Citations

1593 Background: “Financial toxicity” refers to the financial burden imposed by treatment costs on individuals with cancer, constituting a major barrier to achieving equitable cancer outcomes. Recent literature increasingly demonstrates the detrimental impacts of financial toxicity on quality of life (QOL) among individuals with cancer, including individuals who have undergone hematopoietic stem cell transplantation (HSCT). This study evaluates associations among treatment cost burden and various aspects of QOL following HSCT. Methods: Seven hundred one HSCT recipients completed a survey examining their biopsychosocial health one year following transplant. The survey included the Functional Assessment of Cancer Therapy – Bone Marrow Transplantation (FACT-BMT), a multifactorial measure of QOL specific to this population. Treatment cost burden endorsement was measured on a 5-item Likert scale. Hierarchical regression models were developed to assess the incremental effects of demographic characteristics (i.e., Block 1), clinical predictors (Block 2), and cost burden (Block 3) on physical, emotional, social, functional, BMT-specific, general, and composite QOL outcomes. Results: Significant model improvement was observed with the addition of clinical factors ( ΔF(2,650) = 20.28, p < .001), and subsequently, treatment cost burden ( ΔF(1,649) = 110.29, p < .001). In the final model, higher cost burden was associated with poorer physical (β = -0.323, p < .001), emotional (β = -0.301, p < .001), social (β = -0.250, p < .001), functional (β = -0.317, p < .001), BMT-specific (β = -0.341, p < .001), general (β = -0.377, p < .001), and composite QOL (β = -0.381, p < .001). Poorer performance score was associated with each QOL indicator ( p < .001), with allogeneic transplant type associated with poorer functional (β = -0.001, p = .002), but higher emotional (β = 0.118, p = .002), wellbeing. Older age (β = 0.113, p = .003) and female sex predicted higher (β = 0.183, p < .001), while Hispanic ethnicity predicted poorer (β = -0.095, p = .010), social wellbeing. Female sex was associated with poorer QOL specific to BMT concerns (β = -0.118, p = .001). Conclusions: Higher treatment cost burden is associated with poorer overall QOL and its physical, emotional, social, functional, and BMT-specific components one year following HSCT, after controlling for demographic and clinical characteristics. This reflects a critical barrier to equitable cancer care, suggesting that financial toxicity may perpetuate preexisting inequities in QOL, treatment, disease, and survival outcomes that disproportionately impact the underserved. Future research should prioritize 1) better understanding relationships among complex indicators of financial toxicity, QOL, and their underpinning mechanisms and 2) developing solutions to mitigate financial toxicity of HSCT and overall cancer care.

1586 Background: Black and Hispanic breast cancer (BC) survivors have a higher prevalence of obesity and experience less success with weight loss interventions (WLI) than White BC survivors. The BWEL trial (Alliance A011401; NCT02750826) is a phase III randomized trial evaluating the impact of a 2-year telephone-based WLI on invasive disease-free survival in participants (pts) with stage II-III HER2-negative BC and a BMI ≥ 27 kg/m 2 . At 12-months, the WLI induced significant weight loss across demographic factors, including race and ethnicity. However, Black and Hispanic pts lost less weight and completed fewer calls than White pts. Here, we evaluate intervention adherence, engagement and tool utilization in BWEL pts by race and ethnicity. Methods: BWEL randomized pts to a WLI plus health education (HE) or HE alone. WLI pts received