The relationship between tumor size and stage in early versus advanced ovarian cancer

Clinicopathological analysis of giant ovarian tumors

Objective

This study aims to analyze giant ovarian tumors' clinical and pathological characteristics.

Material and Methods

This was an analytical observational study. Medical records of all patients with giant ovarian tumors who underwent surgery between January 2020 and June 2022 at Dr. Soetomo Academic Hospital, Surabaya, Indonesia, were analyzed.

Results

We analyzed 63 patients with ovarian tumors measuring > 20 cm who underwent surgery at Dr. Soetomo Academic Hospital, Surabaya, Indonesia. The mean tumor size was 25.9 cm (largest size was 41 cm). There was no significant difference in tumor size between benign and malignant giant ovarian tumors (p = 0.261). Based on histopathological results, 66.67 % of giant ovarian tumors were malignant, 26.98 % were benign, and 6.35 % were borderline. Among the malignant tumors, the epithelial type accounted for 69 % of cases. Most giant ovarian tumors originated in the left adnexa (68.25 %). There was no significant difference in patient age (p = 0.511), tumor size (p = 0.168), malignancy (p = 0.303), and histopathological type (p = 0.232) regardless of adnexal side. CA125 levels did not differ significantly between malignant and benign giant ovarian tumors (p = 0.604). There was no correlation between malignant ovarian tumor size and CA125 levels, while there was a significant difference between CA125 levels and the adnexal side (p = 0.010).

Conclusions

Most giant ovarian tumors were malignant, diagnosed at an early stage, and predominantly epithelial type. CA125 levels did not correlate with the size of malignant ovarian tumors. Most giant ovarian tumors originate in the left adnexa.

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.

Biofabrication of Modular Spheroids as Tumor-Scale Microenvironments for Drug Screening

To streamline the drug discovery pipeline, there is a pressing need for preclinical models which replicate the complexity and scale of native tumors. While there have been advancements in the formation of microscale tumor units, these models are cell-line dependent, time-consuming and have not improved clinical trial success rates. In this study, two methods for generating 3D tumor microenvironments are compared, rapidly fabricated hydrogel microspheres and traditional cell-dense spheroids. These modules are then bioassembled into 3D printed thermoplastic scaffolds, using an automated biofabrication process, to form tumor-scale models. Modules are formed with SKOV3 and HFF cells as monocultures and cocultures, and the fabrication efficiency, cell architecture, and drug response profiles are characterized, both as single modules and as multimodular constructs. Cell-encapsulated Gel-MA microspheres are fabricated with high-reproducibility and dimensions necessary for automated tumor-scale bioassembly regardless of cell type, however, only cocultured spheroids form compact modules suitable for bioassembly. Chemosensitivity assays demonstrate the reduced potency of doxorubicin in coculture bioassembled constructs and a ≈five-fold increase in drug resistance of cocultured cells in 3D modules compared with 2D monolayers. This bioassembly system is efficient and tailorable so that a variety of relevant-sized tumor constructs could be developed to study tumorigenesis and modernize drug discovery.

A Multistep Tumor Growth Model of High-Grade Serous Ovarian Carcinoma Identifies Hypoxia-Associated Signatures

High-grade serous ovarian carcinoma (HGSC) is associated with late-stage disease presentation and poor prognosis, with a limited understanding of early transformation events. Our study analyzes HGSC tumor progression and organ-specific metastatic dissemination to identify hypoxia-associated molecular, cellular, and histological alterations. Clinical characteristics of the HGSC were replicated in orthotopic xenografts, which involve metastatic dissemination and the prevalence of group B tumors (volume: >0.0625 ≤ 0.5 cm3). Enhanced hyaluronic acid (HA) deposition, expanded tumor vasculature, and increased necrosis contributed to the remodeling of tumor tissue architecture. The proliferative potential of tumor cells and the ability to form glands were also altered during tumor growth. Flow cytometry and label chase-based molecular profiling across the tumor regenerative hierarchy identified the hypoxia-vasculogenic niche and the hybrid epithelial-mesenchymal tumor-cell state as determinants of self-renewal capabilities of progenitors and cancer stem cells. A regulatory network and mathematical model based on tumor histology and molecular signatures predicted hypoxia-inducible factor 1-alpha (HIF1A) as a central node connecting HA synthesis, epithelial-mesenchymal transition, metabolic, vasculogenic, inflammatory, and necrotic pathways in HGSC tumors. Thus, our findings provide a temporal resolution of hypoxia-associated events that sculpt HGSC tumor growth; an in-depth understanding of it may aid in the early detection and treatment of HGSC.

