RESUMEN
Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic (AUROC) is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k = 1 = 0.7618 ± 0.0018 (chance 0.397 ± 0.004, mean ±stdev ). The classifiers find that texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g., cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, preneoplastic/benign/low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e., from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through http://pathobotology.org . We expect our project to cultivate a more connected world of physicians and improve patient care worldwide.
Asunto(s)
Aprendizaje Profundo , Patología , Medios de Comunicación Sociales , Algoritmos , Humanos , PatólogosRESUMEN
Objetivo. Se presenta el caso de una paciente diagnosticada luteoma estromal de ovario con efecto virilizante. Material y métodos. Mujer de 67 años que presenta cuadro de virilización. A pesar de que no fue posible detectar mediante pruebas de imagen ningún tumor productor de andrógenos, se decidió someter a la paciente a una histerectomía y doble anexectomía debido a la fuerte sospecha clínica y analítica de un tumor productor de andrógenos de origen ovárico. Resultados. El estudio anatomopatológico de la pieza quirúrgica demostró la presencia de un luteoma estromal de ovario como causa del cuadro de virilización que presentaba la paciente. Conclusiones. El luteoma estromal de ovario es una rara neoplasia ovárica que se presenta habitualmente en mujeres posmenopáusicas y puede comenzar con síntomas virilizantes o derivados de un ambiente hiperestrogénico. En un 20% de los casos, se diagnostica como un hallazgo incidental (AU)
Objective. We report the case of a patient diagnosed with a stromal luteoma of the ovary with a virilizing effect. Material and methods. A 67-year-old woman presented with symptoms of virilization. Although no androgen-producing tumor was detected on imaging tests, the patient underwent hysterectomy and double oophorectomy due to strong clinical and laboratory suspicion of an androgen-producing tumor of the ovary. Results. Pathologic study of the surgical specimen showed the presence of an ovarian stromal luteoma causing the patient's virilization. Conclusions. Ovarian stromal luteoma is a rare ovarian neoplasm that usually occurs in postmenopausal women and may present as virilization or hyperestrogenism. In 20% of cases, the diagnosis is incidental (AU)