semi-structured telephone-based health coaching, delivered in English or Spanish, and received an activity monitor and wireless scale. Pts self-reported race and ethnicity. Mean values for call duration, call density (time to complete the initial 12-week intensive intervention phase), intervention attrition, and frequency of Fitbit use and weight tracking over 12-months were compared by race and ethnicity, comparing least squares means with Tukey-Kramer adjustment for multiple comparisons with adjusted p-values. Results: Of 3181 pts randomized to the study between 08/2016 and 02/2021, 1591 pts were allocated to the WLI arm. 80.5% of pts were White, 12.8% Black, and 7.1% Hispanic. Average BMI was 34.5 (±5.7) kg/m2. Compared to White pts, Black pts had fewer days of Fitbit usage (113.6 vs. 159.8, p<0.0001) and weight tracking (77.9 vs. 135.6 days, p< 0.0001). Hispanic pts had fewer days of Fitbit usage (108.8 vs. 154.9, p= 0.001) and weight tracking (87 vs. 129.5 days, p=0.0002) compared to non-Hispanic pts. There were no differences in attrition rate, average call duration, or call density by race or ethnicity. Conclusions: In a phase III WLI trial, engagement with tools designed to support weight loss was significantly lower in Black and Hispanic pts. Future work is needed to explore ways to enhance engagement and improve weight loss outcomes for racial and ethnic minority pts. Support: U10CA180821, U10CA180882, UG1CA189823; https://acknowledgments.alliancefound.org . Clinical trial information: NCT02750826 . Measure of engagement Race Ethnicity WhiteN=1281 BlackN=204 p-value Non-HispanicN=1459 HispanicN=113 p-value Withdrew from intervention n (%) 58 (4.5%) 13 (6.4%) 0.56 70 (4.8%) 6 (5.3%) 0.51 Call Duration (min)Mean (SD) 34.5 (7.6) 34.6 (9.1) 0.99 34.6 (7.8) 33.3 (7.9) 0.25 Call density(weeks)Mean (SD) 14.2 (7.2) 14.2 (8.8) 0.99 14.2 (7.3) 14.2 (9.7) 0.99 Days of Fitbit UseMean (SD) 159.8 (132.2) 113.6 (124.7) < 0.0001 154.9 (131.8) 108.8 (125.7) 0.001 Days of weight trackingMean (SD) 135.6 (109.5) 77.9 (87.5) < 0.0001 129.5 (108.8) 87 (96.5) 0.0002

e13661 Background: MM survival has improved significantly, resulting in increased survivorship and caregiver burden and is crucial to understand the real-world impact of MM. We analyzed results from a large, prospective MM patient survey using ML models to identify key predictive factors for survivorship and caregiver burden. Methods: A 45-item questionnaire spanning sociodemographics, treatment preferences, symptom burden and caregiver impact was distributed to MM patients at Mayo Clinic and International Myeloma Foundation support groups. Questions about quality of life (QOL) and treatment satisfaction (TS) represented survivorship outcomes while caregiver workday loss (WL) and job loss (JL) were surrogates for caregiver burden. 6 ML models (multinomial, random forest (RF), XGB (XGBoost), k-NN, GBM, Naïve Bayes) with a training: testing cohort of 80:20 were used to predict each of the 4 outcomes independently, considering model accuracy, C-index, precision, recall, and F1 score (Table), and using R studio (V4.4.1) and Bluesky Statistics (V10.3.4). Results: 2,239 participants (54% male, 89% Whites) with median age 68 yrs completed the survey. 71% respondents had a college degree, 44% resided in Midwest states, 62% reported household income >$75,000, and 67% had Medicare/Medicaid. Median age at MM diagnosis was 67 yrs with 58% diagnosed within the past 5 yrs and 73% currently receiving MM treatment. 