Designing clinical trials based on modern imaging and metastasis-directed treatments in patients with oligometastatic breast cancer: a consensus recommendation from the EORTC Imaging and Breast Cancer Groups.

Breast cancer remains the most common cause of cancer death among women. Despite its considerable histological and molecular heterogeneity, those characteristics are not distinguished in most definitions of oligometastatic disease and clinical trials of oligometastatic breast cancer. After an exhaustive review of the literature covering all aspects of oligometastatic breast cancer, 35 experts from the European Organisation for Research and Treatment of Cancer Imaging and Breast Cancer Groups elaborated a Delphi questionnaire aimed at offering consensus recommendations, including oligometastatic breast cancer definition, optimal diagnostic pathways, and clinical trials required to evaluate the effect of diagnostic imaging strategies and metastasis-directed therapies. The main recommendations are the introduction of modern imaging methods in metastatic screening for an earlier diagnosis of oligometastatic breast cancer and the development of prospective trials also considering the histological and molecular complexity of breast cancer. Strategies for the randomisation of imaging methods and therapeutic approaches in different subsets of patients are also addressed.

MRI Evaluation of Uterine Masses for Risk of Leiomyosarcoma: A Consensus Statement.

Laparoscopic myomectomy, a common gynecologic operation in premenopausal women, has become heavily regulated since 2014 following the dissemination of unsuspected uterine leiomyosarcoma (LMS) throughout the pelvis of a physician treated for symptomatic leiomyoma. Research since that time suggests a higher prevalence than previously suspected of uterine LMS in resected masses presumed to represent leiomyoma, as high as one in 770 women (0.13%). Though rare, the dissemination of an aggressive malignant neoplasm due to noncontained electromechanical morcellation in laparoscopic myomectomy is a devastating outcome. Gynecologic surgeons' desire for an evidence-based, noninvasive evaluation for LMS is driven by a clear need to avoid such harms while maintaining the availability of minimally invasive surgery for symptomatic leiomyoma. Laparoscopic gynecologists could rely upon the distinction of higher-risk uterine masses preoperatively to plan oncologic surgery (ie, potential hysterectomy) for patients with elevated risk for LMS and, conversely, to safely offer women with no or minimal indicators of elevated risk the fertility-preserving laparoscopic myomectomy. MRI evaluation for LMS may potentially serve this purpose in symptomatic women with leiomyomas. This evidence review and consensus statement defines imaging and disease-related terms to allow more uniform and reliable interpretation and identifies the highest priorities for future research on LMS evaluation.

Attitudes and perceptions of radiologists towards online (virtual) oncologic multidisciplinary team meetings during the COVID-19 pandemic-a survey of the European Society of Oncologic Imaging (ESOI).

OBJECTIVES: To explore radiologists' opinions regarding the shift from in-person oncologic multidisciplinary team meetings (MDTMs) to online MDTMs. To assess the perceived impact of online MDTMs, and to evaluate clinical and technical aspects of online meetings. METHODS: An online questionnaire including 24 questions was e-mailed to all European Society of Oncologic Imaging (ESOI) members. Questions targeted the structure and efficacy of online MDTMs, including benefits and limitations. RESULTS: A total of 204 radiologists responded to the survey. Responses were evaluated using descriptive statistical analysis. The majority (157/204; 77%) reported a shift to online MDTMs at the start of the pandemic. For the most part, this transition had a positive effect on maintaining and improving attendance. The majority of participants reported that online MDTMs provide the same clinical standard as in-person meetings, and that interdisciplinary discussion and review of imaging data were not hindered. Seventy three of 204 (35.8%) participants favour reverting to in-person MDTs, once safe to do so, while 7/204 (3.4%) prefer a continuation of online MDTMs. The majority (124/204, 60.8%) prefer a combination of physical and online MDTMs. CONCLUSIONS: Online MDTMs are a viable alternative to in-person meetings enabling continued timely high-quality provision of care with maintained coordination between specialties. They were accepted by the majority of surveyed radiologists who also favoured their continuation after the pandemic, preferably in combination with in-person meetings. An awareness of communication issues particular to online meetings is important. Training, improved software, and availability of support are essential to overcome technical and IT difficulties reported by participants. KEY POINTS: • Majority of surveyed radiologists reported shift from in-person to online oncologic MDT meetings during the COVID-19 pandemic. • The shift to online MDTMs was feasible and generally accepted by the radiologists surveyed with the majority reporting that online MDTMs provide the same clinical standard as in-person meetings. • Most would favour the return to in-person MDTMs but would also accept the continued use of online MDTMs following the end of the current pandemic.

