Time-dependent diffusion MRI-based microstructural mapping for differentiating high-grade serous ovarian cancer from serous borderline ovarian tumor.

PURPOSE: To investigate the value of microstructural characteristics derived from time-dependent diffusion MRI in distinguishing high-grade serous ovarian cancer (HGSOC) from serous borderline ovarian tumor (SBOT) and the associations of immunohistochemical markers with microstructural features. METHODS: Totally 34 HGSOC and 12 SBOT cases who received preoperative pelvic MRI were retrospectively included in this study. Two radiologists delineated the tumors to obtain the regions of interest (ROIs). Time-dependent diffusion MRI signals were fitted by the IMPULSED (imaging microstructural parameters using limited spectrally edited diffusion) model, to extract microstructural parameters, including fraction of the intracellular component (fin), cell diameter (d), cellularity and extracellular diffusivity (Dex). Apparent diffusion coefficient (ADC) values were obtained from standard diffusion-weighted imaging (DWI). The parameters of HGSOCs and SBOTs were compared, and the diagnostic performance was evaluated. The associations of microstructural indexes with immunopathological parameters were assessed, including Ki-67, P53, Pax-8, ER and PR. RESULTS: In this study, fin, cellularity, Dex and ADC had good diagnostic performance levels in differentiating HGSOC from SBOT, with AUCs of 0.936, 0.909, 0.902 and 0.914, respectively. There were no significant differences in diagnostic performance among these parameters. Spearman analysis revealed in the HGSOC group, cellularity had a significant positive correlation with P53 expression (P = 0.028, r = 0.389) and Dex had a significant positive correlation with Pax-8 expression (P = 0.018, r = 0.415). ICC showed excellent agreement for all parameters. CONCLUSION: Time-dependent diffusion MRI had value in evaluating the microstructures of HGSOC and SBOT and could discriminate between these tumors.

An increase of serum CA-125 to two times of nadir level strongly predicts the image-identified relapse of serous ovarian cancer.

Using 70 U/ml or 35 U/ml as CA125 routine abnormal threshold may result in omissions in the relapse detection of Ovarian cancer (OvCa). This study aimed to clarify the association between a biochemical relapse (only the elevation of CA125) and an image-identified relapse to predict the relapsed lesions better. 162 patients who achieved complete clinical response were enrolled from women diagnosed with stage I-IV serous ovarian, tubal, and peritoneal cancers from January 2013 to June 2019 at our center. The CA125 level of 2 × nadir was defined as the indicator of image-identified relapse (P < 0.001). Compared to CA125 level exceeding 35 U/ml, the 2 × nadir of CA125 improve the sensitivity of image-identified relapse (84.9% vs 67.4%, P < 0.001); the 2 × nadir value can act as an earlier warning relapse signal with a longer median time to image-identified relapse (2.7 vs. 0 months, P < 0.001). Of the relapsed population, there was no difference of CA125 changing trend between the neoadjuvant chemotherapy (NACT) and primary debulking surgery (PDS) group after initial treatment. Compared with 35 U/ml, CA125 reaching 2 × nadir during the follow-up process might be a more sensitive and early relapse signal in patients with serous OvCa. This criterion may help guide patients to be recommended for imaging examination to detect potential relapse in time.

Predicting CD27 expression and clinical prognosis in serous ovarian cancer using CT-based radiomics.

