Financial toxicity in lung cancer: an assessment of magnitude, perception, and impact on quality of life.

BACKGROUND: Advances in lung cancer therapy have resulted in improved clinical outcomes. Unfortunately, advances can come at a financial cost to patients and their families that poses a significant risk to overall quality of life (QoL). Financial distress has been shown to be associated with increased symptom burden and decreased treatment compliance but the magnitude of financial distress is not well characterized in lung cancer populations. PATIENTS AND METHODS: Patients with stage II-IV newly diagnosed lung cancer and starting first-line therapy were recruited at a tertiary academic institution between July 2018 and April 2019. The comprehensive score for financial toxicity (COST) was used to assess financial toxicity and the Functional Assessment of Cancer Therapy-Lung (FACT-L) was used to assess QoL. Associations between financial toxicity and baseline variables were assessed using multivariable linear regression and correlations were assessed using the Pearson correlation. RESULTS: In this study, 143 consecutive patients were approached and 91.6% agreed to participate (N = 131). The median age was 65 years (35-90); 52.7% were male (n = 69), and 75.6% were white (n = 99). The inability to afford basic necessities and having <1 month of savings was associated with increased financial toxicity (P < 0.001) after adjusting for other factors such as age, race, insurance, and income. There was also a trend toward increased financial toxicity among those who were employed but on sick leave (P = 0.06). Increased financial toxicity was correlated with a decrease in QoL (correlation coefficient 0.41, P < 0.001). Patients' anticipated out-of-pocket (OOP) expenses for the upcoming 6 months ranged from $0 to $50 000 (median $2150). However, there was no correlation between anticipated OOP expenses and either financial toxicity or QoL. CONCLUSIONS: These data identify key factors for identifying at-risk patients and builds a framework for exploring the benefit of financial counseling interventions, which may improve QoL and oncologic outcomes.

Surgineering: curriculum concept for experiential learning in upper-level biomedical engineering.

PURPOSE: A strong foundation in the fundamental principles of medical intervention combined with genuine exposure to real clinical systems and procedures will improve engineering students' capability for informed innovation on clinical problems. To help build such a foundation, a new course (dubbed Surgineering) was developed to convey fundamental principles of surgery, interventional radiology (IR), and radiation therapy, with an emphasis on experiential learning, hands-on with real clinical systems, exposure to clinicians, and visits to real operating theaters. The concept, structure, and outcomes of the course of the first run of the first semester of the course are described. METHOD: The course included six segments spanning fundamental concepts and cutting-edge approaches in a spectrum of surgical specialties, body and neurological IR, and radiation therapy. Each class involved a minimum of didactic content and an emphasis on hands-on experience with instrumentation, equipment, surgical approaches, anatomical models, dissection, and visits to clinical theaters. Outcomes on the quality of the course and areas for continuing improvement were assessed by student surveys (5-point Likert scores and word-cloud representations of free response) as well as feedback from clinical collaborators. RESULT: Surveys assessed four key areas of feedback on the course and were analyzed quantitatively and in word-cloud representations of: (1) best aspects (hands-on experience with surgeons); (2) worst aspects (quizzes and reading materials); (3) areas for improvement (projects, quizzes, and background on anatomy); and (4) what prospective students should know (a lot background reading for every class). Five-point Likert scores from survey respondents (16/19 students) indicated: overall quality of the course 4.63 ± 0.72 (median 5.00); instructor teaching effectiveness 4.06 ± 1.06 (median 4.00); intellectual challenge 4.19 ± 0.40 (median 4.00); and workload somewhat heavier (62.5%) compared to other courses. Novel elements of the course included the opportunity to engage with clinical faculty and participate in realistic laboratory exercises, work with clinical instruments and equipment, and visit real operating theaters. An additional measure of the success of the course was evidenced by surveys and a strong escalation in enrollment in the following year. CONCLUSIONS: The Surgineering course presents an important addition to upper-level engineering curricula and a valuable opportunity for engineering students to gain hands-on experience and interaction with clinical experts. Close partnership with clinical faculty was essential to the schedule and logistics of the course as well as to the continuity of concepts delivered over the semester. The knowledge and experience gained provides stronger foundation for identification of un-met clinical needs and ideation of new engineering approaches in medicine. The course also provides a valuable prerequisite to higher-level coursework in systems engineering, human factors, and data science applied to medicine.

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.

