intervention to prevent spread of malignant cells and help patients to recover from the disease. Many medical imaging modalities such as histology, histopathology, radiology, X-rays, MRIs, CT scans, phototherapy, PET and ultrasounds are being used in bone cancer detection research. However, hematoxylin and eosin stained histology images are found crucial for early diagnosis of bone cancer. Existing Convolutional Neural Network (CNN) based deep learning techniques are found suitable for medical image analytics. However, the models are prone to mediocre performance unless configured properly with empirical study. Within this article, we suggested a framework centered on deep learning for automatic bone cancer detection. We also proposed a CNN variant known as Bone Cancer Detection Network (BCDNet) which is configured and optimized for detection of a common kind of bone cancer named Osteosarcoma. An algorithm known as Learning based Osteosarcoma Detection (LbOD). It exploits BCDNet model for both binomial and multi-class classification. Osteosarcoma-Tumor-Assessment is the histology dataset used for our empirical study. Our the outcomes of the trial showed that BCDNet outperforms baseline models with 96.29% accuracy in binary classification and 94.69% accuracy in multi-class classification.

Milk-Derived Extracellular Vesicles: A Novel Perspective on Comparative Therapeutics and Targeted Nanocarrier Application

Milk-derived extracellular vesicles (mEVs) are emerging as promising therapeutic candidates due to their unique properties and versatile functions. These vesicles play a crucial role in immunomodulation by influencing macrophage differentiation and cytokine production, potentially aiding in the treatment of conditions such as bone loss, fibrosis, and cancer. mEVs also have the capacity to modulate gut microbiota composition, which may alleviate the symptoms of inflammatory bowel diseases and promote intestinal barrier integrity. Their potential as drug delivery vehicles is significant, enhancing the stability, solubility, and bioavailability of anticancer agents while supporting wound healing and reducing inflammation. Additionally, bovine mEVs exhibit anti-aging properties and protect skin cells from UV damage. As vaccine platforms, mEVs offer advantages including biocompatibility, antigen protection, and the ability to elicit robust immune responses through targeted delivery to specific immune cells. Despite these promising applications, challenges persist, including their complex roles in cancer, effective antigen loading, regulatory hurdles, and the need for standardized production methods. Achieving high targeting specificity and understanding the long-term effects of mEV-based therapies are essential for clinical translation. Ongoing research aims to optimize mEV production methods, enhance targeting capabilities, and conduct rigorous preclinical and clinical studies. By addressing these challenges, mEVs hold the potential to revolutionize vaccine development and targeted drug delivery, ultimately improving therapeutic outcomes across various medical fields.

Prospective Screening of Cancer Syndromes in Patients with Mesenchymal Tumors

Background: The etiology of most mesenchymal tumors is unknown, and knowledge about syndromes with an increased risk of tumors in bone or soft tissue is sparse. Methods: We present a prospective germline analysis of 312 patients with tumors suspected of being sarcomas at a tertiary sarcoma center. Germline and tumor whole genome sequencing, tumor transcriptome, and methylome analyses were performed. Results: Germline pathogenic or likely pathogenic variants associated with an increased risk of tumors were detected in 24 patients (8%), of which 11 (4%) harbored a detectable second hit in the tumor. Second hits were confirmed in genes with (NF1, RB1, TP53, EXT2, and SDHC) and without (ATM, CDC73, MLH1, MSH6, POLG, and KCNQ1) known association with mesenchymal tumor predisposition. Sarcomas from two Lynch syndrome patients showed mismatch repair deficiency, predicting a treatment response to immune checkpoint inhibitors (Level 1 biomarker according to the FDA (Federal Drug Administration) and ESMO (European Society for Medical Oncology)). None of the three CHEK2 carriers had a second hit in the tumor, suggesting a weak link to sarcoma. Conclusions: We conclude that second-hit analyses can be used in standard of care to identify syndrome-related tumors. This approach can help distinguish true manifestations of tumor syndromes from unrelated germline findings and enhance the understanding of germline predisposition in soft tissue tumors. Prospective screening using germline whole genome sequencing should be considered when comprehensive somatic sequencing is introduced into clinical practice.