BACKGROUND: This study aimed to develop and evaluate radiomics models to predict CD27 expression and clinical prognosis before surgery in patients with serous ovarian cancer (SOC). METHODS: We used transcriptome sequencing data and contrast-enhanced computed tomography images of patients with SOC from The Cancer Genome Atlas (n = 339) and The Cancer Imaging Archive (n = 57) and evaluated the clinical significance and prognostic value of CD27 expression. Radiomics features were selected to create a recursive feature elimination-logistic regression (RFE-LR) model and a least absolute shrinkage and selection operator logistic regression (LASSO-LR) model for CD27 expression prediction. RESULTS: CD27 expression was upregulated in tumor samples, and a high expression level was determined to be an independent protective factor for survival. A set of three and six radiomics features were extracted to develop RFE-LR and LASSO-LR radiomics models, respectively. Both models demonstrated good calibration and clinical benefits, as determined by the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis. The LASSO-LR model performed better than the RFE-LR model, owing to the area under the curve (AUC) values of the ROC curves (0.829 vs. 0.736). Furthermore, the AUC value of the radiomics score that predicted the overall survival of patients with SOC diagnosed after 60 months was 0.788 using the LASSO-LR model. CONCLUSION: The radiomics models we developed are promising noninvasive tools for predicting CD27 expression status and SOC prognosis. The LASSO-LR model is highly recommended for evaluating the preoperative risk stratification for SOCs in clinical applications.

Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image-based classifier. PathoRiCH was trained on an in-house cohort (n = 394) and validated on two independent external cohorts (n = 284 and n = 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model's decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.

Neoadjuvant chemotherapy for high-grade serous ovarian cancer: radiologic-pathologic correlation of response assessment and predictors of progression.

PURPOSE: Neoadjuvant chemotherapy is often administered for high-grade serous ovarian carcinoma (HGSC) prior to cytoreductive surgery. We evaluated treatment response by CT (simplified peritoneal carcinomatosis index [S-PCI]), pathology (chemotherapy response score [CRS]), laboratory markers (serum CA-125), and surgical outcomes, to identify predictors of disease-free survival. METHODS: For this retrospective, HIPAA-compliant, IRB-approved study, we identified 396 women with HGSC receiving neoadjuvant chemotherapy between 2010 and 2019. Two hundred and ninety-nine patients were excluded (surgery not performed; imaging/pathology unavailable). Pre- and post-treatment abdominopelvic CTs were assigned CT S-PCI scores 0-24 (higher score indicating more tumor). Specimens were assigned CRS of 1-3 (minimal to complete response). Clinical data were obtained via chart review. Univariate, multivariate, and survival analyses were performed. RESULTS: Ninety-seven women were studied, with mean age of 65 years ± 10. Interreader agreement was good to excellent for CT S-PCI scores (ICC 0.64-0.77). Despite a significant decrease in CT S-PCI scores after treatment (p < 0.001), mean decrease in CT S-PCI did not differ significantly among CRS categories (p = 0.20) or between patients who were optimally versus suboptimally debulked (p = 0.29). In a survival analysis, lower CRS (more viable tumor) was associated with shorter time to progression (p < 0.001). A joint Cox proportional-hazard models showed that only residual pathologic disease (CRS 1/2) (HR 4.19; p < 0.001) and change in CA-125 (HR 1.79; p = 0.01) predicted progression. CONCLUSION: HGSC response to neoadjuvant therapy by CT S-PCI did not predict pathologic CRS score, optimal debulking, or progression, revealing discordance between imaging, pathologic, biochemical, and surgical assessments of tumor response.

A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer.

BACKGROUND: Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. OBJECTIVE: This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. STUDY DESIGN: This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. RESULTS: In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. CONCLUSION: When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Habitat Radiomics Based on MRI for Predicting Platinum Resistance in Patients with High-Grade Serous Ovarian Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aims to explore the feasibility of MRI-based habitat radiomics for predicting response of platinum-based chemotherapy in patients with high-grade serous ovarian carcinoma (HGSOC), and compared to conventional radiomics and deep learning models. MATERIALS AND METHODS: A retrospective study was conducted on HGSOC patients from three hospitals. K-means algorithm was used to perform clustering on T2-weighted images (T2WI), contrast-enhanced T1-weighted images (CE-T1WI), and apparent diffusion coefficient (ADC) maps. After feature extraction and selection, the radiomics model, habitat model, and deep learning model were constructed respectively to identify platinum-resistant and platinum-sensitive patients. A nomogram was developed by integrating the optimal model and clinical independent predictors. The model performance and benefit was assessed using the area under the receiver operating characteristic curve (AUC), net reclassification index (NRI), and integrated discrimination improvement (IDI). RESULTS: A total of 394 eligible patients were incorporated. Three habitats were clustered, a significant difference in habitat 2 (weak enhancement, high ADC values, and moderate T2WI signal) was found between the platinum-resistant and platinum-sensitive groups (P < 0.05). Compared to the radiomics model (0.640) and deep learning model (0.603), the habitat model had a higher AUC (0.710). The nomogram, combining habitat signatures with a clinical independent predictor (neoadjuvant chemotherapy), yielded a highest AUC (0.721) among four models, with positive NRI and IDI. CONCLUSION: MRI-based habitat radiomics had the potential to predict response of platinum-based chemotherapy in patients with HGSOC. The nomogram combining with habitat signature had a best performance and good model gains for identifying platinum-resistant patients.

