MR targeted imaging for the expression of tenascin-C in cervical cancer

Functional roles of magnetic nanoparticles for the identification of metastatic lymph nodes in cancer patients

Staging lymph nodes (LN) is crucial in diagnosing and treating cancer metastasis. Biotechnologies for the specific localization of metastatic lymph nodes (MLNs) have attracted significant attention to efficiently define tumor metastases. Bioimaging modalities, particularly magnetic nanoparticles (MNPs) such as iron oxide nanoparticles, have emerged as promising tools in cancer bioimaging, with great potential for use in the preoperative and intraoperative tracking of MLNs. As radiation-free magnetic resonance imaging (MRI) probes, MNPs can serve as alternative MRI contrast agents, offering improved accuracy and biological safety for nodal staging in cancer patients. Although MNPs' application is still in its initial stages, exploring their underlying mechanisms can enhance the sensitivity and multifunctionality of lymph node mapping. This review focuses on the feasibility and current application status of MNPs for imaging metastatic nodules in preclinical and clinical development. Furthermore, exploring novel and promising MNP-based strategies with controllable characteristics could lead to a more precise treatment of metastatic cancer patients.

Prediction of lymphovascular space invasion using a combination of tenascin-C, cox-2, and PET/CT radiomics in patients with early-stage cervical squamous cell carcinoma

BACKGROUND

Lymphovascular space invasion is an independent prognostic factor in early-stage cervical cancer. However, there is a lack of non-invasive methods to detect lymphovascular space invasion. Some researchers found that Tenascin-C and Cyclooxygenase-2 was correlated with lymphovascular space invasion. Radiomics has been studied as an emerging tool for distinguishing tumor pathology stage, evaluating treatment response, and predicting prognosis. This study aimed to establish a machine learning model that combines radiomics based on PET imaging with tenascin-C (TNC) and cyclooxygenase-2 (COX-2) for predicting lymphovascular space invasion (LVSI) in patients with early-stage cervical cancer.

METHODS

One hundred and twelve patients with early-stage cervical squamous cell carcinoma who underwent PET/CT examination were retrospectively analyzed. Four hundred one radiomics features based on PET/CT images were extracted and integrated into radiomics score (Rad-score). Immunohistochemical analysis was performed to evaluate TNC and COX-2 expression. Mann-Whitney U test was used to distinguish differences in the Rad-score, TNC, and COX-2 between LVSI and non-LVSI groups. The correlations of characteristics were tested by Spearman analysis. Machine learning models including radiomics model, protein model and combined model were established by logistic regression algorithm and evaluated by ROC curve. Pairwise comparisons of ROC curves were tested by DeLong test.

RESULTS

The Rad-score of patients with LVSI was significantly higher than those without. A significant correlation was shown between LVSI and Rad-score (r = 0.631, p < 0.001). TNC was correlated to both the Rad-score (r = 0.244, p = 0.024) and COX-2 (r = 0.227, p = 0.036). The radiomics model had the best predictive performance among all models in training and external dataset (AUCs: 0.914, 0.806, respectively, p < 0.001). However, in testing dataset, the combined model had better efficiency for predicting LVSI than other models (AUCs: 0.801 vs. 0.756 and 0.801 vs. 0.631, respectively).

CONCLUSION

The machine learning model of the combination of PET radiomics with COX-2 and TNC provides a new tool for detecting LVSI in patients with early-stage cervical cancer. In the future, multicentric studies on larger sample of patients will be used to test the model.

TRIAL REGISTRATION

This is a retrospective study and there is no experimental intervention on human participants. The Ethics Committee has confirmed that retrospectively registered is not required.

Superparamagnetic Iron Oxide Nanoparticles (SPIONs) for Diagnosis and Treatment of Breast, Ovarian and Cervical Cancers

Background:

The potential of Super Paramagnetic Iron Oxide Nanoparticles (SPIONs) as theranostic agents for cancer has been investigated extensively. SPIONS can be utilized for diagnostic imaging, drug delivery as well as for therapeutic applications. SPIONS are of particular interest because of their potential for non-invasive diagnosis and non-invasive therapeutic applications. This article is a review of in vivo and clinical studies of SPIONs for diagnosis and treatment of breast, ovarian and cervical cancer. The current limitations of this technology with relation to clinical therapeutic applications and the potential to overcome these limitations are also discussed.

Methods:

NCBI Pubmed was searched for relevant documents by using keyword and MESH based search. The following keyword combinations were used: ‘breast cancer’ and SPION, ‘ovarian cancer’ and SPION, and ‘cervical cancer’ and SPION. The resulting list was manually scanned for the studies involving clinical and in vivo studies.

Results:

The 29 most relevant publications were identified and reviewed.

Conclusion:

Although numerous in vitro and in vivo studies have demonstrated the safety and effectiveness of the use of SPIONs for both diagnostic and therapeutic applications, there is relatively little progress towards translation to clinical applications involving breast, ovarian and cervical cancer.