Multi-task network for automated analysis of high-resolution endomicroscopy images to detect cervical precancer and cancer. (April 2022)

• Record Type: Journal Article
• Authors: Brenes, David; Barberan, CJ; Hunt, Brady; Parra, Sonia G.; Salcedo, Mila P.; Possati-Resende, Júlio C.; Cremer, Miriam L.; Castle, Philip E.; Fregnani, José H.T.G.; Maza, Mauricio; Schmeler, Kathleen M.; Baraniuk, Richard; Richards-Kortum, Rebecca
• Is Part Of: Computerized medical imaging and graphics. Volume 97(2022)
• Publication Date: 2022-04
• Subjects: Endomicroscopy -- Cervical precancer -- Multi-task learning -- Point-of-care; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2022.102052
• Languages: English
• ISSNs: 0895-6111

Abstract

Abstract: Cervical cancer is a public health emergency in low- and middle-income countries where resource limitations hamper standard-of-care prevention strategies. The high-resolution endomicroscope (HRME) is a low-cost, point-of-care device with which care providers can image the nuclear morphology of cervical lesions. Here, we propose a deep learning framework to diagnose cervical intraepithelial neoplasia grade 2 or more severe from HRME images. The proposed multi-task convolutional neural network uses nuclear segmentation to learn a diagnostically relevant representation. Nuclear segmentation was trained via proxy labels to circumvent the need for expensive, manually annotated nuclear masks. A dataset of images from over 1600 patients was used to train, validate, and test our algorithm; data from 20% of patients were reserved for testing. An external evaluation set with images from 508 patients was used to further validate our findings. The proposed method consistently outperformed other state-of-the art architectures achieving a test per patient area under the receiver operating characteristic curve (AUC-ROC) of 0.87. Performance was comparable to expert colposcopy with a test sensitivity and specificity of 0.94 (p = 0.3) and 0.58 (p = 1.0), respectively. Patients with recurrent human papillomavirus (HPV) infections are at a higher risk of developing cervical cancer. Thus, we sought to incorporate HPV DNA test results as a feature to inform prediction. We found that incorporating patient HPV status improved test specificity to 0.71 at a sensitivity of 0.94. Highlights: Deep learning to detect cervical precancer from high-resolution in vivo optical images. Multi-task nuclear segmentation drives the network's attention to nuclear features. Method outperformed state-of-the-art benchmarks even as training data was reduced. Performance for independent validation set matched expert clinical impression. Combining optical image data with HPV test results improved ability to detect cervical precancer.