Strategies to safely rule out pulmonary embolism in COVID-19 outpatients: a multicenter retrospective study.

OBJECTIVES: The objective was to define a safe strategy to exclude pulmonary embolism (PE) in COVID-19 outpatients, without performing CT pulmonary angiogram (CTPA). METHODS: COVID-19 outpatients from 15 university hospitals who underwent a CTPA were retrospectively evaluated. D-Dimers, variables of the revised Geneva and Wells scores, as well as laboratory findings and clinical characteristics related to COVID-19 pneumonia, were collected. CTPA reports were reviewed for the presence of PE and the extent of COVID-19 disease. PE rule-out strategies were based solely on D-Dimer tests using different thresholds, the revised Geneva and Wells scores, and a COVID-19 PE prediction model built on our dataset were compared. The area under the receiver operating characteristics curve (AUC), failure rate, and efficiency were calculated. RESULTS: In total, 1369 patients were included of whom 124 were PE positive (9.1%). Failure rate and efficiency of D-Dimer > 500 µg/l were 0.9% (95%CI, 0.2-4.8%) and 10.1% (8.5-11.9%), respectively, increasing to 1.0% (0.2-5.3%) and 16.4% (14.4-18.7%), respectively, for an age-adjusted D-Dimer level. D-dimer > 1000 µg/l led to an unacceptable failure rate to 8.1% (4.4-14.5%). The best performances of the revised Geneva and Wells scores were obtained using the age-adjusted D-Dimer level. They had the same failure rate of 1.0% (0.2-5.3%) for efficiency of 16.8% (14.7-19.1%), and 16.9% (14.8-19.2%) respectively. The developed COVID-19 PE prediction model had an AUC of 0.609 (0.594-0.623) with an efficiency of 20.5% (18.4-22.8%) when its failure was set to 0.8%. CONCLUSIONS: The strategy to safely exclude PE in COVID-19 outpatients should not differ from that used in non-COVID-19 patients. The added value of the COVID-19 PE prediction model is minor. KEY POINTS: • D-dimer level remains the most important predictor of pulmonary embolism in COVID-19 patients. • The AUCs of the revised Geneva and Wells scores using an age-adjusted D-dimer threshold were 0.587 (95%CI, 0.572 to 0.603) and 0.588 (95%CI, 0.572 to 0.603). • The AUC of COVID-19-specific strategy to rule out pulmonary embolism ranged from 0.513 (95%CI: 0.503 to 0.522) to 0.609 (95%CI: 0.594 to 0.623).

Nivolumab, nivolumab-ipilimumab, and VEGFR-tyrosine kinase inhibitors as first-line treatment for metastatic clear-cell renal cell carcinoma (BIONIKK): a biomarker-driven, open-label, non-comparative, randomised, phase 2 trial.

