Performance of VI-RADS in predicting muscle-invasive bladder cancer after transurethral resection: a single center retrospective analysis.

PURPOSE: To assess VIRADS performance and inter-reader agreement for detecting muscle-invasive bladder cancer (MIBC) following transurethral resection of bladder tumor (TURBT). METHODS: An IRB-approved, HIPAA-compliant, retrospective study from 2016 to 2020 included patients with bladder urothelial carcinoma who underwent MRI after TURBT, and cystectomy within 3 months without post-MRI treatments. Three radiologists blinded to pathology results independently reviewed MR images and assigned a VI-RADS score. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of VI-RADS were assessed for diagnosing MIBC using VI-RADS scores ≥ 3 and ≥ 4. Inter-reader agreement was assessed using Gwet's agreement coefficient (AC) and percent agreement. RESULTS: The cohort consisted of 70 patients (mean age, 68 years ± 11 [SD]; range 39-85; 58 men) and included 32/70 (46%) with MIBC at cystectomy. ROC analysis revealed an AUC ranging from 0.67 to 0.77 and no pairwise statistical difference between readers (p-values, 0.06, 0.08, 0.97). Percent sensitivity, specificity, PPV, NPV and accuracy for diagnosing MIBC for the three readers ranged from 81.3-93.8, 36.8-55.3, 55.6-60.5, 77.3-87.5, and 62.9-67.1 respectively for VI-RADS score ≥ 3, and 78.1-81.3, 47.4-68.4, 55.6-67.6, 72.0-78.8 and 61.4-72.9 respectively for VI-RADS score ≥ 4. Gwet's AC was 0.63 [95% confidence interval (CI): 0.49,0.78] for VI-RADS score ≥ 3 with 79% agreement [95% CI 72,87] and 0.54 [95%CI 0.38,0.70] for VI-RADS score ≥ 4 with 76% agreement [95% CI 69,84]. VIRADS performance was not statistically different among 31/70 (44%) patients who received treatments prior to MRI (p ≥ 0.16). CONCLUSION: VI-RADS had moderate sensitivity and accuracy but low specificity for detection of MIBC following TURBT, with moderate inter-reader agreement.

MRI, clinical, and radiomic models for differentiation of uterine leiomyosarcoma and leiomyoma.

PURPOSE: To assess the predictive ability of conventional MRI features and MRI texture features in differentiating uterine leiomyoma (LM) from uterine leiomyosarcoma (LMS). METHODS: This single-center, IRB-approved, HIPAA-compliant retrospective study included 108 patients (69 LM, 39 LMS) who had pathology, preoperative MRI, and clinical data available at our tertiary academic institution. Two radiologists independently evaluated 14 features on preoperative MRI. Texture features based on 3D segmentation were extracted from T2W-weighted MRI (T2WI) using commercially available texture software (TexRAD™, Feedback Medical Ltd., Great Britain). MRI conventional features, and clinical and MRI texture features were compared between LM and LMS groups. Dataset was randomly divided into training (86 cases) and testing (22 cases) cohorts (8:2 ratio); training cohort was further subdivided into training and validation sets using ten-fold cross-validation. Optimal radiomics model was selected out of 90 different machine learning pipelines and five models containing different combinations of MRI, clinical, and radiomics variables. RESULTS: 12/14 MRI conventional features and 2/2 clinical features were significantly different between LM and LMS groups. MRI conventional features had moderate to excellent inter-reader agreement for all but two features. Models combining MRI conventional and clinical features (AUC 0.956) and MRI conventional, clinical, and radiomics features (AUC 0.989) had better performance compared to models containing MRI conventional features alone (AUC 0.846 and 0.890) or radiomics features alone (0.929). CONCLUSION: While multiple MRI and clinical features differed between LM and LMS groups, the model combining MRI, clinical, and radiomic features had the best predictive ability but was only marginally better than a model utilizing conventional MRI and clinical data alone.

Development, Implementation, and Assessment of a Quality, Research, Education, and Wellness (QREW) Professional Development Lecture Series for Radiology Faculty.