72% reported their spouse as the primary caregiver. XGB was the best predictive model for QOL with gender, household income, stem cell transplant, CAR-T therapy, support from medical staff, and symptom burden (pain, neuropathy, mental well-being) as significant predictors. The RF model was the best for TS with pain and neuropathy levels as the most impactful predictors. For caregiver burden, XGB was the best model for WL with gender, household income, CAR-T therapy, financial strain, lack of support from medical staff, self-workdays missed, self-job loss, and pain and neuropathy levels as significant predictors. For JL, RF was the best model with significant variables being workdays missed by self, increased healthcare needs, symptom burden (pain, neuropathy, and mental well-being), and restricted physical activity. Conclusions: Our study shows the significant impact of MM survivorship and efficiently identifies predictive factors associated with survivorship and caregiver burden from a large prospective dataset using validated ML models. These findings highlight the complex challenges from MM and provide contemporary ML methods to better understand their interplay such that a framework of real-world interventions can be developed. Outcome Best ML Model Accuracy C-Index Precision Recall F1 score QOL XG 0.996 0.996 0.996 0.997 0.996 TS RF 0.984 0.900 0.937 0.948 0.937 WL XGB 0.984 1.000 0.944 0.947 0.945 JL RF 0.989 0.800 0.829 0.848 0.835

e13656 Background: Evidence-based medicine is crucial for clinical decision-making, yet studies suggest that a significant proportion of treatment decisions do not fully incorporate the latest evidence. Large Language Models (LLMs) show promise in bridging this gap, but their reliability for medical recommendations remains uncertain. Methods: We conducted an evaluation study comparing five LLMs’ recommendations across 50 clinical scenarios related to multiple myeloma diagnosis, staging, treatment, and management. The evaluation included three general-purpose LLMs (OpenAI o1-preview, Claude 3.5 Sonnet, Gemini 1.5 Pro), one retrieval-augmented generation (RAG) system (Myelo), and one agentic workflow-based system (HopeAI). General-purpose LLMs generated responses based solely on their internal knowledge, while the RAG system enhanced these capabilities by incorporating external knowledge retrieval. The agentic workflow system extended the RAG approach by implementing multi-step reasoning and coordinating with multiple tools and external systems for complex task execution. Three independent hematologist-oncologists evaluated the LLM-generated responses using standardized scoring criteria developed specifically for this study. Performance assessment encompassed five dimensions: accuracy, relevance, comprehensiveness, hallucination rate, and clinical use readiness. Results: HopeAI demonstrated superior performance across accuracy (82.0%), relevance (85.3%), and comprehensiveness (74.0%), compared to OpenAI o1-preview (64.7%, 57.3%, 36.0%), Claude 3.5 Sonnet (50.0%, 51.3%, 29.3%), Gemini 1.5 Pro (48.0%, 46.0%, 30.0%), and Myelo (58.7%, 56%, 32.7%). Hallucination rates were consistently low across all systems: HopeAI (5.3%), OpenAI o1-preview (3.3%), Claude 3.5 Sonnet (10.0%), Gemini 1.5 Pro (8.0%), and Myelo (5.3%). Clinical use readiness scores were relatively low for all systems: HopeAI (25.3%), OpenAI o1-preview (6.0%), Claude 3.5 Sonnet (2.7%), Gemini 1.5 Pro (4.0%), and Myelo (4.0%). Conclusions: This study demonstrates that while current LLMs show promise in medical decision support, their recommendations require careful clinical supervision to ensure patient safety and optimal care. Further research is needed to improve their clinical use readiness before integration into oncology workflows. These findings provide valuable insights into the capabilities and limitations of LLMs in oncology, guiding future research and development efforts toward integrating AI into clinical workflows.

