Computed tomography reveals hypertrophic remodelling of the diaphragm in cystic fibrosis but not in COPD.

Background: Computed tomography (CT) is increasingly used for assessing skeletal muscle characteristics. In cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD), reduced limb muscle mass predicts poor clinical outcomes. However, the degree to which quantity or quality of respiratory and nonrespiratory muscles is affected by these diseases remains controversial. Methods: Thoracic CT images of 29 CF, 21 COPD and 20 normal spirometry control subjects were analysed to measure indices of muscle quantity (volume or cross-sectional area) and quality (radiodensity) in respiratory (diaphragm, abdominal) and nonrespiratory (pectoralis, lumbar paraspinal) muscles. Multivariable linear regression assessed relationships of CT measurements with body mass index (BMI), forced expiratory volume in 1 s (FEV1) % pred, inflammation and infection biomarkers, nutritional status and CF genotype. Results: Diaphragm volume in CF was significantly higher than in COPD (by 154%) or controls (by 140%). Abdominal muscle area in CF was also greater than in COPD (by 130%). Nonrespiratory muscles in COPD had more low radiodensity muscle (marker of lipid content) compared to CF and controls. In CF but not COPD, higher BMI and FEV1 % pred were independently associated with higher diaphragm and/or abdominal muscle quantity indices. Serum creatinine also predicted respiratory and nonrespiratory muscle quantity in CF, whereas other biomarkers including genotype correlated poorly with muscle CT parameters. Conclusions: Our data suggest that the CF diaphragm undergoes hypertrophic remodelling, whereas in COPD the nonrespiratory muscles show altered muscle quality consistent with greater lipid content. Thoracic CT can thus identify distinctive respiratory and nonrespiratory muscle remodelling signatures associated with different chronic lung diseases.

O-RADS US v2022: An Update from the American College of Radiology's Ovarian-Adnexal Reporting and Data System US Committee.

First published in 2019, the Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized lexicon for ovarian and adnexal lesions, enables stratification of these lesions with use of a numeric score based on morphologic features to indicate the risk of malignancy, and offers management guidance. This risk stratification system has subsequently been validated in retrospective studies and has yielded good interreader concordance, even with users of different levels of expertise. As use of the system increased, it was recognized that an update was needed to address certain clinical challenges, clarify recommendations, and incorporate emerging data from validation studies. Additional morphologic features that favor benignity, such as the bilocular feature for cysts without solid components and shadowing for solid lesions with smooth contours, were added to O-RADS US for optimal risk-appropriate scoring. As O-RADS US 4 has been shown to be an appropriate cutoff for malignancy, it is now recommended that lower-risk O-RADS US 3 lesions be followed with US if not excised. For solid lesions and cystic lesions with solid components, further characterization with MRI is now emphasized as a supplemental evaluation method, as MRI may provide higher specificity. This statement summarizes the updates to the governing concepts, lexicon terminology and assessment categories, and management recommendations found in the 2022 version of O-RADS US.

Staging of Cervical Cancer: A Practical Approach Using MRI and FDG PET.

This review provides a practical approach to the imaging evaluation of patients with cervical cancer (CC), from initial diagnosis to restaging of recurrence, focusing on MRI and FDG PET. The primary updates to the International Federation of Gynecology and Obstetrics (FIGO) CC staging system, as well as these updates' relevance to clinical management, are discussed. The recent literature investigating the role of MRI and FDG PET in CC staging and image-guided brachytherapy is summarized. The utility of MRI and FDG PET in response assessment and posttreatment surveillance is described. Important findings on MRI and FDG PET that interpreting radiologists should recognize and report are illustrated. The essential elements of structured reports during various phases of CC management are outlined. Special considerations, including the role of imaging in patients desiring fertility-sparing management, differentiation of CC and endometrial cancer, and unusual CC histologies, are also described. Finally, future research directions including PET/MRI, novel PET tracers, and artificial intelligence applications are highlighted.

Adnexal Lesion Imaging: Past, Present, and Future.

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

Current Concepts in the Imaging of Uterine Sarcomas.

Uterine sarcomas are a group of rare uterine tumors comprised of multiple subtypes with different histologic characteristics, prognoses, and imaging appearances. Identification of uterine sarcomas and their differentiation from benign uterine disease on imaging is of critical importance for treatment planning to guide appropriate management and optimize patient outcomes. Herein, we review the spectrum of uterine sarcomas with a focus on the classification of primary sarcoma subtypes and presenting the typical MR imaging appearances.