Nomogram for predicting postoperative cancer-specific early death in patients with epithelial ovarian cancer based on the SEER database: a large cohort study

Purpose

Ovarian cancer is a common gynecological malignant tumor. Poor prognosis is strongly associated with early death, but there is no effective tool to predict this. This study aimed to construct a nomogram for predicting cancer-specific early death in patients with ovarian cancer.

Methods

We used data from the Surveillance, Epidemiology, and End Results database of patients with ovarian cancer registered from 1988 to 2016. Important independent prognostic factors were determined by univariate and multivariate logistic regression and LASSO Cox regression. Several risk factors were considered in constructing the nomogram. Nomogram discrimination and calibration were evaluated using C-index, internal validation, and receiver operating characteristic (ROC) curves.

Results

A total of 4769 patients were included. Patients were assigned to the training set (n = 3340; 70%) and validation set (n = 1429; 30%). Based on the training set, eight variables were shown to be significant factors for early death and were incorporated in the nomogram: American Joint Committee on Cancer (AJCC) stage, residual lesion size, chemotherapy, serum CA125 level, tumor size, number of lymph nodes examined, surgery of primary site, and age. The concordance indices and ROC curves showed that the nomogram had better predictive ability than the AJCC staging system and good clinical practicability. Internal validation based on validation set showed good consistency between predicted and observed values for early death.

Conclusion

Compared with predictions made based on AJCC stage or residual lesion size, the nomogram could provide more robust predictions for early death in patients with ovarian cancer.

Modeling EV Kinetics for Use in Early Cancer Detection

Tumor-derived extracellular vesicles (EVs) represent promising biomarkers for monitoring cancers. Technological advances have improved the ability to measure EV reliably in blood using protein, RNA, or lipid detection methods. However, it is less clear how efficacious current EV assays are for the early detection of small and thus curable tumors. Here, a mathematical model is developed to estimate key parameter values and future requirements for EV testing. Tumor volumes in mice correlate well with increases in total number of circulating EV allowing the researchers to calculate EV shed rates for four different published cancer models. Model extrapolations to human physiology show good agreement with published clinical data. Specifically, it is shown that current bulk EV detection systems are ≈104 -fold too insensitive to detect human cancers of ≈1 cm3 . Conversely, it is predicted that emerging single EV methods will allow blood-based detection of cancers of <1 mm3 in humans.

Employment of enhanced permeability and retention effect (EPR): Nanoparticle-based precision tools for targeting of therapeutic and diagnostic agent in cancer

In tumorous tissues, the absence of vasculature supportive tissues intimates the formation of leaky vessels and pores (100 nm to 2 μm in diameter) and the poor lymphatic system offers great opportunity to treat cancer and the phenomenon is known as Enhanced permeability and retention (EPR) effect. The trends in treating cancer by making use of EPR effect is increasing day by day and generate multitudes of possibility to design novel anticancer therapeutics. This review aimed to present various factors affecting the EPR effect along with important things to know about EPR effect such as tumor perfusion, lymphatic function, interstitial penetration, vascular permeability, nanoparticle retention etc. This manuscript expounds the current advances and cross-talks the developments made in the of EPR effect-based therapeutics in cancer therapy along with a transactional view of its current clinical and industrial aspects.

Targeting Mitochondria for Treatment of Chemoresistant Ovarian Cancer

Ovarian cancer is the leading cause of death from gynecologic malignancy in the Western world. This is due, in part, to the fact that despite standard treatment of surgery and platinum/paclitaxel most patients recur with ultimately chemoresistant disease. Ovarian cancer is a unique form of solid tumor that develops, metastasizes and recurs in the same space, the abdominal cavity, which becomes a unique microenvironment characterized by ascites, hypoxia and low glucose levels. It is under these conditions that cancer cells adapt and switch to mitochondrial respiration, which becomes crucial to their survival, and therefore an ideal metabolic target for chemoresistant ovarian cancer. Importantly, independent of microenvironmental factors, mitochondria spatial redistribution has been associated to both tumor metastasis and chemoresistance in ovarian cancer while specific sets of genetic mutations have been shown to cause aberrant dependence on mitochondrial pathways in the most aggressive ovarian cancer subtypes. In this review we summarize on targeting mitochondria for treatment of chemoresistant ovarian cancer and current state of understanding of the role of mitochondria respiration in ovarian cancer. We feel this is an important and timely topic given that ovarian cancer remains the deadliest of the gynecological diseases, and that the mitochondrial pathway has recently emerged as critical in sustaining solid tumor progression.

