An analysis of ovarian tumor diameter and survival

Survival prognosis model for elderly women with epithelial ovarian cancer based on the SEER database

Objectives

We aimed to analyze the risk factors of elderly women with epithelial ovarian cancer (EOC) using data on the SEER database, and to generate a nomogram model their 1-, 3-, and 5-year prognoses. The resulting nomogram model should be useful for clinical diagnoses and treatment.

Methods

We collected clinical data of women older than 70 years with epithelial ovarian cancer (diagnosed on the basis of surgical pathology) from the SEER database including datasets between 2010 and 2019. We randomly grouped the data into two groups (7:3 ratio) using the R language software. We divided the independent prognostic factors obtained by univariate and multi-factor Cox regression analyses into training and validation sets, and we plotted the same independent prognostic factors in a nomogram model of overall survival (OS) at 1, 3, and 5 years. We used the C-index, calibration curve, and area under the curve to validate the nomograms. We further evaluated the model and its clinical applicability using decision curve analyses.

Results

We identified age, race, marital status, histological type, AJCC staging, differentiation degree, unilateral and bilateral tumor involvement, number of positive lymph nodes, chemotherapy, surgery, sequence of systemic treatment versus surgery, and time from diagnosis to treatment as independent prognostic factors for elderly women with EOC (P < 0.5). The C-indexes were 0.749 and 0.735 in the training and validation sets, respectively; the ROC curves showed that the AUC of each prognostic factor was greater than 0.7; and, the AUC values predicted by the line plot were similar in the training and validation sets. The decision curves suggest that this line plot model has a high clinical value for predicting overall survivals at 1, 3, and 5 years in elderly women with EOC.

Conclusion

The nomogram model in this study can provide an accurate assessment of the overall survival of women older than 70 years with EOC at the time of the first treatment, and it provides a basis for individualized clinical treatment.

Improving cancer detection through combinations of cancer and immune biomarkers: a modelling approach

Background

Early cancer diagnosis is one of the most important challenges of cancer research, since in many cancers it can lead to cure for patients with early stage diseases. For epithelial ovarian cancer (which is the leading cause of death among gynaecologic malignancies) the classical detection approach is based on measurements of CA-125 biomarker. However, the poor sensitivity and specificity of this biomarker impacts the detection of early-stage cancers.

Methods

Here we use a computational approach to investigate the effect of combining multiple biomarkers for ovarian cancer (e.g., CA-125 and IL-7), to improve early cancer detection.

Results

We show that this combined biomarkers approach could lead indeed to earlier cancer detection. However, the immune response (which influences the level of secreted IL-7 biomarker) plays an important role in improving and/or delaying cancer detection. Moreover, the detection level of IL-7 immune biomarker could be in a range that would not allow to distinguish between a healthy state and a cancerous state. In this case, the construction of solution diagrams in the space generated by the IL-7 and CA-125 biomarkers could allow us predict the long-term evolution of cancer biomarkers, thus allowing us to make predictions on cancer detection times.

Conclusions

Combining cancer and immune biomarkers could improve cancer detection times, and any predictions that could be made (at least through the use of CA-125/IL-7 biomarkers) are patient specific.

Potential effect of the risk of ovarian cancer algorithm (ROCA) on the mortality outcome of the Prostate, Lung, Colorectal and Ovarian (PLCO) trial

Recently, the Prostate, Lung, Colorectal and Ovarian (PLCO) Trial reported no mortality benefit for annual screening with CA-125 and transvaginal ultrasound (TVU). Currently ongoing is the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS), which utilizes the risk of ovarian cancer algorithm (ROCA), a statistical tool that considers current and past CA125 values to determine ovarian cancer risk. In contrast, PLCO used a single cutoff for CA125, based on current levels alone. We investigated whether having had used ROCA in PLCO could have, under optimal assumptions, resulted in a significant mortality benefit by applying ROCA to PLCO CA125 screening values. A best-case scenario assumed that all cancers showing a positive screen result earlier with ROCA than under the PLCO protocol would have avoided mortality; under a stage-shift scenario, such women were assigned survival equivalent to Stage I/II screen-detected cases. Updated PLCO data show 132 intervention arm ovarian cancer deaths versus 119 in usual care (relative risk, RR = 1.11). Forty-three ovarian cancer cases, 25 fatal, would have been detected earlier with ROCA, with a median (minimum) advance time for fatal cases of 344 (147) days. Best-case and stage-shift scenarios gave 25 and 19 deaths prevented with ROCA, for RRs of 0.90 (95% CI: 0.69-1.17) and 0.95 (95% CI: 0.74-1.23), respectively. Having utilized ROCA in PLCO would not have led to a significant mortality benefit of screening. However, ROCA could still show a significant effect in other screening trials, including UKCTOCS.