Generalizability of Machine Learning Models: Quantitative Evaluation of Three Methodological Pitfalls.

Purpose: To investigate the impact of the following three methodological pitfalls on model generalizability: (a) violation of the independence assumption, (b) model evaluation with an inappropriate performance indicator or baseline for comparison, and (c) batch effect. Materials and Methods: The authors used retrospective CT, histopathologic analysis, and radiography datasets to develop machine learning models with and without the three methodological pitfalls to quantitatively illustrate their effect on model performance and generalizability. F1 score was used to measure performance, and differences in performance between models developed with and without errors were assessed using the Wilcoxon rank sum test when applicable. Results: Violation of the independence assumption by applying oversampling, feature selection, and data augmentation before splitting data into training, validation, and test sets seemingly improved model F1 scores by 71.2% for predicting local recurrence and 5.0% for predicting 3-year overall survival in head and neck cancer and by 46.0% for distinguishing histopathologic patterns in lung cancer. Randomly distributing data points for a patient across datasets superficially improved the F1 score by 21.8%. High model performance metrics did not indicate high-quality lung segmentation. In the presence of a batch effect, a model built for pneumonia detection had an F1 score of 98.7% but correctly classified only 3.86% of samples from a new dataset of healthy patients. Conclusion: Machine learning models developed with these methodological pitfalls, which are undetectable during internal evaluation, produce inaccurate predictions; thus, understanding and avoiding these pitfalls is necessary for developing generalizable models.Keywords: Random Forest, Diagnosis, Prognosis, Convolutional Neural Network (CNN), Medical Image Analysis, Generalizability, Machine Learning, Deep Learning, Model Evaluation Supplemental material is available for this article. Published under a CC BY 4.0 license.

A Brief Review of Artificial Intelligence in Genitourinary Oncological Imaging.

Genitourinary (GU) system is among the most commonly involved malignancy sites in the human body. Imaging plays a crucial role not only in diagnosis of cancer but also in disease management and its prognosis. However, interpretation of conventional imaging methods such as CT or MR imaging (MRI) usually demonstrates variability across different readers and institutions. Artificial intelligence (AI) has emerged as a promising technology that could improve the patient care by providing helpful input to human readers through lesion detection algorithms and lesion classification systems. Moreover, the robustness of these models may be valuable in automating time-consuming tasks such as organ and lesion segmentations. Herein, we review the current state of imaging and existing challenges in GU malignancies, particularly for cancers of prostate, kidney and bladder; and briefly summarize the recent AI-based solutions to these challenges.

Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI.

PURPOSE: Identifying optimal machine learning pipelines for computer-aided diagnosis is key for the development of robust, reproducible, and clinically relevant imaging biomarkers for endometrial carcinoma. The purpose of this study was to introduce the mathematical development of image descriptors computed from spherical harmonics (SPHARM) decompositions as well as the associated machine learning pipeline, and to evaluate their performance in predicting deep myometrial invasion (MI) and histopathological high-grade in preoperative multiparametric magnetic resonance imaging (MRI). PATIENTS AND METHODS: This retrospective study included 128 women with histopathology-confirmed endometrial carcinomas who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. SPHARM descriptors of each tumor were computed on multiparametric MRI images (T2-weighted, diffusion-weighted, dynamic contrast-enhanced-MRI and apparent diffusion coefficient maps). Tensor-based logistic regression was used to classify two-dimensional SPHARM rotationally-invariant descriptors. Head-to-head comparisons with radiomics analyses were performed with DeLong tests with Bonferroni-Holm correction to compare diagnostic performances. RESULTS: With all MRI contrasts, SPHARM analysis resulted in area under the curve, sensitivity, specificity, and balanced accuracy values of 0.94 (95% confidence interval [CI]: 0.85, 1.00), 100% (95% CI: 100, 100), 74% (95% CI: 51, 92), 87% (95% CI: 78, 98), respectively, for predicting deep MI. For predicting high-grade tumor histology, the corresponding values for the same diagnostic metrics were 0.81 (95% CI: 0.64, 0.90), 93% (95% CI: 67, 100), 63% (95% CI: 45, 79) and 78% (95% CI: 64, 86). The corresponding values achieved via radiomics were 0.92 (95% CI: 0.82, 0.95), 82% (95% CI: 65, 93), 80% (95% CI: 51, 94), 81% (95% CI: 70, 91) for deep MI and 0.72 (95% CI: 0.58, 0.83), 93% (95% CI: 65, 100), 55% (95% CI: 41, 69), 74% (95% CI: 52, 88) for high-grade histology. The diagnostic performance of the SPHARM analysis was not significantly different (P = 0.62) from that of radiomics for predicting deep MI but was significantly higher (P = 0.044) for predicting high-grade histology. CONCLUSION: The proposed SPHARM analysis yields similar or higher diagnostic performance than radiomics in identifying deep MI and high-grade status in histology-proven endometrial carcinoma.