Prognostic significance of normal-sized ovary in advanced serous epithelial ovarian cancer

Objective

We compared survival outcomes of advanced serous type epithelial ovarian cancer (EOC) patients with normal-sized ovaries and enlarged-ovarian tumors by propensity score matching analysis.

Methods

The medical records of EOC patients treated at Samsung Medical Center between 2002 and 2015 were reviewed retrospectively. We investigated EOC patients with high grade serous type histology and International Federation of Gynecology and Obstetrics (FIGO) stage IIIB, IIIC, or IV who underwent primary debulking surgery (PDS) and adjuvant chemotherapy to identify patients with normal-sized ovaries. Propensity score matching was performed to compare patients with normal-sized ovaries to patients with enlarged-ovarian tumors (ratio, 1:3) according to age, FIGO stage, initial cancer antigen (CA)-125 level, and residual disease status after PDS.

Results

Of the 419 EOC patients, 48 patients had normal-sized ovary. Patients with enlarged-ovarian tumor were younger (54.0±10.3 vs. 58.4±9.2 years, p=0.005) than those with normal-sized ovary, and there was a statistically significant difference in residual disease status between the 2 groups. In total cohort with a median follow-up period of 43 months (range, 3–164 months), inferior overall survival (OS) was shown in the normal-sized ovary group (median OS, 71.2 vs. 41.4 months; p=0.003). After propensity score matching, the group with normal-sized ovary showed inferior OS compared to the group with enlarged-ovarian tumor (median OS, 72.1 vs. 41.4 months; p=0.031). In multivariate analysis for OS, normal-sized ovary remained a significant factor.

Conclusion

Normal-sized ovary was associated with poor OS compared with the common presentation of enlarged ovaries in EOC, independent of CA-125 level or residual disease.

Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months

OBJECTIVES

To determine if radiomic features, alone or combined with clinical data, are associated with residual tumour (RT) at surgery, and predict the risk of disease progression within 12 months (PD12) in ovarian cancer (OC) patients.

METHODS

This retrospective study enrolled 101 patients according to the following inclusion parameters: cytoreductive surgery performed at our institution (9 May 2007-23 February 2016), assessment of BRCA mutational status, preoperative CT available. Radiomic features of the ovarian masses were extracted from 3D structures drawn on CT images. A phantom experiment was performed to assess the reproducibility of radiomic features. The final radiomic features included in the analysis (n = 516) were grouped into clusters using a hierarchical clustering procedure. The association of each cluster's representative radiomic feature with RT and PD12 was assessed by chi-square test. Multivariate analysis was performed using logistic regression models. P values < 0.05 were considered significant.

RESULTS

Patients with values of F2-Shape/Compactness1 below the median, of F1- GrayLevelCooccurenceMatrix25/0-1InformationMeasureCorr2 below the median and of F1-GrayLevelCooccurenceMatrix25/-333-1InverseVariance above the median showed higher risk of RT (36%, 36% and 35%, respectively, as opposed to 18%, 18% and 18%). Patients with values of F4-GrayLevelRunLengthMatrix25/-333RunPercentage above the median, of F2 shape/Max3DDiameter below the median and F1-GrayLevelCooccurenceMatrix25/45-1InverseVariance above the median showed higher risk of PD12 (22%, 24% and 23%, respectively, as opposed to 6%, 5% and 6%). At multivariate analysis F2-Shape/Max3DDiameter remained significant (odds ratio (95% CI) = 11.86 (1.41-99.88)). To predict PD12, a clinical radiomics model performed better than a base clinical model.

CONCLUSION

This study demonstrated significant associations between radiomic features and prognostic factors such as RT and PD12.

KEY POINTS

• No residual tumour (RT) at surgery is the most important prognostic factor in OC. • Radiomic features related to mass size, randomness and homogeneity were associated with RT. • Progression of disease within 12 months (PD12) indicates worse prognosis in OC. • A model including clinical and radiomic features performed better than only-clinical model to predict PD12.

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.