BACKGROUND: We previously reported a 35-gene expression classifier identifying four clear-cell renal cell carcinoma groups (ccrcc1 to ccrcc4) with different tumour microenvironments and sensitivities to sunitinib in metastatic clear-cell renal cell carcinoma. Efficacy profiles might differ with nivolumab and nivolumab-ipilimumab. We therefore aimed to evaluate treatment efficacy and tolerability of nivolumab, nivolumab-ipilimumab, and VEGFR-tyrosine kinase inhibitors (VEGFR-TKIs) in patients according to tumour molecular groups. METHODS: This biomarker-driven, open-label, non-comparative, randomised, phase 2 trial included patients from 15 university hospitals or expert cancer centres in France. Eligible patients were aged 18 years or older, had an Eastern Cooperative Oncology Group performance status of 0-2, and had previously untreated metastatic clear-cell renal cell carcinoma. Patients were randomly assigned (1:1) using permuted blocks of varying sizes to receive either nivolumab or nivolumab-ipilimumab (ccrcc1 and ccrcc4 groups), or either a VEGFR-TKI or nivolumab-ipilimumab (ccrcc2 and ccrcc3 groups). Patients assigned to nivolumab-ipilimumab received intravenous nivolumab 3 mg/kg plus ipilimumab 1 mg/kg every 3 weeks for four doses followed by intravenous nivolumab 240 mg every 2 weeks. Patients assigned to nivolumab received intravenous nivolumab 240 mg every 2 weeks. Patients assigned to VEGFR-TKIs received oral sunitinib (50 mg/day for 4 weeks every 6 weeks) or oral pazopanib (800 mg daily continuously). The primary endpoint was the objective response rate by investigator assessment per Response Evaluation Criteria in Solid Tumors version 1.1. The primary endpoint and safety were assessed in the population who received at least one dose of study drug. This trial is registered with ClinicalTrials.gov, NCT02960906, and with the EU Clinical Trials Register, EudraCT 2016-003099-28, and is closed to enrolment. FINDINGS: Between June 28, 2017, and July 18, 2019, 303 patients were screened for eligibility, 202 of whom were randomly assigned to treatment (61 to nivolumab, 101 to nivolumab-ipilimumab, 40 to a VEGFR-TKI). In the nivolumab group, two patients were excluded due to a serious adverse event before the first study dose and one patient was excluded from analyses due to incorrect diagnosis. Median follow-up was 18·0 months (IQR 17·6-18·4). In the ccrcc1 group, objective responses were seen in 12 (29%; 95% CI 16-45) of 42 patients with nivolumab and 16 (39%; 24-55) of 41 patients with nivolumab-ipilimumab (odds ratio [OR] 0·63 [95% CI 0·25-1·56]). In the ccrcc4 group, objective responses were seen in seven (44%; 95% CI 20-70) of 16 patients with nivolumab and nine (50% 26-74) of 18 patients with nivolumab-ipilimumab (OR 0·78 [95% CI 0·20-3·01]). In the ccrcc2 group, objective responses were seen in 18 (50%; 95% CI 33-67) of 36 patients with a VEGFR-TKI and 19 (51%; 34-68) of 37 patients with nivolumab-ipilimumab (OR 0·95 [95% CI 0·38-2·37]). In the ccrcc3 group, no objective responses were seen in the four patients who received a VEGFR-TKI, and in one (20%; 95% CI 1-72) of five patients who received nivolumab-ipilimumab. The most common treatment-related grade 3-4 adverse events were hepatic failure and lipase increase (two [3%] of 58 for both) with nivolumab, lipase increase and hepatobiliary disorders (six [6%] of 101 for both) with nivolumab-ipilimumab, and hypertension (six [15%] of 40) with a VEGFR-TKI. Serious treatment-related adverse events occurred in two (3%) patients in the nivolumab group, 38 (38%) in the nivolumab-ipilimumab group, and ten (25%) patients in the VEGFR-TKI group. Three deaths were treatment-related: one due to fulminant hepatitis with nivolumab-ipilimumab, one death from heart failure with sunitinib, and one due to thrombotic microangiopathy with sunitinib. INTERPRETATION: We demonstrate the feasibility and positive effect of a prospective patient selection based on tumour molecular phenotype to choose the most efficacious treatment between nivolumab with or without ipilimumab and a VEGFR-TKI in the first-line treatment of metastatic clear-cell renal cell carcinoma. FUNDING: Bristol Myers Squibb, ARTIC.

Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma.

