A European Society of Oncologic Imaging (ESOI) survey on the radiological assessment of response to oncologic treatments in clinical practice

OBJECTIVES

To present the results of a survey on the assessment of treatment response with imaging in oncologic patient, in routine clinical practice. The survey was promoted by the European Society of Oncologic Imaging to gather information for the development of reporting models and recommendations.

METHODS

The survey was launched on the European Society of Oncologic Imaging website and was available for 3 weeks. It consisted of 5 sections, including 24 questions related to the following topics: demographic and professional information, methods for lesion measurement, how to deal with diminutive lesions, how to report baseline and follow-up examinations, which previous studies should be used for comparison, and role of RECIST 1.1 criteria in the daily clinical practice.

RESULTS

A total of 286 responses were received. Most responders followed the RECIST 1.1 recommendations for the measurement of target lesions and lymph nodes and for the assessment of tumor response. To assess response, 48.6% used previous and/or best response study in addition to baseline, 25.2% included the evaluation of all main time points, and 35% used as the reference only the previous study. A considerable number of responders used RECIST 1.1 criteria in daily clinical practice (41.6%) or thought that they should be always applied (60.8%).

CONCLUSION

Since standardized criteria are mainly a prerogative of clinical trials, in daily routine, reporting strategies are left to radiologists and oncologists, which may issue local and diversified recommendations. The survey emphasizes the need for more generally applicable rules for response assessment in clinical practice.

CRITICAL RELEVANCE STATEMENT

Compared to clinical trials which use specific criteria to evaluate response to oncological treatments, the free narrative report usually adopted in daily clinical practice may lack clarity and useful information, and therefore, more structured approaches are needed.

KEY POINTS

· Most radiologists consider standardized reporting strategies essential for an objective assessment of tumor response in clinical practice. · Radiologists increasingly rely on RECIST 1.1 in their daily clinical practice. · Treatment response evaluation should require a complete analysis of all imaging time points and not only of the last.

Best imaging signs identified by radiomics could outperform the model: application to differentiating lung carcinoid tumors from atypical hamartomas

OBJECTIVES

Lung carcinoids and atypical hamartomas may be difficult to differentiate but require different treatment. The aim was to differentiate these tumors using contrast-enhanced CT semantic and radiomics criteria.

METHODS

Between November 2009 and June 2020, consecutives patient operated for hamartomas or carcinoids with contrast-enhanced chest-CT were retrospectively reviewed. Semantic criteria were recorded and radiomics features were extracted from 3D segmentations using Pyradiomics. Reproducible and non-redundant radiomics features were used to training a random forest algorithm with cross-validation. A validation-set from another institution was used to evaluate of the radiomics signature, the 3D 'median' attenuation feature (3D-median) alone and the mean value from 2D-ROIs.

RESULTS

Seventy-three patients (median 58 years [43‒70]) were analyzed (16 hamartomas; 57 carcinoids). The radiomics signature predicted hamartomas vs carcinoids on the external dataset (22 hamartomas; 32 carcinoids) with an AUC = 0.76. The 3D-median was the most important in the model. Density thresholds < 10 HU to predict hamartoma and > 60 HU to predict carcinoids were chosen for their high specificity > 0.90. On the external dataset, sensitivity and specificity of the 3D-median and 2D-ROIs were, respectively, 0.23, 1.00 and 0.13, 1.00 < 10 HU; 0.63, 0.95 and 0.69, 0.91 > 60 HU. The 3D-median was more reproducible than 2D-ROIs (ICC = 0.97 95% CI [0.95‒0.99]; bias: 3 ± 7 HU limits of agreement (LoA) [- 10‒16] vs. ICC = 0.90 95% CI [0.85‒0.94]; bias: - 0.7 ± 21 HU LoA [- 4‒40], respectively).

CONCLUSIONS

A radiomics signature can distinguish hamartomas from carcinoids with an AUC = 0.76. Median density < 10 HU and > 60 HU on 3D or 2D-ROIs may be useful in clinical practice to diagnose these tumors with confidence, but 3D is more reproducible.

CRITICAL RELEVANCE STATEMENT

Radiomic features help to identify the most discriminating imaging signs using random forest. 'Median' attenuation value (Hounsfield units), extracted from 3D-segmentations on contrast-enhanced chest-CTs, could distinguish carcinoids from atypical hamartomas (AUC = 0.85), was reproducible (ICC = 0.97), and generalized to an external dataset.

KEY POINTS

• 3D-'Median' was the best feature to differentiate carcinoids from atypical hamartomas (AUC = 0.85). • 3D-'Median' feature is reproducible (ICC = 0.97) and was generalized to an external dataset. • Radiomics signature from 3D-segmentations differentiated carcinoids from atypical hamartomas with an AUC = 0.76. • 2D-ROI value reached similar performance to 3D-'median' but was less reproducible (ICC = 0.90).

