Transverse Relaxation Rate of Cyst Fluid Can Predict Malignant Transformation of Ovarian Endometriosis

MR Relaxometry for Discriminating Malignant Ovarian Cystic Tumors: A Prospective Multicenter Cohort Study

BACKGROUND

Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted a retrospective study on magnetic resonance (MR) relaxometry of cyst fluid to distinguish EAOC from OE and reported that this method showed good accuracy. The purpose of this study is to evaluate the accuracy of a non-invasive method in re-evaluating pre-surgical diagnosis of malignancy by a prospective multicenter cohort study.

METHODS

After the standard diagnosis process, the R2 values were obtained using a 3T system. Data on the patients were then collected through the Case Report Form (CRF). Between December 2018 and March 2023, six hospitals enrolled 109 patients. Out of these, 81 patients met the criteria required for the study.

RESULTS

The R2 values calculated using MR relaxometry showed good discriminating ability with a cut-off of 15.74 (sensitivity 80.6%, specificity 75.0%, AUC = 0.750, p < 0.001) when considering atypical or borderline tumors as EAOC. When atypical and borderline cases were grouped as OE, EAOC could be distinguished with a cut-off of 16.87 (sensitivity 87.0%, specificity 61.1%).

CONCLUSIONS

MR relaxometry has proven to be an effective tool for discriminating EAOC from OE. Regular use of this method is expected to provide significant insights for clinical practice.

Research progress in endometriosis-associated ovarian cancer

Endometriosis-associated ovarian cancer (EAOC) is a unique subtype of ovarian malignant tumor originating from endometriosis (EMS) malignant transformation, which has gradually become one of the hot topics in clinical and basic research in recent years. According to clinicopathological and epidemiological findings, precancerous lesions of ovarian clear cell carcinoma (OCCC) and ovarian endometrioid carcinoma (OEC) are considered as EMS. Given the large number of patients with endometriosis and its long time window for malignant transformation, sufficient attention should be paid to EAOC. At present, the pathogenesis of EAOC has not been clarified, no reliable biomarkers have been found in the diagnosis, and there is still a lack of basis and targets for stratified management and precise treatment in the treatment. At the same time, due to the long medical history of patients, the fast growth rate of cancer cells, and the possibility of eliminating the earliest endometriosis-associated ovarian cancer, it is difficult to find the corresponding histological evidence. As a result, few patients are finally diagnosed with EAOC, which increases the difficulty of in-depth study of EAOC. This article reviews the epidemiology, pathogenesis, risk factors, clinical diagnosis, new treatment strategies and prognosis of endometriosis-associated ovarian cancer, and prospects the future direction of basic research and clinical transformation, in order to achieve stratified management and personalized treatment of ovarian cancer patients.

Malignancy in Abdominal Wall Endometriosis: Is There a Way to Avoid It? A Systematic Review

Background: Malignant-associated abdominal wall endometriosis (AWE) is a rare pathology, likely to occur in 1% of scar endometriosis. The objectives of this study were to update the evidence on tumor degeneration arising from AWE to notify about the clinical characteristics, the different treatments offered to patients and their outcomes. Methods: A comprehensive systematic review of the literature was conducted. PubMed, Embase and Cochrane Library databases were used. Prospero (ID number: CRD42024505274). Results: Out of the 152 studies identified, 63 were included, which involved 73 patients. The main signs and symptoms were a palpable abdominal mass (85.2%) and cyclic pelvic pain (60.6%). The size of the mass varied between 3 and 25 cm. Mean time interval from the first operation to onset of malignant transformation was 20 years. Most common cancerous histological types were clear cell and endometrioid subtypes. Most widely accepted treatment is the surgical resection of local lesions with wide margins combined with adjuvant chemotherapy. The prognosis for endometriosis-associated malignancy in abdominal wall scars is poor, with a five-year survival rate of around 40%. High rates of relapse have been reported. Conclusions: Endometrial implants in the abdominal wall should be considered as preventable complications of gynecological surgeries. Special attention should be paid to women with a history of cesarean section or uterine surgery.