RATIONALE AND OBJECTIVES: Professional development is important to academic radiologists. We developed, implemented, and assessed an internal professional development lecture series focusing on the non-interpretative themes of Quality, Research, Education, and Wellness (QREW). MATERIALS AND METHODS: The faculty of a 29-member abdominal radiology division at an academic hospital were invited to deliver 1-hour virtual lectures on noninterpretative topics to division colleagues. Topics were curated by division leadership based on the perceived needs of faculty. Anonymous feedback was collected from attendees for quality improvement purposes and analyzed using descriptive statistics and Fisher's exact test. RESULTS: Over 17 months, 13 QREW lectures were delivered. In total, 91 feedback forms were completed by faculty (mean 7 forms, range 2-12 per session). Of these, 57 responses (63%) were by those <7 years post training ("junior faculty"), 34 responses (37%) by those ≥ 7 years from training ("senior faculty"). Most respondents reported low levels of prior instruction (80/90, 89%) and personal knowledge (49/91, 54%) on topics. Compared to senior faculty, a greater proportion of junior faculty reported less prior instruction (73% vs 98%, P < 0.001) and less personal knowledge (32% vs 65%, P < 0.01). Most respondents agreed or strongly agreed that the topics were important to their clinical practice (87/90, 97%), professional development (86/90, 96%), and personal well-being (82/91, 90%). Faculty identified the QREW program as a major contributor to their professional development. CONCLUSION: A noninterpretative professional development lecture series delivered by radiology faculty in a virtual, interactive format is feasible and effective, particularly for junior faculty.

Current Status of Cancer Genomics and Imaging Phenotypes: What Radiologists Need to Know.

Ongoing discoveries in cancer genomics and epigenomics have revolutionized clinical oncology and precision health care. This knowledge provides unprecedented insights into tumor biology and heterogeneity within a single tumor, among primary and metastatic lesions, and among patients with the same histologic type of cancer. Large-scale genomic sequencing studies also sparked the development of new tumor classifications, biomarkers, and targeted therapies. Because of the central role of imaging in cancer diagnosis and therapy, radiologists need to be familiar with the basic concepts of genomics, which are now becoming the new norm in oncologic clinical practice. By incorporating these concepts into clinical practice, radiologists can make their imaging interpretations more meaningful and specific, facilitate multidisciplinary clinical dialogue and interventions, and provide better patient-centric care. This review article highlights basic concepts of genomics and epigenomics, reviews the most common genetic alterations in cancer, and discusses the implications of these concepts on imaging by organ system in a case-based manner. This information will help stimulate new innovations in imaging research, accelerate the development and validation of new imaging biomarkers, and motivate efforts to bring new molecular and functional imaging methods to clinical radiology. Keywords: Oncology, Cancer Genomics, Epignomics, Radiogenomics, Imaging Markers Supplemental material is available for this article. © RSNA, 2023.

Synoptic Reporting for Pretreatment CT Examination in Patients With Advanced Ovarian Cancer: Impact on Documentation of Disease Sites and Physician Satisfaction.

