Investigating the Incidence of Pulmonary Abnormalities as Identified by Parametric Response Mapping in Patients With Lung Cancer Before Radiation Treatment.

Purpose: Parametric response mapping (PRM) of high-resolution, paired inspiration and expiration computed tomography (CT) scans is a promising analytical imaging technique that is currently used in diagnostic applications and offers the ability to characterize and quantify certain pulmonary pathologies on a patient-specific basis. As one of the first studies to implement such a technique in the radiation oncology clinic, the goal of this work was to assess the feasibility for PRM analysis to identify pulmonary abnormalities in patients with lung cancer before radiation therapy (RT). Methods and Materials: High-resolution, paired inspiration and expiration CT scans were acquired from 23 patients with lung cancer as part of routine treatment planning CT acquisition. When applied to the paired CT scans, PRM analysis classifies lung parenchyma, on a voxel-wise basis, as normal, small airways disease (SAD), emphysema, or parenchymal disease (PD). PRM classifications were quantified as a percent of total lung volume and were evaluated globally and regionally within the lung. Results: PRM analysis of pre-RT CT scans was successfully implemented using a workflow that produced patient-specific maps and quantified specific phenotypes of pulmonary abnormalities. Through this study, a large prevalence of SAD and PD was demonstrated in this lung cancer patient population, with global averages of 10% and 17%, respectively. Moreover, PRM-classified normal and SAD in the region with primary tumor involvement were found to be significantly different from global lung values. When present, elevated levels of PD and SAD abnormalities tended to be pervasive in multiple regions of the lung, indicating a large burden of underlying disease. Conclusions: Pulmonary abnormalities, as detected by PRM, were characterized in patients with lung cancer scheduled for RT. Although further study is needed, PRM is a highly accessible CT-based imaging technique that has the potential to identify local lung abnormalities associated with chronic obstructive pulmonary disease and interstitial lung disease. Further investigation in the radiation oncology setting may provide strategies for tailoring RT planning and risk assessment based on pre-existing PRM-based pathology.

Functionally weighted airway sparing (FWAS): a functional avoidance method for preserving post-treatment ventilation in lung radiotherapy.

Recent changes to the guidelines for screening and early diagnosis of lung cancer have increased the interest in preserving post-radiotherapy lung function. Current investigational approaches are based on spatially mapping functional regions and generating regional avoidance plans that preferentially spare highly ventilated/perfused lung. A potentially critical, yet overlooked, aspect of functional avoidance is radiation injury to peripheral airways, which serve as gas conduits to and from functional lung regions. Dose redistribution based solely on regional function may cause irreparable damage to the 'supply chain'. To address this deficiency, we propose the functionally weighted airway sparing (FWAS) method. FWAS (i) maps the bronchial pathways to each functional sub-lobar lung volume; (ii) assigns a weighting factor to each airway based on the relative contribution of the sub-volume to overall lung function; and (iii) creates a treatment plan that aims to preserve these functional pathways. To evaluate it, we used four cases from a retrospective cohort of SAbR patients treated for lung cancer. Each patient's airways were auto-segmented from a diagnostic-quality breath-hold CT using a research virtual bronchoscopy software. A ventilation map was generated from the planning 4DCT to map regional lung function. For each terminal airway, as resolved by the segmentation software, the total ventilation within the sub-lobar volume supported by that airway was estimated and used as a function-based weighting factor. Upstream airways were weighted based on the cumulative volumetric ventilation supported by corresponding downstream airways. Using a previously developed model for airway radiosensitivity, dose constraints were determined for each airway corresponding to a <5% probability of airway collapse. Airway dose constraints, ventilation scores, and clinical dose constraints were input to a swarm optimization-based inverse planning engine to create a 3D conformal SAbR plan (CRT). The FWAS plans were compared to the patients' prescribed CRT clinical plans and the inverse-optimized clinical plans. Depending on the size and location of the tumour, the FWAS plan showed superior preservation of ventilation due to airflow preservation through open pathways (i.e. cumulative ventilation score from the sub-lobar volumes of open pathways). Improvements ranged between 3% and 23%, when comparing to the prescribed clinical plans, and between 3% and 35%, when comparing to the inverse-optimized clinical plans. The three plans satisfied clinical requirements for PTV coverage and OAR dose constraints. These initial results suggest that by sparing pathways to high-functioning lung subregions it is possible to reduce post-SAbR loss of respiratory function.

Treatment data and technical process challenges for practical big data efforts in radiation oncology.

The term Big Data has come to encompass a number of concepts and uses within medicine. This paper lays out the relevance and application of large collections of data in the radiation oncology community. We describe the potential importance and uses in clinical practice. The important concepts are then described and how they have been or could be implemented are discussed. Impediments to progress in the collection and use of sufficient quantities of data are also described. Finally, recommendations for how the community can move forward to achieve the potential of big data in radiation oncology are provided.