High-Grade Serous Ovarian Cancer: Associations between BRCA Mutation Status, CT Imaging Phenotypes, and Clinical Outcomes

Integrated analysis of spatial transcriptomics and CT phenotypes for unveiling the novel molecular characteristics of recurrent and non-recurrent high-grade serous ovarian cancer

BACKGROUND

High-grade serous ovarian cancer (HGSOC), which is known for its heterogeneity, high recurrence rate, and metastasis, is often diagnosed after being dispersed in several sites, with about 80% of patients experiencing recurrence. Despite a better understanding of its metastatic nature, the survival rates of patients with HGSOC remain poor.

METHODS

Our study utilized spatial transcriptomics (ST) to interpret the tumor microenvironment and computed tomography (CT) to examine spatial characteristics in eight patients with HGSOC divided into recurrent (R) and challenging-to-collect non-recurrent (NR) groups.

RESULTS

By integrating ST data with public single-cell RNA sequencing data, bulk RNA sequencing data, and CT data, we identified specific cell population enrichments and differentially expressed genes that correlate with CT phenotypes. Importantly, we elucidated that tumor necrosis factor-α signaling via NF-κB, oxidative phosphorylation, G2/M checkpoint, E2F targets, and MYC targets served as an indicator of recurrence (poor prognostic markers), and these pathways were significantly enriched in both the R group and certain CT phenotypes. In addition, we identified numerous prognostic markers indicative of nonrecurrence (good prognostic markers). Downregulated expression of PTGDS was linked to a higher number of seeding sites (≥ 3) in both internal HGSOC samples and public HGSOC TCIA and TCGA samples. Additionally, lower PTGDS expression in the tumor and stromal regions was observed in the R group than in the NR group based on our ST data. Chemotaxis-related markers (CXCL14 and NTN4) and markers associated with immune modulation (DAPL1 and RNASE1) were also found to be good prognostic markers in our ST and radiogenomics analyses.

CONCLUSIONS

This study demonstrates the potential of radiogenomics, combining CT and ST, for identifying diagnostic and therapeutic targets for HGSOC, marking a step towards personalized medicine.

Impact of molecular testing on the surgical management of advanced epithelial ovarian cancer

Ovarian carcinoma remains the most lethal gynaecologic malignancy. Half of all high-grade serous ovarian cancers (HGSOCs) have a homologous recombination deficiency (HRD) with regard to the repair of double-strand DNA breaks and are candidate to receive maintenance treatment with PARP inhibitors. While a wealth of literature exists regarding the therapeutic guidance of patients from a medical standpoint, the influence of the HRD status on the surgical outlook has been comparatively limited. In this review, the clinical and biological features of advanced ovarian cancers with BRCA1/2 mutation and/or HRD status are considered with particular reference to their impact on the surgical management and on the medico-surgical sequence. The modification of the surgical indications according to the results of molecular testing in first-line and recurrent settings are discussed.

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.

Imaging approaches for the diagnosis of genetic diseases affecting the female reproductive organs and beyond

This review aims to provide an overview of neoplastic lesions associated with genetic diseases affecting the female reproductive organs. It seeks to enhance our understanding of the radiological aspects in diagnosing genetic diseases including hereditary breast and ovarian cancer syndromes, Lynch syndrome, Peutz-Jeghers syndrome, nevoid basal cell carcinoma syndrome, and Swyer syndrome, and explores the patterns and mechanisms of inheritance that require elucidation. Additionally, we discuss the imaging characteristics of lesions occurring in other regions due to the same genetic diseases.

The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics

Despite significant progress in the prevention, screening, diagnosis, prognosis, and therapy of breast cancer (BC), it remains a highly prevalent and life-threatening disease affecting millions worldwide. Molecular subtyping of BC is crucial for predictive and prognostic purposes due to the diverse clinical behaviors observed across various types. The molecular heterogeneity of BC poses uncertainties in its impact on diagnosis, prognosis, and treatment. Numerous studies have highlighted genetic and environmental differences between patients from different geographic regions, emphasizing the need for localized research. International studies have revealed that patients with African heritage are often diagnosed at a more advanced stage and exhibit poorer responses to treatment and lower survival rates. Despite these global findings, there is a dearth of in-depth studies focusing on communities in the African region. Early diagnosis and timely treatment are paramount to improving survival rates. In this context, radiogenomics emerges as a promising field within precision medicine. By associating genetic patterns with image attributes or features, radiogenomics has the potential to significantly improve early detection, prognosis, and diagnosis. It can provide valuable insights into potential treatment options and predict the likelihood of survival, progression, and relapse. Radiogenomics allows for visual features and genetic marker linkage that promises to eliminate the need for biopsy and sequencing. The application of radiogenomics not only contributes to advancing precision oncology and individualized patient treatment but also streamlines clinical workflows. This review aims to delve into the theoretical underpinnings of radiogenomics and explore its practical applications in the diagnosis, management, and treatment of BC and to put radiogenomics on a path towards fully integrated diagnostics.

New windows of surgical opportunity for gynecological cancers in the era of targeted therapies

Precision medicine through molecular profiling has taken a prominent role in the treatment of solid tumors and it is widely expected that this will continue to expand. With respect to gynecological cancers, a major change has particularly been observed in the treatment landscape of epithelial ovarian, endometrial, and cervical cancers. Regarding the former, maintenance therapy with either poly(ADP-ribose) polymerase inhibitors (PARPi) and/or bevacizumab has become an indispensable treatment option following the traditional combination of cytoreductive surgery and platinum-based chemotherapy. Considering endometrial cancer, the molecular classification system has now been incorporated into virtually every guideline available and molecular-directed treatment strategies are currently being researched, presumably leading to a further transformation of its treatment paradigm. After all, treatment with immune-checkpoint inhibitors that target the programmed cell death 1 (PD-1) receptor has already been shown to significantly improve disease outcomes in these patients, especially in those with mismatch repair deficient, microsatellite stability-high (MMRd-MSI-H) disease. Similarly, in recurrent/metastatic cervical cancer patients, these agents elicited improved survival rates when being added to platinum-based chemotherapy with or without bevacizumab. Interestingly, implications of these targeted therapies for surgical management have been touched on to a minor extent, but are at least as intriguing. This review therefore aims to address the wide-ranging opportunities the molecular tumor characteristics and their corresponding targeted therapies have to offer for the surgical management of epithelial ovarian, endometrial, and cervical cancers, both in the primary and recurrent setting.

Comparing Characteristics of Pelvic High-grade Serous Carcinomas with and without Breast Cancer Gene Variants on MR Imaging

PURPOSE

To compare MRI findings of high-grade serous carcinoma (HGSC) with and without breast cancer (BRCA) gene variants to explore the feasibility of MRI as a genetic predictor.

METHODS

We retrospectively reviewed MRI data from 16 patients with BRCA variant-positive (11 patients of BRCA1 and 5 patients of BRCA2 variant-positive) and 32 patients with BRCA variant-negative HGSCs and evaluated tumor size, appearance, nature of solid components, apparent diffusion coefficient (ADC) value, time-intensity curve, several dynamic contrast-enhanced curve descriptors, and nature of peritoneal metastasis. Age, primary site, tumor stage, bilaterality, presence of lymph node metastasis, presence of peritoneal metastasis, and tumor markers were also compared between the groups with the Mann-Whitney U and chi-square tests.