OBJECTIVES: To validate a deep learning (DL) algorithm for measurement of skeletal muscular index (SMI) and prediction of overall survival in oncology populations. METHODS: A retrospective single-center observational study included patients with metastatic renal cell carcinoma between 2007 and 2019. A set of 37 patients was used for technical validation of the algorithm, comparing manual vs DL-based evaluations. Segmentations were compared using mean Dice similarity coefficient (DSC), SMI using concordance correlation coefficient (CCC) and Bland-Altman plots. Overall survivals (OS) were compared using log-rank (Kaplan-Meier) and Mann-Whitney tests. Generalizability of the prognostic value was tested in an independent validation population (N = 87). RESULTS: Differences between two manual segmentations (DSC = 0.91, CCC = 0.98 for areas) or manual vs. automated segmentation (DSC = 0.90, CCC = 0.98 for areas, CCC = 0.97 for SMI) had the same order of magnitude. Bland-Altman plots showed a mean difference of -3.33 cm2 [95%CI: -15.98, 9.1] between two manual segmentations, and -3.28 cm2 [95% CI: -14.77, 8.21] for manual vs. automated segmentations. With each method, 20/37 (56%) patients were classified as sarcopenic. Sarcopenic vs. non-sarcopenic groups had statistically different survival curves with median OS of 6.0 vs. 12.5 (p = 0.008) and 6.0 vs. 13.9 (p = 0.014) months respectively for manual and DL methods. In the independent validation population, sarcopenic patients according to DL had a lower OS (10.7 vs. 17.3 months, p = 0.033). CONCLUSION: A DL algorithm allowed accurate estimation of SMI compared to manual reference standard. The DL-calculated SMI demonstrated a prognostic value in terms of OS. KEY POINTS: • A deep learning algorithm allows accurate estimation of skeletal muscle index compared to a manual reference standard with a concordance correlation coefficient of 0.97. • Sarcopenic patients according to SMI thresholds after segmentation by the deep learning algorithm had statistically significantly lower overall survival compared to non-sarcopenic patients.

What level of D-dimers can safely exclude pulmonary embolism in COVID-19 patients presenting to the emergency department?

OBJECTIVES: To identify which level of D-dimer would allow the safe exclusion of pulmonary embolism (PE) in COVID-19 patients presenting to the emergency department (ED). METHODS: This retrospective study was conducted on the COVID database of Assistance Publique - Hôpitaux de Paris (AP-HP). COVID-19 patients who presented at the ED of AP-HP hospitals between March 1 and May 15, 2020, and had CTPA following D-dimer dosage within 48h of presentation were included. The D-dimer sensitivity, specificity, and positive and negative predictive values were calculated for different D-dimer thresholds, as well as the false-negative and failure rates, and the number of CTPAs potentially avoided. RESULTS: A total of 781 patients (mean age 62.0 years, 53.8% men) with positive RT-PCR for SARS-Cov-2 were included and 60 of them (7.7%) had CTPA-confirmed PE. Their median D-dimer level was significantly higher than that of patients without PE (4,013 vs 1,198 ng·mL-1, p < 0.001). Using 500 ng·mL-1, or an age-adjusted cut-off for patients > 50 years, the sensitivity and the NPV were above 90%. With these thresholds, 17.1% and 31.5% of CTPAs could have been avoided, respectively. Four of the 178 patients who had a D-dimer below the age-adjusted cutoff had PE, leading to an acceptable failure rate of 2.2%. Using higher D-dimer cut-offs could have avoided more CTPAs, but would have lowered the sensitivity and increased the failure rate. CONCLUSION: The same D-Dimer thresholds as those validated in non-COVID outpatients should be used to safely rule out PE. KEY POINTS: • The median D-dimer level was significantly higher in COVID-19 patients with PE as compared to those without PE (4,013 ng·mL-1 vs 1,198 ng·mL-1 respectively, p < 0.001). • Using 500 ng·mL-1, or an age-adjusted D-dimer cut-off to exclude pulmonary embolism, the sensitivity and negative predictive value were above 90%. • Higher cut-offs would lead to a reduction in the sensitivity below 85% and an increase in the failure rate, especially for patients under 50 years.

Application of Artificial Intelligence to Gastroenterology and Hepatology.

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.

Study of Thoracic CT in COVID-19: The STOIC Project.