Abstract A028: Precise monitoring of osteosarcoma patients by noninvasive assessment of matrix metalloproteinases activities in plasma extracellular vesicles

Abstract Osteosarcoma (OS) is the most prevalent bone cancer in children, characterized by aggressive primary tumors and high metastatic potential. Current treatment protocols involve a combination of neoadjuvant chemotherapy, surgery, and adjuvant chemotherapy, tailored based on histological grade and metastatic risk. Identifying metastatic lesions through routine imaging significantly impacts treatment outcomes, highlighting the need for innovative diagnostic tools for timely monitoring. Noninvasive in vitro diagnostics, analyzing circulating tumor components like tumor-derived extracellular vesicles (EVs), would offer promise for monitoring treatment responses in pediatric sarcomas. Matrix metalloproteinases (MMPs) play a crucial role in tumor progression by degradation of the extracellular matrix; thus, recent research has focused on understanding their functional roles by profiling their proteolytic activities. Tumor EVs are crucial in transporting MMPs to facilitate invasion and metastasis, making them suitable surrogate for noninvasive profiling of MMP activities. Herein, we introduce an innovative OS EV MMP Activity Assay to assess MMP activities in OS patients noninvasively. This assay evaluates six different combinations of surface markers on OS EVs and OS-associated MMPs, generating a unique profile for each patient. Using a logistic regression model, an OS EV MMP Activity Score is generated from the most effective combinations. This Score has shown excellent performance in distinguishing between metastatic and localized OS. In a longitudinal study of six OS patients undergoing treatment, the Score strongly correlated with treatment response as measured by routine imaging. The assay represents a significant advancement in harnessing tumor EVs for diagnostics, providing a noninvasive, quantitative, and reliable approach to assess MMP activities in solid tumors. Citation Format: Junseok Lee, You-Ren Ji, Hyoyong Kim, Zhao Chen, Ryan Zhang, Diego Banuet, Yusef Mathkour, Kenny Vo, Steven Jonas, Yazhen Zhu, Hsian-Rong Tseng. Precise monitoring of osteosarcoma patients by noninvasive assessment of matrix metalloproteinases activities in plasma extracellular vesicles [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A028.

Sex differences in prognosis of primary bone cancer: a propensity score-matched study

Abstract Objectives Sex differences in survival in primary bone cancer have not been fully explored. Hence, this study was conducted to investigate the impact of sex on survival outcomes in patients with primary bone cancer. Methods The Surveillance, Epidemiology, and End Results (SEER)-17 database was used to identify patients with primary bone cancer. One-to-one propensity score matching (PSM) was employed to balance baseline characteristics. Kaplan-Meier curves and log-rank tests were used to evaluate differences in cancer-specific survival (CSS) and overall survival (OS) between sexes. Multivariate Cox regression analysis was performed to verify the independent effect of sex on survival, and sensitivity analysis was performed to determine the robustness of the results. Results A total of 8,791 patients were included, with 4,928 males (56.1 %) and 3,863 females (43.9 %), and a median follow-up time of 38 months. After PSM, 3,812 males and 3,812 females were included in the study, with balanced baseline characteristics between the groups. Post-PSM, females show significantly better CSS (HR=0.88, 95 % CI, 0.81–0.96, p=0.004) and OS (HR=0.87, 95 % CI, 0.81–0.94, p<0.001) compared to males. In multivariable Cox regression, the female sex was identified as an independent protective factor for both CSS (HR=0.86, 95 % CI, 0.79–0.94, p<0.001) and OS (HR=0.83, 95 % CI, 0.77–0.89, p<0.001). Conclusions Based on the analysis of SEER data with a large sample size, this study revealed that female patients with primary bone cancer have significantly better survival compared to males.