Comparative performances of machine learning algorithms in radiomics and impacting factors

There are no current recommendations on which machine learning (ML) algorithms should be used in radiomics. The objective was to compare performances of ML algorithms in radiomics when applied to different clinical questions to determine whether some strategies could give the best and most stable performances regardless of datasets. This study compares the performances of nine feature selection algorithms combined with fourteen binary classification algorithms on ten datasets. These datasets included radiomics features and clinical diagnosis for binary clinical classifications including COVID-19 pneumonia or sarcopenia on CT, head and neck, orbital or uterine lesions on MRI. For each dataset, a train-test split was created. Each of the 126 (9 × 14) combinations of feature selection algorithms and classification algorithms was trained and tuned using a ten-fold cross validation, then AUC was computed. This procedure was repeated three times per dataset. Best overall performances were obtained with JMI and JMIM as feature selection algorithms and random forest and linear regression models as classification algorithms. The choice of the classification algorithm was the factor explaining most of the performance variation (10% of total variance). The choice of the feature selection algorithm explained only 2% of variation, while the train-test split explained 9%.

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.

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).

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.

Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications

Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.

Standardised lesion segmentation for imaging biomarker quantitation: a consensus recommendation from ESR and EORTC

BACKGROUND

Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable.

METHODS

A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds.

RESULTS/CONCLUSIONS

Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refresher training). Recommendations by anatomical area are also specified.

Extraction of Tumor Response Criteria in Semi-Structured Imaging Report

In this study, we extracted information from 6,376 french CT scan semi-structured text reports evaluating the cancer treatment response using the RECIST methodology. We evaluated the performance against manual annotation of 100 reports and measured the evolution of the presence of information over time. The results show high performances of the extraction as well as trends.

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.

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools

The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology.

Adjusting D-dimer to lung disease extent to exclude Pulmonary Embolism in COVID-19 patients (Co-LEAD)

Objective  D-dimer measurement is a safe tool to exclude pulmonary embolism (PE), but its specificity decreases in coronavirus disease 2019 (COVID-19) patients. Our aim was to derive a new algorithm with a specific D-dimer threshold for COVID-19 patients.

Methods  We conducted a French multicenter, retrospective cohort study among 774 COVID-19 patients with suspected PE. D-dimer threshold adjusted to extent of lung damage found on computed tomography (CT) was derived in a patient set ( n  = 337), and its safety assessed in an independent validation set ( n  = 337).

Results  According to receiver operating characteristic curves, in the derivation set, D-dimer safely excluded PE, with one false negative, when using a 900 ng/mL threshold when lung damage extent was <50% and 1,700 ng/mL when lung damage extent was ≥50%. In the derivation set, the algorithm sensitivity was 98.2% (95% confidence interval [CI]: 94.7–100.0) and its specificity 28.4% (95% CI: 24.1–32.3). The negative likelihood ratio (NLR) was 0.06 (95% CI: 0.01–0.44) and the area under the curve (AUC) was 0.63 (95% CI: 0.60–0.67). In the validation set, sensitivity and specificity were 96.7% (95% CI: 88.7–99.6) and 39.2% (95% CI: 32.2–46.1), respectively. The NLR was 0.08 (95% CI; 0.02–0.33), and the AUC did not differ from that of the derivation set (0.68, 95% CI: 0.64–0.72, p  = 0.097). Using the Co-LEAD algorithm, 76 among 250 (30.4%) COVID-19 patients with suspected PE could have been managed without CT pulmonary angiography (CTPA) and 88 patients would have required two CTs.

Conclusion  The Co-LEAD algorithm could safely exclude PE, and could reduce the use of CTPA in COVID-19 patients. Further prospective studies need to validate this strategy.

Twenty Years On: RECIST as a Biomarker of Response in Solid Tumours an EORTC Imaging Group - ESOI Joint Paper