Background There are conflicting data regarding the diagnostic performance of chest CT for COVID-19 pneumonia. Disease extent at CT has been reported to influence prognosis. Purpose To create a large publicly available data set and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter, observational, retrospective cohort study involved 20 French university hospitals. Eligible patients presented at the emergency departments of the hospitals involved between March 1 and April 30th, 2020, and underwent both thoracic CT and reverse transcription-polymerase chain reaction (RT-PCR) testing for suspected COVID-19 pneumonia. CT images were read blinded to initial reports, RT-PCR, demographic characteristics, clinical symptoms, and outcome. Readers classified CT scans as either positive or negative for COVID-19 based on criteria published by the French Society of Radiology. Multivariable logistic regression was used to develop a model predicting severe outcome (intubation or death) at 1-month follow-up in patients positive for both RT-PCR and CT, using clinical and radiologic features. Results Among 10 930 patients screened for eligibility, 10 735 (median age, 65 years; interquartile range, 51-77 years; 6147 men) were included and 6448 (60%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity of CT were 80.2% (95% CI: 79.3, 81.2) and 79.7% (95% CI: 78.5, 80.9), respectively, with strong agreement between junior and senior radiologists (Gwet AC1 coefficient, 0.79). Of all the variables analyzed, the extent of pneumonia at CT (odds ratio, 3.25; 95% CI: 2.71, 3.89) was the best predictor of severe outcome at 1 month. A score based solely on clinical variables predicted a severe outcome with an area under the curve of 0.64 (95% CI: 0.62, 0.66), improving to 0.69 (95% CI: 0.6, 0.71) when it also included the extent of pneumonia and coronary calcium score at CT. Conclusion Using predefined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at 1 month. ClinicalTrials.gov identifier: NCT04355507 Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Rubin in this issue.

What can we learn from the 10 mm lymph node size cut-off on the CT in advanced ovarian cancer at the time of interval debulking surgery?

INTRODUCTION: The benefit of a systematic lymphadenectomy is still debated in patients undergoing neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS) in ovarian cancer (OC). The objective of this study was to evaluate the predictive value of the pre-NACT and post-NACT CT in predicting definitive histological lymph node involvement. The prognostic value of a positive node on the CT was also assessed. MATERIEL AND METHODS: A retrospective, unicentric cohort study was performed including all patients with ovarian cancer who underwent NACT and IDS with a lymphadenectomy between 2005 and 2018. CT were analyzed blinded to pathology, and nodes with small axis ≥ 10 mm on CT were considered positive. Sensitivity (Se), specificity (Sp), and negative (NPV) and positive predictive values (PPV) and their CI95% were calculated. The 2-year recurrence free survival (RFS) and 5-year overall survival (OS) was compared. RESULTS: 158 patients were included, among which 92 (58%) had histologically positive lymph nodes. CT had a Se, Sp, NPV and PPV of 35%, 82%, 47% and 73% before NACT and 20%, 97%, 47% and 91% after NACT, respectively. Patients with nodes considered positive had a non-significant lower 2-year RFS and 5-year OS on the pre-NACT and post-NACT CT. Patients at 'high risk' (nodes stayed positive on the CT or became positive after NACT) also had a non-significant lower 2-year RFS and 5-year OS. CONCLUSION: Presence of enlarged lymph nodes on CT is a weak indicator of lymph node involvement in patients with advanced ovarian cancer undergoing NACT. However, it could be used to assess prognosis.

Intravoxel incoherent motion (IVIM) 3 T MRI for orbital lesion characterization.