e13637 Background: Large language models (LLMs) like GPT-4 (OpenAI) and Claude Opus (Anthropic) showed high accuracy in medical multiple-choice exams, but data on their oncology-specific clinical reasoning and performance is limited. This study evaluates their accuracy and clinical reasoning on breast oncology multiple-choice questions (MCQs) from the American Society of Clinical Oncology (ASCO) question bank. Methods: Using OpenAI and Anthropic Application Programming Interface (APIs), questions were tested without additional prompts under consistent settings (GPT-4 and Claude Opus; Temperature = 0, Tokens = Max). Each question was tested three times to assess precision. Then, Chain-of-thought (COT) prompting was applied to promote LLMs stepwise reasoning to increase their accuracy. Accuracy before and after COT prompting was compared. Incorrect responses were reviewed by board-certified medical oncologists speclized in breast cancer, who scored reasoning clarity, bias, and clinical relevance. Qualitative feedback was descriptively analyzed. Results: A total of 273 breast oncology MCQs were evaluated across the two LLMs. GPT-4 achieved an initial accuracy of 81.3% (222/273; 95% CI: 76.3%-85.5%), compared to Claude Opus, which achieved an accuracy of 79.5% (217/273; 95% CI: 74.3%-83.9%). The Chi-squared test for difference between the models before chain-of-thought (COT) prompting yielded a p-value of 0.59, indicating no statistically significant difference in accuracy between GPT-4 and Claude Opus prior to COT prompting. After COT prompting, the performance of the two models diverged significantly. GPT-4 saw a net decline in accuracy, decreasing by a net of 1 correct answer, resulting in an overall accuracy of 80.95% (221/273). In contrast, Claude Opus experienced a notable improvement, with a net of 19 additional correct answers, leading to an accuracy of 86.4% (236/273). A statistical analysis using a Chi-squared test revealed a difference in accuracy between the two models of 5.5% (p = 0.08), demonstrating that the improvement in accuracy for Claude Opus after COT prompting was borderline statistically significant compared to GPT-4. Thematic analysis of oncologists' feedback revealed that the most common reasons for incorrect answers were reliance on outdated guidelines, misinterpretation of clinical trial data, and failure to consider multidisciplinary or patient-specific approaches in clinical decision-making. Conclusions: Although AI models can achieve high scores on multiple-choice exams, they still require human supervision. These models rely on potentially outdated training data and lack the ability to individualize patient care or apply data from clinical trials. especially in unique or unconventional/non textbook scenarios.

e13622 Background: Chimeric antigen receptor T-cell (CAR-T) therapy has revolutionized treatment of hematologic malignancies; however, severe toxicities like cytokine release syndrome (CRS) remain a limitation to broader utilization. Using a large dataset of patients treated with CAR-T, we developed a machine learning (ML) model leveraging multi-model patient characteristics to predict the risk of CRS onset with clinically meaningful lead times. Methods: A prospectively maintained dataset of CAR-T patients treated at Mayo Clinic was used. Baseline disease/patients characteristics, labs, ECG, echocardiogram, and vital signs (including data from remote patient monitoring program) were collected. A 24-hour rolling window was used to generate daily averages, maximum, minimum, and deltas for each parameter. ECG and echocardiogram data were forward-imputed; vital signs and labs were not imputed. Each day-post-infusion served as a potential event (CRS onset vs. no CRS). A gradient boosting classifier was trained and tested on a subset of patients (n = 351) using 1,000 bootstrap iterations, evaluating 24-, 48-, and 72-hour lead times. Shapley-based feature importance guided simplification to an 11-feature model, externally validated on an independent cohort (n = 51). Models’ performance assessed using the area under the receiver operating characteristic curve (AUC). Results: A total of 402 patients treated with CAR-T were examined. Median age was 64 years and 36% were female. Primary diagnosis was B-cell lymphoma (n = 240) or multiple myeloma (n = 162). A total of 226,332 vital sign measurements and 372,871 lab values, as well as ECG and echocardiogram data, were collected for 30 days post-infusion. Overall, 79.8% of patients experienced CRS within 30 days. The full ML model demonstrated AUCs of 0.90 (95% CI 0.86–0.93), 0.83 (95% CI 0.77–0.88), and 0.82 (95% CI 0.77–0.87) for 24-, 48-, and 72-hour predictions of CRS, respectively. Including “day of CRS onset post CAR-T” as a feature improved the 72-hour AUC to 0.87 (95% CI 0.83–0.90). A model restricted to vital signs alone showed AUCs of 0.88, 0.75, and 0.71 for 24-, 48-, and 72-hour horizons. An 11-feature model—focusing on temperature, blood pressure, fibrinogen, CRP, serum sodium level, QRS interval, and lymphocyte counts—maintained high accuracy at 24 hours (AUC 0.90, 95% CI 0.86–0.93) and was validated externally using an independent dataset with an AUC of 0.92. Conclusions: Our ML model provides a reliable approach for predicting if/when CRS will occur, with up to three days of lead time before clinical manifestation, offering an opportunity for proactive management. The robust performance of a parsimonious 11-feature model supports practical adoption across clinical settings and real-time risk stratification to enhance patient safety. Prospective validation and integration into clinical workflows will be critical to translating these findings into practice.