RESULTS

The mean tumor size of BRCA variant-positive HGSCs was 9.6 cm, and that of variant-negative HGSCs was 6.8 cm, with no significant difference (P = 0.241). No significant difference was found between BRCA variant-positive and negative HGSCs in other evaluated factors, except for age (mean age, 53 years old; range, 32-78 years old for BRCA variant-positive and mean age, 61 years old; range, 44-80 years old for BRCA variant-negative, P = 0.033). Comparing BRCA1 variant-positive and BRCA2 variant-positive HGSCs, BRCA1 variant-positive HGSCs were larger (P = 0.040), had greater Max enhancement (P = 0.013), Area under the curve (P = 0.013), and CA125 (P = 0.038), and had a higher frequency of lymph node metastasis (P = 0.049), with significance.

CONCLUSION

There was no significant difference in the MRI findings between patients with HGSCs with and without BRCA variants. Although studied in small numbers, BRCA1 variant-positive HGSCs were larger and more enhanced than BRCA2 variant-positive HGSCs with higher CA125 and more frequent lymph node metastases, and may represent more aggressive features.

Integrated radiogenomics models predict response to neoadjuvant chemotherapy in high grade serous ovarian cancer

High grade serous ovarian carcinoma (HGSOC) is a highly heterogeneous disease that typically presents at an advanced, metastatic state. The multi-scale complexity of HGSOC is a major obstacle to predicting response to neoadjuvant chemotherapy (NACT) and understanding critical determinants of response. Here we present a framework to predict the response of HGSOC patients to NACT integrating baseline clinical, blood-based, and radiomic biomarkers extracted from all primary and metastatic lesions. We use an ensemble machine learning model trained to predict the change in total disease volume using data obtained at diagnosis (n = 72). The model is validated in an internal hold-out cohort (n = 20) and an independent external patient cohort (n = 42). In the external cohort the integrated radiomics model reduces the prediction error by 8% with respect to the clinical model, achieving an AUC of 0.78 for RECIST 1.1 classification compared to 0.47 for the clinical model. Our results emphasize the value of including radiomics data in integrative models of treatment response and provide methods for developing new biomarker-based clinical trials of NACT in HGSOC.

Tumor biology and impact on timing of surgery in advanced epithelial ovarian cancer

Recent advances in epithelial ovarian cancer research have led to a shift in treatment strategy from the traditional 'organ-centric' to a personalized tumor biology-based approach. Nevertheless, we are still far behind an individualized approach for cytoreductive surgery in advanced ovarian cancer; the gold standard of primary treatment in combination with systemic agents. The impact of tumor biology on treatment sequence is still understudied. It is obvious, that response to platinum-based therapy is crucial for the success of neoadjuvant chemotherapy. While high-grade serous and endometrioid tumors are commonly characterized by an excellent response, other subtypes are considered poor responders or even resistant to platinum. Undoubtedly, neoadjuvant chemotherapy may filter poor responders, but to date, we still do not have appropriate alternatives to platinum-based chemotherapy in the neoadjuvant and first-line setting and 'adjusting' systemic treatment in cases of poor response to neoadjuvant chemotherapy remains elusive. Primary cytoreduction is still considered the gold standard for fit patients with operable tumor dissemination patterns, especially for those ovarian cancer subtypes that show poor response to platinum. Of note, even in high-grade serous ovarian cancer, approximately 20% of tumors are platinum resistant and the benefit of neoadjuvant chemotherapy in this subgroup is limited. Interestingly, these tumors are associated with the mesenchymal molecular subtype, which in turn correlates with high risk for residual disease after cytoreductive surgery and is characterized by the worst survival outcome among high-grade ovarian cancers. This leads to the question, how to best tailor surgical radicality at the onset of patients' presentation to avoid associated morbidity and with a moderate benefit. Here, we give an overview of recent advances of interaction between tumor biology and surgery in ovarian cancer.

CT-based radiomics nomogram analysis for assessing BRCA mutation status in patients with high-grade serous ovarian cancer

BACKGROUND

Radiomics nomogram analysis is widely preoperatively used to assess gene mutations in various tumors.

PURPOSE

To explore the value of computed tomography (CT)-based radiomics nomogram analysis for assessing BRCA gene mutation status of patients with high-grade serous ovarian cancer (HGSOC).

MATERIAL AND METHODS

In total, 96 patients with HGSOC were retrospectively screened and randomly divided into primary (n = 68) and validation cohorts (n = 28). The clinical model was constructed based on clinical features and CT morphological features using univariate and multivariate logistic analyses. Maximum-relevance and minimum-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were performed for feature dimensionality reduction and radiomics score was calculated. The nomogram model combining the clinical model and the radiomics score was constructed using multivariate logistic regression. Receiver operating characteristic (ROC) curves were generated to assess models' performance. The calibration analysis and decision curve analysis (DCA) were also performed.

RESULTS

The clinical model consisted of CA125 level and supradiaphragmatic lymphadenopathy and yielded an area under the curve (AUC) of 0.69 (primary cohort) and 0.81 (validation cohort). The radiomics model was built with seven selected features and showed an AUC of 0.87 (primary cohort) and 0.81 (validation cohort). The nomogram finally showed the highest AUC of 0.89 (primary cohort) and 0.87 (validation cohort). The nomogram presented favorable calibrations in both the primary and validation cohorts. DCA further confirmed the clinical benefits of the constructed nomogram.

CONCLUSION

CT-based radiomics nomogram provides a non-invasive method to discriminate BRCA gene mutation status of HGSOC and potentially helps develop precise medical strategies.

The Challenges and Prospects of p53-Based Therapies in Ovarian Cancer

It has been well established that mutations in the tumor suppressor gene, p53, occur readily in a vast majority of cancer tumors, including ovarian cancer. Typically diagnosed in stages three or four, ovarian cancer is the fifth leading cause of death in women, despite accounting for only 2.5% of all female malignancies. The overall 5-year survival rate for ovarian cancer is around 47%; however, this drops to an abysmal 29% for the most common type of ovarian cancer, high-grade serous ovarian carcinoma (HGSOC). HGSOC has upwards of 96% of cases expressing mutations in p53. Therefore, wild-type (WT) p53 and p53-based therapies have been explored as treatment options via a plethora of drug delivery vehicles including nanoparticles, viruses, polymers, and liposomes. However, previous p53 therapeutics have faced many challenges, which have resulted in their limited translational success to date. This review highlights a selection of these historical p53-targeted therapeutics for ovarian cancer, why they failed, and what the future could hold for a new generation of this class of therapies.

Deep learning magnetic resonance imaging predicts platinum sensitivity in patients with epithelial ovarian cancer

Objective

The aim of the study is to develop and validate a deep learning model to predict the platinum sensitivity of patients with epithelial ovarian cancer (EOC) based on contrast-enhanced magnetic resonance imaging (MRI).

Methods

In this retrospective study, 93 patients with EOC who received platinum-based chemotherapy (≥4 cycles) and debulking surgery at the Sun Yat-sen Memorial Hospital from January 2011 to January 2020 were enrolled and randomly assigned to the training and validation cohorts (2:1). Two different models were built based on either the primary tumor or whole volume of the abdomen as the volume of interest (VOI) within the same cohorts, and then a pre-trained convolutional neural network Med3D (Resnet 10 version) was transferred to automatically extract 1,024 features from two MRI sequences (CE-T1WI and T2WI) of each patient to predict platinum sensitivity. The performance of the two models was compared.