OBJECTIVES: To determine the diagnostic accuracy of MRI intravoxel incoherent motion (IVIM) when characterizing orbital lesions, which is challenging due to a wide range of locations and histologic types. METHODS: This IRB-approved prospective single-center study enrolled participants presenting with an orbital lesion undergoing a 3-T MRI prior to surgery from December 2015 to July 2019. An IVIM sequence with 15 b values ranging from 0 to 2000 s/mm2 was performed. Two neuroradiologists, blinded to clinical data, individually analyzed morphological MRIs. They drew one region of interest inside each orbital lesion, providing apparent diffusion coefficient (ADC), true diffusion coefficient (D), perfusion fraction (f), and pseudodiffusion coefficient (D*) values. T test, Mann-Whitney U test, and receiver operating characteristic curve analyses were performed to discriminate between orbital lesions and to determine the diagnostic accuracy of the IVIM parameters. RESULTS: One hundred fifty-six participants (84 women and 72 men, mean age 54.4 ± 17.5 years) with 167 orbital lesions (98/167 [59%] benign lesions including 54 orbital inflammations and 69/167 [41%] malignant lesions including 32 lymphomas) were included in the study. ADC and D were significantly lower in malignant than in benign lesions: 0.8 × 10-3 mm2/s [0.45] versus 1.04 × 10-3 mm2/s [0.33], p < 0.001, and 0.75 × 10-3 mm2/s [0.40] versus 0.98 × 10-3 mm2/s [0.42], p < 0.001, respectively. D* was significantly higher in malignant lesions than in benign ones: 12.8 × 10-3 mm2/s [20.17] versus 7.52 × 10-3 mm2/s [7.57], p = 0.005. Area under curve was of 0.73, 0.74, 0.72, and 0.81 for ADC, D, D*, and a combination of D, f, and D*, respectively. CONCLUSIONS: Our study showed that IVIM might help better characterize orbital lesions. KEY POINTS: • Intravoxel incoherent motion (IVIM) helps clinicians to assess patients with orbital lesions. • Intravoxel incoherent motion (IVIM) helps clinicians to characterize orbital lymphoma versus orbital inflammation. • Management of patients becomes more appropriate.

Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions.

PURPOSE: To assess the diagnostic performance of radiomic analysis using high temporal resolution (HTR)-dynamic contrast enhancement (DCE) MR sequences compared to BI-RADS analysis to distinguish benign from malignant breast lesions. MATERIALS AND METHODS: We retrospectively analyzed data from consecutive women who underwent breast MRI including HTR-DCE MR sequencing for abnormal enhancing lesions and who had subsequent pathological analysis at our tertiary center. Semi-quantitative enhancement parameters and textural features were extracted. Temporal change across each phase of textural features in HTR-DCE MR sequences was calculated and called "kinetic textural parameters." Statistical analysis by LASSO logistic regression and cross validation was performed to build a model. The diagnostic performance of the radiomic model was compared to the results of BI-RADS MR score analysis. RESULTS: We included 117 women with a mean age of 54 years (28-88). Of the 174 lesions analyzed, 75 were benign and 99 malignant. Seven semi-quantitative enhancement parameters and 57 textural features were extracted. Regression analysis selected 15 significant variables in a radiomic model (called "malignant probability score") which displayed an AUC = 0.876 (sensitivity = 0.98, specificity = 0.52, accuracy = 0.78). The performance of the malignant probability score to distinguish benign from malignant breast lesions (AUC = 0.876, 95%CI 0.825-0.925) was significantly better than that of BI-RADS analysis (AUC = 0.831, 95%CI 0.769-0.892). The radiomic model significantly reduced false positives (42%) with the same number of missed cancers (n = 2). CONCLUSION: A radiomic model including kinetic textural features extracted from an HTR-DCE MR sequence improves diagnostic performance over BI-RADS analysis. KEY POINTS: • Radiomic analysis using HTR-DCE is of better diagnostic performance (AUC = 0.876) than conventional breast MRI reading with BI-RADS (AUC = 0.831) (p < 0.001). • A radiomic malignant probability score under 19.5% gives a negative predictive value of 100% while a malignant probability score over 81% gives a positive predictive value of 100%. • Kinetic textural features extracted from HTR-DCE-MRI have a major role to play in distinguishing benign from malignant breast lesions.

Involvement of radiologists in oncologic multidisciplinary team meetings: an international survey by the European Society of Oncologic Imaging.