Ultrasonographic evaluation of endometrial stripe thickness is insufficient to rule out uterine serous carcinoma.

PURPOSE: Uterine serous carcinoma (USC) is a rare endometrial cancer representing less than 10% of uterine cancers but contributing to up to 50% of the mortality. Delay in diagnosis with this high-grade histology can have significant clinical impact. USC is known to arise in a background of endometrial atrophy. We investigated endometrial stripe (EMS) thickness in USC to evaluate current guidelines for postmenopausal bleeding in the context of this histology. METHODS: Retrospective chart review was conducted using ICD-9 and ICD-10 codes over an 18-year period. We included 139 patients with USC and compared characteristics of patients with EMS ≤ 4 mm and EMS > 4 mm. Chi-square or Fisher's exact tests were used to compare proportions and two-tailed t-tests to compare means. A p-value of < 0.05 was considered statistically significant. RESULTS: Most patients were white, obese, and multiparous. Thirty-two (23%) had an EMS ≤ 4 mm; 107 (77%) had an EMS > 4 mm. There were no statistically significant differences in age at diagnosis or presenting symptoms between groups, and postmenopausal bleeding was the most common symptom in each group. CONCLUSION: Nearly 25% of patients with USC initially evaluated with transvaginal ultrasound were found to have an EMS ≤ 4 mm. If transvaginal ultrasound is used to triage these patients, one in four women will potentially experience a delay in diagnosis that may impact their prognosis.

Histopathological subtyping of high-grade serous ovarian cancer using whole slide imaging.

OBJECTIVE: We have established 4 histopathologic subtyping of high-grade serous ovarian cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse prognosis than the other subtypes. In this study, we modified the histopathologic subtyping algorithm to achieve high interobserver agreement in whole slide imaging (WSI) and to characterize the tumor biology of MT type for treatment individualization. METHODS: Four observers performed histopathological subtyping using WSI of HGSOC in The Cancer Genome Atlas data. As a validation set, cases from Kindai and Kyoto Universities were independently evaluated by the 4 observers to determine concordance rates. In addition, genes highly expressed in MT type were examined by gene ontology term analysis. Immunohistochemistry was also performed to validate the pathway analysis. RESULTS: After algorithm modification, the kappa coefficient, which indicates interobserver agreement, was greater than 0.5 (moderate agreement) for the 4 classifications and greater than 0.7 (substantial agreement) for the 2 classifications (MT vs. non-MT). Gene expression analysis showed that gene ontology terms related to angiogenesis and immune response were enriched in the genes highly expressed in the MT type. CD31 positive microvessel density was higher in the MT type compared to the non-MT type, and tumor groups with high infiltration of CD8/CD103 positive immune cells were observed in the MT type. CONCLUSION: We developed an algorithm for reproducible histopathologic subtyping classification of HGSOC using WSI. The results of this study may be useful for treatment individualization of HGSOC, including angiogenesis inhibitors and immunotherapy.

Metastatic spread of serous ovarian carcinoma to the bilateral breasts: a rare presentation.