BACKGROUND. Imaging reports that consistently document all disease sites with a potential to increase surgical complexity or morbidity can facilitate ovarian cancer treatment planning. OBJECTIVE. The aims of this study were to compare simple structured reports and synoptic reports from pretreatment CT examinations in patients with advanced ovarian cancer in terms of completeness of documenting involvement of clinically relevant anatomic sites as well as to evaluate physician satisfaction with synoptic reports. METHODS. This retrospective study included 205 patients (median age, 65 years) who underwent contrast-enhanced abdominopelvic CT before primary treatment of advanced ovarian cancer from June 1, 2018, to January 31, 2022. A total of 128 reports generated on or before March 31, 2020, used a simple structured report (free text organized into sections); 77 reports generated on or after April 1, 2020, used a synoptic report (a list of 45 anatomic sites relevant to ovarian cancer management, each of which was classified in terms of disease absence versus presence). Reports were reviewed for completeness of documentation of involvement of the 45 sites. For patients who underwent neoadjuvant chemotherapy based on diagnostic laparoscopy findings or underwent primary debulking surgery with suboptimal resection, the EMR was reviewed to identify surgically established sites of disease that were unresectable or challenging to resect. Gynecologic oncology surgeons were electronically surveyed. RESULTS. The mean report turnaround time was 29.8 minutes for simple structured reports versus 54.5 minutes for synoptic reports (p < .001). A mean of 17.6 of 45 sites (range, four to 43 sites) were mentioned by simple structured reports versus 44.5 of 45 sites (range, 39-45) for synoptic reports (p < .001). Forty-three patients had surgically established unresectable or challenging-to-resect disease; involvement of anatomic site(s) with such disease was mentioned in 37% (11/30) of simple structured reports versus 100% (13/13) of synoptic reports (p < .001). All eight surveyed gynecologic oncology surgeons completed the survey. CONCLUSION. A synoptic report improved completeness of pretreatment CT reports in patients with advanced ovarian cancer, including for established sites of unresectable or challenging-to-resect disease. CLINICAL IMPACT. The findings indicate the role of disease-specific synoptic reports in facilitating referrer communication and potentially guiding clinical decision-making.

Practical Tips for Reporting Adnexal Lesions Using O-RADS MRI.

The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI risk stratification system provides a standardized lexicon and evidence-based risk score for evaluation of adnexal lesions. The goals of the lexicon and risk score are to improve report quality and communication between radiologists and clinicians, reduce variability in the reporting language, and optimize management of adnexal lesions. The O-RADS MRI risk score is based on the presence or absence of specific imaging features, including the lipid content, enhancing solid tissue, number of loculi, and fluid type. The probability of malignancy ranges from less than 0.5% when there are benign features to approximately 90% when there is solid tissue with a high-risk time-intensity curve. This information can aid in optimizing management of patients with adnexal lesions. The authors present an algorithmic approach to the O-RADS MRI risk stratification system and highlight key teaching points and common pitfalls. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.

Gynecologic oncology tumor board: the central role of the radiologist.

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.

Adnexal Lesion Imaging: Past, Present, and Future.

Currently, imaging is part of the standard of care for patients with adnexal lesions prior to definitive management. Imaging can identify a physiologic finding or classic benign lesion that can be followed up conservatively. When one of these entities is not present, imaging is used to determine the probability of ovarian cancer prior to surgical consultation. Since the inclusion of imaging in the evaluation of adnexal lesions in the 1970s, the rate of surgery for benign lesions has decreased. More recently, data-driven Ovarian-Adnexal Reporting and Data System (O-RADS) scoring systems for US and MRI with standardized lexicons have been developed to allow for assignment of a cancer risk score, with the goal of further decreasing unnecessary interventions while expediting the care of patients with ovarian cancer. US is used as the initial modality for the assessment of adnexal lesions, while MRI is used when there is a clinical need for increased specificity and positive predictive value for the diagnosis of cancer. This article will review how the treatment of adnexal lesions has changed due to imaging over the decades; the current data supporting the use of US, CT, and MRI to determine the likelihood of cancer; and future directions of adnexal imaging for the early detection of ovarian cancer.

Differentiating Benign From Malignant Cystic Renal Masses: A Feasibility Study of Computed Tomography Texture-Based Machine Learning Algorithms.