OBJECTIVES: Multidisciplinary tumour boards (MTBs) play an increasingly important role in managing cancer patients from diagnosis to treatment. However, many problems arise around the organisation of MTBs, both in terms of organisation-administration and time management. In this context, the European Society of Oncologic Imaging (ESOI) conducted a survey among its members, aimed at assessing the quality and amount of involvement of radiologists in MTBs, their role in it and related issues. METHODS: All members were invited to fill in a questionnaire consisting of 15 questions with both open and multiple-choice answers. Simple descriptive analyses and graphs were performed. RESULTS: A total of 292 ESOI members in full standing for the year 2018 joined the survey. Most respondents (89%) declared to attend MT-Bs, but only 114 respondents (43.9%) review over 70% of exams prior to MTB meetings, mainly due to lack of time due to a busy schedule for imaging and reporting (46.6%). Perceived benefits (i.e. surgical and histological feedback (86.9%), improved knowledge of cancer treatment (82.7%) and better interaction between radiologists and referring clinicians for discussing rare cases (56.9%)) and issues (i.e. attending MTB meetings during regular working hours (71.9%) and lack of accreditation with continuing medical education (CME) (85%)) are reported. CONCLUSIONS: Despite the value and benefits of radiologists' participation in MTBs, issues like improper preparation due to a busy schedule and no counterpart in CME accreditation require efforts to improve the role of radiologists for a better patient care. KEY POINTS: • Most radiologists attend multidisciplinary tumour boards, but less than half of them review images in advance, mostly due to time constraints. • Feedback about radiological diagnoses, improved knowledge of cancer treatment and interaction with referring clinicians are perceived as major benefits. • Concerns were expressed about scheduling multidisciplinary tumour boards during regular working hours and lack of accreditation with continuing medical education.

Incorporating radiomics into clinical trials: expert consensus endorsed by the European Society of Radiology on considerations for data-driven compared to biologically driven quantitative biomarkers.

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Diagnostic Algorithm to Differentiate Benign Atypical Leiomyomas from Malignant Uterine Sarcomas with Diffusion-weighted MRI.

Background Improving the differentiation of uterine sarcomas from atypical leiomyomas remains a clinical challenge and is needed to avoid inappropriate surgery. Purpose To develop a diagnostic algorithm including diffusion-weighted MRI criteria to differentiate malignant uterine sarcomas from benign atypical leiomyomas. Materials and Methods This case-control retrospective study identified women with an atypical uterine mass at MRI between January 2000 and April 2017, with surgery or MRI follow-up after 1 year or longer. A diagnostic algorithm including T2-weighted MRI and diffusion-weighted imaging (DWI) signal and apparent diffusion coefficient (ADC) values was developed to predict for sarcoma. The training set consisted of 51 sarcomas and 105 leiomyomas. Two external validation sets were used to evaluate interreader reproducibility (16 sarcomas; 26 leiomyomas) and impact of reader experience (29 sarcomas; 30 leiomyomas). Wilson confidence intervals (CIs) were calculated for sensitivity and specificity. Results Evaluated were 156 women (median age, 50 years; interquartile range, 44-63 years). Predictive MRI criteria for malignancy were enlarged lymph nodes or peritoneal implants, high DWI signal greater than that in endometrium, and ADC less than or equal to 0.905 × 10-3 mm2/sec. Conversely, a global or focal area of low T2 signal intensity and a low or an intermediate DWI signal less than that in endometrium or lymph nodes allowed readers to confidently diagnose as benign a uterine mass demonstrating one or more of these signs (P < .001) in 100% cases in all three data sets. The sensitivities and specificities of the algorithm for diagnosis of malignancy were 98% (50 of 51 masses; 95% CI: 90%, 100%) and 94% (99 of 105 masses; 95% CI: 88%, 98%) in the training set; 88% (14 of 16 masses; 95% CI: 64%, 97%) and 100% (26 of 26 masses; 95% CI: 87%, 100%) in the validation set; and 83% (24 of 29 masses; 95% CI: 65%, 92%) and 97% (29 of 30 masses; 95% CI: 83%, 99%) for the less experienced reader, respectively. Conclusion A diagnostic algorithm with predictive features including lymphadenopathy, high diffusion-weighted imaging signal with reference to endometrium, and low apparent diffusion coefficient enabled differentiation of malignant sarcomas from atypical leiomyomas, and it may assist inexperienced readers. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Méndez in this issue.