Correlation between preoperative serum levels of five biomarkers and relationships between these biomarkers and cancer stage in epithelial overian cancer

OBJECTIVE

To examine the correlation among the preoperative serum levels of five biomarkers presumed to be useful for early detection of epithelial ovarian cancer and evaluate the relationships between serum levels of these five biomarkers and epithelial ovarian cancer stage.

METHODS

We analyzed 56 newly diagnosed epithelial ovarian cancer patients. Preoperative serum levels of leptin, prolactin, osteopontin (OPN), insulin-like growth factor-II, and CA-125 were determined by ELISA. We also examined the correlation between the serum levels of the biomarkers and ovarian cancer stage. Significant differences in the mean serum levels of two proteins, leptin and CA-125, were observed between stage subsets.

RESULTS

There was a significant negative correlation between prolactin and leptin and a significant positive correlation between prolactin and OPN. Of the five biomarkers, only the mean serum CA-125 level showed a significant positive correlation with cancer stage (Spearman rho=0.24, p<0.01). OPN showed a marginally significant positive correlation with stage (Spearman rho=0.14, p=0.07).

CONCLUSION

We demonstrated the relationship between five biomarkers in epithelial ovarian cancer. These tumor markers may be useful in screening for ovarian cancer, in characterizing disease states, and in developing therapeutic interventions targeting these marker proteins. Large-scale studies that include potential confounding factors and modifiers are necessary to more accurately define the value of these novel biomarkers in ovarian cancer.

Pathology of stage I versus stage III ovarian carcinoma with implications for pathogenesis and screening

The progression of ovarian carcinoma from stage I when it is confined to the ovaries and curable to disseminated abdominal disease, which is usually fatal, is poorly understood. An accurate understanding of this process is fundamental to designing, testing, and implementing an effective screening program for ovarian cancer. Pathologic features of the primary ovarian tumors in 41 FIGO stage I ovarian carcinomas were compared with those in 40 stage III carcinomas. The primary ovarian tumors in stage I cases, when compared with stage III, respectively, were significantly larger (15.4 versus 9.8 cm), were less frequently bilateral (12% versus 75%), more frequently contained a noninvasive component (88% versus 30%), had a higher proportion of a noninvasive component (42% versus 8%), and were more often nonserous (83% versus 20%) (P< 0.001 for all five comparisons). There are significant pathologic differences between the primary ovarian tumors in stage I and III ovarian carcinomas that are very difficult to explain by a simple temporal progression. These findings along with the growing body of literature suggest that early- and advanced-stage ovarian cancers are in many instances biologically different entities. This knowledge may have significant implications for our understanding of the biology of early- and advanced-stage ovarian cancer and therefore on the development of screening strategies for ovarian cancer.

Biology of Epithelial Ovarian Cancer: Implications for Screening Women at High Genetic Risk

Purpose

Our aim was to analyze the clinicopathologic features of screen-detected ovarian cancers identified in women, either at general population risk or high genetic risk of ovarian cancer, who have participated in screening studies.

Methods

Studies published between 1988 and April 2003 were categorized by the population screened and the primary screening modalities used. Each report was examined with reference to the histologic type, stage, and grade of screen-detected cancers. Reports of studies of prophylactically removed ovaries from women at high risk of ovarian cancer were also reviewed.

Results

Of the stage I tumors detected by screening women at population risk, almost half were borderline ovarian tumors, granulosa-cell tumors, or germ-cell tumors, which is disproportionate to their frequency. Furthermore, of the stage I invasive epithelial cancers diagnosed in women at population risk, the majority were endometrioid, clear-cell, and mucinous histologic subtypes. Most ovarian cancers that occur in women at high genetic risk are high-grade serous cancers, and these are infrequently screen detected at an early stage.

Conclusion

The clinicopathologic features of screen-detected ovarian cancers suggest that screening may not reduce mortality in women at increased genetic risk. Prospective screening studies are required in genetically high-risk populations to answer this important question. Women electing surveillance should be aware of the lack of proven benefit and the low likelihood of detecting early stage serous cancers. Bilateral salpingo-oophorectomy appears to be the most effective approach to decrease the risk of ovarian cancer and thereby reduce mortality in high-risk women.