A Rapid Segmentation-Insensitive "Digital Biopsy" Method for Radiomic Feature Extraction: Method and Pilot Study Using CT Images of Non-Small Cell Lung Cancer.

Quantitative imaging approaches compute features within images' regions of interest. Segmentation is rarely completely automatic, requiring time-consuming editing by experts. We propose a new paradigm, called "digital biopsy," that allows for the collection of intensity- and texture-based features from these regions at least 1 order of magnitude faster than the current manual or semiautomated methods. A radiologist reviewed automated segmentations of lung nodules from 100 preoperative volume computed tomography scans of patients with non-small cell lung cancer, and manually adjusted the nodule boundaries in each section, to be used as a reference standard, requiring up to 45 minutes per nodule. We also asked a different expert to generate a digital biopsy for each patient using a paintbrush tool to paint a contiguous region of each tumor over multiple cross-sections, a procedure that required an average of <3 minutes per nodule. We simulated additional digital biopsies using morphological procedures. Finally, we compared the features extracted from these digital biopsies with our reference standard using intraclass correlation coefficient (ICC) to characterize robustness. Comparing the reference standard segmentations to our digital biopsies, we found that 84/94 features had an ICC >0.7; comparing erosions and dilations, using a sphere of 1.5-mm radius, of our digital biopsies to the reference standard segmentations resulted in 41/94 and 53/94 features, respectively, with ICCs >0.7. We conclude that many intensity- and texture-based features remain consistent between the reference standard and our method while substantially reducing the amount of operator time required

Relationship between primary liver hepatocellular carcinoma volumes on portal-venous phase CT imaging

The liver is an important organ in the body. It is located under the rib cage on the right side. The liver performs many important functions, it processes food for nutrients that the body requires and also helps in the detoxification of harmful materials. Like any organ in the body, the liver is susceptible to diseases such as liver cancer. Liver cancer is the growth and spread of unhealthy cells of the liver. There are several risk factor for liver cancer, these are: Cirrhosis (scarring of the liver), long term hepatitis B and hepatitis C infection and diabetes patients with long term drinking problem. Hepatocellular Carcinoma is the most common form of liver cancer in adult population which begins in the main type of liver cell (hepatocyte). Because Hepatocellular carcinoma starts from the primary liver cell itself (hepatocytes), as such it is a primary liver cancer. About 30,000 Americans are diagnosed with primary liver cancer yearly, making it an important disease that plaques our society and therefore needs proper diagnosis. In clinical evaluation of primary liver cancer such as HCC, the use of medical imaging technology has been commonplace. Most medical facilities across the country and globally typically use Computed Tomography (CT) and/or Magnetic Resonance Imaging (MRI) in the diagnosis and treatment follow up of Hepatocellular carcinoma. The medical imaging devices are used to determine the extent and volume of the tumor of the cancerous liver cells. In clinical trials involving the imaging of HCC tumors, the typical protocol used in the CT imaging of HCC involves the use of contrast enhanced dual phase acquisition. This approach is based on the physiology of the blood flow through the liver. Since HCC tumors are hypervascular in nature, it would thus be more apparent in the arterial phase of an acquired CT image. The aforementioned characteristic was tested with a volume paradigm which measure and compare the volume of both the arterial phase and portal venous phase acquired images in the experiment. Overall this study helps in furthering goals to reduce the patient dose from the x-ray tubes during clinical trials. The results of the experiments (n = 19, t = 0.67, p = 0.26), indicates no significant difference between the volume of the HCC tumor images acquired both in the AP and PVP

A Novel Deep Learning Framework for Internal Gross Target Volume Definition from 4D Computed Tomography of Lung Cancer Patients

In this paper, we study the reliability of a novel deep learning framework for internal gross target volume (IGTV) delineation from four-dimensional computed tomography (4DCT), which is applied to patients with lung cancer treated by Stereotactic Body Radiation Therapy (SBRT). 77 patients who underwent SBRT followed by 4DCT scans were incorporated in a retrospective study. The IGTV_DL was delineated using a novel deep machine learning algorithm with a linear exhaustive optimal combination framework, for the purpose of comparison, three other IGTVs base on common methods was also delineated, we compared the relative volume difference (RVI), matching index (MI) and encompassment index (EI) for the above IGTVs. Then, multiple parameter regression analysis assesses the tumor volume and motion range as clinical influencing factors in the MI variation. Experimental results demonstrated that the deep learning algorithm with linear exhaustive optimal combination framework has a higher probability of achieving optimal MI compared with other currently widely used methods. For patients after simple breathing training by keeping the respiratory frequency in 10 BMP, the four phase combinations of 0%, 30%, 50% and 90% can be considered as a potential candidate for an optimal combination to synthesis IGTV in all respiration amplitudes