e13551 Background: The landscape of cancer care is evolving rapidly. In 2023, there were 83 new FDA oncology approvals, and 145 interventional treatment (tx) studies opened across Mayo Clinic. We are exploring novel precision outreach approaches utilizing an LLM-integrated chatbot application to “ping” patients (pts) with specific clinical or genomic characteristics about newly available drugs or clinical trials. As novel outreach methods are considered, we surveyed a patient (pt) support group to understand communication preferences and awareness of adjuvant CDK4/6 inhibitors in tx of breast cancer (BC). Methods: On October 7, 2024, we administered a cross-sectional survey to 41 members of the Breast Cancer Support Network (BCSN) in Rochester, MN as a part of an IRB exempt quality improvement project. Leaders of the BCSN shared information to all participants, noting that the survey’s intent was to represent the pt voice and participation was voluntary. Anonymous survey data were collected with descriptive results reported. Results: 34/41 participants in the BCSN shared demographic information. The majority (83%) were > 50 years of age. 47% reported stage I BC, 12% stage II, 6% stage III, and 35% were unsure of their stage. Most participants (26/34, 76%) reported that their BC was hormone receptor positive (HR+), 2 HER-2 positive, and 6 unsure of their BC receptor status. 24/26 (92%) of pts with HR+ BC were taking or planning to start adjuvant endocrine therapy. 10/34 (29%) were aware that adjuvant CDK 4/6 inhibitors were approved. Of these 10 participants, 2 learned of it from online health resources, 6 from their clinician, 1 from social media, and 1 from a support group. The majority (66%) were unsure if they qualified for these drugs. Regarding receipt of information on new drug approvals/tx options, the highest ranked communication preference was to receive this information in person at an upcoming visit (ranked 1st by 50% of participants), followed by EHR-based message (28%), email (10%), phone call (6%), letter (4%), or an interactive chatbot (0%). Most respondents noted that communication from their healthcare provider was very important (30/37, 81%), with others stating that this was somewhat important (5/37, 14%) or neutral (2/37, 5%). Most noted that receiving information about tx options via video or visual aid before a visit would be very helpful (33/41, 80%). When asked about follow up after learning of a new drug approval, 17/32 (53%) of pts preferred an appointment, 9/32 (28%) a phone call, 3/32 (9%) a decision aid tool, and 3/32 (9%) a questionnaire to assess their understanding. Conclusions: Providing personalized, timely communication about new FDA approved tx options is important for BC survivors and critical for improving care delivery. While technological solutions could broaden awareness, this requires balancing pt preferences for direct communication with clinicians.