Endometriosis-Related Ovarian Cancer: Where Are We Now? A Narrative Review towards a Pragmatic Approach

BACKGROUND

Endometriosis affects more than 10% of reproductive-aged women, causing pelvic pain and infertility. Despite the benign nature of endometriosis, ovarian endometriomas carry a higher risk of developing endometrioid carcinomas (EnOCs) and clear cell ovarian carcinomas (CCCs). Atypical endometriosis, defined as cytological atypia resembling intraepithelial cancer, is considered the precursor of endometriosis-associated ovarian cancer (EAOC). This narrative review aims to provide an overview of EAOC, proposing a practical approach to clinical and therapeutic decision making.

METHODS

An electronic literature search was conducted from inception up to January 2023, using the MEDLINE database via PubMed to evaluate the existing literature on EAOC, including its pathogenesis, the diagnostic process, and the therapeutic possibilities, with articles not relevant to the topic or lacking scientific merit being excluded.

RESULTS

Eighty-one articles were included in the review to present the current state of the art regarding EAOC. A pragmatic clinical flowchart is proposed to guide therapeutic decisions and improve patient outcomes.

CONCLUSIONS

Endometriosis patients may have an increased risk of developing EAOC (either EnOC or CCC). Despite not being fully accepted, the concept of AE may reshape the endometriosis-ovarian cancer relationship. Further research is needed to understand the unaddressed issues.

New insights about endometriosis-associated ovarian cancer: pathogenesis, risk factors, prediction and diagnosis and treatment

Previous studies have shown that the risk of malignant transformation of endometriosis in premenopausal women is approximately 1%, significantly impacting the overall well-being and quality of life of affected women. Presently, the diagnostic gold standard for endometriosis-associated ovarian cancer (EAOC) continues to be invasive laparoscopy followed by histological examination. However, the application of this technique is limited due to its high cost, highlighting the importance of identifying a non-invasive diagnostic approach. Therefore, there is a critical need to explore non-invasive diagnostic methods to improve diagnostic precision and optimize clinical outcomes for patients. This review presents a comprehensive survey of the current progress in comprehending the pathogenesis of malignant transformation in endometriosis. Furthermore, it examines the most recent research discoveries concerning the diagnosis of EAOC and emphasizes potential targets for therapeutic intervention. The ultimate objective is to improve prevention, early detection, precise diagnosis, and treatment approaches, thereby optimizing the clinical outcomes for patients.

Clinicopathological characteristics, molecular features and novel diagnostic strategies for the detection of malignant transformation of endometriosis (Review)

Endometriosis is a benign gynecological disease that affects women of reproductive age. Although malignant transformation of endometriosis is rare, physicians must be aware of this due to the high incidence of clear cell carcinoma of the ovary (CCC) in Japan. The most prevalent histological subtype of ovarian cancer is CCC (~70%) followed by endometrioid carcinoma (30%). The present review discusses the clinicopathological and molecular features of endometriosis-associated ovarian cancer (EAOC) as well as prospects for novel diagnostic strategies. Papers published between 2000 and 2022 in the PubMed and Google Scholar databases were included. Contents of the endometriotic cyst fluid may be involved in carcinogenesis, although the underlying mechanisms are largely unknown. Some studies have proposed a possible mechanism wherein excessive hemoglobin, heme and iron could cause an imbalance in intracellular redox homeostasis in endometriotic cells. Combined with DNA damage and mutations, the imbalances may induce the development of EAOC. Endometriotic cells evolve to adapt to the prolonged unfavorable oxidative microenvironmental stress. On the other hand, macrophages enhance the antioxidative defense mechanism and protect endometriotic cells against oxidative damage through intercellular crosstalk and signaling pathways. Therefore, changes in redox signaling, energy metabolism and the tumor immune microenvironment could be the key elements in the malignant transformation of certain endometriotic cell clones. Additionally, non-invasive bioimaging (i.e., magnetic resonance relaxometry) and biomarkers (i.e., tissue factor pathway inhibitor 2) may be promising tools for early-stage detection of the disease. In conclusion, the present review summarizes the latest advancements in research on the biological characteristics and early diagnosis of malignant transformation of endometriosis.

