- Development of Ultra-High Resolution CT for Differential Diagnosis of Lung Nodules Found Using Low Dose CT
- Investigating Computed Tomography Radiomic-based Features for Predicting Radiation Pneumonitis Development
- Wait Times for Lung Cancer Patients in BC
Development of Ultra-High Resolution CT for Differential Diagnosis of Lung Nodules Found Using Low Dose CT
Lung cancer is the deadliest form of cancer in Canada, representing the highest percentage of estimated new cases (13.5%) and estimated deaths from all cancer cases (26.8%) in 2015. To combat this, the Canadian Task Force on Preventative Health Care has recommended low-dose computed tomography (LDCT) screening on high-risk patients for lung cancer in Canada. The reasoning is that if cancerous tumors are detected at an earlier stage, it costs less to treat the cancer, and the patient has a much higher probability of survival.
However it is not so simple! LDCT screening results in a large number (up to 90%) of false positive cases: Suspicious nodules that turns out not to be malignant (deadly) cancer. Currently, when a suspicious nodule is detected, to determine if the nodule requires treatment the patient may undergo further imaging: CT, nuclear imaging, or MRI; or a biopsy procedure. The risks associated with this diagnostic follow up are small for the imaging, however on a “high-risk” patient, the lung is a fragile organ on which to operate on, pneumothorax (lung collapse), hemorrhage (bleeding), or death may occur.
Our project looks to improve CT technology in order that, upon finding a suspicious nodule, an ultra-high resolution scan may be performed to accurately diagnose the nodule’s malignancy – without having to resort to invasive biopsy procedures! The first step in our investigation is to prove that CT can distinguish between malignant and benign (harmless) cancerous subtypes. We are examining images of various lung tissues (malignant, benign, normal, and non-cancerous lung conditions) under a micro-CT to elucidate features that would allow a pathological diagnosis. Our team consists of pathologists, radiologists, oncologists, medical physicists, and more! We hope to be able to use radiomics: an emerging science of garnering information, including phenotype, from imaging characteristics, in our diagnostic methodology.
If we are able to prove that CT technology is able to diagnose these suspicious nodules detected on a LDCT, we will also be able to determine what resolution (ability to distinguish small features) is required to perform this diagnosis. Essentially, a better resolution means that more ionizing radiation is delivered to the patient. We must be able to accurately perform a diagnosis, but also want to minimize the radiation delivered as this radiation has an (albeit small) risk of causing future cancer. Remember, we want this diagnostic technique to not only be safer than current techniques, e.g. biopsy, but also to be as safe as possible!
We expect that if we are able to prove that this diagnosis is possible using CT, the use of improved technology for diagnostic CT will allow us to achieve our desired resolution. Newer technologies such as improved resolution detectors, variable resolution CT, carbon nanotubes, flying focal spots, liquid-metal-jet anodes, volume-of-interest filtration, and more, have already been developed to improve resolution, and decrease scanning time on a CT. This will allow these ultra-high resolution CT methods to be employed along with a LDCT to make early detection of lung cancer less costly and safer!
For more information on LDCT screening: http://canadiantaskforce.ca/ctfphc-guidelines/2015-lung-cancer/harms-and-benefits/
Investigating Computed Tomography Radiomic-based Features for Predicting Radiation Pneumonitis Development
Lung cancer is the most commonly diagnosed cancer in Canada, leading to the most deaths of any other cancer type. Non-small cell lung cancer (NSCLC) is the most common type of lung cancer; for unresectable cases, treatment recommendation involves chemotherapy and radiation therapy (RT). Radiation pneumonitis (RP), a common radiation induced lung injury, is a major dose-limiting side effect in thoracic RT. When high doses of radiation are delivered to lung tissue in individuals susceptible to RP, a number of unfavorable pathological changes can occur including alveolar inflammation and pulmonary fibrosis. In severe RP cases, patients may experience significant respiratory symptoms that require medical intervention, oxygen supplementation or hospitalization, leading to a reduced quality of life and increased risk of death. Radiation pneumonitis can develop into lung fibrosis months to years after radiation treatment, which can potentially leave the patients with permanent impairment of oxygen transfer. Although fatal RP is uncommon, occurring in less than 2% of those suffering with RP, no patient death should be due to side effects of their cancer treatment.
Building a model that is capable of predicting those patients at the greatest risk of developing RP is crucial, as treatments may be altered to lower this risk thus ensuring safer therapy delivery. Currently there are no clinically useful predictive tools to guide dose modifications or to guide alternative approaches which might modify risk of RP. However, research has shown that early diagnosis and treatment of RP can have a positive impact on patient outcome. Radiomics is the conversion of images into mineable high-dimensional data. Radiomics is capable of extracting more complex diagnostic information from routine medical images, including features that are not identifiable or quantifiable by the physician’s eyes alone. Through the use of radiomics, researchers have been able to identify CT-image features that improve differentiation between post-treatment fibrosis and cancer recurrence, after RT for patients with early-stage lung cancer. Another group of researchers were able to identify a set of image-based texture features that were significantly related to RP development. This demonstrates that the technological advancements in radiomic software are available for the goal of this study to be achieved. Patient outcomes will improve if this model is successful in predicting RP following RT as it will assist clinicians in tailoring the patient’s RT based on the predicted response from the non-invasive CT imaging routinely acquired. It will also allow opportunities for closer follow-up and earlier medical intervention for patients at high risk of developing RP, in order to improve their quality of life and overall outcome.
Wait Times for Lung Cancer Patients in BC
This proposed initiative uses existing methodology to accurately quantify wait times in the thoracic surgical lung cancer pathway for patients treated within the BC Interior. This will allow for the comparison of the fastest and slowest wait times, information that is needed by the Interior Thoracic Group for evaluating thoracic surgical care quality and determining areas of improvement. Currently, the presentation of wait time data is inconsistent between reports and the use of a quantile regression model for measuring these values can be used for quality assurance purposes. This evaluation will also identify and review ways to rectify any points of lag in the lung cancer treatment pathway. Furthermore, this initiative will enable quality assurance monitoring of diagnostic, treatment, and outcomes for surgical lung cancer patients receiving treatment in the BC Interior.