Uncovering nasopharyngeal carcinoma from chronic rhinosinusitis and healthy subjects using routine medical tests via machine learning.

Nasopharyngeal carcinoma (NPC) is one of the most common types of cancers in South China and Southeast Asia. Clinical data has shown that early detection is essential for improving treatment effectiveness and survival rate. Unfortunately, because the early symptoms of NPC are rather minor and similar to that of diseases such as Chronic Rhinosinusitis (CRS), early detection is a challenge. This paper proposes using machine learning methods to detect NPC using routine medical test data, namely Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), k-Nearest-Neighbor (KNN) and Logistic Regression (LR). We collected a dataset containing 523 newly diagnosed NPC patients before treatment, 501 newly diagnosed CRS patients before treatment as well as 600 healthy controls. The routine medical test data including age, gender, blood test features, liver function test features, and urine sediment test features. For comparison, we also used data from Epstein-Barr Virus (EBV) antibody tests, which is a specialized test not included among routine medical tests. In our first test, all four methods were tested on classifying NPC vs CRS vs controls; RF gives the best overall performance. Using only routine medical test data, it gives an accuracy of 83.1%, outperforming LR by 12%. In our second test, using only routine medical test data, when classifying NPC vs non-NPC (i.e. CRS or controls), RF achieves an accuracy of 88.2%. In our third test, when classifying NPC vs. controls, RF using only routine test data achieves an accuracy significantly better than RF using only EBV antibody data. Finally, in our last test, RF trained with NPC vs controls, using routine test data only, continued to perform well on an entirely separate dataset. This is a promising result because preliminary NPC detection using routine medical data is easy and inexpensive to implement. We believe this approach will play an important role in the detection and treatment of NPC in the future.

Three-dimensional printing of patient-specific surgical plates in head and neck reconstruction: A prospective pilot study.

BACKGROUND: Surgical plates have been extensively used in head and neck reconstruction and conventional plates are mass-produced with universal configurations. To overcome disadvantages of conventional surgical plates, we have been exploring patient-specific surgical plates using the three-dimensional (3D) printing technology. We hypothesized that the application of 3D-printed patient-specific surgical plates in head and neck reconstruction is feasible, safe and precise. METHODS: We are conducting a prospective clinical trial to assess the feasibility, safety and accuracy of applying 3D-printed patient-specific surgical plates in head and neck reconstruction. The primary endpoint was the intraoperative success rate. Secondary endpoints included the incidence and severity of postoperative adverse events within six months postoperatively. The accuracy of surgical outcomes was also explored by comparing the planned and final positions of the maxilla, mandible and grafted bone segments. RESULTS: From December 2016 to October 2017, ten patients were enrolled and underwent head and neck reconstruction using 3D-printed patient-specific surgical plates. The patient-specific surgical plates adapted to bone surface precisely and no plate-bending was performed. The intraoperative success rate was 100%. The average follow-up period was 6.5 months. No major adverse events were observed. The mean absolute distance deviation of integral mandible or maxilla was 1.40 ±â€¯0.63 mm, which showed a high accuracy of reconstruction. CONCLUSIONS: The 3D printing of patient-specific surgical plates could be effective in head and neck reconstruction. Surgical procedures were simplified. The precise jaw reconstruction was achieved with high accuracy. Long-term results with a larger sample size are warranted to support a final conclusion. The study protocol has been registered in ClinicalTrials.gov with a No. of NCT03057223.