Response evaluation criteria in solid tumours (RECIST) v1.1 are currently the reference standard for evaluating efficacy of therapies in patients with solid tumours who are included in clinical trials, and they are widely used and accepted by regulatory agencies. This expert statement discusses the principles underlying RECIST, as well as their reproducibility and limitations. While the RECIST framework may not be perfect, the scientific bases for the anticancer drugs that have been approved using a RECIST-based surrogate endpoint remain valid. Importantly, changes in measurement have to meet thresholds defined by RECIST for response classification within thus partly circumventing the problems of measurement variability. The RECIST framework also applies to clinical patients in individual settings even though the relationship between tumour size changes and outcome from cohort studies is not necessarily translatable to individual cases. As reproducibility of RECIST measurements is impacted by reader experience, choice of target lesions and detection/interpretation of new lesions, it can result in patients changing response categories when measurements are near threshold values or if new lesions are missed or incorrectly interpreted. There are several situations where RECIST will fail to evaluate treatment-induced changes correctly; knowledge and understanding of these is crucial for correct interpretation. Also, some patterns of response/progression cannot be correctly documented by RECIST, particularly in relation to organ-site (e.g. bone without associated soft-tissue lesion) and treatment type (e.g. focal therapies). These require specialist reader experience and communication with oncologists to determine the actual impact of the therapy and best evaluation strategy. In such situations, alternative imaging markers for tumour response may be used but the sources of variability of individual imaging techniques need to be known and accounted for. Communication between imaging experts and oncologists regarding the level of confidence in a biomarker is essential for the correct interpretation of a biomarker and its application to clinical decision-making. Though measurement automation is desirable and potentially reduces the variability of results, associated technical difficulties must be overcome, and human adjudications may be required.

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.

[Artificial intelligence and medical imaging]

The use of artificial intelligence methods for image recognition is one of the most developed branches of the AI field and these technologies are now commonly used in our daily lives. In the field of medical imaging, approaches based on artificial intelligence are particularly promising, with numerous applications and a strong interest in the search for new biomarkers. Here, we will present the general methods used in these approaches as well as the potential areas of application.

Risks and benefits of systematic lymphadenectomy during interval debulking surgery for advanced high grade serous ovarian cancer

BACKGROUND

Lymphadenectomy is debated in patients with ovarian cancer. The aim of our study was to evaluate the impact of lymphadenectomy in patients with high-grade serous ovarian cancer receiving neoadjuvant chemotherapy (NACT) followed by interval debulking surgery (IDS).

METHODS

A retrospective, unicentric study including all patients undergoing NACT and IDS was carried out from 2005 to 2018. Patients with and without lymphadenectomy were compared in terms of recurrence free survival (RFS), overall survival (OS), and complication rates.

RESULTS

We included 203 patients. Of these, 133 had a lymphadenectomy (65.5%) and 77 had involved nodes (57.9%). Patients without a lymphadenectomy were older, had a more extensive disease and less complete CRS. No differences were noted between the lymphadenectomy and no lymphadenectomy group concerning 2-year RFS (47.4% and 48.6%, p = 0.87, respectively) and 5-year OS (63.2% versus 58.6%, p = 0.41, respectively). Post-operative complications tended to be more frequent in the lymphadenectomy group (18.57% versus 31.58%, p = 0.09). In patients with a lymphadenectomy, survival was significantly altered if the nodes were involved (positive nodes: 2-year RFS 42.5% and 5-year OS 49.4%, negative nodes: 2-year RFS 60.7% and 5-year OS 82.2%, p = 0.03 and p < 0.001, respectively).

CONCLUSION

Lymphadenectomy during IDS does not improve survival and increases post-operative complications.

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.

Prevalence of pulmonary embolism in patients with COVID-19 at the time of hospital admission

A high prevalence of venous thromboembolism (VTE) has been reported during intensive care unit (ICU) hospitalisation in patients with severe coronavirus disease 2019 (COVID-19) [1, 2]. In most cases, the diagnosis of pulmonary embolism (PE) was incidental as patients underwent computed tomography pulmonary angiography (CTPA) for aggravation of their respiratory condition. Higher mortality is also described in patients with high D-dimer levels suggesting that VTE complication may contribute to unfavourable prognosis [3, 4]. Even though, prevalence of thromboembolic complications during ICU hospitalisation seems to be high, the prevalence of pulmonary embolism at hospital admission for COVID-19 is unknown and may be underestimated.

There is a high prevalence of pulmonary embolism in patients with COVID-19 at the time of hospital admissionhttps://bit.ly/3reaLjv

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.

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

Background There are conflicting data regarding the diagnostic performance of Chest computed tomography (CT) for COVID-19 pneumonia. Disease extent on CT has been reported to influence prognosis. Purpose To create a large publicly available dataset and assess the diagnostic and prognostic value of CT in COVID-19 pneumonia. Materials and Methods This multicenter observational retrospective cohort study (ClinicalTrials.gov: NCT04355507) involved 20 French university hospitals. Eligible subjects presented at the emergency departments of the hospitals involved between March 1st and April 30th, 2020 and underwent both thoracic CT and RT-PCR 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 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 subjects positive for both RT-PCR and CT, using clinical and radiological features. Results Of 10,930 subjects screened for eligibility, 10,735 (median age 65 years, interquartile range, 51-77 years; 6,147 men) were included and 6,448 (60.0%) had a positive RT-PCR result. With RT-PCR as reference, the sensitivity and specificity and 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's AC1 coefficient: 0.79) Of all the variables analysed, the extent of pneumonia on CT (OR 3.25, 95%CI: 2.71, 3.89) was the best predictor of severe outcome at one month. A score based solely on clinical variables predicted a severe outcome with an AUC 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 on CT. Conclusion Using pre-defined criteria, CT reading is not influenced by reader's experience and helps predict the outcome at one month. Published under a CC BY 4.0 license. See also the editorial by Rubin.