Ovarian-Adnexal Reporting and Data System for Magnetic Resonance Imaging (O-RADS MRI): Genesis and Future Directions.

Imaging plays an important role in characterizing and risk-stratifying commonly encountered adnexal lesions. Recently, the American College of Radiology (ACR) released the Ovarian-Adnexal Reporting and Data System (O-RADS) for ultrasound and subsequently for magnetic resonance imaging (MRI). The goal of the recently developed ACR O-RADS MRI risk stratification system is to improve the quality of imaging reports as well as the reproducibility of evaluating adnexal lesions on MRI. This review focuses on exploring this new system and its future refinements.

MRI of Adenomyosis: Where Are We Today?

Purpose of Review: The purpose of this review is to (i) summarize the current literature regarding the role of magnetic resonance imaging (MRI) in diagnosing adenomyosis, (ii) examine how to integrate MRI phenotypes with clinical symptomatology and histological findings, (iii) review recent advances including proposed MRI classifications, (iv) discuss challenges and pitfalls of diagnosing adenomyosis, and (v) outline the future role of MRI in promoting a better understanding of the pathogenesis, diagnosis, and treatment options for patients with uterine adenomyosis. Recent Findings: Recent advances and the widespread use of MRI have provided new insights into adenomyosis and the range of imaging phenotypes encountered in this disorder. Summary: Direct and indirect MRI features allow for accurate non-invasive diagnosis of adenomyosis. Adenomyosis is a complex and poorly understood disorder with variable MRI phenotypes that may be correlated with different pathogeneses, clinical presentations, and patient outcomes. MRI is useful for the assessment of the extent of findings, to evaluate for concomitant gynecological conditions, and potentially can help with the selection and implementation of therapeutic options. Nevertheless, important gaps in knowledge remain. This is in part due to the lack of standardized criteria for reporting resulting in heterogeneous and conflicting data in the literature. Thus, there is an urgent need for a unified MRI reporting system incorporating standardized terminology for diagnosing adenomyosis and defining the various phenotypes.

The Ovarian/Adnexal Reporting and Data System for Ultrasound: From Standardized Terminology to Optimal Risk Assessment and Management.

The American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System (O-RADS) lexicon and risk assessment tool for ultrasound (US) provides a framework for characterization of ovarian and adnexal pathology with the ultimate goal of harmonizing reporting and patient management strategies. Since the first O-RADS US publication in 2018, multiple validation studies have shown O-RADS US to have excellent diagnostic accuracy, with the majority of these studies using O-RADS 4 as the optimal cut-off for detecting ovarian cancer. Most of the existing validation studies include a dedicated training phase and confirm that ORADS US categories and lexicon descriptors are associated with high level inter-read agreement, regardless of radiologist training level or practice experience. O-RADS US has a similar inter-reader agreement when compared to Gynecologic Imaging Reporting and Data System (GIRADS), Assessment of Different Neoplasias in the adnexa (ADNEX), and International Tumor Analysis Group (IOTA) simple rules. System descriptors have been shown to correlate with expected malignancy rates and the O-RADS US risk stratification system has been shown to perform in the expected range of malignancy risk per category. Further directions will focus on clarifying governing concepts and lexicon terminology as well as further refining risk stratification categories based on data from published validation studies.

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.

O-RADS MRI After Initial Ultrasound for Adnexal Lesions: AJR Expert Panel Narrative Review.

The Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) and MRI risk stratification systems were developed by an international group of experts in adnexal imaging to aid radiologists in assessing adnexal lesions. The goal of imaging is to appropriately triage patients with adnexal lesions. US is the first-line imaging modality for assessment, whereas MRI can be used as a problem-solving tool. Both US and MRI can accurately characterize benign lesions such as simple cysts, endometriomas, hemorrhagic cysts, and dermoid cysts, avoiding unnecessary or inappropriate surgery. In patients with a lesion that does not meet criteria for one of these benign diagnoses, MRI can further characterize the lesion with an improved specificity for cancer and the ability to provide a probable histologic subtype in the presence of certain MRI features. This allows personalized treatment, including avoiding overly extensive surgery or allowing fertility-sparing procedures for suspected benign, borderline, or low-grade tumors. When MRI findings indicate a risk of an invasive cancer, patients can be expeditiously referred to a gynecologic oncologic surgeon. This narrative review provides expert opinion on the utility of multiparametric MRI when using the O-RADS US and MRI management systems.