Results

A total of 93 women (mean age, 50.5 years ± 10.5 [standard deviation]) were evaluated (62 in the training cohort and 31 in the validation cohort). The AUCs of the whole abdomen model were 0.97 and 0.98 for the training and validation cohorts, respectively, which was better than the primary tumor model (AUCs of 0.88 and 0.81 in the training and validation cohorts, respectively). In k-fold cross-validation and stratified analysis, the whole abdomen model maintained a stable performance, and the decision function value generated by the model was a prognostic indicator that successfully discriminates high- and low-risk recurrence patients.

Conclusion

The non-manually segmented whole-abdomen deep learning model based on MRI exhibited satisfactory predictive performance for platinum sensitivity and may assist gynecologists in making optimal treatment decisions.

The BUMPy road of peritoneal metastases in ovarian cancer

Ovarian cancer is the most common cause of death due to gynecologic malignancies, with more than 70% of patients presenting with advanced stage disease at the time of diagnosis. The extent and distribution of tumor guide primary treatment selection and clinical management. While primary cytoreductive surgery with complete tumor resection improves survival, patients with extensive peritoneal disease may benefit from neoadjuvant chemotherapy first to reduce tumor burden followed by interval cytoreductive surgery. Imaging plays an essential role in triaging patients including selecting patients who may benefit from neoadjuvant chemotherapy before cytoreductive surgery. Interestingly, there are no universally established criteria to predict resectability and local practices depend on local guidelines and surgeon preferences. Nevertheless, certain anatomical tumor locations are known to be difficult to resect and are associated with suboptimal cytoreduction or require special surgical considerations. This review discusses the recent advances in the initial management of patients with ovarian cancer, a practical approach to the assessment and communication of peritoneal metastases locations on CT and MRI. It also explores recent advances in genomics profiling and radiomics that may influence the initial management of these patients.

CT imaging phenotypes linked to CA125 and HE4 biomarkers are highly predictive in discriminating between hereditary and sporadic ovarian cancer patients

BACKGROUND: Hereditary ovarian cancers (HOC) represent about 23% of ovarian cancer (OC) cases: they are most frequently related to germline mutations in the BRCA genes. OBJECTIVE: We aimed to compare CA125/HE4 serum levels and Computed Tomography (CT) features at time of ovarian cancer (OC) diagnosis in two populations: BRCA mutant and BRCA wild-type (WT) OC, and to investigate the relationship between this laboratory and radiological biomarker and BRCA mutation status. METHODS: This retrospective study included 60 newly diagnosed OC patients with FIGO stage IIIC-IV disease, tested for BRCA1/2 germline mutation status of which preoperative CT scan and serum tumor marker assay were available. RESULTS: The median level of CA125 (708 U/mL) was significantly higher (p < 0.002) in BRCA1/2 mutated patients than in WT patients (176 U/mL), whereas the median level of HE4 (492 pmol/L) was significantly higher (p < 0.002) in WT than in BRCA-mutated patients (252 pmol/L). BRCA mutation carriers showed a higher incidence of bilateral ovarian masses (p = 0.0303) characterized by solid structures (p < 0.00001), higher peritoneal tumor load, macronodular implants >2 cm (p = 0.000099), increased frequency of lymphadenopathies (p = 0.019), and metastasis (p = 0.052) compared to patients with BRCA WT. CONCLUSIONS: Tumor markers and CT patterns may help in identifying BRCA mutation status in OC directing patients towards a personalized treatment.

Diffusion-Weighted Magnetic Resonance Imaging and Morphological Characteristics Evaluation for Outcome Prediction of Primary Debulking Surgery for Advanced High-Grade Serous Ovarian Carcinoma

BACKGROUND

Preoperative assessment of whether a successful primary debulking surgery (PDS) can be performed in patients with advanced high-grade serous ovarian carcinoma (HGSOC) remains a challenge. A reliable model to precisely predict resectability is highly demanded.

PURPOSE

To investigate the value of diffusion-weighted MRI (DW-MRI) combined with morphological characteristics to predict the PDS outcome in advanced HGSOC patients.

STUDY TYPE

Prospective.

SUBJECTS

A total of 95 consecutive patients with histopathologically confirmed advanced HGSOC (ranged from 39 to 77 years).

FIELDS STRENGTH/SEQUENCE

A 3.0 T, readout-segmented echo-planar DWI.

ASSESSMENT

The MRI morphological characteristics of the primary ovarian tumor, a peritoneal carcinomatosis index (PCI) derived from DWI (DWI-PCI) and histogram analysis of the primary ovarian tumor and the largest peritoneal carcinomatosis were assessed by three radiologists. Three different models were developed to predict the resectability, including a clinicoradiologic model combing MRI morphological characteristic with ascites and CA125 level; DWI-PCI alone; and a fusion model combining the clinical-morphological information and DWI-PCI.

STATISTICAL TESTS

Multivariate logistic regression analyses, receiver operating characteristic (ROC) curve, net reclassification index (NRI) and integrated discrimination improvement (IDI) were used. A P < 0.05 was considered to be statistically significant.

RESULTS

Sixty-seven cases appeared as a definite mass, whereas 28 cases as an infiltrative mass. The morphological characteristics and DWI-PCI were independent factors for predicting the resectability, with an AUC of 0.724 and 0.824, respectively. The multivariable predictive model consisted of morphological characteristics, CA-125, and the amount of ascites, with an incremental AUC of 0.818. Combining the application of a clinicoradiologic model and DWI-PCI showed significantly higher AUC of 0.863 than the ones of each of them implemented alone, with a positive NRI and IDI.

DATA CONCLUSIONS

The combination of two clinical factors, MRI morphological characteristics and DWI-PCI provide a reliable and valuable paradigm for the noninvasive prediction of the outcome of PDS.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 2.

Development of survival predictors for high-grade serous ovarian cancer based on stable radiomic features from computed tomography images

Less than 35% of advanced patients with high-grade serous ovarian cancer (HGSOC) survive for 5 years after diagnosis. Here, we developed radiomics-based models to predict HGSOC clinical outcomes using preoperative contrast-enhanced computed tomography (CECT) images. 891 radiomics features were extracted between primary, metastatic, or lymphatic lesions from preoperative venous phase CECT images of 217 patients with HGSOC. A heuristic method, Frequency Appearance in Multiple Univariate preScreening (FAMUS), was proposed to identify stable and task-relevant radiomic features. Using FAMUS, we constructed predictive models of overall survival and disease-free survival in patients with HGSOC based on these stable radiomic features. According to their CT images, patients with HGSOC can be accurately stratified into high-risk or low-risk groups for cancer-related death within 2-6 years or for likely recurrence within 1-5 years. These radiomic models provide convincing and reliable non-invasive markers for individualized prognostic evaluation and clinical decision-making for patients with HGSOC.