The Endometriotic Neoplasm Algorithm for Risk Assessment (e-NARA) Index Sheds Light on the Discrimination of Endometriosis-Associated Ovarian Cancer from Ovarian Endometrioma

BACKGROUND

Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate endometriosis-associated ovarian cancer (EAOC) from ovarian endometrioma (OE) with high accuracy. However, this method has a limitation in discriminating malignancy in clinical use because the R2 value depends on the device manufacturer and repeated imaging is unrealistic. The current study aimed to reassess the diagnostic accuracy of MR relaxometry and investigate a more powerful tool to distinguish EAOC from OE.

METHODS

This retrospective study was conducted at our institution from December, 2012, to May, 2022. A total of 150 patients were included in this study. Patients with benign ovarian tumors (n = 108) mainly received laparoscopic surgery, and cases with suspected malignancy (n = 42) underwent laparotomy. Information from a chart review of the patients' medical records was collected.

RESULTS

A multiple regression analysis revealed that the age, the tumor diameter, and the R2 value were independent malignant predicting factors. The endometriotic neoplasm algorithm for risk assessment (e-NARA) index provided high accuracy (sensitivity, 85.7%; specificity, 87.0%) to discriminate EAOC from OE.

CONCLUSIONS

The e-NARA index is a reliable tool to assess the probability of malignant transformation of endometrioma.

The application of risk models based on machine learning to predict endometriosis-associated ovarian cancer in patients with endometriosis

INTRODUCTION

There is currently no satisfactory model for predicting malignant transformation of endometriosis. The aim of this study was to construct and evaluate a risk model incorporating noninvasive clinical parameters to predict endometriosis-associated ovarian cancer (EAOC) in patients with endometriosis.

MATERIAL AND METHODS

We enrolled 6809 patients with endometriosis confirmed by pathology, and randomly allocated them to training (n = 4766) and testing cohorts (n = 2043). The proportion of patients with EAOC in each cohort was similar. We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method - gradient-boosting decision tree - to construct a predictive model for EAOC and to evaluate the accuracy of the model. We also constructed a multivariate logistic regression model inclusive of the EAOC-associated risk factors using a back stepwise procedure. Then we compared the performance of the two risk-predicting models using DeLong's test.

RESULTS

The occurrence of EAOC was 1.84% in this study. The logistic regression model comprised 10 selected features and demonstrated good discrimination in the testing cohort, with an area under the curve (AUC) of 0.891 (95% confidence interval [CI] 0.821-0.960), sensitivity of 88.9%, and specificity of 76.7%. The risk model based on machine learning had an AUC of 0.942 (95% CI 0.914-0.969), sensitivity of 86.8%, and specificity of 86.7%. The machine learning-based risk model performed better than the logistic regression model in DeLong's test (p = 0.036). Furthermore, in a prospective dataset, the machine learning-based risk model had an AUC of 0.8758, a sensitivity of 94.4%, and a specificity of 73.8%.

CONCLUSIONS

The machine learning-based risk model was constructed to predict EAOC and had high sensitivity and specificity. This model could be of considerable use in helping reduce medical costs and designing follow-up schedules.

The Comparison of Three Predictive Indexes to Discriminate Malignant Ovarian Tumors from Benign Ovarian Endometrioma: The Characteristics and Efficacy

This study aimed to evaluate the prediction efficacy of malignant transformation of ovarian endometrioma (OE) using the Copenhagen Index (CPH-I), the risk of ovarian malignancy algorithm (ROMA), and the R2 predictive index. This retrospective study was conducted at the Department of Gynecology, Nara Medical University Hospital, from January 2008 to July 2021. A total of 171 patients were included in the study. In the current study, cases were divided into three cohorts: pre-menopausal, post-menopausal, and a combined cohort. Patients with benign ovarian tumor mainly received laparoscopic surgery, and patients with suspected malignant tumors underwent laparotomy. Information from a review chart of the patients’ medical records was collected. In the combined cohort, a multivariate analysis confirmed that the ROMA index, the R2 predictive index, and tumor laterality were extracted as independent factors for predicting malignant tumors (hazard ratio (HR): 222.14, 95% confidence interval (CI): 22.27−2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90−33.13, p < 0.001; HR: 0.15, 95% CI: 0.03−0.75, p = 0.021, respectively). In the pre-menopausal cohort, a multivariate analysis confirmed that the CPH index and the R2 predictive index were extracted as independent factors for predicting malignant tumors (HR: 6.45, 95% CI: 1.47−28.22, p = 0.013; HR: 31.19, 95% CI: 8.48−114.74, p < 0.001, respectively). Moreover, the R2 predictive index was only extracted as an independent factor for predicting borderline tumors (HR: 45.00, 95% CI: 7.43−272.52, p < 0.001) in the combined cohort. In pre-menopausal cases or borderline cases, the R2 predictive index is useful; while, in post-menopausal cases, the ROMA index is better than the other indexes.