CT-based Bosniak classification of cystic renal lesions: is version 2019 an improvement on version 2005?

OBJECTIVE: To compare the diagnostic performance and inter-reader agreement of the CT-based v2019 versus v2005 Bosniak classification systems for risk stratification of cystic renal lesions (CRL). METHODS: This retrospective study included adult patients with CRL identified on CT scan between 2005 and 2018. The reference standard was histopathology or a minimum 4-year imaging follow-up. The studies were reviewed independently by five readers (three senior, two junior), blinded to pathology results and imaging follow-up, who assigned Bosniak categories based on the 2005 and 2019 versions. Diagnostic performance of v2005 and v2019 Bosniak classifications for distinguishing benign from malignant lesions was calculated by dichotomizing CRL into the potential for ablative therapy (III-IV) or conservative management (I-IIF). Inter-reader agreement was calculated using Light's Kappa. RESULTS: One hundred thirty-nine patients with 149 CRL (33 malignant) were included. v2005 and v2019 Bosniak classifications achieved similar diagnostic performance with a sensitivity of 91% vs 91% and a specificity of 89% vs 88%, respectively. Inter-reader agreement for overall Bosniak category assignment was substantial for v2005 (κ = 0.78) and v2019 (κ = 0.75) between senior readers but decreased for v2019 when the Bosniak classification was dichotomized to conservative management (I-IIF) or ablative therapy (III-IV) (0.80 vs 0.71, respectively). For v2019, wall thickness was the morphological feature with the poorest inter-reader agreement (κ = 0.43 and 0.18 for senior and junior readers, respectively). CONCLUSION: No significant improvement in diagnostic performance and inter-reader agreement was shown between v2005 and v2019. The observed decrease in inter-reader agreement in v2019 when dichotomized according to management strategy may reflect the more stringent morphological criteria. KEY POINTS: • Versions 2005 and 2019 Bosniak classifications achieved similar diagnostic performance, but the specificity of higher risk categories (III and IV) was not increased while one malignant lesion was downgraded to v2019 Bosniak category II (i.e., not subjected to further follow-up). • Inter-reader agreement was similar between v2005 and v2019 but moderately decreased for v2019 when the Bosniak classification was dichotomized according to the potential need for ablative therapies (I-II-IIF vs III-IV).

Canadian Association of Radiologists Prostate MRI White Paper.

Prostate cancer is the most common malignancy and the third most common cause of death in Canadian men. In light of evolving diagnostic pathways for prostate cancer and the increased use of MRI, which now includes its use in men prior to biopsy, the Canadian Association of Radiologists established a Prostate MRI Working Group to produce a white paper to provide recommendations on establishing and maintaining a Prostate MRI Programme in the context of the Canadian healthcare system. The recommendations, which are based on available scientific evidence and/or expert consensus, are intended to maintain quality in image acquisition, interpretation, reporting and targeted biopsy to ensure optimal patient care. The paper covers technique, reporting, quality assurance and targeted biopsy considerations and includes appendices detailing suggested reporting templates, quality assessment tools and sample image acquisition protocols relevant to the Canadian healthcare context.

CT features associated with underlying malignancy in patients with diagnosed mesenteric panniculitis.

PURPOSE: The purpose of this study was to identify abdominal computed tomography (CT) features associated with underlying malignancy in patients with mesenteric panniculitis (MP). MATERIALS AND METHODS: This single-institution retrospective longitudinal cohort study included patients with MP and a minimum 1-year abdominopelvic CT follow-up or 2-year clinical follow-up after initial abdominopelvic CT examination. Two radiologists, blinded to patients' medical records, conjointly reviewed CT-based features of MP. Electronic medical records were reviewed for newly diagnosed malignancies with the following specific details: type (lymphoproliferative disease or solid malignancy), location (possible mesenteric drainage or distant), stage, time to diagnosis. An expert panel of three radiologists and one hemato-oncologist, who were blinded to the initial CT-based MP features, assessed the probability of association between MP and malignancy based on the malignancy characteristics. RESULTS: From 2006 to 2016, 444 patients with MP were included. There were 272 men and 172 women, with a median age of 64 years (age range: 25-89); the median overall follow-up was 36 months (IQR: 22, 60; range: 12-170). A total of 34 (8%) patients had a diagnosis of a new malignancy; 5 (1%) were considered possibly related to the MP, all being low-grade B-cell non-Hodgkin lymphomas. CT features associated with the presence of an underlying malignancy were the presence of an MP soft-tissue nodule with a short axis >10 mm (P < 0.0001) or lymphadenopathy in another abdominopelvic region (P < 0.0001). Associating these two features resulted in high diagnostic performance (sensitivity 100%; [95% CI: 57-100]; specificity 99% [95% CI: 98-100]). All related malignancies were identified. CONCLUSION: Further workup to rule out an underlying malignancy is only necessary in the presence of an MP soft-tissue nodule >10 mm or associated abdominopelvic lymphadenopathy.