Feasibility of an adapted schedule of carboplatin plus paclitaxel in elderly women with advanced ovarian cancer: A retrospective cohort

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Background: The EWOC-1 trial compared Carboplatin monotherapy (C mono) to two different Carboplatin + Paclitaxel (CP) regimens (weekly or 3-weekly) in vulnerable elderly patients treated for advanced ovarian cancers (OC). This study was closed prematurely because of a worse outcome in the C mono group. Both CP regimens were equivalent in terms of feasibility and efficacy with different toxicity profiles. Optimal CP regimen in elderly patient is still unknown. Here we propose a study of another adapted regimen of CP (aCP) performed in elderly patients in our institution. Methods: We retrospectively analyzed OC patients ≥ 70 years who received a Carboplatin AUC 4-5 d1q3week + Paclitaxel 80 mg/m² d1-d8 q3week regimen between 2015 and 2019. Primary endpoint was treatment feasibility according to the EWOC-1 standard: completion of 6 courses of chemotherapy without early stopping for disease progression, death or unacceptable toxicity (adverse event (AE) related to chemotherapy or treatment procedure leading either to early treatment stopping, to an unplanned hospital admission or to death or to a dose delay lasting more than 14 days or more than 2 dose reductions). Results: We identified 36 pts with a median age of 79 years (table). All patient but one had an ONCODAGE-G8 score ≤ 14, 30.6% of patients had a comorbidity Charlson’s index > 4 and 52.5% had an albumin rate < 35 g/L. The feasibility endpoint was met in 58.3% of patients (IC95% = [25.6; 57.8]). Main causes of treatment failure (TF) were early discontinuation because of toxicity in 6 patients (16.7%) and progressive disease in 3 patients (8.33%). Median PFS was 35.3 months (IC95% = [22.7; NR]) and median OS was 62.1 months (IC95% = [31.4.0; NR]). The most frequent AE were asthenia (all grades = 94.4%, grade 3-4 = 13.9%), anemia (all grades = 94.4%, grade 3-4 = 27.8%), neutropenia (all grades = 66.7%, grade 3-4 = 38.9%) and neuropathy sensory (all grades = 61.1%, no grade 3-4). Non high-grade-serous histological type and a poor Charlson’s score were associated with a higher rate of TF (100% and 63.6%, respectively). Conclusions: These results are consistent with the findings of the EWOC-1 trial in both CP regimens and suggest that aCP could be non-inferior with an acceptable toxicity profile. Further prospective and comparative studies are mandatory to confirm this trend and to better identify predictive factors of TF in OC elderly patients.[Table: see text]

A Magnetic Resonance Imaging Radiomics Signature to Distinguish Benign From Malignant Orbital Lesions

OBJECTIVES

Distinguishing benign from malignant orbital lesions remains challenging both clinically and with imaging, leading to risky biopsies. The objective was to differentiate benign from malignant orbital lesions using radiomics on 3 T magnetic resonance imaging (MRI) examinations.

MATERIALS AND METHODS

This institutional review board-approved prospective single-center study enrolled consecutive patients presenting with an orbital lesion undergoing a 3 T MRI prior to surgery from December 2015 to July 2019. Radiomics features were extracted from 6 MRI sequences (T1-weighted images [WIs], DIXON-T2-WI, diffusion-WI, postcontrast DIXON-T1-WI) using the Pyradiomics software. Features were selected based on their intraobserver and interobserver reproducibility, nonredundancy, and with a sequential step forward feature selection method. Selected features were used to train and optimize a Random Forest algorithm on the training set (75%) with 5-fold cross-validation. Performance metrics were computed on a held-out test set (25%) with bootstrap 95% confidence intervals (95% CIs). Five residents, 4 general radiologists, and 3 expert neuroradiologists were evaluated on their ability to visually distinguish benign from malignant lesions on the test set. Performance comparisons between reader groups and the model were performed using McNemar test. The impact of clinical and categorizable imaging data on algorithm performance was also assessed.

RESULTS

A total of 200 patients (116 [58%] women and 84 [42%] men; mean age, 53.0 ± 17.9 years) with 126 of 200 (63%) benign and 74 of 200 (37%) malignant orbital lesions were included in the study. A total of 606 radiomics features were extracted. The best performing model on the training set was composed of 8 features including apparent diffusion coefficient mean value, maximum diameter on T1-WIs, and texture features. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity on the test set were respectively 0.869 (95% CI, 0.834-0.898), 0.840 (95% CI, 0.806-0.874), 0.684 (95% CI, 0.615-0.751), and 0.935 (95% CI, 0.905-0.961). The radiomics model outperformed all reader groups, including expert neuroradiologists (P < 0.01). Adding clinical and categorizable imaging data did not significantly impact the algorithm performance (P = 0.49).