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Frequency Appearance in Multiple Univariate preScreening (FAMUS) identifies stable and task-relevant radiomic features from computed tomography (CT) images

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Radiomics-based signatures are highly predictive of the clinical outcome of high-grade serous ovarian cancer (HGSOC)

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FAMUS improves the prognostic performance of radiomics-based prediction models

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Developed radiomic models can help clinicians tailor treatment plans for HGSOC

p53 Immunohistochemistry and Mutation Types Mismatching in High-Grade Serous Ovarian Cancer

High-grade serous carcinoma (HGSCa) of the ovary is featured by TP53 gene mutation. Missense or nonsense mutation types accompany most cases of HGSCa that correlate well with immunohistochemical (IHC) staining results—an all (missense) or none (nonsense) pattern. However, some IHCs produce subclonal or mosaic patterns from which TP53 mutation types, including the wild type of the gene, cannot be clearly deduced. We analyzed a total of 236 cases of ovarian HGSCa and tumors of other histology by matching the results of p53 IHC staining and targeted next-generation sequencing (TruSight Tumor 170 panel). Ambiguous IHCs that do not belong to the conventional “all or none” groups were reviewed to distinguish the true wild type (WT) from potentially pathogenic subclonal or mosaic patterns. There were about 9% of sequencing-IHC mismatching cases, which were enriched by the p53 c-terminal encoding nuclear localization signal and oligomerization domain, in which the subcellular locations of p53 protein were affected. Indeed, mutations in the oligomerization domain of the p53 protein frequently revealed an unmatched signal or cytosolic staining (L289Ffs*57 (Ins), and R342*). We conclude that both mutation types and IHC patterns of p53 are important sources of information to provide a precise diagnosis of HGSCa.

Effects of Neoadjuvant Chemotherapy in Ovarian Cancer Patients With Different Germline BRCA1/2 Mutational Status: A Retrospective Cohort Study

Background

Whether neoadjuvant chemotherapy (NAC) followed by interval debulking surgery (IDS) against primary debulking surgery (PDS) has a differential effect on prognosis due to Breast Cancer Susceptibility Genes (BRCA)1/2 mutations has not been confirmed by current studies.

Methods

All patients included in this retrospective study were admitted to Qilu Hospital of Shandong University between January 2009 and June 2020, and germline BRCA1/2 mutation were tested. Patients in stage IIIB, IIIC, and IV, re-staged by International Federation of Gynecology and Obstetrics (FIGO) 2014, were selected for analysis. All patients with NAC received 1-5 cycles of platinum-containing (carboplatin, cisplatin, or nedaplatin) chemotherapy. Patients who received maintenance therapy after chemotherapy were not eligible for this study. All relevant medical records were collected.

Results

A total of 322 patients were enrolled, including 112 patients with BRCA1/2 mutations (BRCAmut), and 210 patients with BRCA1/2 wild-type (BRCAwt). In the two groups, 40 BRCAmut patients (35.7%) and 69 BRCAwt patients (32.9%) received NAC. The progression-free survival (PFS) of BRCAmut patients was significantly reduced after NAC (median: 14.9 vs. 18.5 months; p=0.023); however, there was no difference in overall survival (OS) (median: 75.1 vs. 72.8 months; p=0.798). Whether BRCAwt patients received NAC had no significant effect on PFS (median: 13.5 vs. 16.0 months; p=0.780) or OS (median: 54.0 vs. 56.4 months; p=0.323). Multivariate analyses in BRCAmut patients showed that the predictors of prolonged PFS were PDS (p=0.001), the absence of residual lesions (p=0.012), and FIGO III stage (p=0.020); Besides, PARP inhibitor was the independent predictor for prolonged OS in BRCAmut patients (p=0.000), for BRCAwt patients, the absence of residual lesions (p=0.041) and history of PARP inhibitors (p=0.000) were beneficial factors for OS prolongation.

Conclusions

For ovarian cancer patients with FIGO IIIB, IIIC, and IV, NAC-IDS did not adversely affect survival outcomes due to different BRCA1/2 germline mutational status.

Prediction of BRCA gene mutation status in epithelial ovarian cancer by radiomics models based on 2D and 3D CT images

Background

The objective of this study is to explore the value of two-dimensional (2D) and three-dimensional (3D) radiomics models based on enhanced computed tomography (CT) images in predicting BRCA gene mutations in patients with epithelial ovarian cancer.

Methods

The clinical and imaging data of 106 patients with ovarian cancer confirmed by surgery and pathology were retrospectively analyzed and genetic testing was performed. Radiomics features extracted from the 2D and 3D regions of interest of the patients’ primary tumor lesions were selected in the training set using the maximum correlation and minimum redundancy method. Then, the best features were selected through Lasso tenfold cross-validation. Feature subsets were employed to establish a radiomics model. The model’s performance was evaluated via area under the receiver operating characteristic curve analysis and its clinical validity was assessed by using the model’s decision curve.

Results

On the validation set, the area under the curve values of the 2D, 3D, and 2D + 3D combined models were 0.78 (0.61–0.96), 0.75 (0.55–0.92), and 0.82 (0.61–0.96), respectively. However, the DeLong test P values between the three pairs of models were all > 0.05. The decision curve analysis showed that the radiomics model had a high net benefit across all high-risk threshold probabilities.

Conclusions

The three radiomics models can predict the BRCA gene mutation in ovarian cancer, and there were no statistically significant differences between the prediction performance of the three models.

Assistance by Routine CT Features Combined With 3D Texture Analysis in the Diagnosis of BRCA Gene Mutation Status in Advanced Epithelial Ovarian Cancer

Purpose

To evaluate the predictive value of routine CT features combined with 3D texture analysis for prediction of BRCA gene mutation status in advanced epithelial ovarian cancer.

Method

Retrospective analysis was performed on patients with masses occupying the pelvic space confirmed by pathology and complete preoperative images in our hospital, including 37 and 58 cases with mutant type and wild type BRCA, respectively (total: 95 cases). The enrolled patients’ routine CT features were analyzed by two radiologists. Then, ROIs were jointly determined through negotiation, and the ITK-SNAP software package was used for 3D outlining of the third-stage images of the primary tumor lesions and obtaining texture features. For routine CT features and texture features, Mann-Whitney U tests, single-factor logistic regression analysis, minimum redundancy, and maximum correlation were used for feature screening, and the performance of individual features was evaluated by ROC curves. Multivariate logistic regression analysis was used to further screen features, find independent predictors, and establish the prediction model. The established model’s diagnostic efficiency was evaluated by ROC curve analysis, and the histogram was obtained to conduct visual analysis of the prediction model.

Results

Among the routine CT features, the type of peritoneal metastasis, mesenteric involvement, and supradiaphragmatic lymph node enlargement were correlated with BRCA gene mutation (P < 0.05), whereas the location of the peritoneal metastasis (in the gastrohepatic ligament) was not significantly correlated with BRCA gene mutation (P > 0.05). Multivariate logistic regression analysis retained six features, including one routine CT feature and five texture features. Among them, the type of peritoneal metastasis was used as an independent predictor (P < 0.05), which had the highest diagnostic efficiency. Its AUC, accuracy, specificity, and sensitivity were 0.74, 0.79, 0.90, and 0.62, respectively. The prediction model based on the combination of routine CT features and texture features had an AUC of 0.86 (95% CI: 0.79–0.94) and accuracy, specificity, and sensitivity of 0.80, 0.76, and 0.81, respectively, indicating a better performance than that of any single feature.

Conclusions

Both routine CT features and texture features had value for predicting the mutation state of the BRCA gene, but their predictive efficiency was low. When the two types of features were combined to establish a predictive model, the model’s predictive efficiency was significantly higher than that of independent features.