Development and Validation of Multiparametric MRI-based Radiomics Models for Preoperative Risk Stratification of Endometrial Cancer.

Background Stratifying high-risk histopathologic features in endometrial carcinoma is important for treatment planning. Radiomics analysis at preoperative MRI holds potential to identify high-risk phenotypes. Purpose To evaluate the performance of multiparametric MRI three-dimensional radiomics-based machine learning models for differentiating low- from high-risk histopathologic markers-deep myometrial invasion (MI), lymphovascular space invasion (LVSI), and high-grade status-and advanced-stage endometrial carcinoma. Materials and Methods This dual-center retrospective study included women with histologically proven endometrial carcinoma who underwent 1.5-T MRI before hysterectomy between January 2011 and July 2015. Exclusion criteria were tumor diameter less than 1 cm, missing MRI sequences or histopathology reports, neoadjuvant therapy, and malignant neoplasms other than endometrial carcinoma. Three-dimensional radiomics features were extracted after tumor segmentation at MRI (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced MRI). Predictive features were selected in the training set with use of random forest (RF) models for each end point, and trained RF models were applied to the external test set. Five board-certified radiologists conducted MRI-based staging and deep MI assessment in the training set. Areas under the receiver operating characteristic curve (AUCs) were reported with balanced accuracies, and radiologists' readings were compared with radiomics with use of McNemar tests. Results In total, 157 women were included: 94 at the first institution (training set; mean age, 66 years ± 11 [SD]) and 63 at the second institution (test set; 67 years ± 12). RF models dichotomizing deep MI, LVSI, high grade, and International Federation of Gynecology and Obstetrics (FIGO) stage led to AUCs of 0.81 (95% CI: 0.68, 0.88), 0.80 (95% CI: 0.67, 0.93), 0.74 (95% CI: 0.61, 0.86), and 0.84 (95% CI: 0.72, 0.92), respectively, in the test set. In the training set, radiomics provided increased performance compared with radiologists' readings for identifying deep MI (balanced accuracy, 86% vs 79%; P = .03), while no evidence of a difference was observed in performance for advanced FIGO stage (80% vs 78%; P = .27). Conclusion Three-dimensional radiomics can stratify patients by using preoperative MRI according to high-risk histopathologic end points in endometrial carcinoma and provide nonsignificantly different or higher performance than radiologists in identifying advanced stage and deep myometrial invasion, respectively. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Kido and Nishio in this issue.

ENDO_STAGE Magnetic Resonance Imaging: Classification to Screen Endometriosis.