Giant Cell Tumor of Capitate with Secondary Aneurysmal Bone Cyst: A Case Report

Abstract Background Giant cell tumors (GCTs) of the carpal bones are rare. GCTs of the hand tend to be aggressive, could result in multifocal presentation, and have a high rate of recurrence. Case Description We report a rare case of GCT of the capitate with secondary aneurysmal bone cyst (ABC) in a 40-year-old man. The patient presented with complaints of pain and swelling over his right wrist since 6 months. Radiographs showed the presence of a lytic lesion in the capitate. Magnetic resonance imaging scanning revealed a well-defined, lobulated, multiseptate lytic lesion involving the entire capitate, extending into its subarticular regions. Fine-needle aspiration revealed findings suggestive of a GCT. He underwent en-bloc excision of the capitate, bone grafting of the defect using an appropriately sized bicortical iliac crest autograft, and third carpometacarpal and midcarpal fusion. Histopathological examination of the lesion confirmed the presence of a GCT, with a secondary ABC. At 3 years following surgery, he was asymptomatic with no signs of tumor recurrence, radiocarpal arthritis, or instability. He was able to perform 45 and 60 degrees of wrist extension and flexion, respectively. Literature Review To our knowledge, there are no prior reported cases of GCT of the capitate with a secondary ABC. Clinical Relevance GCTs of the hand in general have a high rate of recurrence. The presence of a super-added ABC could further increase this risk. In the authors' opinion, en-bloc excision of the affected bone, reconstruction of the defect, and appropriate fusion is the optimal mode of management of these cases.

The Importance of 18F-FDG PET/CT in a Rare Case of Multiple Myeloma Presenting With Nasal Plasmacytoma.

Nasopharyngeal plasmacytoma is a rare presentation of multiple myeloma. We emphasize the importance of imaging by presenting the 18F-FDG PET/CT findings of a patient with nasopharyngeal plasmacytoma and lytic hypermetabolic bone lesions.

Bone-Targeted Fluoropeptide Nanoparticle Inhibits NF-κB Signaling to Treat Osteosarcoma and Tumor-Induced Bone Destruction.

Osteosarcoma is a malignant bone cancer usually characterized by symptoms of bone loss due to pathologically enhanced osteoclast activity. Activated osteoclasts enhance bone resorption and promote osteosarcoma cell progression by secreting various cytokines. Intercepting the detrimental interplay between osteoclasts and osteosarcoma cells is considered as an option for osteosarcoma treatment. Here, a bone-targeted fluoropeptide nanoparticle that can inhibit the nuclear factor kappa B (NF-κB) signaling in both osteoclasts and osteosarcoma to address the above issue is developed. The NF-κB essential modulator binding domain (NBD) peptide is conjugated with a fluorous tag to improve its proteolytic stability and intracellular penetration. The NBD peptide is efficiently delivered into cells after fluorination to induce apoptosis of osteocarcoma cells, and inhibits osteoclasts differentiation. The fluorous-tagged NBD peptide is further co-assembled with an oligo (aspartic acid) terminated fluoropeptide to form bone-targeted peptide nanoparticles for osteosarcoma treatment. The targeted nanoparticles efficiently inhibited tumor progression and osteosarcoma-induced bone destruction in vivo. This co-assembled fluoropeptide nanoplatform proposed in this study offers a promising approach for targeted and intracellular delivery of peptide therapeutics in the treatment of various diseases.

Advancing osteosarcoma 3D modeling in vitro for novel tumor microenvironment-targeted therapies development

Osteosarcoma (OS) represents one of the most common primary bone cancers affecting children and young adults. The available treatments have remained unimproved for the past decades, hampered by the poor knowledge of OS etiology/pathophysiology and the lack of innovative, predictive and biologically relevant in vitro models, that can recapitulate the 3D OS tumor microenvironment (TME). Here, we report the development and characterization of an innovative 3D model of OS, composed of OS tumor cells, immune cells (macrophages) and mesenchymal stem cells (MSCs), that formed a multicellular tissue spheroid (MCTS). This fully humanized 3D model was shown to accurately mimic the native histological features of OS, while innately leading to the polarization of macrophages towards an M2-like phenotype, highly aggressive and pro-tumor profile. Upon the exposure to immunomodulatory molecules, the MCTS were shown to be responsive by shifting macrophages polarization, and dramatically altering the TME secretome. In agreement, when treated with immunomodulatory/stimulatory nanoparticles (NPSs), we were able to revert the TME secretome towards an anti-inflammatory profile. This study establishes an advanced 3D OS model capable of shedding light on macrophages and MSCs contributions to disease progression, paving the way for the development of innovative therapeutic approaches targeting the OS TME, while providing a biologically relevant in vitro tool for the efficacy screening of novel OS therapeutic approaches.

