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Schematic of the common sites of disease in ovarian cancer. The features in yellow are sites of potentially non resectable disease. The features in red are sites of non resectable disease
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![Table 2 FIGO staging of ovarian cancer 2014 [11]](publication/305790905/figure/tbl1/AS:670390812692502@1536845033805/FIGO-staging-of-ovarian-cancer-2014-11_Q320.jpg)
CT of the abdomen and pelvis is the first line imaging modality for staging, selecting treatment options and assessing disease response in ovarian cancer. The staging CT provides disease distribution, disease burden and is the imaging surrogate for surgico-pathological FIGO staging. Optimal cyto-reductive surgery offers patients’ the best chance fo...
Citations
... Computed Tomography (CT) with intravenous contrast is the first-line imaging method for staging and follow-up of OC according to the American College of Radiology guidelines [12]. However, according to a meta-analysis, the sensitivity of CT for predicting LNM is not ideal, only 0.47 [13,14]. The diagnostic efficacy of magnetic resonance imaging (MRI) [15] and positron emission tomography/computed tomography (PET/CT) [16] is also not high. ...
Background
Despite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC).
Results
Patients with 401 HGSOC lesions from two institutions were enrolled: institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI]: 0.864–0.933) in the training cohort and 0.855 (95%CI: 0.774–0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone.
Conclusions
The nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.
... When neoadjuvant chemotherapy is offered, an imaging-guided biopsy, planned on the staging CT, confirms the diagnosis and provides the histological sub-type. As described by Sahdev et al., a systematic CT reporting approach is essential to identify and communicate important sites of disease which may alter or preclude surgery at diagnosis or following neoadjuvant chemotherapy [87]. Quantifying the extent of bowel serosal and mesenteric deposits is technically difficult to achieve and communicate. ...
Tubo-ovarian cancer is the most lethal gynaecological cancer. More than 75% of patients are diagnosed at an advanced stage, which is associated with poorer overall survival. Symptoms at presentation are vague and non-specific, contributing to late diagnosis. Multimodal risk models have improved the diagnostic accuracy of adnexal mass assessment based on patient risk factors, coupled with findings on imaging and serum-based biomarker tests. Newly developed ultrasonographic assessment algorithms have standardised documentation and enable stratification of care between local hospitals and cancer centres. So far, no screening test has proven to reduce ovarian cancer mortality in the general population. This review is an update on the evidence behind ovarian cancer diagnostic strategies.
... Recent studies have shown that timely clinical intervention plays a critical role in increasing patients' survival rates. 2 Contrast-enhanced computed tomography (CECT) is a commonly used medical imaging tool for diagnosing ovarian cancer. As a non-invasive diagnostic method, CECT images reflect tissue characteristics that assist doctors in evaluating disease progression and subsequently creating treatment plans 3 . But manually annotating ovarian tumors from CECT images is a time-consuming and labor-intensive task in practical applications. ...
... Next, query patches with a side length of L in the axial plane are centered on the N patch selected points, and 2D patches are determined since 3D spatial context information is difficult to maintain within a small patch area due to anisotropic spacing of ovarian cancer CECT images. 3 By utilizing the coordinate information of 9 points, including the center, vertices, and midpoints, within each query patch, we employ interpolation to extract 9 feature vectors, each containing 32 dimensions, from the corresponding position of the 9 points on the last feature maps of SNet. And then, these vectors are combined to form a 288-dimensional feature vector for each query patch. ...
Background
Ovarian cancer is a highly lethal gynecological disease. Accurate and automated segmentation of ovarian tumors in contrast‐enhanced computed tomography (CECT) images is crucial in the radiotherapy treatment of ovarian cancer, enabling radiologists to evaluate cancer progression and develop timely therapeutic plans. However, automatic ovarian tumor segmentation is challenging due to factors such as inhomogeneous background, ambiguous tumor boundaries, and imbalanced foreground‐background, all of which contribute to high predictive uncertainty for a segmentation model.
Purpose
To tackle these challenges, we propose an uncertainty‐aware refinement framework that aims to estimate and refine regions with high predictive uncertainty for accurate ovarian tumor segmentation in CECT images.
Methods
To this end, we first employ an approximate Bayesian network to detect coarse regions of interest (ROIs) of both ovarian tumors and uncertain regions. These ROIs allow a subsequent segmentation network to narrow down the search area for tumors and prioritize uncertain regions, resulting in precise segmentation of ovarian tumors. Meanwhile, the framework integrates two guidance modules that learn two implicit functions capable of mapping query features sampled according to their uncertainty to organ or boundary manifolds, guiding the segmentation network to facilitate information encoding of uncertain regions.
