The article demonstrates robust clinical evidence that MSCs have significant potential for the regeneration of hyaline articular cartilage in patients. The majority of clinical trials to date have yielded significantly positive results with minimal adverse effects. However the clinical research is still in its infancy. The optimum MSC source, cell concentrations, implantation technique, scaffold, growth factors and rehabilitation protocol for clinical use are yet to be identified. A larger number of randomised control trials are required to objectively compare the clinical efficacy and long-term safety of the various techniques. As the clinical research continues to evolve and address these challenges, it is likely that MSCs may become integrated into routine clinical practice in the near future.

BackgroundOvarian cancer remains the deadliest of all gynaecological cancers. Ultrasound-based models exist to support the classification of adnexal masses but are dependent on human assessment of features on ultrasound. Therefore, we aimed to develop an end-to-end machine learning (ML) model capable of automating the classification of adnexal masses.MethodsIn this retrospective study, transvaginal ultrasound scan images were extracted and segmented from Imperial College Healthcare, UK (ICH development dataset; n=577 masses; 1444 images) and Morgagni-Pierantoni Hospital, Italy (MPH external dataset; n=184 masses; 476 images). Clinical data including age, CA-125 and diagnosis (ultrasound subjective assessment, SA) or histology) were collected. A segmentation and classification model was developed by comparing several models using convolutional neural network-based models and traditional radiomics features. Dice surface coefficient was used to measure segmentation performance and area under the ROC curve (AUC), F1-score and recall for classification performance.FindingsThe ICH and MPH datasets had a median age of 45 (IQR 35-60) and 48 (IQR 38-57) and consisted of 23·1% and 31·5% malignant cases, respectively. The best segmentation model achieved a dice surface coefficient of 0·85 ±0·01, 0·88 ±0·01 and 0·85 ±0·01 in the ICH training, ICH validation and MPH test sets. The best classification model achieved a recall of 1·00 and F1-score of 0·88 (AUC 0·93), 0·94 (AUC 0·89) and 0·83 (AUC 0·90) in the ICH training, ICH validation and MPH test sets, respectively.InterpretationThe ML model provides an end-to-end method of adnexal mass segmentation and classification, with a comparable predictive performance (AUC 0·90) to the published performance of expert subjective assessment (SA, gold standard), and current risk models. Further prospective evaluation of the classification performance of the ML model against existing methods is required.FundingMedical Research Council, Imperial STRATiGRAD PhD programme and Imperial Health Charity.Research in ContextEvidence before this studyAdnexal masses are common, affecting up to 18% of postmenopausal women. Ultrasound is the primary imaging modality for the assessment of adnexal masses. Accurate classification of adnexal masses is fundamental to inform appropriate management. However, all existing classification methods are subjective and rely upon ultrasound expertise.Various models have been developed using ultrasound features and serological markers such as the Risk of malignancy index (RMI), International Ovarian Tumour Analysis (IOTA) Simple Rules (SR), the IOTA Assessment of Different NEoplasia’s in the AdneXa (ADNEX) model, and American College of Radiology (ACR) Ovarian-Adnexal Reporting and Data System Ultrasound (ORADS-US) to support the classification of adnexal masses. Despite modelling efforts, expert subjective assessment remains the gold standard method of classifying adnexal masses.The use of machine learning (ML) within clinical imaging is a rapidly evolving field due to its potential to overcome the subjectivity within image assessment and interpretation. Various studies (n=17) evaluating the use of ML within the classification of adnexal masses on ultrasound have been summarised within a recent meta-analysis by Xu et al, 2022. No studies used a radiomics-based approach to the classification of adnexal masses, and most have not been externally validated within a test set, questioning their generalisability. The largest study to date (Gao et al, 2022), used a deep learning (DL) based approach and was externally validated, yet its performance (F1 score 0·551) was not comparable to existing classification approaches.Added value of this studyWe have developed an end-to-end ML model (ODS) using DL and radiomics-based approaches, capable of identification (automated segmentation) and classification of adnexal masses with a high detection rate for malignancy. The ODS model had a performance comparable to the published performance of existing adnexal mass classification methods and does not rely upon ultrasound experience.Implications of all the available evidenceODS is a high performing, end-to-end model capable of classifying adnexal masses and requires limited ultrasound operator experience. The ODS model is potentially generalisable, having showed consistent performance in both validation (internal) and test (external) sets, highlighting the potential clinical value of a radiomics-based model within the classification of adnexal masses on ultrasound. The ODS model could function as a scalable triage tool, to identify high risk adnexal masses requiring further ultrasound assessment by an expert.

Objective: to correlate the clinical history with imaging findings of women with MRKH Design: Retrospective cohort study Setting: A UK IOTA and ESGO-certified tertiary referral centre for disorders of reproductive development Population: All patients with a diagnosis of MRKH and had undergone an MRI pelvis between 1st January 2011 – 31st April 2021 were included Method: MRI images were analysed by specialist gynaecological radiologists. Clinical data was extracted from an electronic patient record system. Statistical analysis was computed in R (version 4.1.2), R base stats package and ggstatsplot (v0.5.0). Outcome measures: clinical history and predefined imaging features Results: 134 patients were included. Median age at MRI was 18 years (10 – 64 years). Half (48.2%) of women presenting had a history of pain, most often abdominal (84.6%) or vaginal (9.2%). Anlage were identified in 91.8% of women (n = 123). 4.5% of women had imaging features of endometriosis (n = 6). Women with a functional anlage were significantly more likely to experience pain (p <0.001). Pain history was not strongly associated with ectopic ovarian position. Common gynaecological pathology such as endometriosis, ovarian cysts and fibroids were also identified. Conclusions: We identify that majority of women with MRKH will have uterine anlage with a connecting fibrous band, and an ectopic ovarian position 44.0% of cases. Abdominal pain was significantly associated with functional anlage on MRI. Further work is required to identify how other gynaecological pathology impacts women with MRKH.