Ovarian cancer staging: What the surgeon needs to know

Ovarian cancer (OC) is the leading cause of gynecological cancer death, and most cases are diagnosed at advanced stages due to a nonspecific and insidious clinical presentation. Radiologists play a critical role in the decision of which patients are candidates for primary debulking surgery and who may benefit from neoadjuvant chemotherapy. This pictorial review summarizes the dissemination patterns of OC, main imaging findings of metastatic disease, and which findings may alter the treatment plan or predict suboptimal tumor resection.

Development of MRI-Based Radiomics Model to Predict the Risk of Recurrence in Patients With Advanced High-Grade Serous Ovarian Carcinoma

OBJECTIVE. The purpose of our study was to develop a radiomics model based on preoperative MRI and clinical information for predicting recurrence-free survival (RFS) in patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS. This retrospective study enrolled 117 patients with HGSOC, including 90 patients with recurrence and 27 without recurrence; 1046 radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images using a manual segmentation method. L1 regularization-based least absolute shrinkage and selection operator (LASSO) regression was performed to select features, and the synthetic minority oversampling technique (SMOTE) was used to balance our dataset. A support vector machine (SVM) classifier was used to build the classification model. To validate the performance of the proposed models, we applied a leave-one-out cross-validation method to train and test the classifier. Cox proportional hazards regression, Harrell concordance index (C-index), and Kaplan-Meier plots analysis were used to evaluate the associations between radiomics signatures and RFS. RESULTS. The fusion radiomics-based model yielded a significantly higher AUC value of 0.85 in evaluating RFS than the model using contrast-enhanced T1-weighted imaging features alone or T2-weighted imaging features alone (AUC = 0.79 and 0.74 and p = .02 and .01, respectively). Kaplan-Meier survival curves showed significant differences between high and low recurrence risk in patients with HGSOC by different models. The fusion model combining radiomics features and clinical information showed higher performance than the clinical model (C-index = 0.62 and 0.60, respectively). CONCLUSION. The proposed MRI-based radiomics signatures may provide a potential way to develop a prediction model and can help identify patients with advanced HGSOC who have a high risk of recurrence.

The correlation between BRCA status and surgical cytoreduction in high-grade serous ovarian carcinoma

OBJECTIVE

BRCA-associated ovarian cancers are biologically unique; it is unclear if this translates to favorable outcomes at the time of primary cytoreduction (PCS). The aim of this study was to compare the amount of residual disease after PCS in BRCA mutated (BRCAm) and wild-type (BRCAwt) high-grade serous ovarian cancers (HGSC), and to assess whether BRCA status was an independent predictor of complete cytoreduction.

METHODS

We conducted a retrospective analysis of patients with stage III/IV HGSC with known germline and somatic BRCA status, treated with PCS from 2000 to 2017. We compared the complete, optimal and suboptimal cytoreduction rates between the BRCAm and BRCAwt cohorts and built a predictive model to assess whether BRCA status was predictive of complete cytoreduction.

RESULTS

Of 303 treated with PCS, 120 were germline/somatic BRCAm (40%) and 183 were BRCAwt (60%). BRCAm women tended to be younger, but there were no differences between the two groups in preoperative CA-125, disease burden, surgical complexity, length of surgery, or perioperative complications. BRCAm group had a higher rate of complete cytoreduction to no residual disease (0 mm) [72% vs. 48%] (p < 0.001). In a multivariate model, after accounting for age, length of surgery, CA-125 level, stage, disease burden and surgical complexity, BRCAm status was predictive of 0 mm residual disease with odds ratio of 5.3 (95% CI 2.45-11.5; p < 0.001).

CONCLUSIONS

BRCAm status is predictive of complete cytoreduction at the time of PCS. Despite similar disease burden and surgical efforts, one is more likely to achieve complete resection in BRCAm HGSC.

Ultrasound evaluation of ovarian masses and assessment of the extension of ovarian malignancy

The current review sums up the literature on the diagnostic performance of models to predict malignancy in adnexal masses and the ability of ultrasound to make a specific diagnosis in adnexal masses. A summary of the role of ultrasound in assessing the extension of malignant ovarian disease is also provided.

Current update on the molecular genetics and management of hereditary ovarian cancers: a primer for radiologists

More than one-fifth of ovarian cancers are hereditary, with most of them caused by BRCA genes. Malignant ovarian neoplasms are primarily epithelial tumors, a heterogeneous group of tumors with variable genetic backgrounds that translate into different biologic behaviors and morphologic features. Radiologists play an increasingly important role in the diagnosis and management of oncology patients. Familiarity with hereditary ovarian cancers will have a positive impact on patient management and radiologists' involvement in the multidisciplinary approach needed for this delicate patient population. In this paper, we review the basic histologic and genetic backgrounds of ovarian tumors with an emphasis on hereditary ovarian cancers, as well as the effects that these factors have on patient workup, primarily with regard to imaging studies.

Current update on malignant epithelial ovarian tumors

Epithelial ovarian cancer (EOC) represents the most frequently occurring gynecological malignancy, accounting for more than 70% of ovarian cancer deaths. Preoperative imaging plays an important role in assessing the extent of disease and guides the next step in surgical decision-making and operative planning. In this article, we will review the multimodality imaging features of various subtypes of EOC. We will also discuss the role of imaging in the staging, management, and surveillance of EOC.

Noninvasive prediction of residual disease for advanced high-grade serous ovarian carcinoma by MRI-based radiomic-clinical nomogram

OBJECTIVES

To develop a preoperative MRI-based radiomic-clinical nomogram for prediction of residual disease (RD) in patients with advanced high-grade serous ovarian carcinoma (HGSOC).

METHODS

In total, 217 patients with advanced HGSOC were enrolled from January 2014 to June 2019 and randomly divided into a training set (n = 160) and a validation set (n = 57). Finally, 841 radiomic features were extracted from each tumor on T2-weighted imaging (T2WI) and contrast-enhanced T1-weighted imaging (CE-T1WI) sequence, respectively. We used two fusion methods, the maximal volume of interest (MV) and the maximal feature value (MF), to fuse the radiomic features of bilateral tumors, so that patients with bilateral tumors have the same kind of radiomic features as patients with unilateral tumors. The radiomic signatures were constructed by using mRMR method and LASSO classifier. Multivariable logistic regression analysis was used to develop a radiomic-clinical nomogram incorporating radiomic signature and conventional clinico-radiological features. The performance of the nomogram was evaluated on the validation set.

RESULTS

In total, 342 tumors from 217 patients were analyzed in this study. The MF-based radiomic signature showed significantly better prediction performance than the MV-based radiomic signature (AUC = 0.744 vs. 0.650, p = 0.047). By incorporating clinico-radiological features and MF-based radiomic signature, radiomic-clinical nomogram showed favorable prediction ability with an AUC of 0.803 in the validation set, which was significantly higher than that of clinico-radiological signature and MF-based radiomic signature (AUC = 0.623, 0.744, respectively).

CONCLUSIONS

The proposed MRI-based radiomic-clinical nomogram provides a promising way to noninvasively predict the RD status.

KEY POINTS

• MRI-based radiomic-clinical nomogram is feasible to noninvasively predict residual disease in patients with advanced HGSOC. • The radiomic signature based on MF showed significantly better prediction performance than that based on MV. • The radiomic-clinical nomogram showed a favorable prediction ability with an AUC of 0.803.

Prediction of Platinum-based Chemotherapy Response in Advanced High-grade Serous Ovarian Cancer: ADC Histogram Analysis of Primary Tumors

RATIONALE AND OBJECTIVES

To investigate the feasibility of apparent diffusion coefficient (ADC) histogram analysis of primary advanced high-grade serous ovarian cancer (HGSOC) to predict patient response to platinum-based chemotherapy.

