Semi-supervised learning with natural language processing for right ventricle classification in echocardiography - a scalable approach

We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n\ua0=\ua0539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set\ua0n\ua0=\ua011,008) and size (training set\ua0n\ua0=\ua09951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ\ua0=\ua00.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed

Gynecologic cancers in pregnancy: guidelines based on a third international consensus meeting

We aimed to provide comprehensive protocols and promote effective management of pregnant women with gynecological cancers. New insights and more experience have been gained since the previous guidelines were published in 2014. Members of the International Network on Cancer, Infertility and Pregnancy (INCIP), in collaboration with other international experts, reviewed existing literature on their respective areas of expertise. Summaries were subsequently merged into a manuscript that served as a basis for discussion during the consensus meeting. Treatment of gynecological cancers during pregnancy is attainable if management is achieved by collaboration of a multidisciplinary team of health care providers. This allows further optimization of maternal treatment, while considering fetal development and providing psychological support and long-term follow-up of the infants. Nonionizing imaging procedures are preferred diagnostic procedures, but limited ionizing imaging methods can be allowed if indispensable for treatment plans. In contrast to other cancers, standard surgery for gynecological cancers often needs to be adapted according to cancer type and gestational age. Most standard regimens of chemotherapy can be administered after 14 weeks gestational age but are not recommended beyond 35 weeks. C-section is recommended for most cervical and vulvar cancers, whereas vaginal delivery is allowed in most ovarian cancers. Breast-feeding should be avoided with ongoing chemotherapeutic, endocrine or targeted treatment. More studies that focus on the long-term toxic effects of gynecologic cancer treatments are needed to provide a full understanding of their fetal impact. In particular, data on targeted therapies that are becoming standard of care in certain gynecological malignancies is still limited. Furthermore, more studies aimed at the definition of the exact prognosis of patients after antenatal cancer treatment are warranted. Participation in existing registries (www.cancerinpregnancy.org) and the creation of national tumor boards with multidisciplinary teams of care providers (supplementary Box S1, available at Annals of Oncology online) is encouraged.status: publishe