Quality-based pharmacokinetic model selection on DCE-MRI for characterizing orbital lesions.

BACKGROUND: Although several studies have evaluated dynamic contrast-enhanced (DCE) MRI in the orbit, showing its utility when detecting and diagnosing orbital lesions, none have evaluated the pharmacokinetic models. PURPOSE: To provide a quality-based pharmacokinetic model selection for characterizing orbital lesions using DCE-MRI at 3.0T. STUDY TYPE: Prospective. POPULATION: From December 2015 to April 2017, 151 patients with an orbital lesion underwent MRI prior to surgery, including a high temporal resolution DCE sequence, divided into one training and one test dataset with 100 and 51 patients, respectively. FIELD STRENGTH/SEQUENCE: 3T/DCE. ASSESSMENT: Six different pharmacokinetic models were tested. STATISTICAL TESTS: Univariate and multivariate analyses were performed using Wilcoxon-2-sample tests and a logistic regression to compare parameters between malignant and benign tumors for each pharmacokinetic model for the whole cohort. Receiver operating characteristic (ROC) curve analyses were performed on the training dataset to determine area under curve (AUC) and optimal cutoff values for each pharmacokinetic model, then validated on the test dataset to calculate sensitivity, specificity, and accuracy. RESULTS: Regardless of the model, tissue blood flow and tissue blood volume values were significantly higher in malignant vs. benign lesions: 103.8-195.1 vs. 65-113.8, P [<10-4 -2.10-4 ] and 21.3-36.9 vs. 15.6-33.6, P [<10-4 -0.03] respectively. Extracellular volume fraction and permeability-surface area product or transfer constant appeared to be less relevant: 17.3-27.5 vs. 22.8-28.2, P [0.01-0.7], 1.7-4.9, P [0.2-0.9] and 9.5-38.8 vs. 8.1-22.8, P [<10-4 -0.6], respectively. ROC curves showed no significant differences in AUC between the different models. The two-compartment exchange (2CX) model ranked first for quality. DATA CONCLUSION: DCE MRI pharmacokinetic model-derived parameters appeared to be useful for discriminating benign from malignant orbital lesions. The 2CX model provided the best quality of modeling and should be recommended. Perfusion-related DCE parameters appeared to be significantly more relevant to the diagnostic process. Level of Evidence 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1514-1525.

Repeatability of apparent diffusion coefficient and intravoxel incoherent motion parameters at 3.0 Tesla in orbital lesions.

OBJECTIVES: To evaluate repeatability of intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters in the orbit. METHODS: From December 2015 to March 2016, 22 patients were scanned twice using an IVIM sequence with 15b values (0-2,000 s/mm2) at 3.0T. Two readers independently delineated regions of interest in an orbital mass and in different intra-orbital and extra-orbital structures. Short-term test-retest repeatability and inter-observer agreement were assessed using the intra-class correlation coefficient (ICC), the coefficient of variation (CV) and Bland-Altman limits of agreements (BA-LA). RESULTS: Test-retest repeatability of IVIM parameters in the orbital mass was satisfactory for ADC and D (mean CV 12% and 14%, ICC 95% and 93%), poor for f and D*(means CV 43% and 110%, ICC 90% and 65%). Inter-observer repeatability agreement was almost perfect in the orbital mass for all the IVIM parameters (ICC = 95%, 93%, 94% and 90% for ADC, D, f and D*, respectively). CONCLUSIONS: IVIM appeared to be a robust tool to measure D in orbital lesions with good repeatability, but this approach showed a poor repeatability of f and D*. KEY POINTS: • IVIM technique is feasible in the orbit. • IVIM has a good-acceptable repeatability of D (CV range 12-25 %). • IVIM interobserver repeatability agreement is excellent (ICC range 90-95 %). • f or D* provide higher test-retest and interobserver variabilities.