Osteosarcoma cells depend on MCL-1 for survival, and osteosarcoma metastases respond to MCL-1 antagonism plus regorafenib in vivo

Osteosarcoma is the most common form of primary bone cancer, which primarily afflicts children and adolescents. Chemotherapy, consisting of doxorubicin, cisplatin and methotrexate (MAP) increased the 5-year osteosarcoma survival rate from 20% to approximately 60% by the 1980s. However, osteosarcoma survival rates have remained stagnant for several decades. Patients whose disease fails to respond to MAP receive second-line treatments such as etoposide and, in more recent years, the kinase inhibitor regorafenib. BCL-2 and its close relatives enforce cellular survival and have been implicated in the development and progression of various cancer types. BH3-mimetics antagonize pro-survival members of the BCL-2 family to directly stimulate apoptosis. These drugs have been proven to be efficacious in other cancer types, but their use in osteosarcoma has been relatively unexplored to date. We investigated the potential efficacy of BH3-mimetics against osteosarcoma cells in vitro and examined their cooperation with regorafenib in vivo. We demonstrated that osteosarcoma cell lines could be killed through inhibition of MCL-1 combined with BCL-2 or BCL-xL antagonism. Inhibition of MCL-1 also sensitized osteosarcoma cells to killing by second-line osteosarcoma treatments, particularly regorafenib. Importantly, we found that inhibition of MCL-1 with the BH3-mimetic S63845 combined with regorafenib significantly prolonged the survival of mice bearing pulmonary osteosarcoma metastases. Together, our results highlight the importance of MCL-1 in osteosarcoma cell survival and present a potential therapeutic avenue that may improve metastatic osteosarcoma patient outcomes.

AI-Assisted Detection and Localization of Spinal Metastatic Lesions.

The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions. Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection. The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection. The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

Pain in palliative cancer patients – Analysis of the German National Palliative Care Registry

Palliative care aims to improve the quality of life in patients with progressive diseases such as cancer. Effective cancer pain management is a major challenge of palliative treatment. Empirical data on the prevalence of cancer pain, the efficiency of pain treatment and influencing factors are scarce. Here, we investigated pain in cancer patients treated on inpatient palliative care wards in Germany. N = 4779 data sets provided by the German Palliative Care Registry from yearly evaluation periods between 2015 and 2020 were included. Pain ratings were assessed by professionals through a checklist of symptoms and problems (HOPE-SP-CL). More than half of the included patients suffered from moderate/severe pain at the beginning of inpatient palliative care and in 71% of these patients, pain relief was achieved at the end of inpatient treatment. Pain intensity, depression and ECOG performance status at admission were weak predictors of later pain relief. The highest pain intensity at the beginning and least pain relief were found in patients with bone and cartilage cancer. The highest percentage of adequate pain control (81%) was seen in 2020. Data from the German Palliative Care Registry confirmed that although increasingly better addressed over the years, insufficiently controlled cancer pain remains a challenge for palliative care units. Patient-specific (e.g. psychological comorbidity) and cancer-related (e.g. bone or cartilage cancer) risk factors for poor pain treatment underline the need for individualized multimodal pain management including psychological support.

The Landscape of microRNAs in Bone Tumor: A Comprehensive Review in Recent Studies.

Cancer, the second greatest cause of mortality worldwide, frequently causes bone me-tastases in patients with advanced-stage carcinomas such as prostate, breast, and lung cancer. The existence of these metastases contributes to the occurrence of skeletal-related events (SREs), which are defined by excessive pain, pathological fractures, hypercalcemia, and spinal cord com-pression. These injurious incidents leave uncomfortably large holes in each of the cancer patient's life quality. Primary bone cancers, including osteosarcoma (OS), chondrosarcoma (CS), and Ewing's sarcoma (ES), have unclear origins. MicroRNA (miRNA) expression patterns have been changed in primary bone cancers such as OS, CS, and ES, indicating a role in tumor development, invasion, metastasis, and treatment response. These miRNAs are persistent in circulation and ex-hibit distinct patterns in many forms of bone tumors, making them potential biomarkers for early detection and treatment of such diseases. Given their crucial regulatory functions in various bio-logical processes and conditions, including cancer, this study aims to look at miRNAs' activities and possible contributions to bone malignancies, focusing on OS, CS, and ES. In conclusion, miRNAs are valuable tools for diagnosing, monitoring, and predicting OS, CS, and ES outcomes. Further research is required to fully comprehend the intricate involvement of miRNAs in these bone cancers and to develop effective miRNA-based treatments.

Odontogenic carcinosarcoma of the mandible: A case report.