MATERIALS AND METHODS

A total of 70 patients with 102 advanced stage HGSOCs (International Federation of Gynecology and Obstetrics (FIGO) stages III-IV) who received standard treatment of primary debulking surgery followed by the first line of platinum-based chemotherapy were retrospectively enrolled. Patients were grouped as platinum-resistant and platinum-sensitive according to whether relapse occurred within 6 months. Clinical characteristics, including age, pretherapy CA125 level, International Federation of Gynecology and Obstetrics stage, residual tumor, and histogram parameters derived from whole tumor and solid component such as ADC_mean; 10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th percentiles; skewness and kurtosis, were compared between platinum-resistant and platinum-sensitive groups.

RESULTS

No significantly different clinical characteristics were observed between platinum-sensitive and platinum-resistant patients. There were no significant differences in any whole-tumor histogram-derived parameters between the two groups. Significantly higher ADC_mean and percentiles and significantly lower skewness and kurtosis from the solid-component histogram parameters were observed in the platinum-sensitive group when compared with the platinum-resistant group. ADC_mean, skewness and kurtosis showed moderate prediction performances, with areas under the curve of 0.667, 0.733 and 0.616, respectively. Skewness was an independent risk factor for platinum resistance.

CONCLUSION

Pretreatment ADC histogram analysis of primary tumors has the potential to allow prediction of response to platinum-based chemotherapy in patients with advanced HGSOC.

Misdiagnosis of High-grade Serous Ovarian Cancer With BRCA Mutation as Endometriotic Cyst Due to Its Unique Gross Morphology: A Case Report and Literature Review

It is believed that high-grade serous ovarian cancer (HGSOC) is a solid or multilocular-solid cancer. Here, we report the case of a 40-yr-old woman with a left ovarian unilocular cyst. Ultrasonography and computed tomographic examination confirmed that the cyst was thin-walled and homogenous in thickness without mural nodules. It was considered to be an endometriotic cyst. Left ovarian cyst excision specimens proved it to be HGSOC after pathologic examination. Therefore, the patient underwent radical surgery for HGSOC. Pathologic examination of radical resection specimens confirmed that the HGSOC was still in FIGO stage IA and no fallopian tube lesion was found. Considering that the patient had a history of breast cancer in both the breasts at a young age, it was hypothesized that the breast cancer susceptibility gene (BRCA) gene may have a germline mutation. Next-generation sequencing confirmed the BRCA1 (c.3770_3771delAG) germline mutation in this patient. Previous studies have reported the special morphological characteristics and growth pattern of HGSOC with BRCA mutation in the advanced stage. Our case demonstrates that HGSOC with the BRCA mutation can also be a unilocular cyst with a thin wall and uniform thickness without a mural nodule, and in the early stage, may have unique gross morphology.

The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology

With the rapid development of new technologies, including artificial intelligence and genome sequencing, radiogenomics has emerged as a state-of-the-art science in the field of individualized medicine. Radiogenomics combines a large volume of quantitative data extracted from medical images with individual genomic phenotypes and constructs a prediction model through deep learning to stratify patients, guide therapeutic strategies, and evaluate clinical outcomes. Recent studies of various types of tumors demonstrate the predictive value of radiogenomics. And some of the issues in the radiogenomic analysis and the solutions from prior works are presented. Although the workflow criteria and international agreed guidelines for statistical methods need to be confirmed, radiogenomics represents a repeatable and cost-effective approach for the detection of continuous changes and is a promising surrogate for invasive interventions. Therefore, radiogenomics could facilitate computer-aided diagnosis, treatment, and prediction of the prognosis in patients with tumors in the routine clinical setting. Here, we summarize the integrated process of radiogenomics and introduce the crucial strategies and statistical algorithms involved in current studies.

Hereditary ovarian tumour syndromes: current update on genetics and imaging

Hereditary ovarian tumour syndromes are a diverse group of hereditary syndromes characterised by the development of specific histotypes of ovarian neoplasms. While BRCA syndromes are exclusively associated with high-grade serous carcinomas, patients with Lynch syndrome show a preponderance of endometrioid subtype of ovarian and endometrial carcinomas. Distinct non-epithelial phenotypes, such as sex cord stromal tumours with annular tubules, Sertoli-Leydig cell tumours, and small cell carcinoma of the hypercalcaemic type occur in patients with Peutz-Jeghers, DICER1, and rhabdoid tumour predisposition syndromes, respectively. Gorlin-Goltz syndrome is characterised by the development of bilateral, multiple ovarian fibromas in 14-24% of patients. Ovarian steroid cell tumours and broad ligament papillary cystadenomas are characteristically found in women with von Hippel-Lindau syndrome. Recent studies have allowed the characterisation of tumour genetics and associated oncological pathways that contribute to tumourigenesis. Implications of the diagnosis of these syndromes on screening, management, and prognosis are discussed.

Imaging genomics for accurate diagnosis and treatment of tumors: A cutting edge overview

Imaging genomics refers to the establishment of the connection between invasive gene expression features and non-invasive imaging features. Tumor imaging genomics can not only understand the macroscopic phenotype of tumor, but also can deeply analyze the cellular and molecular characteristics of tumor tissue. In recent years, tumor imaging genomics has been a key in the field of medicine. The incidence of cancer in China has increased significantly, which is the main reason of disease death of urban residents. With the rapid development of imaging medicine, depending on imaging genomics, many experts have made remarkable achievements in tumor screening and diagnosis, prognosis evaluation, new treatment targets and understanding of tumor biological mechanism. This review analyzes the relationship between tumor radiology and gene expression, which provides a favorable direction for clinical staging, prognosis evaluation and accurate treatment of tumors.

Ovarian carcinoma in patients with BRCA mutation - a correlation between the growing pattern of peritoneal implants evaluated by CT/MRI and the genotype BRCA1 and BRCA2

Background

Ovarian cancer is the leading cause of death from gynecologic cancer. The risk of developing ovarian cancer is significantly increased in patients that carry a genetic mutation of tumor suppressor gene BRCA1 or BRCA2. The majority of BRCA-associated ovarian/fallopian tube cancers are high-grade serous carcinomas (HGSC). The recognition of patterns of disease is crucial to identify distinctive imaging features that could be useful for predicting prognosis and therapeutic response.

Results

An institutional review board-approved retrospective study was performed and included 34patients (23 BRCA-mutated and 11 BRCA wild-type) with HGSC FIGO III/IV who underwent pre-operative or pre-chemotherapy contrast-enhanced CT/MRI of the abdomen and pelvis between January 2003 and December 2017. Three radiologists independently reviewed the imaging studies and looked for qualitative features of the primary tumor and peritoneal metastases (nodular versus infiltrative pattern). Two pathologists also assessed the histopathologic characteristics of the surgical specimens, with emphasis on the growth pattern of metastatic deposits (expansive/nodular and infiltrative) and inflammatory infiltrate (intra- and/or peritumoral).

No significant associations were found between the different groups of patients (BRCA1-mutant HGSC, BRCA2-mutant HGSC. and BRCA wild-type) and CT/MRI features of ovarian tumors, morphology of peritoneal metastasis, and pathologic characteristics.