529 Background: Neoadjuvant chemotherapy (NAC) is commonly administered to patients (pts) with estrogen receptor alpha (ER) negative (immunohistochemistry [IHC] 0%) and ER-low (IHC 1-10%) breast cancer (BC). For pts without pathological complete response (pCR), ER expression may differ between baseline and residual BC following NAC. We previously demonstrated adjuvant endocrine therapy (ET) omission in ER-low early-stage BC was associated with significantly worse overall survival (OS); however, this effect was restricted to pts with residual BC following NAC and those with higher baseline ER levels (IHC 6-10%) (Choong et al., ASCO 2024). Here, we assessed how often ER was expressed in residual invasive BC for pts treated with NAC for ER-negative and ER-low BC. Methods: Weidentified pts with pre-treatment (tx) ER-negative and ER-low (regardless of HER2 expression) stage I-III BC treated with NAC who underwent BC surgery at Mayo Clinic Rochester between 2009 and 2023. ER IHC was performed in a CAP/CLIA laboratory. We evaluated ER expression in residual invasive BC for pts without pCR. The percent of pts with a post-tx change in ER status was estimated and reported with 95% Wilson confidence intervals. Results: 955 pts (838 [88%] pre-tx ER-negative; 117 [12%] pre-tx ER-low) met inclusion criteria, of whom 69% had HER2-negative and 31% HER2-positive BC. The median age at diagnosis was 52 (range: 24-86). 496 (52%) had residual BC. Residual BC was more common in HER2-negative versus (vs) positive tumors (56% vs 42%, p < 0.001) but did not differ significantly by ER status (ER-negative 51% vs ER-low 57%, p = 0.22). Of those with residual BC, 277/496 (56%) had ER re-testing. Rates of ER re-testing did not vary for pre-tx ER-negative vs -low (57% vs 51%, p = 0.37) but were significantly lower for HER2-positive vs HER2-negative tumors (39% vs 62%, p < 0.001). Among those with post-tx testing, 31/277 (11%, 95% CI: 8-15%) had an increase in ER expression from pretreatment levels (defined as ER IHC < 1% to either 1-10% or > 10%; or ER IHC 1-10% to > 10%). In these 31 pts, the original NAC was for either TNBC (21/31; 68%) or HER2+ BC (10/31; 32%). In pts with pre-tx ER-negative BC, 27/243 (11%, 95% CI: 8-16%) had ER expression in the residual BC including 14/243 (6%, 95% CI: 3-9%) with ER > 10% and 13/243 (5%, 95% CI: 3-9%) with ER 1-10%. For pts with baseline ER-low BC, 4/34 (12%, 95% CI: 5-27%) had ER > 10% in the residual BC. Among the 31 pts where ER increased following NAC, 21/31 (68%) received adjuvant ET, including 17/18 if ER was > 10% in the residual disease and 4/13 in those with ER 1-10%. Conclusions: In pts treated with NAC for ER-negative or ER-low BC not achieving pCR, we identified higher ER expression in the residual breast cancer in > 10% of pts. Given that omission of ET in ER-low BC with residual cancer following NAC is associated with worse survival, repeat biomarker testing should be considered in those without pCR, and ET individualized according to ER expression.

526 Background: The PALLAS trial (NCT02513394) investigated the efficacy of the addition of palbociclib (palbo) to standard adjuvant endocrine therapy (ET) to reduce breast cancer recurrence. Previous analyses of this trial have not shown significant benefit of combination palbo+ET over ET alone. Given prior data showing that extent of neutropenia is associated with response to palbo and other cell cycle-specific therapies, we evaluated whether extent of neutropenia could identify responders to palbo in the adjuvant setting. Methods: PALLAS is a global, open-label, phase III trial that randomized patients (pts) with stage II-III hormone-receptor positive, HER2-negative breast cancer to receive ET for ≥5 years with or without standard-dose palbo for 2 years in 28-day cycles. The primary endpoint is invasive disease-free survival (iDFS). For this exploratory analysis, the palbo population was classified into pts with treatment-emergent high-grade neutropenia (HGN) with maximum grade ≥3 (absolute neutrophil count <1000), or low-grade/no neutropenia (LGN) with maximum grade <2; these groups were compared to each other and to the ET alone group for 5-year iDFS outcomes. Logistic regression examined individual baseline characteristics associated with HGN during the first 3 cycles within the palbo group. Impact of HGN during the first 3, 6, and 12 