Odontogenic carcinosarcoma, a rare and challenging diagnosis, was identified in a 60-year-old male through histopathology, revealing a biphasic neoplasm with malignant epithelial and mesenchymal components. Surgical resection is crucial for management, highlighting the importance of vigilant postoperative follow-up to ensure early detection of any recurrence. One rare mixed malignant odontogenic tumor is odontogenic carcinosarcoma, which comprises malignant epithelial and mesenchymal components. Diagnosing odontogenic carcinosarcoma is challenging due to its rarity and atypical clinical presentation. This study reports a 60-year-old male patient who presented with a painless swelling on the right side of his face and experienced facial asymmetry for 6 months, ultimately diagnosed with odontogenic carcinosarcoma. A biphasic neoplasm with malignant alterations in both epithelial and mesenchymal components was identified upon histopathological examination. MRI imaging showed an expansile multilobulated lytic lesion with cortical erosion and extraosseous extension in the posterior region of the right mandibular body and ramus. Following contrast administration, homogeneous lesion enhancement was observed, with a few small non-enhancing necrotic areas in central parts. The patient subsequently underwent a right hemi-mandibulectomy with resection of adjacent soft tissues and neck dissection due to lymph node involvement. The resulting defect was reconstructed using a pectoralis major flap. No recurrence or metastasis was reported during the 6-month follow-up, reinforcing the positive results. This case highlights the importance of recognizing odontogenic carcinosarcoma and underscores the challenges in diagnosing and managing this rare tumor. Early identification and aggressive treatment can lead to positive outcomes, as evidenced by the absence of recurrence or metastasis in this patient during the follow-up period.

Targeted therapy: P2X3 receptor silencing in bone cancer pain relief.

Bone cancer pain remains a significant clinical challenge, often refractory to conventional treatments. The upregulation of the P2X3 receptor in the dorsal root ganglia has been implicated in the pathogenesis of bone cancer pain. This study aimed to elucidate the role of the P2X3 receptor in this context and assess the therapeutic potential of receptor silencing. Utilizing a rat model with Walker 256 cells to simulate bone cancer pain, researchers conducted molecular analyses, including semi-quantitative RT-PCR and Western Blot, to investigate P2X3 receptor expression in the dorsal root ganglia. Results demonstrated a marked increase in P2X3 receptor levels in the dorsal root ganglia of the bone cancer pain model. Targeted silencing of the P2X3 receptor using specific shRNA delivered via lentiviral vectors significantly reduced pain sensitivity, underscoring the receptor's potential as a valuable therapeutic target. In addition, a comprehensive gene expression analysis leveraging the GEO data set GSE249443 was performed to explore the underlying biological pathways linked to bone cancer pain. This analysis provided insights into the intricate interplay between bone cancer pain and associated biological processes, offering a deeper understanding of the mechanisms involved in pain modulation and progression. In conclusion, this research identifies the P2X3 receptor as a critical molecular target for mitigating bone cancer pain. The selective silencing of the P2X3 receptor emerges as a promising and innovative therapeutic strategy, presenting novel avenues for managing bone cancer pain and potentially revolutionizing treatment approaches in this challenging domain.

AI based diagnostics product design for osteosarcoma cells microscopy imaging of bone cancer patients using CA-MobileNet V3

ObjectiveThe incidence of osteosarcoma (OS) is low, but primary malignant bone tumors rank third among the causes of death in cancer patients under the age of 20. Currently, analysis of cellular structure and tumor morphology through microscopic images remains one of the main diagnostic methods for osteosarcoma. However, this completely manual approach is tedious, time-consuming, and difficult to diagnose accurately due to the similarities in certain characteristics of malignant and benign tumors. MethodsLeveraging the potential of artificial intelligence (AI) in assessing and classifying images, this study explored a modified CA-MobileNet V3 model that was embedded into innovative microscope products to enhance the microscope’s feature extraction capabilities and help reduce misclassification during diagnosis. ResultsThe intelligent recognition model method introduced in this paper has significant advantages in retrieval and classification of osteosarcoma cells and other cell types. Compared with models such as ShuffleNet V2, EfficientNet V2, Mobilenet V3 (without transfer learning), TL-MobileNet V3 (with transfer learning), etc., the model size is only 5.33 MB, is a lightweight model, and the accuracy of the improved model reached 98.69 %. In addition, the artificial intelligence microscope (AIM) with integrated design based on this model can also help improve diagnostic efficiency. ConclusionThe innovative method of the CA-MobileNet V3 automatic classification model based on deep learning provides an efficient and reliable solution for the pathological diagnosis of osteosarcoma. This study contributes to medical image analysis and provides doctors with an accurate and valuable tool for microscopic diagnosis. It also promotes the advancement of artificial intelligence in medical imaging technology.