OBJECTIVE: The Bosniak classification attempts to predict the likelihood of renal cell carcinoma (RCC) among cystic renal masses but is subject to interobserver variability and often requires multiphase imaging. Artificial intelligence may provide a more objective assessment. We applied computed tomography texture-based machine learning algorithms to differentiate benign from malignant cystic renal masses. METHODS: This is an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study of 147 patients (mean age, 62.4 years; range, 28-89 years; 94 men) with 144 cystic renal masses (93 benign, 51 RCC); 69 were pathology proven (51 RCC, 18 benign), and 75 were considered benign based on more than 4 years of stability at follow-up imaging. Using a single image from a contrast-enhanced abdominal computed tomography scan, mean, SD, mean value of positive pixels, entropy, skewness, and kurtosis radiomics features were extracted. Random forest, multivariate logistic regression, and support vector machine models were used to classify each mass as benign or malignant with 10-fold cross validation. Receiver operating characteristic curves assessed algorithm performance in the aggregated test data. RESULTS: For the detection of malignancy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were 0.61, 0.87, 0.72, 0.80, and 0.79 for the random forest model; 0.59, 0.87, 0.71, 0.79, and 0.80 for the logistic regression model; and 0.55, 0.86, 0.68, 0.78, and 0.76 for the support vector machine model. CONCLUSION: Computed tomography texture-based machine learning algorithms show promise in differentiating benign from malignant cystic renal masses. Once validated, these may serve as an adjunct to radiologists' assessments.

Lexicon for adrenal terms at CT and MRI: a consensus of the Society of Abdominal Radiology adrenal neoplasm disease-focused panel.

PURPOSE: Substantial variation in imaging terms used to describe the adrenal gland and adrenal findings leads to ambiguity and uncertainty in radiology reports and subsequently their understanding by referring clinicians. The purpose of this study was to develop a standardized lexicon to describe adrenal imaging findings at CT and MRI. METHODS: Fourteen members of the Society of Abdominal Radiology adrenal neoplasm disease-focused panel (SAR-DFP) including one endocrine surgeon participated to develop an adrenal lexicon using a modified Delphi process to reach consensus. Five radiologists prepared a preliminary list of 35 imaging terms that was sent to the full group as an online survey (19 general imaging terms, 9 specific to CT, and 7 specific to MRI). In the first round, members voted on terms to be included and proposed definitions; subsequent two rounds were used to achieve consensus on definitions (defined as ≥ 80% agreement). RESULTS: Consensus for inclusion was reached on 33/35 terms with two terms excluded (anterior limb and normal adrenal size measurements). Greater than 80% consensus was reached on the definitions for 15 terms following the first round, with subsequent consensus achieved for the definitions of the remaining 18 terms following two additional rounds. No included term had remaining disagreement. CONCLUSION: Expert consensus produced a standardized lexicon for reporting adrenal findings at CT and MRI. The use of this consensus lexicon should improve radiology report clarity, standardize clinical and research terminology, and reduce uncertainty for referring providers when adrenal findings are present.

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.

Interreader Agreement and Intermodality Concordance of O-RADS US and MRI for Assessing Large, Complex Ovarian-Adnexal Cysts.

Purpose To assess interreader agreement of the Ovarian-Adnexal Reporting and Data System (O-RADS) and intermodality concordance between US and MRI for characterizing complex adnexal cysts measuring 5 cm or larger. Materials and Methods This retrospective study included 58 "complex cysts" measuring at least 5 cm in size observed at both US and MRI in 54 women (median age, 37 years ± 12 [SD]; seven postmenopausal women) between July 2017 and June 2020, identified from an electronic US database. A separate set of two blinded radiologists independently reviewed the US or MR images to assign the O-RADS category, and an adjudicator resolved discrepancies (a total of six readers). Lesion outcome (49 benign, eight malignant, one lost to follow-up) was recorded. Interreader agreement of O-RADS US and O-RADS MRI and concordance between US and MRI were analyzed. Results Interreader agreement was fair for US (κ = 0.31), moderate for MRI (κ = 0.43), and moderate between US and MRI (κ = 0.58). A significant positive correlation was found between O-RADS US and MRI (τ = 0.72, P < .001). The O-RADS 4 threshold yielded the highest accuracy for both US and MRI (area under the receiver operating characteristic curve = 0.92 and 0.995, respectively). Considering O-RADS US 4 or 5 as potentially malignant and 1-3 as benign, eight lesions that were assessed as potentially malignant at US were correctly downgraded to benign by using findings at MRI. Using findings at MRI, one malignant lesion that was assessed as benign at US was upgraded to potentially malignant. Conclusion O-RADS US and MRI had excellent performance and positive correlation, but significant interobserver variability remains. Keywords: Ovary, MR Imaging, Ultrasonography © RSNA, 2022 See also the commentary by Baumgarten in this issue.

