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ObjectiveTo develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images.MethodsA DCNN-based diagnostic system was constructed and trained using 13,721 laryngoscopic images of LCA, precancerous laryngeal lesions (PRELCA), benign laryngeal tumors (BLT) and normal tissues (NORM) from 2 tertiary hospitals in China, including 2293 from 206 LCA subjects, 1807 from 203 PRELCA subjects, 6448 from 774 BLT subjects and 3191 from 633 NORM subjects. An independent test set of 1176 laryngoscopic images from other 3 tertiary hospitals in China, including 132 from 44 LCA subjects, 129 from 43 PRELCA subjects, 504 from 168 BLT subjects and 411 from 137 NORM subjects, was applied to the constructed DCNN to evaluate its performance against experienced endoscopists.ResultsThe DCCN achieved a sensitivity of 0.731, a specificity of 0.922, an AUC of 0.922, and the overall accuracy of 0.867 for detecting LCA and PRELCA among all lesions and normal tissues. When compared to human experts in an independent test set, the DCCN’ s performance on detection of LCA and PRELCA achieved a sensitivity of 0.720, a specificity of 0.948, an AUC of 0.953, and the overall accuracy of 0.897, which was comparable to that of an experienced human expert with 10–20 years of work experience. Moreover, the overall accuracy of DCNN for detection of LCA was 0.773, which was also comparable to that of an experienced human expert with 10–20 years of work experience and exceeded the experts with less than 10 years of work experience.ConclusionsThe DCNN has high sensitivity and specificity for automated detection of LCA and PRELCA from BLT and NORM in laryngoscopic images. This novel and effective approach facilitates earlier diagnosis of early LCA, resulting in improved clinical outcomes and reducing the burden of endoscopists.

Purpose A key issue in cancer is apoptosis resistance. However, little is known about the transcription factors which contribute to cellular survival of head and neck squamous cell carcinoma (HNSCC). Experimental design Three batches (54, 64, and 38) of HNSCC specimens were used for cellular and molecular analyses in order to determine the major molecular signaling pathways for cellular survival in HNSCC. Animal models (cell culture and xenografts) were employed to verify the importance of apoptosis resistance in HNSCC. Results Inhibitor of differentiation (Id) family member, Id1, was significantly up-regulated in clinical HNSCC specimens and acted to protect keratinocytes from apoptosis. Transfection of HNSCC cells with Id1 in vitro induced the phosphorylation of Akt (p-Akt) via phosphoinositide kinase-3 (PI3K) and increased the expression of survivin via nuclear factor kappa B (NF-κB). Blockage of the both pathways by specific inhibitors (LY294002 and IκBαM, respectively) abrogated Id1-induced cell survival of keratinocytes. In vivo studies demonstrated that increased expression of Id1 allowed non-tumorigenic keratinocytes (Rhek-1A) to become tumorigenic in nude mice by increased expression of survival genes such as p-Akt and survivin. More importantly, short interfering RNA (siRNA) for Id1 significantly reduced HNSCC tumorvolume of HNSCC in xenograft studies. Analysis of clinical data verified the importance of the Id1 downstream molecule, survivin, in the prognosis of HNSCC patients. Conclusions The above data, taken together, suggest that Id1 and its downstream effectors are potential targets for treatment of HNSCC because of their contribution to apoptosis resistance.

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