The Use of Platelet/Lymphocyte Ratio and Cancer Antigen 125 Combined with Magnetic Resonance Diffusion-Weighted Imaging in Diagnosis of Recurrent Ovarian Cancer and Neuropathic Pain

Diagnostic efficacy of combining diffusion-weighted magnetic resonance imaging with serum Mucin 1, Mucin 13, and Mucin 16 in distinguishing borderline from malignant epithelial ovarian tumors

AIMS

To enhance ovarian tumor diagnosis beyond conventional methods, this study explored combining diffusion-weighted magnetic resonance imaging (DWI-MRI) and serum biomarkers (Mucin 1 [MUC1], MUC13, and MUC16) for distinguishing borderline from malignant epithelial ovarian tumors.

METHODS

A total of 126 patients, including 71 diagnosed with borderline (BEOTs) and 55 with malignant epithelial ovarian tumors (MEOTs), underwent preoperative DWI-MRI. Region of interest (ROI) was manually drawn along the solid component's boundary of the largest tumor, focusing on areas with potentially the lowest apparent diffusion coefficient (ADC). For entirely cystic tumors, a free-form ROI enclosed the maximum number of septa while targeting the lowest ADC. Serum biomarkers were determined using enzyme-linked immunosorbent assay.

RESULTS

Basic morphological traits proved inadequate for malignancy diagnosis, warranting this investigation. BEOTs had an ADC mean of (1.670 ± 0.250) × 103 mm2 /s, while MEOTs had a lower ADC mean of (1.332 ± 0.481) × 103 mm2 /s, with a sensitivity of 63.6% and specificity of 90.1%. Median MUC1 (167.0 U/mL vs. 87.3 U/mL), MUC13 (12.44 ng/mL vs. 7.77 ng/mL), and MUC16 (180.6 U/mL vs. 36.1 U/mL) levels were higher in MEOTs patients. The biomarker performance was: MUC1, sensitivity 50.9%, specificity 100%; MUC13, sensitivity 56.4%, specificity 78.9%; MUC16, sensitivity 83.64%, specificity 100%. Combining serum biomarkers and ADC mean resulted in a sensitivity of 96.4% and specificity of 100%.

CONCLUSION

The integration of DWI-MRI with serum biomarkers (MUC1, MUC13, and MUC16) achieves exceptional diagnostic accuracy, offering a powerful tool for the precise differentiation between borderline and malignant epithelial ovarian tumors.

Crosstalk of Immune Cells and Platelets in an Ovarian Cancer Microenvironment and Their Prognostic Significance

Ovarian cancer (OC) is one of the deadliest gynecological cancers, largely due to the fast development of metastasis and drug resistance. The immune system is a critical component of the OC tumor microenvironment (TME) and immune cells such as T cells, NK cells, and dendritic cells (DC) play a key role in anti-tumor immunity. However, OC tumor cells are well known for evading immune surveillance by modulating the immune response through various mechanisms. Recruiting immune-suppressive cells such as regulatory T cells (Treg cells), macrophages, or myeloid-derived suppressor cells (MDSC) inhibit the anti-tumor immune response and promote the development and progression of OC. Platelets are also involved in immune evasion by interaction with tumor cells or through the secretion of a variety of growth factors and cytokines to promote tumor growth and angiogenesis. In this review, we discuss the role and contribution of immune cells and platelets in TME. Furthermore, we discuss their potential prognostic significance to help in the early detection of OC and to predict disease outcome.