Learning From Experience- Confronting Challenges and Adapting to Change in a Large Academic Abdominal Radiology Practice: Insights From a Faculty Retreat.

RATIONALE: Substantial organizational changes, increasing clinical volumes, and the COVID-19 pandemic presented compound stressors to faculty radiologists in our large academic abdominal radiology division and necessitated multiple changes in our practice. METHODS: To address the challenges and establish group consensus, we conducted a virtual divisional faculty retreat centered on themes of team building, clinical work, trainee education, and faculty mentorship. A pre-retreat survey evaluated satisfaction with aspects of professional life and clinical work practices and invited personal reflections. Survey data were presented in the retreat segments focused on each theme, and subsequent discussion was facilitated in small group breakouts. RESULTS: Responses to the team-building survey revealed common values and sources of gratitude, including health, family and meaningful work and relationships. Faculty reported a strong sense of personal accomplishment, but with varied emotional exhaustion scores. Faculty were satisfied with remote work assignments but identified opportunities to improve the clinical work schedule including reversion of some remote assignments to in-person and increased interventional radiology shift staggering. Compared to pre-COVID practice, faculty respondents perceived giving lower quality and less frequent feedback to trainees; evolving educational resource needs were identified. A more formal approach to faculty mentoring was sought. A post-retreat survey revealed high participant satisfaction. OUTCOMES: In the future, we plan to continue divisional retreat activities to respond to evolving challenges and further improve team building, clinical workflow, trainee education, and faculty mentorship.

Multicenter Evaluation of Multiparametric MRI Clear Cell Likelihood Scores in Solid Indeterminate Small Renal Masses.

Background Solid small renal masses (SRMs) (≤4 cm) represent benign and malignant tumors. Among SRMs, clear cell renal cell carcinoma (ccRCC) is frequently aggressive. When compared with invasive percutaneous biopsies, the objective of the proposed clear cell likelihood score (ccLS) is to classify ccRCC noninvasively by using multiparametric MRI, but it lacks external validation. Purpose To evaluate the performance of and interobserver agreement for ccLS to diagnose ccRCC among solid SRMs. Materials and Methods This retrospective multicenter cross-sectional study included patients with consecutive solid (≥25% approximate volume enhancement) SRMs undergoing multiparametric MRI between December 2012 and December 2019 at five academic medical centers with histologic confirmation of diagnosis. Masses with macroscopic fat were excluded. After a 1.5-hour training session, two abdominal radiologists per center independently rendered a ccLS for 50 masses. The diagnostic performance for ccRCC was calculated using random-effects logistic regression modeling. The distribution of ccRCC by ccLS was tabulated. Interobserver agreement for ccLS was evaluated with the Fleiss κ statistic. Results A total of 241 patients (mean age, 60 years ± 13 [SD]; 174 men) with 250 solid SRMs were evaluated. The mean size was 25 mm ± 8 (range, 10-39 mm). Of the 250 SRMs, 119 (48%) were ccRCC. The sensitivity, specificity, and positive predictive value for the diagnosis of ccRCC when ccLS was 4 or higher were 75% (95% CI: 68, 81), 78% (72, 84), and 76% (69, 81), respectively. The negative predictive value of a ccLS of 2 or lower was 88% (95% CI: 81, 93). The percentages of ccRCC according to the ccLS were 6% (range, 0%-18%), 38% (range, 0%-100%), 32% (range, 60%-83%), 72% (range, 40%-88%), and 81% (range, 73%-100%) for ccLSs of 1-5, respectively. The mean interobserver agreement was moderate (κ = 0.58; 95% CI: 0.42, 0.75). Conclusion The clear cell likelihood score applied to multiparametric MRI had moderate interobserver agreement and differentiated clear cell renal cell carcinoma from other solid renal masses, with a negative predictive value of 88%. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Mileto and Potretzke in this issue.