A Novel Predictive Tool for Discriminating Endometriosis Associated Ovarian Cancer from Ovarian Endometrioma: The R2 Predictive Index

Simple Summary

Ovarian Endometrioma (OE) is a precancerous condition for endometriosis-associated ovarian cancer (EAOC). For many clinicians observing OE outpatients, setting the appropriate time for surgery can be a challenge because there is no suggestive milestone. Out of the fear of malignant transformation, many patients have surgery conducted according to respective faculty standards. This study aims to investigate a novel, noninvasive method not requiring an MRI device. This study partly helps to lift the above restrictions, and has the potential to suggest intervention-appropriate timing to the physician.

Abstract

Background: Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate between ovarian endometrioma (OE) and endometriosis-associated ovarian cancer (EAOC), with a sensitivity and specificity of 86% and 94%, respectively. MRI models that can measure R2 values are limited, and the R2 values differ between MRI models. This study aims to extract the factors contributing to the R2 value, and to make a formula for estimating the R2 values, and to assess whether the R2 predictive index calculated by the formula could discriminate EAOC from OE. Methods: This retrospective study was conducted at our institution from November 2012 to February 2019. A total of 247 patients were included in this study. Patients with benign ovarian tumors mainly received laparoscopic surgery, and the patients suspected of having malignant tumors underwent laparotomy. Information from a chart review of the patients’ medical records was collected. Results: In the investigative cohort, among potential factors correlated with the R2 value, multiple regression analyses revealed that tumor diameter and CEA could predict the R2 value. In the validation cohort, multivariate analysis confirmed that age, CRP, and the R2 predictive index were the independent factors. Conclusions: The R2 predictive index is useful and valuable to the detection of the malignant transformation of endometrioma.

Validation of magnetic resonance relaxometry R2 value and cyst fluid iron level for diagnosis of ovarian endometrioma

Objectives

Magnetic resonance (MR) R2 relaxometry is a safe, noninvasive diagnostic modality for the evaluation of iron levels in the contents of ovarian cysts. The study aims to investigate the sensitivity and specificity of the two methods, R2 value and iron level, in diagnosing OMA patients in the validation set.

Methods

A prospective cohort study was conducted from 2013 to 2019. We investigated how R2 value was affected by iron-related compounds, antioxidants and bioelements in the cysts.

Results

The sensitivity and specificity of CF iron-based diagnosis of OMA was 96.6% and 95.4%, respectively. The sensitivity and specificity for R2 value in diagnosing OMA were 86.2% and 70.7%, respectively. The outcomes of the two tests were highly correlated (r = 0.758; P <0.001). The R2 value was positively correlated with CF levels of iron-related compounds and antioxidants. The R2 value was affected not only by iron ions but also by calcium ions.

Conclusion

Preoperative MR relaxometry may provide a noninvasive alternative to CF iron test in diagnosing OMA. The presence of paramagnetic cations in the cyst may be associated with reduced specificity.

Differentiation between endometriosis-associated ovarian cancers and non- endometriosis-associated ovarian cancers based on magnetic resonance imaging

OBJECTIVES

Endometriosis-associated ovarian cancer (EAOC) patients show different clinical characteristics compared with non-EAOC patients. However, a few studies are focused on the imaging characteristics of EAOC until now. We assessed MRI characteristics in differentiating EAOC and non-EAOC.

