To develop a practice-based training strategy to transition from radiation oncologist to therapist-driven prostate MR-Linac adaptive radiotherapy. In phase 1, 7 therapists independently contoured the prostate and organs-at-risk on T2-weighted MR images from 11 previously treated MR-Linac prostate patients. Contours were evaluated quantitatively (i.e. Dice similarity coefficient [DSC] calculated against oncologist generated online contours) and qualitatively (i.e. oncologist using a 5-point Likert scale; a score ≥ 4 was deemed a pass, a 90% pass rate was required to proceed to the next phase). Phase 2 consisted of supervised online workflow with therapists required no intervention from the oncologist on 10 total cases to advance. Phase 3 involved unsupervised therapist-driven workflow, with offline support from oncologists prior to the next fraction. In phase 1, the mean DSC was 0.92 (range 0.85–0.97), and mean Likert score was 3.7 for the prostate. Five therapists did not attain a pass rate (3–5 cases with prostate contour score < 4), underwent follow-up one-on-one review, and performed contours on a further training set (n = 5). Each participant completed a median of 12 (range 10–13) cases in phase 2; of 82 cases, minor direction were required from the oncologist on 5 regarding target contouring. Radiation oncologists reviewed 179 treatment fractions in phase 3, and deemed 5 cases acceptable but with suggestions for next fraction; all other cases were accepted without suggestions. A training stepwise program was developed and successfully implemented to enable a therapist-driven workflow for online prostate MR-Linac adaptive radiotherapy.

Purpose/Objective: Definitive radiotherapy (RT) is an alternative to radical cystectomy for select patients with muscle invasive bladder cancer (MIBC); however, there is limited data on dose-painted RT approaches. We report the clinical and dosimetric outcomes of a cohort of MIBC patients treated with dose-painted RT. Material/Methods: This was a single institution retrospective study of cT2-4N0M0 MIBC patients treated with external beam radiotherapy (EBRT) to the bladder, and sequential or concomitant boost to the tumor bed. The target delineation was guided by either intravesical injection of Lipiodol or through fusion of the pre-treatment imaging. The majority were treated with daily image-guidance. Kaplan-Meier was used to characterize overall survival (OS) and progression-free survival (PFS). Cumulative incidence function (CIF) was used to estimate local (intravesical) recurrence (LR), regional recurrence (RR) and distant metastasis (DM). Univariable and multivariable cause-specific hazard model was used to assess factors associated with LR and OS. Results: 117 patients were analyzed. The median age was 73 years (range 43, 95). The median EQD2 to the boost volume was 66 Gy (range 52.1, 70). Lipiodol injection was used in 64 patients (55%), all treated with IMRT/VMAT. 95 (81%) received concurrent chemotherapy, of whom, 44 (38%) received neoadjuvant chemotherapy. The median follow-up was 37 months (IQR 16.2, 83.3). At 5-year, OS and PFS were 79% (95% CI 70.5–89.2) and 46% (95% CI 36.5–57.5). Forty-five patients had bladder relapse, of which 30 patients (67%) were at site of the tumor bed. Nine patients underwent salvage-cystectomy. Late high-grade (G3-G4) genitourinary and gastrointestinal toxicity were 3% and 1%. Conclusion: Partial boost RT in MIBC is associated with good local disease control and high rates of cystectomy free survival. We observed a pattern of predominantly LR in the tumor bed, supporting the use of a dose-painted approach/de-escalation strategy to the uninvolved bladder. Prospective trials are required to compare oncological and toxicity outcomes between dose-painted and homogeneous bladder RT techniques.

Objective. Machine learning (ML) based radiation treatment planning addresses the iterative and time-consuming nature of conventional inverse planning. Given the rising importance of magnetic resonance (MR) only treatment planning workflows, we sought to determine if an ML based treatment planning model, trained on computed tomography (CT) imaging, could be applied to MR through domain adaptation. Methods. In this study, MR and CT imaging was collected from 55 prostate cancer patients treated on an MR linear accelerator. ML based plans were generated for each patient on both CT and MR imaging using a commercially available model in RayStation 8B. The dose distributions and acceptance rates of MR and CT based plans were compared using institutional dose-volume evaluation criteria. The dosimetric differences between MR and CT plans were further decomposed into setup, cohort, and imaging domain components. Results. MR plans were highly acceptable, meeting 93.1% of all evaluation criteria compared to 96.3% of CT plans, with dose equivalence for all evaluation criteria except for the bladder wall, penile bulb, small and large bowel, and one rectum wall criteria (p < 0.05). Changing the input imaging modality (domain component) only accounted for about half of the dosimetric differences observed between MR and CT plans. Anatomical differences between the ML training set and the MR linac cohort (cohort component) were also a significant contributor. Significance. We were able to create highly acceptable MR based treatment plans using a CT-trained ML model for treatment planning, although clinically significant dose deviations from the CT based plans were observed. Future work should focus on combining this framework with atlas selection metrics to create an interpretable quality assurance QA framework for ML based treatment planning.

E18744 Background: ICU admission is frequently questioned in cancer patients although its survival benefit has been demonstrated. Most studies focus on short-term outcomes, but scarce data are available regarding long-term outcomes and quality of life. Our aim was to evaluate what overall survival (OS) and functional situation can be expected in cancer patients after ICU discharge. Methods: We retrospectively analyzed all consecutive critically ill patients with solid tumors admitted to the medical ICU of a general hospital during 5 years (2011-2015); post-surgery admissions or programmed procedures were excluded. Follow-up data, ECOG performance status (PS), and prognostic factors related either to the tumor, the treatment or the cause of admission, were obtained from medical records. Kaplan-Meier curves and Cox models were used for prognostic evaluation. Results: 138 patients included, of whom 25% died during ICU admission and 93 (68%) were discharged from hospital. Patients: 65%, male; tumor location: 31.9% lung, 16.7% breast, 12.3% colorectal, 10.1% genitourinary; stage IV: 52.1%; current treatment: 58% chemotherapy, 19.6% radiotherapy, 7.2% biological drugs; treatment intention: 44% curative, 12% palliative, 44% non-curative with potential survival benefit. Median OS after discharge from ICU was 17.3 months (95%CI: 11.7-22.9). Mortality during ICU admission was related to acute complications, with only SAPS II score (a measure of comorbidity and acute severity) as a significant factor (OR: 0.95; p = 0.03), while most of post-discharge mortality was related to tumor progression (52.4%). The only prognostic factor for post-discharge OS in the multivariate analysis was disease status at time of discharge (HR: 1.92; p = 0.047). Among discharged patients, PS at baseline was: ECOG 0-1, 60%; ECOG 2: 31%; ECOG 3-4: 9%. After 12 months, 79% of alive patients kept an ECOG 0-2 (0-1: 52%; 2: 27%). Conclusions: A substantial group of cancer patients obtain a significant survival and maintain an ECOG 0-2 after ICU discharge. Concerns about long-term functional outcomes and survival should not preclude admission of cancer patients to ICUs, but further research on post-ICU quality of life is warranted.