A woman presented with a mass in her right breast. She had previously been treated with carboplatin, paclitaxel and bevacizumab for serous ovarian carcinoma diagnosed 5 years previously and was currently on maintenance olaparib. A right breast mammogram demonstrated periareolar skin thickening and the physical examination revealed an erythematous, non-blanching cutaneous lesion. A punch biopsy revealed high-grade serous carcinoma of ovarian origin, positive for PAX-8, WT-1 and p53. Positron emission tomogram-CT scan showed diffusely increased fluorodeoxyglucose uptake in the right breast. She was treated with external beam radiation therapy to the right breast and regional lymphatics and received 5200 cGy in 20 fractions to the right breast and supraclavicular region with good response. Two weeks after completing radiation therapy, she presented with a new lesion inferior to her left areola, concerning for metastasis to the contralateral breast. Subsequent biopsy of the left breast identified metastatic serous ovarian carcinoma for which she received an additional 5200 cGy in 20 fractions to the breast.

Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer.

Patients with high-grade serous ovarian cancer suffer poor prognosis and variable response to treatment. Known prognostic factors for this disease include homologous recombination deficiency status, age, pathological stage and residual disease status after debulking surgery. Recent work has highlighted important prognostic information captured in computed tomography and histopathological specimens, which can be exploited through machine learning. However, little is known about the capacity of combining features from these disparate sources to improve prediction of treatment response. Here, we assembled a multimodal dataset of 444 patients with primarily late-stage high-grade serous ovarian cancer and discovered quantitative features, such as tumor nuclear size on staining with hematoxylin and eosin and omental texture on contrast-enhanced computed tomography, associated with prognosis. We found that these features contributed complementary prognostic information relative to one another and clinicogenomic features. By fusing histopathological, radiologic and clinicogenomic machine-learning models, we demonstrate a promising path toward improved risk stratification of patients with cancer through multimodal data integration.

Synchronous uterine serous carcinoma and ovarian sex cord stromal tumor (thecoma)-A rare first case report.

Synchronous endometrial and ovarian carcinoma is a rare instance and it accounts for 50 to 70% of all synchronous female genital tract tumors. However, it is very rare to find synchronous endometrial carcinoma and ovarian sex cord-stromal tumor (thecoma). The present case is a 75-year-old woman with a complaint of post-menopausal vaginal bleeding. Radiologically, the magnetic resonance imaging (MRI) pelvis revealed altered signal intensity mass in the uterus. Frozen section and routine histopathological examination were done on radical hysterectomy. Microscopically, serous carcinoma involving uterine corpus and left Fallopian tube was identified along with the unusual finding of contralateral ovarian sex cord-stromal tumor (thecoma), which was confirmed on immunohistochemical examination. It is a very rare association and is first reported in the present study after a thorough search of the published literature. Their relationship based on a high level of estrogen produced by the hyperactive ovary is controversial as serous carcinomas are less hormone-dependent.

Pancreatic serous cystadenocarcinoma diagnosed with liver metastasis at 7 years after the resection of the primary serous neoplastic lesion.

Pancreatic serous neoplasms are rare tumors that are usually benign. However, histopathological differentiation between benign (serous cystadenoma) and malignant (serous cystadenocarcinoma) lesions is difficult. We present the case of a patient with pancreatic serous cystadenocarcinoma that was diagnosed with liver metastasis 7 years after the resection of the primary serous neoplastic lesion. A woman in her 60 s was diagnosed with pancreatic serous cystadenoma based on imaging and histopathological examination findings. The tumor was resected, and the patient was followed up every 6 months to monitor tumor progression. At 7 years after the resection of the primary lesion, liver tumors showing marked flare-like contrast enhancements were detected on arterial phase computed tomography findings and on dynamic magnetic resonance imaging findings acquired 60 s after the administration of a contrast agent. Laparoscopic segmental hepatectomy of S4 and S6 was performed to resect these tumors. Histopathological examination revealed that these tumors were metastatic and developed from the primary lesion. Therefore, a diagnosis of serous cystadenocarcinoma was confirmed. The flare-like contrast enhancement around the metastatic liver lesions on computed tomography and dynamic magnetic resonance images may be an indicator of serous cystadenocarcinoma with liver metastasis that could assist in diagnosis.