CONCLUSIONS

An MRI radiomics signature is helpful in differentiating benign from malignant orbital lesions and may outperform expert radiologists.

Clinical Impact of Lymphadenectomy after Neoadjuvant Chemotherapy in Advanced Epithelial Ovarian Cancer: A Review of Available Data

Recent robust data allow for omitting lymph node dissection for patients with advanced epithelial ovarian cancer (EOC) and without any suspicion of lymph node metastases, without compromising recurrence-free survival (RFS), nor overall survival (OS), in the setting of primary surgical treatment. Evidence supporting the same postulate for patients undergoing complete cytoreductive surgery after neoadjuvant chemotherapy (NACT) is lacking. Throughout a systematic literature review, the aim of our study was to evaluate the impact of lymph node dissection in patients undergoing surgery for advanced-stage EOC after NACT. A total of 1094 patients, included in six retrospective series, underwent either systematic, selective or no lymph node dissection. Only one study reveals a positive effect of lymphadenectomy on OS, and two on RFS. The four remaining series fail to demonstrate any beneficial effect on survival, neither for RFS nor OS. All of them highlight the higher peri- and post-operative complication rate associated with systematic lymph node dissection. Despite heterogeneity in the design of the studies included, there seems to be a trend showing no improvement on OS for systematic lymph node dissection in node negative patients. A well-conducted prospective trial is mandatory to evaluate this matter.

Prevalence and characteristics of pulmonary embolism in 1042 COVID-19 patients with respiratory symptoms: A nested case-control study

INTRODUCTION

Coronavirus disease 2019 (COVID-19) has been associated with cardiovascular complications and coagulation disorders. Previous studies reported pulmonary embolism (PE) in severe COVID-19 patients. Aim of the study was to estimate the prevalence of symptomatic PE in COVID-19 patients and to identify the clinical, radiological or biological characteristics associated with PE.

PATIENTS/METHODS

We conducted a retrospective nested case-control study in 2 French hospitals. Controls were matched in a 1:2 ratio on the basis of age, sex and center. PE patients with COVID-19 were compared to patients in whom PE was ruled out (CTPA controls) and in whom PE has not been investigated (CT controls).

RESULTS

PE was suspected in 269 patients among 1042 COVID-19 patients, and confirmed in 59 patients (5.6%). Half of PE was diagnosed at COVID-19 diagnosis. PE patients did not differ from CT and CTPA controls for thrombosis risk factors. PE patients more often required invasive ventilation compared to CTPA controls (odds ratio (OR) 2.79; 95% confidence interval (CI) 1.33-5.84) and to CT controls (OR 8.07; 95% CI 2.70-23.82). PE patients exhibited more extensive parenchymal lesions (>50%) than CT controls (OR 3.90; 95% CI 1.54-9.94). D-dimer levels were 5.1 (95% CI 1.90-13.76) times higher in PE patients than CTPA controls.

CONCLUSIONS

Our results suggest a PE prevalence in COVID-19 patients close to 5% in the whole population and to 20% of the clinically suspected population. PE seems to be associated with more extensive lung damage and to require more frequently invasive ventilation.

Socio-economic and psychological impact of the COVID-19 outbreak on private practice and public hospital radiologists

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During the COVID-19 outbreak, many radiologists expressed anxiety, depression and insomnia symptoms.

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Working in public hospital was a major protective factor for mental issues.

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Restricted access to education, past medical history and exposition to COVID-19 were common risks factor of anxiety or depression.

Purpose

The COVID-19 pandemic has led to an urgent reorganisation of the healthcare system to prevent hospitals from overflowing and the virus from spreading. Our objective was to evaluate the socioeconomic and psychological impact of the COVID-19 outbreak on radiologists.

Material and methods

French radiologists were invited to answer an online survey during the pandemic through mailing lists. The questionnaire was accessible for nine days. It covered socio-demographic information, exposure to COVID-19 at work and impact on work organisation, and included the Insomnia Severity Index and Hospital Anxiety and Depression Scale. Outcomes were moderate to severe insomnia, definite symptoms of depression or anxiety. Risk and protective factors were identified through multivariate binary logistic regression.