cycles on iDFS was tested using univariate and multivariable landmark Cox regression. Results: The safety population included 5736 pts, 2840 allocated to palbo+ET, 2896 to ET alone. Prior publications reported no new safety signals, low rates of serious infection, and no grade 5 treatment-related events. The palbo+ET group consisted of 1006 (35.4%) LGN and 1834 (64.6%) HGN. 5-year iDFS results are shown in the table. Pts who received palbo+ET and developed HGN by the end of cycle 6 had significantly improved 5-year iDFS compared to those who received ET alone (p=0.04), which remained statistically significant when adjusting for body mass index (BMI), prior chemotherapy, and race. Multivariable logistic regression showed lower BMI, prior chemotherapy, Asian race, and prior mastectomy were significantly associated with HGN (all p<0.05). Conclusions: In this exploratory analysis of the phase III PALLAS adjuvant trial, addition of palbo to ET appeared to be superior to ET alone in pts who developed HGN in the first 6 cycles of treatment but not in those who had LGN. These findings are consistent with observations in the metastatic setting suggesting that neutropenia could be a useful biomarker for palbo concentration and efficacy. Clinical trial information: NCT02513394 . Maximum grade neutropenia measured at end of cycle: 5-year iDFS Palbo+ET HGN (%) Palbo+ET LGN (%) ET alone (%) Hazard Ratio p-value 3 84.9 84.4 1.06 0.54 84.9 82.9 1.17 0.06 6 85.6 84.9 1.08 0.44 85.6 83.4 1.19 0.04 12 86.3 85.9 1.06 0.57 86.3 85.0 1.12 0.20

1543 Background: Treatment toxicity may limit the ability of cancer patients to receive all recommended cycles of therapy. Four to six cycles of docetaxel plus cyclophosphamide (TC) is a common adjuvant chemotherapy regimen for early-stage breast cancer. We assessed the impact of routine collection of patient-reported outcomes (PROs), coupled with a collaborative care model-based symptom management intervention, on the number of cycles of TC received by patients with breast cancer. Methods: The Enhanced, EHR-facilitated Cancer Symptom Control (E2C2) trial was a cluster-randomized, pragmatic clinical trial, conducted between March 2019 and January 2023 at Mayo Clinic Rochester and within the Mayo Clinic Health System in Minnesota and Wisconsin. Patients regularly reported the severity of 6 SPPADE symptoms (Sleep deficit, Pain, Physical function impairment, Anxiety, Depression, and Energy deficit/fatigue) on 11-point numerical rating scales. Each symptom score was interpreted as none to mild (0-3), moderate (4-6), or severe (7-10). The E2C2 intervention included symptom management education modules, clinician decision support aids, and the option to discuss severe symptoms with a nurse, physical therapist, or social worker. Patients with breast cancer who received at least one cycle of TC were included in this analysis. We compared the number of cycles of docetaxel and cyclophosphamide completed by patients in the control condition versus those in the intervention condition. Results: We identified 198 patients with breast cancer who received TC during the control condition and 128 who received TC during the intervention condition. Median age was 61.3 years in the control group and 60.3 years in the intervention group, and 94% and 95% were white, respectively. Those receiving treatment during the control condition, on average, completed 3.53 cycles of docetaxel, while those receiving TC during the intervention condition completed 3.77 cycles ( p = 0.014). Seventy-eight percent in the control group completed at least 4 cycles of docetaxel, compared to 84% in the intervention group. Those receiving TC during the control condition completed an average of 3.71 cycles of cyclophosphamide, compared to 3.78 cycles in the intervention condition (p = 0.231). Eighty-four percent of patients in the control group and 86% in the intervention group completed at least 4 cycles of cyclophosphamide. Conclusions: Routine PRO surveillance, coupled with guideline-based collaborative care interventions, was associated with completion of a greater number of cycles of docetaxel. These findings suggest that routine symptom surveillance and management may enhance the docetaxel tolerance profile and improve treatment adherence in patients with early-stage breast cancer, allowing for delivery of an optimized treatment course.