Results
Firstly, 367 CECT images are collected from the same hospital for experiments. Dice score, Jaccard, Recall, Positive predictive value (PPV), 95% Hausdorff distance (HD95) and Average symmetric surface distance (ASSD) for the testing group of 77 cases are 86.31%, 73.93%, 83.95%, 86.03%, 15.17 mm and 2.57 mm, all of which are significantly better than that of the other state‐of‐the‐art models. And results of visual comparison shows that the compared methods have more mis‐segmentation than our method. Furthermore, our method achieves a Dice score that is at least 20% higher than the Dice scores of other compared methods when tumor volumes are less than 20 cm, indicating better recognition ability to small regions by our method. And then, 38 CECT images are collected from another hospital to form an external testing group. Our approach consistently outperform the compared methods significantly, with the external testing group exhibiting substantial improvements across key evaluation metrics: Dice score (83.74%), Jaccard (69.55%), Recall (82.12%), PPV (81.61%), HD95 (12.31 mm), and ASSD (2.32 mm), robustly establishing its superior performance.
Conclusions
Experimental results demonstrate that the framework significantly outperforms the compared state‐of‐the‐art methods, with decreased under‐ or over‐segmentation and better small tumor identification. It has the potential for clinical application.
... Particular attention should be paid to discussing key sites of disease for surgical planning, particularly sites which are considered difficult to resect or unresectable [21]. This systematic assessment should be performed both for treatment naive patients and for those who have received neoadjuvant therapy. ...
... This systematic assessment should be performed both for treatment naive patients and for those who have received neoadjuvant therapy. Of note, the radiologist should not necessarily deem disease as resectable or unresectable, but rather discuss sites of disease as having potential for complicating surgery or impeding optimal debulking as different surgeons often have varying thresholds for sites to be deemed "unresectable" [21,22]. Such sites include extensive disease in the subdiaphragmatic space, lesser sac, mesenteric root, porta hepatis and implants adjacent to the right hepatic vein, bladder trigone, pelvic sidewall, and lymph nodes superior to the renal veins. ...
This manuscript is a collaborative, multi-institutional effort by members of the Society of Abdominal Radiology Uterine and Ovarian Cancer Disease Focus Panel and the European Society of Urogenital Radiology Women Pelvic Imaging working group. The manuscript reviews the key role radiologists play at tumor board and highlights key imaging findings that guide management decisions in patients with the most common gynecologic malignancies including ovarian cancer, cervical cancer, and endometrial cancer.
... Conventional CT imaging is unable to detect small (< 5 mm) deposits which in HGSOC particularly, often results in reports containing 'non-measurable' findings such as 'haziness, streaking, nodularity, thickening' to describe disease covering the bowel serosa, mesentery or peritoneum, for example. These descriptions are subjective and yet are often the only imaging evidence of disease [3]. ...
... Conventional CT scans use single energy frequency (~ 120kVp) to establish the extent of disease [3]. Dual energy CT (DECT) allows the simultaneous collection of data from different photon spectra. ...
Background
In patients with cancer, the current gold standard for assessing response to treatment involves measuring cancer lesions on computed tomography (CT) imaging. The percentage change in size of specific lesions determines whether patients have had a complete/partial response or progressive disease, according to RECIST criteria. Dual Energy CT (DECT) permits additional measurements of iodine concentration, a surrogate marker of vascularity. Here we explore the role of changes in iodine concentration within cancer tissue on CT scans to assess its suitability for determining treatment response in patients with high grade serous ovarian cancer (HGSOC).
Methods
Suitable RECIST measurable lesions were identified from the CT images of HGSOC patients, taken at 2 different time points (pre and post treatment). Changes in size and iodine concentration were measured for each lesion. PR/SD were classified as responders, PD was classified as non-responder. Radiological responses were correlated with clinical and CA125 outcomes.
Results
62 patients had appropriate imaging for assessment. 22 were excluded as they only had one DECT scan. 32/40 patients assessed (113 lesions) had received treatment for relapsed HGSOC. RECIST and GCIG (Gynaecologic Cancer Inter Group) CA125 criteria / clinical assessment of response for patients was correlated with changes in iodine concentration, before and after treatment. The prediction of median progression free survival was significantly better associated with changes in iodine concentration (p = 0.0001) and GCIG Ca125 / clinical assessment (p = 0.0028) in comparison to RECIST criteria (p = 0.43).
Conclusion
Changes in iodine concentration from dual energy CT imaging may be more suitable than RECIST in assessing response to treatment in patients with HGSOC.
Trial Registration
CICATRIx IRAS number 198179, 14 Dec 2015, https://www.myresearchproject.org.uk/ .
... The most frequent routes for dissemination of ovarian cancer are by direct pelvic invasion and via transcoelomic peritoneal spread. 13 Less frequently, it may also spread along the lymphatics via the utero-ovarian, infundibulopelvic and round ligament pathways. 3 The most common lymphatic spread is along the uteroovarian pathway to the para-aortic and paracaval nodes at the level of the kidney. ...
... Pelvic sidewall invasion is suspected when the distance between the tumour and the muscular pelvic sidewall is <3 mm, or when there is encasement of >90% of the circumference of iliac vessels. 13 Any invasion to the adjacent organs such as the urinary bladder or rectum should be noted since it may require additional surgical input from other subspecialties to achieve optimal debulking ( Figure 2). ...