Results

1515 radiologists answered the survey. Overall, 674 (44.5 %) worked in a highCOVID-19 density area, 671 (44.3 %) were women, and 809 (53.4 %) worked in private practice. Among responders, 186 (12.3 %) expressed insomnia, 222 (14.6 %) anxiety, and 189 (12.5 %) depression symptoms. Lack of protective equipment, increased teleradiology activity and negative impact on education were risk factors for insomnia (respectively OR [95 %CI]:1.7[1.1−2.7], 1.5[1.1−2.2], and 2.5[1.8−3.6]). Female gender, respiratory history, working in COVID-19 high density area, increase of COVID-19 related activity, and impacted education were risk factors for anxiety (OR[95 %CI]:1.7[1.2−2.3], 2[1.1−3.4], 1.5[1.1−2], 1.2[1−1.4], and 2.1[1.5−3]). Conversely, working in a public hospital was a protective factor against insomnia, anxiety, and depression (OR[95 %CI]:0.4[0.2−0.7], 0.6[0.4−0.9], and 0.5[0.3−0.8]).

Conclusions

During COVID-19 pandemic, many radiologists expressed depression, anxiety and insomnia symptoms. Working in a public hospital was a protective factor against every psychological symptom. Socio-economic impact was also major especially in private practice.

Pulmonary embolism in patients with COVID-19 pneumonia

Acute respiratory distress syndrome development in patients with coronavirus disease 2019 (COVID-19) pneumonia is associated with a high mortality rate and is the main cause of death in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [1]. Myocardial injury has also been reported to be significantly associated with fatal outcome, with a 37% mortality rate in patients without prior cardiovascular disease but elevated troponin levels [2]. A D-dimer level of >1 μg·mL⁻¹ has been clearly identified as a risk factor for poor outcome in SARS-Cov-2 infection [3], with recent reports highlighting a high incidence of thrombotic events in intensive care unit (ICU) patients [4]. A normal D-dimer level allows the safe exclusion of pulmonary embolism (PE) in outpatients with a low or intermediate clinical probability of PE, but there is no recommendation to use D-dimer as a positive marker of thrombosis because of lack of specificity.

This study reports an overall 24% (95% CI 17–32%) cumulative incidence of pulmonary embolism in patients with COVID-19 pneumonia, 50% (30–70%) in ICU and 18% (12–27%) in other patientshttps://bit.ly/35s7hjm

Variation in neutrophil to lymphocyte ratio (NLR) as predictor of outcomes in metastatic renal cell carcinoma (mRCC) and non-small cell lung cancer (mNSCLC) patients treated with nivolumab

BACKGROUND

An elevated pre-treatment neutrophil to lymphocytes ratio (NLR) is associated with poor prognosis in various malignancies. Optimal cut-off is highly variable across studies and could not be determined individually for a patient to inform his prognosis. We hypothesize that NLR variations could be more useful than baseline NLR to predict progression-free survival (PFS) and overall survival (OS) in patients (pts) receiving anti-PD1 treatment.

PATIENTS AND METHODS

All pts with metastatic renal cell carcinoma (mRCC) and metastatic non-small cell lung cancer (mNSCLC) who received anti-PD1 nivolumab monotherapy in second-line setting or later were included in this French multicentric retrospective study. NLR values were prospectively collected prior to each nivolumab administration. Clinical characteristics were recorded. Associations between baseline NLR, NLR variations and survival outcomes were determined using Kaplan-Meier's method and multivariable Cox regression models.

RESULTS

161 pts (86 mRCC and 75 mNSCLC) were included with a median follow-up of 18 months. On the whole cohort, any NLR increase at week 6 was significantly associated with worse outcomes compared to NLR decrease, with a median PFS of 11 months vs 3.7 months (p < 0.0001), and a median OS of 28.5 months vs. 18 months (p = 0.013), respectively. In multivariate analysis, NLR increase was significantly associated with worse PFS (HR 2.2; p = 6.10^-5) and OS (HR 2.1; p = 0.005). Consistent results were observed in each cohort when analyzed separately.

CONCLUSION

Any NLR increase at week 6 was associated with worse PFS and OS outcomes. NLR variation is an inexpensive and dynamic marker easily obtained to monitor anti-PD1 efficacy.

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.

[Naftidrofuryl in arterial obstructive disease: A systematic revue of the literature]

INTRODUCTION

Arterial obstructive disease is a disease affecting 11 % of the general population. This prevalence is constantly increasing. Nafronyl is still prescribed despite a decreasing reimbursement rate since 2005. The objective of this study was to summarize data from the scientific literature on the efficacy and safety of nafronyl used for the treatment of peripheral arterial obstructive disease.

METHOD

A systematic review was made on EMBASE, MEDLINE and the Cochrane Library. Randomized controlled trials, systematic reviews and meta-analyses comparing naftidrofuryl with placebo were included. The main outcome was an improvement in the maximum walking distance or pain free walking distance. The quality of the reviews was analysed using a standardised reading grid. Only the best study was retained.

RESULTS

Among 193articles, one meta-analyses were selected. Naftidrofuryl improved the initial pain free walking distance by 60 % at six months, without a demonstrated increase in the risk of adverse reactions.

