Andrea Rockall

Acute abdominal and pelvic pain in pregnancy: ESUR recommendations.

Acute abdominal pain in pregnancy presents diagnostic and therapeutic challenges. Standard imaging techniques need to be adapted to reduce harm to the fetus from X-rays due to their teratogenic and carcinogenic potential. Ultrasound remains the primary imaging investigation of the pregnant abdomen. Magnetic resonance imaging (MRI) has been shown to be useful in the diagnosis of gynaecological and obstetric problems during pregnancy and in the setting of acute abdomen during pregnancy. MRI overcomes some of the limitations of ultrasound, mainly the size of the gravid uterus. MRI poses theoretical risks to the fetus and care must be taken to minimise these with the avoidance of contrast agents. This article reviews the evolving imaging and clinical literature on appropriate investigation of acute abdominal and pelvic pain during established intrauterine pregnancy, addressing its common causes. Guidelines based on the current literature and on the accumulated clinico-radiological experience of the European Society of Urogenital Radiology (ESUR) working group are proposed for imaging these suspected conditions.Key Points• Ultrasound and MRI are the preferred investigations for abdominal pain during pregnancy.• Ultrasound remains the primary imaging investigation because of availability and portability.• MRI helps differentiate causes of abdominopelvic pain when ultrasound is inconclusive.• If MRI cannot be performed, low-dose CT may be necessary.• Following severe trauma, CT cannot be delayed because of radiation concerns.

Functional Imaging to Predict Tumor Response in Locally Advanced Cervical Cancer

Worldwide, cervical cancer is the third commonest cancer. Prognostic factors for cervical cancer include tumor size, histological subtype, histological grade, International Federation of Gynecology and Obstetrics (FIGO) stage, nodal status and performance status. However these known parameters are not sufficient to accurately predict treatment response or prognosis. There is a clinical need for noninvasive prognostic biomarkers to provide more detailed tumor characterization at the baseline and/or early during therapy, which may permit personalized treatment and potentially improve outcomes. Functional imaging techniques have been developing rapidly over the past decade. Imaging parameters derived from PET/CT and functional MRI techniques are emerging as promising response biomarkers. This review details the current evidence and future potential of functional imaging to predict tumor response in locally advanced cervical carcinoma.

Repeatability of quantitative FDG-PET/CT and contrast enhanced CT in recurrent ovarian carcinoma: test retest measurements for tumor FDG uptake, diameter and volume

Purpose: Repeatability of baseline FDG-PET/CT measurements has not been tested in ovarian cancer. This dual-center, prospective study assessed variation in tumor 2[18F]fluoro-2-deoxy-D-glucose (FDG) uptake, tumor diameter, and tumor volume from sequential FDG-PET/CT and contrast-enhanced computed tomography (CECT) in patients with recurrent platinum-sensitive ovarian cancer. Experimental Design: Patients underwent two pretreatment baseline FDG-PET/CT (n = 21) and CECT (n = 20) at two clinical sites with different PET/CT instruments. Patients were included if they had at least one target lesion in the abdomen with a standardized uptake value (SUV) maximum (SUVmax) of ≥2.5 and a long axis diameter of ≥15 mm. Two independent reading methods were used to evaluate repeatability of tumor diameter and SUV uptake: on site and at an imaging clinical research organization (CRO). Tumor volume reads were only performed by CRO. In each reading set, target lesions were independently measured on sequential imaging. Results: Median time between FDG-PET/CT was two days (range 1–7). For site reads, concordance correlation coefficients (CCC) for SUVmean, SUVmax, and tumor diameter were 0.95, 0.94, and 0.99, respectively. Repeatability coefficients were 16.3%, 17.3%, and 8.8% for SUVmean, SUVmax, and tumor diameter, respectively. Similar results were observed for CRO reads. Tumor volume CCC was 0.99 with a repeatability coefficient of 28.1%. Conclusions: There was excellent test–retest repeatability for FDG-PET/CT quantitative measurements across two sites and two independent reading methods. Cutoff values for determining change in SUVmean, SUVmax, and tumor volume establish limits to determine metabolic and/or volumetric response to treatment in platinum-sensitive relapsed ovarian cancer. Clin Cancer Res; 20(10); 2751–60. ©2014 AACR.