... An enlarged node with >1 cm short axis suggests malignant lymphadenopathy. 13 Apart from increased nodal size, necrosis or clustering of lymph nodes are also suspicious features of metastatic involvement. Any enlarged lymph node at the suprarenal para-aortic, portacaval, porta hepatis or celiac axis should be specified because these sites may preclude surgery (Figures 13 and 14). ...
... Compared with traditional MRI analysis in differentiating BOTs from malignancies, radiomics signature results show better performance. In a traditional MRI reading session, the imaging signs always overlap with each other to some extent (for example, large size, solid components, irregular and thick septa) and lead to an inaccurate diagnosis [27,[30][31][32]. A recent study with proton MR spectroscopy (MRS) reported that the SEN and SPE were 91% and 100% for solid components, respectively; additionally, the SEN and SPE were 84% and 82% for cystic components, respectively [12]. ...
Background
Ovarian cancer is the most women malignancy in the whole world. It is difficult to differentiate ovarian cancers from ovarian borderline tumors because of some similar imaging findings.Radiomics study may help clinicians to make a proper diagnosis before invasive surgery.
Purpose
To evaluate the ability of T2-weighted imaging (T2WI)-based radiomics to discriminate ovarian borderline tumors (BOTs) from malignancies based on two-dimensional (2D) and three-dimensional (3D) lesion segmentation methods.
Methods
A total of 95 patients with pathologically proven ovarian BOTs and 101 patients with malignancies were retrospectively included in this study. We evaluated the diagnostic performance of the signatures derived from T2WI-based radiomics in their ability to differentiate between BOTs and malignancies and compared the performance differences in the 2D and 3D segmentation models. The least absolute shrinkage and selection operator method (Lasso) was used for radiomics feature selection and machine learning processing.
Results
The radiomics score between BOTs and malignancies in four types of selected T2WI-based radiomics models differed significantly at the statistical level (p < 0.0001). For the classification between BOTs and malignant masses, the 2D and 3D coronal T2WI-based radiomics models yielded accuracy values of 0.79 and 0.83 in the testing group, respectively; the 2D and 3D sagittal fat-suppressed (fs) T2WI-based radiomics models yielded an accuracy of 0.78 and 0.99, respectively.
Conclusions
Our results suggest that T2WI-based radiomic features were highly correlated with ovarian tumor subtype classification. 3D-sagittal MRI radiomics features may help clinicians differentiate ovarian BOTs from malignancies with high ACC.
... Both CT, MRI and PET are not widely used for the diagnosis of ovarian cancer but for the evaluation of the extent of the tumor and the possible presence of distant metastases. Specifically, CT scan can be used to perform biopsies of suspected metastases in a procedure called CT-guided needle biopsy (53,54). Meanwhile, PET and MRI are mostly used to evaluate the spread of diseases in neighboring lymph node stations and in distant organs, such as the medulla and brain, through the use of radiotracers or contrast agents (for example gadolinium) (55). ...
Ovarian cancer represents one of the most aggressive female tumors worldwide. Over the decades, the therapeutic options for the treatment of ovarian cancer have been improved significantly through the advancement of surgical techniques as well as the availability of novel effective drugs able to extend the life expectancy of patients. However, due to its clinical, biological and molecular complexity, ovarian cancer is still considered one of the most difficult tumors to manage. In this context, several studies have highlighted how a multidisciplinary approach to this pathology improves the prognosis and survival of patients with ovarian cancer. On these bases, the aim of the present review is to present recent advantages in the diagnosis, staging and treatment of ovarian cancer highlighting the benefits of a patient‑centered care approach and on the importance of a multidisciplinary team for the management of ovarian cancer.
... Also, supra-colic omental involvement has to be reported as it necessitate a modified surgical approach. In particular, certain parameters are indicative of potentially non-resectable late-stage disease and therefore should be highlighted to the surgeon; these include large nodular or diffuse peritoneal disease with flat plaques > 2 cm, perihepatic and lesser sac involvement > 2 cm, diffuse suprarenal retroperitoneal nodal disease, para-cardiac nodes ≥ 1 cm, multiple obstructing or diffuse bowel lesions, and extensive mesenteric invasion [64]. ...
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. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
... Upper abdominal disease is related with an increased risk of suboptimal residual disease; moreover, the surgical complexity is higher than that for other parts of the abdomen (2). Although previous studies have reported the feasibility of radical resection of upper abdominal disease and the associated long-term patient survival, lesions in certain anatomical regions including the gallbladder, porta hepatis and omental bursa are still considered major obstacles to achieving optimal residual disease (3,4). ...
As ovarian cancer commonly involves the visceral organs without boundary, more aggressive procedures are adopted during cytoreductive surgery. One of the most difficult aspect of the operation involves the procedure for the gall bladder, porta hepatis, and omental bursa. As the upper abdominal surgical field is not familiar to the gynecologic surgeon, and the vital organs or vessels are densely positioned, these procedures can be challenging for achieving the optimal cytoreductive surgery. The surgical approaches for advanced ovarian cancer that are required in the upper abdomen have evolved with the progress in surgical techniques. This article will discuss the surgical approach by focusing on cholecystectomy, porta hepatis debulking, and omental bursectomy, as well as the regional anatomy in patients with advanced ovarian cancer.