CONCLUSION

The efficacy of naftidrofuryl over the maximum walking distance in peripheral arterial obstructive disease appears similar to physical exercise or simvastatin.

[Encapsulant peritonitis]

INTRODUCTION

Encapsulating peritonitis is a rare but severe chronic fibrotic condition related to the development of a white fibrous membrane surrounding the digestive tract. Idiopathic forms have been described, however the disease is most often secondary to peritoneal dialysis or more rarely to surgery. Treatment is difficult and not codified.

CASE REPORT

We report here the observation of a 36-year-old patient whose diagnosis of encapsulating peritonitis was made after a long sub-occlusive history, eight years after a gastric ulcer perforation.

DISCUSSION

We discuss the possible etiologies and we present a focus on this rare and little-known entity.

Validated imaging biomarkers as decision-making tools in clinical trials and routine practice: current status and recommendations from the EIBALL* subcommittee of the European Society of Radiology (ESR)

Observer-driven pattern recognition is the standard for interpretation of medical images. To achieve global parity in interpretation, semi-quantitative scoring systems have been developed based on observer assessments; these are widely used in scoring coronary artery disease, the arthritides and neurological conditions and for indicating the likelihood of malignancy. However, in an era of machine learning and artificial intelligence, it is increasingly desirable that we extract quantitative biomarkers from medical images that inform on disease detection, characterisation, monitoring and assessment of response to treatment. Quantitation has the potential to provide objective decision-support tools in the management pathway of patients. Despite this, the quantitative potential of imaging remains under-exploited because of variability of the measurement, lack of harmonised systems for data acquisition and analysis, and crucially, a paucity of evidence on how such quantitation potentially affects clinical decision-making and patient outcome. This article reviews the current evidence for the use of semi-quantitative and quantitative biomarkers in clinical settings at various stages of the disease pathway including diagnosis, staging and prognosis, as well as predicting and detecting treatment response. It critically appraises current practice and sets out recommendations for using imaging objectively to drive patient management decisions.

How to differentiate uterine leiomyosarcoma from leiomyoma with imaging

Uterine leiomyomas, the most frequent benign myomatous tumors of the uterus, often cannot be distinguished from malignant uterine leiomyosarcomas using clinical criteria. Furthermore, imaging differentiation between both entities is frequently challenging due to their potential overlapping features. Because a suspected leiomyoma is often managed conservatively or with minimally invasive treatments, the misdiagnosis of leiomyosarcoma for a benign leiomyoma could potentially result in significant treatment delays, therefore increasing morbidity and mortality. In this review, we provide an overview of the differences between leiomyoma and leiomyosarcoma, mainly focusing on imaging characteristics, but also briefly touching upon their demographic, histopathological and clinical differences. The main indications and limitations of available cross-sectional imaging techniques are discussed, including ultrasound, computed tomography, magnetic resonance imaging (MRI) and positron emission tomography/computed tomography. A particular emphasis is placed on the review of specific MRI features that may allow distinction between leiomyomas and leiomyosarcomas according to the most recent evidence in the literature. The potential contribution of texture analysis is also discussed. In order to help guide-imaging diagnosis, we provide an MRI-based diagnostic algorithm which takes into account morphological and functional features, both individually and in combination, in an attempt to optimize radiologic differentiation of leiomyomas from leiomyosarcomas.

Management of epithelial cancer of the ovary, fallopian tube, and primary peritoneum. Long text of the Joint French Clinical Practice Guidelines issued by FRANCOGYN, CNGOF, SFOG, and GINECO-ARCAGY, and endorsed by INCa. Part 1: Diagnostic exploration and staging, surgery, perioperative care, and pathology

An MRI is recommended for an ovarian mass that is indeterminate on ultrasound. The ROMA score (combining CA125 and HE4) can also be calculated (grade A). In presumed early-stage ovarian or tubal cancers, the following procedures should be performed: an omentectomy (at a minimum, infracolic), an appendectomy, multiple peritoneal biopsies, peritoneal cytology (grade C), and pelvic and para-aortic lymphadenectomies (grade B) for all histologic types, except the expansile mucinous subtypes, for which lymphadenectomies can be omitted (grade C). Minimally invasive surgery is recommended for early-stage ovarian cancer, when there is no risk of tumor rupture (grade B). For FIGO stages III or IV ovarian, tubal, and primary peritoneal cancers, a contrast-enhanced computed tomography (CT) scan of the thorax/abdomen/pelvis is recommended (grade B), as well as laparoscopic exploration to take multiple biopsies (grade A) and a carcinomatosis score (Fagotti score at a minimum) (grade C) to assess the possibility of complete surgery (i.e., leaving no macroscopic tumor residue). Complete surgery by a midline laparotomy is recommended for advanced ovarian, tubal, or primary peritoneal cancer (grade B). For advanced cancers, para-aortic and pelvic lymphadenectomies are recommended when metastatic adenopathy is clinically or radiologically suspected (grade B). When adenopathy is not suspected and when complete peritoneal surgery is performed as the initial surgery for advanced cancer, the lymphadenectomies can be omitted because they do not modify either the medical treatment or overall survival (grade B). Primary surgery (before other treatment) is recommended whenever it appears possible to leave no tumor residue (grade B).