Computed Tomography Texture Analysis for Predicting Clinical Outcomes in Patients With Metastatic Renal Cell Carcinoma Treated With Immune Checkpoint Inhibitors.

BACKGROUND: The treatment responses of immune checkpoint inhibitors in metastatic renal cell carcinoma (mRCC) vary, requiring reliable prognostic biomarkers. We assessed the prognostic ability of computed tomography (CT) texture analysis in patients with mRCC treated with programmed death receptor-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors. MATERIALS AND METHODS: Sixty-eight patients with mRCC treated with PD-1/PD-L1 inhibitors between 2012 and 2019 were revaluated. Using baseline and first follow-up CT, baseline and follow-up texture models were developed to predict overall survival (OS) and progression-free survival (PFS) using least absolute shrinkage and selection operator Cox-proportional hazards analysis. Patients were divided into high-risk or low-risk group, and the survival difference was assessed using Kaplan-Meier and log-rank test. Multivariable Cox models were constructed by including only the clinical variables (clinical models) and by combining the clinical variables and the texture models (combined clinical-texture models), and their predictive performance was evaluated using Harrell's C-index. RESULTS: The baseline texture models distinguished longer- and shorter-term survivors for both OS (median, 60.1 vs. 17.0 months; P = .048) and PFS (5.2 vs. 2.8 months; P = .003). The follow-up texture models distinguished longer- and shorter-term overall survivors (40.3 vs. 15.2 months; P = .008) but not for PFS (5.0 vs. 3.6 months; P = .25). The combined clinical-texture model outperformed the clinical model in both predicting the OS (C-index, 0.70 vs. 0.63; P = .03) and PFS (C-index, 0.63 vs. 0.55; P = .04). CONCLUSION: CT texture analysis performed at baseline and early after starting PD-1/PD-L1 inhibitors is associated with clinical outcomes of patients with mRCC.

Adoption of a diagnostic certainty scale in abdominal imaging: 2-year experience at an academic institution.

PURPOSE: Assess use of a diagnostic certainty scale (CS) for abdominal imaging reports and identify factors associated with greater adoption. METHODS: This retrospective, Institutional Review Board-exempt study was conducted at an academic health system. Abdominal radiology reports containing diagnostic certainty phrases (DCPs) generated 4/1/2019-3/31/2021 were identified by a natural language processing tool. Reports containing DCPs were subdivided into those with/without a CS inserted at the end. Primary outcome was monthly CS use rate in reports containing DCPs. Secondary outcomes were assessment of factors associated with CS use, and usage of recommended DCPs over time. Chi-square test was used to compare proportions; univariable and multivariable regression assessed impact of other variables. RESULTS: DCPs were used in 81,281/124,501 reports (65.3%). One-month post-implementation, 82/2310 (3.6%) of reports with DCPs contained the CS, increasing to 1862/4644 (40.1%) by study completion (p < 0.001). Multivariable analysis demonstrated reports containing recommended DCPs were more likely to have the CS (Odds Ratio [OR] 4.5; p < 0.001). Using CT as a reference, CS use was lower for ultrasound (OR 0.73; p < 0.001) and X-ray (OR 0.38; p < 0.001). There was substantial inter-radiologist variation in CS use (OR 0.01-26.3, multiple p values). CONCLUSION: DCPs are very common in abdominal imaging reports and can be further clarified with CS use. Although voluntary CS adoption increased 13-fold over 2 years, > 50% of reports with DCPs lacked the CS at study's end. More stringent interventions, including embedding the scale in report templates, are likely needed to reduce inter-radiologist variation and decrease ambiguity in conveying diagnostic certainty to referring providers and patients.

Predictors of malignancy in incidental adnexal lesions identified on CT in patients with prior non-ovarian cancer.