Screening for ovarian cancer: imaging challenges and opportunities for improvement

The United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) recently reported a reduction in the average overall mortality among ovarian cancer patients screened with an annual sequential, multimodal strategy that tracked biomarker CA125 over time, where increasing serum CA125 levels prompted ultrasound. However, multiple cases were documented wherein serum CA125 levels were rising, but ultrasound screens were normal, thus delaying surgical intervention. A significant factor which could contribute to false negatives is that many aggressive ovarian cancers are believed to arise from epithelial cells on the fimbriae of the fallopian tubes, which are not readily imaged. Moreover, because only a fraction of metastatic tumors may reach a sonographically-detectable size before they metastasize, annual screening with ultrasound may fail to detect a large fraction of early-stage ovarian cancers. The ability to detect ovarian carcinomas before they metastasize is critical and future efforts towards improving screening should focus on identifying unique features specific to aggressive, early-stage tumors, as well as improving imaging sensitivity to allow for detection of tubal lesions. Implementation of a three-stage multimodal screening strategy in which a third modality is employed in cases where the first-line blood-based assay is positive and the second-line ultrasound exam is negative may also prove fruitful in detecting early-stage cases missed by ultrasound.

Laser scanning laser diode photoacoustic microscopy system

The development of low-cost and fast photoacoustic microscopy systems enhances the clinical applicability of photoacoustic imaging systems. To this end, we present a laser scanning laser diode-based photoacoustic microscopy system. In this system, a 905 nm, 325 W maximum output peak power pulsed laser diode with 50 ns pulsewidth is utilized as the light source. A combination of aspheric and cylindrical lenses is used for collimation of the laser diode beam. Two galvanometer scanning mirrors steer the beam across a focusing aspheric lens. The lateral resolution of the system was measured to be ∼21 μm using edge spread function estimation. No averaging was performed during data acquisition. The imaging speed is ∼370 A-lines per second. Photoacoustic microscopy images of human hairs, ex vivo mouse ear, and ex vivo porcine ovary are presented to demonstrate the feasibility and potentials of the proposed system.

Differentiating between borderline and invasive malignancies in ovarian tumors using a multivariate logistic regression model

OBJECTIVE

The objective of this study was to build a model to differentiate between borderline and invasive ovarian tumors.

MATERIALS AND METHODS

We performed a retrospective study involving 148 patients with borderline or invasive ovarian tumors in our institute between 1997 and 2012. Clinical and pathologic data were collected. Logistic regression was used to build the model.

RESULTS

The model was created based on the following variables (p < 0.05): menopausal status; preoperative serum level of cancer antigen 125; the greatest diameter of the tumor; and the presence of solid parts on ultrasound imaging. The sensitivity and specificity of the model were 94.6% [95% confidence interval (CI), 0.887-1] and 78.3% (95% CI, 0.614-0.952) for patients aged ≥ 50 years, and 76.0% (95% CI, 0.622-0.903) and 60.0% (95% CI, 0.438-0.762) for those aged < 50 years, respectively. The performance of the model was tested using cross-validation.

CONCLUSION

Differentiation between borderline and invasive ovarian tumors can be achieved using a model based on the following criteria: menopausal status; cancer antigen 125 level; and ultrasound parameters. The model is helpful to oncologists and patients in the initial evaluation phase of ovarian tumors.

Tumor size as a recommendable variable for accuracy of the prognostic prediction of gastric cancer: a retrospective analysis of 1,521 patients

BACKGROUND

It is still controversial whether tumor size (Ts) should be considered an important indicator for evaluation the prognosis of gastric cancer (GC). The purpose of this study was to elucidate the prognostic prediction superiority of Ts in the large-scale cohort of GC patients.

METHODS

Data from 1,521 patients who underwent the curative resection were analyzed for demonstration the prognostic value of Ts. In addition, a tumor size-node-metastasis (TsNM) classification system was proposed to evaluate the comparative superiorities of the prognostic prediction of GC patients.

RESULTS

With the univariate and multivariate analyses, Ts was identified as an independently prognostic predictor of GC patients, as was T stage. Ts was demonstrated to have smaller Akaike information criterion and Bayesian Information Criterion values within the Cox regression analyses than shown by T stage, which represented the optimum prognostic stratification. TsNM classification was also found to be competent for accurately prognostic evaluation of GC patients. The matched case-control logistic regression showed that TsNM classification could provide very powerful discriminations of patients' overall survival, compared with TNM classification. Additionally, Ts stage was found to enhance the survival discriminations in patients with certain clinicopathological characteristics, including male gender, T4a stage, N0 stage, diffuse type of Lauren classification, or age ≤60 years.

CONCLUSIONS

Ts should be recommended as an important clinicopathologic variable to enhance the accuracy of the prognostic prediction of GC clinical patients.