Conclusion

Identification of specific imaging and pathologic features is important to pursue an optimal personalized cancer treatment strategy and to develop precision medicine in the future.

Glycolytic phenotypes in an evaluation of ovarian carcinoma based on carcinogenesis and BRCA mutation

BACKGROUNDS

Recently, a dualistic carcinogenesis model of ovarian cancer has emerged. We aimed to investigate differences in the glycolytic phenotypes of type I and type II ovarian carcinoma on the basis of FDG uptake and in the pathological features according to tumour grade and histology.

MATERIALS AND METHODS

In total, 386 epithelial ovarian carcinoma patients underwent debulking surgery, and the histopathological results of the patients were retrospectively reviewed from 2003 to 2017. Among these patients, 170 patients had histopathological data that were available due to primary cytoreductive surgery and could be analysed regarding FDG avidity in type I and type II ovarian cancer. The FDG uptake of the tumour (SUVmax), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were analysed according to the tumour grade, histology and type of ovarian carcinogenesis (type I and II) and prognosis.

RESULTS

Among the 386 patients, there was a significant difference in SUVmax among ovarian cancer subtypes. There was a significant increase in SUVmax as the tumour grade increased (8.08 ± 0.63, 10.5 ± 0.40, and 12.7 ± 0.38 for grades I, II and III, respectively, Kruskal-Wallis test, p < 0.0001). Among the 90 type I and 80 type II ovarian carcinoma patients, there was a significant difference in SUVmax (type I and II, 9.47 ± 0.54 and 12.97 ± 0.70, respectively, Mann-Whitney test, p = 0.0003). However, no significant change in SUVmax was observed between BRCA-positive and BRCA-negative patients (N = 80, 13.8 ± 5.78 and 12.4 ± 6.30, Student's t-test, p = 0.3075). Among clinicopathologic and metabolic parameters, type of ovarian cancer, MTV and CA125 were significant factors in the prediction of recurrence.

CONCLUSIONS

The glycolytic phenotype was related to tumour grade and histological subtype, with significant differences between type I and II ovarian cancer. SUVmax of the ovarian cancer would be considered in the differentiation of type I and II ovarian cancer.

Whole solid tumor volume histogram parameters for predicting the recurrence in patients with epithelial ovarian carcinoma: a feasibility study on quantitative DCE-MRI

BACKGROUND

Preoperative prediction of the recurrence of epithelial ovarian carcinoma (EOC) can guide the clinical treatment and improve the prognosis. However, there are still no reliable predictive biomarkers.

PURPOSE

To evaluate whether whole solid tumor volume histogram parameters measured from quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict the recurrence in patients with EOC.

MATERIAL AND METHODS

We followed up 56 patients with surgical and histopathologically diagnosed EOC who underwent quantitative DCE-MRI scans. The differences of the histogram parameters between patients with and without recurrence were compared. Mann-Whitney U test, Pearson's Chi-squared test, or Fisher's exact test, and receiver operating characteristic (ROC) curves were used for statistical analysis.

RESULTS

All histogram parameters of K^trans, k_ep, and v_e were not significantly different between EOC patients with and without recurrence (P>0.05). For 30 patients with high-grade serous ovarian carcinoma (HGSOC), the histogram parameters of K^trans (mean and 5th, 10th, 25th, 50th, 75th percentiles) and k_ep (mean and 50th percentile) in 12 patients with recurrence were significantly lower than those in 18 patients without recurrence (all P<0.05). ROC curves showed that the 5th percentile of K^trans had the largest area under the curve (AUC) of 0.792 for predicting the recurrence in patients with HGSOC. When the threshold value was ≤0.0263/min, the sensitivity, specificity, and accuracy were 100%, 66.7%, and 80%, respectively.

CONCLUSION

Instead of predicting the recurrence of EOC, whole solid tumor volume quantitative DCE-MRI histogram parameters could predict the recurrence of HGSOC and may be potential biomarkers for the prediction of HGSOC recurrence.

Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer

BACKGROUND

Ovarian cancer survival rates have not changed in the last 20 years. The majority of cases are High-grade serous ovarian carcinomas (HGSOCs), which are typically diagnosed at an advanced stage with multiple metastatic lesions. Taking biopsies of all sites of disease is infeasible, which challenges the implementation of stratification tools based on molecular profiling.

MAIN BODY

In this review, we describe how these challenges might be overcome by integrating quantitative features extracted from medical imaging with the analysis of paired genomic profiles, a combined approach called radiogenomics, to generate virtual biopsies. Radiomic studies have been used to model different imaging phenotypes, and some radiomic signatures have been associated with paired molecular profiles to monitor spatiotemporal changes in the heterogeneity of tumours. We describe different strategies to integrate radiogenomic information in a global and local manner, the latter by targeted sampling of tumour habitats, defined as regions with distinct radiomic phenotypes.

CONCLUSION

Linking radiomics and biological correlates in a targeted manner could potentially improve the clinical management of ovarian cancer. Radiogenomic signatures could be used to monitor tumours during the course of therapy, offering additional information for clinical decision making. In summary, radiogenomics may pave the way to virtual biopsies and treatment monitoring tools for integrative tumour analysis.

Cancer genome landscape: a radiologist's guide to cancer genome medicine with imaging correlates

The introduction of high throughput sequence analysis in the past decade and the decrease in sequencing costs has made available an enormous amount of genomic data. These data have shaped the landscape of cancer genome, which encompasses mutations determining tumorigenesis, the signaling pathways involved in cancer growth, the tumor heterogeneity, and its role in development of metastases. Tumors develop acquiring a series of driver mutations over time. Of the many mutated genes present in cancer, only few specific mutations are responsible for invasiveness and metastatic potential, which, in many cases, have characteristic imaging appearance. Ten signaling pathways, each with targetable components, have been identified as responsible for cancer growth. Blockage of any of these pathways form the basis for molecular targeted therapies, which are associated with specific pattern of response and toxicities. Tumor heterogeneity, responsible for the different mutation pattern of metastases and primary tumor, has been classified in intratumoral, intermetastatic, intrametastatic, and interpatient heterogeneity, each with specific imaging correlates. The purpose of this article is to introduce the key components of the landscapes of cancer genome and their imaging counterparts, describing the types of mutations associated with tumorigenesis, the pathways of cancer growth, the genetic heterogeneity involved in metastatic disease, as well as the current challenges and opportunities for cancer genomics research.

Does the method of primary treatment affect the pattern of first recurrence in high-grade serous ovarian cancer?

PURPOSE

To determine if the primary treatment approach (primary debulking surgery (PDS) versus neoadjuvant chemotherapy and interval debulking surgery (NACT-IDS)) influences the pattern of first recurrence in patients with completely cytoreduced advanced high-grade serous ovarian carcinoma (HGSOC).

MATERIALS AND METHODS

This retrospective study included 178 patients with newly diagnosed stage IIIC-IV HGSOC, complete gross resection during PDS (n = 124) or IDS (n = 54) from January 2008-March 2013, and baseline and first recurrence contrast-enhanced computed tomography scans. Clinical characteristics and number of disease sites at baseline were analyzed for associations with time to recurrence. In 135 patients who experienced recurrence, the overlap in disease locations between baseline and recurrence and the number of new disease locations at recurrence were analyzed according to the primary treatment approach.