A solid water pelvic and prostate phantom for imaging, volume rendering, treatment planning, and dosimetry for an RTOG multi-institutional, 3-D dose escalation study. Radiation Therapy Oncology Group.

PURPOSE: With increased interest in 3-D conformal radiation therapy and dose escalation, it is necessary to provide advanced techniques to assure quality in treatment delivery. Multi-institutional trials for these newer treatment techniques require methods of verifying the consistency of treatments between the participating institutions. For this reason, a phantom was designed to address the quality and consistency of Radiation Therapy Oncology Group (RTOG) 3-D prostate treatment protocol. METHODS AND MATERIALS: A solid water pelvic and prostate phantom for imaging, volume rendering, treatment planning, and dosimetry applications for performing comprehensive quality assurance has been designed and fabricated. Its configuration was based upon CT slices obtained from a patient study. Individual slices were machined with corresponding contours of the prostate, bladder, rectum, and the left and right femurs. Most of the phantom is made of solid water (Gammex/RMI, Middleton, WI), while the femurs are made of bone-equivalent material. The CT numbers from patient images were used to adjust the solid water composition within the organ volumes, providing image contrast from the remainder of the phantom. Cylindrical insertion grooves are machined in the phantom to allow placement of ionization chambers and thermal luminal dosimeters (TLDs) for dosimetry applications. During imaging, the cavities are filled with rods fabricated from solid water material. RESULTS: The phantom is being used to evaluate the consistency of a range of processes in radiation therapy simulation, planning, and delivery of 3-D-based treatments for prostate cancer. CONCLUSION: The ultimate study objective is to use the phantom to evaluate the accuracy and consistency of treatments delivered by institutions participating in national collaborative clinical trials involving 3-D conformal dose escalation.

Optically guided intensity modulated radiotherapy.

BACKGROUND AND PURPOSE: Previously, we reported on development of an optically guided system for 3D conformal intracranial radiotherapy using multiple noncoplanar fixed fields. In this paper we report on the extension of our system for stereotactic fractionated radiotherapy to include intensity modulated static ports. METHODS AND MATERIALS: A 3D treatment plan with maximum beam separation is developed in the stereotactic space established by an optically guided system. Gantry angles are chosen such that each beam has a unique entrance and exit pathway, avoids the critical structures, and has a minimal beam's eye view projection. Once, a satisfactory treatment plan is found using this geometric approach an inverse treatment plan is developed using the beam portals established previously. The purpose of adding inverse planing is two fold, on the one hand it allows further reduction of margins around the PTV, while on the other hand it affords the possibility of conformal avoidance of critical structures that are close to or abut the PTV. RESULTS: The use of the optically guided system in conjunction with intensity modulated noncoplanar radiotherapy treatment planning using fixed fields allows the generation of highly conformal treatment plans that exhibit smaller 90, 70, and 50% of prescription dose isodose volumes, improved PITV ratios, comparable or improved EUD, smaller NTD(mean) for the critical structures, and an inhomogeneity index that is within generally accepted limits. CONCLUSION: Because optically guided technology improves the accuracy of patient localization relative to the linac isocenter and allows real-time monitoring of patient position, the planning target volume needs to be corrected only for the limitations of image resolution. Intensity modulated static beam radiotherapy planning then provides the user the ability to further reduce margins on the PTV and to conform very closely to this smaller target volume, and enhances the normal tissue sparing, and high degree of conformality possible with 3D conformal radiotherapy. In addition, since optically guided technology affords improved patient localization and online monitoring of patient position during treatment delivery it allows for safe and efficient delivery of intensity modulated radiotherapy.

Modeling dose distributions from portal dose images using the convolution/superposition method.