Independent predictive value of blood inflammatory composite markers in ovarian cancer: recent clinical evidence and perspective focusing on NLR and PLR

Ovarian cancer (OC) is one of the deadliest malignant tumors affecting women worldwide. The predictive value of some blood inflammatory composite markers in OC has been extensively reported. They can be used for early detection and differential diagnosis of OC and can be used for predicting survival, treatment response, and recurrence in the affected patients. Here, we reviewed the predictive values of composite inflammatory markers based on complete blood count, namely neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio, and systemic inflammation index and markers based on blood protein, namely C-reactive protein-to-albumin ratio and prognostic nutritional index in OC, with a focus on NLR and PLR. We referred to the clinical studies on these six markers, reviewed the patient population, and summarized the marker cut-off values, significance, and limitations of these studies. All these studies were retrospective and most of them were single-center clinical studies with small sample sizes. We found that the cut-off values of these markers have not been unified, and methods used to determine these values varied among studies. The predictive value of these markers on survival was mainly reflected in the postoperative patients of multiple subtypes of ovarian cancer including epithelial OC, high-grade serous ovarian carcinoma, and ovarian clear cell carcinoma. We focused on NLR and PLR and calculated their pooled hazard ratios. NLR and PLR were reliable in predicting overall and progression-free survivals in patients with OC. Therefore, it is necessary to adjust important confounding factors and conduct a long-term follow-up prospective cohort study to further clarify the cut-off values of NLR and PLR and their clinical applications.

Peripheral and tumor-infiltrating immune cells are correlated with patient outcomes in ovarian cancer

OBJECTIVE

At present, there is still a lack of reliable biomarkers for ovarian cancer (OC) to guide prognosis prediction and accurately evaluate the dominant population of immunotherapy. In recent years, the relationship between peripheral blood markers and tumor-infiltrating immune cells (TICs) with cancer has attracted much attention. However, the relationship between the survival of OC patients and intratumoral- or extratumoral-associated immune cells remains controversial.

METHODS

In this study, four machine-learning algorithms were used to predict overall survival in OC patients based on peripheral blood indicators. To further screen out immune-related gene and molecular targets, we systematically explored the correlation between TICs and OC patient survival based on The Cancer Genome Atlas database. Using the TICs score method, patients were divided into a low immune infiltrating cell group and a high immune infiltrating cell group.

RESULTS

The results showed that there was a significant statistical significance between the peripheral blood indicators and the survival prognosis of OC patients. Survival analysis showed that TICs play a crucial role in the survival of OC patients. Four core genes, CXCL9, CD79A, MS4A1, and MZB1, were identified by cross-PPI and COX regression analysis. Further analysis found that these genes were significantly associated with both TICs and survival in OC patients.

CONCLUSIONS

These results suggest that both peripheral blood markers and TICs can be used as prognostic predictors in patients with OC, and CXCL9, CD79A, MS4A1, and MZB1 may be potential therapeutic targets for OC immunotherapy.

A novel classification and its clinical significance in Chiari I malformation with syringomyelia based on high-resolution MRI

BACKGROUND

The existing classification in Chiari I malformation (CM-I) has limited significance for the selection of surgical methods.

OBJECTIVE

The purpose of this study was to investigate the surgery of CM-I with syringomyelia based on the high-resolution MR imaging (HRMRI) findings.

METHODS

Data from 115 patients were collected and retrospectively analyzed. For those with syringomyelia up to the level of C1, HRMRI was performed and according to the communication status between the fourth ventricle and the syringomyelia, patients can be divided into four types, namely Type A: classic communicating; Type B: partial communicating; Type C: non-communicating; Type D: atrophic. All operations were performed with Foramen magnum and Magendie dredging (FMMD), and all intradural factors that may have induced the obstruction of CSF circulation were recorded. The efficiency of operation on syringomyelia was evaluated by mJOA, imaging findings, and complications in the follow-up periods.

RESULTS

The postoperative follow-up period was from 12 to 24 months, with an average of 14.3 months. At 1 year, the mJOA of 115 patients was significantly higher than that before the operations (before surgery 12.1 ± 2.3 vs. after surgery 14. 2 ± 1.4, P < 0.05). In addition, postoperative re-examination showed that the size of the syringomyelia was reduced or completely resolved in patients of Type A, 100% (2/2); Type B, 81% (9/11); Type C, 84% (81/97); and Type D, 20% (1/5).

CONCLUSIONS

According to our new classification based on HRMRI, FMMD is the key to surgical treatment, especially for Type A and Type B patients.