Intra-Tumoral Nerve-Tracing in a Novel Syngeneic Model of High-Grade Serous Ovarian Carcinoma.

Dense tumor innervation is associated with enhanced cancer progression and poor prognosis. We observed innervation in breast, prostate, pancreatic, lung, liver, ovarian, and colon cancers. Defining innervation in high-grade serous ovarian carcinoma (HGSOC) was a focus since sensory innervation was observed whereas the normal tissue contains predominantly sympathetic input. The origin, specific nerve type, and the mechanisms promoting innervation and driving nerve-cancer cell communications in ovarian cancer remain largely unknown. The technique of neuro-tracing enhances the study of tumor innervation by offering a means for identification and mapping of nerve sources that may directly and indirectly affect the tumor microenvironment. Here, we establish a murine model of HGSOC and utilize image-guided microinjections of retrograde neuro-tracer to label tumor-infiltrating peripheral neurons, mapping their source and circuitry. We show that regional sensory neurons innervate HGSOC tumors. Interestingly, the axons within the tumor trace back to local dorsal root ganglia as well as jugular-nodose ganglia. Further manipulations of these tumor projecting neurons may define the neuronal contributions in tumor growth, invasion, metastasis, and responses to therapeutics.

Can addition of frozen section analysis to preoperative endometrial biopsy and MRI improve identification of high-risk endometrial cancer patients?

BACKGROUND: Surgeons sometimes have difficulty determining which result to favor when preoperative results (MRI + preoperative endometrial biopsy [pre-op EB]) differ from intraoperative frozen section histology (FS) results. Investigation of how FS can complement ordinary preoperative examinations like MRI and pre-op EB in identification of patients at high risk of lymph node metastasis (high-risk patients) could provide clarity on this issue. Therefore, the aim of this study is to assess the utility of pre-op EB, MRI and FS results and determine how to combine these results in identification of high-risk patients. METHODS: The subjects were 172 patients with endometrial cancer. Patients with a histological high-grade tumor (HGT), namely, grade 3 endometrioid cancer, clear cell carcinoma or serous cell carcinoma, or with any type of cancer invading at least half of the uterine myometrium were considered high-risk. Tumors invading at least half of the uterine myometrium were classified as high-stage tumors (HST). We compared (a) detection of HGT using pre-op EB versus FS, (b) detection of HST using MRI versus FS, and (c) identification of high-risk patients using MRI + pre-op EB versus FS. Lastly, we determined to what degree addition of FS results improves identification of high-risk patients by routine MRI + pre-op EB. RESULTS: (a) Sensitivity, specificity, and accuracy for detecting HGT were 59.6, 98.4 and 87.8% for pre-op EB versus 55.3, 99.2 and 87.2% for FS (P = 0.44). (b) These figures for detecting HST were 74.4, 83.0 and 80.8% for MRI versus 46.5, 99.2 and 86.0% for FS (P < 0.001). (c) These figures for identifying high-risk patients were 78.3, 85.4 and 82.6% for MRI + pre-op EB versus 55.1, 99.0 and 81.2% for FS (P < 0.001). The high specificity of FS improved the sensitivity of MRI + pre-op EB from 78.3 to 81.2%, but this difference was not statistically significant (P < 0.16). CONCLUSION: Frozen section enables identification of high-risk patients with nearly 100% specificity. This advantage can be used to improve sensitivity for identification of high-risk patients by routine MRI + pre-op EB, although this improvement is not statistically significant.

Cell-stiffness and morphological architectural patterns in clinical samples of high grade serous ovarian cancers.