Post-treatment dose verification refers to the process of reconstructing delivered dose distributions internal to a patient from information obtained during the treatment. The exit dose is commonly used to describe the dose beyond the exit surface of the patient from a megavoltage photon beam. Portal imaging provides a method of determining the dose in a plane distal to a patient from a megavoltage therapeutic beam. This exit dose enables reconstruction of the dose distribution from external beam radiation throughout the patient utilizing the convolution/superposition method and an extended phantom. An iterative convolution/superposition algorithm has been created to reconstruct dose distributions in patients from exit dose measurements during a radiotherapy treatment. The method is based on an extended phantom that includes the patient CT representation and an electronic portal imaging device (EPID). The convolution/superposition method computes the dose throughout the extended phantom, which allows the portal dose image to be predicted in the EPID. The process is then reversed to take the portal dose measurement and infer what the dose distribution must have been to produce the measured portal dose. The dose distribution is modeled without knowledge of the incident intensity distribution, and includes the effects of scatter in the computation. The iterative method begins by assuming that the primary energy fluence (PEF) at the portal image plane is equal to the portal dose image, the PEF is then back-projected through the extended phantom and convolved with the dose deposition kernel to determine a new prediction of the portal dose image. The image of the ratio of the computed PEF to the computed portal dose is then multiplied by the measured portal dose image to produce a better representation of the PEF. Successive iterations of this process then converge to the exiting PEF image that would produce the measured portal dose image. Once convergence is established, the dose distribution is determined by back-projecting the PEF and convolving with the dose deposition kernel. The method is accurate, provided the patient representation during treatment is known. The method was used on three phantoms with a photon energy of 6 MV to verify convergence and accuracy of the algorithm. The reconstructed dose volumes agree to within 3% of the forward computation dose volumes. Furthermore, this technique assumes no prior knowledge of the incident fluence and therefore may better represent the dose actually delivered.

Analysis and convergence of the iterative convolution/superposition dose reconstruction technique for multiple treatment beams and tomotherapy.

An iterative convolution/superposition (C/S) algorithm has been created to reconstruct dose distributions in patients from exit dose measurements during a radiotherapy treatment. The method is based on an extended phantom which includes the patient CT representation and an electronic portal imaging device (EPID). The patient CT is assumed to be a true and rigid representation of the patient at the time of treatment. The C/S method computes the dose throughout the extended phantom which allows the exit dose to be predicted in the EPID. The process is then reversed to take the exit dose measurement and infer what the dose distribution must have been to produce the measured exit dose. The dose distribution is modeled without knowledge of the incident intensity distribution, and includes the effects of scatter in the computation. The iterative method begins by assuming that the exit primary energy fluence (PEF) is equal to the exit dose, the PEF is then backprojected through the extended phantom and superposed with the dose deposition kernel to determine a new prediction of the exit dose. The ratio of the computed PEF to exit dose is then multiplied by the measured exit dose image to produce a better representation of the exit PEF. Successive iterations then converge to the exit PEF image that would produce the measured exit dose image. Once convergence is established, the dose distribution is determined by backprojecting the exit PEF followed by superposition with the dose deposition kernel. The method is used to reconstruct the dose from a stimulated dynamic wedge and verified with film. Convergence and termination of the algorithm is then investigated with no noise and in the presence of noise. The method is then expanded to handle multiple treatment beams by separating the representation of the EPID from the patient or phantom representation in the computation process. Investigation of the effects of noise during the process of iterative dose reconstruction is necessary to understand the capabilities of the algorithm using exit dose images that may contain significant amounts of noise. The capability of the algorithm is evaluated for multiple field treatments to a cube phantom and a prostate patient CT representation in the presence of noise. The method is then used to simulate the dose reconstruction process for tomotherapy using 72 intensity-modulated fan beams. Dose reconstruction is shown to be capable of verifying the dose distributions in patients including multiple beams and dynamic collimation, provided the patient CT is known at the time of treatment.

Calculation of portal dose using the convolution/superposition method.

The convolution/superposition method was used to predict the dose throughout an extended volume, which includes a phantom and a portal imaging device. From the calculated dose volume, the dose delivered in the portal image plane was extracted and compared to a portal dose image. This comparison aids in verifying the beam configuration or patient setup after delivery of the radiation. The phantoms used to test the accuracy of this method include a solid water cube, a Nuclear Associates CT phantom, and an Alderson Rando thorax phantom. The dose distribution in the image plane was measured with film and an electronic portal imaging device in each case. The calculated portal dose images were within 4% of the measured images for most voxels in the central portion of the field for all of the extended volumes. The convolution/superposition method also enables the determination of the scatter and primary dose contributions using the particular dose deposition kernels for each contribution. The ratio of primary dose to total dose was used to extract the primary dose from the detected portal image, which enhances the megavoltage portal images by removing scatter blurring. By also predicting the primary energy fluence, we can find the ratio of computed primary energy fluence to total dose. Multiplying this ratio by the measured dose image estimates the relative primary energy fluence at the portal imager. The image of primary energy fluence possesses higher contrast and may be used for further quantitative image processing and dose modeling.