RESULTS

At univariate and multivariate analyses, NACT-IDS was associated with more overlapping locations between baseline and first recurrence (p ≤ 0.003) and fewer recurrences in new anatomic locations (p ≤ 0.043) compared with PDS. The same results were found in a subgroup that received intra-peritoneal adjuvant chemotherapy after either treatment approach. At univariate analysis, patient age, primary treatment approach, adjuvant chemotherapy route, and number of disease locations at baseline were associated with time to recurrence (p ≤ 0.009). At multivariate analysis, older patient age, NACT-IDS, and greater disease locations at baseline remained significant (p ≤ 0.018).

CONCLUSION

The distribution of disease at the time of first recurrence varied with the choice of primary treatment. Compared to patients treated with PDS, patients who underwent NACT-IDS experienced recurrence more often in the same locations as the original disease.

Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer

PURPOSE

To assess the associations between inter-site texture heterogeneity parameters derived from computed tomography (CT), survival, and BRCA mutation status in women with high-grade serous ovarian cancer (HGSOC).

MATERIALS AND METHODS

Retrospective study of 88 HGSOC patients undergoing CT and BRCA mutation status testing prior to primary cytoreductive surgery. Associations between texture metrics-namely inter-site cluster variance (SCV), inter-site cluster prominence (SCP), inter-site cluster entropy (SE)-and overall survival (OS), progression-free survival (PFS) as well as BRCA mutation status were assessed.

RESULTS

Higher inter-site cluster variance (SCV) was associated with lower PFS (p = 0.006) and OS (p = 0.003). Higher inter-site cluster prominence (SCP) was associated with lower PFS (p = 0.02) and higher inter-site cluster entropy (SE) correlated with lower OS (p = 0.01). Higher values of all three metrics were significantly associated with lower complete surgical resection status in BRCA-negative patients (SE p = 0.039, SCV p = 0.006, SCP p = 0.02), but not in BRCA-positive patients (SE p = 0.7, SCV p = 0.91, SCP p = 0.67). None of the metrics were able to distinguish between BRCA mutation carrier and non-mutation carrier.

CONCLUSION

The assessment of tumoral heterogeneity in the era of personalized medicine is important, as increased heterogeneity has been associated with distinct genomic abnormalities and worse patient outcomes. A radiomics approach using standard-of-care CT scans might have a clinical impact by offering a non-invasive tool to predict outcome and therefore improving treatment effectiveness. However, it was not able to assess BRCA mutation status in women with HGSOC.

Effect of BRCA mutational status on survival outcome in advanced-stage high-grade serous ovarian cancer

Objective

To evaluate impact of germline BRCA mutational status on prognosis in patients with advanced ovarian cancer.

Methods

A total of 128 patients diagnosed with FIGO stage III-IV high-grade serous ovarian cancer (HGSOC) between 2008 and 2017 and underwent BRCA1/2 gene testing at the time of or within two years from cancer diagnosis were included in this study. We compared patients’ clinicopathological characteristics and survival outcomes after primary treatment according to germline BRCA mutational status. Treatment-related factors that might affect patients’ survival outcome were also investigated.

Results

Germline BRCA1/2 mutations were observed in 51 women (39.8%). There were no differences in age and serum CA-125 levels at the time of HGSOC diagnosis, use of neoadjuvant chemotherapy (NAC), extent of debulking surgery, and overall survival (OS) between the BRCA mutation and wild-type BRCA groups. In contrast, the BRCA mutation group displayed longer progression-free survival (PFS) (median, 22.9 vs. 16.9 months, P = 0.001). Multivariate analyses identified germline BRCA1/2 mutation as an independent favorable prognostic factor for PFS (adjusted HR, 0.502; 95% CI, 0.318–0.795; P = 0.003). In the wild-type BRCA group, patients who received NAC as the primary treatment had shorter PFS compared to those who received primary debulking surgery (PDS) (median, 14.2 vs. 16.9 months, P = 0.003). However, in the BRCA mutation group, PFS did not differ between the NAC and PDS groups (P = 0.082).

Conclusions

In advanced-stage HGSOC, patients with germline BRCA1/2 mutations have better prognosis with longer PFS than those lacking BRCA mutations. Prognosis after NAC was different according to the BRCA mutational status.

Electronic supplementary material

The online version of this article (10.1186/s13048-019-0511-7) contains supplementary material, which is available to authorized users.

A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study

Objectives: We used radiomic analysis to establish a radiomic signature based on preoperative contrast enhanced computed tomography (CT) and explore its effectiveness as a novel recurrence risk prognostic marker for advanced high-grade serous ovarian cancer (HGSOC). Methods: This study had a retrospective multicenter (two hospitals in China) design and a radiomic analysis was performed using contrast enhanced CT in advanced HGSOC (FIGO stage III or IV) patients. We used a minimum 18-month follow-up period for all patients (median 38.8 months, range 18.8-81.8 months). All patients were divided into three cohorts according to the timing of their surgery and hospital stay: training cohort (TC) and internal validation cohort (IVC) were from one hospital, and independent external validation cohort (IEVC) was from another hospital. A total of 620 3-D radiomic features were extracted and a Lasso-Cox regression was used for feature dimension reduction and determination of radiomic signature. Finally, we combined the radiomic signature with seven common clinical variables to develop a novel nomogram using a multivariable Cox proportional hazards model. Results: A final 142 advanced HGSOC patients were enrolled. Patients were successfully divided into two groups with statistically significant differences based on radiomic signature, consisting of four radiomic features (log-rank test P = 0.001, <0.001, <0.001 for TC, IVC, and IEVC, respectively). The discrimination accuracies of radiomic signature for predicting recurrence risk within 18 months were 82.4% (95% CI, 77.8-87.0%), 77.3% (95% CI, 74.4-80.2%), and 79.7% (95% CI, 73.8-85.6%) for TC, IVC, and IEVC, respectively. Further, the discrimination accuracies of radiomic signature for predicting recurrence risk within 3 years were 83.4% (95% CI, 77.3-89.6%), 82.0% (95% CI, 78.9-85.1%), and 70.0% (95% CI, 63.6-76.4%) for TC, IVC, and IEVC, respectively. Finally, the accuracy of radiomic nomogram for predicting 18-month and 3-year recurrence risks were 84.1% (95% CI, 80.5-87.7%) and 88.9% (95% CI, 85.8-92.5%), respectively. Conclusions: Radiomic signature and radiomic nomogram may be low-cost, non-invasive means for successfully predicting risk for postoperative advanced HGSOC recurrence before or during the perioperative period. Radiomic signature is a potential prognostic marker that may allow for individualized evaluation of patients with advanced HGSOC.

A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer

The five-year survival rate of epithelial ovarian cancer (EOC) is approximately 35-40% despite maximal treatment efforts, highlighting a need for stratification biomarkers for personalized treatment. Here we extract 657 quantitative mathematical descriptors from the preoperative CT images of 364 EOC patients at their initial presentation. Using machine learning, we derive a non-invasive summary-statistic of the primary ovarian tumor based on 4 descriptors, which we name "Radiomic Prognostic Vector" (RPV). RPV reliably identifies the 5% of patients with median overall survival less than 2 years, significantly improves established prognostic methods, and is validated in two independent, multi-center cohorts. Furthermore, genetic, transcriptomic and proteomic analysis from two independent datasets elucidate that stromal phenotype and DNA damage response pathways are activated in RPV-stratified tumors. RPV and its associated analysis platform could be exploited to guide personalized therapy of EOC and is potentially transferrable to other cancer types.