PURPOSE: To identify imaging features in incidental adnexal lesions which are associated with malignancy on portal venous phase contrast-enhanced CT in patients with known non-ovarian cancer. MATERIALS AND METHODS: This IRB-approved, HIPAA-compliant retrospective study was performed at a tertiary cancer center. Portal venous phase contrast-enhanced CT from January 2010 to December 2015 was reviewed to identify women with non-ovarian malignancy and incidental adnexal lesion, with mean 18 months (range 1-80 months) to definitive diagnosis or last imaging follow-up. Imaging features of adnexal lesions were recorded (size, laterality, shape, attenuation, and composition) and correlated with outcome (benign or malignant) using univariate and multivariate logistic regression analysis. A point-based system was used to predict likelihood of malignancy. RESULTS: Of 276 women (mean age 45 years), 216 (78.3%) had benign lesions, 58 (21.0%) ovarian metastasis, and 2 (0.7%) had primary ovarian malignancy. On logistic regression model, lesion size > 5 cm (p-value, OR, 95% CI 0.01, 9.11, 1.70-48.87), bilaterality (< 0.0001, 28.34, 7.46-107.67), irregular shape (0.01, 12.31, 1.61-94.05), higher-than-simple-fluid attenuation (< 0.0001, 28.27, 5.65-141.59), and heterogeneous composition (0.0017, 10.75, 2.45-47.23) were associated with malignant outcome (AUC 0.97). A point-based system incorporating these five features (possible 0-5 points) had AUC of 0.97. Rate of malignancy was 0% (0/147) if none of the features of malignancy were present, 12.7% (8/63) if one feature was present, 51.7% (15/29) if two features were present, and 100% (37/37) if three or more features present. CONCLUSION: Risk of malignancy of incidental adnexal lesions in women with prior non-ovarian cancer can be estimated based on lesion features seen on portal venous phase contrast-enhanced CT.

Ovarian cancer reporting lexicon for computed tomography (CT) and magnetic resonance (MR) imaging developed by the SAR Uterine and Ovarian Cancer Disease-Focused Panel and the ESUR Female Pelvic Imaging Working Group.

OBJECTIVES: Imaging evaluation is an essential part of treatment planning for patients with ovarian cancer. Variation in the terminology used for describing ovarian cancer on computed tomography (CT) and magnetic resonance (MR) imaging can lead to ambiguity and inconsistency in clinical radiology reports. The aim of this collaborative project between Society of Abdominal Radiology (SAR) Uterine and Ovarian Cancer (UOC) Disease-focused Panel (DFP) and the European Society of Uroradiology (ESUR) Female Pelvic Imaging (FPI) Working Group was to develop an ovarian cancer reporting lexicon for CT and MR imaging. METHODS: Twenty-one members of the SAR UOC DFP and ESUR FPI working group, one radiology clinical fellow, and two gynecologic oncology surgeons formed the Ovarian Cancer Reporting Lexicon Committee. Two attending radiologist members of the committee prepared a preliminary list of imaging terms that was sent as an online survey to 173 radiologists and gynecologic oncologic physicians, of whom 67 responded to the survey. The committee reviewed these responses to create a final consensus list of lexicon terms. RESULTS: An ovarian cancer reporting lexicon was created for CT and MR Imaging. This consensus-based lexicon has 6 major categories of terms: general, adnexal lesion-specific, peritoneal carcinomatosis-specific, lymph node-specific, metastatic disease -specific, and fluid-specific. CONCLUSIONS: This lexicon for CT and MR imaging evaluation of ovarian cancer patients has the capacity to improve the clarity and consistency of reporting disease sites seen on imaging. KEY POINTS: • This reporting lexicon for CT and MR imaging provides a list of consensus-based, standardized terms and definitions for reporting sites of ovarian cancer on imaging at initial diagnosis or follow-up. • Use of standardized terms and morphologic imaging descriptors can help improve interdisciplinary communication of disease extent and facilitate optimal patient management. • The radiologists should identify and communicate areas of disease, including difficult to resect or potentially unresectable disease that may limit the ability to achieve optimal resection.