METHODS

We retrospectively analyzed clinical and MRI characteristics from 54 patients with 67 lesions diagnosed with primary epithelial ovarian carcinoma at the Third Affiliated Hospital of Guangzhou Medical University between January 2012 and October 2020. We studied MRI findings such as maximum diameter, morphology, configuration, locularity, features of mural nodules, lymphadenopathy, peritoneal implants, the presence of hyperintensity on T1WI, and hypointensity on T2WI. We also studied the clinical characteristics. Significant MRI variables in univariate analysis were selected for subsequent multivariate regression analysis. This study evaluated the diagnostic performance of the significant MRI variables in univariate analysis.

RESULTS

We found that the patients with EAOC, compared with those with non-EAOC, were younger, more unilateral, and had earlier FIGO stage. Univariate analysis revealed that morphology, locularity, growth pattern of mural nodules, and hypointensity on T2WI were factors that significantly differed between EAOC and non-EAOC. In the multivariate logistic regression analysis, locularity and hypointensity on T2WI were independent predictors to distinguish EAOC from non-EAOC.

CONCLUSIONS

EAOC typically presented as a unilocular mass with hypointensity on T2WI in cystic components. MRI could help distinguish EAOC from non-EAOC.

ADVANCES IN KNOWLEDGE

MRI is a promising tool for preoperative diagnosis of EAOC.

Relationship between Cyst Fluid Concentrations of Iron and Severity of Dysmenorrhea in Patients with Ovarian Endometrioma

OBJECTIVES AND DESIGN

Endometriosis-related pain can be caused by anatomical distortions as well as environmental factors such as inflammation and oxidative stress. The aim of this study is to investigate the relationship between the severity of dysmenorrhea in patients with ovarian endometrioma (OMA) and cyst fluid (CF) concentrations of irons, including total iron, heme iron, and free iron.

METHOD

Eighty-three patients who were histologically diagnosed with OMA were enrolled in the Department of Gynecology, Nara Medical University Hospital, between 2013 and 2019. The patients were divided into 4 groups according to the severity of dysmenorrhea: no pain, mild, moderate, and severe. Iron concentration was measured by the inductively coupled plasma optical emission spectrometry method.

RESULTS

There were no significant differences among the 4 groups in variables such as age at diagnosis, preoperative CA125, preoperative CA19-9, cyst size, and tumor laterality (unilateral or bilateral). There was a positive correlation between the severity of dysmenorrhea and total iron (p < 0.001) and heme iron (p = 0.016) concentrations. Multiple regression analyses revealed that the CF concentration of total iron (hazard ratio 18.75, 95% confidence interval: 2.26-155.35, p = 0.007) was a significant independent variable associated with the severity of dysmenorrhea. A receiver operating characteristic curve analysis showed that a total iron exceeding 290.8 mg/L was associated with severe dysmenorrhea with a sensitivity of 90.9% and a specificity of 65.7%.

LIMITATIONS

This study excluded patients with adenomyosis, superficial endometriosis, or deep endometriosis, resulting in a smaller number of cases. Iron levels could not be compared to the endometriosis stage using the r-ASRM score.

CONCLUSIONS

There is no clear evidence that iron predicts the severity of endometriosis-related pain. However, iron may be closely associated with dysmenorrhea.

Quantitative analysis of ovarian cysts and tumors by using T2 star mapping

AIM

The aim of this study was to investigate the efficacy of T2 star (T2*) mapping in diagnosing ovarian cysts/ tumors.

METHODS

Pelvic magnetic resonance examinations including T2*WI were performed before surgery in 35 patients. The region of interest, consisted of a 10 mm² diameter circle, was set as much as possible inside ovarian tumors/cysts to measure T2*values, and mean T2* values were compared in ovarian cyst/tumor types, retrospectively. Diagnoses of 40 ovarian cysts/tumors were determined by pathological reports, in which 17 were endometriomas, 13 were mature cystic teratomas, 6 were mucinous cystadenomas and 4 were serous cystadenomas.