Artificial intelligence to diagnose meniscus tears on MRI

PURPOSE

The purpose of this study was to build and evaluate a high-performance algorithm to detect and characterize the presence of a meniscus tear on magnetic resonance imaging examination (MRI) of the knee.

MATERIAL AND METHODS

An algorithm was trained on a dataset of 1123 MR images of the knee. We separated the main task into three sub-tasks: first to detect the position of both horns, second to detect the presence of a tear, and last to determine the orientation of the tear. An algorithm based on fast-region convolutional neural network (CNN) and faster-region CNN, was developed to classify the tasks. The algorithm was thus used on a test dataset composed of 700 images for external validation. The performance metric was based on area under the curve (AUC) analysis for each task and a final weighted AUC encompassing the three tasks was calculated.

RESULTS

The use of our algorithm yielded an AUC of 0.92 for the detection of the position of the two meniscal horns, of 0.94 for the presence of a meniscal tear and of 083 for determining the orientation of the tear, resulting in a final weighted AUC of 0.90.

CONCLUSION

We demonstrate that our algorithm based on fast-region CNN is able to detect meniscal tears and is a first step towards developing more end-to-end artificial intelligence-powered diagnostic tools.

Epidemiology of Lyme borreliosis through two surveillance systems: the national Sentinelles GP network and the national hospital discharge database, France, 2005 to 2016

BACKGROUND

Lyme borreliosis (LB) is the most frequent vector-borne disease in France. Since 2009, surveillance of LB is conducted by a sentinel network of general practitioners (GPs). This system, in conjunction with the national hospitalisation database was used to estimate the incidence and describe the characteristics of LB in France.

AIM

To describe the estimated incidence and trends in GP consultations and hospital admissions for LB in France and identify risk groups and high-incidence regions.

RESULTS

From 2011 to 2016, the mean yearly incidence rate of LB cases was 53 per 100,000 inhabitants (95% CI: 41-65) ranging from 41 in 2011 to 84 per 100 000 in 2016. A mean of 799 cases per year were hospitalised with LB associated diagnoses 2005-16. The hospitalisation incidence rate (HIR) ranged from 1.1 cases per 100,000 inhabitants in 2005 to 1.5 in 2011 with no statistically significant trend. We observed seasonality with a peak during the summer, important inter-regional variations and a bimodal age distribution in LB incidence and HIR with higher incidence between 5 and 9 year olds and those aged 60 years. Erythema migrans affected 633/667 (95%) of the patients at primary care level. Among hospitalised cases, the most common manifestation was neuroborreliosis 4,906/9,594 (51%).

CONCLUSION

Public health strategies should focus on high-incidence age groups and regions during the months with the highest incidences and should emphasise prevention measures such as regular tick checks after exposure and prompt removal to avoid infection.

Gray-level discretization impacts reproducible MRI radiomics texture features

OBJECTIVES

To assess the influence of gray-level discretization on inter- and intra-observer reproducibility of texture radiomics features on clinical MR images.

MATERIALS AND METHODS

We studied two independent MRI datasets of 74 lacrymal gland tumors and 30 breast lesions from two different centers. Two pairs of readers performed three two-dimensional delineations for each dataset. Texture features were extracted using two radiomics softwares (Pyradiomics and an in-house software). Reproducible features were selected using a combination of intra-class correlation coefficient (ICC) and concordance and coherence coefficient (CCC) with 0.8 and 0.9 as thresholds, respectively. We tested six absolute and eight relative gray-level discretization methods and analyzed the distribution and highest number of reproducible features obtained for each discretization. We also analyzed the number of reproducible features extracted from computer simulated delineations representative of inter-observer variability.

RESULTS

The gray-level discretization method had a direct impact on texture feature reproducibility, independent of observers, software or method of delineation (simulated vs. human). The absolute discretization consistently provided statistically significantly more reproducible features than the relative discretization. Varying the bin number of relative discretization led to statistically significantly more variable results than varying the bin size of absolute discretization.

CONCLUSIONS

When considering inter-observer reproducible results of MRI texture radiomics features, an absolute discretization should be favored to allow the extraction of the highest number of potential candidates for new imaging biomarkers. Whichever the chosen method, it should be systematically documented to allow replicability of results.