Introduction: Transvaginal sonography is the first-line imaging technique to diagnose endometriosis, but magnetic resonance imaging is more accurate in staging the extent of lesions, especially for deep pelvic endometriosis. The revised American Society for Reproductive Medicine and Enzian classifications are commonly used to stage the extent of endometriosis. However, a review underlined their weaknesses in terms of complexity, lack of clinical reproducibility and low correlation with surgical complications and fertility outcomes. Thus, to this day, in clinical practice, there is a lack of consensual, standardized or common nomenclature to stage the extent of endometriosis, posing a worldwide challenge. Objectives: The aims of our study were to: (i) develop a new classification (entitled Endo-Stage MRI) based on patterns of endometriosis as observed with magnetic resonance imaging; (ii) compare results with those of the rASRM classification; (iii) estimate the Endo-Stage MRI accuracy to predict the rate of surgical complications; and (iv) propose an Endo-Stage MRI system of triage (low, intermediate, high) that correlates with the risk of surgical complications. The goal is to improve the effectiveness of care pathways and allow for the planning of a multidisciplinary approach when necessary. Patients and methods: A single-center observational study using available clinical and imaging data. According to anatomical locations and the extent of endometriotic lesions, a standardized classification comprising six stages of severity (0-5) was designed. Results: A total of 751 patients with pelvic endometriosis underwent surgery from January 2013 to December 2018 in a tertiary care university hospital. Their Endo-Stage MRI classification was correlated with: (i) the rate of overall complications (grade I-IV Clavien-Dindo classification, (ii) the rate of major complications (grades III-IV) and (iii) the rate of voiding dysfunction requiring self-catheterization lasting more than one month. According to the Endo-Stage MRI classification, stages 0, 1, 2, 3, 4 and 5 were observed in 26 (3%), 156 (21%), 40 (5%), 22 (3%), 290 (39%) and 217 (29%) patients, respectively. Using the proposed Endo-Stage MRI system as triage, low (stages 0-2), intermediate (stages 3-4) and high-risk (stage 5), complications were observed in 29 (13%), 109 (34.9%) and 103 (47.4%) patients, respectively. In multivariate analysis, the Endo-Stage MRI system of triage was strongly predictive of surgical complications and achieved higher accuracy than the revised American Society for Reproductive Medicine classification (AUC: 0.78 (95% CI, 0.76-0.80) vs. 0.61 (95% CI, 0.58-0.64)). Conclusion: Our study proposes a new imaging classification of endometriosis coined Endo-Stage MRI classification. The results suggest that when applied to a clinical situation, it may improve care pathways by providing crucial information for identifying intermediate and/or high-risk stages of endometriosis with increased rates of surgical complications. To make this classification applicable, a multicentric validation study is necessary to assess the relevancy and clinical value of the current anatomical MRI classification.

How to improve O-RADS MRI score for rating adnexal masses with cystic component?

OBJECTIVES: To test the performance of the Ovarian-Adnexal Reporting Data System (O-RADS) MRI in characterizing adnexal masses with cystic components and to test new specific MRI features related to cystic components to improve the ability of the O-RADS MRI score to stratify lesions according to their risk of malignancy. METHODS: The EURopean ADnexal study (EURAD) database was retrospectively queried to identify adnexal masses with a cystic component. One junior and 13 radiologists independently reviewed cases blinded to the pathological diagnosis. For each lesion, the size of the whole lesion, morphological appearance, number of loculi, presence of a thickened wall, thickened septae, signal intensity of the cystic components on T1-weighted/T2-weighted/diffusion weighted, mean value of the apparent diffusion coefficient, and O-RADS MRI score were reported. Univariate and multivariate logistic regression analysis was performed to determine significant features to predict malignancy. RESULTS: The final cohort consisted of 585 patients with 779 pelvic masses who underwent pelvic MRI to characterize an adnexal mass(es). Histology served as the standard of reference. The diagnostic performance of the O-RADS MRI score was 0.944, 95%CI [0.922-0.961]. Significant criteria associated with malignancy included an O-RADS MRI score ≥ 4, ADCmean of cystic component > 1.69, number of loculi > 3, lesion size > 75 mm, the presence of a thick wall, and a low T1-weighted, a high T2-weighted, and a low diffusion-weighted signal intensity of the cystic component. Multivariate analysis demonstrated that an O-RADS MRI score ≥ combined with an ADC mean of the cystic component > 1.69, size > 75 mm, and low diffusion-weighted signal of the cystic component significantly improved the diagnostic performance up to 0.958, 95%CI [0.938-0.973]. CONCLUSION: Cystic component analysis may improve the diagnosis performance of the O-RADS MRI score in adnexal cystic masses. KEY POINTS: • O-RADS MRI score combined with specific cystic features (area under the receiving operating curve, AUROC = 0.958) improves the diagnostic performance of the O-RADS MRI score (AUROC = 0.944) for predicting malignancy in this cohort. • Cystic features that improve the prediction of malignancy are ADC mean > 1.69 (OR = 7); number of loculi ≥ 3 (OR = 5.16); lesion size > 75 mm (OR = 4.40); the presence of a thick wall (OR = 3.59); a high T2-weighted signal intensity score 4 or 5 (OR = 3.30); a low T1-weighted signal intensity score 1, 2, or 3 (OR = 3.45); and a low diffusion-weighted signal intensity (OR = 2.12). • An adnexal lesion with a cystic component rated O-RADS MRI score 4 and an ADC value of the cystic component < 1.69 associated with a low diffusion-weighted signal, has virtually a 0% risk of malignancy.

Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease.

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.