Deep hashing and attention mechanism-based image retrieval of osteosarcoma scans for diagnosis of bone cancer

BackgroundDue to its intricate nature and substantial data size, microscopic image data of osteosarcoma often present a significant obstacle to the effectiveness of conventional image retrieval methods. Therefore, this study investigates a new approach for medical image retrieval using advanced deep hashing techniques and attention mechanisms to address these challenges more effectively. MethodThe proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. Finally, similarity matching via Hamming distance allows rapid and precise identification of similar images. ResultsThe study shows notable advancements: the WRN-AM model achieves 93.2% classification accuracy and 97.09% mAP using 64-bit hash codes. These findings underscore the technique’s effective performance in extracting and categorizing diverse microscopic cell data efficiently and reliably. ConclusionsThis innovative approach provides a robust solution for retrieving and classifying microscopic data of osteosarcoma cells and other cell types, speeding up clinical diagnosis and medical research. It facilitates quicker access and analysis of patient image data, enhancing diagnostic precision and treatment planning for healthcare professionals. Concurrently, it supports researchers in leveraging medical image data more efficiently, fostering progress and innovation in the medical field.

A novel adjunctive diagnostic method for bone cancer: Osteosarcoma cell segmentation based on Twin Swin Transformer with multi-scale feature fusion

BackgroundOsteosarcoma, the most common primary bone tumor originating from osteoblasts, poses a significant challenge in medical practice, particularly among adolescents. Conventional diagnostic methods heavily rely on manual analysis of magnetic resonance imaging (MRI) scans, which often fall short in providing accurate and timely diagnosis. This underscores the critical need for advancements in medical imaging technologies to improve the detection and characterization of osteosarcoma. MethodsIn this study, we sought to address the limitations of current diagnostic approaches by leveraging Hoechst-stained images of osteosarcoma cells obtained via fluorescence microscopy. Our primary objective was to enhance the segmentation of osteosarcoma cells, a crucial step in precise diagnosis and treatment planning. Recognizing the shortcomings of existing feature extraction networks in capturing detailed cellular structures, we propose a novel approach utilizing a twin swin transformer architecture for osteosarcoma cell segmentation, with a focus on multi-scale feature fusion. ResultsThe experimental findings demonstrate the effectiveness of the proposed Twin Swin Transformer with multi-scale feature fusion in significantly improving osteosarcoma cell segmentation. Compared to conventional techniques, our method achieves superior segmentation performance, highlighting its potential utility in clinical settings. ConclusionThe development of our Twin Swin Transformer with multi-scale feature fusion method represents a significant advancement in medical imaging technology, particularly in the field of osteosarcoma diagnosis. By harnessing advanced computational techniques and leveraging high-resolution imaging data, our approach offers enhanced accuracy and efficiency in osteosarcoma cell segmentation, ultimately facilitating better patient care and clinical decision-making.

A natural protopanaxatriol from Panax notoginseng enhances osteosarcoma sensitivity to ferroptosis via ASCL4 upregulation

Current treatments for osteosarcoma, the most common primary bone cancer, including surgery, radiotherapy, and chemotherapy, are largely ineffective against metastatic and chemo-resistant cases, emphasizing the need for novel therapies. In this study, we investigated (20S)-Protopanaxatriol [(20S)-PPT], a natural product from Panax notoginseng, an herb used both as food and medicine, for its ability to enhance osteosarcoma sensitivity to ferroptosis induced by inducers such as RSL3, ML210, and IKE. Specifically, (20S)-PPT increased lipid peroxidation in osteosarcoma cells exposed to RSL3. This effect was mediated by ACSL4 upregulation, as ACSL4 knockdown reversed (20S)-PPT-sensitized ferroptosis. Importantly, (20S)-PPT significantly reduced tumor growth in xenograft models when combined with IKE. Collectively, our study confirms that combining (20S)-PPT with ferroptosis inducers offers a promising novel strategy for treating osteosarcoma.