Radiation Therapy Knowledge and Health Literacy among Culturally Diverse Patients with Prostate Cancer Treated at a Safety-Net Hospital

PURPOSE/OBJECTIVE(S)

Shared decision making is integral to the physician-patient relationship for radiotherapy (RT) patients. It is implicit that patients both comprehend and retain information explained during consultation. However, quality and quantity of patient knowledge following this visit is unknown. The purpose of this study was to evaluate post-consultation RT knowledge and health literacy among a diverse group of patients.

MATERIALS/METHODS

Participants were patients ≥18 years old who received consultation for definitive or salvage RT to the prostate gland/fossa between April 2021 and January 2023 at an urban safety-net hospital. Following consultation, patients completed the Radiation Oncology Knowledge Assessment Survey (ROKAS), designed to measure patient understanding of proposed RT treatment (e.g., treatment frequency, length, safety) and possible short- and long-term side effects (SE). Additional measures included patients' health literacy, health numeracy (numerical medical concepts), acculturation (assimilation to the dominant culture), and socioeconomic factors. ROKAS was administered in both English and Spanish with Spanish-speaking patients offered medical translation if desired. Bivariate Pearson correlations were conducted to examine the relationships between independent variables and post-consultation RT knowledge. Two-sided t-tests were conducted to examine differences in patients' knowledge by language.

RESULTS

Overall, 39 ROKAS were completed by 24 English-speaking and 15 Spanish-speaking patients (mean age 64.4 [SD 6.8], range 52-79). The majority (93%) of patients 'agreed' or 'strongly agreed' that they understood all the RT information presented. However, only 70% of the RT questions were answered correctly with 26% of patients answering all RT questions correctly. Similarly, 95% of patients 'agreed' or 'strongly agreed' with knowing the side effects of their proposed treatment, but only 71% and 74% of short- and long-term SE questions, respectively, were answered correctly. Higher health literacy (p = 0.04) and health numeracy (p = 0.001) were significantly correlated with better understanding of short-term SE, but not with RT knowledge or long-term SE. Spanish-speaking patients had significantly lower scores of health literacy (p = 0.001) and understanding of long-term (p = 0.01), but not short-term SE.

CONCLUSION

There is a significant gap between perceived and measured knowledge of RT treatment and SE in patients who receive consultation for RT to the prostate gland/fossa. Health literacy was significantly associated with improved knowledge of RT and short-term SE. Spanish-speaking patients had poorer understanding of long-term SE than English-speaking patients. Efforts to identify gaps in patient health literacy are needed to target those at risk and ensure that culturally diverse patient populations can engage in shared decision making with their providers.

Contouring Analysis on Synthetic Contrast-Enhanced MR from GRMM-GAN and Implications on MR-Guide Radiation Therapy

PURPOSE/OBJECTIVE(S)

MR-guided linear accelerators have been commercialized making MR-only planning and adaptation an appealing alternative circumventing MR-CT registration. However, obtaining daily contrast-enhanced MR images can be prohibitive due to the increased risk of side effects from repeated contrast injections. In this work, we evaluate the quality of contrast-enhanced multi-modal MR image synthesis network GRMM-GAN (gradient regularized multi-modal multi-discrimination sparse-attention fusion generative adversarial network) for MR-guided radiation therapy.

MATERIALS/METHODS

With IRB approval, we trained the GRMM-GAN based on 165 abdominal MR studies from 65 patients. Each study included T2, T1 pre-contrast (T1pre), and T1 contrast enhanced (T1ce) images. The two pre-contrast MR modalities, T2 and T1pre images were adopted as inputs for GRMM-GAN, and the T1ce image at the portal venous phase was used as an output. Ten MR scans containing 21 liver tumors were selected for contouring analysis. A Turing test was first given to six radiation oncologists, in which 100 real T1ce and synthetic T1ce image slices are randomly given to the radiation oncologists to determine the authenticity of the synthesis. We then invited two radiation oncologists (RadOnc 1 and RadOnc2) to manually contour the 21 liver tumors independently on the real T1ce images. RadOnc2 then performed contouring on the respective synthetic T1ce MRs. DICE coefficient (defined as the intersection over the average of two volumes) and Hausdorff distance (HD, measuring how far two volumes are from each other) were used as analysis metrics. The DICE coefficients were calculated from the two radiation oncologists' contours on the real T1ce MR for each tumor. The DICE coefficients were also calculated from RadOnc 2's contours on real and synthetic MRs. Besides, tumor center shifts were extracted. The tumor center of mass coordinates was extracted from real and synthetic volumes. The difference in the coordinates indicated the shifts in the superior-inferior (SI), right-left (RL), and anterior-posterior (AP) directions between real and synthetic tumor volumes.

RESULTS

An average of 52.3% test score was achieved from the six radiation oncologists, which is close to random guessing. RadOnc 1 and RadOnc 2, who had participated in the contouring analysis, achieved an average DICE of 0.91±0.02 from tumor volumes drawn on the real T1ce MRs. This result sets the inter-operator uncertainty baseline in the real clinical setting. RadOnc 2 achieved an average DICE (real vs. synth) of 0.90±0.04 and HD of 4.76±1.82 mm. Only sub-millimeter (SI: 0.67 mm, RL: 0.41 mm, AP: 0.39 mm) tumor center shifts were observed in all three directions.

CONCLUSION

The GRMM-GAN method has the potential for MR-guided liver radiation when contrast agents cannot be administered daily and provide synthetic contrast-enhanced MR for better tumor targeting. The network can produce synthetic MR images with satisfactory contour agreement and geometric integrity.