Background Computed tomography (CT) scans are routinely obtained in oncology and provide measures of muscle and adipose tissue predictive of morbidity and mortality. Automated segmentation of CT has advanced past single slices to multi‐slice measurements, but the concordance of these approaches and their associations with mortality after cancer diagnosis have not been compared. Methods A total of 2871 patients with colorectal cancer diagnosed during 2012–2017 at Kaiser Permanente Northern California underwent abdominal CT scans as part of routine clinical care from which mid‐L3 cross‐sectional areas and multi‐slice T12–L5 volumes of skeletal muscle (SKM), subcutaneous adipose (SAT), visceral adipose (VAT) and intermuscular adipose (IMAT) tissues were assessed using Data Analysis Facilitation Suite, an automated multi‐slice segmentation platform. To facilitate comparison between single‐slice and multi‐slice measurements, sex‐specific z‐scores were calculated. Pearson correlation coefficients and Bland–Altman analysis were used to quantify agreement. Cox models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for death adjusting for age, sex, race/ethnicity, height, and tumour site and stage. Results Single‐slice area and multi‐slice abdominal volumes were highly correlated for all tissues (SKM R = 0.92, P < 0.001; SAT R = 0.97, P < 0.001; VAT R = 0.98, P < 0.001; IMAT R = 0.89, P < 0.001). Bland–Altman plots had a bias of 0 (SE: 0.00), indicating high average agreement between measures. The limits of agreement were narrowest for VAT ( ± 0.42 SD) and SAT ( ± 0.44 SD), and widest for SKM ( ± 0.78 SD) and IMAT ( ± 0.92 SD). The HRs had overlapping CIs, and similar magnitudes and direction of effects; for example, a 1‐SD increase in SKM area was associated with an 18% decreased risk of death (HR = 0.82; 95% CI: 0.72–0.92), versus 15% for volume from T12 to L5 (HR = 0.85; 95% CI: 0.75–0.96). Conclusions Single‐slice L3 areas and multi‐slice T12–L5 abdominal volumes of SKM, VAT, SAT and IMAT are highly correlated. Associations between area and volume measures with all‐cause mortality were similar, suggesting that they are equivalent tools for population studies if body composition is assessed at a single timepoint. Future research should examine longitudinal changes in multi‐slice tissues to improve individual risk prediction.

Plant-based diets are recommended for cancer survivors, but their relationship with breast cancer outcomes has not been examined. We evaluated whether long-term concordance with plant-based diets reduced the risk of recurrence and mortality among a prospective cohort of 3646 women diagnosed with breast cancer from 2005 to 2013. Participants completed food frequency questionnaires at diagnosis and 6-, 25-, and 72-month follow-up, from which we derived plant-based diet indices, including overall (PDI), healthful (hPDI), and unhealthful (uPDI). We observed 461 recurrences and 653 deaths over a median follow-up of 9.51 years. Using multivariable-adjusted Cox proportional hazards models, we estimated hazard ratios (HR) and 95% confidence intervals for breast cancer recurrence and all-cause, breast-cancer-specific, and non-breast-cancer mortality. Increased concordance with hPDI was associated with a reduced hazard of all-cause (HR 0.93, 95% CI: 0.83–1.05) and non-breast-cancer mortality (HR 0.83, 95% CI: 0.71–0.98), whereas increased concordance with uPDI was associated with increased hazards (HR 1.07, 95% CI: 0.96–1.2 and HR 1.20, 95% CI: 1.02–1.41, respectively). No associations with recurrence or breast-cancer-specific mortality were observed. In conclusion, healthful vs. unhealthful plant-based dietary patterns had differing associations with mortality. To enhance overall survival, dietary recommendations for breast cancer patients should emphasize healthful plant foods.

12057 Background: Older adults with cancer are at an increased risk of treatment related toxicities and excess mortality during cancer treatment. While altered body composition and frailty are associated with worse survival among older adults with cancer, no prior study has examined their combined influence on survival prediction. Methods: Prospective study of older adults (≥60 years) undergoing geriatric assessment (GA) at initial visit with a medical oncologist at UAB from 9/2017-07/2021 with available abdominal computed tomography (CT) within 60 days of GA. Using multi-slice CT images from T12 to L5 level, volumetric skeletal muscle (SMV), visceral (VATV) and subcutaneous (SATV) adipose tissues, and skeletal muscle density (SMD), were derived. Sex-specific z-scores for each measure were determined. A 44-item frailty index was obtained, using the deficit accumulation model. Overall survival (OS) was defined as time from GA to death or last follow-up (11/8/2021). Kaplan-Meier estimates of survival rates were compared using log-rank statistics. Multivariable cox regression models were used to predict OS in a random sub-sample (1:1 split of training:validation set), sequentially adding frailty and each body composition measure and assessing improvement with likelihood ratio tests and Harrel’s C statistic. Results: 815 patients were included (median age 68 years, 61% men, and 75% non-Hispanic Whites. 73% had gastrointestinal malignancies (stage III, 25%, stage, IV 48%). 32% were frail, 31% pre-frail. There was a weak negative correlation between height-adjusted SMV and frailty (r = -0.16), particularly among men (r -0.24). Over a median follow-up of 25.7 months (range 0.3-49.6 months), 268 patients (33%) died. The 2-year survival rate was 75.9%, 68.5% and 52.2% among robust, pre-frail and frail (log-rank p <.001), respectively. In multivariable models adjusted for age, sex, race, cancer type and cancer stage, being frail (vs robust) (Hazards Ratio, HR = 2.32; 95%CI: 1.69-3.2; p <.001) and higher skeletal muscle volume (HR = 0.85; 95%CI: 0.72-0.99; p= 0.04, per SD increment) were independently associated with OS. Adding body composition and frailty to clinical variables led to significant improvement in prediction (Harrel’s C increased from 0.69 to 0.74). In the validation set, discrimination was similar (Harrel’s C = 0.72) and plots suggested good model calibration. Conclusions: CT-based body composition metrics and frailty are independent predictors of OS among older adults with cancer and improve survival prediction compared to routine clinical risk factors.