BackgroundChest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.Methods and findingsWe developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt’s discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4–28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863–0.910), 0.911 (95% CI 0.866–0.947), and 0.985 (95% CI 0.974–0.991), respectively, whereas CheXNeXt’s AUCs were 0.831 (95% CI 0.790–0.870), 0.704 (95% CI 0.567–0.833), and 0.851 (95% CI 0.785–0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825–0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777–0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.ConclusionsIn this study, we developed and validated a deep learning algorithm that classified clinically ...

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Background/Aims: In this study, a subpopulation of stem-like cells in human high grade serous ovarian carcinomas (ovarian cancer stem cells; OCSCs) were isolated and characterized. Methods: Primary high-grade serous ovarian carcinoma (HGSC) fresh biopsies were cultured under serum-free conditions to produce floating spheres. Sphere formation assay, including self-renewal, differentiation potential, chemo-resistance, and tumorigenicity were determined in vitro or in vivo. Results: OCSCs overexpressed stem cell genes (Oct-4, Nanog, Sox-2, Bmi-1, Nestin, CD133, CD44, CD24, ALDH1, CD117, and ABCG2). Immunostaining of spheres showed overexpressed Oct-4, Nanog, and Sox-2. These isolated tumor cells expanded as spheroid colonies for more than 30 passages. In contrast, adherent cells expressed high levels of CA125 and CK7. Flow cytometry analysis showed increased CSC markers (CD44, CD24, CD117, CD133, ABCG2, and ALDH1) in the spheroid cell population. OCSCs displayed higher chemoresistance to cisplatin or paclitaxel compared to adherent cells. Moreover, subcutaneous injection of 1 × 104 sphere-forming cells into NOD/SCID mice gave rise to new tumors similar to the original human tumors and could be passaged in mice. Conclusion: These results revealed that HGSCs are created and propagated by a small number of undifferentiated tumorigenic cells, and therapeutic targeting of these cells could be beneficial for treatment of HGSCs.

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