European Society of Gynaecologic Oncology Quality Indicators for Advanced Ovarian Cancer Surgery

Objectives The surgical management of advanced ovarian cancer involves complex surgery. Implementation of a quality management program has a major impact on survival. The goal of this work was to develop a list of quality indicators (QIs) for advanced ovarian cancer surgery that can be used to audit and improve the clinical practice. This task has been carried out under the auspices of the European Society of Gynaecologic Oncology (ESGO). Methods Quality indicators were based on scientific evidence and/or expert consensus. A 4-step evaluation process included a systematic literature search for the identification of potential QIs and the documentation of scientific evidence, physical meetings of an ad hoc multidisciplinarity International Development Group, an internal validation of the targets and scoring system, and an external review process involving physicians and patients. Results Ten structural, process, or outcome indicators were selected. Quality indicators 1 to 3 are related to achievement of complete cytoreduction, caseload in the center, training, and experience of the surgeon. Quality indicators 4 to 6 are related to the overall management, including active participation to clinical research, decision-making process within a structured multidisciplinary team, and preoperative workup. Quality indicator 7 addresses the high value of adequate perioperative management. Quality indicators 8 to 10 highlight the need of recording pertinent information relevant to improvement of quality. An ESGO-approved template for the operative report has been designed. Quality indicators were described using a structured format specifying what the indicator is measuring, measurability specifications, and targets. Each QI was associated with a score, and an assessment form was built. Conclusions The ESGO quality criteria can be used for self-assessment, for institutional or governmental quality assurance programs, and for the certification of centers. Quality indicators and corresponding targets give practitioners and health administrators a quantitative basis for improving care and organizational processes in the surgical management of advanced ovarian cancer.

European society of urogenital radiology (ESUR) guidelines: MR imaging of pelvic endometriosis.

Endometriosis is a common gynaecological condition of unknown aetiology that primarily affects women of reproductive age. The accepted first-line imaging modality is pelvic ultrasound. However, magnetic resonance imaging (MRI) is increasingly performed as an additional investigation in complex cases and for surgical planning. There is currently no international consensus regarding patient preparation, MRI protocols or reporting criteria. Our aim was to develop clinical guidelines for MRI evaluation of pelvic endometriosis based on literature evidence and consensus expert opinion. This work was performed by a group of radiologists from the European Society of Urogenital Radiology (ESUR), experts in gynaecological imaging and a gynaecologist expert in methodology. The group discussed indications for MRI, technical requirements, patient preparation, MRI protocols and criteria for the diagnosis of pelvic endometriosis on MRI. The expert panel proposed a final recommendation for each criterion using Oxford Centre for Evidence Based Medicine (OCEBM) 2011 levels of evidence.Key Points• This report provides guidelines for MRI in endometriosis.• Minimal and optimal MRI acquisition protocols are provided.• Recommendations are proposed for patient preparation, best MRI sequences and reporting criteria.

Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth

When integrating computational tools, such as automatic segmentation, into clinical practice, it is of utmost importance to be able to assess the level of accuracy on new data and, in particular, to detect when an automatic method fails. However, this is difficult to achieve due to the absence of ground truth. Segmentation accuracy on clinical data might be different from what is found through cross validation, because validation data are often used during incremental method development, which can lead to overfitting and unrealistic performance expectations. Before deployment, performance is quantified using different metrics, for which the predicted segmentation is compared with a reference segmentation, often obtained manually by an expert. But little is known about the real performance after deployment when a reference is unavailable. In this paper, we introduce the concept of reverse classification accuracy (RCA) as a framework for predicting the performance of a segmentation method on new data. In RCA, we take the predicted segmentation from a new image to train a reverse classifier, which is evaluated on a set of reference images with available ground truth. The hypothesis is that if the predicted segmentation is of good quality, then the reverse classifier will perform well on at least some of the reference images. We validate our approach on multi-organ segmentation with different classifiers and segmentation methods. Our results indicate that it is indeed possible to predict the quality of individual segmentations, in the absence of ground truth. Thus, RCA is ideal for integration into automatic processing pipelines in clinical routine and as a part of large-scale image analysis studies.

A framework for optimization of diffusion-weighted MRI protocols for large field-of-view abdominal-pelvic imaging in multicenter studies

PURPOSE To develop methods for optimization of diffusion-weighted MRI (DW-MRI) in the abdomen and pelvis on 1.5 T MR scanners from three manufacturers and assess repeatability of apparent diffusion coefficient (ADC) estimates in a temperature-controlled phantom and abdominal and pelvic organs in healthy volunteers. METHODS Geometric distortion, ghosting, fat suppression, and repeatability and homogeneity of ADC estimates were assessed using phantoms and volunteers. Healthy volunteers (ten per scanner) were each scanned twice on the same scanner. One volunteer traveled to all three institutions in order to provide images for qualitative comparison. The common volunteer was excluded from quantitative analysis of the data from scanners 2 and 3 in order to ensure statistical independence, giving n = 10 on scanner 1 and n = 9 on scanners 2 and 3 for quantitative analysis. Repeatability and interscanner variation of ADC estimates in kidneys, liver, spleen, and uterus were assessed using within-patient coefficient of variation (wCV) and Kruskal-Wallis tests, respectively. RESULTS The coefficient of variation of ADC estimates in the temperature-controlled phantom was 1%-4% for all scanners. Images of healthy volunteers from all scanners showed homogeneous fat suppression and no marked ghosting or geometric distortion. The wCV of ADC estimates was 2%-4% for kidneys, 3%-7% for liver, 6%-9% for spleen, and 7%-10% for uterus. ADC estimates in kidneys, spleen, and uterus showed no significant difference between scanners but a significant difference was observed in liver (p < 0.05). CONCLUSIONS DW-MRI protocols can be optimized using simple phantom measurements to produce good quality images in the abdomen and pelvis at 1.5 T with repeatable quantitative measurements in a multicenter study.

Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi‐atlas (MA) approach

Purpose: As part of a program to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated, and compared three algorithms for fully automatic, multiorgan segmentation in healthy volunteers. Methods: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi‐atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardized, multiparametric whole body MRI protocol at 1.5 T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Fivefold cross‐validation experiments were run on 34 artifact‐free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root‐mean‐square surface distance (RMSSD), and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a ‘per‐organ’ basis. A Mann–Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a ‘per‐organ’ basis, when using different imaging combinations as input for training. Results: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of datasets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC = 0.70 ± 0.18, RE = 0.73 ± 0.18, PR = 0.71 ± 0.14, CNNs: DSC = 0.81 ± 0.13, RE = 0.83 ± 0.14, PR = 0.82 ± 0.10, MA: DSC = 0.71 ± 0.22, RE = 0.70 ± 0.34, PR = 0.77 ± 0.15. Mean surface distance metrics for all the segmented structures were: CFs: ASD = 13.5 ± 11.3 mm, RMSSD = 34.6 ± 37.6 mm and HD = 185.7 ± 194.0 mm, CNNs; ASD = 5.48 ± 4.84 mm, RMSSD = 17.0 ± 13.3 mm and HD = 199.0 ± 101.2 mm, MA: ASD = 4.22 ± 2.42 mm, RMSSD = 6.13 ± 2.55 mm, and HD = 38.9 ± 28.9 mm. The pooled performance of CFs improved when all imaging combinations (T2w + T1w + DWI) were used as input, while the performance of CNNs deteriorated, but in neither case, significantly. CNNs with T2w images as input, performed significantly better than CFs with all imaging combinations as input for all anatomical labels, except for the bladder. Conclusions: Three state‐of‐the‐art algorithms were developed and used to automatically segment major organs and bones in whole body MRI; good agreement to manual segmentations performed by clinical MRI experts was observed. CNNs perform favorably, when using T2w volumes as input. Using multimodal MRI data as input to CNNs did not improve the segmentation performance.

Functional MR Imaging in Gynecologic Cancer

Dynamic-contrast enhanced (DCE) and diffusion-weighted (DW) MR imaging are invaluable in the detection, staging, and characterization of uterine and ovarian malignancies, for monitoring treatment response, and for identifying disease recurrence. When used as adjuncts to morphologic T2-weighted (T2-W) MR imaging, these techniques improve accuracy of disease detection and staging. DW-MR imaging is preferred because of its ease of implementation and lack of need for an extrinsic contrast agent. MR spectroscopy is difficult to implement in the clinical workflow and lacks both sensitivity and specificity. If used quantitatively in multicenter clinical trials, standardization of DCE- and DW-MR imaging techniques and rigorous quality assurance is mandatory.

Correlation of pre-operative CT findings with surgical & histological tumor dissemination patterns at cytoreduction for primary advanced and relapsed epithelial ovarian cancer: A retrospective evaluation

OBJECTIVES Computed tomography (CT) is an essential part of preoperative planning prior to cytoreductive surgery for primary and relapsed epithelial ovarian cancer (EOC). Our aim is to correlate pre-operative CT results with intraoperative surgical and histopathological findings at debulking surgery. METHODS We performed a systematic comparison of intraoperative tumor dissemination patterns and surgical resections with preoperative CT assessments of infiltrative disease at key resection sites, in women who underwent multivisceral debulking surgery due to EOC between January 2013 and December 2014 at a tertiary referral center. The key sites were defined as follows: diaphragmatic involvement(DI), splenic disease (SI), large (LBI) and small (SBI) bowel involvement, rectal involvement (RI), porta hepatis involvement (PHI), mesenteric disease (MI) and lymph node involvement (LNI). RESULTS A total of 155 patients, mostly with FIGO stage IIIC disease (65%) were evaluated (primary=105, relapsed=50). Total macroscopic cytoreduction rates were: 89%. Pre-operative CT findings displayed high specificity across all tumor sites apart from the retroperitoneal lymph node status, with a specificity of 65%. The ability however of the CT to accurately identify sites affected by invasive disease was relatively low with the following sensitivities as relating to final histology: 32% (DI), 26% (SI), 46% (LBI), 44% (SBI), 39% (RI), 57% (PHI), 31% (MI), 63% (LNI). CONCLUSION Pre-operative CT imaging shows high specificity but low sensitivity in detecting tumor involvement at key sites in ovarian cancer surgery. CT findings alone should not be used for surgical decision making.