Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as associated Epstein-Barr virus infection and high radiosensitivity. Radiotherapy has long been recognized as the mainstay for the treatment of NPC. However, the further efficacy brought by radical radiotherapy has reached the bottleneck in advanced patients, who are prone to develop recurrence and distant metastasis after treatment. The application of photon therapy makes it possible for radiation dose escalation in refractory cases and may provide second chance for recurrent patients with less unrecoverable tissue damage. The concept of adaptive radiotherapy is put forward in consideration of target volume shrinkage during treatment. The replanning procedure offers better protection for the organ at risk. However, the best timing and candidates for adaptive radiotherapy is still under debate. The current tendency of artificial intelligence in NPC mainly focuses on image recognition, auto-segmentation and dose prediction. Although artificial intelligence is still in developmental stage, the future of it is promising. To further improve the efficacy of NPC, multimodality treatment is encouraged. In-depth studies on genetic and epigenetic variations help to explain the great heterogeneity among patients, and could further be applied to precise screening and prediction, personalized radiotherapy and the evolution of targeted drugs. Given the clinical benefit of immunotherapy in other cancers, the application of immunotherapy, especially immune checkpoint inhibitor, in NPC is also of great potential. Results from ongoing clinical trials combining immunotherapy with radiotherapy in NPC are expected.

Background: Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning. Methods: An endoscopic images‐based nasopharyngeal malignancy detection model (eNPM‐DM) consisting of a fully convolutional network based on the inception architecture was developed and fine‐tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy‐proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM‐DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts. Results: All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM‐DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM‐DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM‐DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively. Conclusions: The eNPM‐DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images.

Lung cancer is the leading cause of cancer-related deaths. The morbidity and mortality of lung cancer have markedly increased in the past decade with at least 75% of patients with lung cancer having evidence of metastases at the time of diagnosis. It frequently metastasizes to bone resulting in osteolytic lesions with unknown mechanisms. The aim of this study was to identify factors that mediate lung cancer-induced osteoclast activity in vivo. Using a human cytokine antibody array, we first determined cytokine levels in a conditioned medium collected from non-small cell lung cancer A549 and H1299 cells and the non-neoplastic human bronchial epithelial BEAS2B cells. Both A549 and H1229 cells produced significantly higher amount of several cytokines including monocyte chemotactic protein 1 (MCP-1) and interleukin 8 (IL-8) compared with BEAS2B cells. These findings were confirmed by ELISA. From clinical serum specimens, we also observed that MCP-1 and IL-8 levels were increased in lung cancer patients with bone metastases compared with the patients with localized tumor. Next, we investigated the effects of MCP-1 on osteoclast formation in vitro using murine bone marrow-derived monocytes. A549 conditioned medium induced osteoclast formation that was inhibited by neutralizing antibodies against MCP-1. Finally, A549 cells were stably transfected with MCP-1 short hairpin RNA. The MCP-1 knockdown A549 cells were implanted into the tibia of severe combined immunodeficient mice for 4 weeks. The MCP-1 knockdown significantly diminished A549 cell growth. We conclude that MCP-1 promotes lung cancer-induced osteoclast activity and thus bone resorptive lesions in vivo.

Patients with metastatic nasopharyngeal carcinoma (NPC) have variable survival outcomes. We have previously shown that an elevated peripheral blood lymphocyte-to-monocyte ratio (LMR) is associated with an increased metastatic risk in patients with primary NPC. The present study aimed to investigate the prognostic value of pretreatment LMR in a large cohort of metastatic NPC patients. Clinical data of 672 patients with metastatic NPC diagnosed between January 2003 and December 2009 were analyzed. The peripheral lymphocyte and monocyte counts were retrieved, and LMR was calculated. Receiver operating characteristic (ROC) curve analysis and univariate and multivariate COX proportional hazards analyses were performed to evaluate the association of LMR with overall survival (OS). Univariate analysis revealed that an elevated absolute lymphocyte count (≥1.390 × 109/L) and LMR (≥2.475) as well as a decreased monocyte count (<0.665 × 109/L) were significantly associated with prolonged OS. Multivariate Cox proportional hazard analysis showed that LMR (hazard ratio [HR] = 0.50, 95 % confidence interval [CI] = 0.41–0.60, P < 0.001), absolute lymphocyte count (HR = 0.77, 95 % CI = 0.64–0.93, P = 0.007), and monocyte count (HR = 1.98, 95 % CI = 1.63–2.41, P < 0.001) were independent prognostic factors. By stratification analyses, only LMR remained a significant predictor of prognosis. We identified pretreatment LMR as an independent prognostic factor for patients with metastatic NPC. Independent validation of our findings is needed.