RESULTS

The average T2* values of endometrioma was 56.8 ± 8.7 ms (mean ± SEM), which was significantly lower than that of mucinous cystadenoma (334.2 ± 58.5 ms, mean ± SEM) or serous cystadenoma (237.0 ± 45.4 ms, mean ± SEM). There was no difference in T2* values between endometrioma and mature cystic teratoma (64.1 ± 22.6 ms, mean ± SEM). Receiver operating characteristics curve analysis revealed that optimal cut-off value for differential diagnosis of endometrioma and mucinous or serous cystadenoma was 149.2 ms as T2* value, which has an area under the curve of 0.95 (sensitivity = 92.4%, specificity = 78.6%).

CONCLUSION

T2* values were useful to diagnose various types of ovarian cyst/tumor.

Magnetic resonance relaxometry improves the accuracy of conventional MRI in the diagnosis of endometriosis-associated ovarian cancer: A case report

Endometriosis is a precancerous condition for endometriosis-associated ovarian cancer (EAOC). In the present study, conventional magnetic resonance imaging (MRI) and MR relaxometry were used to examine a case of clear cell carcinoma that arose in a pre-existing right-sided benign ovarian endometrioma (OE). The 42-year-old nulliparous woman suspected of EOAC, as assessed by conventional MRI, requested fertility-sparing surgery such as laparoscopic endometrioma cystectomy. Furthermore, the MR transverse relaxation rate (R2) was determined using a single-voxel, multi-echo MR sequence using a 3 Tesla-MR system. An R2 value <12.1 s^-1 was indicative of malignancy, as described in previous studies. In the present study, MR relaxometry identified an R2 value of 7.98 s^-1 in the right cyst, which suggested the malignant transformation of benign OE. Based on these findings, fertility-sparing surgery was contraindicated. In conclusion, MR relaxometry may represent a new clinical approach as an adjunctive modality for the diagnosis of EAOC. When patients exhibiting a pelvic mass suspected of EAOC desire fertility-sparing treatment options, MR relaxometry can facilitate the selection of conservative management.

A diagnostic challenge of seromucinous borderline tumor: A case report

INTRODUCTION

Magnetic resonance (MR) relaxometry provides a noninvasive predictive tool to discriminate between benign ovarian endometrioma (OE) and endometriosis-associated ovarian cancer (EAOC). Transverse relaxation rate R2 value was determined using a single-voxel, multi-echo MR sequence (HISTO) by a 3T-MR system. R2 with cutoff value of 12.1 s was established to discriminate between benign and malignant tumors.

PATIENT CONCERNS

We present a case of a 39-year-old woman who was initially thought to be malignant transformation of endometriosis by diagnostic MR imaging of the vascularized solid components.

DIAGNOSIS

A R2 value of 42.62 s on MR relaxometry demonstrated that this case is non-malignant.

INTERVENTIONS

To confirm the diagnose, left salpingo-oophorectomy by laparoscopic surgery was performed.

OUTCOMES

Histopathological results revealed seromucinous borderline tumor (SMBT). Our experience suggests that preoperative MR relaxometry may be useful for discriminating "borderline (SMBT)" from "malignancy (EAOC)." Furthermore, immunohistochemical studies of this case demonstrated ovarian SMBT cells were positive for estrogen receptor, progesterone receptor, and hepatocyte nuclear factor-1beta. A similar expression pattern was also observed in patients with benign OE.

LESSONS

In many respects, SMBT characteristics differ from those of EAOC but resemble those of benign OE. MR relaxometry unveils a new clinical approach as an adjunctive modality for discriminating SMBT from EAOC.