High grade serous ovarian carcinoma (HGSOC) is recognized as the most frequent type of ovarian cancer and the main cause of ovarian cancer related deaths worldwide. Although homologous recombination deficiency testing has been adopted in the clinical workflow, morphological analysis remains the main diagnostic tool. In this study Atomic Force Microscopy (AFM) was tested in standard hematoxylin and eosin (H&E) stained sections to investigate the biomechanical properties of different architectural growing patterns of HGSOC. Our results showed that AFM was able to discriminate HGSOC morphological growing patterns as well as patients' stage. Micropapillary pattern, which has been associated to poor outcome, had lower Young's moduli. In addition stage IV HGSOC was significantly softer than stage III cancers. Based on our results, AFM analysis could represent an additional tool in HGSOC morphological diagnosis as the biomechanical proprieties of HGSOC were quantitatively associated to tumor staging and architectural pattern.

Added value of contrast-enhanced ultrasound (CEUS) in the diagnosis of primary retroperitoneal serous adenocarcinoma: a case report.

BACKGROUND: Primary retroperitoneal serous adenocarcinoma (PRSA) is a rare malignant disease. Given the rarity of the disease, the imaging features of PRSA are unclear. Contrast-enhanced ultrasound (CEUS) also plays an important role in the evaluation of the differential diagnosis of retroperitoneal lesions. CASE PRESENTATION: We report the case of a 62-year-old woman of with increased CA125 levels for 1 year who was referred to our hospital. After conducting contrast-enhanced computed tomography and magnetic resonance imaging, the mass was misdiagnosed as a chocolate cyst. After transvaginal ultrasound (TUS) combined with CEUS, cystadenocarcinoma was considered as the initial diagnosis. Pathology results confirmed PRSA as the final diagnosis. CONCLUSIONS: CEUS features of PRSA are reported for the first time based on this case, potentially aiding in the differential diagnosis of this rare entity before surgery.

Pretreatment maximum standardized uptake value in 18F-fluorodeoxyglucose positron emission tomography-computed tomography as a prognostic factor for ovarian clear cell carcinoma and low-grade serous carcinoma.

OBJECTIVE: The maximum standardized uptake value (SUVmax) derived by positron emission tomography-computed tomography (PET/CT) can be an index of biological tumor aggressiveness, which is assessed using noninvasive tools before the treatment of epithelial ovarian cancer (EOC). This study aimed to evaluate the prognostic value of the pretreatment SUVmax in patients with EOC. MATERIALS AND METHODS: We reviewed the data of patients with EOC who underwent pretreatment 18F-FDG PET/CT between June 2006 and September 2016. The relationships between pretreatment SUVmax and histological subtypes of EOC were determined. Moreover, progression-free survival (PFS) and overall survival (OS) were evaluated according to the pretreatment SUVmax. Risk factors associated with progression or death were also analyzed. RESULTS: Of 148 patients, 66 (44.6%), 11 (7.4%), 34 (23.0%), 19 (12.8%), 15 (10.1%), and three (2.0%) were diagnosed with high-grade serous carcinoma (HGSC), low-grade serous carcinoma (LGSC), clear cell carcinoma (CCC), endometrioid carcinoma, mucinous carcinoma, and others, respectively. The median SUVmax was marginally lower in LGSC (6.80 vs. 10.5; P = 0.059) and significantly lower in CCC (5.92 vs. 10.5; P = 0.001) than in HGSC. A high pretreatment SUVmax (≥9.30) was a prognostic factor for OS in patients with LGSC (P = 0.046). Furthermore, multivariate analysis revealed that a high SUVmax (≥5.85) was an independent prognostic factor for OS (P = 0.046) in patients with CCC. However, a high SUVmax (≥7.77) was a poor predictor of PFS and OS in patients with EOC (P = 0.156 and P = 0.158, respectively). CONCLUSION: Our findings suggest that the pretreatment SUVmax is not only an independent predictor of survival in patients with CCC but also a significant predictor of survival in patients with LGSC.