Automatic Seed Generation for NPC Image Segmentation

Automatic tumor image segmentation is challenging and difficult particularly in the nasopharynx area since the tumor is surrounded by various organs and image intensities of the tumor and normal tissue are in the similar level. Currently, radiologists manually draw the tumor regions on CT or MR images slide by slide. This is a tedious task and takes a long time. Automatic segmentation can reduce time and radiologists’ workload. In this research, region growing technique is used for the segmentation. With this technique, initial seed representing the tumor cell has to be generated.
Nearby seeds are then examined whether they are the tumor cells. In the original technique, seed is determined manually. Here, a technique for automatic seed generation based on probabilistic reasoning is proposed. This seed is then used for the region growing technique subsequently. CT images of patients who were treated at Ramathibodi hospital are tested and compared with the standard ground truth. The results showed that seed was generated with 87.5% accuracy. The segmented region provided a sensitivity of 0.88. However, this technique could be less accurately with the metastasis case.

3D Visualization of Nasopharyngeal Carcinoma from CT and MR Images

3D tumor visualization is a useful tool for diagnosis and radiation treatment planning since physicians can exactly locate regions of tumor invasion. The tumor size and position can then be estimated correctly. Though there are some commercial software packages for the 3D visualization, they are costly. Thus they are not widely used in Thailand. In this research, software for 3D visualization of nasopharyngeal carcinoma from CT and MR images has been developed.
Thai physicians thus can use the developed software with the lower cost. This increases chances of patients in the rural area to receive better diagnosis and treatment. To create the 3D model, CT or MR images with identified tumor regions by radiologists are employed. The tumor regions gained from these images are reconstructed and registered onto the skull model subsequently. Tumor volume can be estimated. With this software, files from CT or MRI scans can be simply loaded into our software. Physicians can view and easily manipulate the constructed model.

Effective use of Censored Data for NPC Recurrence Prediction

Cancer is a major disease which causes the increasing of human death rate each year. Normally cancer can occur anywhere in the human body. Early detection is difficult, particularly when it occurs in a hidden region. In this region, the cancerous tumor is usually found when it is in the severe stage. This results in difficultly of treatment. In general, the common treatments are radiotherapy, chemotherapy and surgery. The treatments are provided according to the stage and size of the cancer. Cancer that is of our interest is the nasopharyngeal carcinoma (NPC) which is often found in Thailand. Though it is in the hidden location, this cancer is curable. However, after the treatment, the cancer has a high chance to recur. The patients have to be followed up subsequently to check for the recurrence. To determine effective follow-up times, risk of cancer recurrence should be predicted. The recurring cancer can then be early detected prior to developing into the severe stage. Additionally, important prognostic factors that can be used for indicating the cancer recurrence should be determined.
The physicians thus can use these factors to indicate whether the cancer will recur in each patient prior to a thorough recheck. In this research, Neuro-Fuzzy based techniques will be used to predict the cancer recurrence. It combines the advantages of artificial neural networks and fuzzy logic. Thus it should provide better prediction performance. Additionally, using these techniques, we can extract knowledge pertaining risk of having cancer recurrence. The physicians can use the extracted knowledge to simply examine the cancer recurrence in each patient.

Neuro-Fuzzy Based Techniques for Prediction of Nasopharyngeal Carcinoma Recurrence

Nasopharyngeal carcinoma (NPC) is one type of head and neck cancers usually found in people who live in Southeast Asia, South China, Hong Kong and Thailand. Though it is curable, it has high chance of recurrence. After treatments, each patient must be followed up for observation of the cancer recurrence. To provide suitable follow-up planning, recurrence prediction is thus necessary. In this research, prediction of NPC recurrence is focused. Techniques based on artificial intelligence will be applied. These techniques are different from the statistical technique in which they can predict the recurrence individually. Related clinical data and time to recurrence of patients who were treated at Ramathibodi hospital were collected.
In the data collection, some patients might be lost to follow due to various reasons or they might have died because of other causes. These data are called the censored data and they cannot be used directly with the artificial intelligent techniques. Some modifications are thus required. From the previous research, the censored data were excluded from the data set. This resulted in biasing in the prediction. In this research, a technique that can deal with the censored data will be developed in order to provide effective prediction of the cancer recurrence.

Identification of significant SNPs from pool microarray data: Genome-wide association study

Single Nucleotide Polymorphism or SNP is a genetic variation frequently found in a DNA sequence. It occurs when a single nucleotide differs in paired chromosomes. This genetic variation may affect to disease susceptibility or drug response in an individual. Identification of SNPs related to a disease is thus necessary in which it can enhance efficiencies in gene therapy. Additionally, these relevant SNPs can be used for predicting risk of having the disease. To detect such SNPs, a large number of case and control samples are required. In general, SNP microarray is used to detect the SNPs in an individual. However, it is costly particularly in large samples. Identification of SNPs from pooled DNA is thus applied. In this research, susceptibility loci of complex disease from pooled microarray data will be studied. Various feature extraction techniques will be examined.