Modern approaches to noninvasive diagnosis of malignant transformation of endometriosis

Endometriosis-associated ovarian cancer (EAOC) is a rare entity and has highly variable morphological presentations. Mural nodules can be seen in EAOC and benign ovarian endometrioma (OE), which causes a diagnostic dilemma. The differential diagnosis between early-stage EAOC with predominantly cystic appearances and benign OE remains a challenge for physicians. This study will summarize recent knowledge of diagnosis of malignant transformation of endometriosis that have been studied through an innovative approach based on a wide array of novel technologies. Using PubMed database, we focused on the biochemical and technical advancement in the differential diagnosis of benign and malignant endometriosis. Compared with the subjects with benign OE, cyst fluid hemoglobin and iron-related compounds levels were significantly lower in patients with EAOC. This observation opens up the possibility of early diagnosis before morphological variations are captured through ultrasonographic and magnetic resonance (MR) imaging diagnosis. The metallobiology technology offers one solution to this challenge. We discuss the noninvasive diagnosis of EAOC via various imaging methods, including electronic absorption spectroscopy, near infrared approach and MR transverse relaxometry. Special emphasis is given to recent advances in the noninvasive imaging modalities.

Malignant Transformation of an Ovarian Endometrioma during Endometriosis Treatment: A Case Report

Dienogest (DNG) is considered to be effective against ovarian endometrioma (OMA). We report a rare case of OMA transformation to ovarian cancer during long-term endometriosis treatment with a periodic administration of a gonadotropin-releasing hormone agonist (Gn-RH agonist) and DNG. The patient was a 41-year-old Japanese woman. OMA and adenomyosis of the uterus were revealed via computed tomography. Consequently, she underwent conservative treatment without undergoing surgery because her overall status was poor. She received cyclic therapy (Gn-RH agonist and DNG) for approximately eight years. However, she reported lumbago and underwent close medical examination at our hospital after about eight years of treatment. Under the suspicion of malignant transformation, she underwent surgery. The pathological diagnosis was clear cell carcinoma of the right ovary (stage 2B). After surgery, she received six courses of chemotherapy (conventional TC). No evidence of disease was observed after chemotherapy. Our findings suggest that malignant transformation of OMA can occur during DNG treatment. Since the delayed detection of ovarian cancer greatly affects the prognosis, women older than 40 with OMA are encouraged to undergo regular check-ups every few months.

New knowledge and insights about the malignant transformation of endometriosis

Endometriosis may be a definitive risk factor for ovarian cancer, the most fatal gynecological cancer. The ability of endometriosis to transform into malignancy, first described by Dr. Sampson in 1925, is considered a rare occurrence, affecting approximately 1% of ovarian endometriomas. Recently we conducted a retrospective study regarding the malignant transformation of endometriosis in Japanese women. Many studies have reported a consistent correlation between endometriosis and ovarian cancer according to histological subtypes. However, the existing epidemiological evidence linking this association is insufficient to define the role of endometriosis as a cause of ovarian cancer and to influence changes to current clinical practice. Prospective cohort studies are therefore needed to clarify this issue. Additionally, the results of many molecular studies are conflicting, and earlier studies showing the molecular aberrations involved in genomic instability and mutation that enable malignant transformation have not been replicated in later studies. Careful long-term observation of a patient with endometrioma is required to detect possible subsequent incidence of malignant transformation. More importantly, a precise strategy should be set up for better prevention, early detection, specific diagnosis and treatment targeting molecular pathogenesis to understand the mechanisms of endometriosis-associated ovarian cancer. Clinicians need to be aware of the increased ovarian cancer risk in women with endometriosis.

Discrimination of malignant transformation from benign endometriosis using a near-infrared approach

The aim of the present single-center retrospective study was to investigate the discrimination of malignant transformation from ovarian endometrioma (OE) using a near-infrared approach ex vivo. Cystic fluid samples were collected from patients with OE (n=34) and endometriosis-associated ovarian cancer (EAOC) (n=12). The light reflected from each sample of cystic fluid [change in luminance, Δl (cd/m²) = background luminance-cystic fluid luminance at 800 nm] was spectrally measured by a near-infrared CCD camera with band-path filter (800 nm). The Δl in EAOC was significantly lower compared with that in OE. On regression analysis, a positive correlation was observed between the Δl and Hb level in the cystic fluid, and this association was exponential. The diagnostic sensitivity and specificity of Δl was 83.3 and 94.1% at the cutoff value of 21.5 cd/m², with an area under the ROC curve of 0.897. The present ex vivo study potentially provides a powerful near-infrared approach for quantitative discrimination between EAOC and benign OE, with high sensitivity and specificity, which may have clinical applications.